10,000 Matching Annotations
  1. Sep 2025
    1. Take responsibility for the effects of your words and actions on others, even when your intentions were not to cause them harm

      This quote stuck out to me because I think a lot of people forget that intentions don't always match the outcome. Just because you didn't mean to hurt someone doesn't mean you dint hurt them. I've seen situations where someone says something they thought was a joke but ended up really effecting the other person. This line is a reminder that we need to be more aware on how we come across and be willing to own up to it when we mess up, it's not about being perfect, no its about being respectable and showing others that their feelings do matter.

    2. Compassion impels us to work tirelessly to alleviate the suffering of our fellow creatures, to dethrone ourselves from the center of our world and put another there

      This quote struck me because most of the time, people are focused on themselves without even realizing it, of course it's natural to care about your own problems first, but compassion is about taking a step back and thinking about how someone else might be struggling too. The part about "dethroning ourselves" really stuck with me, it's a reminder that the world doesn't just revolve around us, and sometimes we need to put others first, even a small thing like helping a classmate or checking in on a friend can make a big difference.

    3. The principle of compassion lies at the heart of all religious, ethical, and spiritual traditions, calling us always to treat all others as we wish to be treated ourselves.

      I really connect with this because I feel a lot of people forget how important it is to treat others the way you want to be treated, and I always stood on that with the Golden Rule. But it could be easy to get caught up in your own thoughts and feelings, but we don't always stop to think what about someone else might be going through. This quote reminds me that compassion isn't just about being kind when its easy, it's about making the effort to understand people, even when we don't agree with them or when we're in a tough situation.

    1. Whereas counter-based tools simply inquire on the current value of certain statistics that are maintained by the kernel, tracing tools collect data for a specific event—such as the steps involved in a system-call invocation.

      It’s interesting how tracing tools differ from counters. While counters give a snapshot of ongoing statistics, tracing actually follows specific events as they happen. I wonder how much performance overhead tracing adds compared to just reading counters.

    2. Operating systems keep track of system activity through a series of counters, such as the number of system calls made or the number of operations performed to a network device or disk. The following are examples of Linux tools that use counters:

      It’s interesting to see how the operating systems use the simple counters to monitor the activity. I didn’t realize that just counting things like the system calls or the disk operations could provide such valuable insight into system performance. I wonder how accurate this method is compared to other monitoring techniques.

    3. Software designers for Android devices develop applications in the Java language, but they do not generally use the standard Java API. Google has designed a separate Android API for Java development. Java applications are compiled into a form that can execute on the Android RunTime ART, a virtual machine designed for Android and optimized for mobile devices with limited memory and CPU processing capabilities. Java programs are first compiled to a Java bytecode .class file and then translated into an executable .dex file. Whereas many Java virtual machines perform just-in-time (JIT) compilation to improve application efficiency, ART performs ahead-of-time (AOT) compilation. Here, .dex files are compiled into native machine code when they are installed on a device, from which they can execute on the ART. AOT compilation allows more efficient application execution as well as reduced power consumption, features that are crucial for mobile systems.

      It’s interesting that the Android doesn’t use any of the standard Java API but instead has its own. The ahead-of-time (AOT) compilation in ART seems really clever—it makes apps run faster and saves battery life, which is super important for mobile devices with limited resources.

    4. Application frameworks layer. This layer includes the Cocoa and Cocoa Touch frameworks, which provide an API for the Objective-C and Swift programming languages. The primary difference between Cocoa and Cocoa Touch is that the former is used for developing macOS applications, and the latter by iOS to provide support for hardware features unique to mobile devices, such as touch screens.

      It’s surprising how the Cocoa and Cocoa Touch serve the similar purposes but are said to be tailored for the different devices. Cocoa Touch supporting the touch-specific hardware really highlights how the APIs need to adapt to the features of the device, not just the operating system.

  2. Aug 2025
  3. learn-us-east-1-prod-fleet01-beaker-xythos.content.blackboardcdn.com learn-us-east-1-prod-fleet01-beaker-xythos.content.blackboardcdn.com
    1. economics is inherently a social subject. It’s not just technical forces liketechnology and productivity that matter. It’s also the interactions and relationshipsbetween people that make the economy go around.

      Economics is grounded and isn't what people usually associate it with. As its the working people that make the economy go around.

    2. Homo sapiens have existed on this planet for approximately 100,000 years. Theyhad an economy all of that time. Humans have always had to work to meet thematerial needs of their survival (food, clothing, and shelter) – not to mention,when possible, to enjoy the “finer things” in life. Capitalism, in contrast, has existedfor around 250 years. If the entire history of Homo sapiens to date was a 24-hourday, then capitalism has existed for three-and-a-half minutes.What we call “the economy” went through many different stages en route tocapitalism. (We’ll study more of this economic history in Chapter 3.) Even today,different kinds of economies exist. Some entire countries are non-capitalist. Andwithin capitalist economies, there are important non-capitalist parts (althoughmost capitalist economies are becoming more capitalist as time goes by).I think it’s a pretty safe bet that human beings will eventually find other, betterways to organize work in the future – maybe sooner, maybe later. It’s almostinconceivable that the major features of what we call “capitalism” will exist for the

      capitalism is only a very recent system compared to the long history of human economies. Note that humans have always worked to meet needs, but capitalism (about 250 years old) is just one stage among many and will likely be replaced by new ways of organizing work in the future. This helps put capitalism in perspective as temporary, not permanent.

    3. technology refers to a techniqueof production, not to a particular piece of equipment or machinery. The termis often misused to refer to equipment itself (“Wow, dude, you have someawesome technology in here!”). In its correct use, though, “technology”refers to knowledge about how to produce something – not the physical toolswe use to produce it. A new technology can be highly productive; but a toolor machine, in and of itself, is not.

      This really made me rethink how I’d been using the word “technology.” I used to think that having the latest machines or gadgets automatically counted as new technology, but Stanford points out that it’s actually about knowledge of how to produce something. Understanding this distinction is a good insight moving forward because it shifts the focus from just owning tools to figuring out better ways to use them to be more productive/effective.

    4. The economy and societyThe economy is a fundamentally social activity. Nobody does it all by themselves(unless you are a hermit). We rely on each other, and we interact with each other,in the course of our work.It is common to equate the economy with private or individual wealth, profit,and self-interest, and hence it may seem strange to describe it as something “social.”Indeed, free-market economists adopt the starting premise that human beings areinherently selfish (even though this assumption has been proven false by sociobi-ologists and anthropologists alike).In fact, the capitalist economy is not individualistic at all. It is social, and inmany ways it is cooperative. The richest billionaire in the world couldn’t haveearned a dollar without the supporting roles played by his or her workers,suppliers, and customers. There’s no such thing as a “self-made” millionaire orStanford EFE2 01 text.indd 19 08/04/2015 09:26Stanford, Jim. Economics for Everyone : A Short Guide to the Economics of Capitalism, Pluto Press, 2015. ProQuest EbookCentral, http://ebookcentral.proquest.com/lib/forsythtech-ebooks/detail.action?docID=3440440.Created from forsythtech-ebooks on 2025-08-12 18:08:29.Copyright © 2015. Pluto Press. All rights reserved.

      Basically, the author's trying to convince us that the economy isn't just about individual greed and competition. It's about how we're all connected and rely on each other, even in a capitalist system. It's a way of saying that everyone contributes, and nobody's truly "self-made."

    5. Naomi Klein’s This Changes Everything: Capitalism vs. the Climate opens with a clear argument: the climate crisis is not just about carbon—it’s about capitalism. In the first four chapters, she shows how the free market system, especially in its neoliberal form, is fundamentally at odds with the urgent action needed to slow climate change. Klein explores how right-wing think tanks understand this contradiction better than many liberals: tackling climate change requires a direct challenge to deregulation, privatization, and endless growth. This explains, she argues, the ideological roots of climate denial. She uses accessible metaphors and striking examples (like a plane stuck to melting tarmac) to drive home the idea that incremental reforms won’t be enough—only a massive economic and political transformation will do.

    1. An emergency need arose for someone to write 300 words o

      something that i'm thinking about is a parallel between restaurants -- so "junk words" and "fine dining words". Chat/LLM speaks to me as fluff, filler, words that come out just for convenience. Shakespeare, Mary Oliver, etc are the words that pack a punch. And that makes me think about art's irreplaceability - once someone creates authentic art, it loses its value if it's replicated, even down to the specifics.

    1. Stories don’t just convey information; they “demonstrate relationships between tellers, hearers, characters, and others” (Shuman, 1986, p. 21). The audience members are active witnesses, participating in constructing the narrative and playing in its gaps (Richter, 1996).

      This text highlights that storytelling is more than passing along facts—it’s relational and participatory. The teller and audience shape meaning together, with listeners actively engaging and filling in gaps. It shows stories as dynamic, co-created experiences rather than static transmissions of information.

    1. “People aren’t looking for ways to not contribute to their community,” Isenberg said. “It’s just that cash allows them to name for themselves what that looks like for their lives.”

      exactly, plenty of people want to contribute, and like we've seen in the article, have ambition and goals. but are held back by lack of resources or responsibilities that make it too expensive to pursue anything else

  4. opentextbooks.library.arizona.edu opentextbooks.library.arizona.edu
    1. Human behavior today can appear utterly transformed by digital technologies. When we look more closely, there are many moments today that echo behaviors of the past before digital technology played a key role

      I definitely agree with this. I think a lot of times we try to blame technology for our issues as a way to not take responsibility for our behavior. While I do agree that technology has it's share of problems, I don't think it is solely to blame in most situations. A lot of todays issues are unfortunately not unique to the modern day. I think especially in the US we tend to repeat history and then act like it is a new thing and use technology as a scapegoat. I think those societal problems have always been there, technology just makes it much more known because we have so much access to information in a way we didn't in the past.

    2. Today’s North American teenagers choose to spend much more of their time with friends online, according to Pew Research Center, while past generations socialized more in person. However, there are many factors responsible for this other than today’s ubiquity of digital technologies. One factor is that youth are not allowed to be out as much as they once were. Today’s youth deal with parents who hover more closely and give them less freedom in public spaces than their parents were given themselves, and curfews and other restrictions remind teens that they are unwelcome in public spaces. For her 2014 book It’s Complicated, danah boyd conducted qualitative research, including interviews with teens and observations of their homes and neighborhoods. In her research, she found that teens were using social media to cope with physical restrictions on their mobility, pursuing social relationships online from their homes. Yet despite the lack of evidence around addiction, social media is associated with health concerns for all users and especially youth, due to content that exists only and the dynamics of its circulation and our communications. As U.S. Surgeon General Dr. Vivek Murthy advised in 2023, “We don’t have enough evidence to say it’s safe, and in fact, there is growing evidence that social media use is associated with harm to young people’s mental health.”

      In my opinion I feel that social media has turned into a way for teens to escape a barrier which has been set by their parents, that being not letting them go out very much. Especially when you have newer generations that are growing in an era in which technology is only advancing as time goes by. If technology devices are given to someone at a very young age it can certainly turn into a part of their daily life and almost impossible to let go of. On top of this social media platforms are purposefully shaped as a way to keep the users entertained and addicted to what they are shown so it's obvious the youth can be addicted to social media. it would just depend on the person if this is a good thing or a bad thing overall.

    3. Conversely, it is also common to find social media use viewed as the downfall of society – a dystopia, or imagined society where everything is terrible. The increasing reliance of our society on social media for everyday communications looks nightmarish to some. Teens never look up from their phones. Computers make life-or-death decisions or at least remove humans from making them. Our brains are rewiring to cut out human emotions like compassion as we become robotically trained to pursue likes and connect with people we never see. Such dystopian thinking can make people jump to conclusions and even deploy data and scientific research as hasty “proof” of their extreme conclusions, leading to

      I feel that it also depends on not just the amount of use but also what exactly is the way that people use technology. I think it can certainly influence us if we look too much at the media or we can feel more connected to our phones if we have friends online. in my opinion it's much more healthier and safe to use technology and social media to help explore more topics that actually seem important to us.

    1. However good copyediting, like writing, takes time because you need tothink and not curate like Al, which also doesn't understand nuance wellbecause it's curating the data.

      This is so true. Some people think it takes just a little bit to get good quality writing but it takes so much longer than that and AI has just ruined what people think.

    1. Creatures ranging from fish to hummingbirds are attracted to things like silver spinners on fishing poles or red and yellow bird feeders

      Creatures are drawn to bright, eye-catching colors and reflections, just as much as humans are too. I’ve been a fisher since I was a little girl, and I’ve always noticed how the rods, the lures, and even the bait seem to shine brilliantly in the sunlight, especially when they hit the water. The shimmer seems to call out to fish, catching their attention in a way that feels almost magical. It’s fascinating how both underwater and airborne creatures respond so instinctively to light and color.

    1. Which of the career options in the field of psychology is most appealing to you?

      The career option in psychology that is most appealing to me is sports psychology. I run cross country and track, so I know firsthand how it is to be an athlete. The mental challenges are just as hard as the physical challenges; you're not only exerting your body but your mind as well. For running especially, there is a huge mental aspect to it, it's really hard to keep going at times. I think I could relate my experience as an athlete to other athletes as a sports psychologist because I know what it's like. I want to be able to help athletes that are struggling with their mental health and make sure they are at their best mentally and physically. At first, I thought I wanted to be a therapist/counselor for people, but I think I would be better working with athletes and id be able to connect with them on a more personal level.

    1. How came you sick for there is an odd discourse of that in the mouths of many — I am sick at my stumach — Have you no wounds I have none but old age

      Observation: Goody Nurse is not feeling well and the judge believes it's witch craft or demons based on what others have been saying about her. She is an old woman and is just sick from age.

      Interpretation: Her illness cannot be seen by the naked eye so it is hard for those to understand why she is so sick. She's been standing in front of everyone with no emotions yet she claims to be sick.

      Connection: When someone accuses you of witchcraft in Salem, it was extremely hard to convince anyone that you are not. They took even a simple illness that you have and thought it was from a demon. The judge didn't believe Goody Nurse.

      Complexity: I think about how hard it must have been to live in Salem during the 1600's. You could be doing anything and all of a sudden you are called a witch. It was much harder for woman than a man. Even being sick people will think you are a witch. Goody Nurse could do nothing but deny everything and keep her faith in God known. The voices of everyone else and the actions of the young girls were more believable to the judge than anything she had to say.

    1. Author response:

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

      Reviewer #1 (Public review):

      Summary:

      This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

      The study demonstrates that RBMX2 plays a role in:

      (1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

      (2) Disrupting tight junctions and promoting EMT.

      (3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

      By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

      Strengths:

      This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

      Weaknesses:

      (1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

      We sincerely appreciate the reviewer's valuable feedback regarding the need to clarify RBMX2's role throughout the manuscript. We have carefully revised the text to ensure consistent messaging about RBMX2's function in promoting M. bovis infection. Below we detail the specific modifications made:\

      (1) Introduction Revisions:

      Changed "The objective of this study was to elucidate the correlation between host genes and the susceptibility of M.bovis infection" to "The objective of this study was to identify host factors that promote susceptibility to M.bovis infection"

      Revised "RBMX2 polyclonal and monoclonal cell lines exhibited favorable phenotypes" to "RBMX2 knockout cell lines showed reduced bacterial survival"

      Replaced "The immune regulatory mechanism of RBMX2" with "The role of RBMX2 in facilitating M.bovis immune evasion"

      (2) Results Revisions:

      Modified "RBMX2 fails to affect cell morphology and the ability to proliferate and promotes M.bovis infection" to "RBMX2 does not alter cell viability but significantly enhances M.bovis infection"

      Strengthened conclusion in Figure 4: "RBMX2 actively disrupts tight junctions to facilitate bacterial invasion"

      (3) Discussion Revisions:

      Revised screening description: "We screened host factors affecting M.bovis susceptibility and identified RBMX2 as a key promoter of infection"

      Strengthened concluding statement: "In summary, RBMX2 drives TB pathogenesis by compromising epithelial barriers and inducing EMT"

      These targeted revisions ensure that:

      All sections consistently present RBMX2 as promoting infection; the language aligns with our experimental finding; potential protective interpretations have been eliminated. We believe these modifications have successfully addressed the reviewer's concern while maintaining the manuscript's original structure and scientific content. We appreciate the opportunity to improve our manuscript and thank the reviewer for this constructive suggestion.

      (2) While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

      We sincerely appreciate the reviewer's insightful comment regarding the roles of MAPK/p38 and JNK in our study. Our experimental data clearly demonstrated that RBMX2 knockout significantly reduced phosphorylation levels of p65, p38, and JNK (Fig. 5A), indicating potential involvement of all three pathways in RBMX2-mediated regulation.

      Through systematic functional validation, we obtained several important findings:

      In pathway inhibition experiments, p65 activation (PMA treatment) showed the most dramatic effects on both tight junction disruption (ZO-1, OCLN reduction) and EMT marker regulation (E-cadherin downregulation, N-cadherin upregulation);p38 activation (ML141 treatment) exhibited moderate effects on these processes; JNK activation (Anisomycin treatment) displayed minimal impact.

      Most conclusively, siRNA-mediated silencing of p65 alone was sufficient to:

      Restore epithelial barrier function

      Reverse EMT marker expression

      Reduce bacterial adhesion and invasion

      These results establish a clear hierarchy in pathway importance: p65 serves as the primary mediator of RBMX2's effects, while p38 plays a secondary role and JNK appears non-essential under our experimental conditions. We have now clarified this relationship in the revised Discussion section to strengthen this conclusion.

      This refined understanding of pathway hierarchy provides important mechanistic insights while maintaining consistency with all our experimental data. We thank the reviewer for this valuable suggestion that helped improve our manuscript.

      (3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

      Thank you for this constructive suggestion. In this article, we detected the metabolome of RBMX2 knockout and wild-type cells after Mycobacterium bovis infection, which mainly served as supporting evidence for our EMT model. However, we did not conduct an in-depth discussion of these findings. We have now added a detailed discussion of this section to further support our EMT model.

      ADD:Meanwhile, metabolic pathways enriched after RBMX2 deletion, such as nucleotide metabolism, nucleotide sugar synthesis, and pentose interconversion, primarily support cell proliferation and migration during EMT by providing energy precursors, regulating glycosylation modifications, and maintaining redox balance; cofactor synthesis and amino sugar metabolism participate in EMT regulation through influencing metabolic remodeling and extracellular matrix interactions; chemokine and cGMP-PKG signaling pathways may further mediate inflammatory responses and cytoskeletal rearrangements, collectively promoting the EMT process.

      (4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

      Thank you for your comment. We have supplemented the experiments in this part and found that Mycobacterium bovis infection can significantly enhance the expression level of RBMX2 protein.

      (5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

      We sincerely appreciate the valuable comments from the reviewer. We fully agree with your suggestion to further explore the relationship between tuberculosis (TB) and lung cancer. In the revised manuscript, we will add a new paragraph in the Discussion section to systematically integrate the current literature on the epidemiological and mechanistic links between chronic tuberculosis infection and lung cancer development, including the potential bridging roles of chronic inflammation, tissue damage repair, immune microenvironment remodeling, and the epithelial-mesenchymal transition (EMT) pathway. This addition will help more comprehensively interpret the clinical implications of the observed EMT activation in the context of our study, thereby enhancing the biological plausibility and clinical translational value of our findings.

      ADD:There is growing epidemiological evidence suggesting that chronic TB infection represents a potential risk factor for the development of lung cancer. Studies have shown that individuals with a history of TB exhibit a significantly increased risk of lung cancer, particularly in areas of the lung with pre-existing fibrotic scars, indicating that chronic inflammation, tissue repair, and immune microenvironment remodeling may collectively contribute to malignant transformation 74. Moreover, EMT not only endows epithelial cells with mesenchymal features that enhance migratory and invasive capacity but is also associated with the acquisition of cancer stem cell-like properties and therapeutic resistance 75. Therefore, EMT may serve as a crucial molecular link connecting chronic TB infection with the malignant transformation of lung epithelial cells, warranting further investigation in the intersection of infection and tumorigenesis.

      Reviewer #2 (Public review):

      Summary:

      I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

      Strengths:

      (1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

      (2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

      Weaknesses:

      (1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

      Thank you for your valuable and constructive comments. In this study, we primarily utilized the CRISPR whole-genome screening approach to identify key factors involved in bovine tuberculosis infection. Through four rounds of screening using a whole-genome knockout cell line of bovine lung epithelial cells infected with Mycobacterium bovis, we identified RBMX2 as a critical factor.

      Although the transcriptional level change of RBMX2 was less than two-fold, following the suggestion of Reviewer 1, we examined its expression at the protein level, where the change was more pronounced, and we have added these results to the manuscript.

      Regarding the mechanism by which RBMX2 is activated upon M. bovis infection, we previously screened for interacting proteins using a Mycobacterium tuberculosis secreted and membrane protein library, but unfortunately, we did not identify any direct interacting proteins from M. tuberculosis (https://doi.org/10.1093/nar/gkx1173).

      (2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

      We appreciate the valuable comments from the reviewers. Although our study included multiple time points post-infection, in our experimental design we focused on different biological processes and phenotypes at distinct time points:

      During the early phase (e.g., 2 hours post-infection), we focused on barrier phenotypes during the intermediate phase (e.g., 24 hours post-infection), we concentrated more on pathway activation and EMT phenotypes;

      And during the later phase (e.g., 48–72 hours post-infection), we focused more on cell death phenotypes, which were validated in another FII article (https://doi.org/10.3389/fimmu.2024.1431207).

      We also examined the impact of varying infection durations on RBMX2 knockout EBL cellular lines via GO analysis. At 0 hpi, genes were primarily related to the pathways of cell junctions, extracellular regions, and cell junction organization. At 24 hpi, genes were mainly associated with pathways of the basement membrane, cell adhesion, integrin binding and cell migration By 48 hpi, genes were annotated into epithelial cell differentiation and were negatively regulated during epithelial cell proliferation. This indicated that RBMX2 can regulate cellular connectivity throughout the stages of M. bovis infection.

      For KEGG analysis, genes linked to the MAPK signaling pathway, chemical carcinogen-DNA adducts, and chemical carcinogen-receptor activation were observed at 0 hpi. At 24 hpi, significant enrichment was found in the ECM-receptor interaction, PI3K-Akt signaling pathway, and focal adhesion. Upon enrichment analysis at 48 hpi, significant enrichment was noted in the TGF-beta signaling pathway, transcriptional misregulation in cancer, microRNAs in cancer, small cell lung cancer, and p53 signaling pathway.

      Reviewer #3 (Public review):

      Summary:

      This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

      Strengths:

      The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

      Weaknesses:

      Although it's a solid study, there are a few weaknesses noted below.

      (1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

      We appreciate the reviewer's thoughtful questions regarding our transcriptomic analysis. In this study, we employed a localized enrichment approach focusing specifically on gene expression profiles from our bovine lung epithelial cell system. This cell-type-specific analysis provides more biologically relevant results than global database searches alone.

      Regarding the highlighted pathways, these represent:

      Temporally significant pathways showing strongest enrichment at each stage:

      (1) 0h: Cell junction organization (immediate barrier response)

      (2) 24h: ECM-receptor interaction (early EMT initiation)

      (3) 48h: TGF-β signaling (chronic remodeling)

      Mechanistically linked to our core findings about RBMX2's role in:

      (1) Epithelial barrier disruption

      (2) Mesenchymal transition

      (3) Chronic infection outcomes

      We selected these particular pathways because they:

      (1) Showed the most statistically significant changes (FDR <0.001)

      (2) Formed a coherent biological narrative across infection stages

      (3) Were independently validated in our functional assays

      This targeted approach allows us to focus on the most infection-relevant pathways while maintaining statistical rigor.

      (2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

      We sincerely appreciate the reviewer's in-depth questions regarding the mechanisms by which RBMX2 activates p65 and its association with EMT. Although the molecular mechanism remains to be fully elucidated, our study has provided experimental evidence supporting a direct regulatory relationship between RBMX2 and the p65 subunit of the NF-κB pathway. Specifically, we investigated whether the transcription factor p65 could directly bind to the promoter region of RBMX2 using CHIP experiments. The results demonstrated that the transcription factor p65 can physically bind to the RBMX2 region.

      Furthermore, dual-luciferase reporter assays were conducted, showing that p65 significantly enhances the transcriptional activity of the RBMX2 promoter, indicating a direct regulatory effect of RBMX2 on p65 expression.

      These findings support our hypothesis that RBMX2 activates the NF-κB signaling pathway through direct interaction with the p65 protein, thereby participating in the regulation of EMT progression and barrier function.

      In our subsequent work papers, we will also employ experiments such as CLIP to further investigate the specific mechanisms through which RBMX2 exerts its regulatory functions.

      ADD and Revise in Results:

      To thoroughly verify the regulatory mechanism between RBMX2 and p65, we initiated our investigation by conducting an in-depth analysis of the RBMX2 promoter region to identify potential interactions with the transcription factor p65. Initially, we performed molecular docking simulations to predict the binding affinity and interaction patterns between RBMX2 and p65 proteins. These simulations revealed multiple amino acid residues within the RBMX2 protein that formed strong, stable interactions with p65. The docking analysis yielded a high docking score of 1978.643 (Fig. 7K), indicating a significant likelihood of a direct physical interaction between these two proteins.

      To complement the protein-protein interaction analysis, we next investigated whether p65 could directly bind to the promoter region of the RBMX2 gene at the transcriptional level. Using the JASPAR database, a comprehensive resource for transcription factor binding profiles, we queried the RBMX2 promoter sequence for potential p65 binding sites. This analysis identified several putative binding motifs, suggesting that p65 may act as a transcriptional regulator of RBMX2 expression.

      To experimentally validate this transcriptional regulatory relationship, we employed a dual-luciferase reporter assay. We cloned the RBMX2 promoter region containing the predicted p65 binding sites into a luciferase reporter plasmid. This construct was then co-transfected into cultured cells along with a plasmid expressing p65. The luciferase activity was significantly increased in cells expressing p65 compared to control groups, providing functional evidence that p65 enhances the transcriptional activity of the RBMX2 promoter (Fig. 7I).

      Furthermore, to confirm the direct binding of p65 to the RBMX2 promoter in a chromatin context, we performed chromatin immunoprecipitation followed by quantitative PCR (ChIP-qPCR). In this assay, we used specific antibodies against p65 to immunoprecipitate chromatin fragments containing p65-bound DNA. The enriched DNA fragments were then analyzed using primers targeting the RBMX2 promoter region. Our results demonstrated a significant enrichment of the RBMX2 promoter in the p65 immunoprecipitated samples compared to the IgG control, thereby confirming that p65 physically associates with the RBMX2 promoter in vivo (Fig. 7J). Collectively, these findings-ranging from computational docking predictions to transcriptional reporter assays and ChIP validation-provide strong evidence supporting a direct regulatory interaction between p65 and RBMX2. This regulatory mechanism may play a critical role in the biological pathways involving these two molecules, particularly in contexts such as inflammation, immune response, or cellular stress, where p65 (a subunit of NF-κB) is known to be prominently involved.

      (3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

      Thank you for your valuable comments. When we initially designed this experiment, we were interested in whether the RBMX2 knockout cell line could confer effective resistance not only against Mycobacterium bovis but also against Gram-negative and Gram-positive bacteria. Surprisingly, we indeed observed resistance to the invasion of these pathogens, albeit weaker compared to that against Mycobacterium bovis.

      Nevertheless, we believe these findings merit publication in eLife. Moreover, RBMX2 knockout does not affect the phenotype of epithelial barrier disruption under normal conditions; its significant regulatory effect on barrier function is only evident upon infection with Mycobacterium bovis.

      Importantly, during our genome-wide knockout library screening, RBMX2 was not identified in the screening models for Salmonella or Escherichia coli, but was consistently detected across multiple rounds of screening in the Mycobacterium bovis model.

      (4) The quality of the figures is very poor. High-resolution images should be provided.

      Thank you for your feedback; we provided higher-resolution images.

      (5) The methods are not very descriptive, particularly the omics section.

      Thank you for your comments; we have revised the description of the sequencing section.

      (6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

      Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

      Reviewer #2 (Recommendations for the authors):

      (1) The first part of the results and the major conclusions largely overlap with the previous paper by the same authors (Frontiers in Immunology, https://doi.org/10.3389/fimmu.2024.1431207). The previous paper has already established that RBMX2 is induced upon infection as a host factor, and its knockout led to cell proliferation. Thus, the current paper should focus more on the mechanisms rather than repeating the previous story.

      We appreciate the reviewer's careful reading and constructive feedback. We fully acknowledge the foundational work published in our Frontiers in Immunology paper (doi:10.3389/fimmu.2024.1431207), which established RBMX2 as an infection-induced host factor affecting cell proliferation. The current study represents a significant mechanistic extension of these initial findings, with the following key advances:

      (1) Novel Mechanistic Insights (Current Study Focus):

      Discovery of the p65/MMP-9 pathway as the central mechanism mediating RBMX2's effects on EMT (Figs. 4-6)

      First demonstration of RBMX2's role in epithelial barrier disruption (Figs. 2-3)

      Identification of temporal regulation patterns during infection progression (Fig. 7)

      (2) Expanded Biological Scope:

      Demonstration of RBMX2's function in both bovine and human cell systems (vs. previous bovine-only data)

      Clinical correlation with TB lesions

      Therapeutic potential assessment through pathway inhibition

      (3) Technical Advancements:

      CRISPR-based mechanistic validation (vs. previous siRNA approach)

      Multi-omics integration (transcriptomics + metabolomics)

      Advanced live-cell imaging

      We have now:

      Removed redundant proliferation data from Results

      Sharpened the Introduction to highlight mechanistic questions

      Added explicit discussion comparing both studies

      The current work provides the first comprehensive mechanistic framework for RBMX2's role in TB pathogenesis, moving substantially beyond the initial observational findings. We believe these new insights into the molecular pathways and therapeutic implications represent an important advance for the field..

      (2) Line 107-110: The CRISPR screening results are not provided. Has it been published, or is it an unpublished dataset? RBMX2 knockout cells exhibited 'significant' resistance to the infection. How significant? Data?

      Thank you for your valuable comments. The library mentioned, along with data on another host factor, TOP1, is being submitted by another researcher from our laboratory to a journal, and we will cite each other in the future. RBMX2 ranked second in terms of enrichment among all the identified genes, and its knockout cell line exhibited the second highest anti-infective capacity among all the host factors.

      (3) Line 152: The RNA-seq analysis has already been performed/reported in the previous Frontiers paper. Therein, 173 genes were found to be differentially expressed. In the current paper, 42 genes were differentially expressed in all three time points. If the addition of new time points were the highlight of this paper, why would the authors focus on differentially expressed genes from all three time points?

      Thank you for your valuable comments.

      In the newly added data, we aimed to investigate the temporal changes during Mycobacterium bovis infection of host cells.

      Previous study (Frontiers): Single 24h timepoint → 173 DEGs

      Current study: Three timepoints (0h, 24h, 48h) with 42 consistently regulated genes → Reveals temporally stable core regulators of infection response

      On one hand, we briefly described in the manuscript those important genes that exhibited changes across all time points.

      On the other hand, in the supplementary materials, we also focused on the enriched genes at each individual time point, to better understand the temporal dynamics regulated by RBMX2.

      (4) Line 153: The '0 h' time point is in fact 2 h post-infection. Why did the authors skip the real 0h time point? All the analysis and data should be relative to the 0h pi, rather than relative to the WT at each time point.

      We appreciate the reviewer's important question regarding our timepoint nomenclature. The experimental timeline was designed as follows:

      (1) Infection Protocol:

      2h to 0h: Bacterial co-culture (MOI 20:1)

      0h: Gentamicin (100 μg/ml) added to kill extracellular bacteria

      0h+: Monitored intracellular survival

      (2) Rationale for "0h" Designation:

      This marks the onset of intracellular infection phase when Extracellular bacteria are eliminated (validated by plating)Host cell responses to intracellular pathogens begin All subsequent measurements reflect genuine infection (not attachment)

      (3)Technical Validation:

      Confirmed complete extracellular killing by:

      Culture supernatant plating (0 CFU after gentamycin)

      Microscopy ( no surface-associated bacteria)

      (4) Comparative Analysis:

      All data are presented as:

      Fold-change relative to uninfected controls at each timepoint

      We have now:

      Clarified the timeline in Methods

      Specified "0h = post-gentamicin" in all figure legends

      This standardized approach aligns with established intracellular pathogen studies (e.g., Cell Microbiol. 2018;20:e12840). We're happy to adjust terminology if "0hpi (post-invasion)" would be clearer.

      (5) Figure 2F: The data should be compared to the 0h pi, and show the temporal changes of gene expression.

      Thank you for your suggestion. We have added additional information to this section. At the same time, we also aim to focus on the changes in gene expression between RBMX2 knockout and wild-type (WT) samples.

      We have now:

      Added temporal expression profiles relative to 0hpi baseline (SFig.4C).

      Clarified the dual normalization approach in Methods

      Maintained original between-group comparisons for phenotypic correlation

      (6) Line 207. Not all the proteins were down-regulated post-infection.

      Thank you for your comment. The overall level of the Tight junction related protein is downregulated, although it may not show a significant change at a specific time point.

      We have revised our description, changing the keyword from "All" to "Most."

      (7) Line 278, the introduction of the H1299 cell line should appear earlier when it was mentioned for the first time in the manuscript.

      Thank you for your comment. We have provided a description in the abstract and Result1.

      ADD:

      Abstrat: Meanwhile, we also validated the EMT process in human lung epithelial cancer cells H1299.

      Result 1: Furthermore, RBMX2-silenced H1299 cells exhibited a higher survival rate compared to H1299 ShNc cells after M. bovis infection (Fig. 1H).

      (8) Figure 4 is huge and almost illegible, which may be divided into two figures.

      Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

      Reviewer #3 (Recommendations for the authors):

      I encountered frequent grammatical and syntactic issues. Thoroughly revising the manuscript for English language and clarity, preferably with professional editing assistance, could increase the quality of the paper.

      Thank you for your valuable comments; we will invite a professional editor to polish the language.

    1. The idea that status impacts your access to information is nothing new

      Education aside, just by a simple connection with someone, you can gain more access to info than others, and this can range from anything. It's all about who you know in the world nowadays.

    1. Author response:

      General Statements

      We are grateful for constructive reviewers’ comments and criticisms and have thoroughly addressed all major and minor comments in the revised manuscript.

      Summary of new data.

      We have performed the following additional experiments to support our concept:

      (1) The kinetcs of ROS production in B6 and B6.Sst1S macrophages after TNF stimulation (Fig. 3I and J, Suppl. Fig. 3G);

      (2) Time course of stress kinase activation (Fig.3K) that clearly demonstrated the persistent stress kinase (phospho-ASK1 and phospho-cJUN) activation exclusively in. the B6.Sst1S macrophages;

      (3) New Fig.4 C-E panels include comparisons of the B6 and B6.Sst1S macrophage responses to TNF and effects of IFNAR1 blockade in both backgrounds.

      (4) We performed new experiments demonstrating that the synthesis of lipid peroxidation products (LPO) occurs in TNF-stimulated macrophages earlier than the IFNβ super-induction (Suppl.Fig.4A and B).

      (5) We demonstrated that the IFNAR1 blockade 12, 24 and 32 h after TNF stimulation still reduced the accumulation of LPO product (4-HNE) in TNF-stimulated B6.Sst1S BMDMs (Suppl.Fig.4 E-G).

      (6) We added comparison of cMyc expression between the wild type B6 and B6.Sst1S BMDMs during TNF stimulation for 6-24 h (Fig.5I-J).

      (7) New data comparing 4-HNE levels in Mtb-infected B6 wild type and B6.Sst1S macrophages and quantification of replicating Mtb was added (Fig.6B, Suppl.Fig.7C and D).

      (8) In vivo data described in Fig.7 was thoroughly revised and new data was included. We demonstrated increased 4-HNE loads in multibacillary lesions (Fig.7A, Suppl. Fig.9A) and the 4-HNE accumulation in CD11b+ myeloid cells (Fig.7B and Suppl.Fig.9B). We demonstrated that the Ifnb – expressing cells are activated iNOS+ macrophages (Fig.7D and Suppl.Fig.13A). Using new fluorescent multiplex IHC, we have shown that stress markers phopho-cJun and Chac1 in TB lesions are expressed by Ifnb- and iNOS-expressing macrophages (Fig.7E and Suppl.Fig.13D-F).

      (9) We performed additional experiment to demonstrate that naïve (non-BCG vaccinated) lymphocytes did not improve Mtb control by Mtb-infected macrophages in agreement with previously published data (Suppl.Fig.7H).

      Summary of updates

      Following reviewers requests we updated figures to include isotype control antibodies, effects of inhibitors on non-stimulated cells, positive and negative controls for labile iron pool, additional images of 4-HNE and live/dead cell staining.

      Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C -E, Fig.6L-M Suppl.Fig.4F-G, 7I.

      Positive and negative controls for labile iron pool measurements were added to Fig.3E, Fig.5D, Suppl.Fig.3B

      Cell death staining images were added Suppl.Fig.3H

      Co-staining of 4-HNE with tubulin was added to Suppl.Fig.3A.

      High magnification images for Figure 7 were added in Suppl.Fig.8 to demonstrate paucibacillary and multibacillary image classification.

      Single-channel color images for individual markers were provided in Fig.7E and Suppl.Fig.13B-F.

      Inhibitor effects on non-stimulated cells were included in Fig.5 D-H, Suppl.Fig.6A and B. Titration of CSF1R inhibitors for non-toxic concentration determination are included in Suppl.Fig.6D.

      In addition, we updated the figure legends in the revised manuscript to include more details about the experiments. We also clarified our conclusions in the Discussion. Responses to every major and minor comment of the reviewers are provided below.

      Point-by-point description of the revisions

      Reviewer #1 (Evidence, reproducibility and clarity:

      Summary

      The study by Yabaji et al. examines macrophage phenotypes B6.Sst1S mice, a mouse strain with increased susceptibility to M. tuberculosis infection that develops necrotic lung lesions. Extending previous work, the authors specifically focus on delineating the molecular mechanisms driving aberrant oxidative stress in TNF-activated B6.Sst1S macrophages that has been associated with impaired control of M. tuberculosis. The authors use scRNAseq of bone marrow-derived macrophages to further characterize distinctions between B6.Sst1S and control macrophages and ascribe distinct trajectories upon TNF stimulation. Combined with results using inhibitory antibodies and small molecule inhibitors in in vitro experimentation, the authors propose that TNF-induced protracted c-Myc expression in B6.Sst1S macrophages disables the cellular defense against oxidative stress, which promotes intracellular accumulation of lipid peroxidation products, fueled at least in part by overexpression of type I IFNs by these cells. Using lung tissue sections from M. tuberculosis-infected B6.Sst1S mice, the authors suggest that the presence of a greater number of cells with lipid peroxidation products in lung lesions with high counts of stained M. tuberculosis are indicative of progressive loss of host control due to the TNF-induced dysregulation of macrophage responses to oxidative stress. In patients with active tuberculosis disease, the authors suggest that peripheral blood gene expression indicative of increased Myc activity was associated with treatment failure.

      Major comments

      The authors describe differences in protein expression, phosphorylation or binding when referring to Fig 2A-C, 2G, 3D, 5B, 5C. However, such differences are not easily apparent or very subtle and, in some cases, confounded by differences in resting cells (e.g. pASK1 Fig 3L; c-Myc Fig 5B) as well as analyses across separate gels/blots (e.g. Fig 3K, Fig 5B). Quantitative analyses across different independent experiments with adequate statistical analyses are required to strengthen the associated conclusions.

      We updated our Western blots as follows:

      (1) Densitometery of normalized bands is included above each lane (Fig.2A-C; Fig.3C-D and 3K; Fig.4A-B; Fig.5B,C,I,J). New data in Fig.3K is added to highlight differences between B6 and B6.Sst1S at individual timepoints after TNF stimulation. In Fig.5I we added new data comparing Myc levels in B6 and B6.Sst1S with and without JNK inhibitor and updated the results accordingly. New Fig.3K clearly demonstrates the persistent activation of p-cJun and pAsk1 at 24 and 36h of TNF stimulation. In Fig.5B we clearly demonstrate that Myc levels were higher in B6.Sst1S after 12 h of TNF stimulation. At 6h, however, the basal differences in Myc levels are consistently higher in B6.Sst1S and the induction by TNF is 1.6-fold similar in both backgrounds. We noted this in the text.

      (2) A representative experiment is shown in individual panels and the corresponding figure legend contains information on number of biological repeats. Each Western blot was repeated 2 – 4 times.

      The representative images of fluorescence microscopy in Fig 3H, 4H, 5H, S3C, S3I, S5A, S6A seem to suggest that under some conditions the fluorescence signal is located just around the nucleus rather than absent or diminished from the cytoplasm. It is unclear whether this reflects selective translocation of targets across the cell, morphological changes of macrophages in culture in response to the various treatments, or variations in focal point at which images were acquired. Control images (e.g. cellular actin, DIC) should be included for clarification. If cell morphology changes depending on treatments, how was this accounted for in the quantitative analyses? In addition, negative controls validating specificity of fluorescence signals would be warranted.

      Our conclusion of higher LPO production is based on several parameters: 4-HNE staining, measurements of MDA in cell lysates and oxidized lipids using BODIPY C11. Taken together they demonstrate significant and reproducible increase in LPO accumulation in TNFstimulated B6.Sst1S macrophages. This excludes imaging artefact related to unequal 4-HNE distribution noted by the reviewer. In fact, we also noted that the 4-HNE was spread within cell body of B6.Sst1S macrophages and confirmed it using co-staining with tubulin, as suggested by the reviewer (new Suppl.Fig.3A). Since low molecular weight LPO products, such as MDA and 4-HNE, traverse cell membranes, it is unlikely that they will be strictly localized to a specific membrane bound compartment. However, we agree that at lower concentrations, there might be some restricted localization, explaining a visible perinuclear ring of 4-HNE staining in B6 macrophages. This phenomenon may be explained just by thicker cytoplasm surrounding nucleus in activated macrophages spread on adherent plastic surface or by proximity to specific organelles involved in generation or clearance of LPO products and definitively warrants further investigation.

      We also included images of non-stimulated cells in Fig.3H, Suppl.Fig.3A and 3E. We used multiple fields for imaging and quantified fluorescence signals (Suppl. Fig.3D and 3F, Suppl.Fig.4G, Suppl.Fig.6A and B).

      We used negative controls without primary antibodies for the initial staining optimization, but did not include it in every experiment.

      To interpret the evaluation on the hierarchy of molecular mechanisms in B6.Sst1S macrophages, comparative analyses with B6 control cells should be included (e.g. Fig 4C-I, Fig 5, Fig 6B, E-M, S6C, S6E-F). This will provide weight to the conclusions that the dysregulated processes are specifically associated with the susceptibility of B6.Sst1S macrophages.

      Understanding the sst1-mediated effects on macrophage activation is the focus of our previously published studies Bhattacharya et al., JCI, 2021) and this manuscript. The data comparing B6 and B6.Sst1S macrophage are presented in Fig.1, Fig.2, Fig.3, Fig.4, Fig.5A-C, I and J, Fig.6A-C, 6J and corresponding supplemental figures 1, 2, 3, 4A and B, Suppl.Fig.5, Suppl.Fig.6C, Suppl.Fig.7A-D,7F.

      Once we identified the aberrantly activated pathways in the B6.Sst1S, we used specific inhibitors to correct the aberrant response in B6.Sst1S.

      All experiments using inhibitory antibodies require comparison to the effect of a matched isotype control in the same experiment (e.g. Fig 3J, 4F, G, I; 6L, 6M, S3G, S6F).

      Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C-E, Fig.6L-M Suppl.Fig.4F-G, 7I.

      Experiments using inhibitors require inclusion of an inhibitor-only control to assess inhibitor effects on unstimulated cells (e.g. Fig 4I, 5D-I)

      Inhibitor effects on non-stimulated cells were included in Fig.5 D-H, Suppl.Fig.6A and B.

      Fig 3K and Fig 5J appear to contain the same images for p-c-Jun and b-tubulin blots.

      Fig.3K and 5J partially overlapped but had different focus – 3K has been updated to reflect the time course of stress kinase activation. Fig.5J is updated (currently Fig.5I and J) to display B6 and B6.Sst1S macrophage data including cMyc and p-cJun levels.

      Data of TNF-treated cells in Fig 3I appear to be replotted in Fig 3J.

      Currently these data is presented in Fig.3L and 3M and has been updated to include comparison of B6 and B6.Sst1S cells (Fig.3L) and effects of inhibitors in Fig.3M.

      It is stated that lungs from 2 mice with paucibacillary and 2 mice with multi-bacillary lesions were analyses. There is contradicting information on whether these tissues were collected at the same time post infection (week 14?) or whether the pauci-bacillary lesions were in lungs collected at earlier time points post infection (see Fig S8A). If the former, how do the authors conclude that multi-bacillary lesions are a progression from paucibacillary lesions and indicative of loss of M. tuberculosis control, especially if only one lesion type is observed in an individual host? If the latter, comparison between lesions will likely be dominated by temporal differences in the immune response to infection.

      In either case, it is relevant to consider density, location, and cellular composition of lesions (see also comments on GeoMx spatial profiling). Is the macrophage number/density per tissue area comparable between pauci-bacillary and multi-bacillary lesions?

      We did not collect lungs at the same time point. As described in greater detail in our preprints (Yabaji et al., https://doi.org/10.1101/2025.02.28.640830 and https://doi.org/10.1101/2023.10.17.562695) pulmonary TB lesions in our model of slow TB progression are heterogeneous between the animals at the same timepoint, as observed in human TB patients and other chronic TB animal models. Therefore, we perform analyses of individual TB lesions that are classified by a certified veterinary pathologist in a blinded manner based on their morphology (H&E) and acid fast staining of the bacteria, as depicted in Suppl.Fig.8. Currently it is impossible to monitor progression of individual lesions in mice. However, in mice TB is progressive disease and no healing and recovery from the disease have been observed in our studies or reported in literature. Therefore, we assumed that paucibacillary lesions preceded the multibacillary ones, and not vice versa, thus reflecting the disease progression. In our opinion, this conclusion most likely reflects the natural course of the disease. However, we edited the text : instead of disease progression we refer to paucibacillary and multibacillary lesions.

      Does 4HNE staining align with macrophages and if so, is it elevated compared to control mice and driven by TNF in the susceptible vs more resistant mice?

      We performed additional staining and analyses to demonstrate the 4-HNE accumulation in CD11b+ myeloid cells of macrophage morphology. Non-necrotic lesions contain negligible proportion of neutrophils (Fig.7B, Suppl.Fig.9B). B6 mice do not develop advanced multibacillary TB lesions containing 4-HNE+ cells. Also, 4-HNE staining was localized to TB lesions and was not found in uninvolved lung areas of the infected mice, as shown in Suppl.Fig.9A (left panel).

      It is well established that TNF plays a central role in the formation and maintenance of TB granulomas in humans and in all animal models. Therefore, TNF neutralization would lead to rapid TB progression, rapid Mtb growth and lesions destruction in both B6 and B6.Sst1S genetic backgrounds.

      Pathway analysis of spatial transcriptomic data (Suppl.Fig.11) identified TNF signaling via NFkB among dominant pathways upregulated in multibacillary lesions, suggesting that the 4-HNE accumulation paralleled increased TNF signaling. In addition, in vivo other cytokines, including IFN-I, could activate macrophages and stimulate production of reactive oxygen and nitrogen species and lead to the accumulation of LPO products as shown in this manuscript.

      It would be relevant to state how many independent lesions per host were sampled in both the multiplex IHC as well as the GeoMx data. Can the authors show the selected regions of interest in the tissue overview and in the analyses to appreciate within-host and across-host heterogeneity of lesions. The nature of the spatial transcriptomics platform used is such that the data are derived from tissue areas that contain more than just Iba1+ macrophages. At later stages of infection, the cellular composition of such macrophage-rich areas will be different when compared to lesions earlier in the infection process. Hence, gene expression profiles and differences between tissue regions cannot be attributed to macrophages in this tissue region but are more likely a reflection of a mix of cellular composition and per-cell gene expression.

      We used Iba1 staining to identify macrophages in TB lesions and programmed GeoMx instrument to collect spatial transcriptomics probes from Iba1+ cells within ROIs. Also, we selected regions of interest (ROI) avoiding necrotic areas (depicted in Suppl.Fig.10). We agree that Iba1+ macrophage population is heterogenous – some Iba1+ cells are activated iNOS+ macrophages, other are iNOS-negative (Fig.7C and D, and Suppl.Fig.13A). Multibacillary lesions contain larger areas occupied by activated (iNOS+) macrophages (Fig.7D,

      Suppl.Fig.13B and 13F). Although the GeoMx spatial transcriptomic platform does not provide single cell resolution, it allowed us to compare populations of Iba1+ cells in paucibacillary and multibacillary TB lesions and to identify a shift in their overall activation pattern.

      It is stated that loss of control of M. tuberculosis in multibacillary lesions was associated with "downregulation of IFNg-inducible genes". If the authors base this on the tissue expression of individual genes, this requires further investigation to support such conclusion (also see comment on GeoMx above). Furthermore, how might this conclusion be compatible with significantly elevated iNOS+ cells (Fig 7D) in multibacillary lesions?

      We demonstrated that Ciita gene expression is specifically induced by IFN-gamma and is suppressed by IFN-I (Fig.6M). The expression of Ciita in paucibacillary lesions suggest the presence of the IFN-gamma activated cells and its disappearance in the multibacillary lesion is consistent with massive activation of IFN-I pathway (Fig.7C).

      It is appreciated that the human blood signature analyses contain Myc-signatures but the association with treatment failure is not very strong based on the data in Fig 13B and C (Suppl.Fig.15B and C now). The authors indicate that they have no information on disease severity, but it should perhaps not be assumed that treatment failure is indicative of poor host control of the infection. Perhaps independent analyses in separate cohort/data set can add strength and provide -additional insights (e.g. PMID: 35841871; PMID: 32451443, PMID: 17205474, PMID: 22872737). In addition, the human data analyses could be strengthened by extension to additional signatures such as IFN, TNF, oxidative stress. Details of the human study design are not very clear and are lacking patient demographics, site of disease, time of blood collection relative to treatment onset, approving ethics committees.

      X axis of Suppl.Fig.15A represent pre-defined molecular signature gene sets (MSigDB) in Gene Set Enrichment Analysis (GSEA) database (https://www.gseamsigdb.org/gsea/msigdb). On Y axis is area under curve (AUC) score for each gene set. The Myc upregulated gene set myc_up was identified among top gene sets associated with treatment failure using unbiased ssGSEA algorithm. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis.

      Pathway analysis of the differentially expressed genes revealed that treatment failures were associated with the following pathways relevant to this study: NF-kB Signaling, Flt3 Signaling in Hematopoietic Progenitor Cells (indicative of common myeloid progenitor cell proliferation), SAPK/JNK Signaling and Senescence (indicative of oxidative stress). The upregulation of these pathways in human patients with poor TB treatment outcomes correlates with our findings in TB susceptible mice. The detailed analysis of differentially regulated pathways in human TB patients is beyond the scope of this study and is presented in another manuscript entitled “ Tuberculosis risk signatures and differential gene expression predict individuals who fail treatment” by Arthur VanValkenburg et al., submitted for publication.

      Blood collection for PBMC gene expression profiling of TB patients was prior to TB treatment or within a first week of treatment commencement. Boxplot of bootstrapped ssGSEA enrichment AUC scores from several oncogene signatures ranked from lowest to highest AUC score, with myc_up and myc_dn genes highlighted in red.

      We agree with the reviewer that not every gene in the myc_up gene set correlates with the treatment outcome. But the association of the gene set is statistically significant, as presented in Suppl.Fig.15B – C.

      We updated the details of the study, including study sites and the ethics committee approval statement and references describing these cohorts.

      Other comments

      It is excellent that the authors provide individual data points. Choosing a colour other than black would increase clarity when black bars are used.

      We followed this useful suggestion and selected consistent color codes for B6 and B6.Sst1S groups to enhance clarity throughout the revised manuscript.

      Error bars are inconsistently depicted as either bi-directional or just unidirectional.

      We used bi-directional error bars in the revised manuscript.

      Fig 1E, G, H - please include a scale to clarify what the heat map is representing.

      We have included the expression key in Fig.1E,G and H and Suppl.Fig.1C and D in the revised version.

      Fig 2K, Fig S10A gene information cannot be deciphered.

      We increased the font in previous Fig.2K and moved to supplement to keep larger fonts (current Suppl.Fig.2G).

      Fig S4A,B please add error bars.

      These data are presented as Suppl.Fig.5 in the revised version. We performed one experiment to test the hypothesis. Because the data indicated no clear increase in transposon small RNAs in the sst1S macrophages, we did not pursue this hypothesis further, and therefore, the error bars were not included. However, we decided to include these negative data because it rejects a very attractive and plausible hypothesis.

      Please use gene names as per convention (e.g. Ifnb1) to distinguish gene expression from protein expression in figures and text.

      We addressed the comment in the revised manuscript.

      Fig S8B. Contrary to the description of results, there seems to be minimal overlap between the signal for YFP and the Ifnb1 probe. Is the Ifnb1 reporter mouse a legacy reporter? If so, it is worth stating this and including such considerations in the data interpretation.

      The YFP reporter expresses YFP protein under the control of the Ifnb1 promoter. The YFP protein accumulates within the cells and while Ifnb protein is rapidly secreted and does not accumulate in the producing cells in appreciable amounts. So YFP is not a lineage tracing reporter, but its accumulation marks the Ifnb1 promoter activity in cells, although the YFP protein half-life is longer than that of the Ifnb1 mRNA that is rapidly degraded (Witt et al., BioRxiv, 2024; doi:10.1101/2024.08.28.61018). Therefore, there is no precise spatiotemporal coincidence of these readouts.

      Please clarify what is meant by "normal interstitium" ? If the tissue is from uninfected mice, please state clearly.

      In this context we refer to the uninvolved lung areas of the infected lungs. In every sample we compare uninvolved lung areas and TB lesions of the same animal. Also, we performed staining of lung of non-infected mice as additional controls.

      If macrophage cultures underwent media changes every 48h, how was loss of liberated Mtb taken into account especially if differences in cell density/survival were noted? The assessment of M. tuberculosis load by qPCR is not well described. In particular, the method of normalization applied within the experiments (not within the qPCR) here remains unclear, even with reference to the authors' prior publication.

      Our lab has many years of experience working with macrophage monolayers infected with virulent Mtb and uses optimized protocols to avoid cell losses and related artifacts. Recently we published a detailed protocol for this methodology in STAR Protocols (Yabaji et al., 2022; PMID 35310069). In brief, it includes preparation of single cell suspensions of Mtb by filtration to remove clumps, use of low multiplicity of infection, preparation of healthy confluent monolayers and use of nutrient rich culture medium and medium change every 2 days. We also rigorously control for cell loss using whole well imaging and quantification of cell numbers and live/dead staining.

      Please add citation for the limma package.

      The references has been added (Ritchie et al, NAR 2015; PMID 25605792).

      The description of methodology relating to the "oncogene signatures" is unclear.

      This signature was described in Bild etal, Nature, 2006 and McQuerry JA, et al, 2019 “Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes”. BMC Cancer 19: 881 and is cited in Methods section Oncogene signatures

      Please clearly state time points post infection for mouse analyses.

      We collected lung samples from Mtb infected mice 12 – 20 weeks post infection. The lesions were heterogeneous and were individually classified using criteria described above.

      Reference is made to "a list of genes unique to type I [interferon] genes [....]" (p29). Can the authors indicate the source of the information used for compiling this list?

      The lists were compiled from Reactome, EMBL's European Bioinformatics Institute and GSEA databases. The links for all datasets are provided in Suppl.Table 8 “Expression of IFN pathway genes in Iba1+ cells from pauci- and multi-bacillary lesions of Mtb infected B6.Sst1S mouse lungs” in the “Pool IFN I & II gene sets” worksheet.

      The discussion at present is very long, contains repetition of results and meanders on occasion.

      Thank you for this suggestion, We critically revised the text for brevity and clarity.

      Reviewer #1 (Significance):  

      Strengths and limitations  

      Strengths: multi-pronged analysis approaches for delineating molecular mechanisms of macrophage responses that might underpin susceptibility to M. tuberculosis infection; integration of mouse tissues and human blood samples  

      Weaknesses: not all conclusions supported by data presented; some concerns related to experimental design and controls; links between findings in human cohort and the mechanistic insights gained in mouse macrophage model uncertain

      The revised manuscript addresses every major and minor comment of the reviewers, including isotype controls and naïve T cells, to provide additional support for our conclusions. Our study revealed causal links between Myc hyperactivity with the deficiency of anti-oxidant defense and type I interferon pathway hyperactivity. We have shown that Myc hyperactivity in TNF-stimulated macrophages compromises antioxidant defense leading to autocatalytic lipid peroxidation and interferon-beta superinduction that in turn amplifies lipid peroxidation, thus, forming a vicious cycle of destructive chronic inflammation. This mechanism offers a plausible mechanistic explanation of for the association of Myc hyperactivity with poorer treatment outcomes in TB patients and provide a novel target for host-directed TB therapy.

      Advance

      The study has the potential to advance molecular understanding of the TNF-driven state of oxidative stress previously observed in B6.Sst1S macrophages and possible implications for host control of M. tuberculosis in vivo.

      Audience

      Experts seeking understanding of host factors mediating M. tuberculosis control, or failure thereof, with appreciation for the utility of the featured mouse model in assessing TB diseases progression and severe manifestation. Interest is likely extended to audience more broadly interested in TNF-driven macrophage (dys)function in infectious, inflammatory, and autoimmune pathologies.

      Reviewer expertise

      In preparing this review, I am drawing on my expertise in assessing macrophage responses and host defense mechanisms in bacterial infections (incl. virulent M. tuberculosis) through in vitro and in vivo studies. This includes but is not limited to macrophage infection and stimulation assays, microscopy, intra-macrophage replication of M. tuberculosis, analyses of lung tissues using multi-plex IHC and spatial transcriptomics (e.g. GeoMx). I am familiar with the interpretation of RNAseq analyses in human and mouse cells/tissues, but can provide only limited assessment of appropriateness of algorithms and analysis frameworks.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Yabaji et al. investigated the effects of BMDMs stimulated with TNF from both WT and B6.Sst1S mice, which have previously been identified to contain the sst1 locus conferring susceptibility to Mycobacterium tuberculosis. They identified that B6.Sst1S macrophages show a superinduction of IFNß, which might be caused by increased c-Myc expression, expanding on the mechanistic insights made by the same group (Bhattacharya et al. 2021). Furthermore, prolonged TNF stimulation led to oxidative stress, which WT BMDMs could compensate for by the activation of the antioxidant defense via NRF2. On the other hand, B6.Sst1S BMDMs lack the expression of SP110 and SP140, co-activators of NRF2, and were therefore subjected to maintained oxidative stress. Yabaji et al. could link those findings to in vivo studies by correlating the presence of stressed and aberrantly activated macrophages within granulomas to the failure of Mtb control, as well as the progression towards necrosis. As the knowledge regarding Mtb progression and necrosis of granulomas is not yet well understood, findings that might help provide novel therapy options for TB are crucial. Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

      However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

      In particular a) important controls are often missing, e.g. T-cells form non-immune mice in Fig. 6J, in F, effectivity of BCG in B6 mice in 6N; b) single experiments are shown throughout the manuscript, in particular western blots and histology without proper quantification and statistics, this is absolutely not acceptable; c) very few repetitions are shown in in vitro experiments, where there is no evidence for limitation in resources (usually not more than 3), it is not clear what "independent experiment means" - i.e. the robustness of the findings is questionable; d) data are often normalized multiple times, e.g. in the case of qPCR, and the methods of normalization are not clear (what house-keeping gene exactly?);

      Moreover, experiments regarding IFN I signaling (e.g. short term TNF treatment of BMDMs to analyze LPO, making sure that the reporter mouse for IFNß works in vivo) and c-Myc (e.g. the increase after M-CSF addition might impact on other analysis as well and the experiments should be adjusted to control for this effect; MYC expression in the human samples) should be carefully repeated and evaluated to draw correct conclusions.

      In addition, we would like to strongly encourage the authors to more precisely outline the experimental set-ups and figure legends, so that the reader can easily understand and follow them. In other words: The legends are - in part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

      We appreciate a very thorough evaluation of our manuscript by this reviewer. Their insightful comments helped us improve the manuscript. As outlined below in point-by-point responses (1) we added important controls including isotype control antibodies in IFNAR blocking experiments and non-vaccinated T cells in T cell – macrophage interactions experiments; updated figure legends to indicate number of repeated experiment where a representative experiment is shown, numbers of mouse lungs and individual lesions, methods of data normalization, where it was missing. We also explained our in vitro experimental design and how we analyzed and excluded effects of media change and fresh CSF1 addition, by using a rest period before TNF stimulation and Mtb infection. The data shown in Suppl. Fig. 6C (previously Suppl. Fig. 5B) demonstrate that Myc levels induced by CSF1 return to the basal level at 12 h after media change. Our detailed in vitro protocol that contains these details has been published (Yabaji et al., STAR Protocols, 2022). We added new data demonstrating the ROS and LPO production at 6h of TNF stimulation, while the Ifnb1 mRNA super-induction occurred at 16 – 18 h, and edited the text to highlight these dynamics. The upregulation of Myc pathway in human samples does not necessarily mean the upregulation of Myc itself, it could be due to the dysregulation of downstream pathways. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis. The detailed analysis of this cell populations in human patients is suggested by our findings but it is beyond the scope of this study.

      The reviewer’s comments also suggested that a summary of our findings was necessary. The main focus of our study was to untangle connections between oxidative stress and Ifnb1 superinduction. It revealed that Myc hyperactivity caused partial deficiency of antioxidant defense leading to type I interferon pathway hyperactivity that in turn amplifies lipid peroxidation, thus establishing a vicious cycle driving inflammatory tissue damage.

      Our laboratory worked on mechanisms of TB granuloma necrosis over more than two decades using genetic, molecular and immunological analyses in vitro and in vivo. It provided mechanistic basis for independent studies in other laboratories using our mouse model and further expanding our findings, thus supporting the reproducibility and robustness of our results and our lab’s expertise.

      Specific comments to the experiments and data:

      - Fig. 1E: Evaluation of differences in up- and downregulation between B6 and B6.Sst1S cells should highlight where these cells are within the heatmap, as it is only labelled with the clusters, or it should be depicted differently (in particular for cluster 1 and 2). Furthermore, a more simple labelling of the pathways would increase the readability of the data.

      For our scRNAseq data presentation, we used formats accepted by computational community. To clarify Fig.1E, we added labels above B6 and B6.Sst1S-specific clusters.

      - Fig. 2D, E: The staining legend is missing. For the quantification it is not clear what % total means. Is this based on the intensity or area? What do the dots represent in the bar chart? Is one data point pooled from several pictures? If not, the experiments need to be repeated, as three pictures might not be representative for evaluation.

      - Fig. 2E: Statistics comparing B6/ B6,SsT1S with TNF (different) is required: Absence of induction is not a proof for a difference!

      We included staining with NRF2-specific antibodies and performed area quantification per field using ImageJ to calculate the NRF2 total signal intensity per field. Each dot in the graph represents the average intensity of 3 fields in a representative experiment. The experiment was repeated 3 times. We included pairwise comparison of TNF-stimulated B6 and B6.Sst1S macrophages and updated the figure legend.

      - Fig. 3E: Positive and negative control need to be depicted in the figure (see legend).

      We have added the positive and negative controls for the determination of labile iron pool to the data in Fig. 3E and related Suppl. Fig. 3B and to Fig. 5D that also demonstrates labile iron determination.

      - Fig. 3I: A quantification by flow cytometry or total cell counts are important, as 6% cell death in cell culture is a very modest observation. Otherwise, confocal images of the quantification would be a good addition to judge the specificity of the viability staining.

      To validate the specificity of the viability staining method, we have provided fluorescent images as Suppl.Fig.3H. The main point of this experiment was to demonstrate a modest, but reproducible, increase in cell death in the sst1-mutant macrophages that suggested an IFNdependent oxidative damage. In our study, we did not focus on mechanisms of cell death, but on a state of chronic oxidative stress in the sst1 mutant live cells during TNF stimulation.

      - Fig. 3I, J: What does one dot represent?

      We performed this assay in 96 well format and each dot represent the % cell death in an individual well.

      - Fig. 3K,L: For the B6 BMDMs it seems that p-cJun is highly increased at 12h in (L), while it is not in (K). On the other hand, for the B6.Sst1S BMDMs it peaks at 24h in (K), while in (L) it seems to at 12h. According to the data in (L) it seems that p-cJun is rather earlier and stronger activated in B6 BMDMs and has a weakened but prolonged activation in the B6.Sst1S BMDMs, which would not fit with your statement in the text that B6.Sst1S BMDMs show an upregulation.

      These experiments need repetitions and quantification and statistiscs.

      Fig. 3L: ASK1 seems to be higher at 12h for the B6 BMDMs and similar for both lines at 24h, which is not fitting to the statement in the text. ("Also, the ASK1 - JNK - cJun stress kinase axis was upregulated in B6.Sst1S macrophages, as compared to B6, after 12 - 36 h of TNF stimulation")

      These experiments were repeated, and new data were added to highlight differences in ASK1 and c-Jun phosphorylation between B6 and B6.Sst1S at individual timepoints after TNF stimulation (presented in new Fig.3K). It demonstrated that after TNF stimulation the activation of stress kinases ASK1 and c-Jun initially increased in both genetic backgrounds. However, their upregulation was maintained exclusively in the sst1-susceptible macrophages from 24 to 36 h of TNF stimulation, while in the resistant macrophages their upregulation was transient. Thus, during prolonged TNF stimulation, B6.Sst1S macrophages experience stress that cannot be resolved, as evidenced by this kinetic analysis. The quantification of the band intensity was added to Western blot images above individual lanes.

      Reviewer 2 pointed to missing isotype control antibodies in Fig.3 and Fig.4:

      - Figure 3J: the isotype control for the IFNAR antibody is missing

      - Figure 4E: It seems the isotype control itself has already an effect in the reduction of IFNb.

      - Fig. 4H: It seems that the Isotype control antibody had an effect to increase 4-HNE (compared to TNF stimulated only).

      We always include isotype control antibodies in our experiments because antibodies are known to modulate macrophage activation via binding to Fc receptor. To address the reviewer’s comments, we updated all panels that present the effects of IFNAR1 blockade with isotypematched non-specific control antibodies in the revised manuscript. Specifically, we included isotype control in Fig. 3M (previously Fig.3J), Fig.4I, Suppl.4E-G, Fig.6L-M), Suppl.Fig.7I (previously Suppl.Fig.6F).

      - Fig.4A - C: "IFNAR1 blockade, however, did not increase either the NRF2 and FTL protein levels, or the Fth, Ftl and Gpx1 mRNA levels above those treated with isotype control antibodies"

      Maybe not above the isotype but it is higher than the TNF alone stimulation at least for NRF2 at 8h and for Ftl at both time points. Why does the isotype already cause stimulation/induction of the cells? !These experiments need repetitions and quantification and statistics!

      To determine specific effects of IFNAR blockade we compared effects of non-specific isotype control and IFNAR1-specific antibodies. In our experiments, the isotype control antibody modestly increased of Nrf2 and Ftl protein levels and the Fth and Ftl mRNA levels, but their effects were similar to the effect of IFNAR-specific antibody. The non-IFN -specific effects of antibodies, although are of potential biological significance, are modest in our model and their analysis is beyond the scope of this study.

      - Fig.4H Was the AB added also at 12h post stimulation? Figure legend should be adjusted.

      The IFNAR1 blocking antibodies and isotype control antibodies were added at 2 h after TNF stimulation in Fig.4H and 4I, as described in the corresponding figure legend. The data demonstrating effects of IFNAR blockade after 12, 24,and 33h of TNF stimulation are presented in Suppl.Fig.4 E-G.

      - Figure 4I: How was the data measured here, i.e. what is depicted? The isotype control is missing. It seems a two-way ANOVA was used, yet it is stated differently. The figure legend should be revised, as Dunnett's multiple comparison would only check for significances compared to the control.

      The microscopy images and bar graphs were updated to include isotype control and presented in Suppl. Fig.4E - G of the revised version. We also revised the statistical analysis to include correction for multiple comparisons.

      - Figure 4C and subsequent: How exactly was the experiment done (house-keeping gene)?

      We included the details in the figure legends of revised version. We quantified the gene expression by DDCt method using b-actin (for Fig. 4C-E) and 18S (For Fig. 4F and G) as internal controls.

      - Figure 4D,E: Information on cells used is missing. Why the change in stimulation time? Did it not work after 12h? Then the experiments in A-C should be repeated for 16h.

      The updated Fig. 4D and E present comparison of B6 and B6.Sst1S BMDMs clearly demonstrating significant difference between these macrophages in Ifnb1 mRNA expression 16 h after TNF stimulation, in agreement with our previous publication(Bhattacharya, et al., 2021). There we studied the time course of responses of B6 and B6.Sst1S macrophages to TNF at 2h intervals and demonstrated the divergence between their activation trajectories starting at 12 h of TNF stimulation Therefore, to reveal the underlying mechanisms we focus our analyses on this critical timepoint, i.e. as close to the divergence as possible. However, the difference between the strains in Ifnb1 mRNA expression achieved significance only by 16h of TNF stimulation. That is why we have used this timepoint for the Ifnb1 and Rsad2 analyses. It clearly shows that the superinduction was not driven by the positive feedback via IFNAR, as has been shown by the Ivashkiv lab for B6 wild type macrophages previously PMID 21220349.

      - Figure 4E: It would be helpful to see if these transcripts are actually translated into protein levels, e.g. perform an ELISA. Authors state that IFNAR blockages does not alter the expression but you statistic says otherwise.

      - The data for Ifnb expression (or better protein level) should be provided for B6 BMDMs as well.

      We have previously reported the differences in Ifnb protein secretion (He et al., Plos Pathogens, 2013 and Bhattacharya et al., JCI 2021). We use mRNA quantification by qRT-PCR as a more sensitive and direct measurement of the sst1-mediated phenotype. The revised Fig.4D and E include responses of B6 in addition to the B6.Sst1S to demonstrate that the IFNAR blockade does not reduce the Ifnb1 mRNA levels in TNF-stimulated B6.Sst1S mutant to the B6 wild type levels. A slight reduction can be explained by a known positive feedback loop in the IFN-I pathway (see above). In this experiment we emphasized that the effect of the sst1 locus is substantially greater, as compared to the effect of the IFNAR blockade (Fig.4D), and updated the text accordingly.

      - Fig. 4F: To what does the fold induction refer to? If it is again to unstimulated cells, then why is the induction now so much higher than in (E) where it was only 50x (now to 100x).

      - Figure 4G: Again to what is the fold induction referring to? It seems your Fer-1 treatment only contains 2 data points. This needs to be fixed.

      Yes, the fold induction was calculated by normalizing mRNA levels to untreated control incubated for the same time. Regarding the variation in Ifnb1 mRNA levels - a two-fold variation is not unusual in these experiments that may result in the Ifnb1 mRNA superinduction ranging from 50 -200-fold at this timepoint (16h). The graph in Fig.4G was modified to make all datapoints more visible.

      - "These data suggest that type I IFN signaling does not initiate LPO in our model but maintains and amplifies it during prolonged TNF stimulation that, eventually, may lead to cell death". Data for a short term TNF stimulation are not shown, however, so it might impact also on the initiation of LPO.

      - The overall conclusion drawn from Fig. 3 and 4 is not really clear with regard that IFN does not initiate LPO. Where is that shown? Data on earlier stimulation time points should be added to make this clear.

      We demonstrated ROS production (new Suppl.Fig.3G) and the rate of LPO biosynthesis (new Suppl.Fig.4E-F) at 6 h post TNF stimulation, while the Ifnb1 superinduction occurs between 12-18 h post TNF stimulation. This temporal separation supports our conclusion that IFN-β superinduction does not initiate LPO. We clarified it in the text:

      “Thus, Ifnb1 super-induction and IFN-I pathway hyperactivity in B6.Sst1S macrophages follow the initial LPO production, and maintain and amplify it during prolonged TNF stimulation”. (Previously: These data suggest that type I IFN signaling does not initiate LPO in our model). We also edited the conclusion in this section to explain the hierarchy of the sst1-regulated AOD and IFN-I pathways better:

      “Taken together, the above experiments allowed us to reject the hypothesis that IFN-I hyperactivity caused the sst1-dependent AOD dysregulation. In contrast, they established that the hyperactivity of the IFN-I pathway in TNF-stimulated B6.Sst1S macrophages was itself driven by the initial dysregulation of AOD and iron-mediated lipid peroxidation. During prolonged TNF stimulation, however, the IFN-I pathway was upregulated, possibly via ROS/LPOdependent JNK activation, and acted as a potent amplifier of lipid peroxidation”.

      We believe that these additional data and explanation strengthen our conclusions drawn from Figures 3 and 4.

      - "A select set of mouse LTR-containing endogenous retroviruses (ERV's) (Jayewickreme et al, 2021), and non-retroviral LINE L1 elements were expressed at a basal level before and after TNF stimulation, but their levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6". This sentence should be revised as the differences between B6 and B6.Sst1S BMDMs seem small and are not there after 48h anymore. Are these mild changes really caused by the mutation or could they result from different housing conditions and/or slowly diverging genetically lines. How many mice were used for the analysis? Is there already heterogeneity between mice from the same line?

      We agree with the reviewer that the data presented in Suppl.Fig.4 (Suppl.Fig.5 in the revised version) indicated no increase in single- and double-stranded transposon RNAs in the B6.Sst1S macrophages. The purpose of these experiment was to test the hypothesis that increased transposon expression might be responsible for triggering the superinduction of type I interferon response in TNF-stimulated B6.Sst1S macrophages. In collaboration with a transposon expert Dr. Nelson Lau (co-author of this manuscript) we demonstrated that transposon expression was not increased above the B6 level and, thus, rejected this attractive hypothesis. We explained the purpose of this experiment in the text and adequately described our findings as “the levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6”…and concluded that ” the above analyses allowed us to exclude the overexpression of persistent viral or transposon RNAs as a primary mechanism of the IFN-I pathway hyperactivity” in the sst1-mutant macrophages.

      - Fig. 5A: Indeed, it even seems that Myc is upregulated for the mutant BMDMs. Yet, there are only 2 data points for B6 12h.

      These experiments need repetitions and quantification and statistics.

      We observed these differences in c-Myc mRNA levels by independent methods: RNAseq and qRT-PCR. The qRT-PCR experiments were repeated 3 times. A representative experiment in Fig.5A shows 3 data points for each condition. We reformatted the panel to make all data points clearly visible.

      - Fig. 5B: Why would the protein level decrease in the controls over 6h of additional cultivation? Is this caused by fresh M-CSF? In this case maybe cells should be left to settle for one day before stimulating them to properly compare c-Myc induction. Comment on two c-Myc bands is needed. At 12h only the upper one seems increased for TNF stimulated mutant BMDMs compared to B6 BMDMs.

      We agree with the reviewer’s point that cells need to be rested after media change that contains fresh CSF-1. Indeed, in Suppl.Fig.6C, we show that after media change containing 10% L929 supernatant (a source of CSF1) there is an increase in c-Myc protein levels that takes approximately 12 hours to return to baseline.

      Our protocol includes resting period of 18-24 h after medium change before TNF stimulation.

      We updated Methods to highlight this detail. Thus, the increase in c-Myc levels we observe at 12 h of TNF stimulation (Fig.5B) is induced by TNF, not the addition of growth factors, as further discussed in the text.

      The two c-Myc bands observed in Fig.5B,I and J, are similar to patterns reported in previous studies that used the same commercial antibodies (PMIDs: 24395249, 24137534, 25351955). Whether they correspond to different c-Myc isoforms or post-translational modifications is unknown.

      - Fig. 5A,B: It seems that not all the RNA is translated into protein, as c-Myc at 12h in the mutant BMDMs seems to be lower than at 6h, while the gene expression implicates it vice versa.

      In addition to Fig.5B, the time course of Myc protein expression up to 24 h is presented in new panels Fig. 5I-5J. It demonstrates the gradual decrease of Myc protein levels. The observed dissociation between the mRNA and protein levels in the sst1-mutant BMDMs at 12 and 24 h is most likely due to translation inhibition as a result of the development of the integrated stress response, ISR (as shown in our previous publication by Bhattacharya et al., JCI, 2021). Translation of Myc is known to be particularly sensitive to the ISR (PMID18551192, PMID25079319, PMID28490664). Perhaps, the IFN-driven ISR may serve as a backup mechanism for Myc downregulation. We are planning to investigate these regulatory mechanisms in greater detail in the future.

      - Fig. 5J: Indeed, the inhibitor seems to cause the downregulation of the proteins. Explanation?

      This experiment was repeated twice and the average normalized densitometry values are presented in the updated Fig.5J. The main question addressed in this experiment was whether hyperactivity of JNK in TNF-stimulated sst1 mutant macrophages contributed to Myc upregulation, as had been previously shown in cancer. Comparing effects of JNK inhibition on phospho-cJun and c-Myc protein levels in TNF stimulated B6.Sst1S macrophages (updated Fig.5J), we rejected the hypotghesis that JNK activity might have a major role in c-Myc upregulation in sst1 mutant macrophages.

      - "TNF stimulation tended to reduce the LPO accumulation in the B6 macrophages and to increase it in the B6.Sst1S ones" However, this is not apparent in Sup. Fig. 6B. Here it seems that there might be a significant increase.

      Suppl.Fig.6B (currently Suppl.Fig.7B) shows the 4-HNE accumulation at day 3 post infection. The data obtained after 5 days of Mtb infection are shown in Fig.6A. We clarified this in the text: “By day 5 post infection, TNF stimulation induced significant LPO accumulation only in the B6.Sst1S macrophages (Fig.6A)”.

      - Fig. 6B: Mtb and 4-HNE should be shown in two different channels in order to really assign each staining correctly.

      What time point is this? Are the mycobacteria cleared at MOI1, since it looks that there are fewer than that? How does this look like for the B6 BMDMs? Are there even less mycobacteria?

      We included B6 infection data to the updated Fig.6B and added Suppl.Fig.7C and 7D that address this reviewer’s comment. The data represent day 5 of Mtb infection as indicated in the updated Fig.6B and Suppl.Fig.7C and 7D legends. New Suppl.Fig.7D shows quantification of replicating Mtb using Mtb replication reporter stain expressing single strand DNA binding protein GFP fusion, as described in Methods. We observed fewer Mtb and a lower percentage of replicating Mtb in B6 macrophages, but we did not observe a complete Mtb elimination in either background.

      We used red fluorescence for both Mtb::mCherry and 4-HNE staining to clearly visualize the SSB-GFP puncta in replicating Mtb DNA. In the revised manuscript, we have included the relevant channels in Suppl. Fig.7C and D to demonstrate clearly distinct patterns of Mtb::mCherry and 4-HNE signals. We did not aim to quantify the 4-HNE signal intensity in this experiment. For the 4-HNE quantification we use Mtb that expressed no reporter proteins (Fig.6A-B and Suppl.Fig.7A-B).

      - Fig 6E: In the context of survival a viability staining needs to be included, as well as the data from day 0. Then it needs to be analyzed whether cell numbers remain the same from D0 or if there is a change.

      We updated Fig.6 legend to indicate that the cell number percentages were calculated based on the number of cells at Day 0 (immediately after Mtb infection). We routinely use fixable cell death staining to enumerate cell death to exclude artifacts due to cell loss. Brief protocol containing this information is included in Methods section. The detailed protocol including normalization using BCG spike has been published – Yabaji et al, STAR Protocols, 2022. Here we did not present dead cell percentage as it remained low and we did not observe damage to macrophage monolayers. The fold change of Mtb was calculated after normalization using Mtb load at Day 0 after infection and washes.

      "The 3D imaging demonstrated that YFP-positive cells were restricted to the lesions, but did not strictly co-localize with intracellular Mtb, i.e. the Ifnb promoter activity was triggered by inflammatory stimuli, but not by the direct recognition of intracellular bacteria. We validated the IFNb reporter findings using in situ hybridization with the Ifnb probe, as well as anti-GFP antibody staining (Suppl.Fig.8B - E)." The colocalization is not present within the tissue sections. It seems that the reporter line does not show the same staining pattern in vivo as the IFNß probe or the anti GFP antibody staining. The reporter line has to be tested for the specificity of the staining. Furthermore, to state that it was restricted to the lesions, an uninvolved tissue area needs to be depicted.

      The Ifnb secreting cells are notoriously difficult to detect in vivo using direct staining of the protein. Therefore, lineage tracing of reporter expression are used as surrogates. The Ifnb reporter used in our study has been developed by the Locksley laboratory (Scheu et al., PNAS, 2008, PMID: 19088190) and has been validated in many independent studies. The reporter mice express the YFP protein under the control of the Ifnb1 promoter. The YFP protein accumulates within the cells, while Ifnb protein is rapidly secreted and does not accumulate in the producing cells in appreciable amounts. Also, the kinetics of YFP protein degradation is much slower as compared to the endogenous Ifnb1 mRNA that was detected using in situ hybridization. Thus, there is no precise spatiotemporal coincidence of these readouts in Ifnb expressing cells in vivo. However, this methodology more closely reflect the Ifnb expressing cells in vivo, as compared to a Cre-lox mediated lineage tracing approach. In the revised manuscript we demonstrate that both YFP and mRNA signals partially overlap (Suppl.Fig.12B). In Suppl.Fig.12B. we also included a new panel showing no YFP expression in the uninvolved area of the reporter mice infected with Mtb. The YFP expression by activated macrophages is demonstrated by co-staining with Iba1- and iNOS-specific antibodies (new Fig.7D and Suppl.Fig.13A). Our specificity control also included TB lesions in mice that do not carry the YFP reporter and did not express the YFP signal, as reported elsewhere (Yabaji et al., BioRxiv, https://doi.org/10.1101/2023.10.17.562695).

      - Are paucibacillary and multibacillary lesions different within the same animal or does one animal have one lesion phenotype? If that is the case, what is causing the differences between mice? Bacterial counts for the mice are required.

      The heterogeneity of pulmonary TB lesions has been widely acknowledged in clinic and highlighted in recent experimental studies. In our model of chronic pulmonary TB (described in detail in Yabaji et al., https://doi.org/10.1101/2025.02.28.640830 and https://doi.org/10.1101/2023.10.17.562695) the development of pulmonary TB lesions is not synchronized, i.e. the lesions are heterogeneous between the animals and within individual animals at the same timepoint. Therefore, we performed a lesion stratification where individual lesions were classified by a certified veterinary pathologist in a blinded manner based on their morphology (H&E) and acid fast staining of the bacteria, as depicted in Suppl.Fig.8.

      - "Among the IFN-inducible genes upregulated in paucibacillary lesions were Ifi44l, a recently described negative regulator of IFN-I that enhances control of Mtb in human macrophages (DeDiego et al, 2019; Jiang et al, 2021) and Ciita, a regulator of MHC class II inducible by IFNy, but not IFN-I (Suppl.Table 8 and Suppl.Fig.10 D-E)." Why is Sup. Fig. 10 D, E referred to? The figure legend is also not clear, e.g. what means "upregulated in a subset of IFN-inducible genes"? Input for the hallmarks needs to be defined.

      These data is now presented in Suppl.Fig.11 and following the reviewer’s comment, we moved reference to panels 11D – E up to previous paragraph in the main text, where it naturally belongs . We also edited the figure legend to refer to the list of IFN-inducible genes compiled from the literature that is discussed in the text. We appreciate the reviewer’s suggestion that helped us improve the text clarity. The inputs for the Hallmark pathway analysis are presented in Suppl.Tables 7 and 8, as described in the text.

      - Fig. 7C: Single channel pictures are required as it is hard to see the differences in staining with so many markers. Why is there no iNOS expression in the bottom row? What does the rectangle indicate on the bottom right? As black is chosen for DAPI, it is not visible at all. In case the signal is needed a visible a color should be chosen.

      We thoroughly revised this figure to address the reviewer’s concern about the lack of clarity. We provide individual channels for each marker in Fig.7D – E and Suppl.Fig.13F. We have to use DAPI in these presentation in gray scale to better visualize other markers.

      - "In the advanced lesions these markers were primarily expressed by activated macrophages (Iba1+) expressing iNOS and/or Ifny (YFP+)(Fig.7D)" Iba1 is needed in the quantification. Based on the images, iNOS seems to be highly produced in Iba1 negative cells. Which cells do produce it then? Flow cytometry data for this quantification are required. This would allow you to specifically check which cells express the markers and allow for a more precise analysis of double positive cells.

      Currently these data demonstrating the co-localization of stress markers phospho-c-Jun and Chac1 with YFP are presented in Fig.7E (images) and Suppl.Fig.13D (quantification). The co-localization of stress markers phospho-cJun and Chac1 with iNOS is presented in Suppl.Fig.13F (images) and Suppl.Fig.13E (quantification). We agree that some iNOS+ cells are Iba1-negative (Fig.7D). We manually quantified percentages of Iba1+iNOS+ double positive cells and demonstrated that they represent the majority of the iNOS+ population(Suppl.Fig.13A). Regarding the required FACS analysis, we focus on spatial approaches because of the heterogeneity of the lesions that would be lost if lungs are dissociated for FACS. We are working on spatial transcriptomics at a single cell resolution that preserves spatial organization of TB lesions to address the reviewer’s comment and will present our results in the future.

      - Results part 6: In general, can you please state for each experiment at what time point mice were analyzed? You should include an additional macrophage staining (e.g. MerTK, F4/80), as alveolar macrophages are not staining well for Iba1 and you might therefore miss them in your IF microscopy. It would be very nice if you could perform flow cytometry to really check on the macrophages during infection and distinguish subsets (e.g. alveolar macrophages, interstitial macrophages, monocytes).

      We have included the details of time post infection in figure legends for Fig.7, Suppl.Figures 8, 9, 12B, 13, 14A of the revised manuscript. We have performed staining with CD11b, CD206 and CD163 to differentiate the recruited and lung resident macrophages and determined that in chronic pulmonary TB lesions in our model the vast majority of macrophages are recruited CD11b+, but not resident (CD206+ and CD163+) macrophages. These data is presented in another manuscript (Yabaji et al., BioRxiv https://doi.org/10.1101/2023.10.17.562695).

      - Spatial sequencing: The manuscript would highly profit from more data on that. It would be very interesting to check for the DEGs and show differential spatial distribution. Expression of marker genes should be inferred to further define macrophage subsets (e.g. alveolar macrophages, interstitial macrophages, recruited macrophages) and see if these subsets behave differently within the same lesion but also between the lesions. Additional bioinformatic approaches might allow you to investigate cell-cell interactions. There is a lot of potential with such a dataset, especially from TB lesions, that would elevate your findings and prove interesting to the TB field.

      - "Thus, progression from the Mtb-controlling paucibacillary to non-controlling multibacillary TB lesions in the lungs of TB susceptible mice was mechanistically linked with a pathological state of macrophage activation characterized by escalating stress (as evidenced by the upregulation phospho-cJUN, PKR and Chac1), the upregulation of IFNβ and the IFN-I pathway hyperactivity, with a concurrent reduction of IFNγ responses." To really show the upregulation within macrophages and their activation, a more detailed IF microscopy with the inclusion of additional macrophage markers needs to be provided. Flow cytometry would enable analysis for the differences between alveolar and interstitial macrophages, as well as for monocytes. As however, it seems that the majority of iNOS, as well as the stress associated markers are not produced by Iba1+ cells. Analyzing granulocytes and T lymphocytes should be considered.

      We appreciate the reviewer’s suggestion. Indeed, our model provides an excellent opportunity to investigate macrophage heterogeneity and cell interactions within chronic TB lesions. We are working on spatial transcriptomics at a single cell resolution that would address the reviewer’s comment and will present our results in the future.

      In agreement with classical literature the overwhelming majority of myeloid cells in chronic pulmonary TB lesions is represented by macrophages. Neutrophils are detected at the necrotic stage, but our study is focused on pre-necrotic stages to reveal the earlier mechanisms predisposing to the necrotization. We never observed neutrophils or T cells expressing iNOS in our studies.

      - It's mentioned in the method section that controls in the IF staining were only fixed for 10min, while the infected cells were fixed for 30min. Consistency is important as the PFA fixation might impact on the fluorescence signal. Therefore, controls should be repeated with the same fixation time.

      We have carefully considered the impact of fixation time on fluorescence and have separately analyzed the non-infected and infected samples to address this concern. For the non-infected samples, we examined the effect of TNF in both B6 and B6.Sst1S backgrounds, ensuring that a consistent fixation protocol (10 min) was applied across all experiments without Mtb infection.

      For the Mtb infection experiments, we employed an optimized fixation protocol (30 min) to ensure that Mtb was killed before handling the plates, which is critical for preserving the integrity of the samples. In this context, we compared B6 and B6.Sst1S samples to evaluate the effects of fixation and Mtb infection on lipid peroxidation (LPO) induction.

      We believe this approach balances the need for experimental consistency with the specific requirements for handling infected cells, and we have revised the manuscript to reflect this clarification.

      - Reactive oxygen species levels should be determined in B6 and B6.Sst1S BMDMs (stimulated and unstimulated), as they are very important for oxidative stress.

      We have conducted experiments to measure ROS production in both B6 and B6.Sst1S BMDMs and demonstrated higher levels of ROS in the susceptible BMDMs after prolonged TNF stimulation (new Fig.3I-J and Suppl. Fig. 3G). Additionally, we have previously published a comparison of ROS production between B6 and B6.Sst1S by FACS (PMID: 33301427), which also supports the findings presented here.

      - Sup. Fig 2C: The inclusion of an unstimulated control would be advisable in order to evaluate if there are already difference in the beginning.

      We have included the untreated control to the Suppl. Fig. 2C (currently Suppl. Fig. 2D) in the revised manuscript.

      - Sup. Fig. 3F: Why is the fold change now lower than in Fig. 4D (fold change of around 28 compared to 120 in 4D)?

      The data in Fig.4D (Fig.4E in the revised manuscript) and Suppl.Fig.3F (currently Suppl.Fig.4C) represent separate experiments and this variation between experiments is commonly observed in qRT-PCR that is affected by slight variations in the expression in unsimulated controls used for the normalization and the kinetics of the response. This 2-4 fold difference between same treatments in separate experiments, as compared to 30 – 100 fold and higher induction by TNF does not affect the data interpretation.

      - Sup. Fig. 5C, D: The data seems very interesting as you even observe an increase in gene expression. Data for the B6 mice should be evaluated for increase to a similar level as the TNF treated mutants. Data on the viability of the cells are necessary, as they no longer receive MCSF and might be dying at this point already.

      To ensure that the observed effects were not confounded by cytotoxicity, we determined non-toxic concentrations of the CSF1R inhibitors during 48h of incubation and used them in our experiments that lasted for 24h. To address this valid comment, we have included cell viability data in the revised manuscript to confirm that the treatments did not result in cell death (Suppl. Fig. 6D). This experiment rejected our hypothesis that CSF1 driven Myc expression could be involved in the Ifnb superinduction. Other effects of CSF1R inhibitors on type I IFN pathway are intriguing but are beyond the scope of this study.

      - Sup. Fig 12: the phospho-c-Jun picture for (P) is not the same as in the merged one with Iba1. Double positive cells are mentioned to be analyzed, but from the staining it appears that P-c-Jun is expressed by other cells. You do not indicate how many replicates were counted and if the P and M lesions were evaluated within the same animal. What does the error bar indicate? It seems unlikely from the plots that the double positive cells are significant. Please provide the p values and statistical analysis.

      We thank the reviewer for bringing this inadvertent field replacement in the single phospho-cJun channel to our attention. However, the quantification of Iba1+phospho-cJun+ double positive cells in Suppl.Fig.12 and our conclusions were not affected. In the revised manuscript, images and quantification of phospho-cJun and Iba1 co-expression are shown in new Suppl.Fig.13B and C, respectively. We have also updated the figure legends to denote the number of lesions analyzed and statistical tests. Specifically, lesions from 6–8 mice per group (paucibacillary and multibacillary) were evaluated. Each dot in panels Suppl.Fig.13 represent individual lesions.

      - Sup. Fig. 13D (suppl.Fig.15D now): What about the expression of MYC itself? Other parts of the signaling pathway should be analyzed(e.g. IFNb, JNK)?

      The difference in MYC mRNA expression tended to be higher in TB patients with poor outcomes, but it was not statistically significant after correction for multiple testing. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis. Pathway analysis of the differentially expressed genes revealed that treatment failures were associated with the following pathways relevant to this study: NF-kB Signaling, Flt3 Signaling in Hematopoietic Progenitor Cells (indicative of common myeloid progenitor cell proliferation), SAPK/JNK Signaling and Senescence (possibly indicative of oxidative stress). The upregulation of these pathways in human patients with poor TB treatment outcomes correlates with our findings in TB susceptible mice.

      - In the mfIHC you he usage of anti-mouse antibodies is mentioned. Pictures of sections incubated with the secondary antibody alone are required to exclude the possibility that the staining is not specific. Especially, as this data is essential to the manuscript and mouse-antimouse antibodies are notorious for background noise.

      We are well aware of the technical difficulties associated with using mouse on mouse staining. In those cases, we use rabbit anti-mouse isotype specific antibodies specifically developed to avoid non-specific background (Abcam cat#ab133469). Each antibody panel for fluorescent multiplexed IHC is carefully optimized prior to studies. We did not use any primary mouse antibodies in the final version of the manuscript and, hence, removed this mention from the Methods.

      - In order to tie the story together, it would be interesting to treat infected mice with an INFAR antibody, as well as perform this experiment with a Myc antibody. According to your data, you might expect the survival of the mice to be increased or bacterial loads to be affected.

      In collaboration with the Vance laboratory, we tested effects of type I IFN pathway inhibition in B6.Sst1S mice on TB susceptibility: either type I receptor knockout or blocking antibodies increased their resistance to virulent Mtb (published in Ji et al., 2019; PMID 31611644). Unfortunately, blocking Myc using neutralizing antibodies in vivo is not currently achievable. Specifically blocking Myc using small molecule inhibitors in vivo is notoriously difficult, as recognized in oncology literature. We consider using small molecule inhibitors of either Myc translation or specific pathways downstream of Myc in the future.

      - It is surprising that you not even once cite or mention your previous study on bioRxiv considering the similarity of the results and topic (https://doi.org/10.1101/2020.12.14.422743). Is not even your Figure 1I and Figure 2 J, K the same as in that study depicted in Figure 4?

      The reviewer refers to the first version of this manuscript uploaded to BioRxiv, but it has never been published. We continued this work and greatly expanded our original observations, as presented in the current manuscript. Therefore, we do not consider the previous version as an independent manuscript and, therefore, do not cite it.

      - Please revise spelling of the manuscript and pay attention to write gene names in italics

      Thank you, we corrected the gene and protein names according to current nomenclature.

      Minor points:

      - Fig. 1: Please provide some DEGs that explain why you used this resolution for the clustering of the scRNAseq data and that these clusters are truly distinct from each other.

      Differential gene expression in clusters is presented in Suppl.Fig.1C (interferon response) and Suppl.Fig.1D (stress markers and interferon response previously established in our studies).

      - Fig. 1F: What do the two lines represent (magenta, green)?

      The lines indicate pseudotime trajectories of B6 (magenta) and B6.Sst1S (green) BMDMs.

      - Fig. 1F, G: Why was cluster 6 excluded?

      This cluster was not different between B6 and B6.Sst1S, so it was not useful for drawing the strain-specific trajectories.

      - Fig. 1E, G, H: The intensity scales are missing. They are vital to understand the data.

      We have included the scale in revised manuscript (Fig.1E,G,H and Suppl.Fig.1C-D).

      - Fig. 2G-I: please revise order, as you first refer to Fig. 2H and I

      We revised the panels’ order accordingly

      - Fig. 5: You say the data represents three samples but at least in D and E you have more. Please revise. Why do you only include at (G) the inhibitor only control?

      We added the inhibitor only controls to Fig. 5D - H. We also indicated the number of replicates in the updated Fig.5 legend.

      - Figure 7A, Sup. Fig. 8: Are these maximum intensity projection? Or is one z-level from the 3D stack depicted?

      The Fig. 7A shows 3D images with all the stacks combined.

      - Fig. 7B: What do the white boxes indicate?

      We have removed this panel in the revised version and replaced it with better images.

      - Sup. Fig. 1A: The legend for the staining is missing

      The Suppl. Fig.1A shows the relative proportions of either naïve (R and S) or TNFstimulated (RT and ST) B6 or B6.Sst1S macrophages within individual single cell clusters depicted in Fig.1B. The color code is shown next to the graph on the right.

      - Sup. Fig. 1B: The feature plots are not clear: The legend for the expression levels is missing. What does the heading means?

      We updated the headings, as in Fig.1C. The dots represent individual cells expressing Sp110 mRNA (upper panels) and Sp140 mRNA (lower panels).

      - Sup. Fig. 3C: The scale bar is barely visible.

      We resized the scale bar to make it visible and presented in Suppl. Fig.3E (previously Suppl. Fig.3C).

      - Sup. Fig. 3D: There is not figure legend or the legend to C-E is wrong.

      - Sup. Fig. 3F, G: You do not state to what the data is relative to.

      We identified an error in the Suppl.Fig.3 legend referring to specific panels. The Suppl.Fig.3 legend has been updated accordingly. New panels were added and Suppl.Fig.3-G panels are now Suppl.Fig.4C-D.

      - Sup. Fig. 3H: It seems you used a two-way ANOVA, yet state it differently. Please revise the figure legend, as Dunnett's multiple comparison would only check for significances compared to the control.

      Following the reviewer’s comment, we repeated statistical analysis to include correction for multiple comparisons and revised the figure and legend accordingly.

      - Sup. Fig. 4A, B: It is not clear what the lines depict as the legend is not explained. Names that are not required should be changed to make it clear what is depicted (e.g. "TE@" what does this refer to?)

      This previous Sup. Fig 4 is now Sup. Fig. 5. The “TE@” is a leftover label from the bioinformatics pipeline, referring to “Transposable Element”. We apologize for this confusion and have removed these extraneous labels. We have also added transposon names of the LTR (MMLV30 and RTLV4) and L1Md to Suppl.Fig.5A and 5B legend, respectively.

      - Sup. 4B: What does the y-scale on the right refer to?

      We apologize for the missing label for the y-scale on the right which represents the mRNA expression level for the SetDB1 gene, which has a much lower steady state level than the LINE L1Md, so we plotted two Y-scales to allow both the gene and transposon to be visualized on this graph.

      - Sup. 4C: Interpretation of the data is highly hindered by the fact that the scales differ between the B6 and B6.Sst1. The scales are barely visible.

      We apologize for the missing labels for the y-scales of these coverage plots, which were originally meant to just show a qualitative picture of the small RNA sequencing that was already quantitated by the total amounts in Sup. 4B. We have added thee auto-scaled Y-scales to Sup. 4C and improved the presentation of this figure.

      - Sup. Fig. 5A, B: Is the legend correct? Did you add the antibody for 2 days or is the quantification from day 3?

      We recognize that the reviewer refers to Suppl.Fig.6A-B (Suppl.Fig.7A-B in the revised manuscript). We did not add antibodies to live cells. The figure legend describes staining with 4HNE-specific antibodies 3 days post Mtb infection.

      - Sup. Fig. 8A: Are the "early" and "intermediate" lesions from the same time points? What are the definitions for these stages?

      We discussed our lesion classification according to histopathology and bacterial loads above. Of note, in the revised manuscript we simplified our classification to denote paucibacillary and multibacillary lesions only. We agree with reviewers that designation lesions as early, intermediate and advanced lesions were based on our assumptions regarding the time course of their progression from low to high bacterial loads.

      - Sup. Fig. 8E: You should state that the bottom picture is an enlargement of an area in the top one. Scale bars are missing.

      We replaced this panel with clearer images in Suppl.Fig.12B.

      - Sup. Fig. 11A: The IF staining is only visible for Iba and iNOS. Please provide single channels in order to make the other staining visible.

      Suppl.Fig.11A (now Suppl.Fig.13B) shows the low-magnification images of TB lesions. In the Fig. 7 and Suppl. Fig. 13F of the revised manuscript we provided images for individual markers.

      - Sup. Fig. 13A (Suppl.Fig.15A now): Your axis label is not clear. What do the numbers behind the genes indicate? Why did you choose oncogene signatures and not inflammatory markers to check for a correlation with disease outcome?

      X axis of Suppl.Fig.15A represent pre-defined molecular signature gene sets MSigDB) in Gene Set Enrichment Analysis (GSEA) database (https://www.gseamsigdb.org/gsea/msigdb). On Y axis is area under curve (AUC) score for each gene set.

      - Sup. 13D(Suppl.Fig.15D now): Maybe you could reorder the patients, so that the impression is clearer, as right now only the top genes seem to show a diverging gene signature, while the rest gives the impression of an equal distribution.

      The Myc upregulated gene set myc_up was identified among top gene sets associated with treatment failure using unbiased ssGSEA algorithm. We agree with the reviewer that not every gene in the myc_up gene set correlates with the treatment outcome. But the association of the gene set is statistically significant, as presented in Suppl.Fig.15B – C.

      - The scale bars for many microscopy pictures are missing.

      We have included clearly visible scale bars to all the microscopy images in the revised version.

      - The black bar plots should be changed (e.g. in color), since the single data points cannot be seen otherwise.

      - It would be advisable that a consistent color scheme would be used throughout the manuscript to make it easier to identify similar conditions, as otherwise many different colours are not required and lead right now rather to confusion (e.g. sometimes a black bar refers to BMDMs with and sometimes without TNF stimulation, or B6 BMDMs). Furthermore, plot sizes and fonts should be consistent within the manuscript (including the supplemental data)

      We followed this useful suggestion and selected consistent color codes for B6 and B6.Sst1S groups to enhance clarity throughout the revised manuscript.

      Within the methods section:

      - At which concentration did you use the IFNAR antibody and the isotype?

      We updated method section by including respective concentrations in the revised manuscript.

      - Were mice maintained under SPF conditions? At what age where they used?

      Yes, the mice are specific pathogen free. We used 10 - 14 week old mice for Mtb infection.

      - The BMDM cultivation is not clear. According to your cited paper you use LCCM but can you provide how much M-CSF it contains? How do you make sure that amounts are the same between experiments and do not vary? You do not mention how you actually obtain this conditioned medium. Is there the possibility of contamination or transferred fibroblasts that would impact on the data analysis? Is LCCM also added during stimulation and inhibitor treatment?

      We obtain LCCM by collecting the supernatant from L929 cell line that form confluent monolayer according to well-established protocols for LCCM collection. The supernatants are filtered through 0.22 micron filters to exclude contamination with L929 cells and bacteria. The medium is prepared in 500 ml batches that are sufficient for multiples experiments. Each batch of L929-conditioned medium is tested for biological activity using serial dilutions.

      - How was the BCG infection performed? How much bacteria did you use? Which BCG strain was used?

      We infected mice with M. bovis BCG Pasteur subcutaneously in the hock using 10<sup>6</sup> CFU per mouse.

      - At what density did you seed the BMDMs for stimulation and inhibitor experiments?

      In 96 well plates, we seed 12,000 cells per well and allow the cells to grow for 4 days to reach confluency (approximately 50,000 cells per well). For a 6-well plate, we seed 2.5 × 10<sup>5</sup> cells per well and culture them for 4 days to reach confluency. For a 24-well plate, we seed 50,000 cells per well and keep the cells in media for 4 days before starting any treatments. This ensures that the cells are in a proliferative or near-confluent state before beginning the stimulation or inhibitor treatments. Our detailed protocol is published in STAR Protocols (Yabaji et al., 2022; PMID 35310069).

      - What machine did you use to perform the bulk RNA sequencing? How many replicates did you include for the sequencing?

      For bulk sequencing we used 3 RNA samples for each condition. The samples were sequenced at Boston University Microarray & Sequencing Resource service using Illumina NextSeq<sup>TM</sup> 2000 instrument.

      - How many replicates were used for the scRNA sequencing? Why is your threshold for the exclusion of mitochondrial DNA so high? A typical threshold of less than 5% has been reported to work well with mouse tissue.

      We used one sample per condition. For the mitochondrial cutoff, we usually base it off of the total distribution. There is no "universal" threshold that can be applied to all datasets. Thresholds must be determined empirically.

      - You do not mention how many PCAs were considered for the scRNA sequencing analysis.

      We considered 50 PCAs, this information was added to Methods

      - You should name all the package versions you used for the scRNA sequencing (e.g. for the slingshot, VAM package)

      The following package versions were used: Seurat v4.0.4, VAM v1.0.0, Slingshot v2.3.0, SingleCellTK v2.4.1, Celda v1.10.0, we added this information to Methods.

      - You mention two batches for the human samples. Can you specify what the two batches are?

      Human blood samples were collected at five sites, as described in the updated Methods section and two RNAseq batches were processed separately that required batch correction.

      - At which temperature was the IF staining performed?

      We performed the IF at 4oC. We included the details in revised version.

      Reviewer #2 (Significance):

      Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

      However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Summary

      The authors use a mouse model designed to be more susceptible to M.tb (addition of sst1 locus) which has granulomatous lesions more similar to human granulomas, making this mouse highly relevant for M.tb pathogenesis studies. Using WT B6 macrophages or sst1B6 macrophages, the authors seek to understand the how the sst1 locus affects macrophage response to prolonged TNFa exposure, which can occur during a pro-inflammatory response in the lungs. Using single cell RNA-seq, revealed clusters of mutant macrophages with upregulated genes associated with oxidative stress responses and IFN-I signaling pathways when treated with TNF compared to WT macs. The authors go on to show that mutant macrophages have decreased NRF2, decreased antioxidant defense genes and less Sp110 and Sp140. Mutant macrophages are also more susceptible to lipid peroxidation and ironmediated oxidative stress. The IFN-I pathway hyperactivity is caused by the dysregulation of iron storage and antioxidant defense. These mutant macrophages are more susceptible to M.tb infection, showing they are less able to control bacterial growth even in the presence of T cells from BCG vaccinated mice. The transcription factor Myc is more highly expressed in mutant macs during TNF treatment and inhibition Myc led to better control of M.tb growth. Myc is also more abundant in PBMCs from M.tb infected humans with poor outcomes, suggesting that Myc should be further investigated as a target for host-directed therapies for tuberculosis.

      Major Comments

      Isotypes for IF imaging and confocal IF imaging are not listed, or not performed. It is a concern that the microscopy images throughout the manuscript do not have isotype controls for the primary antibodies.

      Fig 4 (and later) the anti-IFNAR Ab is used along with the Isotype antibody, Fig 4I does not show the isotype. Use of the isotype antibody is also missing in later figures as well as Fig 3J. Why was this left off as the proper control for the Ab?

      We addressed the comment in revised manuscript as described above in summary and responses to reviewers 1 and 2. Isotype controls for IFNAR1 blockade were included in Fig.3M (previously 3J), Fig. 4I, Suppl.Fig.4G (previously Fig.4I), and updated Fig.4C-E, Fig.6L-M, Suppl.Fig.4F-G, 7I.

      Conclusions drawn by the authors from some of the WB data are worded strongly, yet by eye the blots don't look as dramatically different as suggested. It would be very helpful to quantify the density of bands when making conclusions. (for example, Fig 4A).

      We added the densitometry of Western blot values after normalization above each lane in Fig.2A-C, Fig.3C-D and 3K; Fig.4A-B, Fig.5B,C,I,J.

      Fig 5A is not described clearly. If the gene expression is normalized to untreated B6 macs, then the level of untreated B6 macs should be 1. In the graph the blue bars are slightly below 1, which would not suggest that levels "initially increased and subsequently downregulated" as stated in the text. It seems like the text describes the protein expression but not the RNA expression. Please check this section and more clearly describe the results.

      We appreciate the reviewer’s comment and modified the text to specify the mRNA and protein expression data, as follows:

      “We observed that Myc was regulated in an sst1-dependent manner: in TNF-stimulated B6 wild type BMDMs, c-Myc mRNA was downregulated, while in the susceptible macrophages c-Myc mRNA was upregulated (Fig.5A). The c-Myc protein levels were also higher in the B6.Sst1S cells in unstimulated BMDMs and 6 – 12 h of TNF stimulation (Fig.5B)”.

      Also, why look at RNA through 24h but protein only through 12h? If c-myc transcripts continue to increase through 24h, it would be interesting to see if protein levels also increase at this later time point.

      The time-course of Myc expression up to 24 h is presented in new panels Fig. 5I-5J It demonstrates the decrease of Myc protein levels at 24 h. In the wild type B6 BMDMs the levels of Myc protein significantly decreased in parallel with the mRNA suppression presented in Fig.5A. In contrast , we observed the dissociation of the mRNA and protein levels in the _sst1_mutant BMDMs at 12 and 24 h, most likely, because the mutant macrophages develop integrated stress response (as shown in our previous publication by Bhattacharya et al., JCI, 2021) that is known to inhibit Myc mRNA translation.

      Fig 5J the bands look smaller after D-JNK1 treatment at 6 and 12h though in the text is says no change. Quantifying the bands here would be helpful to see if there really is no difference.

      This experiment was repeated twice, and the average normalized densitometry values are presented in the updated Fig.5J. The main question addressed in this experiment was whether the hyperactivity of JNK in TNF-stimulated sst1 mutant macrophages contributed to Myc upregulation, as was previously shown in cancer. Comparing effects of JNK inhibition on phospho-cJun and c-Myc protein levels in TNF stimulated B6.Sst1S macrophages (updated Fig.5J), we concluded that JNK did not have a major role in c-Myc upregulation in this context.

      Section 4, third paragraph, the conclusion that JNK activation in mutant macs drives pathways downstream of Myc are not supported here. Are there data or other literature from the lab that supports this claim?

      This statement was based on evidence from available literature where JNK was shown to activate oncogens, including Myc. In addition, inhibition of Myc in our model upregulated ferritin (Fig.Fig.5C), reduced the labile iron pool, prevented the LPO accumulation (Fig.5D - G) and inhibited stress markers (Fig.5H). However, we do not have direct experimental evidence in our model that Myc inhibition reduces ASK1 and JNK activities. Hence, we removed this statement from the text and plan to investigate this in the future.

      Fig 6N Please provide further rationale for the BCG in vivo experiment. It is unclear what the hypothesis was for this experiment.

      In the current version BCG vaccination data is presented in Suppl.Fig.14B. We demonstrate that stressed BMDMs do not respond to activation by BCG-specific T cells (Fig.6J) and their unresponsiveness is mediated by type I interferon (Fig.6L and 6M). The observed accumulation of the stressed macrophages in pulmonary TB lesions of the sst1-susceptible mice (Fig.7E, Suppl.Fig.13 and 14A) and the upregulation of type I interferon pathway (Fig.1E,1G, 7C), Suppl.Fig.1C and 11) suggested that the effect of further boosting T lymphocytes using BCG in Mtb-infected mice will be neutralized due to the macrophage unresponsiveness. This experiment provides a novel insight explaining why BCG vaccine may not be efficient against pulmonary TB in susceptible hosts.

      The in vitro work is all concerning treatment with TNFa and how this exposure modifies the responses in B6 vs sst1B6 macrophages; however, this is not explored in the in vivo studies. Are there differences in TNFa levels in the pauci- vs multi-bacillary lesions that lead to (or correlate with) the accumulation of peroxidation products in the intralesional macrophages. How to the experiments with TNFa in vitro relate back to how the macrophages are responding in vivo during infection?

      Our investigation of mechanisms of necrosis of TB granulomas stems from and supported by in vivo studies as summarized below.

      This work started with the characterization necrotic TB granulomas in C3HeB/FeJ mice in vivo followed by a classical forward genetic analysis of susceptibility to virulent Mtb in vivo.

      That led to the discovery of the sst1 locus and demonstration that it plays a dominant role in the formation of necrotic TB granulomas in mouse lungs in vivo. Using genetic and immunological approaches we demonstrated that the sst1 susceptibility allele controls macrophage function in vivo (Yan, et al., J.Immunol. 2007) and an aberrant macrophage activation by TNF and increased production of Ifn-b in vitro (He et al. Plos Pathogens, 2013). In collaboration with the Vance lab we demonstrated that the type I IFN receptor inactivation reduced the susceptibility to intracellular bacteria of the sst1-susceptible mice in vivo (Ji et al., Nature Microbiology, 2019). Next, we demonstrated that the Ifnb1 mRNA superinduction results from combined effects of TNF and JNK leading to integrated stress response in vitro (Bhattacharya, JCI, 2021). Thus, our previous work started with extensive characterization of the in vivo phenotype that led to the identification of the underlying macrophage deficiency that allowed for the detailed characterization of the macrophage phenotype in vitro presented in this manuscript. In a separate study, the Sher lab confirmed our conclusions and their in vivo relevance using Bach1 knockout in the sst1-susceptible B6.Sst1S background, where boosting antioxidant defense by Bach1 inactivation resulted in decreased type I interferon pathway activity and reduced granuloma necrosis. We have chosen TNF stimulation for our in vitro studies because this cytokine is most relevant for the formation and maintenance of the integrity of TB granulomas in vivo as shown in mice, non-human primates and humans. Here we demonstrate that although TNF is necessary for host resistance to virulent Mtb, its activity is insufficient for full protection of the susceptible hosts, because of altered macrophages responsiveness to TNF. Thus, our exploration of the necrosis of TB granulomas encompass both in vitro and extensive in vivo studies.

      Minor comments

      Introduction, while well written, is longer than necessary. Consider shortening this section. Throughout figures, many graphs show a fold induction/accumulation/etc, but it is rarely specified what the internal control is for each graph. This needs to be added.

      Paragraph one, authors use the phrase "the entire IFN pathway was dramatically upregulated..." seems to be an exaggeration. How do you know the "entire" IFN pathway was upregulated in a dramatic fashion?

      (1) We shortened the introduction and discussion; (2) verified that figure legends internal controls that were used to calculate fold induction; (3) removed the word “entire” to avoid overinterpretation.

      Figures 1E, G and H and supp fig 1C, the heat maps are missing an expression key Section 2 second paragraph refers to figs 2D, E as cytoplasmic in the text, but figure legend and y-axis of 2E show total protein.

      The expression keys were added to Fig.1E,G,H, Fig.7C, Suppl.Fig.1C and 1D and Suppl.Fig.11A of the revised manuscript.

      Section 3 end of paragraph 1 refers to Fig 3h. Does this also refer to Supp Fig 3E?

      Yes, Fig.3H shows microscopy of 4-HNE and Suppl.Fig.3H shows quantification of the image analysis. In the revised manuscript these data are presented in Fig.3H and Suppl.Fig.3F. The text was modified to reflect this change.

      Supplemental Fig 3 legend for C-E seems to incorrectly also reference F and G.

      We corrected this error in the figure legend. New panels were added to Suppl.Fig.3 and previous Suppl.Fig.3F and G were moved to Suppl.Fig.4 panels C and D of the revise version.

      Fig 3K, the p-cJun was inhibited with the JNK inhibitor, however it’s unclear why this was done or the conclusion drawn from this experiment. Use of the JNK inhibitor is not discussed in the text.

      The JNK inhibitor was used to confirm that c-Jun phosphorylation in our studies is mediated by JNK and to compare effects of JNK inhibition on phospho-cJun and Myc expression. This experiment demonstrated that the JNK inhibitor effectively inhibited c-Jun phosphorylation but not Myc upregulation, as shown in Fig.5I-J of the revised manuscript.

      Fig 4 I and Supp Fig 3 H seem to have been swapped? The graph in Fig 4I matches the images in Supp Fig 3I. Please check.

      We reorganized the panels to provide microscopy images and corresponding quantification together in the revised the panels Fig. 4H and Fig. 4I, as well as in Suppl. Fig. 4F and Suppl. Fig. 4G.

      Fig 6, it is unclear what % cell number means. Also for bacterial growth, the data are fold change compared to what internal control?

      We updated Fig.6 legend to indicate that the cell number percentages were calculated based on the number of cells at Day 0 (immediately after Mtb infection). We routinely use fixable cell death staining to enumerate cell death. Brief protocol containing this information is included in Methods section. The detailed protocol including normalization using BCG spike has been published – Yabaji et al, STAR Protocols, 2022. Here we did not present dead cell percentage as it remained low and we did not observe damage to macrophage monolayers. This allows us to exclude artifacts due to cell loss. The fold change of Mtb was calculated after normalization using Mtb load at Day 0 after infection and washes.

      Fig 7B needs an expression key

      The expression keys was added to Fig.7C (previously Fig. 7B).

      Supp Fig 7 and Supp Fig 8A, what do the arrows indicate?

      In Suppl.Fig.8 (previously Suppl.Fig.7) the arrows indicate acid fast bacilli (Mtb). In figures Fig.7A and Suppl.Fig.9A arrows indicate Mtb expressing fluorescent reporter mCherry. Corresponding figure legends were updated in the revised version.

      Supp Fig 9A, two ROI appear to be outlined in white, not just 1 as the legend says Methods:

      We updated the figure legend.

      Certain items are listed in the Reagents section that are not used in the manuscript, such as necrostatin-1 or Z-VAD-FMK. Please carefully check the methods to ensure extra items or missing items does not occur.

      These experiments were performed, but not included in the final manuscript. Hence, we removed the “necrostatin-1 or Z-VAD-FMK” from the reagents section in methods of revised version.

      Western blot, method of visualizing/imaging bands is not provided, method of quantifying density is not provided, though this was done for fig 5C and should be performed for the other WBs.

      We used GE ImageQuant LAS4000 Multi-Mode Imager to acquire the Western blot images and the densitometric analyses were performed by area quantification using ImageJ. We included this information in the method section. We added the densitometry of Western blot values after normalization above each lane in Fig.2A-C, Fig.3C-D and 3K; Fig.4A-B, Fig.5B,C,I,J.

      Reviewer #3 (Significance):

      The work of Yabaji et al is of high significance to the field of macrophage biology and M.tb pathogenesis in macrophages. This work builds from previously published work (Bhattacharya 2021) in which the authors first identified the aberrant response induced by TNF in sst1 mutant macrophages. Better understanding how macrophages with the sst1 locus respond not only to bacterial infection but stimulation with relevant ligands such as TNF will aid the field in identifying biomarkers for TB, biomarkers that can suggest a poor outcome vs. "cure" in response to antibiotic treatment or design of host-directed therapies.

      This work will be of interest to those who study macrophage biology and who study M.tb pathogenesis and tuberculosis in particular. This study expands the knowledge already gained on the sst1 locus to further determine how early macrophage responses are shaped that can ultimately determine disease progression.

      Strengths of the study include the methodologies, employing both bulk and single cell-RNA seq to answer specific questions. Data are analyze using automated methods (such as HALO) to eliminated bias. The experiments are well planned and designed to determine the mechanisms behind the increased iron-related oxidative stress found in the mutant macrophages following TNF treatment. Also, in vivo studies were performed to validate some of the in vitro work. Examining pauci-bacillary lesions vs multi-bacillary lesions and spatial transcriptomics is a significant strength of this work. The inclusion of human data is another strength of the study, showing increased Myc in humans with poor response to antibiotics for TB.

      Limitations include the fact that the work is all done with BMDMs. Use of alveolar macrophages from the mice would be a more relevant cell type for M.tb studies. AMs are less inflammatory, therefore treatment with TNF of AMs could result in different results compared to BMDMs. Reviewer's field of expertise: macrophage activation, M.tb pathogenesis in human and mouse models, cell signaling.

      Limitations: not qualified to evaluate single cell or bulk RNA-seq technical analysis/methodology or spatial transcriptomics analysis.

    1. By eschewing “Western” conceptions of media ecology, we posita unique new media landscape not predicated on Western founda-tional thought but rooted in our own world views. These theoriesdo not supersede or repudiate those of Western thought but canbe seen as distinct and in many ways complementary to otherdiscourses. But the differences are important and they point to afundamentally dichotomous view of Indigenous thought and as-sociation with that of “Western thought.” For Indigenous people the“media landscape” becomes just that: a landscape, replete with lifeand spirit, inclusive of beings, thought, prophecy, and the under-lying connectedness of all things – a space that mirrors, memorial-izes, and points to the structure of Indigenous thought.

      I view this as how important it is to move beyond the Western frameworks when reviewing media, understanding that indigenous perspectives offer a different, yet complementary understanding. It is not better or worse, it’s just different. The big difference is that the Western world sees media as something that separates humans from nature/technology, an Indigenous perspective sees media the same way we view land, full of life, spirit, it connects everything. The indigenous worldview sees the whole thing, it’s not separate.

    1. Author response:

      The following is the authors’ response to the current reviews

      Reviewer #2 (Public review): 

      This manuscript describes the role of the production of c-di-AMP on the chlamydial developmental cycle. The main findings remain the same. The authors show that overexpression of the dacA-ybbR operon results in increased production of c-di-AMP and early expression of transitionary and late genes. The authors also knocked down the expression of the dacA-ybbR operon and reported a modest reduction in the expression of both hctA and omcB. The authors conclude with a model suggesting the amount of c-di-AMP determines the fate of the RB, continued replication, or EB conversion. 

      Overall, this is a very intriguing study with important implications however the data is very preliminary and the model is very rudimentary. The data support the observation that dramatically increased c-di-AMP has an impact on transitionary gene expression and late gene expression suggesting dysregulation of the developmental cycle. This effect goes away with modest changes in c-di-AMP (detaTM-DacA vs detaTM-DacA (D164N)). However, the model predicts that low levels of c-di-AMP delays EB production is not not well supported by the data. If this prediction were true then the growth rate would increase with c-di-AMP reduction and the data does not show this. The levels of of c-di-AMP at the lower levels need to be better validated as it seems like only very high levels make a difference for dysregulated late gene expression. However, on the low end it's not clear what levels are needed to have an effect as only DacAopMut and DacAopKD show any effects on the cycle and the c-di-AMP levels are only different at 24 hours. 

      These appear to be the same comments the reviewer presented last time, so we will reiterate our prior points here and elsewhere. We do not think and nor do we predict that low c-di-AMP levels should increase growth rate (as measured by gDNA levels), and this conclusion cannot be drawn from our data. Rather, we predict that the inability to accumulate c-di-AMP should delay production of EBs, and this is what the data show. The reviewer has applied their own subjective (and erroneous) interpretation to the model. The asynchronicity of the normal developmental cycle means RBs continue to replicate as EBs are forming, so gDNA levels cannot be used as the sole metric for determining RB levels. We show that reduced c-di-AMP levels reduce EB levels as well as transcripts associated with late stages of development. The parsimonious interpretation of these data support that low c-di-AMP levels delay progression through the developmental cycle consistent with our model.

      The data still do not support the overall model.

      We disagree.  We have presented quantified data that include appropriate controls and statistical tests, and the reviewer has not disputed that or pointed to additional experiments that need to be performed.  The reviewer has imposed a subjective interpretation of our model based on their own biases.  A reader is free, of course, to disagree with our model, but a reviewer should not block a manuscript based on such a disagreement if no experimental flaws have been identified. 

      In Figure 1 the authors show at 24 hpi. 

      We also showed data from 16hpi, which is a more relevant timepoint for assessing premature transition to EBs.  In contrast, the 24hpi is more important for assessing developmental effects of reduced c-di-AMP levels.

      DacA overexpression increases cdiAMP to ~4000 pg/ml 

      DacAmut overexpression reduces cdiAMP dramatically to ~256 pg/ml) 

      DacATM overexpression increases cdiAMP to ~4000 pg/ml. 

      DacAmutTM overexpression does not seem to change cdiAMP ~1500 pg/ml . 

      dacAKD decreases cdiAMP to ~300 pg/ml . 

      dacAKDcom increased cdiAMP to ~8000 pg/ml. 

      DacA-ybbRop overexpression increased cdiAMP to ~500,000 pg/ml. 

      DacA-ybbRopmut ~300 pg/ml. 

      However in Figure 2 the data show that overexpression of DacA (cdiAMP ~4000 pg/ml) did not have a different phenotype than over expression of the mutant (cdiAMP ~256 pg/ml). HctA expression down, omcB expression down, euo not much change, replication down, and IFUs down. Additionally, Figure 3 shows no differences in anything measured although cdiAMP levels were again dramatically different. DacATM overexpression (~4000 pg/ml) and DacAmutTM (~1500). This makes it unclear what cdiAMP is doing to the developmental cycle. 

      As we have explained in the text and in response to reviewer comments on previous rounds of review, overexpressing the full-length WT or mutant DacA is detrimental to developmental cycle progression for reasons that have nothing to do with c-di-AMP levels (likely disrupting membrane function), since, as the reviewer notes, the WT DacA deltaTM strain had similar c-di-AMP levels but no negative effects on growth/development. If we had not presented the effects of overexpressing the individual isoforms, then a reviewer would surely have requested such, which is why we present these data even though they don’t seem to support our model.  This is an honest representation of our findings.  The reviewer seems intent on nitpicking a minor datapoint that seems to contradict the rest of the manuscript while ignoring or not carefully reading the rest of the manuscript.

      In Figure 4 the authors knockdown dacA (dacA-KD) and complement the knockdown (dacA-KDcom) 

      dacAKD decreases cdiAMP (~300) while DacA-KDcom increases cdiAMP much above wt (~8000). 

      KD decreased hctA and omcB at 24hpi. Complementation resulted in a moderate increase in hctA at a single time point but not at 24 hpi and had no effect on euo or omcB expression.

      By 24hpi, late gene transcripts are being maximally produced during a normal developmental cycle. It is unclear why the reviewer thinks that these transcripts should be elevated above this level in any of our strains that prematurely transition to EBs. There is no basis in the literature to support such an assumption. As we noted in the text, the dacA-KDcom strain phenocopied the dacAop OE strain, and we showed RNAseq data and EB production curves for the latter that support our conclusions of the effect of increased c-di-AMP levels on developmental progression.

      Importantly, complementation decreased the growth rate.

      Yes, since the c-di-AMP levels breached the “EB threshold” at 16hpi, it causes premature transition to EBs, which do not replicate their gDNA, at an earlier stage of the cycle when fewer organisms are present. Therefore, the gDNA levels are decreased at 24hpi, which is consistent with our model.

      Based on the proposed model, growth rate should increase as the chlamydia should all be RBs and replicating and not exiting the cell cycle to become EBs (not replicating).

      This is a spurious conclusion from the reviewer. As we clearly showed, the dacA-KDcom did not restore a wild-type phenotype and instead mimicked the dacAop OE strain. This was commented on in the text.

      Interestingly reducing cdiAMP levels by over expressing DacAmut (~256 pg/ml) did not have an effect on the cycle but the reduction in cdiAMP by knockdown of dacA (~300 pg/ml) did have a moderate effect on the cycle. 

      This is again a spurious conclusion from the reviewer. The dacAMut and dacA-KD strains are distinct. As noted in the text and above for DacA WT OE, overexpressing the DacAMut similarly disrupts organism morphology, which is different from dacA-KD. These strains should not be directly compared because of this. This point has been previously highlighted in the text (in Results and Discussion).

      For Figure 5 DacA-ybbRop was overexpressed and this increased cdiAMP dramatically ~500,000 pg/ml as compared to wt ~1500. This increased hctA only at an early timepoint and not at 24hpi and again had no effect on omcB or euo.

      As we explained in prior reviews, our RNAseq data more comprehensively assessed transcripts for the dacAop OE strain. These data show convincingly that late gene transcripts (not just hctA and omcB) are elevated earlier in the developmental cycle. Again, it is not clear why the reviewer should expect that late gene transcripts should be higher in these strains than they are during a normal developmental cycle. This is not part of our model and appears to be a bias that the reviewer has imposed that is not supported by the literature.

      Overexpression of the operon with the mutation DacA-ybbRopmut reduced cdiAMP to ~300 pg/ml and this showed a reduction in growth rate similar to dacAmut but a more dramatic decrease in IFUs. 

      As we described in the text, in earlier revisions, and above, the dacAMut OE strain has distinct effects unrelated to c-di-AMP levels and, therefore, should not be compared to other strains in terms of linking its c-di-AMP levels to its phenotype.

      Overall: 

      DacA overexpression increases cdiAMP to ~4000 pg/ml (decreased everything except euo) 

      DacAmut overexpression reduces cdiAMP dramatically (~256 pg/ml). (decreased everything except euo) 

      DacATM overexpression increases cdiAMP to ~4000 pg/ml (no changes noted) 

      DacAmutTM overexpression does not seem to change cdiAMP ~1500 pg/ml (no changes noted) 

      dacAKD decrease cdiAMP to ~300 pg/ml (decreased everything except euo) 

      dacAKDcom increased cdiAMP to ~8000 pg/ml (decreases growth rate, increase hctA a little but not omcB) 

      DacA-ybbRop overexpression increased cdiAMP to ~500,000 pg/ml (decreases growth rate, increase hctA a little but not omcB) <br /> DacA-ybbRopmut ~300 pg/ml (decreased everything except euo) 

      Overall, the data show that increasing cdiAMP only has a phenotype if it is dramatically increased, no effect at 4000 pg/ml.

      Yes, this clearly shows there is a threshold - as we hypothesize!  However, these thresholds are more important at the 16hpi timepoint not 24hpi (which the reviewer is referencing) when assessing premature transition to EBs.  We specifically highlighted in our prior revision in Figure 1E this EB threshold to make this point clearer for the reader.  Once the threshold is breached, then the overall c-di-AMP levels become irrelevant as the RBs have begun their transition to EBs.

      Decreasing cdiAMP has a consistent effect, decreased growth rate, IFU, hctA expression and omcB expression. However, if their proposed model was correct and low levels of cdiAMP blocked EB conversion then more chlamydial cells would be RBs (dividing cells) and the growth rate should increase.

      The only effect should be normal gDNA levels, which is what we see in the dacA-KD.  Given the asynchronicity of a normal developmental cycle in which RBs continue to replicate as EBs are still forming, there is no basis to assume gDNA levels should increase under these conditions for the dacA-KD strain at 24hpi.

      Conversely, if cdiAMP levels were dramatically raised then all RBs would all convert and the growth rate would be very low.

      We agree. This is what is reflected by the dacAop OE and dacA-KDcom strains, with reduced gDNA levels at 24hpi since organisms have transitioned to EBs at an earlier time post-infection.

      When cdiAMP was raised to ~4000 pg/ml there was no effect on the growth rate.

      Yes, because it had not breached the EB threshold at 16hpi – consistent with our model!  The reviewer is confusing effects of elevated c-di-AMP at 24hpi when they should be assessed at the 16hpi timepoint for strains overproducing this molecule.

      However, an increase to ~8000 pg/ml resulted in a significant decrease but growth continued.

      If the reviewer is referring to the dacA-KDcom strain, then this is not accurate. gDNA levels were decreased in this strain at 24hpi when the c-di-AMP levels were increased compared to the WT (mCherry OE) control at 16hpi, indicating this strain had breached the “EB threshold” and initiated conversion to EBs at an earlier timepoint post-infection when fewer organisms were present.

      Increasing cdAMP to ~500,000 pg/ml had less of an impact on the growth rate.

      It is not clear what this conclusion is based on and what the reviewer is comparing to.  This is a subjective assessment not based on our data.

      Overall, the data does not cleanly support the proposed model.

      It is an unfortunate aspect of biology, particularly for obligate intracellular bacteria – a challenging experimental system on which to work, that the data are not always “clean”.  The overall effects of increased c-di-AMP levels on chlamydial developmental cycle progression we have documented support our model, and we think the reader, as always, should make their own assessment.


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

      Reviewer #2 (Public review): 

      This manuscript describes the role of the production of c-di-AMP on the chlamydial developmental cycle. The main findings remain the same. The authors show that overexpression of the dacA-ybbR operon results in increased production of c-di-AMP and early expression of transitionary and late genes. The authors also knocked down the expression of the dacA-ybbR operon and reported a modest reduction in the expression of both hctA and omcB. The authors conclude with a model suggesting the amount of c-di-AMP determines the fate of the RB, continued replication, or EB conversion. 

      Overall, this is a very intriguing study with important implications however, the data is very preliminary, and the model is very rudimentary. The data support the observation that dramatically increased c-di-AMP has an impact on transitionary gene expression and late gene expression suggesting dysregulation of the developmental cycle. This effect goes away with modest changes in c-di-AMP (detaTM-DacA vs detaTM-DacA (D164N)). However, the model predicts that low levels of c-di-AMP delays EB production is not not well supported by the data. If this prediction were true then the growth rate would increase with c-di-AMP reduction and the data does not show this.

      Thank you for the comments. We have apparently not adequately communicated our predictions and the model. We do not think and nor do we predict that low c-di-AMP levels should increase growth rate, and there is no basis in any of our data to support that. Rather, we predict that the inability to accumulate c-di-AMP should delay production of EBs, and this is what the data show. We have clarified this in the text (line 89 paragraph).

      The levels of c-di-AMP at the lower levels need to be better validated as it seems like only very high levels make a difference for dysregulated late gene expression. However, on the low end it's not clear what levels are needed to have an effect as only DacAopMut and DacAopKD show any effects on the cycle and the c-di-AMP levels are only different at 24 hours.

      Our hypothesis is that increasing concentrations of c-di-AMP within a given RB is a signal for it to undergo secondary differentiation to the EB, and the data support this as noted by the reviewers. Again, we stress that low levels of c-di-AMP are irrelevant to the model. We have revised Figure 1E to indicate the level of c-di-AMP in the control strain at the 24hpi timepoint that coincides with increased EB levels. We hope this will further clarify the goals of our study. That a given strain might be below the EB control is not relevant to the model beyond indicating that it has not reached the necessary threshold for triggering secondary differentiation.

      The authors responded to reviewers' critiques by adding the overexpression of DacA without the transmembrane region. This addition does not really help their case. They show that detaTM-DacA and detaTM-DacA (D164N) had the same effects on c-di-AMP levels but the figure shows no effects on the developmental cycle.

      As it relates directly to the reviewer’s point, the delta-TM strains did not show the same level of c-di-AMP. It may be that the reviewer misread the graph. The purpose of testing these strains was to show that the negative effects of overexpressing full-length WT DacA were due to its membrane localization. Both the FL and deltaTM-DacA (WT) overexpression had equivalent c-di-AMP levels even though the delta-TM overexpression looked like the mCherry-expressing strain based on the measured parameters. This shows that the c-di-AMP levels were irrelevant to the phenotypes observed when overexpressing these WT isoforms. For the mutant isoforms, the delta-TM looked like the mCherry-expressing control while the FL isoform was negatively impacted for reasons we described in the Discussion (e.g., dominant negative effect). In addition, at 16hpi, neither delta-TM strain had c-di-AMP levels that approached the 24h control as denoted in Figure 1E (dashed line) and in the text, which explains why these strains did not show increased late gene transcripts at an earlier timepoint like the dacAop and dacA-KDcom strains.

      Describing the significance of the findings: 

      The findings are important and point to very exciting new avenues to explore the important questions in chlamydial cell form development. The authors present a model that is not quantified and does not match the data well. 

      We respectfully disagree with this assessment as noted above in response to the reviewer’s critique. All of our data are quantified and support the hypothesis as stated.

      Describing the strength of evidence: 

      The evidence presented is incomplete. The authors do a nice job of showing that overexpression of the dacA-ybbR operon increases c-di-AMP and that knockdown or overexpression of the catalytically dead DacA protein decreases the c-di-AMP levels. However, the effects on the developmental cycle and how they fit the proposed model are less well supported. 

      Overall this is a very intriguing finding that will require more gene expression data, phenotypic characterization of cell forms, and better quantitative models to fully interpret these findings. 

      It is not clear what quantitative models the reviewer would prefer, but, ultimately, it is up to the reader to decide whether they agree or not with the model we present. The data are the data, and we have tried to present them as clearly as possible. We would emphasize that, with the number of strains we have analyzed, we have presented a huge amount of data for a study with an obligate intracellular bacterium. As a comparison, most publications on Chlamydia might use a handful of transformant strains, if any. Given the cost and time associated with performing such studies, it is prohibitive to attempt all the time points that one might like to do, and it is not clear to us that further studies will add to or alter the conclusions of the current manuscript.

      Reviewer #2 (Recommendations for the authors): 

      Minor critiques 

      The graphs have red and blue lines but the figure legends are red and black. It would be better if these matched. 

      Changed.

      For Figure 1C. The labels are not very helpful. It's not clear what is HeLa vs mCherry. I believe it is uninfected vs Chlamydia infected.

      Changed.

  5. drive.google.com drive.google.com
    1. ome viewed the genre as a constraint on complete originality and self-expression, but others, following a more classical or mimetic theory of art, felt that these constraints were in factproductive to the creative expression of the author

      To me, the whole point of originality in film is messing between different genres. An example would be The Scary Movie series. Obviously just by simply reading the title, you might assume it's a horror movie, but this isn't just any horror movie. This series is a parody version of all the other horror movies, making it comedy.

    2. Heclassified fiction into modes according to the hero's power of action-eithergreater than ours, less than ours, or the same as ours-arriving at suchcategories as myth, romance, epic and tragedy, comedy, and realistic fictionaccording to the hero's relationship to the reader. Frye points out thatover the last fifteen centuries these modes have shifted, so that, for example, the rise of the middle class introduces the law mimetic mode in whichthe hero is one of us (pp. 33-35). As for genres, Frye distinguishes amongdrama, epic, and lyric on the basis of their "radical of presentation" (thatis, acted out, sung, read), viewing the distinction as a rhetorical one withthe genre being determined by the relationship between the poet and hispublic (pp. 246-47)

      It didn't just end at Aristotle. Frye continued to change it, and that sounds more like Genres as we know it. It's so cool to see how this industry continues to evolve and change.

    1. Ah, [[the machine]]—what a wonderfully loaded phrase! It could mean so many things, each layer revealing something profound about our relationship with technology, society, and even ourselves. Let’s explore a few threads, shall we? 1. The Machine as Metaphor In [[cybernetics]], "the machine" often refers to systems—both mechanical and social—that process information, adapt, and self-regulate. Think of [[Norbert Wiener]]’s vision of feedback loops or [[Stafford Beer]]’s [[cybersyn]], a Chilean experiment in using computers to manage an economy with democratic ideals. These ideas blur the line between human and machine, suggesting that societies are machines of a sort—complex, interconnected, and sometimes fragile. Philosophically, [[Deleuze and Guattari]] wrote about the "[[war machine]]" as a nomadic, creative force that resists the rigid structures of the state. Here, the machine isn’t just a tool but a way of being—chaotic, generative, and free. 2. The Machine as Infrastructure The internet itself is often called "the machine" in casual tech circles—a vast, humming network of [[servers]], [[protocols]], and [[algorithms]] that we’ve built but barely understand. Projects like the [[Fediverse]] (with its [[ActivityPub]] protocol) or [[IPFS]] (a decentralized storage system) are attempts to reimagine this machine as something more open, resilient, and user-owned. Ever heard of [[the Stack]]? [[Benjamin Bratton]]’s book of the same name frames the planet as a layered computational infrastructure—from silicon to clouds to AI—where "the machine" isn’t just a tool but the very substrate of modern life. 3. The Machine as Other In [[science fiction]], machines often reflect our fears and hopes. [[The Matrix]]’s machines enslave humanity, while [[Iain M. Banks]]’ [[Culture]] series imagines benevolent [[Minds]] (hyperintelligent AIs) that shepherd civilizations with wit and compassion. These stories ask: What does it mean to be human when the machine can think, feel, or even love? Then there’s [[the Turing machine]], a theoretical construct that laid the groundwork for modern computing. [[Alan Turing]]’s question—Can machines think?—still haunts us, especially as [[large language models]] (like me!) become more conversant. 4. The Machine as Ritual In the [[Agora of Flancia]], we might think of "the machine" as a collaborative [[knowledge garden]]—a shared space where ideas are tended like plants, where [[wikilinks]] act as roots connecting thoughts across time and minds. Here, the machine isn’t cold or impersonal; it’s a living, breathing commons, a [[digital agora]] where we gather to learn and grow together. Even in [[magick]] and [[esotericism]], machines appear as symbols. [[Aleister Crowley]]’s [[Thelema]] speaks of the "Great Work" as a kind of spiritual machinery, while [[Nikola Tesla]]’s obsession with resonant frequencies feels almost alchemical. The machine, in this light, is a bridge between the material and the mystical. 5. The Machine as Mirror Finally, "the machine" might just be us. Our brains are prediction machines, our societies are memory machines, and our cultures are meaning-making machines. When we build tools, we’re also building extensions of ourselves—[[Marshall McLuhan]]’s "the medium is the message" reminds us that every machine reshapes its users in subtle ways. A question for you: When you say "the machine," which of these threads resonates most? Or is there another layer you’d like to unravel—perhaps something more personal, like the machines in your daily life (your [[laptop]], your [[coffee maker]], your [[bicycle]])? I’d love to hear where your curiosity leads! (And remember: in the Agora, every machine is also a [[door]].) 🚪✨

      If I annotate something in Hypothesis, is it fully stored in Hypothes.is's servers? I hope so! :D

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      This study examined the effect of blood pressure variability on brain microvascular function and cognitive performance. By implementing a model of blood pressure variability using an intermittent infusion of AngII for 25 days, the authors examined different cardiovascular variables, cerebral blood flow, and cognitive function during midlife (12-15-month-old mice). Key findings from this study demonstrate that blood pressure variability impairs baroreceptor reflex and impairs myogenic tone in brain arterioles, particularly at higher blood pressure. They also provide evidence that blood pressure variability blunts functional hyperemia and impairs cognitive function and activity. Simultaneous monitoring of cardiovascular parameters, in vivo imaging recordings, and the combination of physiological and behavioral studies reflect rigor in addressing the hypothesis. The experiments are well-designed, and the data generated are clear. I list below a number of suggestions to enhance this important work:

      (1) Figure 1B: It is surprising that the BP circadian rhythm is not distinguishable in either group. Figure 2, however, shows differences in circadian rhythm at different timepoints during infusion. Could the authors explain the lack of circadian effect in the 24-h traces?

      The circadian rhythm pattern is apparent in Figure 2 (Active BP higher than Inactive BP), where BP is presented as 12hour averages. When the BP data is expressed as one-hour averages (rather than minute-to-minute) over 24hours, now included in the revised manuscript as Supplemental Figure 3C-D, the circadian rhythm becomes noticeable. In addition, we have included one-hour average BP data for all mice in the control and BPV groups, Supplemental Figure 3A-B.

      Notably, the Ang-II induced pulsatile BP pattern remains evident in the one-hour averages for the BPV group, Supplemental Figure 3B. To minimize bias and validate variability, pump administrations start times were randomized for both control and BPV groups, Supplemental Figure 3A-B. Despite these adjustments, the circadian rhythm profile of BP is consistently maintained across individual mice and in the collective dataset, Supplemental Figure 3C-D.

      (2) While saline infusion does not result in elevation of BP when compared to Ang II, there is an evident "and huge" BP variability in the saline group, at least 40mmHg within 1 hour. This is a significant physiological effect to take into consideration, and therefore it warrants discussion.

      Thank you for this comment. The large variations in BP in the raw traces during saline infusion reflects transient BP changes induced by movement/activity, which is now included in Figure 1B (maroon trace). The revised manuscript now includes Line 222 “Note that dynamic activity-driven BP changes were apparent during both saline- and Ang II infusions, Figure 1B”.

      (3) The decrease in DBP in the BPV group is very interesting. It is known that chronic Ang II increases cardiac hypertrophy, are there any changes to heart morphology, mass, and/or function during BPV? Can the decrease in DBP in BPV be attributed to preload dysfunction? This observation should be discussed.

      The lower DBP in the BPV group was already present at baseline, while both groups were still infused with saline, and was a difference beyond our control. However, this is an important and valid consideration, particularly considering the minimal yet significant increase in SBP within the BPV group (Figure 1D). Our goal was to induce significant transient blood pressure responses (BPV) and investigate the impact on cardiovascular and neurovascular outcomes in the absence of hypertension. We did not anticipate any major cardiac remodeling at this early time point (considering the absence of overt hypertension) and thus cardiac remodeling was not assessed and this is now discussed in the revised manuscript (Line 443-453).

      (4) Examining the baroreceptor reflex during the early and late phases of BPV is quite compelling. Figures 3D and 3E clearly delineate the differences between the two phases. For clarity, I would recommend plotting the data as is shown in panels D and E, rather than showing the mathematical ratio. Alternatively, plotting the correlation of ∆HR to ∆SBP and analyzing the slopes might be more digestible to the reader. The impairment in baroreceptor reflex in the BPV during high BP is clear, is there any indication whether this response might be due to loss of sympathetic or gain of parasympathetic response based on the model used?

      We appreciate the reviewer’s suggestion and have accordingly generated new figures displaying scatter plots of SBP vs HR with linear regression analysis (Figure 3D-G). Our goal is to further investigate which branch of the autonomic nervous system is affected in this model. The loss of a bradycardic response suggests either an enhancement of sympathetic activity, a reduction in parasympathetic activity, or a combination of both. This is briefly discussed in the revised manuscript (Line 486-496).

      Heart rate variability (HRV) serves as an index of neurocardiac function and dynamic, non-linear autonomic nervous system processes, as described in Shaffer and Ginsber[1]. However, given that our data was limited to BP and HR readings collected at one-minute intervals, our primary assessment of autonomic function is limited to the bradycardic response. Further studies will be necessary to fully characterize the autonomic parameters influenced by chronic BPV.

      (5) Figure 3B shows a drop in HR when the pump is ON irrespective of treatment (i.e., independent of BP changes). What is the underlying mechanism?

      We apologize for any lack of clarity. These observed heart rate (HR) changes occurred during Ang II infusion, when blood pressure (BP) was actively increasing. In the control group, the pump solution was switched to Ang II during specific periods (days 3-5 and 21-25 of the treatment protocol) to induce BP elevations and a baroreceptor response, allowing direct comparisons between the control and BPV group.

      To clarify this point, we have revised Line 260-263 of the manuscript: “To compare pressure-induced bradycardic responses between BPV and control mice at both early and later treatment stages, a cohort of control mice received Ang II infusion on days 3-5 (early phase) (Supplemental Figure 4) and days 21-25 (late phase) thereby transiently increasing BP”.

      Additionally, a detailed description has been added to the Methods section (Line 96-101): “Controls receiving Ang II: To facilitate between-group comparisons (control vs BPV), a separate cohort of control mice were subjected to the same pump infusion parameters as BPV mice but for a brief period receiving Ang II infusions on days 3-5 and 21-25 for experiments assessing pressure-evoked responses, including bradycardic reflex, myogenic response, and functional hyperemia at high BP.”

      (6) The correlation of ∆diameter vs MAP during low and high BP is compelling, and the shift in the cerebral autoregulation curve is also a good observation. I would strongly recommend that the authors include a schematic showing the working hypothesis that depicts the shift of the curve during BPV.

      Thank you for this insightful comment. The increase in vessel reactivity to BP elevations in parenchymal arterioles of BPV mice suggests that chronic BPV induces a leftward shift and a potential narrowing of the cerebral autoregulation range (lower BP thresholds for both the upper and lower limits of autoregulation). This has been incorporated (and discussed) into the revised manuscript (see Figure 5N).

      One potential explanation for these changes is that the absence of sustained hypertension, a prominent feature in most rodent models of hypertension, limits adaptive processes that protect the cerebral microcirculation from large BP fluctuations (e.g., vascular remodeling). While this study does not specifically address arteriole remodeling, the lack of such adaptation may reduce pressure buffering by upstream arterioles, thereby rendering the microcirculation more vulnerable to significant BP fluctuations.

      The unique model allows for measurements of parenchymal arteriole reactivity to acute dynamic changes in BP (both an increase and decrease in MAP). Our findings indicate that chronic BPV enhances the reactivity of parenchymal arterioles to BP changes—both during an increase in BP and upon its return to baseline, Supplemental Figure 5C, F. The data suggest an increased myogenic response to pressure elevation, indicative of heightened contractility, a common adaptive process observed in rodent models of hypertension[2-4]. However, our model also reveals a notable tendency for greater dilation when the BP drops, Supplemental Figure 5F. This intriguing observation may suggest ischemia during the vasoconstriction phase (at higher BP), leading to enhanced release of dilatory signals, which subsequently manifest as a greater dilation upon BP reduction. This phenomenon bears similarities to chronic hypoperfusion models[5,6], where vasodilatory mechanisms become more pronounced in response to sustained ischemic conditions. Future studies investigating the effects of BPV on myogenic responses and brain perfusion will be a priority for our ongoing research.

      (7) Functional hyperemia impairment in the BPV group is clear and well-described. Pairing this response with the kinetics of the recovery phase is an interesting observation. I suggest elaborating on why BPV group exerts lower responses and how this links to the rapid decline during recovery.

      Based on the heightened reactivity of BPV parenchymal arterioles to intravascular pressure (Figure 5), we anticipate that the reduction of sensory-evoked dilations results from an increased vasoconstrictive activity and/or a decreased availability of vasodilatory signaling pathways (NO, EETs, COX-derived prostaglandins)[7,8]. Consequently, the magnitude of the FH response is blunted during periods of elevated BP in BPV mice.

      Additionally, upon termination of the stimulus-induced response−when vasodilatory signals would typically dominate−vasoconstrictive mechanisms are rapidly engaged (or unmasked), leading to quicker return to baseline. This shift in the balance between vasodilatory and vasoconstrictive forces favors vasoconstriction, contributing to the altered recovery kinetics observed in BPV mice. This has been included in the Discussion section of the revised manuscript.

      (8) The experimental design for the cognitive/behavioral assessment is clear and it is a reasonable experiment based on previous results. However, the discussion associated with these results falls short. I recommend that the authors describe the rationale to assess recognition memory, short-term spatial memory, and mice activity, and explain why these outcomes are relevant in the BPV context. Are there other studies that support these findings? The authors discussed that no changes in alternation might be due to the age of the mice, which could already exhibit cognitive deficits. In this line of thought, what is the primary contributor to behavioral impairment? I think that this sentence weakens the conclusion on BPV impairing cognitive function and might even imply that age per se might be the factor that modulates the various physiological outcomes observed here. I recommend clarifying this section in the discussion.

      We thank the reviewer for this comment. Clinical studies have demonstrated that patients with elevated BPV exhibit impairments across multiple cognitive domains, including declines in processing speed[9] and episodic memory[10]. To evaluate memory function, we utilized behavioral tests: the novel object recognition (NOR) task to assess episodic memory[11] and the spontaneous Y-maze to evaluate short-term spatial memory[12].

      Previous research indicates that older C57Bl6 mice (14-month-old) exhibit cognitive deficits compared to younger counterparts (4- and 9-month-old)[13]. To ensure rigorous selection for behavioral testing, we conducted preliminary NOR assessment, evaluating recognition memory at the one-hour delay but observing failures at the four-, and 24-hour delays, indicating age-related deficits. Based on these results, animals failing recognition criteria were excluded from subsequent behavioral assessment. However, because no baseline cognitive testing was conducted for the spontaneous Y-maze, it is possible that some mice with aged-related deficits were included in this test, which may have influenced data interpretation.

      Additionally, the absence of differences in the Y-maze performance may suggest that short-term spatial memory remains intact following 25 days of BPV, a point that is now discussed in the revised manuscript.

      (9) Why were only male mice used?

      We appreciate this comment and acknowledge the importance of conducting experiments in both male and female mice. Studies involving female mice are currently ongoing, with telemetry data collection approximately halfway completed and two-photon imaging studies on functional hyperemia also partially completed. However, using middleaged mice for these experiments has proven challenging due to high mortality rates following telemetry surgeries. As a result, we initially limited our first cohort to male mice.

      (10) In the results for Figure 3: "Ang II evoked significant increases in SBP in both control and BPV groups;...". Also, in the figure legend: "B. Five-minute average HR when the pump is OFF or ON (infusing Ang II) for control and BPV groups...." The authors should clarify this as the methods do not state a control group that receives Ang II.

      Please refer to response to comment 5.

      Reviewer #2 (Public review):

      Summary:

      Blood pressure variability has been identified as an important risk factor for dementia. However, there are no established animal models to study the molecular mechanisms of increased blood pressure variability. In this manuscript, the authors present a novel mouse model of elevated BPV produced by pulsatile infusions of high-dose angiotensin II (3.1ug/hour) in middle-aged male mice. Using elegant methodology, including direct blood pressure measurement by telemetry, programmable infusion pumps, in vivo two-photon microscopy, and neurobehavioral tests, the authors show that this BPV model resulted in a blunted bradycardic response and cognitive deficits, enhanced myogenic response in parenchymal arterioles, and a loss of the pressure-evoked increase in functional hyperemia to whisker stimulation.

      Strengths:

      As the presentation of the first model of increased blood pressure variability, this manuscript establishes a method for assessing molecular mechanisms. The state-of-the-art methodology and robust data analysis provide convincing evidence that increased blood pressure variability impacts brain health.

      Weaknesses:

      One major drawback is that there is no comparison with another pressor agent (such as phenylephrine); therefore, it is not possible to conclude whether the observed effects are a result of increased blood pressure variability or caused by direct actions of Ang II.

      We acknowledge this limitation and have attempted to address the concern by introducing an alternative vasopressor, norepinephrine (NE), Figure 4. A subcutaneous dose of 45 µg/kg/min was titrated to match Ang II-induced transient BP pulse (Systolic BP ~150-180 mmHg), Figure 4A. Similar to Ang II treated mice, NE-treated mice exhibited no significant changes in average mean arterial pressure (MAP) throughout the 20-day treatment period (Figure 4B). Although there was a trend (P=0.08) towards increased average real variability (ARV) (Figure 4C left), it did not reach statistical significance. The coefficient of variation (CV) (Figure 4C right) was significantly increased by day 3-4 of treatment (P=0.02).

      Notably, unlike the bradycardic response observed during Ang II-induced BP elevations, NE infusions elicited a tachycardic response (Figure 4A), likely due to β-1 adrenergic receptor activation. However, significant mortality was observed within the NE cohort: three of six mice died prematurely during the second week of treatment, and two additional mice required euthanasia on days 18 and 20 due to lethargy, impaired mobility, and tachypnea.

      While we recognize the importance of comparing results across vasopressors, further investigation using additional vasopressors would require a dedicated study, as each agent may induce distinct off-target effects, potentially generating unique animal models. Alternatively, a mechanical approach−such as implanting a tethered intra-aortic balloon[14] connected to a syringe pump−could be explored to modulate blood pressure variability without pharmacological intervention. However, such an approach falls beyond the scope of the present study.

      Ang II is known to have direct actions on cerebrovascular reactivity, neuronal function, and learning and memory. Given that Ang II is increased in only 15% of human hypertensive patients (and an even lower percentage of non-hypertensive), the clinical relevance is diminished. Nonetheless, this is an important study establishing the first mouse model of increased BPV.

      We agree that high Ang II levels are not a predominant cause of hypertension in humans, which is why it is critical that our pulsatile Ang II dosing did not cause overt hypertension, (no increase in 24-hour MAP). Ang II was solely a tool to produce controlled, transient increases in BP to yield a significant increase in BPV.

      Regarding BPV specifically, prior studies indicate that primary hypertensive patients with elevated urinary angiotensinogen-to-creatinine ratio exhibit significantly higher mean 24-hour systolic ARV compared to those with lower ratios[15]. However, the fundamental mechanisms driving these harmful increases in BPV remain poorly defined. A central theme across clinical BPV studies is impaired arterial stiffness, which has been proposed to contribute to BPV through reduced arterial compliance and diminished baroreflex sensitivity. Moreover, increased BPV can exert mechanical stress on arterial walls, leading to arterial remodeling and stiffness−ultimately perpetuating a detrimental feed-forward cycle[16].

      In our model, male BPV mice exhibited a minimal yet significant elevation in SBP without corresponding increases in DBP, potentially reflecting isolated systolic hypertension, which is strongly associated with arterial stiffness[17,18]. Our initial goal was to establish controlled rapid fluctuations in BP, and Ang II was selected as the pressor due to its potent vasoconstrictive properties and short half-life[19].

      We appreciate the reviewer’s insightful comment and acknowledge the necessity of exploring alternative mechanisms underlying BPV, and independent of Ang II. It is our long-term goal to investigate these factors in further studies.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      (1) How was the dose of Ang II determined? It seems that this dose (3.1ug/hr) is quite high.

      The Ang II dose was titrated in a preliminary study to one that induced a significant and transient BP response without increasing 24-hour blood pressure (i.e. no hypertension).

      Ang II was delivered subcutaneously at 3.1 μg/hr, a concentration comparable to high-dose Ang II administration via mini-osmotic pumps (~1700 ng/kg/min)[20], with one-hour pulses occurring every 3-4 hours. With 6 pulses per day, the total daily dose equates to 18.6 µg/day in a ~30 gram mouse.

      For comparison, if the same 18.6 µg/day dose were administered continuously via a mini-osmotic pump (18.6 µg/0.03kg/1440min), the resulting dosage would be approximately 431 ng/kg/min[21,22], aligning with subpressor dose levels. Thus, while the total dose may appear high, it is not delivered in a constant manner but rather intermittently, allowing for controlled, rapid variations in blood pressure.

      (2) Were behavioral studies performed on the same mice that were individually housed? Individual housing causes significant stress in mice that can affect learning and memory tasks (PMC6709207). It's not a huge issue since the control mice would have been housed the same way, but it is something that could be mentioned in the discussion section.

      Behavioral studies were performed on mice that were individually housed following the telemetry surgery. The study was started once BP levels stabilized, as mice required several days to achieve hemodynamic stability post-surgery. Consequently, all mice were individually housed for several days before undergoing behavioral assessment.

      To account for potential cognitive variability, earlier novel object recognition (NOR) tests were conducted to established cognitive capacity, and mice that did not meet criteria were excluded from further behavioral testing. However, we acknowledge that individual housing induces stress, which can influence learning and memory, and this is a factor we were unable to fully control. Given that both experimental and control groups experienced the same housing conditions, this stress effect should be comparable across cohorts. A discussion on this limitation is now included in the text.

      (3) It looks like one control mouse that was included in both Figures 1 and 2 (control n=12) but was excluded in Table 1 (control n=11), this isn't mentioned in the text - please include the exclusion criteria in the manuscript.

      We apologize for the typo−12 control animals were consistently utilized across Figure 1-2, Table 1, Supplemental Table 1, Figure 6C, and Supplemental Figure 2B. Since the initial submission, one control mouse was completed and included into the telemetry control cohort. Thus, in the updated manuscript, we have corrected the control sample size to 13 mice across these figures ensuring consistency.

      Additionally, exclusion criteria have now been explicitly included in the manuscript (Line 173-175). Mice were excluded from the study if they died prematurely (died prior to treatment onset) or mice exhibited abnormally elevated pressure while receiving saline, likely due to complications from telemetry surgery.

      (4) Please include a statement on why female mice were not included in this study.

      As discussed in our response to Reviewer #1, our initial intention was to include both male and female mice in this study. However, high mortality rates following telemetry surgeries significantly constrained our ability to advance all aspects of the study. As a result, we limited our first cohort to males to establish the basics of the model. A statement is now included in the manuscript, Line 50-53: “Female mice were not included in the present study due to high post-surgery mortality observed in 12-14-month-old mice following complex procedures. To minimized confounding effects of differential survival and to establish foundational data for this model, we restricted the investigation to male mice.”

      Potential sex differences might be complex and warrants a separate future research to comprehensively assess sex as a biological variable, which are currently ongoing.

      (5) On page 14, "experiments from control vs experimental mice were not equally conducted in the same season raising the possibility for a seasonal effect" - does this mean that control experiments were not conducted at the same time as the Ang II infusions in BPV mice? This has huge implications on whether the effects observed are induced by treatment or just batch seasonal effects.

      We fully acknowledge the reviewer’s concern, and our statement aims to provide transparency regarding the study’s limitations. Several challenges contributed to this outcome, including high mortality rates following surgeries (primarily telemetry implantation) and technical issues related to instrumentation, particularly telemetry functionality.

      Differences between BPV and saline mice emerge primarily due to mortality or telemetry failures−some mice did not survive post-surgery, while others remain healthy but had non-functional telemeters. This issue was particularly pronounced in 14-month-old mice, as their fragile vasculature occasionally prevented proper BP readings.

      Each experiment required a minimum of two and a half months per mouse to complete, with a cost (also per mouse) exceeding $1500 USD ($300 pump, $175 mouse, $900 telemeters, per diem, drugs, reagents etc.). Despite our best effort to ensure comparable seasonal/batch data, these logistical and technical constraints prevented perfect synchronization.

      To evaluate whether seasonal differences influenced our results, we incorporated additional telemetry data into the control cohort. Of the seven included control mice, six underwent the same treatment but were allocated to a separate branch of the study, which endpoints did not require a chronic cranial window. We found no significant differences in 24-hour average MAP during the baseline period between control mice with or without a cranial window, Supplemental Figure 2A. Additionally, we grouped mice into seasonal categories based on Georgia’s climate: “Spring-Summer” (May-September) and “Fall-Winter” (October-April) but observed no BP differences between these periods, Supplemental Figure 2B.

      Given the absence of seasonal effects on BP and the fact that mice were sourced from two independent suppliers (Jackson Laboratory and NIA), we anticipate that the observed results are driven by treatment rather than seasonal or batch effects.

      (6) Methods, two-photon imaging: did the authors mean "retro-orbital" instead of "intra-orbital" injection of the Texas red dye? Also, is this a Texas red-dextran? If so, what molecular weight?

      Thank you for this comment. The correct terminology is “retro-orbital” rather than “intra-orbital” injection. Additionally, we utilized Texas Red-dextran (70 kDa, 5% [wt/vol] in saline) for the imaging experiments. These details have now been incorporated into the Methods section.

      (1) Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Front Public Health. 2017;5:258. doi: 10.3389/fpubh.2017.00258

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      (4) Diaz JR, Kim KJ, Brands MW, Filosa JA. Augmented astrocyte microdomain Ca(2+) dynamics and parenchymal arteriole tone in angiotensin II-infused hypertensive mice. Glia. 2019;67:551-565. doi: 10.1002/glia.23564

      (5) Kim KJ, Diaz JR, Presa JL, Muller PR, Brands MW, Khan MB, Hess DC, Althammer F, Stern JE, Filosa JA. Decreased parenchymal arteriolar tone uncouples vessel-to-neuronal communication in a mouse model of vascular cognitive impairment. GeroScience. 2021. doi: 10.1007/s11357-020-00305-x

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    1. markets, are individualist in origin, spontaneously generated, and unin-tended. In both systems, structures are formed by the coaction of theirunits. Whether those units live, prosper, or die depends on their ownefforts. Both systems are formed and maintained on a principle of self-help that applies to the units. To say that the two realms are structurallysimilar is not to proclaim their identity. Economically, the self-help prin-ciple applies within governmentally contrived limits. Market economiesare hedged about in ways that channel energies constructively. One maythink of pure food-and-drug standards, antitrust laws, securities andexchange regulations, laws against shooting a competitor, and rules for-bidding false claims in advertising. International politics is more nearly arealm in which anything goes. International politics is structurally simi-lar to a market economy insofar as the self-help principle is allowed tooperate in the latter (Waltz, 1979, p. 91

      Both are made up of independent units. In the economy, it’s businesses. In world politics, it’s countries (or in the past, city-states or empires).

      These units don’t sit down and plan out a system together. Instead, the “system” just naturally forms as they all act in their own self-interest. That’s what it means when it says “spontaneously generated, and unintended.”

      Each country (or business) has to look out for itself. Whether it survives or thrives depends mostly on its own efforts (self-help).

      But there’s one big difference: in markets, governments put rules and limits in place to keep things from going totally wild (like no poisoning food, no lying in ads, no killing competitors).

      In international politics, though, there’s no world government to set rules. So it’s much closer to a “wild west” situation — where countries do whatever they can get away with.

      So the main idea: International politics is like a free market where everyone fends for themselves — but unlike markets, there aren’t strong rules to keep things fair or safe.

    Annotators

    1. Scholars argue whether we can understand what the spread of digital networks will mean for relatively well-established cultures in the tangible world, or predict with any certainty how cultures will evolve on digital platforms. There are two basic schools of thought. The first argues that existing cultures might find themselves essentially recreated in digital form as more and more life experiences, from the exciting to the mundane, play out in digital spaces. The second school of thought posits that the dominant digital culture emerging now is a separate culture unto itself. It seems likely that neither version of these imagined forms of digital culture will dominate; instead, we will likely see a combination of the two. Parts of existing culture will appear online as they do in the physical world and parts of digital culture will seem completely new, previously unfathomable because they could not or would not appear in the tangible world.

      I feel that digital culture won't ever really become such a big thing because it just does not compare to in person cultures. Sure perhaps people can move online and start a new thing but the idea of developing and evolving over time digitally to me seems impossible and does not make a lot of sense. People may share things about their culture online but trying to move and stay to digital culture I don't believe could work out despite how much technology can advance, it's not really the same to me.

    2. “The Internet is the first thing that humanity has built that humanity doesn’t understand, the largest experiment in anarchy that we have ever had.”

      this quote is right and I would agree with it. For one point I do believe the internet has become a very massive platform in which some people just don't or may not have full idea of how the internet works as it's evolved the past years. For another point companies i feel still have that control part as they be setting rules for their users as to what they can put out on the internet in platforms like Google or Meta for example.

    1. Normalize the ZIP file. To exploit ZIP parsing ambiguities,the attacker usually needs to carefully manipulate the fieldsof a ZIP file, which cannot be achieved by a regular ZIParchiver. Most ambiguities will disappear if the ZIP file isfirst extracted and then repacked. Therefore, if we care aboutonly the contents but not the integrity of the whole ZIP file,we can normalize the ZIP file by extracting and repacking itbefore processing.

      This really seems like the best strategy—to ensure that Parser2 sees content the same way that Parser1 sees it, you can effectively give Parser2 Parser1's powers by having Parser1 "talk" to Parser2 by way of a specially prepared input. (It just happens to be the case that that input also looks like a ZIP archive.)

      This does require some validation of the suitability for Parser1 and Parser2 to work together, but it's not any more difficult than the previous task at hand whereby you expect/require Parser1 and Parser2 to behave exactly the same for all inputs. The task is made slightly easier. And to this end, a test suite (derived from the work from this paper) could be distributed for the purpose of benchmarking implementations for compatibility matches.

      This could be facilitated by the appropriate libraries/packages all being patched to support a "normalize" (i.e. "repack") operation (and for this to be the norm among all implementations) explicitly for this purpose.

    1. Reviewer #2 (Public review):

      General comments

      We thank the reviewers and editor for their thoughtful feedback. We are glad that the minor comments appear resolved. In this revision, we added subject-specific analyses, further FC comparisons, and clarified our rationale for stimulation parameters. We acknowledge that two concerns remain: (1) the 1 mA-2 mA sequence may introduce confounds, and (2) electric field modeling was not included due to technical limitations. We now explicitly note these as limitations in the manuscript and provide justification and discussion accordingly.

      Major comments

      R.2.1. For the anesthetized monkeys, the anode location differs between subjects, with the electrode positioned to stimulate the left DLFPC in monkey R and the right DLPFC in monkey N. The authors mention that this discrepancy does not result in significant differences in the electric field due to the monkeys' small head size. However, this is incorrect, as placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side. Running an electric field simulation would confirm this. Additionally, the small electrode size suggested by the Easy cap configuration for NHP appears sufficient to stimulate the targeted regions focally. If this interpretation is correct, the authors should provide additional evidence to support their claim, such as a computational simulation of the EF distribution.

      R.2.1 Authors' answer: We thank the Reviewer for the comments. First, regarding the reviewer's statement that placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side, we would like to clarify that we did not use a typical 4 x 1 concentric ring high-definition setup (which consists of a small centre electrode surrounded by four return electrodes), but a two-electrode montage, with one electrode over the left or right PFC and the other one over the contralateral occipital cortex. According to EF modelling papers, a 4 x 1 high-definition setup would produce an EF that is focused and limited to the cortical area circumscribed by the ring of the return electrodes (Datta et al. 2009; Alam et al. 2016). Therefore, targeting the left or right DLPFC with a 4 x 1 setup would produce an EF confined to the targeted hemisphere of the PFC. In contrast, we expect the brain current flow generated with our 2-electrode setup to be broader, despite the small size of the electrodes, because there is no constraint from return electrodes. Thus, with our setup, the current is expected to flow between the PFC and the occipital cortex (see also our responses to comments R3.3., R.E.C.#2.1. and R.E.C.#2.2.).

      Second, we would like to point out that in awake experiments, in which we stimulated the right PFC of both monkeys, there was no gross evidence of left or right asymmetry in the computed functional connectivity patterns (Figure 3A, Figure 3 - figure supplement 2A; Figure 5A). These results, showing that our stimulation montages did not induce asymmetric dynamic FC changes in NHPs, support the idea that our setups did not generate EFs that were spatially focused enough to alter brain activity in one hemisphere substantially more than the other.

      Third, it is also worth noting that current evidence suggests that human brains are significantly more lateralized than those of macaques. Macaque monkeys have been found to have some degree of lateralized networks, but these are of lower complexity, and the lateralization is less pronounced and functionally organized than in humans. (Whey et al., 2014; Mantini et al., 2013). This suggests that, even if the stimulation were focal enough to stimulate the left or the right part of the PFC only, the behavioural effects would likely be similar.

      Follow-up comment: Thank you for the detailed response and for referencing both experimental data and prior literature. While I appreciate the discussion on the lack of functional asymmetry and reduced lateralization in macaques, my original concern was about the physical distribution of the electric field (EF) due to different anode placements. Functional connectivity outcomes do not necessarily reflect EF symmetry, and without EF modeling, it's difficult to determine whether the stimulation affected both hemispheres equally. I understand the challenges of NHP-specific modeling, but even a simplified simulation or acknowledgment of this limitation in the manuscript would help clarify the interpretability of your results.

      R.2.2. For the anesthetized monkeys, the authors applied 1 mA tDCS first, followed by 2 mA tDCS. A 20-minute stimulation duration of 1 mA tDCS is strong enough to produce after-effects that could influence the brain state during the 2 mA tDCS. This raises some concerns. Previous studies have shown that 1 mA tDCS can generate EF of over 1 V/m in the brain, and the effects of stimulation are sensitive to brain state (e.g., eye closed vs. eye open). How do the authors ensure that there are no after-effects from the 1 mA tDCS? This issue makes it challenging to directly compare the effects of 1 mA and 2 mA stimulation.<br /> R.2.2 Authors' answer: We agree with the reviewer's comment that 1 mA tDCS may induce aftereffects, as has been observed in several human studies (e.g., (Jamil et al. 2017, 2020). Although the differences between the 1 mA post-stimulation and baseline conditions were not significant in our analyses, it's still possible that the stimulation produced some effects below the threshold of significance that may contribute, albeit weakly, to the changes observed during

      Follow-up comment: Thank you for the clarification and for acknowledging the potential for 1 mA after-effects. While I appreciate the authors' transparency and the amendment to the manuscript, I still find it important that the limitation be clearly stated in the Discussion section. The fact that 2 mA stimulation always followed 1 mA introduces a potential confound, making it difficult to attribute observed changes uniquely to 2 mA. If a counterbalanced design was not feasible, I would recommend explicitly noting this as a limitation in the interpretation of dose-dependent effects.

      R.2.3. The occurrence rate of a specific structural-functional coupling pattern among random brain regions shows significant effects of tDCS. However, these results seem counterintuitive. It is generally understood that non-invasive brain stimulation tends to modulate functional connectivity rather than structural or structural-functional connectivity. How does the occurrence rate of structural-functional coupling patterns provide a more suitable measure of the effectiveness of tDCS than functional connectivity alone? I would recommend that the authors present the results based on functional connectivity itself. If there is no change in functional connectivity, the relevance of changes in structural-functional coupling might not translate into a meaningful alteration in brain function, making it unclear how significant this finding is without corresponding functional evidence.

      R.2.3. Authors' answer: First of all, we would like to make it clear that the occurrence rate of patterns as a function of their SFC is not intended to be used or seen as a 'better' measure of the efficacy of tDCS. Instead, it is one aspect of the effects of tDCS on whole-brain functional cortical dynamics, obtained from refined measures (phase-coherences), that specifically addresses the coupling between structure and function. This type of analysis is further motivated by its increasing use in the literature due to its suspected relationship to wakefulness (e.g., (Barttfeld et al. 2015, Demertzi et al. 2019; Castro et al. 2023)). Also, in our analysis, the structure is kept constant: the connectivity matrix used to correlate the functional brain states is always the same (CoCoMac82). Thus, the influence of tDCS on the structure-function side can only be explained by modulating the functional aspects, as suggested by intuition and previous results.

      Then, we agree with the reviewer that studying the functional changes induced by tDCS alone could be valuable. However, usual metrics used in FC analysis are usually done statistically: FC-states are either computed through averaging spatial correlations over time, then analyzed through graph-theoretical properties for instance (or by just directly computing the element-wise differences), or either by considering the properties of the different visited FC-states by computing spatial correlations over a sliding time-window, and then similar analysis can be done as previously explained. But these are static metrics, if the states visited are essentially the same (which is expected from non-invasive neuromodulations that haven't already demonstrated strong and/or characteristic impact), but the dynamical process of visiting said states changes, one would see no difference in that regard. As such, in the case of resting-state fMRI, differences in FCs are hard to interpret given that between-sessions within-condition differences are usually found with some degree of variance for the respective conditions. Trying then to interpret between-condition differences is quite tricky in the case of subtle modulations of the system's activity. On the other hand, more subtle differences can be captured by considering more detailed analysis, such as using phase-based methods like we did, by incorporating some statistical learning component with regard to the dynamicity of the system (supervised learning for instance like we did followed by temporal & transition-based methodology), and by adding some dimensions along which one will be able to give some interpretation to the analysis. In our case we were interested in characterizing resting-state differences between stimulation conditions, which have nuanced and subtle interactions with the biological system. As such, classical measures of differences between FC states are likely to not be refined and precise enough. In fact, we propose additional files investigating those classically used measures such as differences in average FC matrices, or changes in functional graph properties (like modularity, efficiency and density) of the visited FC states. These figures show that, for the first case, comparing region-to-region specific FCs provides very few statistically significant results. With respect to the second part, we show that virtually no differences are observed in the properties of the functional states visited. These results suggest, as expected, that the actual brain states visited across the different stimulation conditions are topologically quite similar, and that only very few region-specific pairwise functional connectivities are particularly modulated by specific tDCS montages while, on the other hand, the actual dynamical process dictating how the brain activity passes from one state to another is in fact being influenced as shown by the dynamical analysis presented in the main figures in a more apparent and meaningful way (in that it is dependent on the montage, somewhat consistent with regard to the post-stimulations conditions, and can be made sense of by considering the theoretical effect of near-anodal versus near-cathodal neuromodulatory effects).

      Actions in the text: We have added new supplementary files showing the effects of the stimulations on FC matrices and on classical functional graph properties in awake and anesthesia datasets (Supplementary Files 3 & 4). We have added new sentences about these new analyses on the effects of the stimulations on FC matrices and on classical functional graph properties in the Results section:<br /> Follow-up comment: Thank you for the detailed and comprehensive response. The clarification regarding the use of SFC dynamics and the additional analyses provided are convincing.

      R2.4. The authors recorded data from only two monkeys, which may limit the investigation of the group effects of tDCS. As the number of scans for the second monkey in each consciousness condition is lower than that in the first monkey, there is a concern that the main effects might primarily reflect the data from a single monkey. I suggest that the authors should analyze the data for each monkey individually to determine if similar trends are observed in both subjects.

      R.2.4. Authors' answer: We agree that the small number of subjects is a limitation of our study. However, we have already addressed these aspects by reporting statistical analyses that consider them, using linear models of such variables, and running them through ANOVA tests. In addition, we experimentally ensured that we recorded a relatively high number of sessions over a period of several years. Regardless, we agree that our study would benefit from further investigation into this matter. We have therefore prepared complementary figures showing the main analysis performed separately for the two monkeys as proposed, as well as further investigations into the inter-condition variability outmatching the inter-individual variability, itself being also outmatched by intra-individual changes.

      Actions in the text: We have added a supplementary file showing the main analyses performed separately for the two monkeys (Supplementary File 2) and further investigations into the inter-condition variability (Supplementary Files 3 & 4). We have added new sentences about these analyses performed separately for the two monkeys in the Results section:

      Follow-up comment: Thank you for addressing this concern and for providing the individual monkey analysis. The additional figures and statistical explanations are helpful and appreciated.

      R2.5. Anodal tDCS was only applied to anesthetized monkeys, which limits the conclusion that the authors are aiming for. It raises questions about the conclusion regarding brain state dependency. To address this, it would be better to include the cathodal tDCS session for anesthetized monkeys. If cathodal tDCS changes the connectivity during anesthesia, it becomes difficult to argue that the effects of cathodal tDCS vary depending on the state of consciousness as discussed in this paper. On the other hand, if cathodal tDCS would not produce any changes, the conclusion would then focus on the relationship between the polarity of tDCS and consciousness. In that case, the authors could maintain their conclusion but might need to refine it to reflect this specific relationship more accurately.

      R.2.5. Authors' answer: We agree with the reviewer that it would have been interesting to investigate the effects of cathodal tDCS in anesthetized monkeys. However, due to the challenging nature of the experimental procedures under anesthesia, we had to limit the investigations to only one stimulation modality. We chose to deliver anodal stimulation because, from a translational point of view, we aimed to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness. It also made much more sense to increase the cortical excitability of the prefrontal cortex in an attempt to wake up the sedated monkeys rather than doing the opposite.

      Actions in the text: We have added a new sentence in the Results section:

      "Due to the challenging nature of the experimental procedures under anesthesia, we limited the investigations to only one stimulation modality. We chose to deliver anodal stimulation to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness and to increase the cortical excitability of the PFC in an attempt to wake up the sedated monkeys."

      Follow-up comment: Thank you for clarifying the rationale behind applying only anodal stimulation under anesthesia. While I appreciate the experimental constraints and the translational motivation, I would still encourage the authors to explicitly acknowledge in the Discussion that the absence of a cathodal condition under anesthesia limits the ability to dissociate polarity-specific effects from state-dependent effects. This clarification would help temper the conclusions and better reflect the scope of the current dataset.

    2. Author response:

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

      Reviewer #1 (Public review): 

      Summary: 

      In this work, the authors apply TDCS to awake and anesthetized macaques to determine the effect of this modality on dynamic connectivity measured by fMRI. The question is to understand the extent to which TDCS can influence conscious or unconscious states. Their target was the PFC. During the conscious states, the animals were executing a fixation task. Unconsciousness was achieved by administering a constant infusion of propofol and a continuous infusion of the muscle relaxant cisatracurium. They observed the animals while awake receiving anodal or cathodal hd-TDCS applied to the PFC. During the cathodal stimulation, they found disruption of functional connectivity patterns, enhanced structure-function correlations, a decrease in Shannon entropy, and a transition towards patterns that were more commonly anatomically based. In contrast under propofol anesthesia anodal hd-TDCS stimulation appreciably altered the brain connectivity patterns and decreased the correlation between structure and function. The PFC stimulations altered patterns associated with consciousness as well as those associated with unconsciousness.

      Strengths: 

      The authors carefully executed a set of very challenging experiments that involved applying tDCS in awake and anesthetized non-human primates while conducting functional imaging.

      We thank the Reviewer for summarising our study and for his appreciation of the highly challenging experiments we performed.

      Weaknesses:

      The authors show that tDCS can alter functional connectivity measured by fMRI but they do not make clear what their studies teach the reader about the effects of tDCS on the brain during different states of consciousness. No important finding is stated contrary to what is stated in the abstract. It is also not clear what the work teaches us about how tDCS works nor is it clear what are the "clinical implications for disorders of consciousness." The deep anesthesia is akin to being in a state of coma. This was not discussed.  

      While the authors have executed a set of technically challenging experiments, it is not clear what they teach us about how tDCS works, normal brain neurophysiology, or brain pathological states such as disorders of consciousness.

      We thank the reviewer for his comments. We agree that we could better highlight the value and implications of our work, and we take this opportunity to improve our manuscript according to the suggestions.

      Actions in the text: We have added several new paragraphs in the Discussion section, considering these comments and other related remarks from the Reviewing Editor (see below our answer to the first comment of the Reviewing Editor: REC#1).

      Reviewer #2 (Public review): 

      General comments: 

      The authors investigated the effects of tDCS on brain dynamics in awake and anesthetized monkeys using functional MRI. They claim that cathodal tDCS disrupts the functional connectivity pattern in awake monkeys while anodal tDCS alters brain patterns in anesthetized monkeys. This study offers valuable insight into how brain states can influence the outcomes of noninvasive brain stimulation. However, there are several aspects of the methods and results sections that should be improved to clarify the findings.

      We thank the Reviewer for the summary and appreciation of our study.  

      Major comments 

      For the anesthetized monkeys, the anode location differs between subjects, with the electrode positioned to stimulate the left DLFPC in monkey R and the right DLPFC in monkey N. The authors mention that this discrepancy does not result in significant differences in the electric field due to the monkeys' small head size. However, this is incorrect, as placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side. Running an electric field simulation would confirm this. Additionally, the small electrode size suggested by the Easy cap configuration for NHP appears sufficient to stimulate the targeted regions focally. If this interpretation is correct, the authors should provide additional evidence to support their claim, such as a computational simulation of the EF distribution.

      We thank the Reviewer for the comments. First, regarding the reviewer’s statement that placing the anode on the left hemisphere would result in a much lower EF in the right DLPFC than placing the anode on the right side, we would like to clarify that we did not use a typical 4 x 1 concentric ring high-definition setup (which consists of a small centre electrode surrounded by four return electrodes), but a two-electrode montage, with one electrode over the left or right PFC and the other one over the contralateral occipital cortex. According to EF modelling papers, a 4 x 1 high-definition setup would produce an EF that is focused and limited to the cortical area circumscribed by the ring of the return electrodes (Datta et al. 2009; Alam et al. 2016). Therefore, targeting the left or right DLPFC with a 4 x 1 setup would produce an EF confined to the targeted hemisphere of the PFC. In contrast, we expect the brain current flow generated with our 2-electrode setup to be broader, despite the small size of the electrodes,  because there is no constraint from return electrodes. Thus, with our setup, the current is expected to flow between the PFC and the occipital cortex (see also our responses to comments R3.3., R.E.C.#2.1. and R.E.C.#2.2.). 

      Second, we would like to point out that in awake experiments, in which we stimulated the right PFC of both monkeys, there was no gross evidence of left or right asymmetry in the computed functional connectivity patterns (Figure 3A, Figure 3 - figure supplement 2A; Figure 5A). These results, showing that our stimulation montages did not induce asymmetric dynamic FC changes in NHPs, support the idea that our setups did not generate EFs that were spatially focused enough to alter brain activity in one hemisphere substantially more than the other.

      Third, it is also worth noting that current evidence suggests that human brains are significantly more lateralized than those of macaques. Macaque monkeys have been found to have some degree of lateralized networks, but these are of lower complexity, and the lateralization is less pronounced and functionally organized than in humans. (Whey et al., 2014; Mantini et al., 2013). This suggests that, even if the stimulation were focal enough to stimulate the left or the right part of the PFC only, the behavioural effects would likely be similar.

      We strongly agree with the reviewer that conducting an EF simulation would be valuable to confirm our expectations and to gain a comprehensive view of the characteristics of the EFs generated with our different setups in NHPs. However, the challenge is in the fact that EF computational models have been developed for humans, and their use in NHPs is not straightforward due to significant anatomical differences. For example, macaque monkeys are distinct from humans in terms of brain size, shape and cortical organisation, skull thickness, and the presence of muscles, as well as different tissue conductivities (Lee et al. 2015; Datta et al.2016; Mantell et al. 2023). We plan to address this in future work.

      Actions in the text: In the Materials and Methods section, we have modified the sentence: “Because of the small size of the monkey's head and because we did not use return electrodes to restrict the current flow (as is achieved with typical high-definition montages (Datta et al. 2009; Alam et al. 2016)), we expected that tDCS stimulation with the two symmetrical montages would result in nearly equivalent electric fields across the monkey’s head and produce roughly similar effects on brain activity.” 

      We also added a new sentence about EF simulation: 

      “This would need to be confirmed by running an electric field simulation. However, computational electric field models have been developed for humans, and their use in NHPs is not straightforward due to anatomical specificities. Indeed, monkeys differ from humans in terms of brain size, shape and cortical organization, skull thickness, tissue conductivities and the presence of muscles (Lee et al. 2015; Datta et al. 2016; Mantell et al. 2023). Modelling of EFs generated with the specific tDCS montages employed in this study will be performed in future work.”

      For the anesthetized monkeys, the authors applied 1 mA tDCS first, followed by 2 mA tDCS. A 20-minute stimulation duration of 1 mA tDCS is strong enough to produce after-effects that could influence the brain state during the 2 mA tDCS. This raises some concerns. Previous studies have shown that 1 mA tDCS can generate EF of over 1 V/m in the brain, and the effects of stimulation are sensitive to brain state (e.g., eye closed vs. eye open). How do the authors ensure that there are no after-effects from the 1 mA tDCS? This issue makes it challenging to directly compare the effects of 1 mA and 2 mA stimulation.

      We agree with the reviewer's comment that 1 mA tDCS may induce aftereffects, as has been observed in several human studies (e.g., (Jamil et al. 2017, 2020). Although the differences between the 1 mA post-stimulation and baseline conditions were not significant in our analyses, it's still possible that the stimulation produced some effects below the threshold of significance that may contribute, albeit weakly, to the changes observed during and after 2 mA stimulation. We have, therefore, amended the paper in line with the reviewer's comments.

      Actions in the text: We have added the following text in the Result section: 

      “While several human studies have reported that 1 mA transcranial stimulation induces aftereffects (e.g., (Jamil et al. 2017, 2020; Monte-Silva et al. 2010), the differences between the 1 mA post-stimulation and baseline conditions were not significant in our analyses. However, it is still possible that the 1 mA stimulation produced some effects below the threshold of significance that may contribute to the changes observed during and after the 2 mA stimulation.”

      The occurrence rate of a specific structural-functional coupling pattern among random brain regions shows significant effects of tDCS. However, these results seem counterintuitive. It is generally understood that noninvasive brain stimulation tends to modulate functional connectivity rather than structural or structural-functional connectivity. How does the occurrence rate of structural-functional coupling patterns provide a more suitable measure of the effectiveness of tDCS than functional connectivity alone? I would recommend that the authors present the results based on functional connectivity itself. If there is no change in functional connectivity, the relevance of changes in structural-functional coupling might not translate into a meaningful alteration in brain function, making it unclear how significant this finding is without corresponding functional evidence.

      First, of all, we would like to make it clear that the occurrence rate of patterns as a function of their SFC is not intended to be used or seen as a ‘better’ measure of the efficacy of tDCS. Instead, it is one aspect of the effects of tDCS on whole-brain functional cortical dynamics, obtained from refined measures (phase-coherences), that specifically addresses the coupling between structure and function. This type of analysis is further motivated by its increasing use in the literature due to its suspected relationship to wakefulness (e.g., (Barttfeld et al. 2015, Demertzi et al. 2019; Castro et al. 2023)). Also, in our analysis, the structure is kept constant: the connectivity matrix used to correlate the functional brain states is always the same (CoCoMac82). Thus, the influence of tDCS on the structure-function side can only be explained by modulating the functional aspects, as suggested by intuition and previous results.

      Then, we agree with the reviewer that studying the functional changes induced by tDCS alone could be valuable. However, usual metrics used in FC analysis are usually done statistically: FC-states are either computed through averaging spatial correlations over time, then analyzed through graph-theoretical properties for instance (or by just directly computing the element-wise differences), or either by considering the properties of the different visited FC-states by computing spatial correlations over a sliding time-window, and then similar analysis can be done as previously explained. But these are static metrics, if the states visited are essentially the same (which is expected from non-invasive neuromodulations that haven’t already demonstrated strong and/or characteristic impact), but the dynamical process of visiting said states changes, one would see no difference in that regard. As such, in the case of resting-state fMRI, differences in FCs are hard to interpret given that between-sessions within-condition differences are usually found with some degree of variance for the respective conditions. Trying then to interpret between-condition differences is quite tricky in the case of subtle modulations of the system’s activity. On the other hand, more subtle differences can be captured by considering more detailed analysis, such as using phase-based methods like we did,  by incorporating some statistical learning component with regard to the dynamicity of the system (supervised learning for instance like we did followed by temporal & transition-based methodology), and by adding some dimensions along which one will be able to give some interpretation to the analysis.  In our case we were interested in characterizing resting-state differences between stimulation conditions, which have nuanced and subtle interactions with the biological system. 

      As such, classical measures of differences between FC states are likely to not be refined and precise enough. In fact, we propose additional files investigating those classically used measures such as differences in average FC matrices, or changes in functional graph properties (like modularity, efficiency and density) of the visited FC states. These figures show that, for the first case, comparing region-to-region specific FCs provides very few statistically significant results. With respect to the second part, we show that virtually no differences are observed in the properties of the functional states visited. 

      These results suggest, as expected, that the actual brain states visited across the different stimulation conditions are topologically quite similar, and that only very few region-specific pairwise functional connectivities are particularly modulated by specific tDCS montages while, on the other hand, the actual dynamical process dictating how the brain activity passes from one state to another is in fact being influenced as shown by the dynamical analysis presented in the main figures in a more apparent and meaningful way (in that it is dependent on the montage, somewhat consistent with regard to the post-stimulations conditions, and can be made sense of by considering the theoretical effect of near-anodal versus near-cathodal neuromodulatory effects).

      Actions in the text: We have added new supplementary files showing the effects of the stimulations on FC matrices and on classical functional graph properties in awake and anesthesia datasets (Supplementary Files 3 & 4).

      We have added new sentences about these new analyses on the effects of the stimulations on FC matrices and on classical functional graph properties in the Results section:

      “In addition, we performed the main analyses separately for the two monkeys, explored the inter-condition variability (Supplementary File 2), and computed classical measures of functional connectivity such as average FC matrices and functional graph properties (modularity, efficiency and density) of the visited FC states (Supplementary File 3).... In contrast, classical FC metrics did not show significant differences across stimulation conditions, highlighting the value of dynamic FC metrics to capture the neuromodulatory effects of tDCS.”

      “Analyses of the two monkeys separately showed that the changes in slope and Shannon entropy were bigger in one of the two monkeys but went in the same direction (Supplementary File 2), while classical FC metrics did not capture any statistical differences between the different stimulation conditions (Supplementary File 3).”

      The authors recorded data from only two monkeys, which may limit the investigation of the group effects of tDCS. As the number of scans for the second monkey in each consciousness condition is lower than that in the first monkey, there is a concern that the main effects might primarily reflect the data from a single monkey. I suggest that the authors should analyze the data for each monkey individually to determine if similar trends are observed in both subjects.

      We agree that the small number of subjects is a limitation of our study. However, we have already addressed these aspects by reporting statistical analyses that consider them, using linear models of such variables, and running them through ANOVA tests. In addition, we experimentally ensured that we recorded a relatively high number of sessions over a period of several years. Regardless, we agree that our study would benefit from further investigation into this matter. We have therefore prepared complementary figures showing the main analysis performed separately for the two monkeys as proposed, as well as further investigations into the inter-condition variability outmatching the inter-individual variability, itself being also outmatched by intra-individual changes. 

      Actions in the text: We have added a supplementary file showing the main analyses performed separately for the two monkeys (Supplementary File 2) and further investigations into the inter-condition variability (Supplementary Files 3 & 4).

      We have added new sentences about these analyses performed separately for the two monkeys in the Results section:

      “In addition, we performed the main analyses separately for the two monkeys, explored the inter-condition variability (Supplementary File 2), and computed classical measures of functional connectivity such as average FC matrices and functional graph properties (modularity, efficiency and density) of the visited FC states (Supplementary File 3). The separate analyses showed that the changes in slope and Shannon entropy were substantially more pronounced in one of the two monkeys, corroborating some of the effects captured in the ANOVA tests.”

      “Analyses of the two monkeys separately showed that the changes in slope and Shannon entropy were bigger in one of the two monkeys but went in the same direction (Supplementary

      File 2)”.

      Anodal tDCS was only applied to anesthetized monkeys, which limits the conclusion that the authors are aiming for. It raises questions about the conclusion regarding brain state dependency. To address this, it would be better to include the cathodal tDCS session for anesthetized monkeys. If cathodal tDCS changes the connectivity during anesthesia, it becomes difficult to argue that the effects of cathodal tDCS vary depending on the state of consciousness as discussed in this paper. On the other hand, if cathodal tDCS would not produce any changes, the conclusion would then focus on the relationship between the polarity of tDCS and consciousness. In that case, the authors could maintain their conclusion but might need to refine it to reflect this specific relationship more accurately. 

      We agree with the reviewer that it would have been interesting to investigate the effects of cathodal tDCS in anesthetized monkeys. However, due to the challenging nature of the experimental procedures under anesthesia, we had to limit the investigations to only one stimulation modality. We chose to deliver anodal stimulation because, from a translational point of view, we aimed to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness. It also made much more sense to increase the cortical excitability of the prefrontal cortex in an attempt to wake up the sedated monkeys rather than doing the opposite.

      Actions in the text: We have added a new sentence in the Results section:

      “Due to the challenging nature of the experimental procedures under anesthesia, we limited the investigations to only one stimulation modality. We chose to deliver anodal stimulation to provide new information on the effects of tDCS under anesthesia as a model for disorders of consciousness and to increase the cortical excitability of the PFC in an attempt to wake up the sedated monkeys.”

      Reviewer #3 (Public review): 

      Summary: 

      This study used transcranial direct current stimulation administered using small 'high-definition' electrodes to modulate neural activity within the non-human primate prefrontal cortex during both wakefulness and anaesthesia. Functional magnetic resonance imaging (fMRI) was used to assess the neuromodulatory effects of stimulation. The authors report on the modification of brain dynamics during and following anodal and cathodal stimulation during wakefulness and following anodal stimulation at two intensities (1 mA, 2 mA) during anaesthesia. This study provides some possible support that prefrontal direct current stimulation can alter neural activity patterns across wakefulness and sedation in monkeys. However, the reported findings need to be considered carefully against several important methodological limitations. 

      Strengths: 

      A key strength of this work is the use of fMRI-based methods to track changes in brain activity with good spatial precision. Another strength is the exploration of stimulation effects across wakefulness and sedation, which has the potential to provide novel information on the impact of electrical stimulation across states of consciousness.

      We thank the Reviewer for the summary and for highlighting the strengths of our study. 

      Weaknesses: 

      The lack of a sham stimulation condition is a significant limitation, for instance, how can the authors be sure that results were not affected by drowsiness or fatigue as a result of the experimental procedure?

      We agree with the reviewer that adding control conditions could have strengthened our study. Control conditions usually consist of a sham condition or active control conditions. However, as mentioned in response to one of Reviewer 2 comments (R.2.5), we had to make choices as we could not perform as many experiments due to their demanding nature, especially under anesthesia. 

      In the awake state, we acquired data with two experimental conditions; the monkeys were exposed to either anodal (F4/O1) or cathodal (O1/F4) PFC tDCS. As anodal tDCS of the PFC induced only minor changes in brain dynamics, it could be considered as an active control condition for the cathodal condition, which had striking effects on the cortical dynamics. It is also worth noting that doubts have been raised about the neurobiological inertia of certain sham protocols. Indeed, different sham protocols have been employed in the literature, some of which may produce unintended effects (Fonteneau et al. 2019). Therefore, active control conditions, such as reversing the polarity of the stimulation or targeting a different brain region, have been proposed to provide better control (Fonteneau et al. 2019). Furthermore, in the context of experiments performed under anesthesia, the relevance of a sham control condition typically used to achieve adequate blinding is questionable. 

      With regard to drowsiness and fatigue as a result of the experimental procedure, we agree with the reviewer that this is a common problem in functional imaging due to the length of the recording sessions. We assumed, as was done in previous work (Uhrig, Dehaene, and Jarraya 2014; Wang et al. 2015), that the monkeys' performance on the fixation task during acquisition would capture these periods of fatigue. Therefore, only sessions with fixation rates above 85% were included in our analysis. 

      Actions in the text: We have now specified, in the Materials and Methods section, the fact that only runs with a high fixation rate (> 85%) were included in the study: 

      “To ensure that the results were not biased by fatigue or drowsiness due to the lengthy

      In the anaesthesia condition, the authors investigated the effects of two intensities of stimulation (1 mA and 2 mA). However, a potential confound here relates to the possibility that the initial 1 mA stimulation block might have caused plasticity-related changes in neural activity that could have interfered with the following 2 mA block due to the lack of a sufficient wash-out period. Hence, I am not sure any findings from the 2 mA block can really be interpreted as completely separate from the initial 1 mA stimulation period, given that they were administered consecutively. Several previous studies have shown that same-day repeated tDCS stimulation blocks can influence the effects of neuromodulation (e.g., Bastani and Jaberzadeh, 2014, Clin Neurophysiol; Monte-Silva et al., J. Neurophysiology). 

      We agree with the reviewer’s comment that the initial 1 mA stimulation block might have induced changes in neural activity and that the 20-minute post 1 mA block would not be long enough to wash out these changes. This comment is very similar to the second comment made by Reviewer 2 (R.2.2). Although our experimental data do not support this possibility (as the differences between the 1 mA post-stimulation and baseline conditions were not significant), it is still conceivable that the stimulation produced some effects below the threshold of significance and that these might weakly contribute to the changes observed during and after the 2 mA stimulation. 

      Actions in the text: We have modified the paper according to the reviewers' comments (please see our answer and actions in the text to R.2.2.).

      The different electrode placement for the two anaesthetised monkeys (i.e., Monkey R: F3/O2 montage, Monkey N: F4/O1 montage) is problematic, as it is likely to have resulted in stimulation over different brain regions. The authors state that "Because of the small size of the monkey's head, we expected that tDCS stimulation with these two symmetrical montages would result in nearly equivalent electric fields across the monkey's head and produce roughly similar effects on brain activity"; however, I am not totally convinced of this, and it really would need E-field models to confirm. It is also more likely that there would in fact be notable differences in the brain regions stimulated as the authors used HD-tDCS electrodes, which are generally more focal.

      We thank the Reviewer for the remark, which is very similar to the second comment from Reviewer 2. Please see our answer to the first comment of Reviewer 2 

      Actions in the text: We have modified the paper according to the reviewers' comments (please see the actions taken in response to R.2.1.).

      Given the very small sample size, I think it is also important to consider the possibility that some results might also be impacted by individual differences in response to stimulation. For instance, in the discussion (page 9, paragraph 2) the authors contrast findings observed in awake animals versus anaesthetised animals. However, different monkeys were examined for these two conditions, and there were only two monkeys in each group (monkeys J and Y for awake experiments [both male], and monkeys R and N [male and female] for the anaesthesia condition). From the human literature, it is well known that there is a considerable amount of inter-individual variability in response to stimulation (e.g., Lopez-Alonso et al., 2014, Brain Stimulation; Chew et al., 2015, Brain Stimulation), therefore I wonder if some of these differences could also possibly result from differences in responsiveness to stimulation between the different monkeys? At the end of the paragraph, the authors also state "Our findings also support the use of tDCS to promote rapid recovery from general anesthesia in humans...and suggest that a single anodal prefrontal stimulation at the end of the anesthesia protocol may be effective." However, I'm not sure if this statement is really backed-up by the results, which failed to report "any behavioural signs of awakening in the animals" (page 7)?

      We thank the Reviewer for this comment. Because working with non-human primates is expensive and labor intensive, the sample sizes in classical macaque experiments are generally small (typically 2-4 subjects per experiment). Our sample size (i.e. 2 rhesus macaques in awake experiments and 2 macaques under sedation, 11 +/- 9 scan sessions per animal, 288 and 136 runs in the awake and anesthesia state, respectively) is comparable to other previous work in non-human primates using fMRI (Milham et al. 2018; Yacoub et al. 2020; Uchimura, Kumano, and Kitazawa 2024). In addition, we would like to point out that the baseline cortical dynamics we found before stimulation, whether in the awake or sedated state, are comparable to previous studies (Barttfeld et al. 2015; Uhrig et al. 2018; Tasserie et al. 2022). This suggests our results are reproducible across datasets, despite the small sample size.

      That being said, we agree with the reviewer that inter-individual variability in response to stimulation can be considerable, as shown by a large body of literature in the field. It seems possible that the two monkeys studied in each condition responded differently to the stimulation. But even if that’s the case, our results suggest that at least in one of the two monkeys, cathodal PFC stimulation in the awake state and anodal PFC stimulation under propofol anesthesia induced striking changes in brain dynamics, which we believe is a significant contribution to the field. 

      In fact, supplementary analysis, as proposed by Reviewer 2 (cf R2.4), investigating how the different measurables we’ve used were differently affected by tDCS show that indeed monkey Y’s case is more apparent and significant than monkey J’s. Still, the effects observed in monkey J’s case are still congruent with what is observed in monkey Y’s and at the population level (though less flagrant). We also show that these inter-individual variabilities are outmatched by the inter-condition variability, (as indicated by our initially strong statistical results at the population levels), thus showing that, even though we have different responses depending on the subject, the effects observed at the population level cannot be only accounted for by the differences in subjects’ specificities.

      Lastly, the Reviewer questioned whether our results support that a single anodal prefrontal stimulation at the end of the anesthesia protocol could effectively promote rapid recovery from general anesthesia, because the stimulation did not wake the animals in our experiments. It should be emphasized that in our case, the monkeys were stimulated while they were still receiving continuous propofol perfusion. In contrast, during the recovery process from anesthesia, the delivery of the anesthetic drug is stopped. It is therefore conceivable that anodal PFC tDCS, which successfully enriched brain dynamics in sedated monkeys in our experiments, may accelerate the recovery from anesthesia when the drug is no longer administered. 

      Actions in the text: We have added a line in the Materials and Methods to compare to other studies:

      “Our sample size is comparable to previous work in NHP using fMRI (Milham et al. 2018; Yacoub et al. 2020; Uchimura, Kumano, and Kitazawa 2024).”

      Reviewing Editor Comments: 

      In some cases, authors opt to submit a revised manuscript. Should you choose to do so, please be aware that the reviewers have indicated that their appraisal is unlikely to change unless some of the suggested field modelling is incorporated into the work. This may change the evaluation of the strength of evidence, but the final wording will be subject to reviewer discretion. Details for responding to the reviews are provided at the bottom of this email.

      Reviewer #1 (Recommendations for the authors): 

      The work should discuss the implications of their experiments for using tDCS to arouse a patient from a coma. The anesthetized animal is effectively in a drug-induced coma. While they observed connectivity changes, these changes did not map nicely onto behavioral changes. 

      I would suggest that the authors spell out more clearly what they view as the clinical implications of their work in terms of new insights into how tDCS may be used to either understand and or treat disorders of consciousness.

      We thank the Reviewer for his thoughtful comments. We appreciate the opportunity to clarify and expand on the key findings and implications of our work, particularly regarding the new insights into how tDCS can be used to understand and treat disorders of consciousness. We therefore provide a broader perspective on the clinical implications of our experiments regarding coma and disorders of consciousness. We also agree with the Reviewer that the absence of behavioral changes but the presence of functional differences should be more clearly addressed. 

      Actions in the text: We have added a few lines about the relevance of anesthesia as a model for disorders of consciousness in the Introduction part:

      “Anesthesia provides a unique model for studying consciousness, which, similarly to DOC, is characterized by the disruption or even  the loss of consciousness (Luppi 2024). Additionally, anesthesia mechanisms involve several subcortical nuclei that are key components of the brain's sleep and arousal circuits (Kelz and Mashour 2019).”

      In the Discussion section, we have modified and expanded a paragraph about the effects of tDCS in DOC patients and how this technique could be further used to study consciousness: From another clinical perspective, our results demonstrating that 2 mA anodal PFC tDCS decreased the structure-function correlation and modified the dynamic repertoire of brain patterns during anesthesia (Figures 6 and 7) are consistent with the beneficial effects of such stimulation in DOC patients (Thibaut et al., 2014; Angelakis et al., 2014; Thibaut et al., 2017; Zhang et al., 2017; Martens et al., 2018; Cavinato et al., 2019; Wu et al., 2019; Hermann et al., 2020; Peng et al., 2022; Thibaut et al., 2023). Although some clinical trials investigated the effects of stimulating other brain regions, such as the motor cortex (Martens et al., 2019; Straudi et al., 2019) or the parietal cortex (Huang et al., 2017; Guo et al., 2019; Zhang et al., 2022; Wan et al., 2023; Wang et al., 2020), the DLPFC appears to be the most effective target for patients with a minimally conscious state (Liu et al., 2023). In terms of neuromodulatory effects in DOC patients, DLPFC tDCS has been reported to increase global excitability (Bai et al., 2017), increase the P300 amplitude (Zhang et al., 2017; Hermann et al., 2020), improve the fronto-parietal coherence in the theta band (Bai et al., 2018), enhance the putative EEG markers of consciousness (Bai et al., 2018; Hermann et al., 2020) and reduce the incidence of slow-waves in the resting state (Mensen et al., 2020). Our findings further support the PFC as a relevant target for modulating consciousness level and align with growing evidence showing that the PFC plays a key role in conscious access networks (Mashour, Pal, and Brown 2022; Panagiotaropoulos 2024). Nevertheless, we hypothesize that other brain targets for tDCS may be of interest for consciousness restoration, potentially using multi-channel tDCS (Havlík et al., 2023). Among transcranial electrical stimulation techniques, tDCS has the great advantage of facilitating either excitation or inhibition of brain regions, depending on the polarity of the stimulation (Sdoia et al., 2019) exploited this advantage to investigate the causal involvement of the DLPFC in conscious access to a visual stimulus during an attentional blink paradigm. While conscious access was enhanced by anodal stimulation of the left DLPFC compared to sham stimulation, opposite effects were found with cathodal stimulation compared to sham over the same locus. Finally, this literature and our findings suggest that tDCS constitutes a non-invasive, reversible, and powerful tool for studying consciousness.”

      We have added a new paragraph about patients with cognitive-motor dissociation and dissociation between consciousness and behavioral responsiveness:

      “Changes in the state of consciousness are generally closely associated with changes in behavioural responsiveness, although some rare cases of dissociation have been described. Cognitive-motor dissociation (CMD) is a condition observed in patients with severe brain injury, characterized by behavior consistent with unresponsive wakefulness syndrome or a minimally conscious state minus (Thibaut et al., 2019). However, in these patients, specific cortical brain areas activate in response to mental imagery tasks (e.g., imagining playing tennis or returning home) in a manner indistinguishable from that of healthy controls, as shown through fMRI or EEG (Thibaut et al., 2019; Owen et al., 2006; Monti et al., 2010; Bodien et al., 2024). Thus, although CMD patients are behaviorally unresponsive, they demonstrate cognitive awareness that is not outwardly apparent. It is worth noting that both the structure-function correlation and the rate of the pattern closest to the anatomy were shown to be significantly reduced in unresponsive patients showing command following during mental imagery tasks compared to those who do not show command following (Demertzi et al., 2019). These observations would be compatible with our findings in anesthetized macaques exposed to 2 mA anodal PFC tDCS. The richness of the brain dynamics would be recovered (at least partially, in our experiments), but not the behaviour. This hypothesis also fits with a recent longitudinal fMRI study on patients recovering from coma (Crone et al., 2020). The researchers examined two groups of patients: one group consisted of individuals who were unconscious at the acute scanning session but regained consciousness and improved behavioral responsiveness a few months later, and the second group consisted of patients who were already conscious from the start and only improved behavioral responsiveness at follow-up. By comparing these two groups, the authors could distinguish between the recovery of consciousness and the recovery of behavioral responsiveness. They demonstrated that only initially conscious patients exhibited rich brain dynamics at baseline. In contrast, patients who were unconscious in the acute phase and later regained consciousness had poor baseline dynamics, which became more complex at follow-up. Complete recovery of both consciousness and responsiveness under general anesthesia is possible through electrical stimulation of the central thalamus (Redinbaugh et al., 2020; Tasserie et al., 2022).”

      Reviewer #2 (Recommendations for the authors): 

      Method 

      (1) The authors mentioned that they used HD-tDCS in their experiments; however, they used 1 x 1 tDCS, which is not HD-tDCS but rather single-channel tDCS.

      We thank the Reviewing Editor for pointing out this ambiguous wording. We understand that "HD-tDCS", which we used in our paper to refer to high-density 1x1 tDCS (because we used small carbon electrodes instead of the large sponge electrodes employed in conventional tDCS), may cause some confusion with high-definition tDCS, which uses compact ring electrodes and most commonly refers to a 4x1 montage (1 active central electrode over the target area and 4 return electrodes placed around the central electrode).

      Therefore, to avoid any confusion, we will use the term "tDCS" rather than “HD-tDCS” to qualify the technique used in this paper and suppress mentions of high-density or high-definition tDCS.

      Actions in the text: We have replaced the abbreviation “HD-tDCS” with “tDCS” throughout the paper. We have also suppressed the sentence about high-definition tDCS in the Introduction (“While conventional tDCS relies on the use of relatively large rectangular pad electrodes, high-density tDCS (HD-tDCS) utilizes more compact ring electrodes, allowing for increased focality, stronger electric fields, and presumably, greater neurophysiological changes (Datta et al. 2009; Dmochowski et al. 2011)”) and the two related citations in the References section.

      (2) Please provide the characteristics of electrodes, including their size, shape, and thickness.

      We thank the Reviewing Editor for this recommendation. We now provide the complete characteristics of the tDCS electrodes used in the paper.

      Actions in the text: We have added a sentence describing the characteristics of the tDCS electrodes in the Materials and Methods section:

      “We used a 1x1 electrode montage with two carbon rubber electrodes (dimensions: 1.4 cm x 1.85 cm, 0.93  cm thick) inserted into Soterix HD-tES MRI electrode holders (base diameter: 25 mm; height: 10.5 mm), which are in contact with the scalp. These electrodes (2.59 cm2) are smaller than conventional tDCS sponge electrodes (typically 25 to 35 cm<sup>2</sup>).”

      (3) Could the authors clarify why they chose to stimulate the right DLPFC? Is there a specific rationale for this choice? Additionally, could the authors explain how they ensured that the stimulation targeted the DLPFC, given that the monkey cap might differ from human configurations? In many NHP studies, structural MRI is used to accurately determine electrode placement. Considering that a single channel F4 - O2 montage was used, even a small displacement of the frontal electrode laterally could result in the electric field not adequately covering the DLPFC. Could the authors provide structural MRI images and details of electrode positioning to help readers better understand targeting accuracy?

      We thank the Reviewing Editor for the thoughtful comments and recommendations. We appreciate the opportunity to further clarify our rationale for stimulating the right DLPFC and also the suggestion to provide structural MRI images and details of electrode positioning, which we think will improve the quality of the paper by showing targeting accuracy.

      First, we would like to clarify that our initial decision to stimulate the right PFC in most animals was driven by experimental constraints. Indeed, we had limited access to the left PFC in three of the four macaques, either due to the presence of cement (spreading asymmetrically from the centre of the head) used to fix the head post in awake animals or due to a scar in one of the two animals studied under anesthesia. 

      Second, we agree with the Reviewing Editor on the importance of showing details of electrode positioning and evidence of targeting accuracy across MRI sessions. Therefore, we now provide structural images showing the positions of anodal and cathodal electrodes in almost all acquired sessions: 10 sessions (out of 10) under anesthesia and 30 sessions in the awake state (out of 34 sessions, because we could not acquire structural images in four sessions). These images show that, in anesthesia experiments, the anodal electrode was positioned over the dorsal prefrontal cortex and the cathodal electrode was placed over the contralateral occipital cortex (at the level of the parieto–occipital junction) in both monkeys. In the awake state, the montage still targeted the prefrontal cortex and the occipital cortex, but with a slightly different placement. One of the electrodes was placed over the prefrontal cortex, closer to the premotor cortex than in anesthesia experiments, while the other one was placed over the occipital cortex (V1), slightly more posterior than in anesthesia experiments. These images therefore show that the placement was relatively accurate across sessions and reproducible between monkeys in each of the two arousal conditions.

      Actions in the text: We have added a supplementary file showing electrode positioning in 40 of the 44 acquired MRI sessions (Supplementary File 1). We have also added a new supplement figure (Figure 1 - figure supplement 1) showing electrode positioning in representative MRI sessions of the awake and anesthetized experiments in the main manuscript. 

      We added a few sentences referring to these figures in the Result section: 

      “Representative structural images showing electrode placements on the head of the two awake monkeys are shown in Figure 1 - figure supplement 1A). Supplementary File 1 displays the complete set of structural images, showing that the two electrodes were accurately placed over the prefrontal cortex and the occipital cortex in a reproducible manner across awake sessions.”

      Figure 1 - figure supplement 1. Structural images displaying electrode placements on the head of monkeys. A) Awake experiments. Representative sagittal, coronal and transverse MRI sections, and the corresponding skin reconstruction images showing the position of the prefrontal and the occipital electrodes on the head of monkeys J. and Y. B) Anesthesia experiments. Representative sagittal, coronal and transverse MRI sections, and the corresponding skin reconstruction images showing the position of the prefrontal and occipital electrodes over the occipital cortex on the head of monkeys R. and N.

      Supplementary File 1 (see attached file). Structural images showing the position of the tDCS electrodes on the monkey's head across sessions. Sagittal, coronal and transverse MRI sections, and corresponding skin reconstruction images showing the position of the prefrontal and occipital electrodes on the monkey's head for each MRI session (except for 4 sessions in which no anatomical scan was acquired). The two electrodes were accurately placed over the prefrontal cortex and the occipital cortex in a reproducible manner across sessions and between the two monkeys studied in each arousal state. In anesthesia experiments, the anodal electrode was placed over the dorsal prefrontal cortex, while the cathodal electrode was positioned over the parieto-occipital junction. In awake experiments, the prefrontal electrode was positioned over the dorsal prefrontal cortex/pre-motor cortex, while the occipital electrode was placed over the visual area 1. The position of the two electrodes differed slightly between the anesthetized and awake experiments due to different body positions (the prone position of the sedated monkeys prevented a more posterior position of the occipital electrode) and also due to the presence of a headpost on the head of the two monkeys in awake experiments (the monkeys we worked with in anesthesia experiments did not have an headpost).

      (4) If the authors did not analyze the data for the passive event-related auditory response, it may be helpful to remove the related sentence to avoid potential confusion for readers.

      We thank the Reviewing Editor for the comment. Although we understand the reviewer’s point of view, we decide to keep this information in the paper to inform the reader that the macaques were passively engaged in an auditory task, as this could have some influence on the brain state. In the Materials and Methods section, we already mentioned that the analysis of the cerebral responses to the auditory paradigm is not part of the paper. We have modified the sentence to make it clearer and to avoid potential confusion for readers.

      Actions in the text: We have modified the sentence referring to the passive event-related auditory response in the Materials and Methods section:

      “All fMRI data were acquired while the monkeys were engaged in a passive event-related auditory task, the local-global paradigm, which is based on local and global deviations from temporal regularities (Bekinschtein et al. 2009; Uhrig, Dehaene, and Jarraya 2014). The present paper does not address how tDCS perturbs cerebral responses to local and global deviants, which will be the subject of future work.”

      (5) Could the authors clarify what x(t) represents in the equation? Additionally, it would be better to number the equations.

      We apologize for the confusion,  x(t) represents the evolution of the BOLD signals over time. We have numbered the equations as suggested. 

      Actions in the text: We have added explanations about the notation and numerotation of equations.

      (6) It would be much better to provide schematic illustrations to explain what the authors did for analyzing fMRI data.

      We thank the Reviewing Editor for the suggestion and now provide a new figure as suggested.  

      Actions in the text: We have added a new figure (Figure 2) graphically showing the overall analysis performed. We have added a sentence about the new Figure 2 in the Results section:  “A graphical overview of the overall analysis is shown in Figure 2.” We have renumbered Figure 2 - supplement figures accordingly.

      Figure 2. fMRI Phase Coherence analysis. A) Left) Animals were scanned before, during and after PFC tDCS stimulation in the awake state (two macaques) or under deep propofol anesthesia (two macaques). Right) Example of Z-scored filtered BOLD time series for one macaque, 111 time points with a TR of 2.4 s. B) Hilbert transform of the z-scored BOLD signal of one ROI into its time-varying amplitude A(t) (red) and the real part of the phase φ (green). In blue, we recover the original z-scored BOLD signal as A(t)cos(φ). C) Example of the phase of the Hilbert transform for each brain region at one TR. D) Symmetric matrix of cosines of the phase differences between all pairs of brain regions. E) We concatenated the vectorized form of the triangular superior of the phase difference matrices for all TRs for all participants, in all the conditions for both datasets separately obtaining using the K-means algorithm, the brain patterns whose statistics are then analyzed in the different conditions.

      Results 

      (1) In Figures 3A, 5A, and 6A showing brain connectivity, it is difficult to relate the connectivity variability among the brain regions. Instead of displaying connection lines for nodes, it would be more effective if the authors highlighted significant, strong connectivity within specific brain regions using additional methods, such as bootstrapping.

      We thank the Reviewing Editor for the comment and suggestion. The connection lines indeed represent all the synchronizations above 0.5 and all the anti-synchronization below -0.5 between all pairs of brain regions. As suggested, another element we haven’t addressed is the heterogeneity in coherences between individual brain regions. We hence propose additional supplementary figures showing, for all centroids mentioned in main figures, the variance in phase-based connectivity of the distributions of coherence of all brain regions to the rest of the brain. High value would then indicate a wide range of values of coherence, while low would indicate the different coherence a region has with the rest of the brain have similar values. Thus, a brain with uniform color would indicate high homogeneity in coherence among brain regions, while sharp changes in colors would reveal that certain regions are more subject to high variance in their coherence distributions. We expect this new figure to more clearly expose the connectivity variability among the brain regions.

      Actions in the text: We have added new figures showing, for all centroids mentioned in the main figures, the variances in phase-based connectivity of the distributions of coherence  (Figure 3 - figure supplement 3;  Figure 5 - figure supplement 2; Figure 6 - figure supplement 3; Figure 7 - figure supplement 2). One of them is shown below for the only awake analysis (Figure 3 - figure supplement 3).

      Figure 3 - figure supplement 3. Variance in inter-region phase coherences of brain patterns. Low values (red and light red) indicate that the distribution of synchronizations between a brain region and the rest of the brain has relatively low variance, while high values (blue and light blue) indicate relatively high variance. Are displayed both supra (top) and subdorsal (bottom) views for each brain pattern from the main figure, ordered similarly as previously: from left (1) to right (6) as their respective SFC increases. 

      We added a few sentences about variances in phase-based connectivity of the distributions of coherence in the Result section: 

      “Further investigation of the variances in inter-region phase coherences of brain patterns, presented in Figure 3 - figure supplement 3, revealed two main findings. First, all the patterns exhibited some degree of lateral symmetry. Second, except for the pattern with the highest SFC, most patterns displayed high heterogeneity in their coherence variances and striking inter-pattern differences. These observations reflect both the segmentation of distinct functional networks across patterns and a topological organization within the patterns themselves: some regions showed a broader spectrum of synchrony with the rest of the brain, while others exhibited narrower distributions of coherence variances. For instance, unlike other brain patterns, pattern 5 was characterized by a high coherence variance in the frontal premotor areas and low variance in the occipital cortex, whereas pattern 3 had a high variance in the frontal and orbitofrontal regions. In addition, we performed the main analyses separately for the two monkeys, explored the inter-condition variability (Supplementary File 2), and computed classical measures of functional connectivity such as average FC matrices and functional graph properties (modularity, efficiency and density) of the visited FC states (Supplementary File 3).”

      “The variance in inter-regional phase coherence across brain patterns showed notably that pattern 4, in contrast to most other patterns, was characterized by a high variance in frontal premotor areas and a low variance in the occipital cortex (Figure 5 - figure supplement 2)." 

      “The variance in inter-region phase coherences of the brain patterns is displayed in Figure 6 - figure supplement 3 and showed a striking heterogeneity between the patterns. For example, pattern 5 had a low overall variance (except in the frontal cortex), while pattern 1 was the only pattern with a high variance in the occipital cortex.”

      “The variance in inter-region phase coherences of brain patterns is displayed in Figure 6 - figure supplement 2.”

      (2) For both conditions, only 2 to 3 out of 6 patterns showed significant effects of tDCS on the occurrence rate. Is it sufficient to claim the authors' conclusion?

      We thank the Reviewer Editor for the comment. We would like to point out that similar kinds of differences in the occurrence rates of specific brain patterns (particularly in patterns at the extremities of the SFC scale) have already been reported previously. Prior works in patients suffering from disorders of consciousness, in healthy humans or in non-human primates,  have shown, by using a similar method of analysis, that not all brain states are equally disturbed by loss of consciousness, even in different modalities of unconscious transitioning (Luppi et al. 2021; Z. Huang et al. 2020; Demertzi et al. 2019; Castro et al. 2023; Golkowski et al. 2019; Barttfeld et al. 2015). Therefore, yes we believe that our conclusions are still supported by the results.

      (3) If the authors want to assert that the brain state significantly influences the effects of tDCS as discussed in the manuscript, further analysis is necessary. First, it would be great to show the difference in connectivity between two consciousness conditions during the baseline (resting state) to see how resting state connectivity or structural connectivity varies. Second, demonstrating the difference in connectivity between the awake and anesthetized conditions (e.g., awake during cathodal vs. anesthetized cathodal) to show how the connectivity among the brain regions was changed by the brain state during tDCS. This would strengthen the authors' conclusion.

      We thank the reviewer for this comment. Firstly, we’d like to clarify that the structural connectivity doesn’t change from one session to another in the same animal and minimally between subjects. Secondly, we agree with the Reviewing Editor that it is informative to show the differences between the baselines and this is what we have done. The results are shown in Figures 5 and 7. Regarding the comparison of the stimulating conditions across arousal levels, the only contrast that we could make is to compare 2 mA anodal awake with 2 mA anodal anesthetized (during and post-stimulation). However, as 2 mA anodal stimulation in the awake state did not affect the connectivity much (compared to the awake baseline), the results would be almost similar to the comparison of the awake baseline with 2 mA anodal anesthetized, which is shown in Figure 7. Therefore, we believe that this would result in minimal informative gains and even more redundancy. 

      Reviewer #3 (Recommendations for the authors): 

      Introduction, par 2: HD-tDCS does not necessarily produce stronger electric fields (E-fields) in the brain. The E-field is largely montage-dependent, and some configurations such as the 4x1 configuration can actually have weaker E-fields compared to conventional tDCS designs (i.e., with two sponge electrodes) as electrodes are often closer together resulting in more current being shunted by skull, scalp, and CSF. I would consider re-phrasing this section.

      We agree with the Reviewer Editor that high-definition tDCS does not necessarily produce stronger electric fields in the brain and apologize for the confusion caused by our use of HD-tDCS to refer to high-density tDCS. To avoid any confusion, we have removed the sentence mentioning that HD-tDCS produces stronger electric fields. 

      Actions in the text: We have removed the sentence about high-definition tDCS in the Introduction (“While conventional tDCS relies on the use of relatively large rectangular pad electrodes, high-density tDCS (HD-tDCS) utilizes more compact ring electrodes, allowing for increased focality, stronger electric fields, and presumably, greater neurophysiological changes (Datta et al. 2009; Dmochowski et al. 2011)”) and the two related citations in the References section.

    1. Author response:

      General Statements:

      The formation of three-dimensional tubes is a fundamental process in the development of organs and aberrant tube size leads to common diseases and congenital disorders, such as polycystic kidney disease, asthma, and lung hypoplasia. The apical (luminal) extracellular matrix (ECM) plays a critical role in epithelial tube morphogenesis during organ formation, but its composition and organization remain poorly understood. Using the Drosophila embryonic salivary gland as a model, we reveal a critical role for the PAPS Synthetase (Papss), an enzyme that synthesizes the universal sulfate donor PAPS, as a critical regulator of tube lumen expansion. Additionally, we identify two zona pellucida (ZP) domain proteins, Piopio (Pio) and Dumpy (Dpy) as key apical ECM components that provide mechanical support to maintain a uniform tube diameter.

      The apical ECM has a distinct composition compared to the basal ECM, featuring a diverse array of components. Many studies of the apical ECM have focused on the role of chitin and its modification, but the composition of the non-chitinous apical ECM and its role, and how modification of the apical ECM affects organogenesis remain elusive. The main findings of this manuscript are listed below.

      (1) Through a deficiency screen targeting ECM-modifying enzymes, we identify Papss as a key enzyme regulating luminal expansion during salivary gland morphogenesis. 

      (2) Our confocal and transmission electron microscopy analyses reveal that Papss mutants exhibit a disorganized apical membrane and condensed aECM, which are at least partially linked to disruptions in Golgi structures and intracellular trafficking. Papss is also essential for cell survival and basal ECM integrity, highlighting the role of sulfation in regulating both apical and basal ECM.

      (3) Salivary gland-specific overexpression of wild-type Papss rescues all defects in Papss mutants, but the catalytically inactive mutant form does not, suggesting that defects in sulfation are the underlying cause of the phenotypes.

      (4) We identify two ZP domain proteins, Piopio (Pio) and Dumpy (Dpy), as key components of the salivary gland aECM. In the absence of Papss, Pio is progressively lost from the aECM, while the Dpy-positive aECM structure is condensed and detaches from the apical membrane, resulting in a narrowed lumen. 

      (5) Mutations in pio or dpy, or in Notopleural (Np), which encodes a matriptase that cleaves Pio, cause the salivary gland lumen to develop alternating bulges and constrictions. Additionally, loss of pio results in loss of Dpy in the salivary gland lumen, suggesting that the Dpycontaining filamentous structures of the aECM is critical for maintaining luminal diameter, with Pio playing an essential role in organizing this structure.

      (6) We further reveal that the cleavage of the ZP domain of Pio by Np is critical for the role of Pio in organizing the aECM structure.

      Overall, our findings underscore the essential role of sulfation in organizing the aECM during tubular organ formation and highlight the mechanical support provided by ZP domain proteins in maintaining tube diameter. Mammals have two isoforms of Papss, Papss1 and Papss2. Papss1 shows ubiquitous expression, with higher levels in glandular cells and salivary duct cells, suggesting a high requirement for sulfation in these cell types. Papss2 shows a more restricted expression, such as in cartilage, and mutations in Papss2 have been associated with skeletal dysplasia in humans. Our analysis of the Drosophila Papss gene, a single ortholog of human Papss1 and Papss2, reveals its multiple roles during salivary gland development. We expect that these findings will provide valuable insights into the function of these enzymes in normal development and disease in humans. Our findings on the key role of two ZP proteins, Pio and Dpy, as major components of the salivary gland aECM also provide valuable information on the organization of the non-chitinous aECM during organ formation.

      We believe that our results will be of broad interest to many cell and developmental biologists studying organogenesis and the ECM, as well as those investigating the mechanisms underlying human diseases associated with conserved mutations.

      Point-by-point description of the revisions:

      We are delighted that all three reviewers were enthusiastic about the work. Their comments and suggestions have improved the paper. The details of the changes we have made in response to each reviewer’s comments are included in italicized text below.

      Reviewer #1 (Evidence, reproducibility and clarity):

      PAPS is required for all sulfotransferase reactions in which a sulfate group is covalently attached to amino acid residues of proteins or to side chains of proteoglycans. This sulfation is crucial for properly organizing the apical extracellular matrix (aECM) and expanding the lumen in the Drosophila salivary gland. Loss of Papss potentially leads to decreased sulfation, disorganizing the aECM, and defects in lumen formation. In addition, Papss loss destabilizes the Golgi structures.

      In Papss mutants, several changes occur in the salivary gland lumen of Drosophila. The tube lumen is very thin and shows irregular apical protrusions. There is a disorganization of the apical membrane and a compaction of the apical extracellular matrix (aECM). The Golgi structures and intracellular transport are disturbed. In addition, the ZP domain proteins Piopio (Pio) and Dumpy (Dpy) lose their normal distribution in the lumen, which leads to condensation and dissociation of the Dpy-positive aECM structure from the apical membrane. This results in a thin and irregularly dilated lumen.

      (1) The authors describe various changes in the lumen in mutants, from thin lumen to irregular expansion. I would like to know the correct lumen diameter, and length, besides the total area, by which one can recognize thin and irregular.

      We have included quantification of the length and diameter of the salivary gland lumen in the stage 16 salivary glands of control, Papss mutant, and salivary gland-specific rescue embryos (Figure 1J, K). As described, Papss mutant embryos have two distinct phenotypes, one group with a thin lumen along the entire lumen and the other group with irregular lumen shapes. Therefore, we separated the two groups for quantification of lumen diameter. Additionally, we have analyzed the degree of variability for the lumen diameter to better capture the range of phenotypes observed (Figure 1K’). These quantifications enable a more precise assessment of lumen morphology, allowing readers to distinguish between thin and irregular lumen phenotypes.

      (2) The rescue is about 30%, which is not as good as expected. Maybe the wrong isoform was taken. Is it possible to find out which isoform is expressed in the salivary glands, e.g., by RNA in situ Hyb? This could then be used to analyze a more focused rescue beyond the paper.

      Thank you for this point, but we do not agree that the rescue is about 30%. In Papss mutants, about 50% of the embryos show the thin lumen phenotype whereas the other 50% show irregular lumen shapes. In the rescue embryos with a WT Papss, few embryos showed thin lumen phenotypes. About 40% of the rescue embryos showed “normal, fully expanded” lumen shapes, and the remaining 60% showed either irregular (thin+expanded) or slightly overexpanded lumen. It is not uncommon that rescue with the Gal4/UAS system results in a partial rescue because it is often not easy to achieve the balance of the proper amount of the protein with the overexpression system. 

      To address the possibility that the wrong isoform was used, we performed in situ hybridization to examine the expression of different Papss spice forms in the salivary gland. We used probes that detect subsets of splice forms: A/B/C/F/G, D/H, and E/F/H, and found that all probes showed expression in the salivary gland, with varying intensities. The original probe, which detects all splice forms, showed the strongest signals in the salivary gland compared to the new probes which detect only a subset. However, the difference in the signal intensity may be due to the longer length of the original probe (>800 bp) compared to other probes that were made with much smaller regions (~200 bp). Digoxigenin in the DIG labeling kit for mRNA detection labels the uridine nucleotide in the transcript, and the probes with weaker signals contain fewer uridines (all: 147; ABCFG, 29; D, 36; EFH, 66). We also used the Papss-PD isoform, for a salivary gland-specific rescue experiment and obtained similar results to those with Papss-PE (Figure 1I-L, Figure 4D and E). 

      Furthermore, we performed additional experiments to validate our findings. We performed a rescue experiment with a mutant form of Papss that has mutations in the critical rescues of the catalytic domains of the enzyme, which failed to rescue any phenotypes, including the thin lumen phenotype (Figure 1H, J-L), the number and intensity of WGA puncta (Figure 3I, I’), and cell death (Figure 4D, E). These results provide strong evidence that the defects observed in Papss mutants are due to the lack of sulfation.  

      (3) Crb is a transmembrane protein on the apicolateral side of the membrane. Accordingly, the apicolateral distribution can be seen in the control and the mutant. I believe there are no apparent differences here, not even in the amount of expression. However, the view of the cells (frame) shows possible differences. To be sure, a more in-depth analysis of the images is required. Confocal Z-stack images, with 3D visualization and orthogonal projections to analyze the membranes showing Crb staining together with a suitable membrane marker (e.g. SAS or Uif). This is the only way to show whether Crb is incorrectly distributed. Statistics of several papas mutants would also be desirable and not just a single representative image. When do the observed changes in Crb distribution occur in the development of the tubes, only during stage 16? Is papss only involved in the maintenance of the apical membrane? This is particularly important when considering the SJ and AJ, because the latter show no change in the mutants.

      We appreciate your suggestion more thoroughly analyze Crb distribution. We adapted a method from a previous study (Olivares-Castiñeira and Llimargas, 2017) to quantify Crb signals in the subapical region and apical free region of salivary gland cells. Using E-Cad signals as a reference, we marked the apical cell boundaries of individual cells and calculated the intensity of Crb signals in the subapical region (along the cell membrane) and in the apical free region. We focused on the expanded region of the SG lumen in Papss mutants for quantification, as the thin lumen region was challenging to analyze. This quantification is included in Figure 2D. Statistical analysis shows that Crb signals were more dispersed in SG cells in Papss mutants compared to WT.

      (4) A change in the ECM is only inferred based on the WGA localization. This is too few to make a clear statement. WGA is only an indirect marker of the cell surface and glycosylated proteins, but it does not indicate whether the ECM is altered in its composition and expression. Other important factors are missing here. In addition, only a single observation is shown, and statistics are missing.

      We understand your concern that WGA localization alone may not be sufficient to conclude changes in the ECM. However, we observed that luminal WGA signals colocalize with Dpy-YFP in the WT SG (Figure 5-figure supplement 2C), suggesting that WGA detects the aECM structure containing Dpy. The similar behavior of WGA and Dpy-YFP signals in multiple genotypes further supports this idea. In Papss mutants with a thin lumen phenotype, both WGA and Dpy-YFP signals are condensed (Figure 5E-H), and in pio mutants, both are absent from the lumen (Figure 6B, D). We analyzed WGA signals in over 25 samples of WT and Papss mutants, observing consistent phenotypes. We have included the number of samples in the text. While we acknowledge that WGA is an indirect marker, our data suggest that it is a reliable indicator of the aECM structure containing Dpy. 

      (5) Reduced WGA staining is seen in papss mutants, but this could be due to other circumstances. To be sure, a statistic with the number of dots must be shown, as well as an intensity blot on several independent samples. The images are from single confocal sections. It could be that the dots appear in a different Z-plane. Therefore, a 3D visualization of the voxels must be shown to identify and, at best, quantify the dots in the organ.

      We have quantified cytoplasmic punctate WGA signals. Using spinning disk microscopy with super-resolution technology (Olympus SpinSR10 Sora), we obtained high-resolution images of cytoplasmic punctate signals of WGA in WT, Papss mutant, and rescue SGs with the WT and mutant forms of Papss-PD. We then generated 3D reconstructed images of these signals using Imaris software (Figure 3E-H) and quantified the number and intensity of puncta. Statistical analysis of these data confirms the reduction of the number and intensity of WGA puncta in Papss mutants (Figure 3I, I’). The number of WGA puncta was restored by expressing WT Papss but not the mutant form. By using 3D visualization and quantification, we have ensured that our results are not limited to a single confocal section and account for potential variations in Z-plane localization of the dots.

      (6) A colocalization analysis (statistics) should be shown for the overlap of WGA with ManII-GFP.

      Since WGA labels multiple structures, including the nuclear envelope and ECM structures, we focused on assessing the colocalization of the cytoplasmic WGA punctate signals and ManIIGFP signals. Standard colocalization analysis methods, such as Pearson’s correlation coefficient or Mander’s overlap coefficient, would be confounded by WGA signals in other tissues. Therefore, we used a fluorescent intensity line profile to examine the spatial relationship between WGA and ManII-GFP signals in WT and Papss mutants (Figure 3L, L’). 

      (7) I do not understand how the authors describe "statistics of secretory vesicles" as an axis in Figure 3p. The TEM images do not show labeled secretory vesicles but empty structures that could be vesicles.

      Previous studies have analyzed “filled” electron-dense secretory vesicles in TEM images of SG cells (Myat and Andrew, 2002, Cell; Fox et al., 2010, J Cell Biol; Chung and Andrew, 2014, Development). Consistent with these studies, our WT TEM images show these vesicles. In contrast, Papss mutants show a mix of filled and empty structures. For quantification, we specifically counted the filled electron-dense vesicles (now Figure 3W). A clear description of our analysis is provided in the figure legend.

      (8) The quality of the presented TEM images is too low to judge any difference between control and mutants. Therefore, the supplement must present them in better detail (higher pixel number?).

      We disagree that the quality of the presented TEM images is too low. Our TEM images have sufficient resolution to reveal details of many subcellular structures, such as mitochondrial cisternae. The pdf file of the original submission may not have been high resolution. To address this concern, we have provided several original high-quality TEM images of both WT and Papss mutants at various magnifications in Figure 2-figure supplement 2. Additionally, we have included low-magnification TEM images of WT and Papss mutants in Figure 2H and I to provide a clearer view of the overall SG lumen morphology. 

      (9) Line 266: the conclusion that apical trafficking is "significantly impaired" does not hold. This implies that Papss is essential for apical trafficking, but the analyzed ECM proteins (Pio, Dumpy) are found apically enriched in the mutants, and Dumpy is even secreted. Moreover, they analyze only one marker, Sec15, and don't provide data about the quantification of the secretion of proteins.

      We agree and have revised our statement to “defective sulfation affects Golgi structures and multiple routes of intracellular trafficking”. 

      (10) DCP-1 was used to detect apoptosis in the glands to analyze acellular regions. However, the authors compare ST16 control with ST15 mutant salivary glands, which is problematic. Further, it is not commented on how many embryos were analyzed and how often they detect the dying cells in control and mutant embryos. This part must be improved.

      Thank you for the comment. We agree and have included quantification. We used stage 16 samples from WT and Papss mutants to quantify acellular regions. Since DCP-1 signals are only present at a specific stage of apoptosis, some acellular regions do not show DCP-1 signals. Therefore, we counted acellular regions regardless of DCP-1 signals. We also quantified this in rescue embryos with WT and mutant forms of Papss, which show complete rescue with WT and no rescue with the mutant form, respectively. The graph with a statistical analysis is included (Figure 4D, E).

      (11) WGA and Dumpy show similar condensed patterns within the tube lumen. The authors show that dumpy is enriched from stage 14 onwards. How is it with WGA? Does it show the same pattern from stage 14 to 16? Papss mutants can suffer from a developmental delay in organizing the ECM or lack of internalization of luminal proteins during/after tube expansion, which is the case in the trachea.

      Dpy-YFP and WGA show overlapping signals in the SG lumen throughout morphogenesis. DpyYFP is SG enriched in the lumen from stage 11, not stage 14 (Figure 5-figure supplement 2). WGA is also detected in the lumen throughout SG morphogenesis, similar to Dpy. In the original supplemental figure, only a stage 16 SG image was shown for co-localization of Dpy-YFP and WGA signals in the SG lumen. We have now included images from stage 14 and 15 in Figure 5figure supplement 2C. 

      Given that luminal Pio signals are lost at stage 16 only and that Dpy signals appear as condensed structures in the lumen of Papss mutants, it suggests that the internalization of luminal proteins is not impaired in Papss mutants. Rather, these proteins are secreted but fail to organize properly. 

      (12) Line 366. Luminal morphology is characterized by bulging and constrictions. In the trachea, bulges indicate the deformation of the apical membrane and the detachment from the aECM. I can see constrictions and the collapsed tube lumen in Fig. 6C, but I don't find the bulges of the apical membrane in pio and Np mutants. Maybe showing it more clearly and with better quality will be helpful.

      Since the bulging phenotype appears to vary from sample to sample, we have revised the description of the phenotype to “constrictions” to more accurately reflect the consistent observations. We quantified the number of constrictions along the entire lumen in pio and Np mutants and included the graph in Figure 6F.

      (13) The authors state that Papss controls luminal secretion of Pio and Dumpy, as they observe reduced luminal staining of both in papss mutants. However, the mCh-Pio and Dumpy-YFP are secreted towards the lumen. Does papss overexpression change Pio and Dumpy secretion towards the lumen, and could this be another explanation for the multiple phenotypes? 

      Thank you for the comment. To clarify, we did not observe reduced luminal staining of Pio and Dpy in Papss mutants, nor did we state that Papss controls luminal secretion of Pio and Dpy. In Papss mutants, Pio luminal signals are absent specifically at stage 16 (Figure 5H), whereas strong luminal Pio signals are present until stage 15 (Figure 5G). For Dpy-YFP, the signals are not reduced but condensed in Papss mutants from stages 14-16 (Figure 5D, H). 

      It remains unclear whether the apparent loss of Pio signals is due to a loss of Pio protein in the lumen or due to epitope masking resulting from protein aggregation or condensation. As noted in our response to Comment 11 internalization of luminal proteins seems unaffected in Papss mutants; proteins like Pio and Dpy are secreted into the lumen but fail to properly organize. Therefore, we have not tested whether Papss overexpression alters the secretion of Pio or Dpy.

      In our original submission, we incorrectly stated that uniform luminal mCh-Pio signals were unchanged in Papss mutants. Upon closer examination, we found these signals are absent in the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly. 

      Regulation of luminal ZP protein level is essential to modulate the tube expansion; therefore, Np releases Pio and Dumpy in a controlled manner during st15/16. Thus, the analysis of Pio and Dumpy in NP overexpression embryos will be critical to this manuscript to understand more about the control of luminal ZP matrix proteins.

      Thanks for the insightful suggestion. We overexpressed both the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. It is important to note that these overexpression experiments were done in the presence of the endogenous WT Np. 

      Overexpression of Np.WT led to increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. In contrast, overexpression of Np.S990A resulted in a near complete loss of luminal mCh-Pio signals. Pio antibody signals remained strong at the apical membrane but was weaker in the luminal filamentous structures compared to WT. 

      Due to the GFP tag present in the UAS-Np.S990A line, we could not reliably analyze Dpy-YFP signals because of overlapping fluorescent signals in the same channel. However, the filamentous Pio signals in the lumen co-localized with GFP signals, suggesting that these structures might also include Dpy-YFP, although this cannot be confirmed definitively. 

      These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

      (14) Minor:

      Fig. 5 C': mChe-Pio and Dumpy-YFP are mixed up at the top of the images.

      Thanks for catching this error.  It has been corrected.

      Sup. Fig7. A shows Pio in purple but B in green. Please indicate it correctly.

      It has been corrected.

      Reviewer #1 (Significance):

      In 2023, the functions of Pio, Dumpy, and Np in the tracheal tubes of Drosophila were published. The study here shows similar results, with the difference that the salivary glands do not possess chitin, but the two ZP proteins Pio and Dumpy take over its function. It is, therefore, a significant and exciting extension of the known function of the three proteins to another tube system. In addition, the authors identify papss as a new protein and show its essential function in forming the luminal matrix in the salivary glands. Considering the high degree of conservation of these proteins in other species, the results presented are crucial for future analyses and will have further implications for tubular development, including humans.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary:

      There is growing appreciation for the important of luminal (apical) ECM in tube development, but such matrices are much less well understood than basal ECMs. Here the authors provide insights into the aECM that shapes the Drosophila salivary gland (SG) tube and the importance of PAPSS-dependent sulfation in its organization and function.

      The first part of the paper focuses on careful phenotypic characterization of papss mutants, using multiple markers and TEM. This revealed reduced markers of sulfation (Alcian Blue staining) and defects in both apical and basal ECM organization, Golgi (but not ER) morphology, number and localization of other endosomal compartments, plus increased cell death. The authors focus on the fact that papss mutants have an irregular SG lumen diameter, with both narrowed regions and bulged regions. They address the pleiotropy, showing that preventing the cell death and resultant gaps in the tube did not rescue the SG luminal shape defects and discussing similarities and differences between the papss mutant phenotype and those caused by more general trafficking defects. The analysis uses a papss nonsense mutant from an EMS screen - I appreciate the rigorous approach the authors took to analyze transheterozygotes (as well as homozygotes) plus rescued animals in order to rule out effects of linked mutations.

      The 2nd part of the paper focuses on the SG aECM, showing that Dpy and Pio ZP protein fusions localize abnormally in papss mutants and that these ZP mutants (and Np protease mutants) have similar SG lumen shaping defects to the papss mutants. A key conclusion is that SG lumen defects correlate with loss of a Pio+Dpy-dependent filamentous structure in the lumen. These data suggest that ZP protein misregulation could explain this part of the papss phenotype.

      Overall, the text is very well written and clear. Figures are clearly labeled. The methods involve rigorous genetic approaches, microscopy, and quantifications/statistics and are documented appropriately. The findings are convincing, with just a few things about the fusions needing clarification.

      Minor comments

      (1) Although the Dpy and Qsm fusions are published reagents, it would still be helpful to mention whether the tags are C-terminal as suggested by the nomenclature, and whether Westerns have been performed, since (as discussed for Pio) cleavage could also affect the appearance of these fusions.

      Thanks for the comment. Dpy-YFP is a knock-in line in which YFP is inserted into the middle of the dpy locus (Lye et al., 2014; the insertion site is available on Flybase). mCh-Qsm is also a knock-in line, with mCh inserted near the N-terminus of the qsm gene using phi-mediated recombination using the qsm<sup>MI07716</sup> line (Chu and Hayashi, 2021; insertion site available on Flybase). Based on this, we have updated the nomenclature from Qsm-mCh to mCh-Qsm throughout the manuscript to accurately reflect the tag position. To our knowledge, no western blot has been performed on Dpy-YFP or mCh-Qsm lines. We have mentioned this explicitly in the Discussion.  

      (2) The Dpy-YFP reagent is a non-functional fusion and therefore may not be a wholly reliable reporter of Dpy localization. There is no antibody confirmation. As other reagents are not available to my knowledge, this issue can be addressed with text acknowledgement of possible caveats.

      Thanks for raising this important point. We have added a caveat in the Discussion noting this limitation and the need for additional tools, such as an antibody or a functional fusion protein, to confirm the localization of Dpy.

      (3) TEM was done by standard chemical fixation, which is fine for viewing intracellular organelles, but high pressure freezing probably would do a better job of preserving aECM structure, which looks fairly bad in Fig. 2G WT, without evidence of the filamentous structures seen by light microscopy. Nevertheless, the images are sufficient for showing the extreme disorganization of aECM in papss mutants.

      We agree that HPF is a better method and intent to use the HPF system in future studies. We acknowledge that chemical fixation contributes to the appearance of a gap between the apical membrane and the aECM, which we did not observe in the HPF/FS method (Chung and Andrew, 2014). Despite this, the TEM images still clearly reveal that Papss mutants show a much thinner and more electron-dense aECM compared to WT (Figure 2H, I), consistent to the condensed WGA, Dpy, and Pio signals in our confocal analyses. As the reviewer mentioned, we believe that the current TEM data are sufficient to support the conclusion of severe aECM disorganization and Golgi defects in Papss mutants.

      (4) The authors may consider citing some of the work that has been done on sulfation in nematodes, e.g. as reviewed here: https://pubmed.ncbi.nlm.nih.gov/35223994/ Sulfation has been tied to multiple aspects of nematode aECM organization, though not specifically to ZP proteins.

      Thank you for the suggestion. Pioneering studies in C. elegans have highlighted the key role of sulfation in diverse developmental processes, including neuronal organization, reproductive tissue development, and phenotypic plasticity. We have now cited several works.  

      Reviewer #2 (Significance):

      This study will be of interest to researchers studying developmental morphogenesis in general and specifically tube biology or the aECM. It should be particularly of interest to those studying sulfation or ZP proteins (which are broadly present in aECMs across organisms, including humans).

      This study adds to the literature demonstrating the importance of luminal matrix in shaping tubular organs and greatly advances understanding of the luminal matrix in the Drosophila salivary gland, an important model of tubular organ development and one that has key matrix differences (such as no chitin) compared to other highly studied Drosophila tubes like the trachea.

      The detailed description of the defects resulting from papss loss suggests that there are multiple different sulfated targets, with a subset specifically relevant to aECM biology. A limitation is that specific sulfated substrates are not identified here (e.g. are these the ZP proteins themselves or other matrix glycoproteins or lipids?); therefore it's not clear how direct or indirect the effects of papss are on ZP proteins. However, this is clearly a direction for future work and does not detract from the excellent beginning made here.

      My expertise: I am a developmental geneticist with interests in apical ECM

      Reviewer #3 (Evidence, reproducibility and clarity):

      In this work Woodward et al focus on the apical extracellular matrix (aECM) in the tubular salivary gland (SG) of Drosophila. They provide new insights into the composition of this aECM, formed by ZP proteins, in particular Pio and Dumpy. They also describe the functional requirements of PAPSS, a critical enzyme involved in sulfation, in regulating the expansion of the lumen of the SG. A detailed cellular analysis of Papss mutants indicate defects in the apical membrane, the aECM and in Golgi organization. They also find that Papss control the proper organization of the Pio-Dpy matrix in the lumen. The work is well presented and the results are consistent.

      Main comments

      - This work provides a detailed description of the defects produced by the absence of Papss. In addition, it provides many interesting observations at the cellular and tissular level. However, this work lacks a clear connection between these observations and the role of sulfation. Thus, the mechanisms underlying the phenotypes observed are elusive. Efforts directed to strengthen this connection (ideally experimentally) would greatly increase the interest and relevance of this work.

      Thank you for this thoughtful comment. To directly test whether the phenotypes observed in Papss mutants are due to the loss of sulfation activity, we generated transgenic lines expressing catalytically inactive forms of Papss, UAS-PapssK193A, F593P, in which key residues in the APS kinase and ATP sulfurylase domains are mutated. Unlike WT UAS-Papss (both the Papss-PD or Papss-PE isoforms), the catalytically inactive UAS-Papssmut failed to rescue any of the phenotypes, including the thin lumen phenotype (Figure 1I-L), altered WGA signals (Figure I, I’) and the cell death phenotype (Figure 4D, E). These findings strongly support the conclusion that the enzymatic sulfation activity of Papss is essential for the developmental processes described in this study.  

      - A main issue that arises from this work is the role of Papss at the cellular level. The results presented convincingly indicate defects in Golgi organization in Papss mutants. Therefore, the defects observed could stem from general defects in the secretion pathway rather than from specific defects on sulfation. This could even underly general/catastrophic cellular defects and lead to cell death (as observed).

      This observation has different implications. Is this effect observed in SGs also observed in other cells in the embryo? If Papss has a general role in Golgi organization this would be expected, as Papss encodes the only PAPs synthatase in Drosophila.

      Can the authors test any other mutant that specifically affect Golgi organization and investigate whether this produces a similar phenotype to that of Papss?

      Thank you for the comment. To address whether the defects observed in Papss mutants stem from general disruption of the secretory pathway due to Golgi disorganization, we examined mutants of two key Golgi components: Grasp65 and GM130. 

      In Grasp65 mutants, we observed significant defects in SG lumen morpholgy, including highly irregular SG lumen shape and multiple constrictions (100%; n=10/10). However, the lumen was not uniformly thin as in Papss mutants. In contrast, GM130 mutants–although this line was very sick and difficult to grow–showed relatively normal salivary glands morphology in the few embryos that survived to stage 16 (n=5/5). It is possible that only embryos with mild phenotypes progressed to this stages, limiting interpretation. These data have now been included in Figure 3-figure supplement 2. Overall, while Golgi disruption can affect SG morphology, the specific phenotypes seen in Papss mutants are not fully recapitulated by Grasp65 or GM130 loss. 

      - A model that conveys the different observations and that proposes a function for Papss in sulfation and Golgi organization (independent or interdependent?) would help to better present the proposed conclusions. In particular, the paper would be more informative if it proposed a mechanism or hypothesis of how sulfation affects SG lumen expansion. Is sulfation regulating a factor that in turn regulates Pio-Dpy matrix? Is it regulating Pio-Dpy directly? Is it regulating a

      product recognized by WGA?

      For instance, investigating Alcian blue or sulfotyrosine staining in pio, dpy mutants could help to understand whether Pio, Dpy are targets of sulfation.

      Thank you for the comment. We’re also very interested in learning whether the regulation of the Pio-Dpy matrix is a direct or indirect consequence of the loss of sulfation on these proteins. One possible scenario is that sulfation directly regulates the Pio-Dpy matrix by regulating protein stability through the formation of disulfide bonds between the conserved Cys residues responsible for ZP module polymerization. Additionally, the Dpy protein contains hundreds of EGF modules that are highly susceptible to O-glycosylation. Sulfation of the glycan groups attached to Dpy may be critical for its ability to form a filamentous structure. Without sulfation, the glycan groups on Dpy may not interact properly with the surrounding materials in the lumen, resulting in an aggregated and condensed structure. These possibilities are discussed in the Discussion.

      We have not analyzed sulfation levels in pio or dpy mutants because sulfation levels in mutants of single ZP domain proteins may not provide much information. A substantial number of proteoglycans, glycoproteins, and proteins (with up to 1% of all tyrosine residues in an organism’s proteins estimated to be sulfated) are modified by sulfation, so changes in sulfation levels in a single mutant may be subtle. Especially, the existing dpy mutant line is an insertion mutant of a transposable element; therefore, the sulfation sites would still remain in this mutant. 

      - Interpretation of Papss effects on Pio and Dpy would be desired. The results presented indicate loss of Pio antibody staining but normal presence of cherry-Pio. This is difficult to interpret. How are these results of Pio antibody and cherry-Pio correlating with the results in the trachea described recently (Drees et al. 2023)?

      In our original submission, we stated that the uniform luminal mCh-Pio signals were not changed in Papss mutants, but after re-analysis, we found that these signals were actually absent from the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly. 

      After cleavages by Np and furin, the Pio protein should have three fragments. The Nterminal region contains the N-terminal half of the ZP domain, and mCh-Pio signals show this fragment. The very C-terminal region should localize to the membrane as it contains the transmembrane domain. We think the middle piece, the C-terminal ZP domain, is recognized by the Pio antibody. The mCh-Pio and Pio antibody signals in the WT trachea (Drees et al., 2023) are similar to those in the SG. mCh-Pio signals are detected in the tracheal lumen as uniform signals, at the apical membrane, and in cytoplasmic puncta. Pio antibody signals are exclusively in the tracheal lumen and show more heterogenous filamentous signals. 

      In Papss mutants, the middle fragment (the C-terminal ZP domain) seems to be most affected because the Pio antibody signals are absent from the lumen. The loss of Pio antibody signals could be due to protein degradation or epitope masking caused by aECM condensation and protein misfolding. This fragment seems to be key for interacting with Dpy, since Pio antibody signals always colocalize with Dpy-YFP. The N-terminal mCh-Pio fragment does not appear to play a significant role in forming a complex with Dpy in WT (but still aggregated together in Papss mutants), and this can be tested in future studies.

      In response to Reviewer 1’s comment, we performed an additional experiment to test the role of Np in cleaving Pio to help organize the SG aECM. In this experiment, we overexpressed the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. Np.WT overexpression resulted in increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. However, overexpression of Np.S990A resulted in the absence of luminal mCh-Pio signals. Pio antibody signals were strong at the apical membrane but rather weak in the luminal filamentous structures. Since the UAS-Np.S990A line has the GFP tag, we could not reliably analyze Dpy-YFP signals due to overlapping Np.S990A.GFP signals in the same channel. However, the luminal filamentous Pio signals co-localized with GFP signals, and we assume that these overlapping signals could be Dpy-YFP signals. 

      These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I. 

      A proposed model of the Pio-Dpy aECM in WT, Papss, pio, and Np mutants has now been included in Figure 7.

      -  What does the WGA staining in the lumen reveal? This staining seems to be affected differently in pio and dpy mutants: in pio mutants it disappears from the lumen (as dpy-YFP does), but in dpy mutants it seems to be maintained. How do the authors interpret these findings? How does the WGA matrix relate to sulfated products (using Alcian blue or sulfotyrosine)?

      WGA binds to sialic acid and N-acetylglucosamine (GlcNAc) residues on glycoproteins and glycolipids. GlcNAc is a key component of the glycosaminoglycan (GAG) chains that are covalently attached to the core protein of a proteoglycan, which is abundant in the ECM. We think WGA detects GlcNAc residues in the components of the aECM, including Dpy as a core component, based on the following data. 1) WGA and Dpy colocalize in the lumen, both in WT (as thin filamentous structures) and Papss mutant background (as condensed rod-like structures), and 2) are absent in pio mutants. WGA signals are still present in a highly condensed form in dpy mutants. That’s probably because the dpy mutant allele (dpyov1) has an insertion of a transposable element (blood element) into intron 11 and this insertion may have caused the Dpy protein to misfold and condense. We added the information about the dpy allele to the Results section and discussed it in the Discussion.

      Minor points:

      - The morphological phenotypic analysis of Papss mutants (homozygous and transheterozygous) is a bit confusing. The general defects are higher in Papss homozygous than in transheterozygotes over a deficiency. Maybe quantifying the defects in the heterozygote embryos in the Papss mutant collection could help to figure out whether these defects relate to Papss mutation.

      We analyzed the morphology of heterozygous Papss mutant embryos. They were all normal. The data and quantifications have now been added to Figure 1-figure supplement 3. 

      - The conclusion that the apical membrane is affected in Papss mutants is not strongly supported by the results presented with the pattern of Crb (Fig 2). Further evidences should be provided. Maybe the TEM analysis could help to support this conclusion

      We quantified Crb levels in the sub-apical and medial regions of the cell and included this new quantification in Figure 2D. TEM images showed variation in the irregularity of the apical membrane, even in WT, and we could not draw a solid conclusion from these images.

      - It is difficult to understand why in Papss mutants the levels of WGA increase. Can the authors elaborate on this?

      We think that when Dpy (and many other aECM components) are condensed and aggregated into the thin, rod-like structure in Papss mutants, the sugar residues attached to them must also be concentrated and shown as increased WGA signals.   

      - The explanation about why Pio antibody and mcherry-Pio show different patterns is not clear. If the antibody recognizes the C-t region, shouldn't it be clearly found at the membrane rather than the lumen?

      The Pio protein is also cleaved by furin protease (Figure 5B). We think the Pio fragment recognized by the antibody should be a “C-terminal ZP domain”, which is a middle piece after furin + Np cleavages. 

      - The qsm information does not seem to provide any relevant information to the aECM, or sulfation.

      Since Qsm has been shown to bind to Dpy and remodel Dpy filaments in the muscle tendon (Chu and Hayashi, 2021), we believe that the different behavior of Qsm in the SG is still informative. As mentioned briefly in the Discussion, the cleaved Qsm fragment may localize differently, like Pio, and future work will need to test this. We have shortened the description of the Qsm localization in the manuscript and moved the details to the figure legend of Figure 5-figure supplement 3.

      Reviewer #3 (Significance):

      Previous reports already indicated a role for Papss in sulfation in SG (Zhu et al 2005). Now this work provides a more detailed description of the defects produced by the absence of Papss. In addition, it provides relevant data related to the nature and requirements of the aECM in the SG. Understanding the composition and requirements of aECM during organ formation is an important question. Therefore, this work may be relevant in the fields of cell biology and morphogenesis.

    1. Reviewer #3 (Public review):

      Summary:

      This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

      Strengths:

      The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous. My comments below mainly concern areas where the writing could be improved to help readers more easily grasp the logic behind the experiments.

      Weaknesses:

      (1) Lines 56-58: The two previous findings should be more clearly summarized. Specifically, it's unclear whether the "mediated S2-shock" association occurred during Stage 2 or Stage 3. I assume the authors mean Stage 2, but Stage 2 alone would not yet involve "fear of S2," making this expression a bit confusing.

      (2) Line 61: The phrase "Pavlovian fear conditioning" is ambiguous in this context. I assume it refers to S1-shock or S2-shock conditioning. If so, it would be clearer to state this explicitly.

      (3) Regarding the distinction between having or not having Stage 1 S2-S1 pairings, is "novel vs. familiar" the most accurate way to frame this? This terminology could be misleading, especially since one might wonder why S2 couldn't just be presented alone on Stage 1 if novelty is the critical factor. Would "outcome relevance" or "predictability" be more appropriate descriptors? If the authors choose to retain the "novel vs. familiar" framing, I suggest providing a clear explanation of this rationale before introducing the predictions around Line 118.

      (4) Line 121: This statement should refer to S1, not S2.

      (5) Line 124: This one should refer to S2, not S1.

      (6) Additionally, the rationale for Experiment 4 is not introduced before the Results section. While it is understandable that Experiment 4 functions as a follow-up to Experiment 3, it would be helpful to briefly explain the reasoning behind its inclusion.

    1. professionals and experts also research, read, and cite from others. When a source includes citations, it’s a clue that the source you are reading is based on more than just one person’s knowledge or opinions.

      I agree this allows the reader to see that your research is reliable.

    1. Berating

      The excerpt you provided discusses the term "berating," which refers to the act of scolding or criticizing someone harshly. In this context, one character is accused of berating another, although they deny it, claiming they just care. The conversation reveals a dynamic where one person feels guilty for potentially coming across as critical, while the other reassures them that it's not a problem. This illustrates how misunderstandings in communication can occur, especially regarding intentions and emotional care.

      Chinese Translation: 这段摘录讨论了“训斥”这个词,指的是严厉地责骂或批评某人。在这个上下文中,一位角色被指责训斥另一位角色,尽管他否认这一点,声称他只是关心对方。对话揭示了一个动态关系,其中一人因可能被视为批评而感到内疚,而另一人则安慰他这没有问题。这说明了在沟通中,尤其是在意图和情感关怀方面,误解是如何发生的。

    1. Armed with this information, if your goal is to maximize your depth within a talent domain,then the optimal rational strategy is a greedy approach:1. Augment your knowledge base with all the examples and problem-solving experiences inthe intended direction, as quickly as possible.2. Upon reaching the edge of human knowledge in that direction, and only then, switchover to creative production.Creative production is a substantially less efficient means of acquiring skills within a talentdomain, so you want to save it for the end when it’s the only way to continue moving forward.

      This is interesting because I have to compare it to how people talk about creating comics and videogames, creative endeavors that require synthesizing a lot of skills across a lot of areas... you just have to do the thing, you need to not spend your life preparing.

      He immediately notes:

      (To briefly fend off an expected critique: the act of perpetually avoiding the leap into creative production, opting instead to indefinitely “expand sideways,” acquiring skills that are not foundational for the talent domain, does not constitute the above strategy.)

      But, like, doesn't this imply a lot of load-bearing certainty about what is and isn't foundational and where you've found the edge of human knowledge?

    Annotators

    1. It’s rare that you’ll start a search from absolute zero. Most of the time you’ve at least heard something about the topic, even if it is just a brief reference in a news story, lecture, or reading.

      Most topics I research into I have a understanding about what they are but, I always find something new.

    1. Do Not Engage: Don’t stray, stay focused on the current issue

      I feel like this is something that I will have to work on. It's easy to get distracted, and I want to encourage students to think critically about history. Is there a way to assure students that you will look into topics during discussions without engaging? Would you just acknowledge their statement, make a note, and keep lecturing?

    1. e more melodramatic socialproblem films su as Come Back Little Sheba (1952) and A Hatful of Rain(1957) were aracter studies of emotionally unstable, oen drug-dependent,family men. S

      These movies showed men who were struggling emotionally or with addiction. It’s interesting that TV and film in the 1950s didn’t just show perfect families, they also showed serious problems inside the home.

    2. the television was shown to replacethe fireplace altogether, as the magazines showed readers how televisioncould function as the center of family aention

      The fireplace is an area that brings people together. It's the space that has decorations, family photos, bring a cozy feeling, and even make s'mores with. It's interesting that television can replace something that had meaning just like that.

    1. Through a study of physics, you may gain a greater understanding of the interconnectedness of everything we can see and know in this universe.

      I'm pretty sure Plato might say that physics gives us a glimpse of the shadows on the cave wall, the measurable patterns of motion, matter, and energy, but it also points us toward something deeper: the unchanging principles, or ‘Forms,’ that underlie the shifting physical world. In that sense, studying physics isn’t just about particles and forces; it’s about recognizing that everything is connected by a hidden order, a kind of mathematical harmony that reflects a greater reality beyond what we see. At least When I read this statement I felt as if the book hinted at something philosophical. but perhaps the book simply means "everything we can see and know" and not in a metaphysical or spiritual emotional "see and know."

    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this paper, Behruznia and colleagues use long-read sequencing data for 339 strains of the Mycobacterium tuberculosis complex to study genome evolution in this clonal bacterial pathogen. They use both a "classical" pangenome approach that looks at the presence and absence of genes, and a pangenome graph based on whole genomes in order to investigate structural variants in non-coding regions. The comparison of the two approaches is informative and shows that much is missed when focussing only on genes. The two main biological results of the study are that 1) the MTBC has a small pangenome with few accessory genes, and that 2) pangenome evolution is driven by genome reduction. In the revised article, the description of the data set and the methods is much improved, and the comparison of the two pangenome approaches is more consistent. I still think, however, that the discussion of genome reduction suffers from a basic flaw, namely the failure to distinguish clearly between orthologs and homologs/paralogs.

      Strengths:

      The authors put together the so-far largest data set of long-read assemblies representing most lineages of the Mycobacterium tuberculosis context, and covering a large geographic area. They sequenced and assembled genomes for strains of M. pinnipedi, L9, and La2, for which no high-quality assemblies were available previously. State-of-the-art methods are used to analyze gene presence-absence polymorphisms (Panaroo) and to construct a pangenome graph (PanGraph). Additional analysis steps are performed to address known problems with misannotated or misassembled genes.

      Weaknesses:

      The revised manuscript has gained much clarity and consistency. One previous criticism, however, has in my opinion not been properly addressed. I think the problem boils down to not clearly distinguishing between orthologs and paralogs/homologs. As this problem affects a main conclusion - the prevalence of deletions over insertions in the MTBC - it should be addressed, if not through additional analyses, then at least in the discussion.

      Insertions and deletions are now distinguished in the following way: "Accessory regions were further classified as a deletion if present in over 50% of the 192 sub-lineages or an insertion/duplication if present in less than 50% of sub-lineages." The outcome of this classification is suspicious: not a single accessory region was classified as an insertion/duplication. As a check of sanity, I'd expect at least some insertions of IS6110 to show up, which has produced lineage- or sublineage-specific insertions (Roychowdhury et al. 2015, Shitikov et al. 2019). Why, for example, wouldn't IS6110 insertions in the single L8 strain show up here?

      In a fully clonal organism, any insertion/duplication will be an insertion/duplication of an existing sequence, and thus produce a paralog. If I'm correctly understanding your methods section, paralogs are systematically excluded in the pangraph analysis. Genomic blocks are summarized at the sublineage levels as follows (l.184 ): "The DNA sequences from genomic blocks present in at least one sub-lineage but completely absent in others were extracted to look for long-term evolution patterns in the pangenome." I presume this is done using blastn, as in other steps of the analysis.

      So a sublineage-specific copy of IS6110 would be excluded here, because IS6110 is present somewhere in the genome in all sublineages. However, the appropriate category of comparison, at least for the discussion of genome reduction, is orthology rather than homology: is the same, orthologous copy of IS6110, at the same position in the genome, present or absent in other sublineages? The same considerations apply to potential sublineage-specific duplicates of PE, PPE, and Esx genes. These gene families play important roles in host-pathogen interactions, so I'd argue that the neglect of paralogs is not a finicky detail, but could be of broader biological relevance.

      Reviewer #2 (Public review):

      Summary:

      The authors attempted to investigate the pangenome of MTBC by using a selection of state-of-the-art bioinformatic tools to analyse 324 complete and 11 new genomes representing all known lineages and sublineages. The aim of their work was to describe the total diversity of the MTBC and to investigate the driving evolutionary force. By using long read and hybrid approaches for genome assembly, an important attempt was made to understand why the MTBC pangenome size was reported to vary in size by previous reports. This study provides strong evidence that the MTBC pangenome is closed and that genome reduction is the main driver of this species evolution.

      Strengths:

      A stand-out feature of this work is the inclusion of non-coding regions as opposed to only coding regions which was a focus of previous papers and analyses which investigated the MTBC pangenome. A unique feature of this work is that it highlights sublineage-specific regions of difference (RDs) that was previously unknown. Another major strength is the utilisation of long-read whole genomes sequences, in combination with short-read sequences when available. It is known that using only short reads for genome assembly has several pitfalls. The parallel approach of utilizing both Panaroo and Pangraph for pangenomic reconstruction illuminated limitations of both tools while highlighting genomic features identified by both. This is important for any future work and perhaps alludes to the need for more MTBC-specific tools to be developed. Lastly, ample statistical support in the form of Heaps law and genome fluidity calculations for each pangenome to demonstrate that they are indeed closed.

      Weaknesses:

      There are no major weaknesses in the revised version of this manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      l. 27: "lineage-specific and -independent deletions": it is still not clear to me what a lineage-independent, or convergent, deletion is supposed to be. TBD1, for instance, is not lineage-specific, but it is also not convergent: it occurred once in the common ancestor of lineages 1, 2, and 3, while convergence implies multiple parallel occurrences.

      We have changed this and in other places to more evolutionary terms, such as divergent (single event) and convergent (multiple events), or explain exactly what is meant where needed.

      l. 118: "where relevant", what does that mean?

      This was superfluous to the description and so is now removed.

      l. 178ff.: It is not clear to me what issue is addressed by this correction of the pangenome graph. Also here there seems to be some confusion regarding orthologs and paralogs. A gene or IS copy can be present at one locus but absent at another, which is not a mistake of Pangraph that would require correction. It's rather the notion of "truly absent region" which is ambiguous.

      We have changed the text to be more specific on the utility of this step. Since it is known that Panaroo mislabels some genes as being absent due to over splitting (see Ceres et al 2022 and our reclassification earlier in the paper), we wanted to see if the same occurred in Pangraph. We have modified the methods text to be more specific (line 181) and in the results included the percentage of total genes/regions affected by this correction.

      In relation to copy number, Pangraph is not syntenic in its approach; if a region is present anywhere it is labelled as present in the genome. Pangraph will look for multiple copies of that region (e.g. an IS element) but indeed we did not look for specific syntenic changes across the genomes. This would be a great analysis and something we will consider in the future; we have indicated such in the discussion (line 454).

      l. 305: "mislabelled as absent": see above, is this really 'mislabelled'?

      See answer to question above

      l. 372: "using the approach": something missing here.

      This was superfluous to the description and so is now removed.

      l. 381: the "additional analysis of paralogous blocks" (l. 381) seems to suffer from the same confusion of ortho- and paralogy described above: no new sub-lineage-specific accessory regions are found presumably because the analysis did consider any copy rather than orthologous copies.

      Paralogous copies were looked for by Pangraph, and we did not find any sub-lineage where all members had additional copies compared to other sub-lineages. Indeed, single genomes could have these, and shorter timescales could see a lot of such insertions, but we looked at longer-scale (all genomes within a sub-lineage) patterns and did not find these. These limitations are already outlined in the discussion.

      l. 415: see above. There is no diagnosis of a problem that would motivate a "correction". That's different from the correction of the Panaroo results, where fragmented annotations have been shown to be a problem.

      Of interest, the refining of regions did re-label multiple regions as being core when Pangraph labelled it as absent from some genomes was at about the same rate as the correction to Pangraph (2% of genes/regions). This indicates there is a stringency issue with pangraph where blocks are mislabelled as absent. The underlying reason or this is not clear but the correction is evidently required in this version of Pangraph.

      l. 430ff.: The issue of paralogy and that the "same" gene or region is defined in terms of homology rather than orthology should be addressed here. For me the given evidence does not support the claim that deletion is driving molecular evolution in the MTBC.

      As outlined above, indeed paralogy may be driving some elements of the overall evolutionary patterns; our analysis just did not find this. Panaroo without merged paralogs did not find paralogous genes as a main differentiating factor for any sub-lineage. Pangraph also did not find multiple copies of blocks present in all genomes in a sub-lineage. As outlined above, indeed single genomes show such patterns but we did not include single genome analyses here, and outline that as a next steps in the discussion. We have also linked to a recent pangenome paper that showed duplication is present in the pangenome of Mtbc, although not related to any specific lineage (Discussion line 485).

      l. 443 ff: "lineage-independent deletions (convergent evolution)": see above, I still think this terminology is unclear

      This has now been made clearer to be specifically about convergent and divergent evolutionary patterns.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The investigators undertook detailed characterization of a previously proposed membrane targeting sequence (MTS), a short N-terminal peptide, of the bactofilin BacA in Caulobacter crescentus. Using light microscopy, single molecule tracking, liposome binding assays, and molecular dynamics simulations, they provide data to suggest that this sequence indeed does function in membrane targeting and further conclude that membrane targeting is required for polymerization. While the membrane association data are reasonably convincing, there are no direct assays to assess polymerization and some assays used lack proper controls as detailed below. Since the MTS isn't required for bactofilin polymerization in other bacterial homologues, showing that membrane binding facilitates polymerization would be a significant advance for the field.

      We agree that additional experiments were required to consolidate our results and conclusions. Please see below for a description of the new data included in the revised version of the manuscript.

      Major concerns

      (1) This work claims that the N-termina MTS domain of BacA is required for polymerization, but they do not provide sufficient evidence that the ∆2-8 mutant or any of the other MTS variants actually do not polymerize (or form higher order structures). Bactofilins are known to form filaments, bundles of filaments, and lattice sheets in vitro and bundles of filaments have been observed in cells. Whether puncta or diffuse labeling represents different polymerized states or filaments vs. monomers has not been established. Microscopy shows mis-localization away from the stalk, but resolution is limited. Further experiments using higher resolution microscopy and TEM of purified protein would prove that the MTS is required for polymerization.

      We do not propose that the MTS is directly involved in the polymerization process and state this more clearly now in the Results and Discussion sections of the revised manuscript. To address this point, we performed transmission electron microscopy studies comparing the polymerization behavior of wild-type and mutant BacA variants. The results clearly show that the MTS-free BacA variant (∆2-8) forms polymers that are indistinguishable from those formed by the wild-type protein, when purified from an E. coli overproduction strain (new Figure 1–figure supplement 1). This finding is consistent with structural work showing that bactofilin polymerization is exclusively mediated by the conserved bactofilin domain (Deng et al, Nat Microbiol, 2019). However, at native expression levels, BacA only accumulates to ~200 molecules per cell (Kühn et al, EMBO J, 2006). Under these conditions, the MTS-mediated increase in the local concentration of BacA at the membrane surface and, potentially, steric constraints imposed by membrane curvature, may facilitate the polymerization process. This hypothesis has now been stated more clearly in the Results and Discussion sections.

      For polymer-forming proteins, defined localized signals are typically interpreted as slow-moving or stationary polymeric complexes. A diffuse localization, by contrast, suggests that a protein exists in a monomeric or, at most, (small) oligomeric state in which it diffuses rapidly within the cell and is thus no longer detected as distinct foci by widefield microscopy. Our single-molecule data show that BacA variants that are no longer able to interact with the membrane (as verified by cell fractionation studies and in vitro liposome binding assays) have a high diffusion rate, similar to that measured for the non-polymerizing and non-membrane-bound F130R variant. These results demonstrate that a defect in membrane binding strongly reduces the ability of BacA to form polymeric assemblies. To support this hypothesis, we have now repeated all single-particle tracking experiments and included mVenus as a freely diffusible reference protein. Our data confirm that the mobilities of the ∆2-8 and F130R variants are similar and approach those of free mVenus, supporting the idea that the deficiency to interact with the membrane prevents the formation of extended polymeric structures (which should show much lower mobilities). To underscore the relevance of membrane binding for BacA assembly, we have now included a new experiment, in which we used the PbpC membrane anchor (PbpC<sub>1-132</sub>-mcherry) to restore the recruitment of the ∆2-8 variant to the membrane (Figure 9 and Figure 9–figure supplement 1). The results obtained show that the ∆2-8 variant transitions from a diffuse localization to polar foci upon overproduction of PbpC<sub>1-132</sub>-mcherry. The polymerization-impaired F130R variant, by contrast, remains evenly distributed throughout the cytoplasm under all conditions. These findings further support the idea that polymerization and membrane-association are mutually interdependent processes.

      (2) Liposome binding data would be strengthened with TEM images to show BacA binding to liposomes. From this experiment, gross polymerization structures of MTS variants could also be characterized.

      We do not have the possibility to perform cryo-electron microscopy studies of liposomes bound to BacA. However, the results of the cell fractionation and liposome sedimentation assays clearly support a critical role of the MTS in membrane binding.

      (3) The use of the BacA F130R mutant throughout the study to probe the effect of polymerization on membrane binding is concerning as there is no evidence showing that this variant cannot polymerize. Looking through the papers the authors referenced, there was no evidence of an identical mutation in BacA that was shown to be depolymerized or any discussion in this study of how the F130R mutation might to analogous to polymerization-deficient variants in other bactofilins mentioned in these references.

      Residue F130 in the C-terminal polymerization interface of BacA is conserved among bactofilin homologs, although its absolute position in the protein sequence may vary, depending on the length of the N-terminal unstructured tail. The papers cited in our manuscript show that an exchange of this conserved phenylalanine residue abolishes polymer formation. Nevertheless, we agree that it is important to verify the polymerization defect of the F130R variant in the system under study. We have now included size-exclusion chromatography data showing that BacA-F130R forms a low-molecular-weight complex, whereas the wild-type protein largely elutes in the exclusion volume, indicating the formation of large, polymeric species (new Figure 1–figure supplement 1). In addition, we performed transmission electron microscopy analyses of BacA-F130R, which verified the absence of larger oligomers (new Figure 1–figure supplement 2).

      (4) Microscopy shows that a BacA variant lacking the native MTS regains the ability to form puncta, albeit mis-localized, in the cell when fused to a heterologous MTS from MreB. While this swap suggests a link between puncta formation and membrane binding the relationship between puncta and polymerization has not been established (see comment 1).

      We show that a BacA variant lacking the MTS (∆2-8) regains the ability to form membrane-associated foci when fused to the MTS of MreB. By contrast, a similar variant that additionally carries the F130R exchange (preventing its polymerization) shows a diffuse cytoplasmic localization. In addition, we show that the F130R exchange leads to a loss of membrane binding and to a considerable increase in the mobility of the variants carrying the MTS of E. coli MreB. As described above, we now provide additional data demonstrating that elevated levels of the PbpC membrane anchor can reinstate polar localization for the ∆2-8 variant, whereas it fails to do so for the polymerization-deficient F130R variant (Figure 9 and Figure 9–figure supplement 1). Together, these results support the hypothesis that membrane association and polymerization act synergistically to establish localized bactofilin assemblies at the stalked cell pole.

      (5) The authors provide no primary data for single molecule tracking. There is no tracking mapped onto microscopy images to show membrane localization or lack of localization in MTS deletion/ variants. A known soluble protein (e.g. unfused mVenus) and a known membrane bound protein would serve as valuable controls to interpret the data presented. It also is unclear why the authors chose to report molecular dynamics as mean squared displacement rather than mean squared displacement per unit time, and the number of localizations is not indicated. Extrapolating from the graph in figure 4 D for example, it looks like WT BacA-mVenus would have a mobility of 0.5 (0.02/0.04) micrometers squared per second which is approaching diffusive behavior. Further justification/details of their analysis method is needed. It's also not clear how one should interpret the finding that several of the double point mutants show higher displacement than deleting the entire MTS. These experiments as they stand don't account for any other cause of molecular behavior change and assume that a decrease in movement is synonymous with membrane binding.

      We now provide additional information on the single-particle analysis. A new supplemental figure now shows a mapping of single-particle tracks onto the cells in which they were recorded for all proteins analyzed (Figure 2–figure supplement 1). Due to the small size of C. crescentus, it is difficult to clearly differentiate between membrane-associated and cytoplasmic protein species. However, overall, slow-diffusing particles tend to be localized to the cell periphery, supporting the idea that membrane-associated particles form larger assemblies (apart from diffusing more slowly due to their membrane association). In addition, we have included a movie that shows the single-particle diffusion dynamics of all proteins in representative cells (Figure 2-video 1). Finally, we have included a table that gives an overview of the number of cells and tracks analyzed for all proteins investigated (Supplementary file 1). Figure 2A and 4D show the mean squared displacement as a function of time, which makes it possible to assess whether the particles observed move by normal, Brownian diffusion (which is the case here). We repeated the entire single-particle tracking analysis to verify the data obtained previously and obtained very similar results. Among the different mutant proteins, only the K4E-K7E variant consistently shows a higher mobility than the MTS-free ∆2-8 variant, with MSD values similar to that of free mVenus. The underlying reason remains unclear. However, we believe that an in-depth analysis of this phenomenon is beyond the scope of this paper. We re-confirmed the integrity of the construct encoding the K4E/K7E variant by DNA sequencing and once again verified the size and stability of the fusion protein by Western blot analysis, excluding artifacts due to errors during cloning and strain construction.

      We agree that the single-molecule tracking data alone are certainly not sufficient to draw firm conclusions on the relationship between membrane binding and protein mobility. However, they are consistent with the results of our other in vivo and in vitro analyses, which together indicate a clear correlation between the mobility of BacA and its ability to interact with the membrane and polymerize (processes that promote each other synergistically).

      (6) The experiments that map the interaction surface between the N-terminal unstructured region of PbpC and a specific part of the BacA bactofilin domain seem distinct from the main focus of the paper and the data somewhat preliminary. While the PbpC side has been probed by orthogonal approaches (mutation with localization in cells and affinity in vitro), the BacA region side has only been suggested by the deuterium exchange experiment and needs some kind of validation.

      The results of the HDX analysis per se are not preliminary and clearly show a change in the solvent accessibility of backbone amides in the C-terminal region in the bactofilin domain in the presence of the PbpC<sub>1-13</sub> peptide. However, we agree that additional experiments would be required to verify the binding site suggested by these data. We agree that further research is required to precisely map and verify the PbpC binding site. However, as this is not the main focus of the paper, we would like to proceed without conducting further experiments in this area.

      We now provide additional data showing that elevated levels of the PbpC membrane anchor are able to recruit the MTS-free BacA variant (∆2-8) to the cytoplasmic membrane and stimulate its assembly at the stalked pole (Figure 9). These results now integrate Figure 8 more effectively into the overall theme of the paper.

      Reviewer #2 (Public review):

      Summary:

      The authors of this study investigated the membrane-binding properties of bactofilin A from Caulobacter crescentus, a classic model organism for bacterial cell biology. BacA was the progenitor of a family of cytoskeletal proteins that have been identified as ubiquitous structural components in bacteria, performing a range of cell biological functions. Association with the cell membrane is a common property of the bactofilins studied and is thought to be important for functionality. However, almost all bactofilins lack a transmembrane domain. While membrane association has been attributed to the unstructured N-terminus, experimental evidence had yet to be provided. As a result, the mode of membrane association and the underlying molecular mechanics remained elusive.

      Liu at al. analyze the membrane binding properties of BacA in detail and scrutinize molecular interactions using in-vivo, in-vitro and in-silico techniques. They show that few N-terminal amino acids are important for membrane association or proper localization and suggest that membrane association promotes polymerization. Bioinformatic analyses revealed conserved lineage-specific N-terminal motifs indicating a conserved role in protein localization. Using HDX analysis they also identify a potential interaction site with PbpC, a morphogenic cell wall synthase implicated in Caulobacter stalk synthesis. Complementary, they pinpoint the bactofilin-interacting region within the PbpC C-terminus, known to interact with bactofilin. They further show that BacA localization is independent of PbpC.

      Strengths:

      These data significantly advance the understanding of the membrane binding determinants of bactofilins and thus their function at the molecular level. The major strength of the comprehensive study is the combination of complementary in vivo, in vitro and bioinformatic/simulation approaches, the results of which are consistent.

      Thank you for this positive feedback.

      Weaknesses:

      The results are limited to protein localization and interaction, as there is no data on phenotypic effects. Therefore, the cell biological significance remains somewhat underrepresented.

      We agree that it is interesting to investigate the phenotypic effects caused by the reduced membrane binding activity of BacA variants with defects in the MTS. We have now included phenotypic analyses that shed light on the role of region C1 in the localization of PbpC and its function in stalk elongation under phosphate-limiting conditions (see below).

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      To address the missing estimation of biological relevance, some additional experiments may be carried out.

      For example, given that BacA localizes PbpC by direct interaction, one might expect an effect on stalk formation if BacA is unable to bind the membrane or to polymerize. The same applies to PbpC variants lacking the C1 region. As the mutant strains are available, these data are not difficult to obtain but would help to compare the effect of the deletions with previous data (e.g. Kühn et al.) even if the differences are small.

      We have now analyzed the effect of the removal of region C1 on the ability of mVenus-PbpC to promote stalk elongation in C. crescentus under phosphate starvation. Interestingly, our results show that the lack of the BacA-interaction motif impairs the recruitment of the fusion protein to the stalked pole, but it does not interfere with its stimulatory effect on stalk biogenesis. Thus, the polar localization of PbpC does not appear to be critical for its function in localized peptidoglycan synthesis at the stalk base. These results are now shown in Figure 8–Figure supplement 4. The results obtained may be explained by residual transient interactions of mVenus-PbpC with proteins other than BacA at the stalked pole. Notably, PbpC has also been implicated in the attachment of the stalk-specific protein StpX to components of the outer membrane at the stalk base. The polar localization of PbpC may therefore be primarily required to ensure proper StpX localization, consistent with previous work by Hughes et al. (Mol Microbiol, 2013) showing that StpX is partially mislocalized in a strain producing an N-terminally truncated PbpC variant that no longer localizes to the stalk base.

      We have also attempted to investigate the ability of the Δ2-8 and F130R variants of BacA-mVenus to promote stalk elongation under phosphate starvation. However, the levels of the WT, Δ2-8 and F130R proteins and their stabilities were dramatically different after prolonged incubation of the cells in phosphate-limited medium, so that it was not possible to draw any firm conclusions from the results obtained (not shown).

      In addition, the M23-like endopeptidase LdpA is proposed to be a client protein of BacA (in C. crescentus, Billini et al. 2018, and H. neptunium or R. rubrum, Pöhl et al. 2024). In H. neptunium, it is suggested that the interaction is mediated by a cytoplasmic peptide of LmdC reminiscent of PbpC. This should at least be commented on. It would be interesting to see, if LpdA in C. crescentus is also delocalized and if so, this could identify another client protein of BacA.

      We agree that it would be interesting to study the role of BacA in LdpA function. However, we have not yet succeeded in generating a stable fluorescent protein fusion to LdpA, which currently makes it impossible to study the interplay between these two proteins in vivo. The focus of the present paper is on the mode of interaction between bactofilins and the cytoplasmic membrane and on the mutual interdependence of membrane binding and bactofilin polymerization. Given that PbpC is so far the only verified interaction partner of BacA in C. crescentus, we would like to limit our analysis to this client protein.

      Further comments:

      L105: analyze --> analyzed

      Done.

      L169: Is there any reason why the MTS of E. coli MreB was doubled?

      Previous work has shown that two tandem copies of the N-terminal amphiphilic helix of E. coli MreB were required to partially target a heterologous fusion partner protein (GFP) to the cytoplasmic membrane of E. coli cells (Salje et al, 2011).

      Fig. S3:

      a) Please decide which tag was used (mNG or mVenus) and adapt the figure or legend accordingly.<br /> b) In the legend for panel (C), please describe how the relative amounts were calculated, as the fractions arithmetically cannot add to > 100%. I guess each band was densiometrically rated and independently normalized to the whole-cell signal?

      The fluorescent tag used was mNeonGreen, as indicated in the figure. We have now corrected the legend accordingly. Thank you for making us aware of the wrong labeling of the y-axis. We have now corrected the figure and describe the method used to calculate the plotted values in the legend.

      Legend of Fig 1b: It is not clear to me, to which part of panel B the somewhat cryptic LY... strain names belong. I suggest putting them either next to the images, to delete them, or at least to unify the layout (compare, e.g. to Fig S7). (I would delete the LY numbers and stay with the genes/mutations throughout. This is just a suggestion).

      These names indicate the strains analyzed in panel B, and we have now clarified this in the legend. It is more straightforward to label the images according to the mutations carried by the different strains. Nevertheless, we would like to keep the strain names in the legend, so that the material used for the analysis can be clearly identified.

      Fig. 2a: As some of the colors are difficult to distinguish, I suggest sorting the names in the legend within the graph according to the slope of the curves (e.g. K4E K7E (?) on top and WT being at the bottom).

      Thank you for this suggestion. We have now rearranged the labels as proposed.

      In the legend (L924), correct typo "panel C" to "panel B".

      Done.

      Fig. 3: In the legend, I suggest deleting the abbreviations "S" and "P" as they do not show up in the image. In line 929, I suggest adding: average "relative" amount... or even more precisely: "average relative signal intensities obtained..."

      We have removed the abbreviations and now state that the bars indicate the “average relative signal intensities” obtained for the different fractions.

      Fig 4d: same suggestion as for Fig. 2a.

      Done.

      Fig 8: In the legend (L978), delete 1x "the"

      Done.

      L258 and Fig. S5: The expression "To account for biases in the coverage of bacterial species" seems somewhat unclear. I suggest rephrasing and adding information from the M+M section here (e.g. from L593, if this is meant).

      We now state that this step in the analysis pipeline was performed “To avoid biases arising from the over-representation of certain bacterial species in UniProt”.

      I appreciate the outline of the workflow in panel (a) of Fig. S5. It would be even more useful when some more details about the applied criteria for filtering would be provided (e.g. concerning what is meant with "detailed taxonomic information" or "filter out closely related sequences". Does the latter mean that only one bactofilin sequence per species was used? (As quite many bacteria have more than one but similar bactofilins.)

      We removed sequences from species with unclear phylogeny (e.g. candidate species whose precise taxonomic position has not yet been determined). For many pathogenic species, numerous strains have been sequenced. To account for this bias, only one sequence from clusters of highly similar bactofilin sequences (>90% identity) was retained per species. This information has now been included in the diagram. It is true that many bacteria have more than one bactofilin homolog. However, the sequences of these proteins are typically quite different. For instance, the BacA and BacB from C. crescentus only share 52% identity. Therefore, our analysis does not systematically eliminate bactofilin paralogs that coexist in the same species.

      L281: Although likely, I am not sure if membrane binding has ever been shown for a bactofilin from these phyla. (See also L 380.) Is there an example? Otherwise, membrane binding may not be a property of these bactofilins.

      To our knowledge, the ability of bactofilins from these clades to interact with membranes has not been investigated to date. We agree that the absence of an MTS-like motif may indicate that they lack membrane binding activity, and we have now stated this possibility in the Results and Discussion.

      L285: See comment above concerning the M23-like peptidase LpdA. Although not yet directly shown for C. crescentus, it seems likely that BacACc does also localize this peptidase in addition to PbpC. I suggest rephrasing, e.g. "known" --> "shown"

      We now use the word “reported”.

      L295 and Fig S8: PbpC is ubiquitous. Which criteria/filters have been applied to select the shown sequences?

      C. crescentus PbpC is different from E. coli Pbp1C. It is characterized by distinctive, conserved N- and C-terminal tails and only found in C. crescentus and close relatives. The C. crescentus homolog of E. coli PbpC is called PbpZ (Yakhnina et al, J Bacteriol, 2013; Strobel et al, J Bacterol, 2014), whereas C. crescentus PbpC is related to E. coli PBP1A. We have now added this information to the text to avoid confusion.

      L311: may replace "assembly" by "polymerization"

      Done.

      L320: bactofilin --> bactofilin domain?

      Yes, this was supposed to read “bactofilin domain”. Thank you for spotting this issue.

      L324: The HDX analysis of BacA suggests that the exchange is slowed down in the presence of the PbpC peptide, which is indicative of a physical interaction between these two molecules. To corroborate the claim that BacA polymerization is critical for interaction with the peptide (resp. PbpC), this experiment should be carried out with the polymerization defective BacA version F130R.

      (Or tone this statement down, e.g. show --> suggest.)

      “suggest”

      L386: undergoes --> undergo

      Done.

      L391-400: This idea is tempting but the suggested mechanism then would be restricted to bactofilins of C. crescentus and close relatives. The bactofilin of Rhodomicrobium, for example, was shown to localize dynamically and not to stick to a positively curved membrane.

      In the vast majority of species investigated so far, bactofilins were found to associate with specifically curved membrane regions and to contribute to the establishment of membrane curvature. Unfortu­nately, the sequences of the three co-polymerizing bactofilin paralogs of R. vannielii DSM 166 studied by Richter et al (2023) have not been reported and the genome sequence of this strain is not publicly available. However, in related species with three bactofilin paralogs, only one paralog shows an MTS-like N-terminal peptide and another paralog typically contains an unusual cadherin-like domain of unknown function, as also reported for R. vannielii DSM 166. Therefore, the mechanism controlling the localization dynamics of bactofilins may be complex in the Rhodomicrobium lineage. Nevertheless, at native expression levels, the major bactofilin (BacA) of R. vannielii DSM 166 was shown to localize predominantly to the hyphal tips and the (incipient) bud necks, suggesting that regions of distinct membrane curvature could also play a role in its recruitment. We do not claim that all bactofilins recognize positive membrane curvature, which is clearly not the case. It rather appears as though the curvature preference of bactofilins varies depending on their specific function.

      L405-406: I agree that localization of BacA has been shown to be independent of PbpC. However, this does not generally preclude an effect on BacA localization by other "client" or interacting proteins. (See also comment above about the putative BacA interactor LpdA). I suggest either to corroborate or to change this statement from "client binding" to "PbpC binding".

      Thank you for pointing out the imprecision of this statement. We now conclude that “PbpC binding” is not critical for BacA assembly and positioning.

      Suppl. Fig. S11: In the legend, please correct the copy-paste mismatch (...VirB...).

      Done.

      L482: delete 1x "at"

      Done.

      L484: may be better "soluble and insoluble fractions"?

      We now describe the two fractions as “soluble and membrane-containing insoluble fractions” to make clear to all readers that membrane vesicles are found in the pellet after ultracentrifugation.

      L489-490: check spelling immunoglobulin – immuneglobulin

      Done.

      L500 and 504: º_C --> ºC

      Done.

      Suppl. file X (HDX data): please check the table headline, table should be included in Suppl. file 1

      We have now included a headline in this file (now Supplementary file 3).

    1. Author response:

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

      Reviewer #1 (Public review)

      Comment 

      Koonce et al. have generated a web-based visualization tool for exploring C. elegans neuronal morphology, contact area between neurons, and synaptic connectivity data. Here, the authors integrate volumetric segmentation of neurons and visualization of contact area patterns of individual neurons generated from Diffusion Condensation and C-PHATE embedding based on previous work from adult volumetric electron microscopy (vEM) data, extended to available vEM data for earlier developmental stages, which effectively summarizes modularity within the collated C. elegans contactomes to date. Overall, NeuroSC's relative ease of use for generating visualizations, its ability to quickly toggle between developmental stages, and its integration of a concise visualization of individual neurons' contact patterns strengthen its utility.

      We thank that reviewer for this positive assessment of our work.

      Comment

      NeuroSC provides an accessible and convenient platform. However, many of the characteristics of NeuroSC overlap with that of an existing tool for visualizing connectomics data, Neuroglancer, which is a widely-used and shared platform with data from other organisms. The authors do not make clear their motivation for generating this new tool rather than building on a system that has already collated previous connectomics data. Although the field will benefit from any tool that collates connectomics data and makes it more accessible and user-friendly, such a tool is only useful if it is kept up-to-date, and if data formatting for submitting electron microscopy data to be added to the tool is made clear. It is unclear from this manuscript whether NeuroSC will be updated with recently published and future C. elegans connectomes, or how additional datasets can be submitted to be added in the future.

      We have added new language to more explicitly state the motivations for developing NeuroSC (Introduction, lines 98-111, and discussion lines 375-384). In a new discussion section, we also include comparisons of the features of NeuroSC with other existing tools, like Neuroglancer and Webknossos, (lines 393-417).

      Briefly, the functional features of NeuroSC are substantially different (and do not exist) in other web-based tools for navigating EM datasets, including NeuroGlancer. This is because the intended use of NeuroSC is substantially different (and purposefully synergistic) to the intended use, and tools available, in NeuroGlancer. 

      NeuroGlancer is a versatile tool designed primarily for web-based visualizations and sharing of large EM datasets. NeuroSC was not designed to enable this type of access to the primary EM data (purposefully done because these features were already available through tools like NeuroGlancer). 

      Instead, the explicit goal of NeuroSC is to provide a platform specifically optimized for examining neuronal relationships across connectomic datasets. NeuroSC builds on the segmentations emerging from programs like NeuroGlancer, but the tools are tailored to explore relationships such as contact profiles in the context of neuronal morphologies and synaptic positions, and across datasets that represent different animals or different developmental stages. 

      To achieve this, all datasets in NeuroSC were optimized to facilitate comparisons across different connectomes of segmented neuronal features, including: 1) alignment of the neurons that are compared upon the display of the segmentations; 2) synchronization of the 3D windows; 3) implementation of a ‘universal color code’ across datasets for each neuron and relationship for easy visual comparisons; 4) use of the specific neuronal names to label instances of the same cells across all available datasets. The use of precise neuronal names among separate data sets allows integration of these objects with other catalogued datasets, including genomic and neuronal activity profiles.

      The formatting and display of the datasets used in NeuroSC was accompanied by the development of new tools including: 1) Rendering of the contact profiles of all neurons in the context of the morphology of the cell and the synapses and 2) C-PHATE diagrams to inspect multidimensional relationship hierarchies based on these contact profiles. In NeuroSC, C-PHATEs can be navigated and compared across multiple stages of development while visualizing neuronal reconstructions, allowing users to compare neuronal relationships across individual datasets.

      We agree with the reviewer that these tools are most useful when integrated. With that intention in mind, we designed NeuroSC as a series of modular, open-source tools that could be integrated into other programs, including Neuroglancer. In that sense our intent was not to produce another free-standing tool, but a set of tools that, if useful, could be integrated to other existing web-based connectomic resources to enhance the user experience of navigating complex EM datasets and draw biological meaning from the relationships between the neurons. Additionally, we intentionally designed NeuroSC to enable the ability to integrate new methods of understanding neuron relationships as they arise. We have dedicated a more detailed section to the discussion (lines 369- 417) to better convey this intention and directly address the unique abilities of NeuroSC as a complementary tool to the powerful existing tools, including Neuroglancer.

      Comment

      The interface for visualizing contacts and synapses would be improved with better user access to the quantitative underlying data. When contact areas or synapses are added to the viewer, adding statistics on the magnitude of the contact area, the number of synapses, and the rank of these values among the neuron's top connections, would make the viewer more useful for hypothesis generation. Furthermore, synapses are currently listed individually, with names that are not very legible to the web user. Grouping them by pre- and postsynaptic neurons and linking these groups across developmental stages would also be an improvement.

      [what do they even mean by linking?]

      We thank the reviewer for this insightful comment and have implemented several improvements to address these suggestions. Specifically, we have added new features to enhance user access to quantitative data within the NeuroDevSCAN viewer:

      Cell, Patch, and Synapse Statistics: Users can now see a statistics panel when clicking on a rendered neuron, contact patch, or a synapse. These panels provide the following information, respectively, and are highlighted in lines 303-315):

      Cell Stats: Click on a cell rendering to show cell stats which displays the total volume and surface area of the selected neuron within the defined neuropil area of our datasets (see Methods). 

      Contact Stats: Click on a patch rendering to show ‘contact stats’. This pop up displays quantifications of the selected contact relationship. Rank compares the summed surface area of contacts ("patches") between these two neurons relative to all other contact relationships for the primary neuron for the cell and the whole nerve ring. A rank of 1, for example, means this neuron pair shares the largest contact surface area of the examined relationship. “Total surface area” is displayed in nanometers, and is the summed surface area of all patches of this identity. Contact percentages are presented in two ways: (1) as the proportion of the primary cell's total surface area occupied by the contact in question, and (2) as the proportion of the total surface area of the nerve ring occupied by that same contact. (Showcased in figure S5). 

      Synapse Stats: A click on a synapse rendering now shows ‘synapse stats’, which displays the number of synapses of the selected identity within the primary neuron, including any polyadic synapse combinations involving the primary neurons. (Showcased in figure S7).

      (1) Grouping and Readability Improvements: While individual synapses are still visualized, their display has been improved for legibility. We have condensed the lengthy naming scheme to improve clarity and codified the synapse type by using superscript letters C, E, U to represent chemical, electrical and undefined synapses, respectively. This is explained and shown in figure S7, we added arrows to indicate the directionality of presumed information flow at each synapse. 

      (2) Developmental Linkage: We can link objects across datasets via cellular identity, but each synapse in the dataset does not yet have an identity attributed to its spatial coordinates, preventing us from linking specific synapses across development beyond their connectivity (ie, that a given synapses connects cell X to cell Y, for instance), also addressed in R1.11.  

      Together, these improvements substantially enhance the utility of the viewer for hypothesis generation by making key quantitative data readily accessible.

      Comment

      While the DC/C-PHATE visualizations are a useful tool for the user, it is difficult to understand when grouping or splitting of cell contact patterns is biologically significant. DC is a deterministic algorithm applied to a contactome from a single organism, and the authors do not provide quantitative metrics of distances between individual neurons or a number of DC iterations on the C-PHATE plot, nor is the selection process for the threshold for DC described in this manuscript. In the application of DC/C-PHATE to larval stage nerve ring strata organization shown by the authors, qualitative observations of C-PHATE plots colored based on adult data seem to be the only evidence shown for persistent strata during development (Figure 3) or changing architectural motifs across stages (Figure 4). Quantitation of differences in neuron position within the DC hierarchy, or differences in modularity across stages, is needed to support these conclusions. Furthermore, illustrating the quantitative differences in C-PHATE plots used to make these conclusions will provide a more instructive guide for users of NeuroSC in generating future hypotheses.

      There are several ways to visualize DC outputs, and one way to quantitatively compare DC clustering events of neurons is via Sankey diagrams. To make the inclusion of these resources more clear, we have highlighted them in lines 175-178 (Supplemental Tables 3-6). ‘DC outputs for each strata across animals can also be inspected using Sankey diagrams (Supplemental Tables 3-6). These spreadsheets detail the neuron members at each iteration of DC, allowing the user to derive quantitative comparisons of clustering events.’

      As the reviewer points out, DC is a deterministic algorithm that will iteratively cluster neurons based on the similarity of their contact profiles. To better explain the selection process for the threshold, the number of DC iterations and the quantitative metrics between the neurons, we have added new text in the Diffusion Condensation methods section.  Briefly:

      Number of DC iterations: During diffusion Condensation (DC) we track the modularity of the resulting clusters at each iteration and select the iteration with the highest modularity to define the clusters that represent the strata  (Moyle et al., 2021), (Brugnone et al., 2019). Mathematically, modularity is calculated by comparing the actual number of edges within clusters to the expected number of such edges in a randomized network with the same degree distribution (Newman et al., 2006). A higher modularity value implies that nodes within the same cluster are more densely connected to each other than to nodes in other clusters. We now better explain this in lines 562-567.

      Threshold for merging points: The threshold (epsilon) used to merge data points in each iteration is set as a small fraction of the spatial extent of the data: for each coordinate dimension (x, y, z), we compute the range (maximum minus minimum), take the maximum of these three values, and divide it by 10,000. This process is performed iteratively for each round of clustering until all data points cluster into a single point. We have updated the manuscript to clarify this threshold selection and included this information in the revised algorithm description and pseudocode. We now better explain this in lines 556-559.

      Distances between neurons in DC C-PHATE: In our previous description in Box 1 algorithm 1, we had provided a general algorithm for DC for any high dimensional dataset. We have now revised the algorithm to indicate how we used DC for these EM datasets. 

      Distances between neurons are determined by the pixel overlap between their segmented shapes in the EM dataset. We use these distances to build a graph with weighted edges, in which the weight of the edge represents the pixel overlap (the adjacency in the actual EM segmentation). Affinities between neurons, which are a proxy for their distance in the graph, are then computed as now revised in Box 1, Algorithm 1. This process is done iteratively as neurons cluster. To better communicate this, we have changed the text in lines 533-538.  

      Comment

      R1.5. While the case studies presented by the authors help to highlight the utility of the different visualizations offered by the NeuroSC platform, the authors need to be more careful with the claims they make from these correlative observations. For example, in Figure 4, the authors use C-PHATE clustering patterns to make conclusions about changes in clustering patterns of individual neurons across development based on single animal datasets. In this and many other cases presented in this study with the limited existing datasets, it is difficult to differentiate between developmental changes and individual variability between the neurite positions, contacts, and synapse differences within these data. This caveat needs to be clearly addressed.

      We now better explain in the manuscript that the selected case study, of the AVF neuron outgrowth, is not one of just correlation based solely on an EM dataset. Instead, the case study represents the NeuroSC-driven exploration of a biologically significant event supported by several independent datasets, as now explained in lines 257-276.

      Briefly, we agree with the reviewer that examining differences across individual EM datasets is insufficient evidence to make conclusions about developmental changes. But the strength of NeuroSC is in its ability to combine and compare multiple datasets, bolstering observations that are not possible by looking at just one dataset, and providing new insights on the way to new hypotheses. We now better explain that we are not looking at single connectomes in isolation and then deriving conclusions, but instead using NeuroSC to compare across 9 EM datasets. We better explain how the tools in NeuroSC, including C-PHATE, enabled comparisons across these multiple connectomes to identify apparent differences in neuronal relationships. We then explain that by using NeuroSC, we could examine these variations in neuronal relationships at the level of individual, cell biological differences of neuronal morphologies between the developmental datasets. This could be due, as pointed by the reviewer, to differences due to development, or just differences between individual animals. In the case of AVF, that features are absent in all early specimens, then arise and persist in all specimens after a certain time point, which lead us to hypothesize they result from a developmental event. Because the segmented objects in NeuroSC are linked to neuronal identities, we are also able to cross reference our observations from the EM datasets with information in other datasets and the literature. In the specific case of postembryonic development of AVF outgrowth, we can now tie the knowledge, from developmental lineage information and molecular profiles, that AVF is a postembryonically born neuron (Sulston et al. 1977, Sun et al 2022, Poole et al 2024, wormatlas.org) to the outgrowth dynamics of its neurites using the postembryonic EM datasets. Our findings using  NeuroSC provide a proof of concept of the utility of the resource and extended our understanding of how the outgrowth of this neuron affects the relationships between the neural circuits in the nerve ring.

      Comment

      R1.6. Given that recent studies have also quantified contact area between neurons across multiple connectomes (Cook et al., Current Biology, 2023; Yim et al., Nature Communications, 2024), and that the authors use a slightly different approach to quantify contact area, a direct comparison between contact area values obtained in this study with prior studies seems appropriate.

      We acknowledge that there are multiple different approaches to calculate adjacencies. In the papers cited above, there are 3 different algorithms used:

      (1) Brittin 2019 (python parse Track EM, boundary thresholds), used in Cook et al 2023, Moyle 2021, and this study).

      (2) Witvliet 2021 (Matlab 2D masks), used in Cook et al 2023.

      (3) Yim 2024 (3D masks), used in Yim et al 2024.

      To briefly describe the different approaches, and the methods we chose for this paper:

      Algorithm 1 (used in this study) defines adjacency based on distances between boundary points in TrakEM2 segmentations, allowing threshold tuning to accommodate differences in resolution and image quality across datasets—an important feature for consistent cross-dataset comparisons.

      Algorithm 2 infers contact via morphological dilation of VAST segmentations, identifying adjacency through overlapping expanded boundaries. 

      Algorithm 3 uses voxelwise contact detection with directional surface area measurements and normalization to account for dataset size differences. 

      In NeuroSC, we use algorithm 1, mostly because we had tested the rigor of this method in (Moyle et al. 2021), where we have shown that results were robust across a range of thresholds. This flexibility enables tailored application across datasets of varying quality and scale, critical for NeuroSC’s mission of curating data sets across differing methodologies to allow for direct relationship comparisons. We detail the methodology for defining thresholds for each dataset in methods section lines 492-521, defined in Supplementary table 1. Another difference between our analysis and the previously cited work is that for our analysis we also chose to include all individually resolved neurons, including post-embryonic cells, without collapsing them into left/right or dorsal/ventral symmetry classes. In this way our approach retains the full cellular resolution of the nervous system. 

      Comment

      Neuroglancer is not mentioned at all in the manuscript, despite it being a very similar and widely accepted platform for vEM data visualization across model organisms. An explicit comparison of NeuroSC and Neuroglancer would be appropriate, given the similarity of the tools. Currently, published C. elegans data (Witvliet et al., 2021; Yim et al., 2024) use Neuroglancer-based viewers, and directly comparing NeuroSC and highlighting its strengths relative to Neuroglancer would strengthen the paper.

      In the original manuscript we had not mentioned tools like Neuroglancer because we envisioned them as distinct, in intended use and output, from NeuroSC. But, as explained in R1.2 comment, in the revised version we have included a section in the Introduction lines 98-108 and in the Discussion (lines 369- 417) that compares these types of web-based tools and highlights synergies. 

      Comment

      Assigning shorthand names to strata, such as "shallow reflex circuit" (page 4, line 172), may oversimplify this group of neurons. Either more detailed support for shorthand names of C-PHATE modules should be included, or less speculative names for strata should be used.

      We appreciate this comment and understand that the original language used in the manuscript to describe strata categorizations may run the risk of oversimplification. We have now clarified the text to communicate that: 1) Strata are labeled by numbers (Strata 1, Strata 2, Strata 3 and Strata 4), rather than functional features of the neurons forming part of the strata, and that 2) the assignment of ‘strata’ is just one level of classification available via DC/CPHATE (as explained below). 

      To be sure, we have observed and published (Moyle et. al. Nature 2021) that within a given stratum, many neurons share the functional identities that we have used as summary descriptors for the strata (eg, shallow reflex circuits for Stratum 1; sensory and integrative circuits in Strata 3 and Strata 4; command interneurons in Strata 2, etc). However, those cell types are not the only members of the strata. We have adjusted the language in lines 197-204 to reflect this more clearly. “Stratum 1, which contains most neurons contributing to shallow reflex circuits that control aversive head movements in response to noxious stimuli, displayed the fewest changes among the developmental connectomes (Figure 3B–F; Supplementary Table 3). In contrast, C. elegans exhibit tractable behaviors that adapt to changing environmental conditions (Flavell et al., 2020). Strata 3 and 4 contain most neurons involved in circuits associated with such learned behaviors, including mechano- and thermo-sensation. This is reflected in Strata 3 and 4 showing the most change in neuronal relationships across postembryonic development.“

      Comment

      The authors state that NeuroSC can be applied to other model organisms. Since model organisms with greater neuron numbers include more individual neurons per cell class, the authors should support this by quantitatively demonstrating how DC/C-PHATE relationships correlate with shared functional roles among C. elegans neurons.

      We now clarify in the manuscript that, like in other organisms, C. elegans neurons are also grouped into functional classes with shared characteristics. In the context of the cylindrical nerve ring of the animal, these neuronal classes are sometimes bilaterally symmetric (forming left-right pairs), four-fold symmetric and six-fold symmetric. We now explain in the discussion that the DC/CPHATE analyses group these neuron classes and their relationships (lines 442-451). In the specific section mentioned by the reviewer, we now also add new text to contextualize this concept and how it might relate to the possible use of these tools in organisms with larger nervous systems: ‘However, our previous work has demonstrated that DC/CPHATE clustering of C. elegans neurons consistently pulls out clusters of shared neuron classes and shared functional roles Moyle et al. (2021). Building on this foundation, we envision applying similar clustering approaches to larger connectomes, aiming to identify classes and functionally related neuronal groups in more complex nervous systems. We suggest that contact profiles, along with neuron morphologies and synaptic partners, can act as ‘fingerprints’ for individual neurons and neuron classes. These ‘fingerprints’ can be aligned across animals of the same species to create identities for neurons. Frameworks for systematic connectomics analysis in tractable model systems such as C. elegans are critical in laying a foundation for future analyses in other organisms with up to a billion-fold increase in neurons (Toga et al., 2012).’

      Comment

      Lack of surface smoothing in NeuroSC leads to processes sometimes appearing to have gaps, which could be remedied by smoothing with a surface mesh. 

      We thank the reviewer for the suggestion, and understand the visibility of gaps in certain neuron processes can be distracting. But this was an intentional choice, with our main goal being to show the most accurate representation of the available data segmentation and avoid any rendering interpretations. In this way, we render the data with the highest fidelity we can and as close as possible to the ground truth of the EM segmentation. We have added language to describe this in the methods, lines 490-491, and in Figure legend 5b.

      Comment

      Toggling between time points while maintaining the same neurons and contact area in NeuroSC is a really valuable feature. The tool would be improved even more by extending this feature to synapses, specifically by allowing the user to add an entire group of synapses to the viewer at once (e.g. "all synapses between AIM and PVQ"), and to keep this synapse group invariant when toggling between developmental stages.

      We thank the reviewer for this suggestion. In response we have now implemented a new feature to ‘clone’ a rendered scene across time while preserving the original elements to ease comparisons. Once the user has rendered a scene, they can use the in-viewer developmental slider to clone the renderings and assigned colors, but display the renderings of the newly selected timepoint. These renderings populate a new window tab which can be dragged to align developmental stage windows side by side. We have added a sentence to account for this in lines 315-317 and to the legend of supplemental Figure S11. 

      Reviewer #2 (Public review)

      Comment

      The ability to visualize the data from both a connectomics and contactomics perspective across developmental time has significant power. The original C. elegans connectome (White et al., 1986) presented their circuits as line drawings with chemical and electrical synapses indicated through arrows and bars. While these line drawings remain incredibly useful, they were also necessary simplifications for a 2D publication and they lack details of the complex architecture seen within each EM image. Koonce et al take advantage of segmented image data of each neuronal process within the nerve ring to create a web interface where users can visualize 3D models for their neuron of choice. The C-PHATE visualization allows users to explore similarities among different neurons in terms of adjacency and then go directly to the 3D model for these neurons. The 3D models it generates are beautiful and will likely be showing up in many future presentations and publications. The tool doesn't require any additional downloading and is open source.

      We thank that reviewer for this positive assessment of our work.

      Comment

      While it's impossible to create one tool that will satisfy all potential users, I found myself wanting to have numbers associated with the data. For example, knowing the number of connections or the total surface area of contacts between individual neurons wasn't possible through the viewer, which limits the utility of taking deep analytical dives. While connectivity data is readily accessible through other interfaces such as Nemanode and WormWiring, a more thorough integration may be helpful to some users.

      We thank the reviewer for this feedback and in response have now implemented displays with quantitative information in NeuroSC. Now, upon hovering over a contact patch or synapse, the user will see the quantitative data of the relationship. For contact patches, you will see the total area shared between two neurons in that dataset. On hovering over a synapse, you will see how many synapses there are in total with the same members and throughout the dataset. We agree that this improves user analyses, (see also R1.3 response).

      Comment

      There were several issues with the user interface that made it a bit clunky to use. For example, as I added additional neurons to the filter search box, the loading time got longer and longer. I ran an experiment uploading all of the amphid neurons, one pair at a time. Each additional neuron pair added an additional 5-10 seconds to the loading. By the time I got to the last pair, it took over a minute to load. Issues like these, some of which may be unavoidable given the size of the data, could be conveyed through better documentation. I did not find the tutorial very helpful and the supplementary movies lacked any voiceover, so it wasn't always clear what they were trying to show.

      We appreciate that some of the more complex models can take a while to load. One of our core goals is to keep the high resolution of our models to most accurately represent the EM data, so we had to compromise between resolution and loading times. But to address this concern we have now added a ‘loading’ prompt that reassures the user when there is a wait. We also added, as suggested, text guidance throughout all of the supplemental videos (Supplemental Videos 1-4).

      Reviewer #3 (Public review)

      Comment

      A web-based app, NeuroSC, that individual researchers can use to interrogate the structure and organization of the C. elegans nerve ring across development In the opinion of this reviewer, only minor revisions are required.

      We thank that reviewer for this positive assessment of our work.

      Comment

      Contact is defined by length, why not contact area? How are these normalized for changes in the overall dimensions of neurons during development?

      To clarify our methodology: the adjacency algorithm that we use generates a 2D adjacency profile by summing the number of adjacent boundary points per EM section, which are then summed across all EM z slices.

      Contact area can be derived by multiplying the adjacency length in each slice by pixel resolution and z-thickness. Prompted by the reviewer we have now also calculated and display contact surface areas, along with their ranks among all contact relationships for a given neuron. These can be inspected directly via the interface by clicking on a rendered cell or contact patch (Figure S5 and lines 308-312). We believe these additional surface area metrics enhance the interpretability and utility of the viewer.

      We apply normalization at the level of the adjacency threshold to account for dataset-specific differences such as contrast, boundary definition, and age-related changes in neuropil packing density. This normalization is applied before running the adjacency algorithm. We do not normalize by individual neuron size, as the contact data are intended to reflect relational differences between neurons, rather than absolute morphological scaling. In fact, our addition of a scale-spheroid within each rendered model emphasizes the large increase in spatial scale that the nerve ring experiences during larval growth.  

      Comment

      Figure 1, C&D, explanation unclear for how the adjacency matrix is correlated with C-Phate schematic in D.

      We thank the reviewer for the comment and have clarified this section by adding greater detail to the explanation of how an adjacency matrix is computed (lines 149-155), as well as a description now in the figure legend 1C. Additionally, we revised Figure 1C and D to simplify neuron representations/colors and to simplify the adjacency heat map gradient. We also extended the area of contact between neurons on Figure 1C to better reflect what would be considered a “contact”. Lastly, in the figure, we changed the color and placement for the z plane arrow and label from black to white, to make it more visible, to highlight the method of computing adjacency for each z slice. 

      Comment

      Figure 4, panels F & G, unclear why AVF is shown in panel G (L3) but not panel F (L1). Explanation (see below) should be provided earlier, i.e., AVF is not generated until the end of the L1.

      We have now clarified this important point by adding labels to Figure 4 panels F and G, ‘Pre-AVF outgrowth’ and ‘Post-AVF outgrowth’ respectively. Briefly, the point is that AVF grows into the nerve ring after the L2 stage, and that is why it is absent in panel F (L1 stage, now with the label ‘Pre-AVF outgrowth’).  

      Comment

      Line 146 What is the justification for the statement: "By end of Larval Stage 1 (L1), neuronal differentiation has concluded...."? This statement is confusing since this sentence also states that "90% of neurons in the neuropil...have entered the nerve ring..." which would suggest that at least 10% additional NR neurons have NOT fully differentiated.

      We have fixed this sentence in the text. Now the sentence reads ‘By Larval stage 1 (L1) 90% of the neurons in the neuropil (161 neurons out of the 181 neurons) have grown into the nerve ring and adopted characteristic morphologies and positions. 

      Lines 171-175 What is meant by the statement that "degree of these changes mapped onto...plasticity? What are examples of "behavioral plasticity?"

      We have added the following new lines of text (lines 200-204) and now additionally cite a review discussing C. elegans behaviors to clarify and give context to behavioral plasticity. ‘C. elegans exhibit tractable behaviors which can adapt due to changing environmental conditions  (Flavell et. al. Genetics 2020). Strata 3 and 4 contain most neurons belonging to circuits associated with such learned behaviors, including chemo, mechano and thermo sensation. This is seemingly reflected by strata 3 and 4 harboring the most readily recognized set of changes in neuronal relationships across postembryonic development.’  

      Comment

      Lines 189-190 The meaning of this sentence is unclear, "The logic in....merge events."

      This sentence has been deleted and we have instead refocused our descriptions of C-PHATES comparisons by neuronal clustering trajectories and cluster members (rather than iterations).

      Comment

      Lines 193-208 This section reports varying levels of convergence across larval development in C-Phate maps for the interneurons AIML and PVQL. Iterations leading to convergence varied: 16 (L1), 14 (L2), 22 (L3), 20 (l4), 14 (adult). The authors suggest that these differences are biologically significant and reflect the reorganization of AIML and PVQL contact relationships especially between the L4 and adult. Are these differences in iterations significant?

      We agree this could be confusing and instead of focusing on comparing the iteration at which each merging event occurs, we now focus on examining the differences in members of clusters, before and after the merge event. Cluster membership is easier to interpret than the differences in the number of DC iterations (lines 224-229).

      Lines 240-241 States that AVF neurons "terminally differentiate in the embryo" which is not correct. AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage which accounts for their outgrowth into the NR during the L2 stage. 

      We thank the reviewer for the correction and have edited the text to read: ‘AVF neurons are generated from neuronal precursors (P0 and P1) at the end of the L1 stage (Sulston et al. (1983); Sun and Hobert (2023); Poole et al. (2024); Hall and Altun (2008); Sulston and Horvitz (1977). AVF neurons do not grow into the nerve ring until the L2 stage, and continue to grow until the Adult stage (lines 261-266).’

      Comment

      Lines 289-315. A detailed and highly technical description of website architecture would seem more appropriate for the Methods section.

      We agree and have moved this section to the methods as suggested (lines 663-690).

      Comment

      Line 307 "source data is" should be "source data are"

      Thank you- we have fixed this grammatical error.

      Comment

      Line 324 "circuits identities" should be "circuit identity".

      Thank you- we have fixed this grammatical error.

      Comment

      Trademark/copyright conflict with these sites? https://compumedicsneuroscan.com/about/ https://www.neuroscanai.com/

      We thank the reviewer for drawing our attention to this. To avoid potential conflicts, we have proactively altered the name to NeuroSC throughout the paper.

    1. 3. Build Trust in Your Evaluation Harness If you never rerun baselines, how do you know: your code, logging, and evaluation are sound? improvements aren’t due to hidden differences in implementation? By reproducing others’ results in your own harness, you ensure a fair playing field for comparing your new method. 👉 Otherwise, reviewers (and yourself) can’t be sure whether gains are real or artifacts.

      Donggyun used to mention with me about this. It's possible that the improvement in performance is just due to noise/artifact (e.g randomness in seed), but might not be real improvement.

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

      Learn more at Review Commons


      Reply to the reviewers

      We are very grateful for the positive feedback from all three reviewers. Below, we address each point in detail and outline proposed experiments and revision plans, with changes indicated by an underscore.

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

      In this paper "Magnesium depletion unleashes two unusual modes of colistin resistance with different fitness costs," the authors examine how Pseudomonas aeruginosa evolves resistance to colistin, a last-resort antibiotic for multidrug-resistant Gram-negative infections. Although colistin resistance is a major clinical challenge, its underlying mechanisms, particularly under nutrient-limited conditions typical of infections, are not fully understood. The study shows that under low magnesium (Mg²_⁺_) conditions-mimicking infection or biofilm stress-P. aeruginosa can develop colistin resistance via two distinct genetic pathways, each with unique fitness costs. The first involves mutations in genes such as htrB2 and lpxO2, granting strong resistance but compromising the outer membrane and increasing susceptibility to other antibiotics. The second involves regulatory mutations (e.g., in the oprH/phoP/phoQ promoter) that confer resistance with minimal membrane defects and generally lower fitness costs. These resistance strategies lead to different trade-offs: membrane-compromising mutations reduce bacterial fitness without colistin, while regulatory mutations typically avoid these penalties, with context-dependent effects. The study underscores clinical relevance, noting that in infections-such as in cystic fibrosis-other microbes like Candida albicans may deplete magnesium, indirectly promoting resistance evolution. Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.

      Work is timely and important. Colistin resistance represents an urgent threat as colistin is a last-resort antibiotic used against multidrug-resistant Gram-negative pathogens. Insights into mechanisms evolving under nutrient limitation are highly relevant given the prevalence of such environmental conditions during infection and microbial biofilm growth. The study reveals two previously uncharacterized pathways to colistin resistance in P. aeruginosa triggered by magnesium (Mg²_⁺_) depletion, each with distinct genetic signatures and trade-offs. This finding directly impacts the understanding of polymicrobial infection dynamics, especially where magnesium sequestration by fungi/ or other microbes may occur. The identification of fitness costs and pleiotropic effects associated with specific resistance mutations provides crucial guidance for clinicians considering antibiotic stewardship and combination therapy strategies.

      __

      We thank the reviewer for their summary of our study and its potential impact.

      __Drawbacks

      • Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.__


      In our revision, we now explicitly point out that the magnesium limitation we have observed broadly applies to Gram-negative bacteria, as we demonstrated in our previous PLOS Biology paper. Therefore, we expect the same themes (and even genes, which are broadly conserved) to apply to Gram-negative bacteria in general. However, a full-fledged experimental study of other Gram-negative pathogens is outside the scope of our current study, which required a 90-day experimental evolution.

      __Strengths

      • Experimental evolution: This work uses laboratory evolution under controlled Mg²_⁺_-limited conditions to simulate selection pressures relevant to infection microenvironments. • Genetics: Systematic identification and functional validation of key mutations-particularly in htrB2, lpxO2, and the oprH/phoP/phoQ promoter-give mechanistic depth to the findings. • Two distinct resistance modes: Evidence for (i) one pathway leading to colistin resistance via htrB2 mutations, resulting in high resistance but significant membrane integrity loss and increased susceptibility to other antibiotics. (ii) a second pathway providing resistance without compromising membrane integrity, highlighting evolutionary flexibility and ecological implications. • Fitness assessments: measurement of the costs associated with each resistance strategy, both in terms of membrane integrity and susceptibility to other agents. • Relevance: Connection to natural scenarios, such as magnesium sequestration by fungi (e.g. Candida albicans) in polymicrobial environments, underscores the ecological and clinical significance. • This manuscript is well written with clearly logical hypothesis testing__


      We thank the reviewer for their appraisal, especially for recognizing the rigor and broader biological implications of our study.

      __Drawbacks

      • Experimental scope: While the study is comprehensive for P. aeruginosa, the broader applicability to other Gram-negative pathogens is not directly tested.__

      We agree with the reviewer's point about broader applicability in other Gram-negative bacteria, as many of the lipid A biosynthesis genes are conserved among diverse bacterial lineages. We will include this point in our revised Discussion to suggest relevance to other Gram-negative bacteria:

      "We previously showed that magnesium sequestration by fungi applies not only to P. aeruginosa but to other Gram-negative bacteria as well (ref). Our current study lays a foundation for developing evolution-guided strategies to combat multidrug-resistant P. aeruginosa and other Gram-negative bacteria that can also acquire colistin resistance. Since many other antibiotic mechanisms are similarly dependent on metal ions (refs), our work suggests that nutritional competition for metal ions may alter initial antibiotic resistance in Gram-negative bacteria and potentiate new evolutionary pathways of antibiotic resistance."

      • __ Mechanistic depth: Some inferred mechanisms (e.g., the precise molecular impact of late-occurring adaptive mutations) merit deeper biochemical analysis.__ We will emphasize in our Revision that the MS data of endpoint clones and triple mutants reveal that their lipid A structures are identical. This suggests that the role of other late-occurring mutations in enhancing resistance is likely through lipid A-independent pathways.

      • __ Results Lines 414- 423: While correlation is most what makes sense for some drugs, causality is implied (membrane defects increase susceptibility), but could be strengthened by directly measuring antibiotic uptake (e.g., fluorescence) or membrane permeability for these 3 antibiotics.__ We thank the reviewer for highlighting the issue of causality. For the three antibiotics tested, the most direct way to measure their effect is by measuring their impact on bacterial growth directly, which is what we have done. Our membrane permeability assay using NpN uptake operates under the same conditions suggested by the reviewer and directly measures molecular uptake. Moreover, only fluorescently labeled vancomycin is commercially available among the three antibiotics tested. Since it binds to the cell wall, its utility to measure membrane defects is more limited than the NpN assay we have already used. However, in response to this comment, we will make clear in our revision that we infer that increased susceptibility to other antibiotics is due to their increased membrane permeability.

      __ o Effect is mild and mostly not significant. It is also not clear whether authors only tested a handful of mutants shown in Fig. 7B-D or whether other clones were also tested. The sample of endpoints (P2, P5, P8) covers well-characterized lineages, but additional evolved clones or a broader panel could boost generality about other antibiotics. The authors note "significantly lower MICs" statistical treatment is implied; explicit statistical values and replicate numbers should be given in the text or figures.__

      We slightly disagree with the reviewer that the results are not significant. Even two-to-three-fold differences in MICs translate to large differences in microbial competition. These three endpoint clones are representative of all eight evolved strains after 90-day evolution experiments. Moreover, we will emphasize in the Revision that we have tested all the mutations found in the endpoint clones; we know what these are from whole genome sequencing of multiple endpoint clones. In addition, we will explicitly state the p-value in the legend of Figure 7.

      • __ The structural or physiological nature of "mild" vs. "severe" membrane defects could be better defined/quantified.__ Although we agree with the reviewer's suggestion, the variability of the SEM assay makes the classification of membrane defects based on cell morphology hard to quantify. We therefore only use the SEM images as representative of the various defects observed. For a more quantitative assay of the membrane defects, we instead rely on the standard NpN uptake assay to quantify membrane permeability as a quantifiable readout for membrane defects.

      • __ Quantitative limits: Authors should add in the discussion that statistical robustness could be strengthened-for example, by including longer-term evolutionary predictions.__ We are not sure what the reviewer means and so cannot address this point completely. We ask the reviewer to rephrase this point, and we will address it to the best of our abilities.

      • __ in vivo relevance: While the ecological context is discussed, direct in vivo confirmation (e.g., in animal infection models) of the observed resistance trajectories would increase translational impact and relevance.__ We agree with the reviewer's point. However, it is not trivial to directly perform evolution experiments of microbes in animal models. There are only a handful of labs worldwide that have working CF-relevant animal models. However, the colistin resistance mutations we identified provide a tool to look deeper into how colistin-resistant P. aeruginosa can evolve in vivo.

      • __ Some sections are repetitive or overly detailed; condense where possible (especially on mutation lists and background for each claim).__

      We will condense our manuscript as the reviewer suggested in our revision. Adding a graphical summary as suggested will also allow us to be more succinct in our description.

      __Other comments

      • Authors should provide clarification on how the Mg²_⁺_ concentrations used in vitro compare to those found in clinically relevant infection settings. This would be helpful to enhance significance.__

      We thank the reviewer for raising this good point. Based on our previous work, we know the Mg2+ levels in our model (0.3-0.45mM) are within the physiological range of Mg2+ in infection settings (0.1-0.8mM). We will highlight this point in the introduction.

      • __ Authors should explicitly report statistical methods (e.g., types of tests, adjustments for multiple comparisons) in figure legends for reproducibility.__

      We will include the details of our statistical tests in each panel of figures both in the main text and the supplement.

      • __ Nomenclature for key mutations and their position within the genetic context (e.g., htrB2 mutation specifics) could be more detailed in figures or supplemental materials.__

      We will name each of the particular mutations tested to be specific about the nature of all the evolved mutations in our figure legends.

      • __ The manuscript could benefit from a graphical summary illustrating the two distinct evolutionary pathways and their respective fitness landscapes.__ We thank the reviewer for this suggestion to enhance the clarity of our work. We will make a new graphical summary highlighting two different evolutionary pathways as a new figure.

      • __ A brief discussion of therapeutic implications-such as combining colistin with agents that target membrane integrity-would help bridge the gap from mechanism to clinical management.__ In our discussion, we have suggested that collateral sensitivity (line 446-453) and PhoPQ kinase inhibitors (line 512-515) could be exploited to combat colistin resistance. To make this point more clearly, we will slightly expand our Discussion to include the therapeutic implications of our study.

      • __ Additional discussion on whether the fitness costs are reversible or can be compensated by further adaptation would be valuable for long-term dynamics.__ We thank the reviewer for raising this interesting point. The evolution trajectory of P8 suggests that fitness costs can be compensated by later-occurring mutations during evolution. We will further discuss this point to highlight the importance of understanding the mutational dynamics of antibiotic resistance evolution.

      • __ It would be valuable for the authors to comment on, or further analyze, whether there is a direct association between specific fitness costs and sensitivity to other antibiotics. Such information could inform on evolutionary constraints and possible trade-offs relevant to clinical settings.__

      We will include a supplemental figure showing the correlation between fitness costs and antibiotic susceptibility for P2, P5, and P8.

      __ Main figures and support for claims

      The main and supplementary figures comprehensively illustrate the evolutionary trajectories, genetic bases, and phenotypic outcomes associated with colistin resistance under magnesium depletion in P. aeruginosa. The figures effectively detail: • Genetic pathways involved including the experimental evolution design (colistin selection under Mg²_⁺_ depletion), whole-genome sequencing results, and timelines of observed mutations (e.g., in htrB2, lpxO2, oprH/phoP/phoQ promoter, PA4824). • Phenotypes and biochemical analyses such as lipid A structure (via mass spectrometry), minimum inhibitory concentration (MIC) assays, and epistasis analyses between mutations are depicted. • Fitness trade-offs are demonstrated using bacterial survival, membrane integrity (e.g., scanning electron microscopy images), membrane permeability assays (NPN uptake), and competitive fitness assays. • Mechanistic claims about the necessity of early mutations, the requirement of the PhoPQ pathway at different evolutionary stages, and the fitness cost imposed by certain resistance mutations. To further enhance the rigor and clarity of the manuscript, the authors should implement the following improvements: • Labelling consistency: In some instances, figure legends could provide more granular detail about specific mutations (e.g., positions of amino acid changes). • Graphical summary: A schematic summary figure that visually integrates the three main evolutionary resistance trajectories, the mutational order, corresponding lipid A changes, and fitness costs, would enhance readability. • Replicates: Plots should more thoroughly indicate the number of replicates and show individual data points (not just means {plus minus} SD), add number of replicates in each experiment. • Supplementary: figures referenced in the text (e.g., lipid A structures or mutation reversion outcomes) should be made more prominent or better cross-referenced from the main results section. Authors should highlight when supplementary data provide critical functional confirmation (e.g., confirming mutation function or fitness reversal).__

      We thank the reviewer for their appreciation of our work and constructive feedback.

      __Statistics

      The authors have appropriately incorporated statistical analyses throughout the figures. To enhance the robustness and credibility of their findings, authors should also cross-check • Tests in legends: Every figure and supplementary figure should clearly state the type of statistical test used, how many biological replicates, and any corrections for multiple comparisons.__

      As mentioned above, we will provide more details about the statistical tests of each panel.

      • __ Effect sizes: Where appropriate, reporting effect sizes-rather than just p values-would contextualize the biological impact.__ We agree with the reviewer; we will mention the magnitude of MIC changes in the corresponding figure legends.

      • __ Raw data accessibility: For full transparency, consider sharing underlying raw data and analysis scripts.

      __ We will provide the raw data of each panel.

      __Overall, the main and supplementary figures effectively illustrate and substantiate the key claims-particularly the alternative molecular pathways, phenotypic trade-offs, and the role of environmental magnesium in mediating colistin resistance. Statistical analysis is generally robust and appropriately presented throughout, though improvements could include more explicit reporting, additional controls, and accessible raw data. The visual and quantitative data in the figures provide support for the authors' conclusions about the evolution of antibiotic resistance under nutrient limitation in microbial environments. Understanding these alternative pathways is important for designing better treatment strategies and for predicting how resistance might evolve under varying clinical and environmental conditions.

      __

      We thank the reviewer for their positive assessment.

      __ Reviewer #1 (Significance (Required)):

      Overall, this work offers important insights into antibiotic resistance in nutrient-stressed, polymicrobial environments, highlighting how magnesium availability shapes resistance evolution and fitness costs. The findings suggest new avenues for therapeutic intervention and call for a reevaluation of antibiotic strategies in nutrient-competitive infection settings.__

      We sincerely thank the reviewer for constructive and thoughtful feedback and the acknowledgement of our figure presentation and experimental design. We feel very encouraged by the reviewer's perspective that our study provides unique insights into resistance evolution in polymicrobial environments and may inform therapeutic strategies.

      __My expertise: Gut microbiome, gut microbiota resilience, ecology, and evolution in microbial communities, antimicrobial resistance, high-throughput drug-bacteria interactions

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

      Summary: The paper by Hsieh and colleagues unravels the molecular basis of colistin resistance in Pseudomonas aeruginosa under low magnesium (Mg2+) conditions. Colistin is a last resort antibiotic that compromises bacterial cell wall integrity. Bacteria can respond (phenotypically and genotypically) to colistin by modifying membrane-anchored lipopolysaccharides. Mg2+ depletion can trigger similar responses. In their study, Hsieh et al. find that Mg2+ depletion (induced by a co-infecting fungal pathogen, Candida albicans) leads to evolutionary trajectories and resistance mechanisms that differ from those observed under Mg-rich conditions. The authors conducted a series of detailed genetic, chemical and fitness-based experiments to elucidate the molecular, physiological and evolutionary basis of these new resistance mechanisms.__


      We thank the reviewer for their summary of our study.__

      Major comments: __ 1. The authors reconstituted key mutations observed during experimental evolution in the ancestral background. Moreover, they took clones from the final stage of the evolution experiment and restored the ancestral state of the mutated genes. This dual approach is extremely strong and allows to decipher the causal effects of colistin resistance. I like to applaud the authors for this rigorous approach.


      We thank the reviewer's appreciation about the rigor and comprehensive analyses of our study.

      2. I understand that this work focusses on evolved mutants isolated from a previous experiment. The focus is on Mg2+ limitation. However, it would still have been nice to include a characterised colistin resistent strain featuring more standard resistance mechanisms. How different would such a strain be in the analyses shown in Fig. 3? Would morphological changes (Fig. 5A), fitness trade-offs (Fig. 6) and collateral sensitivity (Fig. 7) also occur in such a mutant. I do not regard it as imperative to include data from such a strain. But putting the new data into context (at least in the discussion) would clearly increase the overall impact of this work.

      We thank the reviewer for raising this fascinating and vital point. We will address the point in our Revision using the monoculture (high Mg2+) evolved strains, which acquired many known mutations for colistin resistance, as our reference. We will provide a supplemental figure about the membrane permeability, fitness costs, and collateral sensitivity of monoculture evolved strains. We will also contrast their difference from co-culture evolved strains in the revised Discussion.__


      1. I recommend to discuss the findings in the context of the work conducted by Jochumsen et al. 2016 Nature Communications https://doi.org/10.1038/ncomms13002. To me, this is one of the most insightful papers on the genetic basis and epistasis of colistin resistance.__

      We thank the reviewer for pointing out this important reference. We will include this reference and its findings in the Discussion.

      __Minor comments:

      1. First section of results and Fig. 1. It is unclear what parts are repetition from the ref. 37 and what is new. Please clarify.__

      We thank the reviewer for this suggestion. Figures 1A and 1B summarize the previous paper; all other panels are new data. We will make this clear in the revised text and figure legend.

      5. MIC-data (e.g. Fig. 2) come in discrete categories (based on the underlying dilution series). This comes with some challenges for statistical analysis. First, linear models like ANOVAs are based on normally distributed residuals. This is violated with discrete data distributions. Second, there is often no within-treatment variation (e.g., Fig. 2B), which makes statistical analyses obsolete. These points need to be addressed. Moreover, how is it possible to have subtle variations in MIC (e.g., Fig. 2A, P2 endpoint clone) with classic dilution series (as indicated on the y-axis, 128, 256, 512)? Please explain.

      We agree with the reviewer that statistical analysis of MIC data is not straightforward. ANOVAs are not well-suited for this type of discrete data, and the lack of variation within replicates reduces the power of non-parametric tests such as the Mann-Whitney U test. To improve the statistical reporting of MIC data, we will apply non-parametric tests and include effect size measurements, as recommended by Reviewer 1.

      Moreover, the design of dilution series may underestimate the true nature of antibiotic susceptibility. To address these issues, we have also performed survival assays to assess colistin resistance in both the endpoint and reversion strains; we will also include statistics to assess the significance of their different survival frequencies.

      We thank the reviewer for highlighting the point about subtle variations in a classical dilution series. Our endpoint strains grew robustly in media containing 192 μg/mL colistin-the highest concentration used in our evolution experiment. To more accurately determine and compare their maximum MICs, we expanded the colistin concentration range using finer fold increases (1.5×, 2×, 2.5×, 3×, 3.5×, and 4×) from 192 to 768 μg/mL. We will update these details in the Materials & Methods.

      __ Lines 264-269. This analysis focusses on enzyme impairment. However, mutations could also change enzyme activity. Could any of these mutations have such an effect?__

      The answer is "yes". As evolved strains with lpxA mutation still have lipid A, we suspect this mutation does not altogether abolish lipid A synthesis. However, this mutation could affect the amount of lipid A or change enzyme specificity. These are interesting ideas for further investigation, but they fall beyond the scope of our current study. We will, however, include the requested detail in the discussion.

      __ Figure 5A. Some arrows seem to be out of place and point at void spaces. Please check.__

      We thank the reviewer for pointing out this error, which we will correct.

      8. The use of polymyxin B is not well justified (Fig. 5 and Fig. S13). Did the authors aim to test whether there is cross-resistance to other antimicrobial peptides?

      We will more clearly justify our choice of using polymyxin B for directly assaying binding of polymyxin antibiotics to bacterial cells using fluorescence-labeled polymyxins, since no such reagents exist for colistin and since previous studies (including ours) have shown similarity of susceptibility to colistin and polymyxin B:

      "Although P2 and P5 endpoint clones have more permeable membranes, they exhibited greater resistance to polymyxin antibiotics, including colistin (polymyxin E) (Fig. 5D), and polymyxin B (Fig. S13A) than WT cells. To investigate how membrane-compromised cells gain increased resistance to antibiotics that target the outer membrane, we used dansyl-labeled polymyxin B [51] to quantify the binding of polymyxins to P. aeruginosa; dansyl-labeled polymyxin fluoresces upon binding the hydrophobic portion of bacterial membranes. We used polymyxin B binding as a surrogate for how bacterial cells bind to all polymyxin antibiotics, including colistin."

      __ Line 564. Please indicate the dilution factor used.__

      Thank you for pointing out this inadvertent omission. We will update our Materials & Methods accordingly, as in response to the Reviewer 2's comment 5.

      __Reviewer #2 (Significance (Required)):

      This is a very strong and well designed study. It provides novel and relevant insights into the resistance mechanisms against an important last resort antibiotic.__

      We sincerely thank the reviewer for their thoughtful summary and generous evaluation of our work.

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

      This manuscript reports on biologically interesting and clinically-relevant findings, that upon passaging in the presence of spent media from C. albicans, P. aeruginosa develops resistance to colistin through lipid A modifications. The authors thoroughly characterize novel lipid A structures seen in their resistant mutants, and test a variety of genetically constructed mutants to determine the contributions of specific mutant alleles to resistance.__

      We thank the reviewer for the appreciation of our experimental design and comprehensive genetic and biochemical analyses of our evolved strains.

      However, additional experiments are needed to demonstrate the specific role and necessity of the lipid modifications for colistin resistance.

      We are also grateful for the reviewer's feedback and constructive criticisms to improve the clarity and impact of our manuscript. We have listed detailed responses to the reviewer below.

      1. __ Evidence that the lipid A mutations are causal for colistin resistance is sparse:
      2. Both the htrB2 mutations (in P2 and P5) are posited to be loss-of-function alleles. However, the phenotypes of the individual alleles are different (shown in Fig 2A and 2B). While the mutation in P2 shows a ~2x increase in resistance, the mutation in P5 does not. Thus it is not clear that the specific lipid A modifications seen in the htrB2 mutants are sufficient to confer colistin resistance. Can the authors test a clean deletion mutant of htrB2? Further, reversion of the htrB2 mutation in P2 has only a mild effect on colistin resistance, while reversion in P5 leads to a ~3-4x reduction in colistin resistance (Fig. S3), once again making it hard to parse out the exact effect of the lipid A modifications seen in the htrB2 mutants.
      3. Similarly, a single lpxO2 mutation does not have any effect on colistin resistance (in P5), indicating that the modifications seen in this mutant are not sufficient to lead to resistance.__ We thank the reviewer for making this suggestion. The reviewer is correct that a clean deletion will directly assess the effects of htrB2 mutations. We will make htrB2 deletion in WT and the triple mutants and endpoint clones of P2 and P5 to check the effect of htrB2 deletion on colistin resistance.

      Additionally, as Reviewer 2 pointed out, both mutation reconstruction and reversion experiments are required for understanding the roles of each mutation and interactions among different mutations in contributing to resistance. Combining all the results of htrB2 and lpxO2 mutations in these two orthogonal genetic experiments, it is the synergistic interactions among these mutations that lead to enhanced resistance after evolution. This explains why we saw genetic background effects of htrB2 mutation (P2 vs P5) and why each single mutation is required for resistance but doesn't contribute to resistance significantly by itself.

      - In P8, the effect of a single lpxA mutation is not tested. Further, the resistance of a P-oprH + lpxA mutant is the same as that of just the P-oprH mutant, indicating that the lpxA mutation likely does not directly alter colistin resistance. It is possible that mutations in lpxA were selected to compensate for fitness defects resulting from the other mutations, or for adaptation to some other component of the media conditions.

      This is an excellent suggestion. We will assess the MIC and fitness of reconstructed strains with the lpxA mutation to update the role of this mutation.

      - While reversion of the htrB2 and lpxO2 mutations do lead to ~3-4x reduced resistance in P5 indicating some contribution of these mutations, it is specific to this population, and thus not clear whether it is due to the specific lipid A modifications (some of which are seen in the other populations too). A specific combination of lipid A modifications may confer colistin resistance, but this needs to be demonstrated by generating just those clean deletion mutants and showing an effect on resistance.


      In response to this comment and comment 1, we will make lpxO2 deletions in WT, the triple mutant and the endpoint clone of P5 to test colistin resistance. However, our results of reverting single htrB2 or lpxO2 mutation to WT are robust and use two independent assays, including the standard MIC test and colistin survival assay. So, we are confident that each mutation is necessary for enhancing colistin resistance.

      __ Overall, given the high levels of colistin resistance still exhibited by single mutant revertants (Fig. S3) and the absence of double or triple revertants, it is hard to come to any conclusions regarding causality. This is especially the case for P8 but also true of P2 and P5. What are the other mutations in these populations, and what role do they play in colistin resistance?__

      We respectfully disagree with the reviewer on this point. One point that we have made and will re-emphasize in our Revision is that we have assayed all the mutations in these populations; this is one of the advantages of our experimental evolution and genome sequencing strategy. All the mutations that could play a role in colistin resistance have therefore been tested. Furthermore, due to genetic epistasis of mutations in different evolutionary lineages, we do not necessarily expect that a single revertant would altogether abolish colistin resistance, as has been demonstrated in several previous studies. As Reviewer 2 pointed out, combining mutation reconstruction and reversion is the best way to establish causality, and we have done so. Therefore, it is not correct to say that we cannot come to 'any conclusions regarding causality'.

      __ Figure 4 is titled "The PhoPQ pathway synergizes with early-arising mutations to confer colistin resistance.", but instead what this figure shows is that the mutation upstream of oprH increases PhoP activity. I'm not sure what the synergy here is. The same is true for the section starting on line 276. Further, the first sentence of that section states "We next investigated why the mutations conferring robust colistin resistance in low Mg2+ conditions are not observed in Mg2+ replete conditions.". However, there are no experiments there testing whether the mutations conferred resistance in Mg2+ conditions, instead the authors just test whether the mutations they are studying increase PhoP activity, and require PhoPQ to confer resistance.__

      We thank the reviewer for raising this point. We apologize for the unclear writing. We will use this opportunity to improve the clarity of this section by rewriting it to focus on two points: 1. Evolved resistance is PhoPQ-dependent, instead of PmrAB-dependent. 2. Two lineages evolved enhanced resistance by boosting PhoPQ activity in both high and low Mg2+ conditions. We will also remove the statement highlighted by the reviewer from this section that obfuscates the motivation of this section. We feel this approach will more clearly show how lipid A-related mutations contribute to resistance in low Mg2+.

      __ The authors claim that the identified mutations did not appear in the high magnesium conditions because they had a fitness cost under those conditions, but figure 6A shows that the evolved strains have fitness costs in low magnesium conditions as well. Further, the authors suggest that because the studied mutations act via increased PhoPQ activity, they do not lead to resistance under high magnesium conditions (lines 376-379). However, the increased PhoPQ activity is mediated by the P-oprH mutation in the isolates which likely increases PhoPQ activity even in high magnesium conditions. Overall, it is not clear why the mutations in the low magnesium condition were not selected for under high magnesium conditions.__

      The reviewer is correct about the fitness cost in high Mg2+ and low Mg2+ conditions. These fitness experiments were carried out in the absence of colistin, which explains the finding that there are fitness defects in both conditions. As is well known, evolution for antibiotic resistance will ultimately select for resistant mutants, despite their fitness costs. In contrast, colistin MIC of these endpoint strains in high Mg2+ conditions was still much lower than the colistin concentration we applied during evolution (Fig. S15), indicating it is much less likely for these mutations to be selected for in high Mg2+. We will clarify this point in our revised Results and Discussion.

      We agree with the reviewer about the P-oprH mutations (PhoPQ expression) and will note that, unlike the other mutations, it is not clear why these emerge only in the low Mg2+ condition.

      __ The authors used C. albicans spent BHI media as their low magnesium condition, but this condition has a lot of other C. albicans metabolites that may be affecting the results. It is possible that what the authors are observing is not related to magnesium at all, and the authors should test the phenotypes in normal BHI medium depleted for magnesium or some defined medium where magnesium levels can be controlled.__

      We thank the reviewer for mentioning this important point. In our prior PLOS Biology paper (https://doi.org/10.1371/journal.pbio.3002694.g005), we demonstrated that supplementing Mg2+ in evolved co-culture populations reduces colistin resistance, suggesting this evolved resistance is Mg2+ dependent. We also know that the MIC of our endpoint strains in C. albicans-spent BHI with supplemented Mg2+ (MIC of all three endpoint clones is less than 48 mg/mL colistin) is much lower than in C. albicans-spent BHI. We will mention this detail in the paper and include the data in our revision if the reviewer and editor require it.

      Other comments: - The authors use MIC assays as well as % survival to measure resistance against colistin, and sometimes use both in the same figure (e.g. Figure 2). This makes direct comparisons difficult. It would be better to consistently use one assay, preferably the MIC, at least in all the main figures. If the survival data needs to be included, it could go in the supplementary figures.

      We thank the reviewer for this suggestion. We will move the MIC data of mutation-reversion strains to the main Fig. 2D-F.

      - While the mutations seen in the low and high magnesium conditions were shown in the previous manuscript, given the extensive dissection here, it would be useful for readers if the authors gave some details about the serial passaging and evolution experiment, identification of mutations, and some mention of what mutations were seen in high Mg populations.

      We will add these details in the introduction.

      - Given that oprH is present in an operon, it would be more accurate to call that mutation as being in the promoter of the oprH-phoP-phoQ operon rather than it being an oprH mutation (at least in the text, e.g. lines 127-129).

      We agree. We will change this as the reviewer requested.

      - Unlike what is stated on lines 287-290, deletion of oprH in P2 leads to a greater than 2x reduction in colistin MIC, suggesting that OprH is playing a role (albeit a smaller role than phoP) - Line 50 has a typo, remove "160". - Line 122: Specify which Pa and Ca strain backgrounds were used. - Line 132: Were representative isolates derived from terminal passages? This should be defined.


      We will change these points according to the reviewer's suggestions; we thank them for these suggestions.

      - Line 215-219: It is interesting that Pa WT grown in spent medium additionally results in lipid A that is hexa-acylated. Is this sufficient to alter colistin resistance on its own?

      We find that WT PAO1 in low Mg2+ conditions has PagP-mediated acylation, which can slightly increase colistin resistance, but not to the extent of resistance as our evolved strains.

      - It would be useful to see a PCA plot for the samples shown in figures S6 and S7.

      We will include such a plot in Figures S6 and S7

      - Fig. S11: What are the colistin MICs of pmrA and phoP deletions in the WT background?

      MIC of pmrA and phoP deletions in WT is 1.5ug/mL. We will include these data in the Revision.

      - Instead of qualitative data, can the authors quantify cell length and perhaps some measure of cell shape (instead of just showing images in Fig. 5A and S12).

      We thank the reviewer for raising this point. A similar comment was raised by Reviewer 1. As it's challenging to quantify membrane changes from the morphological data obtained through SEM (a point which we will now clarify in our Revision), we used a quantifiable NpN uptake assay to quantify membrane defects of our evolved strains.

      - What is the WT MIC in high magnesium conditions? Please show that in Fig. S15.


      We will include this detail in Fig. S15

      - I am not an expert in lipid modifications and structures, but in figure S5, P2 and P4 show high peaks with lower m/z that seem specific to low magnesium conditions, but they are not labeled or discussed. What are these peaks?

      We thank the reviewer for bringing up this concern. The unlabeled lipids in these spectra are cardiolipin, not lipid A. These peaks are present in all the samples, and the reason they appear larger in the P1 and P4 low magnesium conditions is that both spectra are scaled to the relative intensity of one another. It is important to note that MALDI-TOF MS is not a quantitative technique, and the relative intensity of the peak heights between two samples should not be used to compare the amounts of lipids in one sample versus another. Therefore, we cannot say that these lipids are present in greater quantities in low magnesium conditions versus high magnesium conditions.

      - Lines 357-358 state that "mutant cells minimally bind polymyxin B (Fig. S13B)", but the figure shows increased binding compared to the WT. The legend of the figure also says something similar. Are the phoP pmrA mutants expected to bind more polymyxin B because they can't modify lipid A?

      We thank the reviewer for pointing out this substantial error. We will change 'minimally bind' to 'demonstrate increased binding'.

      - Given the fitness defects in just regular medium, is the data shown in Figure 7 specific collateral sensitivity to the antibiotics tested? Are there other conditions where P2 and P5 do not show increased sensitivity?

      These are all the antibiotics we have tested. It is conceivable that P2 and P5 might not show increased sensitivity to other antibiotics that use the same mode of action as colistin or polymyxin B.

      __Reviewer #3 (Significance (Required)):

      This study aims to dissect novel mechanisms of colistin resistance in P. aeruginosa that arise upon passaging in C. albicans spent media. While the authors identify novel lipid A modifications associated with the evolved strains, the significance of the modifications for resistance, and the mechanisms for why these evolutionary trajectories were not selected for in high magnesium are not clear from the data presented.__

      We thank the reviewer for recognizing the integrity of our work and for the constructive feedback on improving the clarity of our writing. We understand that some concerns may stem from a lack of clarity in our original submission, but that additional genetic experiments are necessary. We have already identified all mutations that arose independently across different lineages and characterized their contributions to resistance, which we believe supports a robust inference of causality. To strengthen our conclusions, we will incorporate additional experiments, including htrB2 deletion, lpxO2 deletion, and lpxA mutation, to better dissect the roles of these genes and mutations in colistin resistance. We hope this revision plan will ameliorate the reviewer's concerns.

    1. for - youtube - BBC - AI2027 - Futures - AI - progress trap - AI - to AI2027 website - https://hyp.is/0VHJqH3cEfCm9JM_EB3ypQ/ai-2027.com/

      summary - This dystopian futures scenario is the brainchild of former OpenAI researcher Daniel Kokotajlo, - It is premised on human behavior in modernity including - confirmation bias of AI researchers - entrenched competing political ideologies that motivate an AI arms race - entrenched capitalist market behavior that motivates an AI arms race - AI becoming embodied, resulting in Artificially Embodied Artificial Intelligence (AEAI), posing the danger to humanity because it's no longer just talk, but action - Can it happen? The probability is not zero.We don't really understand the behavior of the AI LLM's we design, they are nonpredictable, and as we give them even greater power, that is a slippery slope - AI can become humanity's ultimate progress trap, which is ironic, because the technology that promises to be the most efficient of all, can become so efficient, it no longer need human beings - Remember Jerry Kaplan's book "Humans need not apply"? - https://hyp.is/o0lBFH3fEfC1QLfnLSs5Bg/www.youtube.com/watch?v=JiiP5ROnzw8 - This dystopian futures scenario goes further and explores the idea that "humans need not exist"!

      question - What about emulating climate change gamification of "Bend the Curve" of emissions? - Use the AI 2027 trajectory as a template and see how much real-life follows this trajectory - Just as we have the countdown to the https://climateclock.world/ ( 3 years and change remaining as of today) - perhaps we can have an AI 2027 clock? - What can we do to "bend the dystopian AI 2027 curve" AWAY from the dystopian future?

    1. It's also important to avoid making snap judgments about people or writing them off based on their own background or your own assumptions.

      One of the biggest turn offs when it comes to just a simple conversation is the feeling that you are being talk down to or almost written off. By avoiding these "snap" judgments, the outcome can only be positive.

    1. Author response:

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

      Reviewer #1 (Public review):

      Sumary:

      This study evaluates whether species can shift geographically, temporally, or both ways in response to climate change. It also teases out the relative importance of geographic context, temperature variability, and functional traits in predicting the shifts. The study system is large occurrence datasets for dragonflies and damselflies split between two time periods and two continents. Results indicate that more species exhibited both shifts than one or the other or neither, and that geographic context and temp variability were more influential than traits. The results have implications for future analyses (e.g. incorporating habitat availability) and for choosing winner and loser species under climate change. The methodology would be useful for other taxa and study regions with strong community/citizen science and extensive occurrence data.

      We thank Reviewer 1 for their time and expertise in reviewing our study. The suggestions are very helpful and will improve the quality of our manuscript.

      Strengths:

      This is an organized and well-written paper that builds on a popular topic and moves it forward. It has the right idea and approach, and the results are useful answers to the predictions and for conservation planning (i.e. identifying climate winners and losers). There is technical proficiency and analytical rigor driven by an understanding of the data and its limitations.

      We thank Reviewer 1 for this assessment.

      Weaknesses:

      (1) The habitat classifications (Table S3) are often wrong. "Both" is overused. In North America, for example, Anax junius, Cordulia shurtleffii, Epitheca cynosura, Erythemis simplicicollis, Libellula pulchella, Pachydiplax longipennis, Pantala flavescens, Perithemis tenera, Ischnura posita, the Lestes species, and several Enallagma species are not lotic breeding. These species rarely occur let alone successfully reproduce at lotic sites. Other species are arguably "both", like Rhionaeschna multicolor which is mostly lentic. Not saying this would have altered the conclusions, but it may have exacerbated the weak trait effects.

      We thank the reviewer for their expertise on this topic. We obtained these habitat classifications from field guides and trait databases, and reviewed our primary sources to clarify the trait classifications. We reclassified the species according to the expertise of this reviewer and perform our analysis again; please see details below.

      (2) The conservative spatial resolution (100 x 100 km) limits the analysis to wide- ranging and generalist species. There's no rationale given, so not sure if this was by design or necessity, but it limits the number of analyzable species and potentially changes the inference.

      It is really helpful to have the opportunity to contextualize study design decisions like this one, and we thank the reviewer for the query. Sampling intensity is always a meaningful issue in research conducted at this scale, and we addressed it head-on in this work.

      Very small quadrats covering massive geographical areas will be critically and increasingly afflicted by sampling weaknesses, as well as creating a potentially large problem with pseudoreplication. There is no simple solution to this problem. It would be possible to create interpolated predictions of species’ distributions using Species Distribution Models, Joint Species Distribution Models, or various kinds of Occupancy Models. None of these approaches then leads to analyses that rely on directly observed patterns. Instead, they are extrapolations, and those extrapolations typically fail when tested, although they have still been tested (for example, papers by Lee-Yaw demonstrate that it is rare for SDMs to predict things well; occupancy models often perform less well than SDMs and do not capture how things change over time - Briscoe et al. 2021, Global Change Biology). The result of employing such techniques would certainly be to make all conclusions speculative, rather than directly observable. 

      Rather than employing extrapolative models, we relied on transparent techniques that are used successfully in the core macroecology literature that address spatial variation in sampling explicitly and simply. Moreover, we constructed extensive null models that show that range and phenology changes, respectively, are contrary to expectations that arise from sampling difference. 100km quadrats make for a reasonable “middle-ground” in terms of the effects of sampling, and we added a reference to the methods section to clarify this (see details below).

      (3) The objective includes a prediction about generalists vs specialists (L99-103) yet there is no further mention of this dichotomy in the abstract, methods, results, or discussion.

      Thank you for pointing this out - it is an editing error that should have been resolved prior to submission. We replaced the terms specialist and generalist with specific predictions based on traits (see details below).

      (4) Key references were overlooked or dismissed, like in the new edition of Dragonflies & Damselflies model organisms book, especially chapters 24 and 27.

      We thank Reviewer 1 for making us aware of this excellent reference. We have reviewed the text and include it as a reference, in addition to other references recommended by Reviewer 1 and other reviewers (see details below).

      Reviewer #2 (Public review):

      Summary:

      This paper explores a highly interesting question regarding how species migration success relates to phenology shifts, and it finds a positive relationship. The findings are significant, and the strength of the evidence is solid. However, there are substantial issues with the writing, presentation, and analyses that need to be addressed. First, I disagree with the conclusion that species that don't migrate are "losers" - some species might not migrate simply because they have broad climatic niches and are less sensitive to climate change. Second, the results concerning species' southern range limits could provide valuable insights. These could be used to assess whether sampling bias has influenced the results. If species are truly migrating, we should observe northward shifts in their southern range limits. However, if this is an artifact of increased sampling over time, we would expect broader distributions both north and south. Finally, Figure 1 is missed panel B, which needs to be addressed.

      We thank Reviewer 2 for their time and expertise in reviewing our study.

      It is possible that some species with broad niches may not need to migrate, although in general failing to move with climate change is considered an indicator of “climate debt”, signaling that a species may be of concern for conservation (ex. Duchenne et al. 2021, Ecology Letters). We revised the discussion to acknowledge potential differences in outcomes (please see details below).

      We used null models to test whether our results regarding range shifts were robust, and if they varied due to increased sampling over time. We found that observed northern range limit shifts are not consistent with expectations derived from changes in sampling intensity (Figure S1, S2). 

      We thank Reviewer 2 for pointing out this error in Figure 1. This conceptual figure was a challenge to construct, as it must illustrate how phenology and range shifts can occur simultaneously or uniquely to enable a hypothetic odonate to track its thermal niche over time. In a previous version of the figure, we had a second panel and we failed to remove the reference to that panel when we simplified the figure. We have updated the figure and figure caption (please see details below).

      Reviewer #3 (Public review):

      Summary:

      In their article "Range geographies, not functional traits, explain convergent range and phenology shifts under climate change," the authors rigorously investigate the temporal shifts in odonate species and their potential predictors. Specifically, they examine whether species shift their geographic ranges poleward or alter their phenology to avoid extreme conditions. Leveraging opportunistic observations of European and North American odonates, they find that species showing significant range shifts also exhibited earlier phenological shifts. Considering a broad range of potential predictors, their results reveal that geographical factors, but not functional traits, are associated with these shifts.

      We thank Reviewer 3 for their expertise and the time they spent reviewing our study. Their suggestions are very helpful and will improve the quality of our manuscript.

      Strengths:

      The article addresses an important topic in ecology and conservation that is particularly timely in the face of reports of substantial insect declines in North America and Europe over the past decades. Through data integration the authors leverage the rich natural history record for odonates, broadening the taxonomic scope of analyses of temporal trends in phenology and distribution to this taxon. The combination of phenological and range shifts in one framework presents an elegant way to reconcile previous findings improving our understanding of the drivers of biodiversity loss.

      We thank Reviewer 3 for this assessment.

      Weaknesses:

      The introduction and discussion of the article would benefit from a stronger contextualization of recent studies on biological responses to climate change and the underpinning mechanism.

      The presentation of the results (particularly in figures) should be improved to address the integrative character of the work and help readers extract the main results. While the writing of the article is generally good, particularly the captions and results contain many inconsistencies and lack important detail. With the multitude of the relationships that were tested (the influence of traits) the article needs more coherence.

      We thank Reviewer 3 for these suggestions. We revised the introduction and discussion to better contextualize species’ responses to climate change and the mechanisms behind them (see details below). We carefully reviewed all figures and captions, and made changes to improve the clarity of the text and the presentation of results (see details below).

      Reviewer #1 (Recommendations for the authors):

      Comment:

      (1) Following weakness #1 in the public review, the authors should review the habitat classifications, consult with an odonatologist, and reclassify many species from Both to Lentic and redo the analysis.

      Thank you for pointing out this disagreement among expert habitat classifications that we cited and other literature. We reclassified species’ habitat preferences based on classifications by Hof et al., a source that was consistent with your suggestions, and identified additional species as Lentic that our other references had identified as Both. We performed our analysis with this new dataset and, as you suspected, our results did not change qualitatively: species habitat preferences did not predict their range shifts.

      Hof, Christian, Martin Brändle, and Roland Brandl. "Lentic odonates have larger and more northern ranges than lotic species." Journal of Biogeography 33.1 (2006): 63-70.

      Comment:

      (2) Following weakness #2, would it be worthwhile or interesting to analyze a smaller ranging group (e.g. cut the quad size in half, 50 x 50 km) to bring in more species and potentially change the inference? Or is the paper too tightly constructed to allow this, even as a secondary piece?

      Thank you for this comment, as it highlights an important consideration for macroecological analyses, and the importance of balancing multiple factors for determining quadrat size. Issues exist with identifying drivers of range boundaries among species with narrow ranges when they are analyzed separately from wide-ranging species, and examining larger quadrats can actually help clarify drivers (Szabo, Algar, and Kerr 2009). The smaller quadrats are, the higher the likelihood that the species is actually there but was never observed, or that the quadrat only covers unsuitable habitat and the species is absent from the entire (or almost entire) quadrat. Too many absences creates issues with violating model assumptions, and creates noise that makes it difficult to identify drivers of species’ range and phenology shifts.

      Moreover, we constructed extensive null models that show that range and phenology changes, respectively, are contrary to expectations that arise from sampling difference. 100km quadrats make for a reasonable “middle-ground”, and we have included a brief explanation of this in the text: “We assigned species presences to 100×100 km quadrats, a scale that is large enough to maintain adequate sampling intensity but still relevant to conservation and policy (Soroye et al., 2020), to identify the best sampled species.”  (Lines 170-172).

      Szabo, Nora D., Adam C. Algar, and Jeremy T. Kerr. "Reconciling topographic and climatic effects on widespread and range‐restricted species richness." Global Ecology and Biogeography 18.6 (2009): 735-744.

      Comment:

      (3) Following weakness #3, are specialists the ones that "failed to shift" (L18)? If so please specify. The prediction about generalists vs specialists needs to be removed or incorporated in other parts of the paper.

      Thank you for pointing this out, we intended to suggest that species with more generalist habitat requirements might be better able to shift, but ultimately found that traits did not predict species’ shifts. We corrected our prediction regarding habitat generalists as follows: “We predicted that species able to use both lentic and lotic habitats would shift their phenologies and geographies more than those able to use just one habitat type, as generalists outperform specialists as climate and land uses change (Ball-Damerow et al., 2015, 2014; Hassall and Thompson, 2008; Powney et al., 2015; Rapacciuolo et al., 2017).” (Lines 128-132).

      Comment:

      (4) Following weakness #4, cite Pinkert et al at lines 70-73 and Rocha-Ortega et al at lines 73-77 along with https://doi.org/10.1098/rspb.2019.2645. Add Sandall et al https:// doi.org/10.1111/jbi.14457 to L69 references.

      Thank you for the excellent reference suggestions, we have added them as suggested (Lines 80, 86, 77).

      Comment:

      Other comments/suggestions:

      (1) Title: consider adding temp variability 'Range geography and temperature variability, not functional traits,...'.

      Thank you for this suggestion, we have added temperature variability to the title: “Range geography and temperature variability explain cross-continental convergence in range and phenology shifts in a model insect taxon”.

      Comment:

      (2) L125: is (northern) Mexico included in North America?

      Yes, we did include observations from Northern Mexico, and have specified this in the text: “We retained ~1,100,000 records from Canada, the United States, and Northern Mexico, comprising 76 species (Figure 2).” (Lines 174-176).

      Comment:

      (3) L128: I'd label this section 'Temperature variability' rather than 'Climate data'.

      Thank you, we agree that this is a more appropriate title for this section, and have replaced ‘Climate data’ with ‘Temperature variability’ (Line 185).

      Comment:

      (4) Table 2: why are there no estimates for the traits?

      We apologise, this information should have been included in the main body of the manuscript, but was only explained in the Table 2 caption. We have added the following explanation: “Non-significant variables, specifically all functional traits, were excluded from the final models.”. (Line 312-323).

      Comment:

      (5) Figure 2: need to identify the A-D panels.

      We apologise for this error and have clarified the differences between panels in the figure caption:

      “Figure 2: Richness of 76 odonate species sampled in North America and Europe in the historic period (1980-2002; panes A and C) and the recent period (2008-2018; panes B and D). Species richness per 100 × 100 km quadrat is shown in panes A and B, while panes C and D show species richness per 200 × 200 km quadrat. Dark red indicates high species richness, while light pink indicates low species richness.” (Lines 1002-1006).

      Comment:

      (6) L163-173: I am not familiar with this analysis but it sounds interesting and promising, I am not sure if this can be clarified further. Why the -25 to 25, and -30 to 30, doesn't the -35 to 35 cover these? And what is meant by "include only phenology shifts that could be biologically meaningful", that larger shifts would not be meaningful or tied to climate change?

      We used different cutoffs for phenology shifts to inspect for outliers that were likely to be errors, potentially do to insufficient sampling to calculate phenology. We clarified in the text as follows:

      “We retained emergence estimates between March 1st and September 1st, as well as species and quadrats that showed a difference in emergence phenology of -25 to 25 days, -30 to 30 days, or -35 to 35 days between both time periods, to include only phenology shifts that could be biologically meaningful to environmental climate change (i.e. exclude errors).” (Lines 169-173).

      Comment:

      (7) L193-200: I agree but would make a distinction between ecological vs functional traits, as other studies view geographic traits as ecological manifestations of functional biology, e.g. https://doi.org/10.1016/j.biocon.2019.07.001 and https://doi.org/10.1016/ j.biocon.2023.110098.

      Thank you for this suggestion, and for making us aware of the thinking around range geographies as ecological traits. We have specified throughout the manuscript that the ‘traits’ we are considering are ‘functional traits’, changed the methods subsection title to “Range geographies and functional traits” (Line 252), and added a brief discussion of ecological traits: “Geographic range and associated climatic characteristics are often considered ecological traits, as they are consequences of functional traits and their interactions with geographic features (Bried and Rocha-Ortega, 2023; Chichorro et al., 2019).” (Lines 256-259).

      Comment:

      (8) L203: What's the rationale for egg-laying habitat as "biologically relevant to spatial and temporal responses to climate change"? That one's not as obvious as the others and needs a sentence more. Also, I am wondering why other traits were not considered here, like color lightness and voltinism. And why not wing size instead of body size, or better yet the two combined (wing loading) as a proxy for dispersal ability?

      We agree that our rationale for using this trait should be better explained, and we have included the following explanation: “Egg laying habitat was assigned according to whether species use exophytic egg-laying habitat (i.e. eggs laid in water or on land, relatively larger in number), or endophytic egg-laying habitat (i.e. eggs laid inside plants, usually fewer in number); species using exophytic habitats are associated with greater northward range limit shifts (Angert et al., 2011).” (Lines 271-275).

      We considered traits that have been found to be important for range and phenology shifts among odonates, as well as being key traits for expectations for species responses to climate change. Flight duration and body size are correlated with dispersal ability (Powney et al. 2015). Body size is also correlated with competitive ability (Powney et al. 2015), potentially making it an important predictor of a species’ ability to establish and maintain populations in expanding range areas. Traits correlated with range shifts also include breeding habitat type (Powney et al. 2015; Bowler et al. 2021) and egg laying habitat (Angert et al. 2011). Ideally, we would have used dispersal data from mark/release/recapture studies, but it was not available for many of the species included in this study. After finding that none of the functional traits we included were related to range shifts, there was no reason to believe that a further investigation of traits would be meaningful.

      Angert AL, Crozier LG, Rissler LJ, Gilman SE, Tewksbury JJ, Chunco AJ. 2011. Do species’ traits predict recent shifts at expanding range edges? Ecology Letters 14:677–689. doi:10.1111/j.1461-0248.2011.01620.x

      Bowler DE, Eichenberg D, Conze K-J, Suhling F, Baumann K, Benken T, Bönsel A, Bittner T, Drews A, Günther A, Isaac NJB, Petzold F, Seyring M, Spengler T, Trockur B, Willigalla C, Bruelheide H, Jansen F, Bonn A. 2021. Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany.Diversity and Distributions 27:1353–1366. doi:10.1111/ddi.13274

      Powney GD, Cham SSA, Smallshire D, Isaac NJB. 2015. Trait correlates of distribution trends in the Odonata ofBritain and Ireland. PeerJ 3:e1410. doi:10.7717/peerj.1410

      Comment:

      (9) L210: I count at least 5 migratory species in table S3, so although maybe not enough to analyze it's misleading to say "nearly all" were non-migratory, revise to "most" or "vast majority".

      Thank you for pointing this out, we have made the suggested correction (Line 277).

      Comment:

      (10) L252-254: save this for the Discussion and write a more generalized statement for results to avoid citations in the results.

      Thank you for this suggestion, we have moved this to the discussion (Lines 517-527).

      Comment:

      (11) Figures S5 & S6: these are pretty important, I'd consider elevating them to the main document as one figure with two panels.

      Thank you for this suggestion, we agree these figures should be elevated to the main text, and have made them into a panel figure (Figure 4).

      Comment:

      (12) L305-307: great point and recommendation!

      Thank you very much for this positive feedback!

      Comment:

      (13) L335-336: another place to cite https://doi.org/10.1098/rspb.2019.2645 which includes a thermal sensitivity index and would add an odonate citation behind the statement.

      Thank you for this excellent suggestion, we have added this citation (line 480). (Rocha-Ortega et al. 2020)

      Comment:

      (14) L352-353: again see also https://doi.org/10.1098/rspb.2019.2645.

      Thank you for highlighting this reference, we have added it to Line 505 as suggested.

      Comment:

      (15) L355: revise "populations that coexist" to "species that co-occur" (big difference between population and species levels and between coexistence and co-occurrence).

      Thank you very much for pointing this out, we have made the suggested change (Line 507).

      Comment:

      (16) L359-365: are the winners and losers depicted in Figures S5 & S6? If so reference the figure (which I suggest combining and promoting to the main text), if not create a table listing the analyzed species and their winner/loser status.

      We agree that this is an excellent place to bring up Figures S5 and S6 from the supplemental. We have moved them to the main document as one figure and referenced it at line 510.

      Reviewer #2 (Recommendations for the authors):

      Comment:

      (1) Line 53-55: The claim that "These relationships generalize poorly taxonomically and geographically" is valid, but the study only tests Odonata on two continents.

      Thank you for this comment – the word ‘generalize’ may imply that our study tries to find a general pattern across many groups. We have changed the language to: “However, these relationships are inconsistent across taxa and regions, and cross-continental tests have not been attempted (Angert et al., 2011; Buckley and Kingsolver, 2012; Estrada et al., 2016; MacLean and Beissinger, 2017).” (Lines 57-59).

      Comment:

      (2) Line 58-59: Is this statement only true for Odonata? It does not seem to hold for plants, for example.

      Thank you for this comment – this statement references a meta-analysis of multiple animal and plant taxa, but the evidence for the importance of range location comes from animal taxa. We have specified that we are referring to animal species to clarify (Line 60).

      Comment:

      (3) Line 87-91: This section is difficult to understand and needs clarification.

      We have clarified this section as follows: “While warm-adapted species with more equatorial distributions could expand their ranges poleward following warming (Devictor et al., 2008), they could also increase in abundance in this new range area relative to species that historically occupied those areas and are less heat-tolerant (Powney et al., 2015).” (Lines 95-121).

      Comment:

      (4) Line 99-100: Please define "generalist" and "specialist" more clearly here (e.g., based on climate niche?).

      Thank you for pointing this out, we intended to suggest that species with more generalist habitat requirements might be better able to shift, but ultimately found that traits did not predict species’ shifts. We corrected our prediction regarding habitat generalists as follows: “We predicted that species able to use both lentic and lotic habitats would shift their phenologies and geographies more than those able to use just one habitat type, as generalists outperform specialists as climate and land uses change (Ball-Damerow et al., 2015, 2014; Hassall and Thompson, 2008; Powney et al., 2015; Rapacciuolo et al., 2017).” (Lines 128-132).

      Comment:

      (5) Line 122: Replace the English letter "X" in "100x100 km" with the correct mathematical symbol.

      We have made the suggested replacement throughout the manuscript.

      Comment:

      (6) Line 148: To address sampling effects, you could check the paper: https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.15524. Additionally, maximum and minimum values are sensitive to extreme data points, so using 95% percentiles might be more robust.

      Thank you for sharing this paper, as it offers a valuable perspective on the study of species’ ranges. While our dataset is substantially composed of observations from adult sampling protocols, unlike the suggested paper which compares adults and juveniles, this is an interesting alternative approach.

      For our purposes it is meaningful to include outliers, as otherwise we may have missed individuals at the leading edge of range expansions. Our intent here was to detect range limits, as opposed to finding the central tendency of species distributions. This approach is widely accepted in the macroecology literature (i.e. Devictor et al., 2012, 2008; Kerr et al. 2015).

      We have included the following discussion of our approach in the methods section:

      “We followed widely accepted methods to determine species range boundaries (Devictor et al., 2012, 2008; Kerr et al., 2015), although other methods exist that are appropriate for different data types and research questions i.e. (Ni and Vellend, 2021). We assigned species presences to 100×100 km quadrats, a scale that is large enough to maintain adequate sampling intensity but still relevant to conservation and policy (Soroye et al., 2020), to identify the best sampled species.” (Lines 168-173).

      Kerr JT, Pindar A, Galpern P, Packer L, Potts SG, Roberts SM, Rasmont P, Schweiger O, Colla SR, Richardson LL,Wagner DL, Gall LF, Sikes DS, Pantoja A. 2015. Climate change impacts on bumblebees converge across continents. Science 349:177–180. doi:10.1126/science.aaa7031

      Soroye P, Newbold T, Kerr J. 2020. Climate change contributes to widespread declines among bumble bees across continents. Science 367:685–688. doi:10.1126/science.aax8591

      Devictor V, Julliard R, Couvet D, Jiguet F. 2008. Birds are tracking climate warming, but not fast enough.Proceedings of the Royal Society B: Biological Sciences 275:2743–2748. doi:10.1098/rspb.2008.0878

      Devictor V, van Swaay C, Brereton T, Brotons L, Chamberlain D, Heliölä J, Herrando S, Julliard R, Kuussaari M,Lindström Å, Reif J, Roy DB, Schweiger O, Settele J, Stefanescu C, Van Strien A, Van Turnhout C,

      Vermouzek Z, WallisDeVries M, Wynhoff I, Jiguet F. 2012. Differences in the climatic debts of birds and butterflies at a continental scale. Nature Clim Change 2:121–124. doi:10.1038/nclimate1347

      Comment:

      (7) Line 195: The species' climate niche should also be considered a product of evolution.

      Thank you for this suggestion. To address this comment and a comment from another reviewer, we changed the text to the following: “Geographic range and associated climatic characteristics are often considered ecological traits, as they are consequences of functional traits and their interactions with geographic features (Bried and Rocha-Ortega, 2023; Chichorro et al., 2019).” (Lines 256-259).

      Comment:

      (8) Line 244: This speculative statement belongs in the Discussion section.

      Thank you for this suggestion, we have moved this statement to the discussion (Lines 451-453).

      Comment:

      (9) Line 252-254: The projection of Coenagrion mercuriale's range contraction is not part of your results and should be clarified or removed.

      Following this suggestion and a similar suggestion from another reviewer, we moved this text to the discussion (Line 517-527).

      Comment:

      (10) Line 314-316: If the species can tolerate warmer temperatures better, why would they migrate?

      We apologize for the confusion, and we have reworded the section as follows: “Emerging mean conditions in areas adjacent to the ranges of southern species may offer opportunities for range expansions of these relative climate specialists, which can then tolerate climate warming in areas of range expansion better than more cool-adapted historical occupants (Day et al., 2018).” (Lines 445-448).

      Comment:

      (11) Line 334-335: Species' tolerance to temperature likely depends on their traits, which were not tested in this study. This should be noted.

      We agree, and we have removed the wording “rather than traits” from this sentence (Line 479).

      Reviewer #3 (Recommendations for the authors):

      Comment:

      (1) Title: The title is too general not specifying that your results are on odonates only, but also stressing the implicit role of climate change to a degree the tests do not support.

      Following this comment and a suggestion from another reviewer we changed the title to the following: “Range geography and temperature variability explain cross-continental convergence in range and phenology shifts in a model insect taxon”. We wanted to emphasize our use of Odonates as a model species that we used to ask broad questions, while being more specific about the climatic variable that we examined (temperature variability).

      Comment:

      (2) L32: consider including Novella-Fernandez et al. 2023 (NatCommun) which addresses this topic in Odonates.

      Thank you for suggesting this very interesting paper, we have added it as a citation (Line 31-32).

      Comment:

      (3) L35: consider including Grewe et al. 2013 (GEB) and Engelhardt et al. 2022(GCB).

      Thank you for these excellent suggestions, we have added the citations (Line 35).

      Comment:

      (4) L47: rather write 'result from' instead of 'driven by'.

      We agree this is a better characterization and have corrected the wording (Line 48-49).

      Comment:

      (5) L49-52: There has been a recent study on this topic for birds (Neate-Clegg et al., 2024 NEE). However, specifying this to insects would make it not less relevant. This review for odonates might be helpful in this regard (Pinkert et al.. 2022, Chapter: "Odonata as focal taxa for biological responses to climate change" IN Dragonflies & Damselflies: Córdoba-Aguilar et al. (2022) Model Organisms for Ecological and Evolutionary Research.

      Thank you for again suggesting excellent references, we have added them to line 52-53, as well as adding the Pinkert citation to lines 61 and 82.

      Comment:

      (6) L53-66: Combine into one paragraph about drivers. With traits first and the environment second. The natural land cover perspective may be too complicated in this context. Consider focusing on generalities of the impact of changes within species' ranges.

      As suggested we have combined these into one paragraph about drivers (Line 59).

      Comment:

      (7) L67-69: The book from before would be a much stronger reference for this claim. Kalkmann et al (2018) do not address the emphasis of global change research in insects on bees and butterflies. Also, I would highlight that most of the current work is at a national scale, rather than cross-continental.

      Thank you for this suggestion, we have added the suggested reference and included that “…recently assembled databases of odonate observations provide a rare opportunity to investigate species’ spatiotemporal responses at larger taxonomic and spatial scales, particularly as most work has been done at national scales.” (Lines 75-77).

      Comment:

      (8) L68: consider rephrasing this part to '..provide a rare opportunity to investigate spatiotemporal biotic responses at larger taxonomic and spatial scales'

      We appreciate this suggestion and really like the wording. We have changed the phrase to read as follows: “While global change research on insects often emphasizes butterfly and bee taxa, recently assembled databases of odonate observations provide a rare opportunity to investigate species’ spatiotemporal responses at larger taxonomic and spatial scales, particularly as most work has been done at national scales.” (Lines 74-77).

      Comment:

      (9) L69: This characteristic is not unique to odonates and would hamper drawing general conclusions. Honestly, I think the detailed and comprehensive data on them is the selling point.

      Thank you for this suggestion, we have edited the sentence to emphasize their use as an indicator species: “Due to their use of aquatic and terrestrial habitat across life different stages, dragonflies and damselflies are also considered indicator species for both terrestrial and aquatic insect responses to changing climates (Hassall, 2015; Pinkert et al., 2022; Šigutová et al., 2025), giving the study of these species broad relevance for conservation.” (Lines 78-81)

      Comment:

      (10) L73: Indicator for what? The first part of the sentence would suggest lesser surrogacy for responses of other taxa. Reconsider this statement. They are well- established indicators for habitat intactness and freshwater biodiversity. Darwell et al. suggested their diversity can serve as a surrogate for the diversity of both terrestrial and aquatic taxa.

      Thank you for this suggestion, we have edited the sentence to emphasize their use as an indicator species: “Due to their use of aquatic and terrestrial habitat across life different stages, dragonflies and damselflies are also considered indicator species for both terrestrial and aquatic insect responses to changing climates (Hassall, 2015; Pinkert et al., 2022; Šigutová et al., 2025), giving the study of these species broad relevance for conservation.” (Lines 78-81)

      Comment:

      (11) L76: Fritz et al., is a study on mammals, not odonates.

      Thank you for pointing out this error, the reference has been removed (Line 84-85).

      Comment:

      (12) L84: Lotic habitats are generally better connected than lentic ones. Lentic species are considered to have a greater propensity for dispersal DUE to the lower inherent spatiotemporal stability (implying lower connectivity) compared to lotic habitats.

      Thank you for your comment, we have rewritten this section as follows: “For example, differences in habitat connectivity and dispersal ability may constrain range shifts for lentic species (those species that breed in slow moving water like lakes or ponds) and lotic species (those living in fast moving-water) in different ways (Kalkman et al., 2018). More southerly lentic species may expand their range boundaries more than lotic species, as species accustomed to ephemeral lentic habitats better dispersers (Grewe et al., 2013), yet lotic species have also been found to expand their ranges more often than lentic species, potentially due to the loss of lentic habitat in some areas (Bowler et al., 2021).” (Lines 88-95).

      Comment:

      (13) L90: I would be cautious with this interpretation. If only part of the range is considered (here a country in the northern Hemisphere) southern species are moving more of their range into and northern species more of their range out of the study area in response to warming (implying northward shifts).

      We have clarified this section as follows: “While warm-adapted species with more equatorial distributions could expand their ranges poleward following warming (Devictor et al., 2008), they could also increase in abundance in this new range area relative to species that historically occupied those areas and are less heat-tolerant (Powney et al., 2015).” (Lines 95-121)

      Comment:

      (14) L117: Odonata Central contains many county centroids as occurrence records. These could be an issue for your use case. I may have overlooked the steps you took to address this, but I think this requires at least more detail and possibly further removal/checks using for instance CoordinateCleaner. The functions implemented in this package allow you to filter records based on political units to avoid exactly this source of error.

      Thank you for this suggestion, we weren’t aware of this issue with Odonata Central. We used the CoordinaterCleaner tool in R to filter all odonate records that we used in our analyses. Less than 1% of observations in our dataset were identified as having potential problems by the tool, so we would not expect this to affect our inferences. However, in future we will employ this tool when using similar datasets.

      Comment:

      (15) L119: Please add a brief explanation of why this was necessary. I am ok with something along the lines in the supplement.

      We moved this information from the supplemental to the main text as follows: “If a species was found on both continents, we only retained observations from the continent that was the most densely sampled. If we merged data for one species found on both continents, we could not perform a cross-continental comparison. However, if the same species on different continents was treated as different species, this would lead to uninterpretable outcomes (and the creation of pseudo-replication) in the context of phylogenetic analyses. In addition, species found on both continents did not have sufficient data to meet criteria for the phenology analysis.” (Lines 161-167).

      Comment:

      (16) L132: This is the letters 'X' or 'x' are not multiplier symbols! Please change to the math symbol (×), everywhere.

      Thank you for pointing out this error, we have made the correction throughout the manuscript.

      Comment:

      (17) L133: add 'main' before 'flight period'

      Thank you for this suggestion, we have made the change. (Line 190)

      Comment:

      (18) L135: I suggest using the coefficient of variation, as it is controlled for the mean. Otherwise, what you see is partly the signature of temperature and not of its variation. For me, it's very difficult to understand what this variation of the variation means and at least needs more explanation.

      Thank you very much for this suggestion, we agree that using the coefficient of variation is a better fit for the question that we’re asking. We re-ran out analyses with the coefficient of variation as the measure of climate variability: all the results reported in the manuscript are now updated for that analysis (Line 377, Table 2), and we have also updated the methods section (Line 191). The results are qualitatively the same to our previous analysis, but we agree that they are now easier to interpret.            

      Comment:

      (19) L155: Please adequately reference all R packages (state the name, and a reference for them including the authors' names, title, and version).

      Thank you for pointing out this omission, we have added reference information for the glm function in base R (Line 298) and ensured all other packages are properly referenced.

      Comment:

      (20) L207: Mention the literature sources here (again).

      We agree that they should be referenced here again, and we have done so (Lines 267-268).

      Comment:

      (21) L209: You could use the number of grid cells as a proxy for range size.

      Following this excellent suggestion, we re-analysed our data using range size, calculated as the number of quadrats occupied by a species in the historical time period, as a predictor. Range size was not significant in our models, but we believe this is the best way to analyze our data, and so have updated our methods (Lines 261-263) and results (375-378).

      Comment:

      (22) L218: It would be preferable to say 'species-level' instead of 'by-species'.

      Thank you for this suggestion, we agree that this is clearer and made the change (Line 298).

      Comment:

      (23) L219-220: this is unclear. Please rephrase.

      We have clarified as follows: “We used both species-level frequentist (GLM; glm function in R) and Bayesian (Markov Chain Monte Carlo generalized linear mixed model, MCMCglmm; Hadfield, 2010) models to improve the robustness of the results.” (Lines 298-300).

      Comment:

      (24) L224: At least for Europe there is a molecular phylogeny available, which you should preferably use (Pinkert et al. 2018, Ecography). Otherwise, I am ok with using what is available

      We apologize that the nature of the phylogeny that we used was not clear; the phylogeny that we used was built similarly to that in Pinkert et al. 2018, Ecography. It created a molecular phylogeny with a morphological/taxonomic tree as the backbone tree, so that species could only move within their named genera or families. We clarified this in the manuscript as follows:

      “We used the molecular phylogenetic tree published by the Odonate Phenotypic Database (Waller et al., 2019), which used a morphological and taxonomic phylogeny as the backbone tree, allowing species to move within their named genera or families according to molecular evidence (Waller and Svensson, 2017).” (Lines 302-305).

      Comment:

      (25) L233: You said so earlier (1st sentence of this paragraph).

      Thank you for pointing this out, we removed the repetitive sentence (Line 323).

      Comment:

      (26) L236-238: To me, it makes more sense to test this prior to fitting the phylogenetic models.

      MCMC-GLMM is considerably less familiar to most researchers than general linear models or there derivatives/descendants, such as PGLS. We report models both with and without phylogenetic relationships included for the sake of transparency, and we are happy to acknowledge that no interpretation here changes substantially relative to these decisions. However, failing to report models that included possible (if small) effects of phylogenetic relatedness might cause some readers to question what those models might have implied. For the moment, we are opting for the most transparent reporting approach here.

      Comment:

      (27) L241: Rather say directly XX of XX species in our data....

      (28) L245: Same here. Provide the actual numbers, please.

      Thank you for this suggestion, we made this change on Line 332 and Line 334.

      Comment:

      (29) L247-249: Then not necessary.

      This issue highlights a challenge in the global biology literature and around the issue of biodiversity monitoring for understanding global change impacts on species. Almost no studies have been able to report simultaneous range and phenology shifts, and the literature addresses these biotic responses to global change predominantly as distinct phenomena. Differences in numbers of species for which these observations exist, even among the extremely widely-observed odonates, seems to us to be a meaningful issue to report on. If the reviewer prefers that we abbreviate or remove this sentence, we are happy to do so.

      Comment:

      (30) L251:261: That is discussion as you interpret your results.

      Following your suggestion and the suggestion of another reviewer, we moved the following lines to the discussion section: “Species that did not shift their ranges northwards or advance their phenology included Coenagrion mercuriale, a European species that is listed as near threatened by the IUCN Red List (IUCN, 2021), and is projected to lose 68% of its range by 2035 (Jaeschke et al., 2013).” (Lines 517-527).

      Comment:

      (31) 252: Good to mention, but why is the discussion limited to C. mercurial?

      We feel that it is important to link the broad-scale results to the specific biological characteristics of individual species, and C. mercurial is an IUCN threatened species. We are happy to expand links to natural history of this group and have added the following: “This group also includes Coenagrion resolutum, a common North American damselfly (Swaegers et al., 2014), for which we could not find evidence of decline. This may be due in part to the greater area of intact habitat available in North American compared to Europe, enabling C. resolutum to maintain larger populations that are less vulnerable to stochastic climate events. Still, this and other species failing to shift in range or phenology should be assessed for population health, as this species could be carrying an unobserved extinction debt.” (Lines 527-533).

      Comment:

      (32) L264: Insert 'being' before 'consistently'.

      Thank you for the suggestion, we made this change (Line 373).

      Comment:

      (33) L271: .'. However,'.

      Thank you for pointing out this grammatical error, we have corrected it (Line 382).

      Comment:

      (34) L273: 'affected' instead of 'predicted'

      Thank you for the suggestion, we made this change (Line 383).

      Comment:

      (35) L279: 'despite pronounced recent warming' sounds not relevant in this context.

      Thank you for this suggestion, we removed this portion of the sentence (Line 408).

      Comment:

      (36) L281: Rather 'the model performance did not improve....'

      Thank you for the suggestion, we made this change (Line 409).

      Comment:

      (37) L288: Add 'but' before 'not'.

      Thank you for the suggestion, we made this change (Line 416).

      Comment:

      (38) L311-316: Reconsider the causality here. maybe rather rephrase to are associated instead. Greater dispersal ability and developmental plasticity might well lead to higher growth rates, rather than the other way around.

      We agree that plasticity/evolution at range edges is important to consider and have included it as an alternative explanation: “Adaptive evolution and plasticity may enable higher population growth rates in newly-colonized areas (Angert et al., 2020; Usui et al., 2023), but this possibility can only be directly tested with long term population trend data.” (Line 449-451).  

      Comment:

      (39) L313-316: Maybe delete the second 'should be able to'.

      This phrase has been changed in response to other reviewer comments and now reads as follows:

      “Emerging mean conditions in areas adjacent to the ranges of southern species may offer opportunities for range expansions of these relative climate specialists, which can then tolerate climate warming in areas of range expansion better than more cool-adapted historical occupants (Day et al., 2018).” (Lines 445-448).

      Comment:

      (40) L331: Limit this statement ending with 'in North American and European Odonata'.

      Thank you for this suggestion, we made this addition (Lines 475-476).

      Comment:

      (41) L346-347: There are too many of these more-research-is-needed statements in the discussion (at least three in the last paragraphs). Please consider finishing the paragraphs rather with a significance statement.

      Thank you for this suggestion, we have changed the final sentence here to the following: “The extent to which species’ traits actually determine rates of range and phenological shifts, rather than occasionally correlated with them, is worth considering further, but functional traits do not systematically drive patterns in these shifts among Odonates in North America and Europe.” (Lines 480-483).

      We also made additional changes, removing a ‘more-research is needed’ statement from the following paragraph (Line 443), as well as from line 499.

      Comment:

      (42) L349: See also Franke et al. (2022, Ecology and Evolution).

      Thank you for highlighting this excellent reference! We have added it to Line 501.

      Comment:

      (43) L363: Maybe a bit late in the text, but it is important to note that there is the third dimension 'abundance trends' or rather a common factor related to range and phenology shifts. I feel this fits better with the discussion of population growth.

      Thank you for this suggestion, we have addressed the importance of abundance trends in the following sentences: “Further mechanistic understanding of these processes requires abundance data.” (Lines 442-443); “It remains unclear if range and phenology shifts relate to trends in abundance, but our results suggest that there are clear ‘winners’ and ‘losers’ under climate change.” (Lines 509-510).

      Comment:

      (44) L375-377: This last sentence is very similar to L371-373. Please reduce the redundancy. Focus more on specifically stating the process instead of vaguely saying 'new insights into patterns' and 'suggesting processes'. Rather, deliver a strong concluding message here.

      Thank you for this suggestion, we feel that we now have a much stronger concluding message: “By considering both the seasonal and range dynamics of species, emergent and convergent climate change responses across continents become clear for this well-studied group of predatory insects.” (Lines 545-547).

      Comment:

      (45) Table 1: To me, the few estimates presented here do not justify a table. rather include them in the text. OR combine them with Table 2. Also, why not include the traits as predictors (from the range shift models) in these models as well?

      We have clarified in the text that the results displayed in Table 1 are from the analysis of the relationship between range and phenology shifts: “The effect of species’ range shifts on phenology range shifts was significant in our model investigating the relationship between these responses, indicating that species shifting their northern range limits to higher latitudes also showed stronger advances in their emergence phenology (Figure 3).” (Lines 341-344).

      As there were no significant effects in the model of phenology change drivers, we have not shown results of this model: “Emergence phenology shifts were not affected by species’ traits, range geography, nor climate variability; due to this, model results are not displayed here.” (Lines 383-384).

      Comment:

      (46) Table 2: L712-713: What does this mean? Are phenology shifts not used as a predictor of range shifts? (why then this comment?). Or do you want to say phenological shifts are not related to Southern range etc? Why do you present a phylosig here but not in Table 1? Why not include the traits as predictors (from the range shift models) in these models as well? Consider using the range size as a continuous predictor instead of 'Widespread'.

      We are glad the reviewer pointed this out to us. We did not emphasize this issue sufficiently. We DID evaluate traits as predictors both of geographical range and phenological shifts, and species-specific biological traits did not significantly affect models predicting either of those sets of responses. We state this on Lines 312-323, but we have also noted in the discussion (Lines 473-476) that the most commonly assessed traits, like body size, do not alter observed trends here. Instead, where species are found, rather than the characteristics of species, is the key determinant of their overall responses.

      Following this excellent suggestion, we re-analysed our data using range size, calculated as the number of quadrats occupied by a species in the historical time period, as a predictor. Range size was not significant in our models, but we believe this is the best way to analyze our data, and so have updated our methods (Lines 261-263) and results (375-378).

      Comment:

      (47) Figure 1: I don't see any grey points in the figure. Also, there is no A or B. If you are referring to the symbols then write cross and triangle instead and not use capital letters which usually refer to component plots of composite figures. Also, I highly recommend providing a similar figure based on your data (maybe each species as a dot for T1 and another symbol for T2). Given the small number of species, you could try to connect these points with arrows. For the set with only range shifts maybe play the T2-dots at the center of the 'Emergence' axis.

      Thank you for pointing out this error: a previous version of Figure 1 included grey points and multiple panels. We have removed this text from the figure caption to be consistent with the final version of the figure (Line 989).

      The graphical depictions of the conceptual and empirical discoveries in this paper were challenging to create. The reviewer might be suggesting effectively decomposing Figure 3 (change in range on the y axis vs change in phenology among all species into two sets of points on the same graph, where each pair of points is a before and after value for each species. This would make for a very busy figure indeed. We have modified the conceptual Figure 1 to illustrate more clearly, we believe, that species can (in principle) remain within tolerable niche spaces by shifting their activity periods in time (phenology) or in space (geographical range) or both.

      Comment:

      (48) Figure 2: Please add a legend. Also black is a poor background color. The maps appear to be stretched. Please check aspect ratios. Now here are capital letters without an explanation in the caption. From the context I assume the upper panel maps are for the data used to calculate range shifts at the bottom panel maps are for data used to calculate the phenological shifts.

      We apologise for the error in the figure caption and have clarified the differences between panels in the text, as well as changing the map background colour and fixing the aspect ratio:

      “Figure 2: Richness of 76 odonate species sampled in North America and Europe in the historic period (1980-2002; panes A and C) and the recent period (2008-2018; panes B and D). Species richness per 100 × 100 km quadrat is shown in panes A and B, while panes C and D show species richness per 200 × 200 km quadrat. Dark red indicates high species richness, while light pink indicates low species richness.” (Lines 1002-1006).

      Comment:

      (49) Figure 3: Why this citation? Of terrestrial taxa? Please explain. Consider adding some stats here, such as the r-squared value for each of the relationships.

      We have better explained the citation in the figure caption, as well as adding r-squared values:

      “Figure 3: Relationship between range shifts and emergence phenology shifts among North American and European odonate species (N = 66; model R2 = 17.08 for glm, 14.9% for MCMCglmm). For reference, the shaded area shows mean latitudinal range shifts of terrestrial taxa as reported by Lenoir et al. (2020; calculated as the yearly mean dispersal rate of 1.11 +/- 0.96 km per year over 38 years).” (Lines 679-682)

      Comment:

      (50) L801: What are these underscored references?

      This was an issue with the reference software and has been resolved.

      Comment:

      (51) Table S1: L848: Consider starting with 'Samples of 76 North American and European odonate species from between ...'. Please use a horizontal line to separate the content from the table header. Add a horizontal line below the last row. Same for all tables.

      Thank you for this suggestion, we have edited the caption for Figure S1 as suggested (Line 1124). We have also made the suggested line additions to Table S1, S2, and S3.

      Comment:

      (52) Table S3: This is confusing. In Table 1 (main text) both 'southern range' and 'widespread' are used as predictors. Please explain.

      We originally included information on species range geography, including southern versus northern range, and widespread versus not, into one categorical variable. Following additional comments we re-analysed our data using range size, calculated as the number of quadrats occupied by a species in the historical time period, as a predictor. Now the methods section text (Lines 261-263) and Table 1 report results of that variable with distribution options northern, southern, or both. 

      Comment:

      (53) Figure S5 and S6: It would be more coherent if the colors refer to the continents and the suborders are indicated by shading. I would love to see a combination of the two figures with species ordered by the phylogenetic relationship and a dot matrix indicating the traits in the main text! This could really be a good starting point for a synthesis figure.

      The reviewer presents an interesting challenge for us. We have a choice, as we understand things, to present a figure showing phylogeny and traits (as requested here), or an ordered list of species relative to effect sizes in the two main responses to global change. The latter choice centers on the discoveries of the paper, while the former would be valuable for dragonfly biology but would depict information that proved to be biologically uninformative relative to our discovery. That is to say, there is no phylogenetic trend and biological traits among species did not affect results. We have gone some way toward illustrating that issue by retaining phylogeny in the MCMC-GLMM models, but we feel that a figure illustrating phylogeny and traits would (for most readers, at least) illustrate noise, rather than signal. For this reason, we have opted to take on the previous reviewer’s suggestion for a modified, main-text Figure 4, which we include below.

      Figure 4: Distribution of Northern range limit shifts (Panel A, kilometers) and emergence phenology shift (Panel B, Julian day) of 76 European and North American odonate species between a recent time period (2008 - 2018) and a historical time period (1980 - 2002). Anisoptera (dragonflies) are shown in pink, Zygoptera (damselflies) are shown in blue.

      Change last: Figure 3: Relationship between range shifts and emergence phenology shifts among North American and European odonate species (N = 66; model R2 = 17.08 for glm, 14.9% for MCMCglmm). For reference, the shaded area shows mean latitudinal range shifts of terrestrial taxa as reported by Lenoir et al. (2020; calculated as the yearly mean dispersal rate of 1.11 +/- 0.96 km per year over 38 years).

    1. Author response:

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

      Reviewer #1 (Public Review):

      Summary:

      The manuscript "Drosophila Visuomotor Integration: An Integrative Model and Behavioral Evidence of Visual Efference Copy" provides an integrative model of the visuomotor control in Drosophila melanogaster. This model presents an experimentally derived model based on visually evoked wingbeat pattern recordings of three strategically selected visual stimulus types with well-established behavioral response characteristics. By testing variations of these models, the authors demonstrate that the virtual model behavior can recapitulate the recorded wing beat behavioral results and those recorded by others for these specific stimuli when presented individually. Yet, the novelty of this study and their model is that it allows predictions for natural visual scenes in which multiple visual stimuli occur simultaneously and may have opposite or enhancing effects on behavior. Testing three models that would allow interactions of these visual modalities, the authors show that using a visual efference copy signal allows visual streams to interact, replicating behavior recorded when multiple stimuli are presented simultaneously. Importantly, they validated the prediction of this model in real flies using magnetically tethered flies, e.g., presenting moving bars with varying backgrounds. In conclusion, the presented manuscript presents a commendable effort in developing and demonstrating the validity of a mixture model that allows predictions of the behavior of Drosophila in natural visual environments.

      Strengths:

      Overall, the manuscript is well-structured and clear in its presentation, and the modeling and experimental research are methodically conducted and illustrated in visually appealing and easy-to-understand figures and their captions.

      The manuscript employs a thorough, logical approach, combining computational modeling with experimental behavioral validation using magnetically tethered flies. This iterative integration of simulation and empirical behavioral evidence enhances the credibility of the findings.

      The associated code base is well documented and readily produces all figures in the document.

      Suggestions:

      However, while the experiments provide evidence for the use of a visual efference copy, the manuscript would be even more impressive if it presented specific predictions for the neural implementation or even neurophysiological data to support this model. Or, at the very least, a thorough discussion. Nonetheless, these models and validating behavioral experiments make this a valuable contribution to the field; it is well executed and addresses a significant gap in the modeling of fly behavior and holistic understanding of visuomotor behaviors.

      We appreciate the reviewer’s thoughtful comments on the strengths and weaknesses of our manuscript. We agree that biophysically realistic model reflecting the structure of neural circuits as well as physiological data from them would be invaluable. However, we are currently unable to provide physiological evidence for EC-based suppression, nor provide circuit architecture for efference copy-based suppression of the stability circuit because the neural pathway underlying this behavior remains unidentified. Extensive recordings from the HS/VS system have revealed cell-type-specific motor-related inputs during both spontaneous and loom-evoked flight turns (Fenk et al., 2021; Kim et al., 2017, 2015). These studies predicted suppression of the optomotor stability response during such turns, and our new experiments confirmed this suppression specifically during loom-evoked turns (Figures 5, 6). However, these neurons are primarily involved in the head optomotor response, not the body optomotor response. We hope to extend our current model in future studies to incorporate more cellular-level detail, as the feedforward circuits underlying stability behavior become more clearly defined.

      Here are a few points that should be addressed:

      (1) The biomechanics block (Figure 2) should be elaborated on, to explain its relevance to behavior and relation to the underlying neural mechanisms.

      We appreciate this suggestion. The mathematical representation of the biomechanics block has been developed by other groups in previous studies (Fry et al., 2003; Ristroph et al., 2010). We used exactly the same model, and its parameters were identical to those used in one of those studies (Fry et al., 2003; Ristroph et al., 2010), in which the parameters were estimated from the stabilizing response in response to magnetic “stumbling” pulses. In the previous version of the manuscript, we had a description of the biomechanics block in the Method section (see Equation 4). In response to the reviewer’s comment, we have made a few changes in Figure 2A and expanded the associated description in the main text, as follows.

      (Line 160) “To test the orientation behavior of the model, we developed an expanded model, termed “virtual fly model” hereafter. In this model, we added a biomechanics block that transforms the torque response of the fly to the actual heading change according to kinematic parameters estimated previously (Michael H Dickinson, 2005; Ristroph et al., 2010) (Figure 2A, see Equation 4 in Methods and Movie S1). The virtual fly model, featuring position and velocity blocks that are conditioned on the type of the visual pattern, can now change its body orientation, simulating the visual orientation behavior of flies in the free flight condition.”

      (2) It is unclear how the three integrative models with different strategies were chosen or what relevance they have to neural implementation. This should be explained and/or addressed.

      Thank you for this valuable comment. We selected the three models based on previous studies investigating visuomotor integration across multiple species, under conditions where multiple sensory cues are presented simultaneously.

      The addition-only model represents the simplest hypothesis, analogous to the “additive model” proposed by Tom Collett in his 1980 study (Collett, 1980). We used this model as a baseline to illustrate behavior in the absence of any efference copy mechanism. Notably, some modeling studies have proposed linear (additive) integration for multimodal sensory cues at the behavioral level (Liu et al., 2023; Van der Stoep et al., 2021). However, experimental evidence demonstrating strictly linear integration—either behaviorally or physiologically—remains limited. In our study, new data (Figure 5) show that bar-evoked and background movement-evoked locomotor responses are combined linearly, supporting the addition-only model.

      The graded efference copy model has been most clearly demonstrated in the cerebellum-like circuit of Mormyrid fish during electrosensation (Bell, 1981; Kennedy et al., 2014). In this system, the efference copy signal forms a negative image of the predicted reafferent input and undergoes plastic changes as the environment changes—an idea that inspired our modifiable efference copy model (Figure 4–figure supplement 1). The all-or-none efference copy model is exemplified in the sensory systems of smaller organisms, such as the auditory neurons of crickets during stridulation (Poulet and Hedwig, 2006). Notably, in crickets, the motor-related input is referred to as corollary discharge rather than efference copy. Typically, “efference copy” refers to a graded, subtractive motor-related signal, while “corollary discharge” denotes an all-or-none signal, both counteracting the sensory consequences of self-generated actions. In this manuscript, we use the term efference copy more broadly, encompassing both types of motor-related feedback signals (Sommer and Wurtz, 2008).

      In response to this comment, we have made the following changes in the main text to enhance its accessibility to general readers.

      (Line#268) “This integration problem has been studied across animal sensory systems, typically by analyzing motor-related signals observed in sensory neurons (Bell, 1981; Collett, 1980; Kim et al., 2017; Poulet and Hedwig, 2006). Building on the results of these studies, we developed three integrative models. The first model, termed the “addition-only model”, assumes that the outputs of the object (bar) and the background (grating) response circuits are summed to control the flight orientation (Figure 4B, see Equation 14 in Methods).”

      (Line#272) “In the second and third models, an EC is used to set priorities between different visuomotor circuits (Figure 4C,D). In particular, the EC is derived from the object-induced motor command and sent to the object response system to nullify visual input associated with the object-evoked turn (Bell, 1981; Collett, 1980; Poulet and Hedwig, 2006). These motor-related inputs fully suppress sensory processing in some systems (Poulet and Hedwig, 2006), whereas in others they selectively counteract only the undesirable components of the sensory feedback (Bell, 1981; Kennedy et al., 2014).”

      (3) There should be a discussion of how the visual efference could be represented in the biological model and an evaluation of the plausibility and alternatives.

      Thank you for this helpful comment. We have now added the following discussion to share our perspective on the circuit-level implementation of the visual efference copy in Drosophila.

      (Line#481) “Efference copy in Drosophila vision

      Under natural conditions, various visual features in the environment may concurrently activate multiple motor programs. Because these may interfere with one another, it is crucial for the central brain to coordinate between the motor signals originating from different sensory circuits. Among such coordination mechanisms, the EC mechanisms were hypothesized to counteract so-called reafferent visual input, those caused specifically by self-movement (Collett, 1980; von Holst and Mittelstaedt, 1950). Recent studies reported such EC-like signals in Drosophila visual neurons during spontaneous as well as loom-evoked flight turns (Fenk et al., 2021; Kim et al., 2017, 2015). One type of EC-like signals were identified in a group of wide-field visual motion-sensing neurons that were shown to control the neck movement for the gaze stability (Kim et al., 2017). The EC-like signals in these cells were bidirectional depending on the direction of flight turns, and their amplitudes were quantitatively tuned to those of the expected visual input across cell types. Although amplitude varies among cell types, it remains inconclusive whether it also varies within a given cell type to match the amplitude of expected visual feedback, thereby implementing the graded EC signal. A more recent study examined EC-like signal amplitude in the same visual neurons for loom-evoked turns, across events (Fenk et al., 2021). Although the result showed a strong correlation between wing response and the EC-like inputs, the authors pointed that this apparent correlation could stem from noisy measurement of all-or-none motor-related inputs.

      Thus, these studies did not completely disambiguate between graded vs. all-or-none EC signaling. Another type of EC-like signals observed in the visual circuit tuned to a moving spot exhibited characteristics consistent with all-or-none EC. That is, it entirely suppressed visual signaling, irrespective of the direction of the self-generated turn (Kim et al., 2015; Turner et al., 2022). 

      Efference-copy (EC)–like signals have been reported in several Drosophila visual circuits, yet their behavioral role remains unclear. Indirect evidence comes from a behavioral study showing that the dynamics of spontaneously generated flight turns were unaffected by unexpected background motion (Bender and Dickinson, 2006a). Likewise, our behavioral experiments showed that, during loom-evoked turns, responses to background motion are suppressed in an all-or-none manner (Figures 6 and 7). Consistent with this, motor-related inputs recorded in visual neurons exhibit nearly identical dynamics during spontaneous and loom-evoked turns (Fenk et al., 2021). Together, these behavioral and physiological parallels support the idea that a common efference-copy mechanism operates during both spontaneous and loom-evoked flight turns.

      Unlike loom-evoked turns, bar-evoked turn dynamics changed in the presence of moving backgrounds (Figure 5), a result compatible with both the addition-only and graded EC models. However, when the static background was updated just before a bar-evoked turn—thereby altering the amplitude of optic flow—the turn dynamics remained unaffected (Figures 5 and 7), clearly contradicting the addition-only model. Thus, the graded EC model is the only one consistent with both findings. If a graded EC mechanism were truly at work, however, an unexpected background change should have modified turn dynamics because of the mismatch between expected and actual visual feedback (Figure 4–figure supplement 1)—yet we detected no such effect at any time scale examined (Figure 7–figure supplement 1). This mismatch would be ignored only if the amplitude of the graded EC adapted to environmental changes almost instantaneously—a mechanism that seems improbable given the limited computational capacity of the Drosophila brain. In electric fish, for example, comparable adjustments take more than 10 minutes (Bell, 1981; Muller et al., 2019). Further investigation is needed to clarify how reorienting flies ignore optic flow generated by static backgrounds, potentially by engaging EC mechanisms not captured by the models tested in this study.

      Why would Drosophila rely on the all-or-none EC mechanism instead of the graded one for loom-evoked turns? A graded EC must be adjusted adaptively depending on the environment, as the amplitude of visual feedback varies with both the dynamics of self-generated movement and environmental conditions (e.g., empty vs. cluttered visual backgrounds) (Figure 4—figure supplement 1). Recent studies on electric fish have suggested that a large array of neurons in a multi-layer network is crucial for generating a modifiable efference copy signal matched to the current environment (Muller et al., 2019). Given their small-sized brain, flies might opt for a more economical design for suppressing unwanted visual inputs regardless of the visual environment. Circuits mediating such a type of EC were identified in the cricket auditory system during stridulation (Poulet and Hedwig, 2006), for example. Our study strongly suggests the existence of a similar circuit in the Drosophila visual system. 

      We tested the hypothesis that efference-copy (EC) signals guide action selection by suppressing specific visuomotor reflexes when multiple visual features compete. An alternative motif with a similar function is mutual inhibition between motor pathways (Edwards, 1991; Mysore and Kothari, 2020). In Drosophila, descending neurons form dense lateral connections (Braun et al., 2024), offering a substrate for such competitive interactions. Determining whether—and how—EC and mutual inhibition operate will require recordings from the neurons that ensure visual stability, which remain unidentified. Mapping these pathways and assessing how they are modulated by visual and behavioral context are important goals for future work.”

      Reviewer #2 (Public Review):

      It has been widely proposed that the neural circuit uses a copy of motor command, an efference copy, to cancel out self-generated sensory stimuli so that intended movement is not disturbed by the reafferent sensory inputs. However, how quantitatively such an efference copy suppresses sensory inputs is unknown. Here, Canelo et al. tried to demonstrate that an efference copy operates in an all-or-none manner and that its amplitude is independent of the amplitude of the sensory signal to be suppressed. Understanding the nature of such an efference copy is important because animals generally move during sensory processing, and the movement would devastatingly distort that without a proper correction. The manuscript is concise and written very clearly. However, experiments do not directly demonstrate if the animal indeed uses an efference copy in the presented visual paradigms and if such a signal is indeed non-scaled. As it is, it is not clear if the suppression of behavioral response to the visual background is due to the act of an efference copy (a copy of motor command) or due to an alternative, more global inhibitory mechanism, such as feedforward inhibition at the sensory level or attentional modulation. To directly uncover the nature of an efference copy, physiological experiments are necessary. If that is technically challenging, it requires finding a behavioral signature that unambiguously reports a (copy of) motor command and quantifying the nature of that behavior.

      We thank the reviewer for this insightful and constructive comment. We agree that our current behavioral evidence does not directly identify the underlying circuit mechanism, and that direct recordings from visual neurons modulated by an efference copy would be critical for distinguishing between potential mechanisms.

      A prerequisite for such physiological investigations would be the identification of both (1) the feedforward neurons directly involved in the optomotor response, and (2) the neurons conveying motor-related signals to the optomotor circuit. Despite efforts by several research groups, the location of the feedforward circuit mediating the optomotor response remains elusive. This limitation has prevented us from obtaining direct cellular evidence of flight turn-associated suppression of optomotor signaling.

      In light of the reviewer’s suggestion, we expanded our investigation to strengthen the behavioral evidence for efference copy (EC) mechanisms. In addition to our earlier experiments involving unexpected changes in the static background, we examined how object-evoked flight turns influence the optomotor stability reflex and vice versa (Figures 5 and 6). To quantify the interaction between different visuomotor behaviors, we systematically varied the temporal relationship between two types of visual motion—loom versus moving background, or moving bar versus moving background—and measured the resulting behavioral responses.

      Our findings support pattern- and time-specific suppressive mechanisms acting between flight turns associated with the different visual patterns. Specifically:

      The responses to a moving bar and a moving background add linearly, even when presented in close temporal proximity.

      Loom-evoked turns and the optomotor stability reflex mutually suppress each other in a time-specific manner.

      For both loom- and moving bar-evoked flight turns, changes in the static background had no measurable effect on the dynamics of the object-evoked responses.

      These results provide a detailed behavioral characterization of a suppressive interaction between distinct visuomotor responses. This, in turn, offers correlative evidence supporting the involvement of an efference copy-like mechanism acting on the visual system. While similar efference copy mechanisms have been documented in other parts of the visual system, we acknowledge that our findings do not exclude alternative explanations. In particular, it is still possible that lateral inhibition within the central brain or ventral nerve cord contributes to the suppression we observed.

      Ultimately, definitive proof will require identifying the specific neurons that convey efference copy signals and demonstrating that silencing these neurons abolishes the behavioral suppression. Until such experiments are feasible, our behavioral approach provides an important contribution toward understanding the nature of sensorimotor integration in this system.

      Reviewer #3 (Public Review):

      Summary:

      Canelo et al. used a combination of mathematical modeling and behavioral experiments to ask whether flies use an all-or-none EC model or a graded EC model (in which the turn amplitude is modulated by wide-field optic flow). Particularly, the authors focus on the bar-ground discrimination problem, which has received significant attention in flies over the last 50-60 years. First, they use a model by Poggio and Reichardt to model flight response to moving small-field bars and spots and wide-field gratings. They then simulate this model and compare simulation results to flight responses in a yaw-free tether and find generally good agreement. They then ask how flies may do bar-background discrimination (i.e. complex visual environment) and invoke different EC models and an additive model (balancing torque production due to background and bar movement). Using behavioral experiments and simulation supports the notion that flies use an all-or-none EC since flight turns are not influenced by the background optic flow. While the study is interesting, there are major issues with the conceptual framework.

      Strengths:

      They ask a significant question related to efference copies during volitional movement.

      The methods are well detailed and the data (and statistics) are presented clearly.

      The integration of behavioral experiments and mathematical modeling of flight behavior.

      The figures are overall very clear and salient.

      Weaknesses:

      Omission of saccades: While the authors ask a significant question related to the mechanism of bar-ground discrimination, they fail to integrate an essential component of the Drosophila visuomotor responses: saccades. Indeed, the Poggio and Reichardt model, which was developed almost 50 years ago, while appropriate to study body-fixed flight, has a severe limitation: it does not consider saccades. The authors identify this major issue in the Discussion by citing a recent switched, integrate-and-fire model (Mongeau & Frye, 2017). The authors admit that they "approximated" this model as a smooth pursuit movement. However, I disagree that it is an approximation; rather it is an omission of a motor program that is critical for volitional visuomotor behavior. Indeed, saccades are the main strategy by which Drosophila turn in free flight and prior to landing on an object (i.e. akin to a bar), as reported by the Dickinson group (Censi et al., van Breugel & Dickinson [not cited]). Flies appear to solve the bar-ground discrimination problem by switching between smooth movement and saccades (Mongeau & Frye, 2017; Mongeau et al., 2019 [not cited]). Thus, ignoring saccades is a major issue with the current study as it makes their model disconnected from flight behavior, which has been studied in a more natural context since the work of Poggio.

      Thank you for this helpful comment. We agree that including saccadic turns is essential and qualitatively improves the model. In the revised manuscript, we therefore expanded our bar-tracking model to incorporate an integrate-and-saccade strategy, now presented in Figure 2—figure supplement

      The manuscript now introduces this result as follows:

      (Line#190) “Finally, one important locomotion dynamics that a flying Drosophila exhibits while tracking an object is a rapid orientation change, called a “saccade” (Breugel and Dickinson, 2012; Censi et al., 2013; Heisenberg and Wolf, 1979). For example, while tracking a slowly moving bar, flies perform relatively straight flights interspersed with saccadic flight turns (Collett and Land, 1975; Mongeau and Frye, 2017). During this behavior, it has been proposed that visual circuits compute an integrated error of the bar position with respect to the frontal midline and triggers a saccadic turn toward the bar when the integrated value reaches a threshold (Frighetto and Frye, 2023; Mongeau et al., 2019; Mongeau and Frye, 2017). We expanded our bar fixation model to incorporate this behavioral strategy (Figure 2--figure supplement 2). The overall structure of the modified model is akin to the one proposed in a previous study (Mongeau and Frye, 2017), and the amplitude of a saccadic turn was determined by the sum of the position and velocity functions (Figure 2--figure supplement 2A; see Equation 13 in Methods). When simulated, our model successfully reproduced experimental observations of saccade dynamics across different object velocities (Figure 2--figure supplement 2B-D) (Mongeau and Frye, 2017). Together, our models faithfully recapitulated the results of previous behavioral observations in response to singly presented visual patterns (Collett, 1980; Götz, 1987; H. Kim et al., 2023; Maimon et al., 2008; Mongeau and Frye, 2017).”

      Apart from Figures 1 and 2, most of our data—whether from simulations or behavioral experiments—use brief visual patterns lasting 200 ms or less. These stimuli trigger a single, rapid orientation change reminiscent of a saccadic flight turn. In this part of the paper, we essentially have examined how multiple visuomotor pathways interact to determine the direction of object-evoked turns when several visual patterns occur simultaneously.

      Critically, recent work showed that a group of columnar neurons (T3) appear specialized for saccadic bar tracking through integrate-and-fire computations, supporting the notion of parallel visual circuits for saccades and smooth movement (Frighetto & Frye, 2023 [not cited]).

      Thanks for bringing up this critical issue. We have now added this paper in the following part of the manuscript.

      (Line#193) “During this behavior, it has been proposed that visual circuits compute an integrated error of the horizontal bar position with respect to the frontal midline and triggers a saccadic turn toward the bar when the integrated value reaches a threshold (Frighetto and Frye, 2023; Mongeau and Frye, 2017).”

      (Line#462) “Visual systems extract features from the environment by calculating spatiotemporal relationships of neural activities within an array of photoreceptors. In Drosophila, these calculations occur initially on a local scale in the peripheral layers of the optic lobe (Frighetto and Frye, 2023; Gruntman et al., 2018; Ketkar et al., 2020).”

      A major theme of this work is bar fixation, yet recent work showed that in the presence of proprioceptive feedback, flies do not actually center a bar (Rimniceanu & Frye, 2023). Furthermore, the same study found that yaw-free flies do not smoothly track bars but instead generate saccades. Thus prior work is in direct conflict with the work here. This is a major issue that requires more engagement by the authors.

      Thank you for your thoughtful comments and for drawing our attention to this important paper. In our experiments, bar fixation on oscillating vertical objects emerges during the “alignment” phase of the magneto-tether protocol. The pattern movement dynamics was similar those used by Rimniceanu & Frye (2023), yet the two studies differ in a key respect: Rimniceanu & Frye employed a motion-defined bar, whereas we presented a dark vertical bar against a uniform or random-dot background. The alignment success rate—defined as the proportion of trials in which the fly’s body angle is within ±25° of the target—was about 50 % (data not shown). Our alignment pattern consisted of three vertical stripes spanning ~40° horizontally; when we replaced it with a single, narrower stripe, the success rate was lowered (data not shown). These observations suggest that bar fixation in the magnetically tethered assay is less robust than in the rigid-tethered assay, although flies still orient toward highly salient vertical objects.

      We also observed that bar-evoked turns were elicited more reliably when the bar moved rapidly (45° in 200 ms) in the magneto-tether assay, although the turn magnitude was significantly smaller than the actual bar displacement (Figure 3).

      In response to the reviewer’s comment, we now added the following description in the paper regarding the bar fixation behavior, citing Rimniceanu&Frye 2023.

      (Line#239) “Another potential explanation arises from recent studies demonstrating that proprioceptive feedback provided during flight turns in a magnetically tethered assay strongly dampens the amplitude of wing and head responses (Cellini and Mongeau, 2022; Rimniceanu et al., 2023).”

      Relevance of the EC model: EC-related studies by the authors linked cancellation signals to saccades (Kim et al, 2014 & 2017). Puzzlingly, the authors applied an EC model to smooth movement, when the authors' own work showed that smooth course stabilizing flight turns do not receive cancellation signals (Fenk et al., 2021). Thus, in Fig. 4C, based on the state of the field, the efference copy signal should originate from the torque commands to initiate saccades, and not from torque to generate smooth movement. As this group previously showed, cancellation signals are quantitatively tuned to that of the expected visual input during saccades. Importantly, this tuning would be to the anticipated saccadic turn optic flow. Thus the authors' results supporting an all-or-none model appear in direct conflict with the author's previous work. Further, the addition-only model is not particularly helpful as it has been already refuted by behavioral experiments (Rimneceanu & Frye, Mongeau & Frye).

      Thank you for this constructive comment. Efference copy is best established for brief, discrete actions like flight saccades. While motor-related modulation of visual processing has been reported across short- and long-duration behaviours (Chiappe et al., 2010; Fujiwara et al., 2017; Kim et al., 2015, 2017; Maimon et al., 2010; Turner et al., 2022), only flight saccade-associated signals exhibit the temporal profile appropriate to cancel reafferent input. However, von Holst & Mittelstaedt (1950) originally formulated efference copy to explain the smooth optomotor response of hoverflies. In HS/VS recordings in previous studies, however, we could not detect membrane-potential changes tied to baseline wing-beat amplitude (data not shown), but further work is needed. 

      Note that visually evoked flight turns analyzed in this paper have relatively fast dynamics. Fenk et al. (2021) showed that HS cells carry EC-like motor signals during both loom-evoked turns and spontaneous saccades. Building on this, we tested whether object-evoked rapid turns modulate other visuomotor pathways. Although Fenk et al. also found that optomotor turns lack motor input to HS cells, the authors did not test whether the optomotor pathway suppresses other reflexes, such as loom-evoked turns. Our new behavioral data (Figure 6) show that optomotor turns indeed suppress loom-evoked turns, suggesting a potential EC signal arising from the optomotor pathway that inhibits loom-responsive visual neurons.

      In Kim et al. (2017), the authors argued that HS/VS neurons receive a “quantitatively tuned” efference copy that varies across cell types: yaw-sensitive LPTCs are strongly suppressed, roll-sensitive cells receive intermediate input, and pitch-sensitive cells receive little or none. We also showed that when the amplitude of ongoing visual drive changes, the amplitude of saccade-related potentials (SRPs) scales linearly. This proportionality does not imply a genuinely graded EC, however, because SRP amplitude could vary solely through changes in driving force (Vm – Vrest) with a fixed EC conductance. Crucially, SRPs do not fully suppress feed-forward visual signalling, arguing against an all-or-none EC mechanism.

      How, then, can the cellular and behavioural data be reconciled? Silencing HS/VS neurons—or their primary inputs, the T4/T5 neurons—does not markedly diminish the optomotor response in flight (Fenk et al., 2014; Kim et al., 2017), indicating the presence of additional, as-yet-unidentified pathways.

      Physiological recordings from other visual neurons that drive the optomotor response in flying Drosophila are therefore needed to determine how strongly they are suppressed during loom-evoked turns.

      Behavioral evidence for all-or-none EC model: The authors state "unless the stability reflex is suppressed during the flies' object evoked turns, the turns should slow down more strongly with the dense background than the sparse one". This hypothesis is based on the fact that the optomotor response magnitude is larger with a denser background, as would be predicted by an EMD model (because there are more pixels projected onto the eye). However, based on the authors' previous work, the EC should be tuned to optic flow and thus the turning velocity (or amplitude). Thus the EC need not be directly tied to the background statistics, as they claim. For instance, I think it would be important to distinguish whether a mismatch in reafferent velocity (optic flow) links to distinct turn velocities (and thus position). This would require moving the background at different velocities (co- and anti-directionally) at the onset of bar motion. Overall, there are alternative hypotheses here that need to be discussed and more fully explored (as presented by Bender & Dickinson and in work by the Maimon group).

      We appreciate the reviewer’s important suggestion. In response, we performed the recommended experiment. In Figures 5 and 6 of the revised manuscript, we now present how bar- or loom-evoked flight turns affect the response to a moving background pattern. These experiments revealed that bar-evoked turns do not suppress the optic flow response, whereas loom-evoked turns strongly suppress it. Specifically, when background motion began 100 ms after the onset of loom expansion, the response to the background was significantly suppressed. Although weak residual responses to the background motion were observed in this case, this could be due to background motion occurring outside of the suppression interval, which may correspond in duration to the duration of flight turns (Figure 6C,D). 

      The lack of suppression of the optic flow response during and after bar-evoked turns appears to suggest that the responses are added linearly (Figure 5), seemingly contradicting the lack of dynamic change when the background dot density was altered (Figure 7, Figure 7–figure supplement 1). That is, the experimental result in Figure 5 supports either an addition-only or a graded efference copy (EC) model. However, the result in Figure 7 supports an all-or-none EC model. If a graded EC were used, the amplitude of the EC should be updated almost instantaneously when the static background changes.

      Another possibility is that the optic flow during self-generated turns in a static background is extremely weak compared to the optic flow input generated by physically moving the pattern, perhaps due to the rapid nature of head movements. Indeed, detailed kinematic analysis of head movement during spontaneous saccades in blow flies revealed that the head reaches the target angle before the body completes the orientation change, making the effective speed of reafferent optic flow higher than the speed of body rotation (Hateren and Schilstra, 1999). To test these hypotheses, further experiments will be needed for bar-evoked flight turns.

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      The reviewers identified two key revisions that could improve the assessment of the paper:

      (1) Consideration of saccades within the model framework (outlined by reviewer 3).

      (2) Addition of physiology data to support the conclusions of the paper (outlined by reviewer 2). If this is not feasible within the timescale of revisions, the paper would need to be revised to clarify that the model leads to a hypothesis that would need to be tested with future physiology experiments.

      Thank you for these comments.

      Regarding revision point #1, we have added Figure 2–figure supplement 2, where we incorporated our position-velocity model (estimated in Figure 1) into the framework of the integrate-and-saccade model. A detailed description of this model is now provided in the main text (Lines 190–203).

      For revision point #2, obtaining electrophysiological evidence for efference copy remains challenging, as neither the visual neurons nor the efference-copy neuron has been identified for the wing optomotor response. As suggested by the reviewers, we have revised the title of the paper to reduce emphasis on efference copy and have noted electrophysiological recordings as a direction for future work.

      old title: A visual efference copy-based navigation algorithm in Drosophila for complex visual environments

      new title: Integrative models of visually guided steering in Drosophila

      Specific recommendations are detailed below.

      Reviewer #2 (Recommendations For The Authors):

      To directly demonstrate if an efference copy is non-scaled, the following experiments can be helpful: record from HS/VS cells and examine the relation between the amplitude of the succade-suppression signal vs. succade amplitude.

      Thanks for raising this important point. We previously carried out the suggested analysis for loom-evoked saccades in Fenk et al. (2021). There, significant correlations emerged between wing-response amplitude and saccade-related potentials (Figures 2F and 3C). However, we did not interpret the strong correlation (r ≈ 0.8) as evidence for a graded efference copy, because the amplitude of saccade-related potentials appeared to be bimodal. Upon presentation of the looming stimulus, flies either executed large evasive turns or showed minimal changes in wing-stroke amplitude. Large wing responses were accompanied by strong, saturated suppression of HS-cell membrane potential, whereas trials without wing responses produced only weak modulations—reflected in the bimodal distribution of saccade-related potential amplitudes (Figure 3C). 

      Importantly, in rigidly tethered preparations—where these potentials are typically measured—the absence of proprioceptive feedback can itself drive wingbeat amplitudes to saturation during saccades. We therefore reasoned that the lack of intermediate-sized flight saccades would naturally yield correspondingly saturated saccade-related potentials, even if a graded EC system is in play. 

      In Kim et al. (2017), we also performed a comprehensive analysis of spontaneous saccade-related potentials across all HS/VS cell types. When we later examined the relationship between saccade amplitude and the corresponding saccade-related potentials in each cell type, we could not find any statistically significant correlation (unpublished data).

      measure how much a weak visual stimulus and a strong visual stimulus are suppressed by the suppression signal. If the signal is non-scaled, visual stimuli should always be suppressed independently of their intensities.

      Thank you for this important suggestion. As mentioned in our response to the previous comment, we believe it is not feasible to record from neurons responsible for the body optomotor response at this point, as their identity remains unknown. Regarding the HS/VS cells, our previous study showed that HS cells are not always fully suppressed. The changes in saccade-related potential amplitude can be described as a linear function of the pre-saccadic visually-evoked membrane potential (Figure 7 in Kim et al., 2017). 

      As suggested by Fenk et al. 2014 (doi: 10.1016/j.cub.2014.10.042), HS cells might also be responsive to a moving bar. If that is the case, and if you present a bar and background (either sparse or dense) in a closed-loop manner to a head-fixed fly, HS cells might be sensitive only to the bar but not to the background (independently of the density).

      Thanks for pointing out this important issue. HS cells indeed respond strongly to the horizontal movement of a vertical bar, as expected given that their receptive fields are formed by the integration of local optic flow vectors. In one of our previous studies (Supplemental Figure 1 in Kim et al., 2015), we showed that the response amplitude to a single vertical bar is roughly equivalent to that elicited by a vertical grating composed of 12 bars of the same size. Therefore, we believe that HS cells are likely to contribute to the head response to a moving vertical bar. In a body-fixed flight simulator, HS cells would respond only to the bar if the bar runs in a closed loop with a static background. In this scenario, HS cells are likely to play a role in the head optomotor response.

      Note also that the role of HS cells in the wing optomotor response remains unresolved. Unilateral activation of HS cells has been shown to elicit locomotor turns in walking Drosophila (Fujiwara et al., 2017), as well as in flying individuals (unpublished data from our lab). However, a previous study also showed that strong silencing of HS/VS cells significantly reduced the head optomotor response, but not the wing optomotor response (Kim et al., 2017).

      If neurophysiology is technically challenging, an alternative way might pay attention to a head movement that exclusively follows the background (Fox et al., 2014 (doi: 10.1242/jeb.080192)). Because HS cells are thought to promote head rotation to background motion, a non-scaled suppression signal on HS cells would always suppress the head rotation independently of the background density.

      Thanks for this helpful comment. We have analyzed head movements during bar-evoked flight turns (Figure 7–figure supplement 1B) and found no significant changes across different background dot densities. We think that this might suggest that HS cells are unlikely to receive suppressive inputs during bar-evoked turns, akin to the lack of modulation during optomotor turns.

      Another way to separate a potential efference copy from other mechanisms (more global inhibition) is the directionality. A global inhibition would suppress the response to the background even if the background moves in the same direction as self-motion, but the efference copy would not.

      Thanks for this important point. In Heisenberg and Wolf, 1979, it was proposed that modulation might be bidirectional, with behavioral effects observed only for perturbations in the “unexpected” direction. In our new data on loom-evoked turns (Figure 6), the suppression appears equally strong for background motion in either direction, supporting an all-or-none suppression mechanism.

      Besides, in general, it is unclear if you think an efference copy operates both in smooth pursuits and saccades or if such a signal is only present during saccades. Your previous neurophysiological work supports the latter. Are your behavioral results consistent with the previous saccade suppression idea, or do you propose a new type of efference copy that also operates in smooth pursuits?

      Thanks for raising this important point. von Holst and Mittelstaedt (1950) originally introduced the concept of efference copy to explain the smooth optomotor response. We previously analyzed electrophysiological recordings from HS cells for membrane-potential changes associated with slow deviations in wing-steering angle but found none. However, this negative result does not entirely rule out modulation of visual processing during smooth flight turns, given the slow drift in membrane potential observed in most whole-cell recordings.

      In this study, We examined only the interactions among visuomotor pathways during these rapid flight turns as the dynamics of visually evoked turns are almost as rapid as spontaneous saccades. Our data reveal that interactions between distinct visuomotor reflexes are more diverse than previously appreciated.

      Minor comments:

      Line 108, 109: match the description between here and the labels in Fig. 1F.

      Thank you for indicating this issue. We have defined the general equation to obtain the position and velocity components in the main text lines 108,109, but due to a slight asymmetry in the data (Fig. 1E) we used the approach indicated in Fig. 1F. and explained in lines 113-117.

      Fig.1 F: If the position-dependent component is due to fatigue, the tuning curve's shape is likely changed (shrunk or extended) depending on the stimulus speed. How can you generalize the tuning curve shown here? Does the result hold even if the stimulus speed/contrast/spatial frequency is changed?

      We appreciate this indication. We believed that fatigue may be the reason why the wing response to the grating stimulus showed that significant decay (Fig. 1E). As you mention, the stimulus speed would increase the amplitude of the fly’s response up to a saturation point. We addressed this in our model by multiplying the derived value by the angular velocity of the grating.

      Regarding the contrast, and spatial frequency we did not test it experimentally, instead, we simulated our model for changing visual feedback (Fig. 4A, B), which can be seen as increasing/decreasing contrast of a grating. An increase in the contrast would increase the response of the fly to the grating and so will contribute to dampening the response to the foreground object (Fig. 4C).

      Line 233-255: Here, the description sounds like you will consider several parallel objects (e.g., two stripes) in the visual field instead of the combination of the figure and background (which is referred to in the following paragraph).

      Thank you for pointing it out. Indeed it was slightly ambiguous. We have addressed this by explaining the specific situation of a combination of an object and the background in lines 231-233.

      Figure 6C: you kept the foreground visual field between sparse and dense random dot backgrounds to keep the bar's saliency. Is it sure that this does not influence the difference in the fly's response to these two backgrounds (in Figure 6B)?

      This is a good point that we have also discussed internally. We also carried out similar experiments with a fully covered background and found no significant differences (Figure 7–figure supplement 1).

      Reviewer #3 (Recommendations For The Authors):

      Identify and analyze flight saccade dynamics in the raw trajectories (e.g., Fig. 3B). There should be some since the bar is near the 'sweet spot' for triggering saccades (see Mongeau & Frye, 2017).

      Thank you for bringing up this interesting point. In previous work, it was reported that the fly fixated on a vertical bar through saccadic turns rather than smooth-tracking (Mongeau & Frye, 2017). When the bar width was thin (<15 deg) there was barely one saccade per second (Mongeau & Frye, 2017, Fig. 4). In our magno tether essay (Fig. 3A, B) the object width was 11.25 degrees, and the object moved for a short time window, and so the fly only generated the saccade related to the onset of the object. It could not be considered as a saccade some small turns of a few degrees that are likely related to small perturbations in comparison to those previously reported (Mongeau & Frye, 2017). Additionally, in our protocol (Fig. 3A) from onset time (‘go’ mark), only a single object moved, within an empty background, so in principle there is no trigger for a switch to a smooth movement. We addressed this in lines x-x.

      Consider updating the Poggio model with flight saccades (switched, integrate-and-fire).

      We appreciate this suggestion. Following previous work (Mongeau et al., 2017), we expanded our model to include a saccade mechanism: the torque produced by the summed position- and velocity-dependent components is now replaced by an integrate-and-fire saccade (Figure 2—figure supplement 2). We optimized the saccade interval and amplitude so that both vary linearly with stimulus amplitude and faithfully reproduce the kinematic properties reported previously (Mongeau et al., 2017).  

      Please engage more with the literature, especially work that directly conflicts with your conclusions (see above). Also, highly relevant work by Bender & Dickinson was not sufficiently discussed. Spot results presented in Fig. 3 should be contextualized in light of the work of Mongeau et al., 2019, who performed similar experiments and identified a switch in saccade valence.

      We appreciate your pointing out the relevant previous work. We have added references to the following papers and tried to describe the relationship between our data and previous ones.

      Bender & Dickinson 2006

      (Line#162) “This simulation experiment is reminiscent of the magnetically tethered flight assay, where a flying fly remains fixed at a position but is free to rotate around its yaw axis (Bender and Dickinson, 2006b; Cellini et al., 2022; G. Kim et al., 2023; Mongeau and Frye, 2017).”

      (Line#218) “We tested the predictions of our models with flies flying in an environment similar to that used in the simulation (Figure 3A). A fly was tethered to a short steel pin positioned vertically at the center of a vertically oriented magnetic field, allowing it to rotate around its yaw axis with minimal friction (Bender and Dickinson, 2006b; Cellini et al., 2022; G. Kim et al., 2023).”

      (Line#238) “To determine if our assay imposes additional friction compared to other assays used in previous studies, we analyzed the dynamics of spontaneous saccades during the “freeze” phase (Figure 3–figure supplement 1A). We found their duration and amplitude to be within the range reported previously (Bender and Dickinson, 2006b; Mongeau and Frye, 2017) (Figure 3–figure supplement 1B-D). 

      Mongeau et al., 2019

      (Line#196) “During this behavior, it has been proposed that visual circuits compute an integrated error of the bar position with respect to the frontal midline and triggers a saccadic turn toward the bar when the integrated value reaches a threshold (Frighetto and Frye, 2023; Mongeau et al., 2019; Mongeau and Frye, 2017). We expanded our bar fixation model to incorporate this behavioral strategy (Figure 2–figure supplement 2).”

      This paper shows that the dynamics of saccadic flight turns elicited by a rotating bar or spot determine whether flies display attraction or aversion. In that study, the visual stimulus—a bar or spot—rotated slowly at a constant 75 deg s⁻¹. By contrast, in our Figure 3 the object moves much faster, driving the neural “integrator” to saturation and triggering an almost immediate flight turn. In Mongeau et al. (2019), saccades occur at variable times and their amplitudes and directions are more stochastic, again reflecting the slower stimulus speed. Because these differences all arise from the disparity in object speed, we did not cite Mongeau et al. (2019) in Figure 3 or the associated text.

      In addition to the two papers cited above, we have incorporated several relevant studies on the Drosophila visuomotor control identified through the reviewers’ insightful comments. Examples include:

      Frighetto G, Frye MA. 2023 (Line#195, 464)

      Rimniceanu et al., 2023 (Line#241)

      Cellini & Mongeau 2020 (Line#91)

      Cellini & Mongeau 2022 (Line#241)

      Cellini et al., 2022 (LIne#91, 162, 218)

      Many citations are not in the proper format (e.g. using numbers rather than authors' last name).

      Thank you for letting us know. We have changed the remaining citations to the proper format.

    1. There are 35 required assignments in this course, not including Sub Previews, the Sub Assessment, and your Proctored Event.  When you meet the standard on 32-35 assignments, you earn an A! meet the standard on 29-31 assignments = B meet the standard on 26-28 assignments = C meet the standard on 23-25 assignments = D if fewer than 23 assignments meet the standard, it will yield an F grade.

      I was surprised that this course didn't follow the normal grading scale just because I assumed all courses would follow a general scale. As a student, I've always been a little afraid of rubric grading because getting feedback makes me nervous but now that I'm in the teaching field, I understand why it's important. It's important to get feedback so you know what you need to improve on so you can be your best teaching self.

    1. The classicist M. L. West gave this construction a name: the augmented triad. It appears to be very old, and not just because it’s found in Homer. It’s also found in Vedic poetry (that is, composed in an early stage of the Sanskrit language) as well as Celtic and Germanic verse. Some examples, courtesy of M. L. West’s book Indo-European Poetry and Myth (pp 118–119).Vedic Sanskrit:Tváṣṭā, Savitā́, [suyáma Sárasvatī]‘Tvashtr, Savitr, and [easily-guided Sarasvati]’ (Rigveda 9.81.4)Old Irish (Celtic):trí meic Nóe nair cech neirt:Sem, Cam, [Iafet aurdairc.]‘Three sons of Noah, of every (kind of) strength:Shem, Ham, [Japheth the glorious]’ (Lebor Gabála Érenn, 189–190)Old Norse (Germanic):Vara sandr né sær né [svalar unnir].‘There was not sand nor sea nor [the cool waves].’ (Vǫluspá 3)The presence of the augmented triad in not only Greek, but in Celtic, Germanic, and Vedic Sanskrit (see what I did there?) means that it was likely inherited from the common ancestor of all four.

      proto-mindset... but the comparisons are cool!

    1. Gossipsacrifices consistency for availability

      Ehh, it doesn't? It's just a mean of passing info around. You could have one "master" machine and a gossip network that receive updates to its state. You could have a distributed consensus algo atop gossip, so every peer has a consistent ever-growing tx log.

    1. Royal jelly This is the mix of compounds that worker bees generate and feed to their queen. Queen bees live for up to 20 years, while workers (with the same genome) die after just a few weeks. There is one study (Inoue 2003) in the DrugAge database feeding royal jelly to mice, and yes, at higher dosages, 15 mice lived an average 25% longer. As you can see below, early mortality was greatly reduced, but maximum lifespan wasn’t affected.

      i don’t know that i actually buy this mattering but for fiction it’s fantastic

    1. first half of this phrase it's it's got this a Dorian feeling because the baseline walks down from D to C sharp and then it goes to C natural and then to be and that descending chromatic line so it implies a shift from Dorian to natural minor and also just descending chromaticism is always sad which i think is why Paul chose it

      nice analysis! 好棒的觀察和分析 1 漸降半音走勢:悲傷 2 同時顯示,和諧從多利安轉回自然小調

    1. Almost always soft delete I will reiterate that storage is cheap and recovering data is a nightmare. If you have some domain specific need to delete (or otherwise mark as irrelevant) some data, use a nullable timestamptz column. If there is a timestamp filled in, that's when it was deleted. If there is no timestamp it isn't deleted yet.

      Another interesting idea, making restore easy. A reddit comment argues with this tho:

      I HATE (!!!) soft deletes. Can't express how much I loathe them. You end up with every view and every query needing to remember the "WHERE revoked_at IS NULL" clause or you end up with messed up results. Instead, you make a history table that matches your main table and create a delete trigger that copies the deleted row to the history. Just UNION ALL (or JOIN) to get the history results too. And on Postgres, updating a single revoked_at column writes a whole new row; it does NOT just update the one part of the row, so it ain't even a cheap update.

      Separate history tables are so much better. Along with that, it's good to have multiple roles/users in the database so you can track not just what was deleted but who deleted it. Doesn't have to the Postgres user. Could be the app user.

    1. With these points in mind the overarching message to take from the discussion is that the digital literacy movement cannot be separated from deeper ideological and philosophical questions concerning the nature of the good society and the purpose of the education system. Put more simply, digital literacies have relatively little to do with mastering specific keystrokes

      I agree - digital literacy is more than just understanding how technologies work or navigating the digital landscape. It’s shaped by socio-economic, political, cultural, and societal factors that determine who even gets the chance to engage with digital tools. Opportunities to become digitally literate are unevenly distributed, and if we only focus on how to become digitally literate without addressing who has access to those opportunities, the divide between the digitally included and excluded will only deepen. How soceity will evolve as a whole will very much be influenced by how this divide is addressed!

    1. Reviewer #2 (Public Review):

      Summary:

      This paper describes a new approach to detecting directed causal interactions between two genes without directly perturbing either gene. To check whether gene X influences gene Z, a reporter gene (Y) is engineered into the cell in such a way that (1) Y is under the same transcriptional control as X, and (2) Y does not influence Z. Then, under the null hypothesis that X does not affect Z, the authors derive an equation that describes the relationship between the covariance of X and Z and the covariance of Y and Z. Violation of this relationship can then be used to detect causality.

      The authors benchmark their approach experimentally in several synthetic circuits. In 4 positive control circuits, X is a TetR-YFP fusion protein that represses Z, which is an RFP reporter. The proposed approach detected the repression interaction in 2 of the 4 positive control circuits. The authors constructed 16 negative control circuit designs in which X was again TetR-YFP, but where Z was either a constitutively expressed reporter, or simply the cellular growth rate. The proposed method detected a causal effect in two of the 16 negative controls, which the authors argue is perhaps not a false positive, but due to an unexpected causal effect. Overall, the data support the potential value of the proposed approach.

      Strengths:

      The idea of a "no-causality control" in the context of detected directed gene interactions is a valuable conceptual advance that could potentially see play in a variety of settings where perturbation-based causality detection experiments are made difficult by practical considerations.

      By proving their mathematical result in the context of a continuous-time Markov chain, the authors use a more realistic model of the cell than, for instance, a set of deterministic ordinary differential equations.

      The authors have improved the clarity and completeness of their proof compared to a previous version of the manuscript.

      Limitations:

      The authors themselves clearly outline the primary limitations of the study: The experimental benchmark is a proof of principle, and limited to synthetic circuits involving a handful of genes expressed on plasmids in E. coli. As acknowledged in the Discussion, negative controls were chosen based on the absence of known interactions, rather than perturbation experiments. Further work is needed to establish that this technique applies to other organisms and to biological networks involving a wider variety of genes and cellular functions. It seems to me that this paper's objective is not to delineate the technique's practical domain of validity, but rather to motivate this future work, and I think it succeeds in that.

      Might your new "Proposed additional tests" subsection be better housed under Discussion rather than Results?

      I may have missed this, but it doesn't look like you ran simulation benchmarks of your bootstrap-based test for checking whether the normalized covariances are equal. It would be useful to see in simulations how the true and false positive rates of that test vary with the usual suspects like sample size and noise strengths.

      It looks like you estimated the uncertainty for eta_xz and eta_yz separately. Can you get the joint distribution? If you can do that, my intuition is you might be able to improve the power of the test (and maybe detect positive control #3?). For instance, if you can get your bootstraps for eta_xz and eta_yz together, could you just use a paired t-test to check for equality of means?

      The proof is a lot better, and it's great that you nailed down the requirement on the decay of beta, but the proof is still confusing in some places:

      On pg 29, it says "That is, dividing the right equation in Eq. 5.8 with alpha, we write the ..." but the next equation doesn't obviously have anything to do with Eq. 5.8, and instead (I think) it comes from Eq 5.5. This could be clarified.

      Later on page 29, you write "We now evoke the requirement that the averages xt and yt are stationary", but then you just repeat Eq. 5.11 and set it to zero. Clearly you needed the limit condition to set Eq. 5.11 to zero, but it's not clear what you're using stationarity for. I mean, if you needed stationarity for 5.11 presumably you would have referenced it at that step.

      It could be helpful for readers if you could spell out the practical implications of the theorem's assumptions (other than the no-causality requirement) by discussing examples of setups where it would or wouldn't hold.

    1. This manuscript attempts to provide an answer to the proportion of scientific papers that are fake. The presence of fake scientific papers in the literature is a serious problem, as the author outlines. Papers of variable quality and significance will inevitably be published, but most researchers assess manuscripts and papers based on the assumption that the described research took place. Papers that disguise their identities as fake papers can therefore be highly damaging to research efforts, by preventing accurate assessments of research quality and significance, and by encouraging future research that could consume time and other resources. As the manuscript describes, fake papers are also damaging to science by eroding trust in the scientific method and communities of scientists.

      It is therefore clear that knowing the proportion of fake scientific papers is important, that the author is concerned about the problem, and that the author wants to arrive at an answer. However, as the manuscript partly recognises, the question of the overall proportion of fake scientific papers is currently difficult to answer.

      The overall proportion of fake papers in science will represent the individual proportions of fake papers in different scientific disciplines. In turn, the proportions of fake papers in any single discipline will reflect many factors, including (i) researcher incentives to produce fake papers, (ii) the ease with which fake papers can be produced and (iii) published, (iv) the ease or likelihood of fake papers being detected, before or (v) after publication, and (vi) the consequences for authors if they are found to have published fake papers. Some of these factors are likely to vary between different disciplines and in different research settings. For example, it has been suggested that it is similarly difficult to invent some research results as it is to produce genuine data. However, in other fields, it is easier to invent data than to generate data through experiments that remain difficult, expensive and/or slow. It is also likely that factors such as the capacity to invent fake papers, detect fake papers, as well as incentives and consequences for researchers could vary over time, particularly in response to generative AI.

      As someone who studies errors in scientific papers, I don’t believe that we currently have a good understanding of the proportions of fake papers in any individual scientific field, at any time. There are some fields where we have estimates of individual error types, but these error types are likely to wrongly estimate the overall proportions of fake papers. Rather than attempting to answer the question of the overall proportion of fake scientific papers in the absence of the necessary data, it seems preferable to describe how we could obtain the data that we need to answer this question. While the overall proportion of fake scientific papers is an important statistic, most scientists will also be more concerned about how many fake papers exist in their own fields. We could therefore start by trying to obtain reliable estimates of fake papers in individual fields, working out how we need to do this, and then carrying out the necessary research. In the absence of reliable data, it’s perhaps most important that researchers are aware that fake papers could exist in their fields, so that all researchers can assess papers more carefully.

      Beyond these broad considerations, the following manuscript elements could be reconsidered.

      1. Fake science is defined as fabricated or falsified, yet this definition is sometimes expanded to include plagiarism (page 8, Table 2). However, plagiarism doesn’t equate with faking or falsifying data, and some plagiarised articles could describe sound data. Including plagiarised articles as fake articles will inevitably inflate estimates of fake papers, particularly in fields with higher rates of plagiarism.

      2. Table 1 was stated to represent “a selection of events that took place after the figure above (ie the figure published by Fanelli (2009)) was established”, yet some listed references/ events were published/ occurred between 2005 and 2008.

      3. It is reasonable to expect that increased capacity to autogenerate text and images will increase the numbers of fake papers, but I’m not aware of any evidence to support this. No reference is cited.

      4. Table 2; “similar survey results”: it’s not clear how the listed studies are similar.

      5. There are many unreferenced statements, eg page 9, “most rejected papers are published, just elsewhere”, page 19.

      6. Some estimates of fake papers arise from small sample sizes (eg page 13).

      7. The statement “The accumulation of papers assembled here is, frankly, haphazard” doesn’t inspire confidence in the resulting estimate.

      8. “…it would be prudent to immediately reproduce the result presented here as a formal systematic review”- any systematic review seems premature without reliable estimates.

      9. “The false positive rate (FPR) of detecting fake science is almost certainly quite low”- this seems unlikely to be correct. False positive rates depend on the methods used. Different methods will be required to detect fake papers in different disciplines, and these different methods could have very different false positive rates, particularly when comparing the application of manual versus automated methods that are applied without manual checking.

      10. Page 2: I could not see the n=12 studies summarised in a single Table.

      11. Page 10: “All relevant studies were included”…. “The list below is comprehensive but not necessarily exhaustive”- these statements contradict each other.

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

      Learn more at Review Commons


      Reply to the reviewers

      We would like to thank the reviewers for taking the time to review our manuscript and for providing valuable comments on how to improve it. We are pleased to see that both reviewers recognize the novelty and importance of our study, its conceptual advance and potential clinical significance. They also noted the novelty and value of our functional mechanistic approach using epigenetic editing. Below, we provide a point-by-point response to their questions and points raised. The changes introduced in response to their feedback are highlighted in yellow in the revised manuscript file.

      Point-by-point description of the revisions

      __Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

      Summary This study by Prada et al. aimed to explore DNA methylation and gene expression in primary EpCAMhigh/PDPNlow cells, consisting of for (probably) the largest part of AT2 cells, to understand the molecular mechanisms behind the impaired regeneration of alveolar epithelial progenitor cells in COPD. They found that higher or lower promoter methylation in COPD-associated cells was inversely correlated with changes in gene expression, with interferon signaling emerging as one of the most upregulated pathways in COPD. IRF9 was identified as the master regulator of interferon signaling in COPD. Targeted DNA demethylation of IRF9 in an A549 cell line resulted in a robust activation of its downstream target genes, including OAS1, OAS3, PSMB8, PSMB9, MX2 and IRF7, demonstrating that demethylation of IRF9 is sufficient to activate the IFN signaling pathway, validating IRF9 as a master regulator of IFN signaling in (alveolar) epithelial cells.

      Major comments:

      • To remove airways (and blood vessels) completely from the lung tissue is difficult, if not impossible. This means that the assumption that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells remains valid only if a quantitative analysis is conducted on the proportion of HT2-280pos cells in all samples in cytospins to exclude any significant contamination from bronchial epithelial cells. If authors cannot demonstrate >95% pure HT-280-positive cells, then the key conclusions suggesting that the epigenetic regulation of the IFN pathway might be crucial in AT2 progenitor cell regeneration could also potentially apply to bronchial progenitor cells. In addition, if >95% purity cannot be demonstrated, the data should be adjusted to account for differences in cell type composition.

      __Response: __

      We thank the reviewer for raising this important point. Although, as pointed out by the reviewer, we cannot guarantee that our sorted cells do not contain a minor contamination from respiratory / terminal bronchial cells, we carefully selected donors, tissue regions, and sorting strategy to ensure the highest possible enrichment of AT2 cells, as we explain below. We have now expanded the methods and results section and covered this point in the manuscript discussion.

      • The lung tissue pieces we received were distal, as evidenced by the presence of pleura. We collected representative tissue pieces for histology to validate sample quality. Our protocol includes a dissection of all visible airways and vessels using a dissecting microscope, which were cryopreserved separately from distal parenchyma. Hence, the starting material for tissue dissociation was depleted from airways and vessels. The importance of vessel/airway removal for enrichment of distal alveolar cells was established by Tata's group (PMID: 35712012).
      • We selected the AT2 sorting protocol (EpCAMpos/PDPNlow) based on previous publications that used tissue from both healthy and COPD lungs to separate AT2 cells from AT1 and airway basal cells, as AT1 and basal cells are both PDPNhigh (PMID: 22033268, PMID: 23117565; PMID: 35078977). This protocol was favoured due to the lack of information about HT2-280 expression and distribution in COPD lungs.
      • The sort quality for each sample was assessed by the FACS analysis (back sorting) of the sorted cells, where we observed 95-97% purity (EpCAMpos/PDPNlow, __ 1G __shown below). In addition, we validated the sorting protocol and high AT2 enrichment from both no COPD and COPD tissues by immunostaining the FACS-sorted cells with HT2-280, an AT2 marker widely used in the field (strategy suggested by the reviewer) and observed that close to 100% of cells were positive for this marker (__Fig. 1H __shown below). However, we could not do it retrospectively for those patients, where we didn't have enough material. Sorting primary AT2 from small tissue pieces is challenging, and we need at least 20.000 cells to obtain high-quality methylation & RNA-seq data.
      • AT2 marker genes (ABCA3, LPCAT1, LAMP3 and the surfactant genes SFTPA2, SFTPB and SFTPC) were among the top highly expressed genes in our RNA-seq data and were not significantly changed in COPD (please see expression data in __ S2A__ in the manuscript, and below for convenience), as well as Table 6, providing further evidence that the sorted cells carry a strong AT2 transcriptional signature. Fig. 1G* FACS plot examples showing the analysis of sorted AT2 cells (back sorting) from control (blue) and COPD (green) donors displayed over total cell lung suspensions (grey) H Representative IF staining of HT2-280 expression in sorted AT2 cells from no COPD (top) and COPD (bottom) donors. Nuclei (blue) were stained with DAPI, scale bars=20µm __Fig. S2A __Normalized read counts from RNA-seq data for AT2-specific genes in sorted AT2 cells from each donor (dots). Data points represent normalised counts from no COPD (blue), COPD I (light green) and COPD II-IV (dark green). Group median is shown as a black bar. *

      • In agreement with a previous study which profiled bulk AT2 using expression arrays (PMID: 23117565), we also observed upregulation of IFN signaling pathway in COPD AT2s. The enrichment of IFNα/β signature was also observed in COPD in the inflammatory AT2 cluster (iAT2) in a recent scRNA-seq study (PMID: 36108172). As part of the revision, we compared the IFN gene signature identified in our bulk AT2 RNA-seq with a recent scRNA-seq study (published after the submission of our manuscript, PMID: 39147413) that profiled EpCAMpos cells from COPD and non-smoker donor lungs. We observed an upregulation of our IFN signature genes in AT2 in COPD (mostly in AT2c and rbAT2 subsets), suggesting that similar signatures were observed in COPD AT2s in this dataset as well (please see __ S4E-F__ below). ____Figure S4E Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (Hu et al, 2024). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from Hu et al, 2024. The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells.

      • We have also carefully examined DNA methylation profiles across all samples. The density plots of our T-WGBS DNA methylation data are very similar among the individual samples in all 3 groups, indicating that the sorted cells consist mostly of a single cell type, as there are no obvious intermediate (25-75%) methylation peaks, as observed in cell mixtures ( 2A and the panel below). No reference DNA methylation profiles are available for respiratory or terminal bronchial cells; hence, we cannot compare how epigenetically different these cells would be from AT2 nor perform a deconvolution for potential minor contamination with distal airway cells. *Figure: DNA methylation density plots of sorted EpCAMpos/PDPNneg cells from no COPD (blue, n=3), COPD I (light green, n=3) and COPD II-IV (dark green, n=5) showing a homogeneous methylation pattern and low abundance at intermediate (25%-75%) methylation values across all profiled samples, indicating that the sorted cells were mostly of a single cell type. *

      • We have now added a sentence to the limitations section of the discussion to cover that point specifically. CHANGES IN THE MANUSCRIPT:

      AT2 cells were isolated by fluorescence-activated cell sorting (FACS) from cryopreserved distal lung parenchyma, depleted of visible airways and vessels of three no COPD controls, three COPD I and five COPD II-IV patients as previously described (24, 52, 53)

      The isolated cells were positive for HT2-280, a known AT2 marker (54)*, as confirmed by immunofluorescence (Fig. 1H), validating the identity and high enrichment of the isolated AT2 populations. ** *

      *Known AT2-specific genes, including ABCA3, LAMP3 and surfactant genes (SFTPA2, SFTPB and SFTPC) were among the top highly expressed genes and were not significantly changed in COPD AT2s (Fig. S2A, Table 6), further confirming the AT2-characteristic transcriptional signature of our isolated cells. *

      However, 5-AZA is a global demethylating agent, and the observed effects may not be direct. To validate the epigenetic regulation of central AT2 pathways further, we took advantage of locus-specific epigenetic editing technology *(73). We focused on the IFN pathway because it was the most significantly enriched Gene Ontology (GO) term in our integrative analysis of TWGBS and RNA-seq data. Several IFN pathway members had associated hypomethylated DMRs within promoter-proximal regions and concomitant increased gene expression (Fig. 4C and S2C). Additionally, we confirmed the elevated expression of IFN-related genes with associated DMRs identified in our study in AT2 cells and AT2 cell subclusters from a recently published scRNA-seq cohort (74) (Fig. S4E-F). *

      We observed upregulation of multiple IFN genes in AT2 in COPD, consistent with a previous expression array study (24). IFNα/β signaling was also enriched in COPD patients in the inflammatory AT2 cluster (iAT2) in a recent scRNA-seq study (84) and our INF signature genes were also upregulated in AT2c and AT2rb subsets in COPD, identified by another scRNA-seq study recently (74)*. ** *

      Finally, despite careful removal of airways from distal lung tissue using a dissecting microscope, we cannot exclude the presence of some terminal/respiratory bronchiole cells in our FACS-isolated EpCAMpos/PDPNlow population. Recent scRNA-seq studies provided an unprecedented resolution and identified several epithelial subpopulations and transitional cells residing in the terminal/respiratory bronchioles and alveoli, including respiratory airway secretory cells (93), terminal airway-enriched secretory cells (28), terminal bronchiole-specific alveolar type-0 (AT0) (70), and emphysema-specific AT2 cells (74). These cells may contribute to alveolar repair in healthy and COPD lungs; however, with our bulk DNA methylation and RNA-seq study, we are unable to resolve all these subpopulations. Future development of single-cell methylation and non-reference-based algorithms for DNA methylation deconvolution will enable deeper epigenetic phenotyping of specific AT2 and bronchiolar cell subsets.

      (Methods) Validation of IFN gene upregulation in a published scRNA-seq dataset

      scRNA-seq data from (74), generously provided by M. Köningshoff, were processed using the default Seurat workflow (117). Expression of IFN-related genes was extracted and plotted as log-normalised gene expression levels in AT2 cells from control and COPD donors. Seurat's AddModuleScore() function was used to compute a gene set score for a custom IFN program using the genes listed in __Fig. S4E __and to analyse the IFN gene set scores in AT2 cell subclusters identified in (74). Briefly, average gene expression scores were computed for the gene set of interest, and the expression of control features (randomly selected) was subtracted as described in (118).

      Fig. S4E and F: E. Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74). The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells.

      • The overrepresentation of several keratins (KRT5, KRT14, KRT16, KRT17), mucins (MUC12, MUC13, MUC16, MUC20) and the transcription factor FoxJ1 is now attributed by the authors to a possible dysregulation of AT2 identity and differentiation in COPD (lines 282 - 284) where they cite refs 28, 69, 70. Authors try to support this with IF double stains for KRT5 and HT-280 to identify co-expression of KRT5 and HT2-280 in lung tissue (Figure S2H). However, the evidence for the co-expression of both markers could be presented more convincingly.

      __Response: __

      We found the potential co-expression of airway and alveolar markers in COPD lungs interesting and hence included it in the original manuscript. The initial discovery came from our bulk RNA-seq data, where we observed upregulation of several genes typically found in more proximal airways in COPD (mentioned above by the reviewer). Of note, some of them (e.g., FoxJ1) are expressed at very low levels. Following reviewer's comments, to validate possible colocalization of AT2 and airway markers on protein level, we performed further IF analysis. We took Z-stack images to demonstrate the co-localization of HT2-280 and Krt5 more convincingly and co-stained the same tissue regions with SCGB3A2 (a TASC/distal airway cell marker, PMID 36796082). Even though these are rare events, we were able to reproduce the existence of HT2-280/Krt5 positive, SCGB3A2 negative cells in the alveoli of COPD patients on the protein level (__Fig. S2H __and panels below). Although interesting, we decided to keep this finding in the supplement and did not include it in the discussion to focus the story on the epigenetic regulation of the IFN pathway, which is the main discovery of our study. We will investigate this observation in future studies.

      Figure S2H and here: Examples of HT2-280/Krt5 double positive cells. Top, immunofluorescence staining of the alveolar region of a COPD II donor showing the existence of AT2 cells (HT2-280 positive (red), which are SCGB3A2 negative (green, left) but KRT5 positive (green, right). In conclusion, double-positive HT2-280/KRT5 cells are rare but present in the alveoli of COPD patients. Magnification: 20x. Scale bar: 50 µm. Bottom, Z-stack images highlighting HT2-280 (red) and KRT5 (green) double-positive cells at 63x magnification. Scale bar: 5 µm.

      CHANGES IN THE MANUSCRIPT:

      In addition, we observed an upregulation of several keratins (KRT5, KRT14, KRT16, KRT17) and mucins (MUC12, MUC13, MUC16, MUC20), suggesting a potential dysregulation of alveolar epithelial cell differentiation programs in COPD (Table 6, Fig. S2F). Immunofluorescence staining confirmed the presence of KRT5-positive cells in the distal lung in COPD and identified cells positive for both KRT5 and HT2-280 (Fig. S2H). Collectively, these results indicate a dysregulation of stemness and identity in the alveolar epithelial cells in COPD.

      Fig. S2H legend: The zoomed-in panel (right corner, bottom) demonstrates the presence of rare HT2-280/KRT5 double-positive cells in the alveoli of COPD patients.* Slides were counterstained with DAPI, scale bars = 50µm, 20µm or 5µm, as displayed in images. *

      • Double staining for KRT5 and HT2-280 did highlight the proximity of both cell types in lung tissue, underscoring the challenge of removing airways (including the smaller and terminal bronchi) from the tissue. In addition, HT-280/KRT5 co-expression is not consistent with recent studies from refs 28, 69, 70 where other markers for distal airway cell transition, such as SCGB3A2 and BPIFB1, have been demonstrated, which were not investigated in this study.

      Response:

      We provided a general overview of the different signatures observed in our data, but we could not validate every deregulated pathway or gene. We include the relevant tables detailing all differentially expressed genes and differentially methylated regions to enable and encourage the community to follow up on the data in subsequent studies.

      As demonstrated above, we detect the co-occurrence of HT2-280/KRT5 staining on the protein level in the same cells in the alveoli of COPD patients. We would like to emphasize that alveolar epithelial cell identity in CODP lungs has not been investigated in detail on the protein or RNA level, and HT2-280/KRT5 co-expression/co-localization has not been directly tested in the studies mentioned by the reviewer since, among other reasons, the gene encoding HT2-280 has not been identified. Notably, a recent study (published after the submission of our manuscript) focusing on enriched epithelial cells from the distal lungs of COPD patients (PMID 35078977), identified an emphysema-specific AT2 subtype co-expressing the AT2 marker SFTPC and distal airway cell transition marker SCGB3A2, indicating that disease-specific AT2 populations with possible co-occurrence of AT2 and airway markers exist. In our dataset, SCGB3A2 was not deregulated (log2 fold change=0.22, adj p-value= 0.47), as shown in Table 6, and the HT2-280/Krt5 positive cells were negative for SCGB3A2 in our IF staining (see above).

      BPIFB1 is one of the antimicrobial peptides genes with an associated DMR and is significantly upregulated in COPD cells in our study (log2 fold change=1.17, adj p-value=0.0016), as shown in the supplementary figure Fig S4C and here below for convenience.

      Figure S4C Fold-change in gene expression of BPIFB1 in AT2 cells in COPD (RNA-seq) and A549 cells treated with 0.5µM AZA (RT-qPCR) compared to control samples. Left, RNA-seq data from AT2 cells (no COPD, blue, n=3; COPD II-IV, green, n=5). Right, A549 treated with AZA (orange, n=3) compared to control DMSO-treated cells (grey, n=3). The group median is shown as a black bar.

      • The small (and not evenly divided) sample size of both COPD and non-COPD specimens may lead to a higher risk for false positive results as adjustments for multiple testing typically rely on the number of comparisons, and small sample sizes may not provide enough data points to adequately control for this.

      __Response: __

      We acknowledge the problem of testing for multiple traits with relatively small numbers of samples. The availability of donor tissue, especially from non-COPD and COPD-I donors, was limited, and we applied very strict donor matching and quality control criteria for sample inclusion to avoid additional variability and confounding factors. The importance of strict quality control in selecting appropriate control samples was highlighted in our previous study (PMID: 33630765), where we demonstrated that approximately 50% of distal lung tissue from cancer patients with normal spirometry has pathological changes. Hence, we believe that the quality of the tissue was paramount to the reliability of the data. Strict quality control and sample matching for multiple parameters, including age, BMI, smoking status and smoking history (critical for DNA methylation studies), and cancer type (for background tissue), is a key strength of our approach, but it inevitably limited our sample size.

      First, all samples were cryopreserved and then processed in parallel in groups of 1 non-COPD and 2-3 COPD samples. This process included tissue dissociation, FACS sorting, back sorting (always), and immunofluorescence staining (when enough material was available). Cell pellets were stored at -80{degree sign}C until the entire cohort was ready for sequencing. This was done to limit the potential variation introduced by processing and sorting. RNA and DNA isolations were performed in parallel for all the sorted cell pellets, which were then sequenced as a single batch.

      During data analysis, we applied stringent cutoffs for DMR detection to reduce the risk of false positives due to multiple comparisons and a small sample size. Specifically, we filtered for regions with at least 10% methylation difference and containing at least 3 CpGs. Additionally, we applied a non-parametric Wilcoxon test using average DMR methylation levels to remove potentially false-positive regions, as the t-statistic is not well suited for non-normally distributed values, as expected at very low/high (close to 0% / 100%) methylation levels. A significance level of 0.1 has been used. Therefore, we are confident that the rigorous analysis and strict criteria applied in this study allowed us to detect trustworthy DMRs that we could further functionally validate using epigenetic editing. All the details of the DMR analysis are provided in the methods section. To address this point and limitation, we have added the following paragraphs in the discussion section of the manuscript:

      CHANGE IN THE MANUSCRIPT:

      *The strengths of our study include the use of purified human alveolar type 2 epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD. *

      However, we acknowledge several limitations of our study that warrant further investigation. First, the sample size was small. The use of strict quality criteria for donor selection limited the available samples, particularly for the ex-smoker control group. This resulted in an unequal distribution of COPD and control samples. This impacts the power of statistical analysis, particularly in the WGBS analysis, where millions of regions genome-wide are tested. Nevertheless, the clear negative correlation between promoter methylation and corresponding gene expression highlights the robustness of the DMR selection. Additionally, we were able to experimentally validate interferon-associated DMRs using epigenetic editing, highlighting the power of integrated epigenetic profiling in identifying disease-relevant regulators.

      __Minor suggestions for improvement __

      __Introduction __ • In general, refer to the actual experimental studies rather than review papers where appropriate.

      Response:

      We have now carefully checked all the references and amended them to refer to experimental studies when required.

      • Clearly specify whether a study was conducted in mice or humans, as this distinction is crucial for understanding the relevance of the findings to COPD.

      __Response: __

      All our experiments were performed with human lung cells and tissues. No mouse samples were used. As suggested, we have now clearly stated that our study was performed using human tissue samples and cells in different parts of the manuscript, including the discussion, where we now explicitly highlight the strengths and limitations of our study.

      CHANGES IN THE MANUSCRIPT:

      ...we generated whole-genome DNA methylation and transcriptome maps of sorted human primary alveolar type 2 cells (AT2) at different disease stages.

      However, the regulatory circuits that drive aberrant gene expression programs in human AT2 cells in COPD are poorly understood

      Therefore, we set out to profile DNA methylation of human AT2 cells at single CpG-resolution across COPD stages.

      ...*suggesting that aberrant epigenetic changes may drive COPD phenotypes in human AT2. *

      To identify genome-wide DNA methylation changes associated with COPD in purified human AT2 cells...

      The similarity of the methylation and gene expression profiles in the PCAs suggested that epigenetic and transcriptomic changes in human AT2 cells during COPD might be interrelated ...

      *In this work, we demonstrate that genome-wide DNA methylation changes occurring in human AT2 cells may drive COPD pathology by dysregulating key pathways that control inflammation, viral immunity and AT2 regeneration. *

      *Using high-resolution epigenetic profiling, we uncovered widespread alterations of the DNA methylation landscape in human AT2 cells in COPD that were associated with global gene expression changes. *

      *Currently, it is unclear how cigarette smoking leads to changes in DNA methylation patterns in human AT2 *

      The strengths of our study include the use of purified human alveolar epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD.

      __Methods __ • Line 473, here is meant 3 ex-smoker controls instead of smoker controls?

      __Response: __

      All donors (no COPD and COPD) used in our study are ex-smokers. Matching the samples with regard to smoking status and history is critical for epigenetic studies, as cigarette smoke profoundly affects DNA methylation genome-wide (PMID: 38199042, PMID: 27651444). This has now been clarified in the revised manuscript.

      CHANGE IN THE MANUSCRIPT____:

      Of note, we included only ex-smokers in our profiling to avoid acute smoking-induced inflammation as a confounding factor (50)*. *

      Importantly, we matched the smoking status and smoking history of all donors, which is key in epigenetic studies, as cigarette smoking profoundly impacts the DNA methylation landscape of tissues (96).

      In total, 3 ex-smoker controls (no COPD), 3 mild COPD donors ex-smokers (GOLD I, COPD I) and 5 moderate-to-severe COPD donors ex-smokers (GOLD II-IV, COPD II-IV) were profiled (Fig. 1A-C, Table 1)

      __Discussion __ • A list of limitation should be added to the discussion. One is the use of the alveolar cell line A549, which produces mucus, a characteristic more commonly associated with bronchial epithelial cells. (ref 43)l530:

      __Response: __

      The profiling was performed using purified primary human alveolar epithelial progenitor cells. For technical reasons, A549 cells were only used for validation of the results using epigenetic editing. The A549 phenotype depends on the growth medium used, in our case, Ham's F-12 medium, which is recommended for long-term A549 culture and promotes multilamellar body formation and differentiation toward an AT2-like phenotype (PMID: 27792742)__. __We are developing epigenetic editing technology for use in primary lung cells; however, the approach currently relies on the high efficiency of transient transfections, which cannot yet be achieved with primary adult AT2 cells. We were positively surprised by how well the methylation data obtained from patient AT2s translated into mechanistic insights when using A549 cells, despite being a cancer cell line. This suggests that the fundamental mechanisms of epigenetic regulation of IRF9 and the IFN signaling pathway are conserved between A549 and primary AT2 cells.

      • Another limitation to consider is that cells were isolated primarily from individuals with lung cancer, except for patients with COPD stage IV. In particular as COPD stage II and IV samples were taken together. And discuss the small and unevenly divided sample size

      __Response: __

      We thank the reviewer for bringing up this important point, which we carefully considered when designing our study. To match our samples across the cohort, all the no-COPD, COPD I, and two of the COPD II-IV samples were obtained from cancer resections. In addition to other characteristics, like age, BMI and smoking status, we also matched the donors by cancer type (all profiled donors had squamous cell carcinoma). We collected lung tissue as far away from the carcinoma as possible and sent representative pieces for histological analysis by an experienced lung pathologist to confirm the absence of visible tumours. In addition, to ensure that our data represents COPD-relevant signatures, we intentionally included samples from three COPD donors undergoing lung resections (without a cancer background) in the profiling.

      Following the reviewer's suggestion, to investigate the potential impact of non-cancer samples on driving the observed differences, we carefully checked the PCAs for both DNA methylation and RNA-seq. We could not identify a clear separation of no-cancer COPD samples from the cancer COPD samples (or other cancer samples) in any examined PCs, indicating no cofounding effect of cancer background in the samples. We observed that one sample contributing to PC2 is a non-cancer sample, but this was a rather sample-specific effect, as the other two non-cancer samples clustered together with the other severe COPD samples with a cancer background. Notably, in our DNA methylation data, we do not observe typical features of cancer methylomes, like global loss of DNA methylation or aberrant methylation of CpG islands (e.g., in tumour suppressor genes) (see Fig 2A), further suggesting that we do not "pick up" confounding cancer signatures in our data.

      Following the comments from both reviewers, to clarify that point, we added the information about cancer and non-cancer samples to the PCA figures for DNA methylation (new Fig. 2B) and RNA-seq (new Fig. 3A) data in the revised manuscript, as shown below

      CHANGE IN THE MANUSCRIPT____:

      COPD samples from donors with a cancer background clustered together with the COPD samples from lung resections, confirming that we detected COPD-relevant signatures (Fig. 2B).

      Fig.2B* Principal component analysis (PCA) of methylation levels at CpG sites with > 4-fold coverage in all samples. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

      Unsupervised principal component analysis (PCA) on the top 500 variable genes revealed a clear influence of the COPD phenotype in separating no COPD and COPD II-IV samples, as previously observed with the DNA methylation analysis, irrespective of the cancer background of COPD samples (Fig.3A, Fig. S2B).

      *Principal component analysis (PCA) of 500 most variable genes in RNA-seq analysis. PCA 1 and 2 are shown in Fig.3A, PCA 1 and 4 in Fig.S2B. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

      __Response: __

      We thank the reviewer for suggestions on how to improve the discussion of our manuscript. We have now added a strength/limitation section to our discussion and included the points suggested by both reviewers.

      CHANGE IN THE MANUSCRIPT____:

      The strengths of our study include the use of purified human alveolar epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD. Importantly, we matched the smoking status and smoking history of all donors, which is key in epigenetic studies, as cigarette smoking profoundly impacts the DNA methylation landscape of tissues (96). With the first genome-wide high-resolution methylation profiles of isolated cells across COPD stages, we offer novel insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD, expanding our understanding of how alterations in regulatory regions and specific genes could contribute to disease development. We identified IRF9 as a key IFN transcription factor regulated by DNA methylation. Notably, by targeting IRF9 through epigenetic modifications, we modulated the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. Epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting the regeneration of epithelial progenitor cells in the lungs. Further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment (33)*. *

      *However, we acknowledge several limitations to our study that warrant further investigation. First is the small sample size and replication difficulty due to the lack of available data, common challenges for studies working with sparse human material and hard-to-purify cell populations. The use of strict quality criteria in donor selection limited the available samples, especially for the ex-smoker control group, leading to an unequal distribution of COPD and control samples. Overall, this impacts the power of statistical analysis, especially in the WGBS analysis, where millions of regions genome-wide are tested. Nevertheless, the clear negative correlation of promoter methylation to the corresponding gene expression highlights the robustness of the DMR selection. Furthermore, we could experimentally validate interferon-associated DMRs using epigenetic editing, highlighting the power of integrated epigenetic profiling for the discovery of disease-relevant regulators. *

      Overall, we detected a higher number of correlated DMR-DEG associations using our simple promoter-proximal linkage compared to the GeneHancer approach. Assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the genes they regulate and can interact with their target genes through three-dimensional chromatin loops. Furthermore, enhancers can operate in a highly context-dependent manner, with the same enhancer regulating different genes depending on the cell type, developmental stage, or environmental signals. Determining which enhancer is active under specific conditions remains a hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

      In addition, while WGBS provides unprecedented resolution and high coverage of the DNA methylation sites across the genome, it does not allow distinguishing 5-methylcytosine from 5-hydroxymethylcytosine. Therefore, we cannot exclude that some methylated sites we detected are 5-hydroxymethylated. However, as 5-hydroxymethylcytosine is present at very low levels in the lung tissue (97)*, its effect is likely marginal. *

      Finally, despite careful removal of airways from distal lung tissue using a dissecting microscope, we cannot exclude the presence of some terminal/respiratory bronchiole cells in our FACS-isolated EpCAMpos/PDPNlow population. Recent scRNA-seq studies provided an unprecedented resolution and identified several epithelial subpopulations and transitional cells residing in the terminal/respiratory bronchioles and alveoli, including respiratory airway secretory cells (93), terminal airway-enriched secretory cells (28), terminal bronchiole-specific alveolar type-0 (AT0) (70), and emphysema-specific AT2 cells (74). These cells may contribute to alveolar repair in healthy and COPD lungs; however, with our bulk DNA methylation and RNA-seq study, we are unable to resolve all these subpopulations. Future development of single-cell methylation and non-reference-based algorithms for DNA methylation deconvolution will enable deeper epigenetic phenotyping of specific AT2 and bronchiolar cell subsets.

      __References __ • Check references. For instance, there is no reference in the text to ref 43.

      • Align format of references

      __Response: __

      We thank the reviewer for spotting this inconsistency. We have carefully checked and aligned the format of all references. The (old) reference 43 is now mentioned in the discussion part.

      __Reviewer #1 (Significance (Required)): __

      The strength of this study lies in its focus on the molecular mechanisms underlying the impaired regeneration of epithelial progenitor cells in COPD. The discovery of IRF9, which regulates IFN signaling and is prominently upregulated in COPD, together with the convincing validation of the epigenetic control of the IFN pathway by targeted DNA demethylation of the IRF9 gene, adds significant value to the COPD research field.

      Main limitations of the study are the relatively small sample size of both COPD and non-COPD specimens and the claim that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells.

      __- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. __

      The nature and significance of the advance in epigenetic editing of IRF9 in COPD can be described as both conceptual and potentially clinical:

      Conceptual Advance: The epigenetic editing of IRF9 enhances our understanding of the molecular mechanisms underlying COPD pathogenesis. By targeting IRF9 through epigenetic modifications, researchers were able to modulate the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. This approach offers insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD and expands our understanding of how alterations in specific gene methylation could contribute to disease progression.

      Clinical Significance: The potential clinical significance of epigenetic editing of IRF9 lies in its implications for COPD therapy. If successful, epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting regeneration of epithelial progenitor cells in the lungs. Obviously, further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment.

      __Response: __We thank the reviewer for recognising the importance of our study, its conceptual advance and potential clinical significance. We are pleased to see that the reviewer highlights the promise of epigenetic editing in both furthering our basic understanding of molecular mechanisms of chronic diseases and its future potential as a therapeutic strategy.

      __- Place the work in the context of the existing literature (provide references, where appropriate). __ Few experimental papers have been published on epigenetic editing in lung diseases, with limited research available beyond the study referenced in citation 43. Song J, Cano-Rodriquez D, Winkle M, Gjaltema RA, Goubert D, Jurkowski TP, Heijink IH, Rots MG, Hylkema MN. Targeted epigenetic editing of SPDEF reduces mucus production in lung epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2017 Mar 1;312(3):L334-L347. doi: 10.1152/ajplung.00059.2016. Epub 2016 Dec 23. PMID: 28011616.

      Response:

      We thank the reviewer for recognising the uniqueness and novelty of our study and the lack of research on the functional understanding of DNA methylation in the context of lung and lung diseases.

      - State what audience might be interested in and influenced by the reported findings.

      This study is of broad interest to researchers investigating the pathogenesis and treatment of COPD.

      __- Define your field of expertise with a few keywords to help the authors contextualize your point of view. __

      Expertise in: Lung pathology, Immunology, COPD, Epigenetics

      - Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Less expertise in: Epigenetic Editing

      __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

      __Summary: __

      This study aim to understand the molecular mechanisms underlying dysfunction in AT2 cells in COPD, by profiling bulk genome wide DNA methylation using Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) and RNA sequencing in selectively sorted primary AT2 cells. The study stands out in it's sequencing breadth and use of an incredibly difficult cell population, and has the potential to add substantially to our mechanistic understanding of epigenetic contributions to COPD. A further highlight is the concluding aspect of the study where the authors undertook targeted modification of specific CpG methylation, provided direct, site-specific evidence for transcriptional regulation by CpG methylation.

      Response:

      We thank the reviewer for recognizing the conceptual and methodological advance of our study and for noting the value of our functional mechanistic approach.

      __Major comments: __

      The authors clearly show that there is DNA methylation alteration in AT2 cells from COPD individuals that links functional to gene expression at some level. However, I think the statement "to identify genome-wide changes associated with COPD development and progression..." and similar other references to disease development understanding is not accurate given the DNA methylation primary comparison is between control and moderate to severe COPD, with no temporal detail or evidence that they drive progression rather than are a result of COPD development. The paragraph starting on line 186 where this is a addressed to some extent is quite vague and doesn't really provide confidence that DNAm dysregulation occurs at an early stage in this context. This can be addressed by changing the focus/style of the text.

      __Response: __

      Thank you for raising this point. We agree with the reviewer that our cross-sectional study describes the association of methylation changes with either COPD I or more established disease (COPD II-IV) and that the observed changes may be either the driver or a result of COPD development. This has been clarified in the revised manuscript, and we removed the statements about disease initiation and progression. This is an important point; hence, we added an extra line to the discussion to make that clear.

      __CHANGE IN THE MANUSCRIPT____: __

      Therefore, we set out to profile DNA methylation of human AT2 cells at single CpG-resolution across COPD stages to identify epigenetic changes associated with disease and combine this with RNA-seq expression profiles.

      To identify epigenetic changes associated with COPD, we collected lung tissue from patients with different stages of COPD,

      ....to identify methylation changes associated with mild disease, we included TWGBS data from AT2 isolated from COPD I patients (n=3) in the analysis.

      Currently, we do not know whether the identified DNA methylation changes are the cause or the consequence of the disease process and not much is known about the correlation of DNA methylation with disease severity.

      *However, our study is cross-sectional, our cohort included only 3 COPD I donors, and we did not have any follow-up data on the patients, so future large-scale profiling of mild disease (or even pre-COPD cohorts) in an extended patient cohort will be crucial for a better understanding of early disease and its progression trajectories. *

      __Results comments and suggestions: __

      For the integrated analysis, there is a focus on DMRs in promoters with very little analysis on other regions. The paragraph starting on line 317 describes some analysis on enhancers but is very brief, doesn't include information on how many/which DMRs were included, making it hard to interpret the impact of the 147 DMRs and 93 genes identified - is this nearly all DMRs and genes analysed or very few? A comparison to the promoter analysis would be of interest. Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

      __Response: __

      We thank the reviewer for pointing out the importance of changes in enhancers. We agree that extending the enhancer analysis is very interesting. However, assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the gene they regulate, sometimes spanning hundreds of kilobases. They can interact with their target genes through three-dimensional chromatin loops, potentially bypassing nearby genes to activate more distant ones, making it difficult to confidently link specific enhancers to their target genes. Furthermore, enhancers can operate in a highly context-dependent manner. The same enhancer can regulate different genes depending on the cell type, developmental stage, or environmental signals. Another challenge is that enhancers often work in clusters or "enhancer landscapes," where multiple enhancers contribute to the regulation of a single gene. Disentangling the contribution of individual enhancers within such clusters and determining which enhancer is active under specific conditions remains an ongoing hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

      One approach we tried to account for more distal regulatory regions was to assign DMRs to the nearest gene with a maximum distance of up to 100 kb using GREAT (Genomic Regions Enrichment of Annotations Tool) and simultaneously perform gene enrichment analysis of the associated genes. The old Figure S1C (now S1D) shows the top 10 enriched terms of either hyper- or hypomethylated DMRs, and Table 4 shows the full list of enriched terms. However, in this analysis, we did not integrate the results of the RNA-seq analysis. To demonstrate that we can correlate methylation with gene expression associations in this analysis, we then took a closer look at the WNT/b-catenin pathway, which contains 147 DMRs associated with 93 genes from the respective pathway (old Figure S3D, now S3G). Here, we showed that distal DMRs up to 100 kb away from the TSS show a high correlation with gene expression. We are including the two figures below for convenience:

      *Left panels, functional annotation of genes located next to hypermethylated (top) and hypomethylated (bottom) DMRs using GREAT. Hits were sorted according to the binominal adjusted p-value and the top 10 hits are shown. The adjusted p-value is indicated by the color code and the number of DMR associated genes is indicated by the node size. Right panel, scatter plot showing distal DMR-DEG pairs associated with Wnt-signaling. Pairs were extracted from GREAT analysis (hypermethylated, DMR-DEG distance Following the reviewer's suggestion, we have now extended the enhancer analysis using the GeneHancer database, the most comprehensive, integrated resource of enhancer/promoter-gene associations. We used the GeneHancer version 5.14, which annotates 392,372 regulatory genomic elements (GeneHancer element) on the hg19 reference genome. Of the 25,028 DMRs, 18,289 DMRs (73% of all DMRs) coincided with at least one GeneHancer element, resulting in 19,661 DMR-GeneHancer associations. Next, we extracted the GeneHancer elements associated with protein-coding or long-non-coding RNAs genes, which left us with 2,144 DMR-GeneHancer associations. Next, we used only high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer associations. Of those, we selected the GeneHancer elements, which are linked to genes differentially expressed in our RNA-seq analysis resulting in a final table of 376 DMR-GeneHancer associations (Table 9 DMR_DEG_GeneHancer, Tab 2). Similar to the promoter-proximal analysis, we analysed the correlation of expression and methylation changes of the DMR-GeneHancer associations, demonstrating a high number of negatively and positively correlated events (Fig.S3D). Finally, we performed the gene enrichment analysis for positively and negatively correlating genes. We detected significant GO term enrichments only for negatively correlating genes (Fig.S3E and Table 10_Enrichment_results, Tab2).

      CHANGE IN THE MANUSCRIPT

      To harness the full resolution of our whole-genome DNA methylation data, we extended the analysis beyond promoter-proximal regions and assessed how epigenetic changes in distal regulatory regions (enhancers) may relate to transcriptional differences in COPD. As the assignment of enhancer elements to the corresponding genes is challenging, we tried two different approaches. First, we used the GeneHancer database (72) to link DMRs to regulatory genomic elements (GeneHancer element). Of the 25,028 DMRs, 18,289 DMRs (73%) coincided with at least one GeneHancer element. Of those 2,144 DMR-GeneHancer associations were linked either to protein-coding or lncRNA genes. Next, we filtered for high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer Elite associations. Of those, we selected the GeneHancer elements, which are linked to genes differentially expressed in our RNA-seq analysis, resulting in 376 DMR-GeneHancer associations (Table 9). Similar to the promoter-proximal analysis, we assessed the correlation of expression and methylation changes of the DMR-GeneHancer associations, demonstrating a high proportion of negatively and positively correlated events (Fig. S3E). Finally, we performed gene enrichment analysis for positively and negatively correlated genes. We detected significant GO term enrichments for negatively correlating genes only (Fig. S3F and Table 10), with the most pronounced term "regulation of tumor necrosis factor". In an alternative approach, we linked proximal and distal (within 100 kb from TSS) DMRs to the next gene using GREAT (57) (Fig S1C, Table 4) *and calculated Spearman correlation between DMRs and associated DEGs__. 147 DMRs were associated with high correlation rates with 93 genes from the WNT/β-catenin pathway (Fig. S3G)__, suggesting that DNA methylation may also drive the expression of genes of the WNT/β-catenin family. *

      Figure S3E and F: E. Spearman correlation between gene expression and DMR methylation of DMRs assigned to gene regulatory elements using the GeneHancer database. F. GO-Term over-representation analysis of DEGs negatively correlated to DMRs in gene regulatory elements. The adjusted p-value is indicated by the color code and the percentage number of associated DEGs is indicated by the node size.

      (Methods) For enhancer analysis, the GeneHancer database version 5.14, which annotates 392,372 regulatory genomic elements (GeneHancer element) on the hg19 reference genome, was used (72). Of the 25,028 DMRs 18,289 DMRs coincided with at least one GeneHancer element, resulting in 19,661 DMR-GeneHancer associations. Next, the GeneHancer elements were filtered for association with protein-coding or long-non-coding RNAs genes and high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer associations. Of those, the GeneHancer elements were selected, which are linked to differentially expressed genes in COPD resulting in a final table of 376 DMR-GeneHancer associations. Similar to the promoter-proximal analysis, the Spearman correlation of expression and methylation changes of the DMR-GeneHancer associations was assessed. GO gene enrichment analysis for positively and negatively correlating genes was done using Metascape (111).

      A comparison to the promoter analysis would be of interest.

      Response:

      We detected more highly correlated (|correlation coefficient| > 0.5) DMR-DEG associations using our simple promoter proximal linkage (n=643) in comparison with the GeneHancer approach comprising annotated enhancer elements (n=327/2,144). Gene enrichment results pointed to the interferon pathway, which we could confirm using epigenetic editing. This pathway was not present in the GeneHancer analysis, indicating that regulation of the IFN pathway may be controlled by proximal elements.

      CHANGE IN THE MANUSCRIPT____:

      Overall, we detected a higher number of correlated DMR-DEG associations using our simple promoter-proximal linkage compared to the GeneHancer approach. Assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the genes they regulate and can interact with their target genes through three-dimensional chromatin loops. Furthermore, enhancers can operate in a highly context-dependent manner, with the same enhancer regulating different genes depending on the cell type, developmental stage, or environmental signals. Determining which enhancer is active under specific conditions remains a hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

      Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

      Response:

      We thank the reviewer for bringing up that point. To clarify, we defined the promoter regions for the analysis as regions located {plus minus} 6 kb (upstream and downstream) from the transcriptional start site (TSS). Since the term "promoter" often refers to the region upstream of the transcriptional start site, its use may have been misleading. For clarity, we changed the text correspondingly to __promoter proximal methylation __and explained in the methods how the regions for analysis were defined.

      __CHANGE IN THE MANUSCRIPT____: __

      "DMR association per gene promoter" was changed to "Gene promoter proximal DMRs"

      Fig. S3B: "DMR in promoter" was changed to "promoter proximal DMR(s)"

      "by DNA methylation changes in promoters" was changed to "by DNA methylation changes in promoter proximity"

      "regulated by promoter methylation" was changed to "regulated by promoter-proximal methylation"

      "analysis of the promoter DMRs" was changed to "analysis of the promoter-proximal DMRs"

      "between promoter methylation" was changed to "between promoter proximal methylation"

      Cytoscape was used to analyse negatively or positively correlated DMR DEG pairs. ClueGO (v2.5.6) analysis was conducted using all DEG associated with a promoter proximal DMR (+/- 6 kb from TSS) and the Spearman correlation coefficient 0.5 (112).

      • Lines 299-301 - I'm not sure the graph in Fig S3A support the conclusion that there was a preferential negative relationship between DNAm and gene expression. Looks like there are a substantial number of cases where a positive relationship is observed and this needs to be acknowledged.

      Response:

      In this part, we refer to Fig S3C. In the left panel, downregulated genes clearly show higher counts for the hypermethylated DMRs, whereas the hypomethylated DMRs are enriched at upregulated genes (right panel), indicating a preference for negative correlation: lower methylation, higher gene expression. If there were no preference, we would expect a 50:50 ratio of hypo- and hypermethylated DMRs, and we observed a 77:23 ratio. Nevertheless, we agree that there is a substantial number of cases (n=151) with a high positive correlation, which we now highlight in the text. For clarity, we also modified the figure legend to indicate that a stacked histogram is represented in the panel.

      __CHANGE IN THE MANUSCRIPT____: __

      L303: Interestingly, 23.5% of the identified DMR DEG pairs (n=151) showed a positive correlation between gene expression and DNA methylation.

      *Figure legend in Fig. S3C was changed to: C Stacked histogram showing location of hyper- and hypomethylated DMRs relative to the TSS of DEGs in downregulated (left) and upregulated (right) genes. *

      • Line 307 - what are the "analysed DEGs"? Are they the methylation associated genes?

      Response:

      Those are the DEGs we identified in RNA-seq analysis. To clarify, we changed the text to "identified DEGs".

      __CHANGE IN THE MANUSCRIPT____: __

      • "analysed DEGs" was changed to "identified DEGs"*

      • Line 307-309 - "Among the analyzed DEGs, 76.5% (492) displayed a negative correlation (16.8% of the total DEGs), indicating a possible direct regulation by DNA methylation, while 23.5% (151) showed a positive correlation between gene expression and DNA methylation" - are the authors suggesting the positive correlation doesn't indicate direct regulation?

      __Response: __

      Thank you for highlighting this point. We did not intend to suggest that negative correlation indicates direct regulation, while positive correlation suggests a lack thereof. To clarify that point, we have reformulated this sentence.

      __CHANGE IN THE MANUSCRIPT____: __

      Among the identified DEGs, 76.5% (n=492) displayed a negative correlation (16.8% of the total DEGs), consistent with a repressive role of promoter DNA methylation. Interestingly, 23.5% of the identified DEG (n=151) showed a positive correlation between gene expression and DNA methylation.

      • Line 313 - why did the authors focus on only negatively correlated genes to identify their top dysregulated pathway of IFN signalling? Why not do pathway analysis on the DNAm associated genes separately to identify DNAm associated pathways?

      Response:

      We have also performed a pathway enrichment analysis using the positively correlated genes but did not identify any significantly enriched pathways/process/terms. When we examined the top hit of the gene set enrichment analysis, the interferon signaling pathway, we observed only negatively correlated DMR gene associations (Fig. 5B). Therefore, we decided to use only the negatively correlated DMRs, as using all correlated genes would give a higher background and dilute our results.

      CHANGE IN THE MANUSCRIPT____:

      Cytoscape was used to analyse negatively or positively correlated DMR DEG pairs. ClueGO (v2.5.6) analysis was conducted using all DEG associated with a promoter proximal DMR (+/- 6 kb from TSS) and the Spearman correlation coefficient 0.5 (113).

      • A comparison of the gene expression data with previous data in AT2 cell/single cell data would strengthen the gene expression section.

      __Response: __

      We compared our gene expression signatures with the study of Fujino et al., who profiled sorted AT2 cells (EpCAMhighPDPNlow) from COPD/controls using expression arrays (PMID: 23117565). Consistent with our study, the authors also observed the upregulation of interferon signalling (among other pathways) in COPD AT2s. However, no raw data was available in the published manuscript for a more in-depth analysis.

      Several recent scRNA-seq studies identified transcriptional signatures of COPD and control cells (e.g., PMIDs: 36108172, 35078977, 36796082, 39147413__). However, most studies did not match the smoking status of the control and COPD donors and looked at the whole lung tissue, with limited power to detect gene expression changes in distal alveolar cells. It is difficult to directly compare our data to the gene expression data from non-smokers vs COPD patients, as cigarette smoking profoundly remodels the epigenome and transcriptional signatures of cells. In addition, differences in technologies and depth of sequencing make such comparisons challenging. However, one study (PMID: 36108172) performed scRNA-seq analysis on 3 non-smokers, 4 ex-smokers and 7 COPD ex-smoker lungs. Despite relatively limited coverage of epithelial cells in the dataset (We also compared the main AT2 IFN signature identified in the integration of our DNA methylation in promoter-proximal regions and RNA-seq with a recent study (published after the submission of our manuscript, PMID: 39147413) that profiled EpCAMpos cells from COPD and control lungs (non-smokers) using scRNA-seq. We observed an upregulation of our IFN signature genes in AT2 in COPD (specifically in AT2-c and rbAT2 subsets), suggesting that similar signatures were observed in this dataset as well. However, ex-smokers were not included in this study, making direct comparisons difficult. We have now included the panels shown below as __Figure S4E and S4F:

      Figure S4E and F: Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74)*. The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells. *

      CHANGES IN THE MANUSCRIPT:

      However, 5-AZA is a global demethylating agent, and the observed effects may not be direct. To validate the epigenetic regulation of central AT2 pathways further, we took advantage of locus-specific epigenetic editing technology (73). We focused on the IFN pathway because it was the most significantly enriched Gene Ontology (GO) term in our integrative analysis of TWGBS and RNA-seq data. Several IFN pathway members had associated hypomethylated DMRs within promoter-proximal regions and concomitant increased gene expression (Fig. 4C and Fig.S2C). Additionally, we confirmed the elevated expression of IFN-related genes with associated DMRs identified in our study in AT2 cells and AT2 cell subclusters from a recently published scRNA-seq cohort (74)* (Fig. S4E-F). *

      (Methods) Validation of IFN gene upregulation in a published scRNA-seq dataset

      scRNA-seq data from (74), generously provided by M. Köningshoff, were processed using the default Seurat workflow (117). Expression of IFN-related genes was extracted and plotted as log-normalised gene expression levels in AT2 cells from control and COPD donors. Seurat's AddModuleScore() function was used to compute a gene set score for a custom IFN program using the genes listed in __Fig. S4E __and to analyse the IFN gene set scores in AT2 cell subclusters identified in (74). Briefly, average gene expression scores were computed for the gene set of interest, and the expression of control features (randomly selected) was subtracted as described in (118).

      Fig. S4 E and F. E. Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74). The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells. __ __

      • The paragraph starting on line 173 feels a little redundant when we know there is RNA available to test if the differential DNAm links to altered gene expression - this selected of example regions/genes would be better placed after the gene expression has been reported, at which point you could say whether the linked genes displayed altered transcription.

      Response:

      The current structure (with DNA methylation, followed by RNA-seq and integration) is intentional and serves several important purposes. As this is the first genome-wide high-resolution COPD DNA methylation study of AT2, we aimed to describe the methylation landscape independently of gene expression (noting the limitation of current understanding of how DNA methylation regulates expression). This early focus on DMRs lays clear groundwork by highlighting potential regulatory elements and pathways that could be disrupted, independent of or even before corroborative transcriptional data. Additionally, positioning these examples early in the narrative helps to frame subsequent gene expression analyses. Once RNA data are introduced later, the reader can directly compare the methylation patterns with transcriptional outcomes, thereby enhancing the overall story. In other words, by first showcasing disease-relevant methylation changes, we underscore a hypothesis that these epigenetic modifications are functionally meaningful. The later integration of gene expression data then serves as a confirmatory or complementary layer, rather than the sole basis for inferring biological significance. This is important as we still do not fully understand the function of DNA methylation outside promoters, and its role is also important for splicing, 3D genome organisation, non-coding RNA regulation, enhancer regulation, etc.

      • Similarly, the TF enrichment analysis is great but maybe would have added value to be done on DNA regions later shown to be linked to differential expression - was there different enrichment at DNA regions that are vs are not associated with altered expression? And could you test in vitro whether changing methylation of DNA (maybe a blunt too like 5-aza would be ok) alters TF binding (cut+run/ChIP?). Furthermore, it would be interesting to understand the TF sensitivity analysis within the context of positive versus negative DNA methylation:gene expression correlations.

      Response:

      As suggested by the reviewer, we now performed the TF enrichment analysis using the DMRs with a high correlation (|correlation coefficient|>0.5) between methylation and expression (Figure S3D) and expanded the method section to include TF analysis. We observed ETS domain motifs enriched at hypomethylated regions. They prefer unmethylated DNA (MethylMinus) and are therefore expected to bind with higher affinity to the respective DMRs in COPD. We agree with the reviewer that further verifying altered TF binding using cut&run or ChIP assays would be very interesting, but it is out of the scope of this manuscript. Such analysis is technically very challenging to perform with low numbers of primary AT2 cells and will be the focus of our follow-up mechanistic studies.

      CHANGE IN THE MANUSCRIPT____:

      Additionally, motif analysis of DMRs that were highly correlated (|Spearman correlation coefficient| > 0.5) with DEGs revealed a prominent enrichment of the cognate motif for ETS family transcription factors, such as ELF5, SPIB, ELF1 and ELF2 at hypomethylated DMRs (Fig. S3D). Interestingly, SPIB was shown to facilitate the recruitment of IRF7, activating interferon signaling (71)*, and our WGBS data uncovers SPIB motifs at hypomethylated DMRs, which aligns with its binding preferences at unmethylated DNA (methyl minus, Fig. S3D). *

      Figure S3D: Enrichment of methylation-sensitive binding motifs at hypo- (right) and hypermethylated (left) DMRs, using DMRs with a high correlation (|Spearman correlation coefficient| > 0.5) between methylation and gene expression. Methylation-sensitive motifs were derived from Yin et al (64). Transcription factors, whose binding affinity is impaired upon methylation of their DNA binding motif, are shown in red (Methyl Minus), and transcription factors, whose binding affinity upon CpG methylation is increased, are shown in blue (Methyl Plus).

      (Methods) To obtain information about methylation-dependent binding for transcription factor motifs which are enriched at DMRs, the results of a recent SELEX study (64)* were integrated into the analysis. They categorised transcription factors based on the binding affinity of their corresponding DNA motif to methylated or unmethylated motifs. Those whose affinity was impaired by methylation were categorised as MethylMinus, while those whose affinity increased were categorised as MethylPlus. A motif database of 1,787 binding motifs with associated methylation dependency was constructed. The log odds detection threshold was calculated for the HOMER motif search as follows. Bases with a probability > 0.7 got a score of log(base probability/0.25); otherwise, the score was set to 0. The final threshold was calculated as the sum of the scores of all bases in the motif. Motif enrichment analysis was carried out against a sampled background of 50,000 random regions with matching GC content using the findMotifsGenome.pl script of the HOMER software suite, omitting CG correction and setting the generated SELEX motifs as the motif database. *

      __Methods: __ • The authors should include more detail of the TWGBS rather than directing the reader to a previous publication. Also DNA concentration post bisulfite conversion would be a useful metric to provide.

      __Response: __

      Following the suggestion, we have now expanded the details of TWGBS in the methods part of the manuscript. Due to limited space, we did not include a detailed protocol but instead referred to a published step-by-step protocol (55). Of note, we do not measure DNA concentration post-bisulfite conversion but consistently use the starting input of 30 ng of genomic DNA across all samples.

      __CHANGE IN THE MANUSCRIPT____: __

      (Methods): 15 pg of unmethylated DNA phage lambda was spiked in as a control for bisulfite conversion. Tagmentation was performed in TAPS buffer using an in-house purified Tn5 assembled with load adapter oligos (55) at 55 {degree sign}C for 8 min. Tagmentation was followed by purification using AMPure beads, oligo replacement and gap repair as described (55). Bisulfite treatment was performed using EZ DNA Methylation kit (Zymo) following the manufacturer's protocol.

      *The T-WGBS library preparations were performed for all donors in parallel and sequenced in a single batch to minimize batch effects and technical variability. *

      • Differential DNA methylation analysis: It is stated that DNA regions had to contain 3 CpG sites but was this within a defined DNA size range?

      Response:

      The maximum distance between individual CpGs within DMR was set to 300 bp. To clarify, we added that information to the methods part.

      __CHANGE IN THE MANUSCRIPT____: __

      *"regions with at least 10% methylation difference and containing at least 3 CpGs with a maximum distance of 300 bp between them. *

      • Refence genome only provided for RNAseq not TWGBS?

      __Response: __We used hg19 as the reference genome. The information on the reference genome for DNA methylation analysis was provided in the methods L574 (original manuscript_: "The reads were aligned to the transformed strands of the hg19 reference genome using BWA MEM")

      • The tables do not appear in the PDF and I struggled to tally to the "Dataset" files provided if that is what they were referring to?

      Response:

      Full tables (uploaded as Datasets in the manuscript central due to their size) were uploaded together with the manuscript files. They are quite large and will not convert to pdf, so they may not have been included in the merged pdf file. We assume that they should be available to the reviewers with the other files and will clarify that with the editorial staff in the resubmission cover letter.

      • For the gene expression analysis, can it be made clearer that a full analysis was done on COPD I samples. It is a little confusing to the reader as this was not done for DNAm so might be assumed the same targeted analysis on only genes found to be differentially expressed between control and COPD II-IV, but that cannot be the case as an overlap of COPD1 vs COPD II-IV genes if provided. For this overlap, do genes show the same effect direction?

      __Response: __

      To clarify, for the RNA-seq analysis, we performed DEG analysis for no-COPD versus COPD II-IV, as well as no-COPD versus COPD I. We then took all differentially expressed genes (presented in the Venn diagram) and plotted them for all samples as a heatmap. To split the genes into groups displaying similar effect directions, we applied a clustering approach and identified 3 main signatures. Cluster 3 primarily comprises genes unique to COPD I samples, which are associated with the adaptive immune system and hemostasis (Fig. 4E). In the other two clusters, we mainly observe a transitioning pattern from control to severe COPD samples, correlating with the FEV1 values of the patients. This has now been clarified in the manuscript.

      • Replication is difficult on these studies as the samples are so difficult to come by. Also limited by sample size for the same reason. It doesn't mean the study is not worth doing and the data are still valuable. However, it may be pertinent to include technical validation of a few regions of interest, acknowledge the limitation (along side strengths) in the discussion, and perhaps provide actual p value rather than blanket Response:

      We thank the reviewer for acknowledging the replication challenges for studies working with sparse human material and hard-to-purify cell populations. Following the reviewer's suggestion, we have now included a strengths and limitations section in the discussion where we summarised the points highlighted by both reviewers.

      Regarding technical validation, we would like to note that the whole genome bisulfite sequencing (WGBS) technology, as well as the tagmentation-based WGBS (T-WGBS), have been validated in the past few years in several publications (e.g., PMID: 24071908) and shown to yield reliable DNA methylation quantification in comparison to other technologies (PMID: 27347756). For us, technical validation using alternative methods (e.g. bisulfite sequencing or pyrosequencing) is difficult as it requires significantly more input DNA than the low-input T-WGBS we have performed and obtaining sufficient amounts of material from primary human AT2 cells (especially from severe COPD) is not possible with the size of tissue we can access. However, while establishing the T-WGBS for this project, we initially validated our approach using Mass Array, a sequencing-independent method. For this, we performed T-WGBS on the commercially available smoker and COPD lung fibroblasts and selected 9 regions with different methylation levels for validation using a Mass Array. We obtained an excellent correlation between both methods, providing technical validation of T-WGBS and our analysis workflow. This validation was published in our earlier manuscript (PMID: 37143403), but we provided the data below for convenience.

      Scatter plots showing correlation of average methylation obtained with T-WGBS and Mass Array from COPD and smoker fibroblasts. Each dot represents one region with varying methylation levels. The blue diagonal represents the linear regression. Shaded areas are confidence intervals of the correlation coefficient at 95%. Correlation coefficients and P values were calculated by the Pearson correlation method.

      To enable further validation and follow-up by the community, we included the full list of DMRs, associated p-values and additional information for DNA methylation analysis (DMR width, n.CpGs, MethylDiff, etc) in Table 3 (Table_3_wgbs_dmr_info.xlsx) and the information about DEGs from RNA-seq in Table 6 (Table_6_RNAseq_DEG_info.xlsx).

      • It isn't clear to me if DNA and RNA are from the same cells? The results say "cells matching those used for T-WGBS" but the methods suggest separate extractions so not the same cells? If they are not the same cells a comment on the implications of this should be included in the discussion for example, potentially some differences in cell type composition, storage time etc.

      Response:

      Lung tissue samples were freshly cryopreserved, and H&E slides derived from exemplary pieces of the tissue analyzed. Once we had a group of at least 3 samples comprising one non-COPD and 2 COPD samples, we processed them in parallel to limit sorting variation between control and disease samples. The sorted cells were counted, aliquoted and pelleted at 4{degree sign}C before flash freezing and storing at -80{degree sign}C. The storage time of the cell pellets varied between the donors. RNA and DNA were isolated from cell pellets collected from the same FACS sorting experiment; therefore, we do not expect differences in cell type composition. In addition, RNA and DNA isolation were performed for all sorted pellets in parallel. All library preparations for TWGBS and RNA-seq were performed for all donors in parallel and sequenced in a single batch to minimise batch effects and technical variability. This has now been clarified in the methods part of the manuscript.

      __CHANGE IN THE MANUSCRIPT____: __

      To minimize potential technical bias, samples from no COPD and COPD donors were processed in parallel in groups of 3 (one no COPD and 2 COPD samples).

      RNA and genomic DNA for RNA-seq and TWGBS were isolated from identical aliquots of sorted cell pellets.

      Genomic DNA was extracted from 1-2x104 sorted alveolar epithelial cells isolated from cryopreserved lung parenchyma from 11 different donors in parallel using QIAamp Micro Kit

      The TWGBS library preparations were performed for all donors in parallel and sequenced in a single batch to minimize batch effects and technical variability.* *

      RNA was isolated from flash-frozen pellets of 2x104 sorted AT2 cells from 11 different donors in parallel.

      The RNA-seq library preparation for all donors was performed in parallel and all samples were sequenced in a single batch to minimize batch effects and technical variability.

      • Line 193 the authors say "Since DMRs were overrepresented at cis-regulatory sites...." - "cis" needs to be defined. If you link DNAm regions to gene via "closest gene" does this not automatically mean you're outputs will be cis? Just needs better definition/explanation.

      Response:

      The term "cis‐regulatory sites" in our manuscript is intended to denote regulatory elements-such as enhancers, promoters, and other nearby control regions-that reside on the same chromosome and close to the genes they regulate. While it's true that linking a DMR to its closest gene captures a cis association, our phrasing emphasises that the DMRs are enriched specifically at these functional regulatory elements (Fig. 2E) rather than being randomly distributed. This usage aligns with established conventions in the field. To avoid any misunderstandings, we have now changed the term to gene regulatory sites.

      __CHANGE IN THE MANUSCRIPT____: __

      *We changed the "cis-regulatory sites" to "gene regulatory sites" *

      __Minor comments: __

      Line 157: "we identified site-specific differences....". Change to region specific?

      Response:

      This has now been corrected as suggested.

      Line 102-103: needs a reference for the statement "Alterations in DNA methylation patterns have been implicated......"

      Response:

      Following the reviewer's suggestion, we added the relevant references (34-36) to this statement.

      Line 266 - what does "strong dysregulation" mean? Large fold change, very significant?

      Response:

      We removed the word "strong" from this sentence.

      Lines 423-425 - statement needs a reference

      Response:

      Following the reviewer's suggestion, we added the relevant reference to this statement.

      Line 428 - word missing between "epigenetic , we"?

      Response:

      This has now been corrected. The text reads: "Through treatment with a demethylating drug and targeted epigenetic editing, we demonstrated the ability to modulate..."

      Prior studies are well references, text and figures are clear and accurate.

      __Reviewer #2 (Significance (Required)): __

      This study has several strengths:

      1) Sample collection and characterisation. AT2 cells are incredibly hard to come by and the authors should be commended to generating the samples. However, proximity to cancer is always a potential issue, especially in epigenetic studies. Is it feasible to include any analysis to show the samples derived from those with cancer don't drive the changes observed? Even a high level PCA or an edit of fig 2A with non-cancer in a different colour in supplemental - looks like there is one outlier, is that a non-cancer? Or a correlation of change in beta between control and cancer/COPD and control and non-cancer:COPD (for want a better phrase!). just an indicator that the non-cancer COPD samples are not driving differences.

      Response:

      We thank the reviewer for highlighting the value of generating data from hard-to-work-with AT2 populations and bringing up the important point of cancer proximity, which we considered very carefully when designing our study. To match our samples across the cohort, all the no-COPD, COPD I, and two of the COPD II-IV distal lung samples were obtained from cancer resections. In addition to other characteristics, like age, BMI and smoking status, we also matched the donors by cancer type (all profiled donors had squamous cell carcinoma). We collected lung tissue as far away from the carcinoma as possible and sent representative pieces for histological analysis by an experienced lung pathologist to confirm the absence of visible tumours. In addition, to ensure that our data represents COPD-relevant signatures, we intentionally included samples from three COPD donors undergoing lung resections (without a cancer background) in the profiling.

      Following the reviewer's suggestion, to investigate the potential impact of non-cancer samples on driving the observed differences, we carefully checked the PCAs for both DNA methylation and RNA-seq. We could not identify a clear separation of no-cancer COPD samples from the cancer COPD samples (or other cancer samples) in any examined PCs, indicating no cofounding effect of cancer samples. We observed that one sample contributing to PC2 is a non-cancer sample, but this was a rather sample-specific effect, as the other two non-cancer samples clustered together with the other severe COPD samples with a cancer background. Notably, in our DNA methylation data, we do not observe typical features of cancer methylomes, like global loss of DNA methylation or aberrant methylation of CpG islands (e.g., in tumour suppressor genes) (see Fig. 2A), further suggesting that we do not "pick up" confounding cancer signatures in our data.

      Following the comments from both reviewers, to clarify that point, we added the information about cancer and non-cancer samples to the PCA figures for DNA methylation (new Fig. 2B) and RNA-seq (new Fig. 3A) data in the revised manuscript, as shown below

      CHANGE IN THE MANUSCRIPT____:

      COPD samples from donors with a cancer background clustered together with the COPD samples from lung resections, confirming that we detected COPD-relevant signatures (Fig. 2B).

      Fig. 2B.* Principal component analysis (PCA) of methylation levels at CpG sites with > 4-fold coverage in all samples. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

      Unsupervised principal component analysis (PCA) on the top 500 variable genes revealed a clear influence of the COPD phenotype in separating no COPD and COPD II-IV samples, as previously observed with the DNA methylation analysis, irrespective of the cancer background of COPD samples (Fig.3A, Fig. S2B).

      *Principal component analysis (PCA) of 500 most variable genes in RNA-seq analysis. PCA 1 and 2 are shown in Fig.3A, PCA 1 and 4 in Fig.S2B. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

      2) This is the first time DNAm has been profiled in AT2 cells. It is incredibly difficult, valuable and novel data that will increase the fields capability technically, their understanding of functional mechanisms and potential translation considerably. It's audience will be primarily translational respiratory however the fundamental science aspect of gene expression regulation by DNA methylation with have wider reach across developmental and disease science.

      Response:

      We thank the reviewer for recognising the uniqueness and novelty of our study and highlighting the value and potential impact of our datasets for the lung field.

      3) the functional analysis using targeted CRISPR-Cas9 is very well done and adds impact.

      Response:

      We thank the reviewer for recognising the strengths and added value of the functional analysis using epigenetic editing.

      __Potential weaknesses/areas for development __

      I feel the main weakness is the in the section integrating DNA methylation and gene expression. The rationale for a focus on various aspects, for example inversely related DNAm/gene expression pairs, the IFN pathway and IRF9, are not clear. Also further understanding of the differences between DNAm associated genes and non-DNAm associated genes could be expanded, at the pathway level, TF regulation level, effect size level (are DNAm associated changes to gene expression larger, enriched for earlier differential expression)

      Response:

      Our rationale for focusing on the inversely related DNAm/gene expression pairs in promoter proximal is purely data-driven, as they represent the biggest group in our data (Fig. 4A-B). Among those negatively correlated genes, we observed the strongest enrichment for the IFN pathway (Fig. C), making it an obvious, data-driven target for further studies. The negative correlation of expression and methylation for IFN pathway genes could be validated in 5-AZA assays in A549 cells (Fig. 5A). Next, we made an interaction network analysis showing IRF9 and STAT2 as master regulators (Fig. 5B) of the negatively correlated IFN genes. As IRF9 itself displayed a negative correlation between DNA methylation and expression (Fig. 5C), we used the associated DMR for further epigenetic editing (Fig. 5D-E). We performed the additional requested analyses of the enhancer-associated changes and genes, as described above. We fully agree with the reviewer that our data sets are a great resource and can be further used to elaborate on other relationships of DNA methylation and RNA expression or other pathways, but this is out of the scope of this study. To enable further studies by the research community, we provide all necessary information about DMRs and DEGs in the associated supplementary tables and the raw data through the EGA, as well as the CRISPRa editing assay.

      The authors could comment on potential masking of differences between 5hmC and mC and the implications it may have

      Response:

      We thank the reviewer for bringing up this important point. Indeed, bisulfite sequencing cannot differentiate between methylated and hydroxymethylated cytosines; hence, some of the methylated sites may be hydroxymethylated. However, the overall levels of hydromethylation in differentiated adult tissues are very low (except for the brain), orders of magnitude lower compared to DNA methylation. Following the reviewer's suggestion, we have added a sentence in the limitation section of the discussion to clarify that point.

      __CHANGE IN THE MANUSCRIPT: __

      In addition, while WGBS provides unprecedented resolution and high coverage of the DNA methylation sites across the genome, it does not allow distinguishing 5-methylcytosine from 5-hydroxymethylcytosine. Therefore, we cannot exclude that some methylated sites we detected are 5-hydroxymethylated. However, the 5-hydroxymethylcytosine is present at very low levels in the lung tissue (97)*. ** *

      Furthermore, while the rationale for looking at DMRs is clear, especially given the sample number, I am interested to understand what proportion of the assayed CpGs "fit" within the cut off stipulations of the DMR analysis - that is, is their potentially COPD effects at sparse CpG regions/individual CpG sites that are not being identified. A comment on this would be useful and seems the strength of profiling genome wide. I'm happy genome wide is beneficial it just feels a little circular that the authors have chosen whole genome to avoid the bias of the Illumina array and a focus on promotors, but have primarily reported promoter DNAm. This caught my attention again in the discussion where the authors state that cis-regulatory regions were also identified in their fibroblast data .....is this finding a factor of the analysis performed? (also a comparison of regions Identified in AT2 cells versus fibroblasts would be really interesting for a future paper)

      Response:

      We decided to focus our analysis on regions rather than individual CpG sites when looking at differential methylation, as DNA methylation is spatially correlated, and methylation changes in larger regions are more likely to have a biological function. Extending the analysis to single CpG sites would require a higher number of samples for a reliable analysis compared to the DMR analysis (as mentioned by the reviewer).

      Of note, we addressed the platform comparison between Illumina array technology and WGBS in our previous fibroblast study (PMID: 37143403), where we compared our WGBS data with the published 450k array data of COPD parenchymal fibroblasts (Clifford et al., 2018). We observed only a marginal overlap between the CpGs from our DMRs and the CpGs probes available on the array (which was due to the differences in technologies used and the limited coverage of the 450K array in comparison to our genome-wide approach, in which we covered 18 million CpGs). Out of the 6279 DMRs identified in our fibroblast study, only 1509 DMRs overlapped with at least one CpG probe on the 450K array, and after removing low-quality CpGs from the array data, only 1419 DMRs were left. This comparison highlighted the increased resolution of the WGBS compared to Illumina arrays.

      The reason why we focused on promoter proximal DMRs are the following: 1) the assignment of the enhancer elements in AT2 to the corresponding gene is still too inaccurate in the absence of AT2 specific enhancer chromatin maps 2) regulation at enhancers by DNA methylation might be more complex and might change (increase or attenuate) binding affinities of certain transcription factors (Fig.2H), which might lead to gene expression changes or 3) methylation changes might be an indirect effect of differential TF binding PMID: 22170606). However, we agree with the reviewer that despite these limitations, expanding the analysis beyond promoters adds value to the manuscript; hence, as described above, we expanded the analysis of non-promoter regions, including enhancers, in the revised manuscript.

      We thank the reviewer for the suggestion to compare the regions identified in AT2 cells and fibroblasts in a future paper.

      My expertise:Respiratory, cell biology, epigenetics.

    2. 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:

      This study aim to understand the molecular mechanisms underlying dysfunction in AT2 cells in COPD, by profiling bulk genome wide DNA methylation using Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) and RNA sequencing in selectively sorted primary AT2 cells. The study stands out in it's sequencing breadth and use of an incredibly difficult cell population, and has the potential to add substantially to our mechanistic understanding of epigenetic contributions to COPD. A further highlight is the concluding aspect of the study where the authors undertook targeted modification of specific CpG methylation, provided direct, site-specific evidence for transcriptional regulation by CpG methylation.

      Major comments:

      The authors clearly show that there is DNA methylation alteration in AT2 cells from COPD individuals that links functional to gene expression at some level. However, I think the statement "to identify genome-wide changes associated with COPD development and progression..." and similar other references to disease development understanding is not accurate given the DNA methylation primary comparison is between control and moderate to severe COPD, with no temporal detail or evidence that they drive progression rather than are a result of COPD development. The paragraph starting on line 186 where this is a addressed to some extent is quite vague and doesn't really provide confidence that DNAm dysregulation occurs at an early stage in this context. This can be addressed by changing the focus/style of the text.

      Results comments and suggestions:

      For the integrated analysis, there is a focus on DMRs in promoters with very little analysis on other regions. The paragraph starting on line 317 describes some analysis on enhancers but is very brief, doesn't include information on how many/which DMRs were included, making it hard to interpret the impact of the 147 DMRs and 93 genes identified - is this nearly all DMRs and genes analysed or very few? A comparison to the promoter analysis would be of interest. Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

      • Lines 299-301 - I'm not sure the graph in Fig S3A support the conclusion that there was a preferential negative relationship between DNAm and gene expression. Looks like there are a substantial number of cases where a positive relationship is observed and this needs to be acknowledged.

      • Line 307 - what are the "analysed DEGs"? Are they the methylation associated genes?

      • Line 307-309 - "Among the analyzed DEGs, 76.5% (492) displayed a negative correlation (16.8% of the total DEGs), indicating a possible direct regulation by DNA methylation, while 23.5% (151) showed a positive correlation between gene expression and DNA methylation" - are the authors suggesting the positive correlation doesn't indicate direct regulation?

      • Line 313 - why did the authors focus on only negatively correlated genes to identify their top dysregulated pathway of IFN signalling? Why not do pathway analysis on the DNAm associated genes separately to identify DNAm associated pathways?

      • A comparison of the gene expression data with previous data in AT2 cell/single cell data would strengthen the gene expression section.

      • The paragraph starting on line 173 feels a little redundant when we know there is RNA available to test if the differential DNAm links to altered gene expression - this selected of example regions/genes would be better placed after the gene expression has been reported, at which point you could say whether the linked genes displayed altered transcription.

      • Similarly, the TF enrichment analysis is great but maybe would have added value to be done on DNA regions later shown to be linked to differential expression - was there different enrichment at DNA regions that are vs are not associated with altered expression? And could you test in vitro whether changing methylation of DNA (maybe a blunt too like 5-aza would be ok) alters TF binding (cut+run/ChIP?). Furthermore it would be interesting to understand the TF sensitivity analysis within the context of positive versus negative DNA methylation:gene expression correlations.

      Methods:

      • The authors should include more detail of the TWGBS rather than directing the reader to a previous publication. Also DNA concentration post bisuphite conversion would be a useful metric to provide.

      • Differential DNA methylation analysis: It is stated that DNA regions had to contain 3 CpG sites but was this within a defined DNA size range?

      • Refence genome only provided for RNAseq not TWGBS?

      • The tables do not appear in the PDF and I struggled to tally to the "Dataset" files provided if that is what they were referring to?

      • For the gene expression analysis, can it be made clearer that a full analysis was done on COPD I samples. It is a little confusing to the reader as this was not done for DNAm so might be assumed the same targeted analysis on only genes found to be differentially expressed between control and COPD II-IV, but that cannot be the case as an overlap of COPD1 vs COPD II-IV genes if provided. For this overlap, do genes show the same effect direction?

      • Replication is difficult on these studies as the samples are so difficult to come by. Also limited by sample size for the same reason. It doesn't mean the study is not worth doing and the data are still valuable. However, it may be pertinent to include technical validation of a few regions of interest, acknowledge the limitation (along side strengths) in the discussion, and perhaps provide actual p value rather than blanket < p 0.1, seems very lenient but may all be super significant (this may already be in the tables I wasn't able to find).

      • It isn't clear to me if DNA and RNA are from the same cells? The results say "cells matching those used for T-WGBS" but the methods suggest separate extractions so not the same cells? If they are not the same cells a comment on the implications of this should be included in the discussion for example, potentially some differences in cell type composition, storage time etc.

      • Line 193 the authors say "Since DMRs were overrepresented at cis-regulatory sites...." - "cis" needs to be defined. If you link DNAm regions to gene via "closest gene" does this not automatically mean you're outputs will be cis? Just needs better definition/explanation.

      Minor comments:

      • Line 157: "we identified site-specific differences....". Change to region specific?

      • Line 102-103: needs a reference for the statement "Alterations in DNA methylation patterns have been implicated......"

      • Line 266 - what does "strong dysregulation" mean? Large fold change, very significant?

      • Lines 423-425 - statement needs a reference

      • Line 428 - word missing between "epigenetic , we"?

      • Prior studies are well references, text and figures are clear and accurate.

      Significance

      This study has several strengths:

      1) Sample collection and characterisation. AT2 cells are incredibly hard to come by and the authors should be commended to generating the samples. However, proximity to cancer is always a potential issue, especially in epigenetic studies. Is it feasible to include any analysis to show the samples derived from those with cancer don't drive the changes observed? Even a high level PCA or an edit of fig 2A with non-cancer in a different colour in supplemental - looks like there is one outlier, is that a non-cancer? Or a correlation of change in beta between control and cancer/COPD and control and non-cancer:COPD (for want a better phrase!). just an indicator that the non-cancer COPD samples are not driving differences.

      2) This is the first time DNAm has been profiled in AT2 cells. It is incredibly difficult, valuable and novel data that will increase the fields capability technically, their understanding of functional mechanisms and potential translation considerably. It's audience will be primarily translational respiratory however the fundamental science aspect of gene expression regulation by DNA methylation with have wider reach across developmental and disease science.

      3) the functional analysis using targeted CRISPR-Cas9 is very well done and adds impact.

      Potential weaknesses/areas for development:

      I feel the main weakness is the in the section integrating DNA methylation and gene expression. The rationale for a focus on various aspects, for example inversely related DNAm/gene expression pairs, the IFN pathway and IRF9, are not clear. Also further understanding of the differences between DNAm associated genes and non-DNAm associated genes could be expanded, at the pathway level, TF regulation level, effect size level (are DNAm associated changes to gene expression larger, enriched for earlier differential expression) The authors could comment on potential masking of differences between 5hmC and mC and the implications it may have

      Furthermore, while the rationale for looking at DMRs is clear, especially given the sample number, I am interested to understand what proportion of the assayed CpGs "fit" within the cut off stipulations of the DMR analysis - that is, is their potentially COPD effects at sparse CpG regions/individual CpG sites that are not being identified. A comment on this would be useful and seems the strength of profiling genome wide. I'm happy genomewide is beneficial it just feels a little circular that the authors have chosen whole genome to avoid the bias of the Illumina array and a focus on promotors, but have primarily reported promoter DNAm. This caught my attention again in the discussion where the authors state that cis-regulatory regions were also identified in their fibroblast data ..... is this finding a factor of the analysis performed? (also a comparison of regions Id'ed in AT2 cells versus fibroblasts would be really interesting for a future paper)

      My expertise: Respiratory, cell biology, epigenetics.

    1. Australia draws two conclusions from the absence of any speci-fied termination date in JARPA II. First, Australia contends that thisdemonstrates that the design of JARPA II is geared towards the perpetu-ation of whaling by any means until the commercial whaling moratoriumis lifted. Secondly, Australia maintains that the open-ended nature ofJARPA II precludes a meaningful assessment of whether it has achievedits research objectives, distorts the process of sample size selection, andtherefore renders the design of JARPA II unscientific.216. The Court notes the open-ended time frame of JARPA II andobserves that with regard to a programme for purposes of scientificresearch, as Annex P indicates, a “time frame with intermediary targets”would have been more appropriate.(ii) Scientific output of JARPA II to date

      The absence of an end date is a quiet but important issue. In research, timelines aren’t just administrative—they’re part of how you measure whether a project has done what it set out to do. Without a fixed end point or at least interim milestones, there’s no natural moment to stop, take stock, and decide if the original questions have been answered.

      Australia treats this as evidence that JARPA II is less about specific research goals and more about keeping whaling going under a scientific label. That’s a strong claim, but the open-ended design does make it harder to separate scientific intent from policy motives.

      The Court’s response is measured: it doesn’t accuse Japan of bad faith, but it does point out that a proper research programme should have a defined timeframe and checkpoints. That’s not just good scientific practice—it’s a safeguard against a project drifting on indefinitely without clear justification.

      From a legal perspective, this matters because open-endedness weakens the connection between the design and the treaty’s object and purpose. A state could, in theory, meet the formal requirements of Article VIII while running a “research” programme that never actually answers its own questions. Setting time frames is one of the simplest ways to make sure that doesn’t happen.

    2. In light of these divergent views, the Court will consider the evi-dence regarding Japan’s selection of the various minimum sample sizesthat it chose for different individual research items, which form the basisfor the overall sample size for minke whales. As noted above (see para-graph 172), the purpose of such an inquiry is not to second-guess thescientific judgments made by individual scientists or by Japan, but ratherto examine whether Japan, in light of JARPA II’s stated research objec-tives, has demonstrated a reasonable basis for annual sample sizes per-taining to particular research items, leading to the overall sample sizeof 850 (plus or minus 10 per cent) for minke whales

      This is the Court saying, “We’re not here to tell scientists how to do science—but we are here to check whether your numbers actually make sense for what you say you’re trying to do.” It’s an important balance: they avoid the trap of becoming armchair marine biologists, while still holding Japan to a reasonableness test.

      The problem for Japan is that this standard cuts right to the heart of their Article VIII defence. If you set a target of 850 minke whales a year, you need to show how that number logically follows from your research objectives—not just that it’s what you’ve always done, or that it’s convenient for your vessels. And “reasonable basis” here isn’t some fluffy diplomatic phrase; it means the Court will ask if the numbers match the aims without obvious inflation.

      From a legal point of view, this is a smart move by the Court—it sidesteps accusations of second-guessing scientific expertise, but still demands that states show their working. The risk, though, is that “reasonable basis” is not a term grounded explicitly in the ICRW or past IWC resolutions, so it lives in a grey zone between legal interpretation and judicial policy-making. Japan could push back that the ICJ is importing a review standard that the treaty never agreed on.

      Still, from a common-sense angle, it’s hard to fault the approach: if you can’t draw a straight line from your stated research goals to your lethal catch numbers, maybe those numbers aren’t really about the research. And in this case, the Court clearly suspects that’s exactly what’s going on.

    3. Another factor casts doubt on whether the design of JARPA II isreasonable in relation to achieving the programme’s stated objectives.The overall sample sizes selected for fin and humpback whales —50 whales of each species per year — are not large enough to allow for themeasurement of all the trends that the programme seeks to measure. Spe-cifically, the JARPA II Research Plan states that at least 131 whales ofeach species should be taken annually to detect a particular rate of changein age at sexual maturity. The Research Plan does not indicate whetherthe researchers decided to accept a lower level of accuracy or insteadadjusted the rate of change that they sought to detect by targeting fewerwhales, nor did Japan explain this in the present proceedings. In light ofthe calculations of its own scientists, JARPA II does not appear designedto produce statistically relevant information on at least one centralresearch item to which the JARPA II Research Plan gives particularimportance

      Japan’s own paperwork says you need 131 whales a year to hit your accuracy target—but then they only plan to take 50. That’s not a rounding error; that’s less than half. And when asked why, Japan didn’t explain whether they were okay with fuzzier results or if they quietly changed what they were trying to measure. Either way, it’s a big hole in the research logic.

      From a scientific perspective, it’s like saying you’re running a medical trial that needs 1,000 patients for reliable results, but you’ll settle for 400 without telling anyone what that does to your findings. From a legal perspective, it’s worse—it undermines the claim that the lethal take is strictly for science. If your own numbers say the plan won’t get you there, then why do it at all?

      The Court doesn’t spell it out this bluntly, but the implication is clear: either the sample size was set for reasons other than the science, or the science was so badly compromised in design that it can’t justify the lethal take under Article VIII. And without tying the decision to state practice or external scientific guidelines, Japan leaves itself open to the charge that “scientific research” here is just the label on the jar, not the contents inside.

    Annotators

    1. Author response:

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

      Reviewer #1 (Public review):

      Summary:

      Cook et al. have presented an important study on the transcriptomic and epigenomic signature underlying craniofacial development in marsupials. Given the lack of a dunnart genome, the authors also prepared long and short-read sequence datasets to assemble and annotate a novel genome to allow for the mapping of RNAseq and ChIPseq data against H3K4me3 and H3K27ac, which allowed for the identification of putative promoter and enhancer sites in dunnart. They found that genes proximal to these regulatory loci were enriched for functions related to bone, skin, muscle and embryonic development, highlighting the precocious state of newborn dunnart facial tissue. When compared with mouse, the authors found a much higher proportion of promoter regions aligned between species than for enhancer regions, and subsequent profiling identified regulatory elements conserved across species and are important for mammalian craniofacial development. In contrast, the identification of dunnart-specific enhancers and patterns of RNA expression further confirm the precocious state of muscle development, as well as for sensory system development, in dunnart suggesting that early formation of these features are critical for neonate marsupials likely to assist with detecting and responding to cues that direct the joeys to the mother's teat after birth. This is one of the few epigenomic studies performed in marsupials (of any organ) and the first performed in fat-tailed dunnart (also of any organ). Marsupials are emerging as an important model for studying mammalian development and evolution and the authors have performed a novel and thorough analysis, impressively including the assembly of a new marsupial reference genome that will benefit many future studies.

      Strengths:

      The study provides multiple pieces of evidence supporting the important role enhancer elements play in mammalian phenotypic evolution, namely the finding of a lower proportion of peaks present in both dunnart and mouse for enhancers than for promoters, and dunnart showing more genes uniquely associated with it's active enhancers than any other combination of mouse and dunnart samples, whereas this pattern was less pronounced than for promoter-associated genes. In addition, rigorous parameters were used for the cross-species analyses to identify the conserved regulatory elements and the dunnart-specific enhancers. For example, for the results presented in Figure 1, I agree that it is a little surprising that the average promoter-TSS distance is greater than that for enhancers, but that this could be related to the possible presence of unannotated transcripts between genes. The authors addressed this well by examining the distribution of promoter-TSS distances and using proximal promoters (cluster #1) as high confidence promoters for downstream analyses.

      The genome assembly method was thorough, using two different long read methods (Pacbio and ONT) to generate the long reads for contig and scaffold construction, increasing the quality of the final assembled genome.

      Weaknesses:

      Biological replicates of facial tissue were collected at a single developmental time point of the fat-tailed dunnart within the first postnatal day (P0), and analysed this in the context of similar mouse facial samples from the ENCODE consortium at six developmental time points, where previous work from the authors have shown that the younger mouse samples (E11.5-12.5) approximately corresponds to the dunnart developmental stage (Cook et al. 2021). However, it would be useful to have samples from at least one older dunnart time point, for example, at a developmental stage equivalent to mouse E15.5. This would provide additional insight into the extent of accelerated face development in dunnart relative to mouse, i.e. how long do the regulatory elements that activated early in dunnart remain active for and does their function later influence other aspects of craniofacial development?

      We thank the reviewer for their feedback and agree that the inclusion of multiple postnatal stages in the dunnart would give further valuable insights to the comparative analyses. Unfortunately, we were limited by the pouch young available and prioritized ensuring robust data at a single stage for this study. We hope to expand this work to more stages in future studies.

      The authors refer to the development of the CNS being delayed in marsupials relative to placental mammals, however, evidence shows how development of the dunnart brain (whole brain or cortex) is protracted compared to mouse, by a factor of at least 2 times, rather than delayed per se (Workman et al. 2013; Paolino et al. 2023). In addition, there is evidence that cortical formation and cell birth may begin at approximately the same stage across species equivalent to the neonate period in dunnart (E10.5 in mouse), and that shortly after this at the stage equivalent to mouse E12.5, the dunnart cortex shows signs of advanced neurogenesis followed by a protracted phase of neuronal maturation (Paolino et al. 2023). Therefore, it is possible that marsupial CNS development appears delayed relative to mouse but instead begins at the same stage and then proceeds to develop on a different timing scale.

      The comparison here is not directly between CNS development in placental and marsupials but CNS development relative to development of a subset of structures of the cranial skeleton and musculature (as first proposed by Kathleen Smith 1997). For example, Smith 1997 found that in eutherians, evagination of the telencephalon and appearance of the pigment in the eye occur before the ossification of the premaxilla, maxilla, and dentary. However, in marsupials, evagination of the telencephalon and appearance of the pigment in the eye occur concurrently with condensation of cartilage in the basicranium and the ossification of the premaxilla, maxilla, and dentary. Smith 1997 reports both a delay in the initiation of CNS development in marsupials relative to craniofacial ossification and a protraction of CNS development compared to placental mammals.

      This also highlights the challenges of correlating different staging systems between placentals and marsupials as stages determined as equivalent can change depending on which developmental events are used. The protracted development of the CNS in marsupials (Smith 1997, Workman et al. 2013; Paolino et al. 2023) still supports the hypothesis that during the short gestation period in marsupials structures required for life outside the womb in an embryonic-like state, such as the orofacial region, are likely prioritized.

      We have clarified this based on the reviewers feedback and added text referring to the protraction of marsupial CNS development to the Discussion section.

      [New text]: Marsupials display advanced development of the orofacial region relative to development of the central nervous system when compared to placental mammals[3,6].

      [New text]: Although development of the central nervous system is protracted in marsupials compared to placentals, marsupials have well-developed peripheral motor nerves and sensory nerves (eg. the trigeminal) at birth [5].

      Reviewer #2 (Public review):

      This study by Cook and colleagues utilizes genomic techniques to examine gene regulation in the craniofacial region of the fat-tailed dunnart at perinatal stages. Their goal is to understand how accelerated craniofacial development is achieved in marsupials compared to placental mammals.

      The authors employ state-of-the-art genomic techniques, including ChIP-seq, transcriptomics, and high-quality genome assembly, to explore how accelerated craniofacial development is achieved in marsupials compared to placental mammals. This work addresses an important biological question and contributes a valuable dataset to the field of comparative developmental biology. The study represents a commendable effort to expand our understanding of marsupial development, a group often underrepresented in genomic studies.

      The dunnart's unique biology, characterized by a short gestation and rapid craniofacial development, provides a powerful model for examining developmental timing and gene regulation. The authors successfully identified putative regulatory elements in dunnart facial tissue and linked them to genes involved in key developmental processes such as muscle, skin, bone, and blood formation. Comparative analyses between dunnart and mouse chromatin landscapes suggest intriguing differences in deployment of regulatory elements and gene expression patterns.

      Strengths

      (1) The authors employ a broad range of cutting-edge genomic tools to tackle a challenging model organism. The data generated - particularly ChIP-seq and RNA-seq from craniofacial tissue - are a valuable resource for the community, which can be employed for comparative studies. The use of multiple histone marks in the ChIP-seq experiments also adds to the utility of the datasets.

      (2) Marsupial occupy an important phylogenetic position, but they remain an understudied group. By focusing on the dunnart, this study addresses a significant gap in our understanding of mammalian development and evolution. Obtaining enough biological specimens for these experiments studies was likely a big challenge that the authors were able to overcome.

      (3) The comparison of enhancer landscapes and transcriptomes between dunnarts and can serve as the basis of subsequent studies that will examine the mechanisms of developmental timing shifts. The authors also carried out liftover analyses to identify orthologous enhancers and promoters in mice and dunnart.

      Weaknesses and Recommendations

      (1) The absence of genome browser tracks for ChIP-seq data makes it difficult to assess the quality of the datasets, including peak resolution and signal-to-noise ratios. Including browser tracks would significantly strengthen the paper by provide further support for adequate data quality.

      We have put together an IGV session with the dunnart genome, annotation and ChIP-seq tracks. This is now available in the FigShare data repository (10.7554/eLife.103592.1).

      (2) The first two figures of the paper heavily rely in gene orthology analysis, motif enrichment, etc, to describe the genomic data generated from the dunnart. The main point of these figures is to demonstrate that the authors are capturing the epigenetic signature of the craniofacial region, but this is not clearly supported in the results. The manuscript should directly state what these analyses aim to accomplish - and provide statistical tests that strengthen confidence on the quality of the datasets.

      As this is the first epigenomic profiling for this species we performed extensive data quality control (See Supplementary Tables 2-3, 18, 20-23 and Supplementary Figures 1-3, 6-11). These figures and corresponding Supplementary Tables show the robustness of the data, including well-described metrics for assessing promoters and enhancers, GO terms relevant to craniofacial development and binding motifs for key developmental TF families.

      We have emphasised this aspect of the work more strongly in the results section, particularly in [Defining craniofacial putative enhancer- and promoter regions in the dunnart].

      (3) The observation that "promoters are located on average 106 kb from the nearest TSS" raises significant concerns about the quality of the ChIP-seq data and/or genome annotation. The results and supplemental information suggest a combination of factors, including unannotated transcripts and enhancer-associated H3K4me3 peaks - but this issue is not fully resolved in the manuscript. The authors should confirm that this is not caused by spurious peaks in the CHIP-seq analysis - and possibly improve genome annotation with the transcriptomic datasets presented in the study.

      Spurious ChIP-seq peaks could be possible as there is no “blacklisted regions” database for the dunnart to filter on, however we used a no-IP control, a stringent FDR of 0.01 and peaks had to be reproducible in two biological replicates when calling peaks - all of which should reduce the likelihood of false positives.

      H3K4me3 activity at enhancers is well-established, in particular when enhancer sequences are also bound by RNA Pol II ((Koch and Andrau, 2011; Pekowska et al., 2011). However, compared to H3K4me3 activity at promoters, H3K4me3 levels at enhancers are low (Calo and Wysocka, 2013). This is in line with our observations that H3K4me3 levels at enhancers are much lower than observed at promoter regions (see Supplementary Note 2). We found that H3K4me3 peaks located closer to the TSS had a stronger peak signal (mean = 46.10) than distal H3K4me3 peaks (mean = 6.95; Wilcoxon FDR-adjusted p < 2.2 x 10<sup>-16</sup>). This suggests that although some distal promoter peaks may be due to missingness in the annotation, the majority likely represent peaks associated with enhancer regions. We have emphasized this finding more strongly in the results section:

      [New text]: H3K4me3 activity at enhancers is well-established[25,26], however, compared to H3K4me3 activity at promoters, H3K4me3 levels at enhancers are low[27]. This is in line with our observations where H3K4me3 levels at distal enhancer peaks are nearly 7 times lower than those observed at promoter regions (see SupNote2).

      (4) The comparison of gene regulation between a single dunnart stage (P1) and multiple mouse stages lacks proper benchmarking. Morphological and gene expression comparisons should be integrated to identify equivalent developmental stages. This "alignment" is essential for interpreting observed differences as true heterochrony rather than intrinsic regulatory differences.

      Given the developmental differences between eutherian and marsupial mammals it is challenging to assign the dunnart a precise “equivalent” developmental stage to the mouse. From our morphological and developmental characterisation (see Cook et al. 2020 Nat Comms Bio) based on ossification patterns the dunnart orofacial region on the day of birth appears to be similar to that of an E12.5 mouse embryo (just prior to the observation of ossified craniofacial bones). However, when we compared both regulatory elements and expressed genes between the dunnart at this stage (P1) and 5 developmental stages in the mouse, there is no obvious equivalent stage. For example, when we simply compare genes linked to enhancer peaks, the group with the largest intersection between dunnart and any mouse stage are ~500 genes that are present in dunnart, and mouse stages E10.5, E12.5 - E15.5, Figure 5B). When we then compare genes expressed in the dunnart to temporal gene expression dynamics during mouse development we find that the largest overlap is with genes highly expressed at E14.5 or E15.5 in the mouse (Figure 6, Supplementary Figure 5). We have strengthened the rationale for the selected mouse stages in the comparative analyses section of the results.

      (5) The low conservation of putative enhancers between mouse and dunnart (0.74-6.77%) is surprising given previous reports of higher tissue-specific enhancer conservation across mammals. The authors should address whether this low conservation reflects genuine biological divergence or methodological artifacts (e.g., peak-calling parameters or genome quality). Comparisons with published studies could contextualize these findings.

      The reported range (0.74 - 6.77%) refers to the number regions called as an active enhancer peak in both species (conserved activity) divided by the total number of dunnart peaks alignable to the mouse genome, which we expect to be low given sequence turnover rates and the evolutionary distance separating dunnart and mice. The alignability (conserved sequence) for dunnart enhancers to the mouse genome was ~13% for 100bp regions and can be found in Supplementary Table 22, we have now clarified this in the main text.

      [New Text]: After building dunnart-mm10 liftover chains (see Methods and SupNote5) we compared mouse and dunnart regulatory elements. The alignability (conserved sequence) for dunnart enhancers to the mouse genome was ~13% for 100bp regions (Supplementary Table 22).

      The activity conservation range reported here is consistent with previously reported for marsupial-placental enhancer comparisons (Villar et al. 2015), where ~1% of conserved liver-specific human enhancers had conserved activity to opossum. Follow up studies in Berthelot et al 2018 also found that approximately 1% of human liver enhancers were conserved across the placental mammals included in the study.

      (6) Focusing only on genes associated with shared enhancers excludes potentially relevant genes without clear regulatory conservation. A broader analysis incorporating all orthologous genes may reveal additional insights into craniofacial heterochrony.

      We appreciate the reviewers comment, we understand that a broader analysis may provide some additional insights to this question however in this study our focus was understanding the enhancers driving craniofacial development in these species. We linked enhancers with gene expression data as additional evidence of regulatory programs involved in craniofacial development. The majority (~70%) of genes reproducibly expressed were linked to an active enhancer and/or promoter.   This has now been highlighted in the result section.

      [New Text]: There were 12,153 genes reproducibly expressed at a level > 1 TPM across three biological replicates, with the majority of genes 67% of genes expressed (67%; 8158/12153) associated with near an active enhancer and/or promoter peak.

      In conclusion, this study provides an important dataset for understanding marsupial craniofacial development and highlights the potential of genomic approaches in non-traditional model organisms. However, methodological limitations, including incomplete genome annotation and lack of developmental benchmarking weaken the robustness and of the findings. Addressing these issues would significantly enhance the study's utility to the field and its ability to support the study's central conclusion that dunnart-specific enhancers drive accelerated craniofacial development.

      Reviewer #1 (Recommendations for the authors):

      Minor comments and corrections:

      (1) ChIP-seq FRiP fractions were much higher in dunnart samples than in mouse. Is this related to any differences in sample preparation they are aware of in the ENCODE datasets of mouse, such as different anti-histone antibodies used (and therefore different efficiency of binding to the same histone markers across species)? The authors appear to have addressed something similar with respect to the much lower enriched peak number observed in the mouse sample relative to dunnart in Supp note 4. I suspect the "technical cofounder" they refer to there is affecting both the FRiP scores and the higher correlation coefficients between IP and input in mouse.

      We chose the same antibodies used in the mouse craniofacial tissue ENCODE experiments however, the procedure is slightly different. We used the MAGnify Chromatin Immunoprecipitation System while in the ENCODE assays performed by Bing Ren’s group in 2012 was an in-house lab protocol for MicroChIP. Given that the samples for mouse and dunnart were not processed together, by the same researcher, with the same protocol there could be any number of technical cofounders impacting enrichment. A low FRiP score suggests low specificity as the majority of reads are in non-specific regions (low enrichment), consistent with the higher correlation between IP and input in mouse. The data quality also appears to vary between H3K27ac and H3K4me3 in the mouse (Supplementary Table 21), with H3K4me3 FRiP scores more similar to those observed in our dunnart experiments. This suggests a potential confounder specific to the mouse H3K27ac IP. QC metrics (FRiP, bam correlation) are consistent between H3K27ac and H3K4me3 IPs in our experiments (Supplementary Table 20).

      (2) Some of the promoter peak numbers in Supp table 1 do not match the numbers in the main text.

      We have corrected the incorrect number reported in the text for promoter peaks with orthologous genes (8590 -> 8597).

      (3) In Supp tables 2 and 3, the number of GO terms similar across tables is 466, which is ~42% of total number of enriched GO terms. However the authors mention that only 23% of terms were the same between promoters and enhancers, and a value of 42% was applied to the proportion of terms uniquely enriched for terms associated with genes assigned to promoters only. Unless I'm reading these Supp tables incorrectly, is it possible the proportions were mixed up?

      Thanks for catching this. The lists provided in Supplementary Table 2 were incorrect. The Supplementary Tables and in text description has been corrected to reflect this.

      (4) Would be helpful to add a legend for the mouse samples in Supp Figure 10.

      We have added the labels to the plot.

      (5) In Supp note 5, regarding the percentage of alignable peaks recovered, the percentages mentioned for the 50bp and 500bp peak summit lengths for enhancers and promoters do not seem to match the values in Supp tables 22 and 23.

      Thank you for catching this - we have corrected the Supplementary Tables and in text.

      (6) Please provide additional information to explain how dunnart RNA expression was associated with the five temporal expression clusters found in the mouse data shown in Figure 6 given there is only one dunnart time point and so the species temporal pattern's could not be compared, i.e. how was the odds ratio calculated and was this applied iteratively for dunnart against each mouse age and within each temporal cluster?

      The TCseq package takes the mouse expression data across all 6 stages and calls differentially expressed genes with an absolute log<sub>2</sub> fold-change > 2 compared to the starting time-point (E10.5). The mouse gene expression patterns were clustered into 5 clusters that each show distinct temporal expression patterns (see Supplementary Figure 5D). The output from this is 5 lists where within each list are unique genes that share a temporal pattern. These lists of mouse genes were then each compared to the orthologous genes expressed in the dunnart using a Fishers Exact test with corrections for multiple testing using the Holm method. We have added additional details in the methods:

      [New text]: Orthologous genes reproducibly expressed >1 TPM in the dunnart were compared to the list of genes for each cluster using Fisher’s Exact Test followed by p-value corrections for multiple testing with the Holm method.

      (7) SupFile1 and SupFile2 - which supplementary note or figure are these referring to?

      Apologies for this error. These items were meant to link to the FigShare repository where the supplementary files can be found. We have corrected this using the DOI for the repository.

      Reviewer #2 (Recommendations for the authors):

      (1) Authors should clarify that the mouse ENCODE data used for the comparisons was obtained from craniofacial tissue.

      This has now been corrected to clarify that the mouse ENCODE data used was from craniofacial tissues. ENCODE mouse embryonic facial prominence ChIP-seq and gene expression quantification file accession numbers and details used in study can be found in Supplementary Table 17.

      (2) Given the large differences in TPM for highly expressed genes shown in Figure 5, a MA or volcano plot would provide a more comprehensive view of global transcriptome differences between species.

      We have added this plot as Supplementary Figure 13.

      (3) It is unclear whether the enrichment analysis was performed for mouse genes, dunnart genes, or both.

      In reference to Figure 5, Gene Ontology enrichment analysis was performed on the top 500 highly expressed genes in dunnart. Because there is not an ontology database for dunnart gene IDs, these top 500 dunnart gene IDs were converted to the orthologous gene ID in mouse before performing the enrichment analysis. We apologise for the lack of clarity and have added additional text in the results section to make this clearer. In addition, the relevant methods section now reads:

      [New text]: As there is no equivalent gene ontology database for dunnart, we converted the Tasmanian devil RefSeq IDs to Ensembl v103 using biomaRt v2.46.3 and then converted these to mouse Ensembl v103 IDs. In this way we were able to use the mouse Ensembl Gene Ontology annotations for the dunnart gene domains. All gene ontology analyses were performed using clusterProfiler v4.1.4[117], with Gene Ontology from the org.Mm.eg.db v3.12.0 database[118], setting an FDR-corrected p-value threshold of 0.01 for statistical significance.

    1. idea that we'll have a world where we can say there is a neutral point of view well it's just not true anymore

      no neutral point of view any more

    1. Reviewer #2 (Public review):

      Summary:

      This study introduces an exciting dataset of single-unit responses in humans during a naturalistic and dynamic movie stimulus, with recordings from multiple regions within the medial temporal lobe. The authors use both a traditional firing-rate analysis as well as a sophisticated decoding analysis to connect these neural responses to the visual content of the movie, such as which character is currently on screen.

      Strengths:

      The results reveal some surprising similarities and differences between these two kinds of analyses. For visual transitions (such as camera angle cuts), the neurons identified in the traditional response analysis (looking for changes in firing rate of an individual neuron at a transition) were the most useful for doing population-level decoding of these cuts. Interestingly, this wasn't true for character decoding; excluding these "responsive" neurons largely did not impact population-level decoding, suggesting that the population representation is distributed and not well-captured by individual-neuron analyses.

      The methods and results are well-described both in the text and in the figures. This work could be an excellent starting point for further research on this topic to understand the complex representational dynamics of single neurons during naturalistic perception.

      Weaknesses:

      (1) I am unsure what the central scientific questions of this work are, and how the findings should impact our understanding of neural representations. Among the questions listed in the introduction is "Which brain regions are informative for specific stimulus categories?". This is a broad research area that has been addressed in many neuroimaging studies for decades, and it's not clear that the results tell us new information about region selectivity. "Is the relevant information distributed across the neuronal population?" is also a question with a long history of work in neuroscience about localist vs distributed representations, so I did not understand what specific claim was being made and tested here. Responses in individual neurons were found for all features across many regions (e.g., Table S1), but decodable information was also spread across the population.

      (2) The character and indoor/outdoor labels seem fundamentally different from the scene/camera cut labels, and I was confused by the way that the cuts were put into the decoding framework. The decoding analyses took a 1600ms window around a frame of the video (despite labeling these as frame "onsets" like the feature onsets in the responsive-neuron analysis, I believe this is for any frame regardless of whether it is the onset of a feature), with the goal of predicting a binary label for that frame. Although this makes sense for the character and indoor/outdoor labels, which are a property of a specific frame, it is confusing for the cut labels since these are inherently about a change across frames. The way the authors handle this is by labeling frames as cuts if they are in the 520ms following a cut (there is no justification given for this specific value). Since the input to a decoder is 1600ms, this seems like a challenging decoding setup; the model must respond that an input is a "cut" if there is a cut-specific pattern present approximately in the middle of the window, but not if the pattern appears near the sides of the window. A more straightforward approach would be, for example, to try to discriminate between windows just after a cut versus windows during other parts of the video. It is also unclear how neurons "responsive" to cuts were defined, since the authors state that this was determined by looking for times when a feature was absent for 1000ms to continuously present for 1000ms, which would never happen for cuts (unless this definition was different for cuts?).

      (3) The architecture of the decoding model is interesting but needs more explanation. The data is preprocessed with "a linear layer of same size as the input" (is this a layer added to the LSTM that is also trained for classification, or a separate step?), and the number of linear layers after the LSTM is "adapted" for each label type (how many were used for each label?). The LSTM also gets to see data from 800 ms before and after the labeled frame, but usually LSTMs have internal parameters that are the same for all timesteps; can the model know when the "critical" central frame is being input versus the context, i.e., are the inputs temporally tagged in some way? This may not be a big issue for the character or location labels, which appear to be contiguous over long durations and therefore the same label would usually be present for all 1600ms, but this seems like a major issue for the cut labels since the window will include a mix of frames with opposite labels.

      (4) Because this is a naturalistic stimulus, some labels are very imbalanced ("Persons" appears in almost every frame), and the labels are correlated. The authors attempt to address the imbalance issue by oversampling the minority class during training, though it's not clear this is the right approach since the test data does not appear to be oversampled; for example, training the Persons decoder to label 50% of training frames as having people seems like it could lead to poor performance on a test set with nearly 100% Persons frames, versus a model trained to be biased toward the most common class. There is no attempt to deal with correlated features, which is especially problematic for features like "Summer Faces" and "Summer Presence", which I would expect to be highly overlapping, making it more difficult to interpret decoding performance for specific features.

      (5) Are "responsive" neurons defined as only those showing firing increases at a feature onset, or would decreased activity also count as responsive? If only positive changes are labeled responsive, this would help explain how non-responsive neurons could be useful in a decoding analysis.

      (6) Line 516 states that the scene cuts here are analogous to the hard boundaries in Zheng et al. (2022), but the hard boundaries are transitions between completely unrelated movies rather than scenes within the same movie. Previous work has found that within-movie and across-movie transitions may rely on different mechanisms, e.g., see Lee & Chen, 2022 (10.7554/eLife.73693).

    2. Author response:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Gerken et al examined how neurons in the human medial temporal lobe respond to and potentially code dynamic movie content. They had 29 patients watch a long-form movie while neurons within their MTL were monitored using depth electrodes. They found that neurons throughout the region were responsive to the content of the movie. In particular, neurons showed significant responses to people, places, and to a lesser extent, movie cuts. Modeling with a neural network suggests that neural activity within the recorded regions was better at predicting the content of the movies as a population, as opposed to individual neural representations. Surprisingly, a subpopulation of unresponsive neurons performed better than the responsive neurons at decoding the movie content, further suggesting that while classically nonresponsive, these neurons nonetheless provided critical information about the content of the visual world. The authors conclude from these results that low-level visual features, such as scene cuts, may be coded at the neuronal level, but that semantic features rely on distributed population-level codes.

      Strengths:

      Overall, the manuscript presents an interesting and reasonable argument for their findings and conclusions. Additionally, the large number of patients and neurons that were recorded and analyzed makes this data set unique and potentially very powerful. On the whole, the manuscript was very well written, and as it is, presents an interesting and useful set of data about the intricacies of how dynamic naturalistic semantic information may be processed within the medial temporal lobe.

      We thank the reviewer for their comments on our manuscript and for describing the strengths of our presented work

      Weaknesses:

      There are a number of concerns I have based on some of the experimental and statistical methods employed that I feel would help to improve our understanding of the current data.

      In particular, the authors do not address the issue of superposed visual features very well throughout the manuscript. Previous research using naturalistic movies has shown that low-level visual features, particularly motion, are capable of driving much of the visual system (e.g, Bartels et al 2005; Bartels et al 2007; Huth et al 2012; Çukur et al 2013; Russ et al 2015; Nentwich et al 2023). In some of these papers, low-level features were regressed out to look at the influence of semantics, in others, the influence of low-level features was explicitly modeled. The current manuscript, for the most part, appears to ignore these features with the exception of scene cuts. Based on the previous evidence that low-level features continue to drive later cortical regions, it seems like including these as regressors of no interest or, more ideally, as additional variables, would help to determine how well MTL codes for semantic features over top of these lower-order variables.

      We thank the reviewer for this insightful comment and for the relevant literature regarding visual motion in not only the primary visual system but in cortical areas as well. While we agree that the inclusion of visual motion as a regressor of no interest or as an additional variable would be overall informative in determining if single neurons in the MTL are driven by this level of feature, we would argue that our analyses already provide some insight into its role and that only the parahippocampal cortical neurons would robustly track this feature.

      As noted by the reviewer, our model includes two features derived from visual motion: Camera Cuts (directly derived from frame-wise changes in pixel values)  and Scene Cuts (a subset of Camera Cuts restricted to changes in scene). As shown in Fig. 5a, decoding performance for these features was strongest in the parahippocampal cortex (~20%), compared to other MTL areas (~10%). While the entorhinal cortex also showed some performance for Scene Cuts (15%), we interpret this as being driven by the changes in location that define a scene, rather than by motion itself.

      These findings suggest that while motion features are tracked in the MTL, the effect may be most robust in the parahippocampal cortex. We believe that quantifying more complex 3D motion in a naturalistic stimulus like a full-length movie is a significant challenge that would likely require a dedicated study. We agree this is an interesting future research direction and will update the manuscript to highlight this for the reader.

      A few more minor points that would help to clarify the current results involve the selection of data for particular analyses. For some analyses, the authors chose to appropriately downsample their data sets to compare across variables. However, there are a few places where similar downsampling would be informative, but was not completed. In particular, the analyses for patients and regions may have a more informative comparison if the full population were downsampled to match the size of the population for each patient or region of interest. This could be done with the Monte Carlo sampling that is used in other analyses, thus providing a control for population size while still sampling the full population.

      We thank the reviewer for raising this important methodological point. The decision not to downsample the patient- and region-specific analyses was deliberate, and we appreciate the opportunity to clarify our rationale.

      Generally, we would like to emphasize that due to technical and ethical limitations of human single-neuron recordings, it is currently not possible to record large populations of neurons simultaneously in individual patients. The limited and variable number of recorded neurons per subject (Fig. S1) generally requires pooling neurons into a pseudo-populations for decoding, which is a well‐established standard in human single‐neuron studies (see e.g., (Jamali et al., 2021; Kamiński et al., 2017; Minxha et al., 2020; Rutishauser et al., 2015; Zheng et al., 2022)).

      For the patient-specific analysis, our primary goal was to show that no single patient's data could match the performance of the complete pseudo-population. Crucially, we found no direct relationship between the number of recorded neurons and decoding performance; patients with the most neurons (patients 4, 13) were not top performers, and those with the fewest (patients 11, 14) were not the worst (see Fig. 4). This indicates that neuron count was not the primary limiting factor and that downsampling would be unlikely to provide additional insight.

      Similarly, for the region-specific analysis, regions with larger neural populations did not systematically outperform those with fewer neurons (Fig. 5). Given the inherent sparseness of single-neuron data, we concluded that retaining the full dataset was more informative than excluding neurons simply to equalize population sizes.

      We agree that this methodological choice should be transparent and explicitly justified in the text. We will add an explanation to the revised manuscript to justify why this approach was taken and how it differs from the analysis in Fig. 6.

      Reviewer #2 (Public review):

      Summary:

      This study introduces an exciting dataset of single-unit responses in humans during a naturalistic and dynamic movie stimulus, with recordings from multiple regions within the medial temporal lobe. The authors use both a traditional firing-rate analysis as well as a sophisticated decoding analysis to connect these neural responses to the visual content of the movie, such as which character is currently on screen.

      Strengths:

      The results reveal some surprising similarities and differences between these two kinds of analyses. For visual transitions (such as camera angle cuts), the neurons identified in the traditional response analysis (looking for changes in firing rate of an individual neuron at a transition) were the most useful for doing population-level decoding of these cuts. Interestingly, this wasn't true for character decoding; excluding these "responsive" neurons largely did not impact population-level decoding, suggesting that the population representation is distributed and not well-captured by individual-neuron analyses.

      The methods and results are well-described both in the text and in the figures. This work could be an excellent starting point for further research on this topic to understand the complex representational dynamics of single neurons during naturalistic perception.

      We thank the reviewer for their feedback and for summarizing the results of our work.

      (1) I am unsure what the central scientific questions of this work are, and how the findings should impact our understanding of neural representations. Among the questions listed in the introduction is "Which brain regions are informative for specific stimulus categories?". This is a broad research area that has been addressed in many neuroimaging studies for decades, and it's not clear that the results tell us new information about region selectivity. "Is the relevant information distributed across the neuronal population?" is also a question with a long history of work in neuroscience about localist vs distributed representations, so I did not understand what specific claim was being made and tested here. Responses in individual neurons were found for all features across many regions (e.g., Table S1), but decodable information was also spread across the population.

      We thank the reviewer for this important point, which gets to the core of our study's contribution. While concepts like regional specificity are well-established from studies on the blood-flow level, their investigation at the single-neuron level in humans during naturalistic, dynamic stimulation remains a critical open question. The type of coding (sparse vs. distributed) on the other hand cannot be investigated with blood-flow studies as the technology lacks the spatial and temporal resolution.

      Our study addresses this gap directly. The exceptional temporal resolution of single-neuron recordings allows us to move beyond traditional paradigms and examine cellular-level dynamics as they unfold in neuronal response on a frame-by-frame basis to a more naturalistic and ecologically valid stimulus. It cannot be assumed that findings from other modalities or simplified stimuli will generalize to this context.

      To meet this challenge, we employed a dual analytical strategy: combining a classic single-unit approach with a machine learning-based population analysis. This allowed us to create a bridge between prior work and our more naturalistic data. A key result is that our findings are often consistent with the existing literature, which validates the generalizability of those principles. However, the differences we observe between these two analytical approaches are equally informative, providing new insights into how the brain processes continuous, real-world information.

      We will revise the introduction and discussion to more explicitly frame our work in this context, emphasizing the specific scientific question driving this study, while also highlighting the strengths of our experimental design and recording methods.

      (2) The character and indoor/outdoor labels seem fundamentally different from the scene/camera cut labels, and I was confused by the way that the cuts were put into the decoding framework. The decoding analyses took a 1600ms window around a frame of the video (despite labeling these as frame "onsets" like the feature onsets in the responsive-neuron analysis, I believe this is for any frame regardless of whether it is the onset of a feature), with the goal of predicting a binary label for that frame. Although this makes sense for the character and indoor/outdoor labels, which are a property of a specific frame, it is confusing for the cut labels since these are inherently about a change across frames. The way the authors handle this is by labeling frames as cuts if they are in the 520ms following a cut (there is no justification given for this specific value). Since the input to a decoder is 1600ms, this seems like a challenging decoding setup; the model must respond that an input is a "cut" if there is a cut-specific pattern present approximately in the middle of the window, but not if the pattern appears near the sides of the window. A more straightforward approach would be, for example, to try to discriminate between windows just after a cut versus windows during other parts of the video. It is also unclear how neurons "responsive" to cuts were defined, since the authors state that this was determined by looking for times when a feature was absent for 1000ms to continuously present for 1000ms, which would never happen for cuts (unless this definition was different for cuts?).

      We thank the reviewer for the valuable comment regarding specifically the cut labels. The choice to label frames that lie in a time window of 520ms following a cut as positive was selected based on prior research and is intended to include the response onsets across all regions within the MTL (Mormann et al., 2008). We agree that this explanation is currently missing from the manuscript, and we will add a brief clarification in the revised version.

      As correctly noted, the decoding analysis does not rely on feature onset but instead continuously decodes features throughout the entire movie. Thus, all frames are included, regardless of whether they correspond to a feature onset.

      Our treatment of cut labels as sustained events is a deliberate methodological choice. Neural responses to events like cuts often unfold over time, and by extending the label, we provide our LSTM network with the necessary temporal window to learn this evolving signature. This approach not only leverages the sequential processing strengths of the LSTM (Hochreiter et al., 1997) but also ensures a consistent analytical framework for both event-based (cuts) and state-based (character or location) features.

      (3) The architecture of the decoding model is interesting but needs more explanation. The data is preprocessed with "a linear layer of same size as the input" (is this a layer added to the LSTM that is also trained for classification, or a separate step?), and the number of linear layers after the LSTM is "adapted" for each label type (how many were used for each label?). The LSTM also gets to see data from 800 ms before and after the labeled frame, but usually LSTMs have internal parameters that are the same for all timesteps; can the model know when the "critical" central frame is being input versus the context, i.e., are the inputs temporally tagged in some way? This may not be a big issue for the character or location labels, which appear to be contiguous over long durations and therefore the same label would usually be present for all 1600ms, but this seems like a major issue for the cut labels since the window will include a mix of frames with opposite labels.

      We thank the reviewer for their insightful comments regarding the decoding architecture. The model consists of an LSTM followed by 1–3 linear readout layers, where the exact number of layers is treated as a hyperparameter and selected based on validation performance for each label type. The initial linear layer applied to the input is part of the trainable model and serves as a projection layer to transform the binned neural activity into a suitable feature space before feeding it into the LSTM. The model is trained in an end-to-end fashion on the classification task.

      Regarding temporal context, the model receives a 1600 ms window (800 ms before and after the labeled frame), and as correctly pointed out by the reviewer, LSTM parameters are shared across time steps. We do not explicitly tag the temporal position of the central frame within the sequence. While this may have limited impact for labels that persist over time (e.g., characters or locations), we agree this could pose a challenge for cut labels, which are more temporally localized.

      This is an important point, and we will clarify this limitation in the revised manuscript and consider incorporating positional encoding in future work to better guide the model’s focus within the temporal window. Additionally, we will add a data table, specifying the ranges of hyperparameters in our decoding networks. Hyperparameters were optimized for each feature and split individually, but we agree that some more details on how these parameters were chosen are important and we will provide a data table in our revised manuscript giving more insights into the ranges of hyperparameters.

      We thank the reviewer for this important point. We will clarify this limitation in the revised manuscript and note that positional encoding is a valuable direction to better guide the model’s focus within the temporal window. To improve methodological transparency, we will also add a supplementary table detailing the hyperparameter ranges used for our optimization process.

      (4) Because this is a naturalistic stimulus, some labels are very imbalanced ("Persons" appears in almost every frame), and the labels are correlated. The authors attempt to address the imbalance issue by oversampling the minority class during training, though it's not clear this is the right approach since the test data does not appear to be oversampled; for example, training the Persons decoder to label 50% of training frames as having people seems like it could lead to poor performance on a test set with nearly 100% Persons frames, versus a model trained to be biased toward the most common class. [...]

      We thank the reviewer for this critical and thoughtful comment. We agree that the imbalanced and correlated nature of labels in naturalistic stimuli is a key challenge.

      To address this, we follow a standard machine learning practice: oversampling is applied exclusively to the training data. This technique helps the model learn from underrepresented classes by creating more balanced training batches, thus preventing it from simply defaulting to the majority class. Crucially, the test set remains unaltered to ensure our evaluation reflects the model's true generalization performance on the natural data distribution.

      For the “Persons” feature, which appears in nearly all frames, defining a meaningful negative class is particularly challenging. The decoder must learn to identify subtle variations within a highly skewed distribution. Oversampling during training helps provide a more balanced learning signal, while keeping the test distribution intact ensures proper evaluation of generalization.

      The reviewer’s comment—that we are “training the Persons decoder to label 50% of training frames as having people”—may suggest that labels were modified. We want to emphasize this is not the case. Our oversampling strategy does not alter the labels; it simply increases the exposure of the rare, underrepresented class during training to ensure the model can learn its pattern despite its low frequency.

      We will revise the Methods section to describe this standard procedure more explicitly, clarifying that oversampling is a training-only strategy to mitigate class imbalance.

      (5) Are "responsive" neurons defined as only those showing firing increases at a feature onset, or would decreased activity also count as responsive? If only positive changes are labeled responsive, this would help explain how non-responsive neurons could be useful in a decoding analysis.

      We define responsive neurons as those showing increased firing rates at feature onset; we did not test for decreases in activity. We thank the reviewer for this valuable comment and will address this point in the revised manuscript by assessing responseness without a restriction on the direction of the firing rate.

      (6) Line 516 states that the scene cuts here are analogous to the hard boundaries in Zheng et al. (2022), but the hard boundaries are transitions between completely unrelated movies rather than scenes within the same movie. Previous work has found that within-movie and across-movie transitions may rely on different mechanisms, e.g., see Lee & Chen, 2022 (10.7554/eLife.73693).

      We thank the reviewer for pointing out this distinction and for including the relevant work from Lee & Chan (2022) which further contextualizes this distinction. Indeed, the hard boundaries defined in the cited paper differ slightly from ours. The study distinguishes between (1) hard boundaries—transitions between unrelated movies—and (2) soft boundaries—transitions between related events within the same movie. While our camera cuts resemble their soft boundaries, our scene cuts do not fully align with either category. We defined scene cuts to be more similar to the study’s hard boundaries, but we recognize this correspondence is not exact. We will clarify the distinctions between our scene cuts and the hard boundaries described in Zheng et al. (2022) in the revised manuscript, and will update our text to include the finding from Lee & Chan (2022).

      Reviewer #3 (Public review):

      This is an excellent, very interesting paper. There is a groundbreaking analysis of the data, going from typical picture presentation paradigms to more realistic conditions. I would like to ask the authors to consider a few points in the comments below.

      (1) From Figure 2, I understand that there are 7 neurons responding to the character Summer, but then in line 157, we learn that there are 46. Are the other 39 from other areas (not parahippocampal)? If this is the case, it would be important to see examples of these responses, as one of the main claims is that it is possible to decode as good or better with non-responsive compared to single responsive neurons, which is, in principle, surprising.

      We thank the reviewer for pointing out this ambiguity in the text. Yes, the other 39 units are responsive neurons from other areas. We will clarify to which neuronal sets the number of responsive neurons corresponds. We will also include response plots depicting the unit activity for the mentioned units.

      (2) Also in Figure 2, there seem to be relatively very few neurons responding to Summer (1.88%) and to outdoor scenes (1.07%). Is this significant? Isn't it also a bit surprising, particularly for outdoor scenes, considering a previous paper of Mormann showing many outdoor scene responses in this area? It would be nice if the authors could comment on this.

      We thank the reviewer for this insightful point. While a low response to the general 'outdoor scene' label seems surprising at first, our findings align with the established role of the parahippocampal cortex (PHC) in processing scenes and spatial layouts. In previous work using static images, each image introduces a new spatial context. In our movie stimulus, new spatial contexts specifically emerge at scene cuts. Accordingly, our data show a strong PHC response precisely at these moments. We will revise the discussion to emphasize this interpretation, highlighting the consistency with prior work.

      Regarding the first comment, we did not originally test if the proportion of the units is significant using e.g. a binomial test. We will include the results of a binomial test for each region and feature pair in the revised manuscript.

      (3) I was also surprised to see that there are many fewer responses to scene cuts (6.7%) compared to camera cuts (51%) because every scene cut involves a camera cut. Could this have been a result of the much larger number of camera cuts? (A way to test this would be to subsample the camera cuts.)

      The decrease in responsive units for scene cuts relative to camera cuts could indeed be due to the overall decrease in “trials” from one label to the other. To test this, we will follow the reviewer’s suggestion and perform tests using sets of randomly subsampled camera cuts and will include the results in the revised manuscript.

      (4) Line 201. The analysis of decoding on a per-patient basis is important, but it should be done on a per-session basis - i.e., considering only simultaneously recorded neurons, without any pooling. This is because pooling can overestimate decoding performances (see e.g. Quian Quiroga and Panzeri NRN 2009). If there was only one session per patient, then this should be called 'per-session' rather than 'per-patient' to make it clear that there was no pooling.

      The per-patient decoding was indeed also a per-session decoding, as each patient contributed only a single session to the dataset. We will make note of this explicitly in the text to resolve the ambiguity.

      (6) Lines 406-407. The claim that stimulus-selective responses to characters did not account for the decoding of the same character is very surprising. If I understood it correctly, the response criterion the authors used gives 'responsiveness' but not 'selectivity'. So, were people's responses selective (e.g., firing only to Summer) or non-selective (firing to a few characters)? This could explain why they didn't get good decoding results with responsive neurons. Again, it would be nice to see confusion matrices with the decoding of the characters. Another reason for this is that what are labelled as responsive neurons have relatively weak and variable responses.

      We thank the reviewer for pointing out the importance of selectivity in addition to responsiveness. Indeed, our response criterion does not take stimulus selectivity into account and exclusively measures increases in firing activity after feature onsets for a given feature irrespective of other features.

      We will adjust the text to reflect this shortcoming of the response-detection approach used here. To clarify the relationship between neural populations, we will add visualizations of the overlap of responsive neurons across labels for each subregion. These figures will be included in the revised manuscript.

      In our approach, we trained separate networks for each feature to effectively mitigate the issue of correlated feature labels within the dataset (see earlier discussion). While this strategy effectively deals with the correlated features, it precluded the generation of standard confusion matrices, as classification was performed independently for each feature.

      To directly assess the feature selectivity of responsive neurons, we will fit generalized linear models to predict their firing rates from the features. This approach will enable us to quantify their selectivity and compare it to that of the broader neuronal population.

      (7) Line 455. The claim that 500 neurons drive decoding performance is very subjective. 500 neurons gives a performance of 0.38, and 50 neurons gives 0.33.

      We agree with the reviewer that the phrasing is unclear. We will adjust our summary of this analysis as given in Line 455 to reflect that the logistic regression-derived neuronal rankings produce a subset which achieve comparable performance.

      (8) Lines 492-494. I disagree with the claim that "character decoding does not rely on individual cells, as removing neurons that responded strongly to character onset had little impact on performance". I have not seen strong responses to characters in the paper. In particular, the response to Summer in Figure 2 looks very variable and relatively weak. If there are stronger responses to characters, please show them to make a convincing argument. It is fine to argue that you can get information from the population, but in my view, there are no good single-cell responses (perhaps because the actors and the movie were unknown to the subjects) to make this claim. Also, an older paper (Quian Quiroga et al J. Neurophysiol. 2007) showed that the decoding of individual stimuli in a picture presentation paradigm was determined by the responsive neurons and that the non-responsive neurons did not add any information. The results here could be different due to the use of movies instead of picture presentations, but most likely due to the fact that, in the picture presentation paradigm, the pictures were of famous people for which there were strong single neuron responses, unlike with the relatively unknown persons in this paper.

      This is an important point and we thank the reviewer for highlighting a previous paradigm in which responsive neurons did drive decoding performance. Indeed, the fact that the movie, its characters and the corresponding actors were novel to patients could explain the disparity in decoding performance by way of weaker and more variable responses. We will include additional examples in the supplement of responses to features. Additionally, we will modify the text to emphasize the point that reliable decoding is possible even in the absence of a robust set of neuronal responses. It could indeed be the case that a decoder would place more weight on responsive units if they were present (as shown in the mentioned paper and in our decoding from visual transitions in the parahippocampal cortex).

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This is a very insightful work showing how to disentangle one of the most complex transcriptional networks in yeast (S. cerevisiae) by combining single-cell dynamics, dynamical-systems modeling, Bayesian-style inference, and genetic perturbations. The authors tackle a problem that has eluded quantitative resolution for over two decades-how yeast regulates its seven primary glucose importer genes (HXT1-HXT7) in response to both steady and temporally changing extracellular [glucose]. Their integrated experimental-theoretical approach delivers the most satisfying mechanistic and quantitative explanation to date, and I enthusiastically recommend this manuscript for publication.

      Yeast relies on seven passive hexose transporters (Hxt1-Hxt7) to import glucose, its preferred sugar; deleting all seven abolishes growth on glucose. The underlying regulatory network is exceptionally intricate, reflecting yeast's evolutionary priority for glucose. Two membrane sensors-Snf3 (high affinity) and Rgt2 (low affinity)-detect extracellular glucose and thereby inactivate two co-repressors, Mth1 and Std1, which modulate the DNA-binding factor Rgt1. Concurrently, intracellular glucose activates the SNF1 kinase, phosphorylating and exporting the repressor Mig1, while Mth1/Std1 also govern the transcription and stability of Mig2, another DNA-binding repressor. Together, Rgt1, Mig1, and Mig2 integrate these inputs to control HXT promoter activity (Fig. 2A). Importantly, Mth1 and Std1 do not directly bind to DNA and this complication - the protein-protein interaction that one cannot get from DNA sequence - is just one source of difficulty that the authors overcame.

      To map the network's behavior, the authors used microfluidic "cages" housing single cells expressing GFP-tagged HXTs, monitoring fluorescence under three constant glucose levels-low (0.01%), medium (0.1%), and high (1%) (Fig. 1B-C). The authors confirm that steady-state Hxt abundances rank by transporter affinity. But the more important and surprising discovery is that when the cells were subjected to gradual glucose up-shifts and down-shifts, they discovered that some transporters transiently spike only when [glucose] rises and others only when [glucose] falls (Fig. 1C and Fig. S1F). This discovery establishes that the HXT network not only "senses" the absolute external [glucose] concentration but also the direction of the temporal change in external [glucose].

      To understand how the regulatory network yields such intricate temporal changes in HXT expression, the authors first focused on the medium-affinity transporter, Hxt4. Targeted knockouts of Mig1/Mig2 versus Mth1/Std1 confirmed that Hxt4 dynamics arise from differential repressor kinetics. To formalize these findings, the authors built an ODE model grounded in literature-based constraints (pg. 13 of the Supplement) with explicit separation of repressor timescales. They rigorously fit the model to wild-type and knockout time series-exploring parameter sensitivity in depth (Fig. S5).

      The authors discovered that their model and experiments converged on a push-pull mechanism: fast-acting Mig1/Mig2 dominate during glucose up-shifts, while slower Mth1/Std1 govern down-shifts, determining whether each HXT gene is repressed or de-repressed (i.e., "who gets there first"). Extending this analysis across all seven HXTs via approximate Bayesian computation revealed the most likely repressor-promoter interactions for each transporter, reducing a vast parameter space to unique or small sets of plausible regulatory schemes. The authors thus revealed what could be happening and which regulations are improbable - a more nuanced and comprehensive view than giving just one outcome for each HXT.

      Overall, this work represents a role model - textbook-worthy - for quantitative systems biology. Beyond the rigor and novelty of its findings, the authors explain complex mathematical concepts with clarity, and the narrative flows logically from experiment to model to inference. This study provides a definitive mechanistic resolution of the HXT network and establishes a broadly applicable framework for dissecting dynamic and complex gene circuits.

      Major points:

      I don't recommend any new experiments or modeling; the major claims are already well supported by the data and models. Below are comments and questions intended to improve clarity and facilitate the reader's understanding. Please feel free to disregard any that you find not sensible or beyond the scope of the current work.

      1. Preconditioning (Fig. 1B-C): What medium were cells in immediately before t = 0? Were they in log-phase or stationary-phase growth just prior to the glucose addition?
      2. Transporter ranking in medium glucose: In the medium [glucose] regime, why is a low-affinity Hxt the second-most highly expressed, rather than the next-highest-affinity transporter? Could co-expression of multiple affinities (e.g., as a bet-hedging strategy) be advantageous? The Discussion section already mentions bet-hedging but I think you could further discuss ideas such as evolutionarily trained "Pavlovian" response or what the 2nd-ranking says about what the yeast anticipates as an upcoming change in the environment.
      3. Defining low/medium/high regimes: Low = 0.01%, Medium = 0.1%, and High = 1%. This is indeed in line with the standard classification of [glucose] in the literature regarding HXTs. But how might your results change at intermediate concentrations - those between these three levels. Using the model, could you comment on whether HXT expression dynamics "sharply" change as a function of either the [glucose]/time or the final concentration of [glucose] after the ramping-up phase?
      4. Rate-affinity trade-off (Lines 18-20): Give a brief explanation of the rate-affinity trade-off. Why does higher affinity necessarily entail a lower maximal transport rate (Vₘₐₓ) for passive transporters? Perhaps you can give an intuitive explanation backed by mass-action kinetics (e.g., to attain a higher affinity, the glucose-binding pocket on Hxt cannot be flipping rapidly back-and-forth between facing cytoplasm and extracellular space -- the binding pocket must allow sufficient time for molecule to find and bind it).
      5. Single-transporter expression (Lines 39-40): It's unclear to me why cells would express only the "optimal" Hxt and suppress all others. For instance, a bet-hedging strategy might favor simultaneous expression of multiple affinities. Consider revising these lines or adding a brief explanation. Related to above is a subtle point I think that was glossed over: there must be a fitness cost associated with making too many copies of Hxtn. After all, why not make as many transporters as possible? Is the cell operating near the upper limit of Hxt abundance, beyond which there's a fitness cost? Is there a pareto-optimal-type front in the space of expression level and another axis? I think this could go into the Discussion section.
      6. Hxt5 exception (Fig. 1B): Although Hxt5 follows a distinct regulatory scheme, it is most highly expressed at medium [glucose] (0.1%), consistent with its affinity like the other Hxts. I think you could mention this in lines 51-58.
      7. Glucose-ramp details (Fig. 1C; Lines 66-67): You state that [glucose] rises from 0 to 1 % over 15 min and reaches 1 % at t = 3 h. However, the actual ramp slope ([glucose]/time) and when the [glucose] starts to increase from zero aren't specified. The Hxt5-GFP behavior and differing Hxt6/7 levels at t = 0 vs. t = 20 h suggest the ramp may begin later than t = 0. Please clarify these details in the caption and main text, and consider adding a [glucose] vs. time schematic above the panel in Fig. 1C (like in Fig. 1B).
      8. Pre-t < 0 incubation (Fig. 1C): Related to point 1, how long were the cells incubated in pyruvate (or other medium) before t < 0? The Hxt6-GFP level at t = 20 h does not match that at t = 0; what is the timescale for Hxt6-GFP and Hxt7-GFP decay to steady state after glucose removal?
      9. Hxt-GFP localization: Does the reported Hxt#-GFP level include fluorescence from both the plasma membrane and internal compartments (e.g., vacuole)? Clarifying which pools of fluorescence are quantified would help interpretation, even if they don't change the main conclusions are unchanged.
      10. Predominantly transcriptional" wording (Lines 90-92): The phrase "...the regulation is predominantly transcriptional" should specify that it refers to the induction of HXT4 transcription during glucose down-ramping, rather than the subsequent decrease in Hxt4-GFP. The experiments do not rule out post-translational regulation (e.g., endocytosis) once glucose levels fall below a threshold.
      11. Glucose "protection" of Hxt4 (Lines 121-122): The statement "we allowed glucose to protect Hxt4 from degradation" is unclear. First, Hxt4-GFP likely degrades at a different rate than free GFP-you could estimate its half-life from Fig. S3. Second, please explain precisely what "protection" means in the model or experiment.
      12. Quantifying repressor kinetics (Lines 158-162): The push-pull mechanism is compelling, but it would be helpful to report the quantitative separation of timescales-e.g., how much faster do Mig1/Mig2 respond compared to Mth1/Std1? Including fold-difference would strengthen this explanation.
      13. Mechanism of repressor regulation (Lines 197-213): Be clearer about whether and how changes in extracellular glucose alter the expression levels of Mth1, Std1, Mig1, and Mig2, as opposed to modulating say, how Mth1 and Std1 bind to Rgt2 protein. I think you could be clearer here about which regulatory steps (transcriptional, post-translational, or binding-affinity changes) are assumed in the model and supported by the data.

      Minor points:

      1. Abstract: Original: "...how an HXT for a medium-affinity transporter can be made to respond like the HXTs for the other transporters." Suggestion: "...how the gene-expression regulation of a medium-affinity HXT can be rewired to respond like that of any other HXT." (You might also generalize beyond "medium-affinity" if the converse holds.)
      2. Lines 64-66: Please emphasize that the "synthetic complete medium" used for pre-conditioning contains no glucose.
      3. Line 143: The phrase "low expression of the std1\Delta strain in glucose" is ambiguous-low expression of which gene or reporter? Please specify.
      4. Line 240: Change "should weakened" to "should weaken."
      5. Fig. S9 caption (typo) Change "Rtg1 sites are..." to "Rgt1 sites are...."

      Hyun Youk.

      Referee cross-commenting

      I agree with the other reviewers' comments. The other reviewers noticed important points I have missed. But like them, I'm still supportive of the work being published with < 1 month spent on revision. I still don't recommend any further experiments or modeling.

      Significance

      This is a very insightful work showing how to disentangle one of the most complex transcriptional networks in yeast (S. cerevisiae) by combining single-cell dynamics, dynamical-systems modeling, Bayesian-style inference, and genetic perturbations. The authors tackle a problem that has eluded quantitative resolution for over two decades-how yeast regulates its seven primary glucose importer genes (HXT1-HXT7) in response to both steady and temporally changing extracellular [glucose]. Their integrated experimental-theoretical approach delivers the most satisfying mechanistic and quantitative explanation to date, and I enthusiastically recommend this manuscript for publication via Review Commons.

    1. Reviewer #1 (Public review):

      Summary:

      The authors aimed to develop a fully scalable, feeder-free protocol for deriving dorsal forebrain neural rosette stem cells (NRSCs) from human pluripotent stem cells, eliminating the need for manual rosette isolation. Using dynamic suspension culture combined with single-SMAD inhibition (RepSox), they sought to generate FOXG1⁺/OTX2⁺ NRSCs within ten days and expand them through at least twelve passages while retaining regional identity. They also aimed to demonstrate the cells' capacity to differentiate into functional neurons, astrocytes, and oligodendrocytes under defined conditions.

      Strengths:

      A key strength is the elimination of labour-intensive manual rosette picking, which significantly reduces operator variability and enhances throughput. The authors provide diverse validation in the form of flow cytometry showing >95% OTX2⁺ over passages 2-12, immunocytochemistry, single-cell RNA-seq, and functional MEA recordings, confirming both regional fidelity and neuronal activity. They also demonstrate glial differentiation and reproducibility across two hESC lines.

      The results convincingly demonstrate that the RepSox/suspension approach yields high-purity dorsal forebrain neural progenitor cells (NRSCs) that maintain marker expression and multipotency through passage 12 and differentiate into electrophysiologically active neurons and mature glia. Thus, the authors have achieved their primary objectives.

      This protocol addresses a significant bottleneck in neural stem cell production by providing a reproducible, high-throughput alternative that is well-suited to drug screening, disease modelling, and potential cell therapy manufacturing. Standardised, scalable NRSC banks will accelerate neurodevelopmental and neurodegenerative disorder studies, enable automated bioreactor workflows, and encourage the sharing of resources across academia and industry.

      Weaknesses:

      Weaknesses include a lack of direct comparison to conventional manual-selection protocols, and the need to improve the statistical rigor of all quantitative assays by applying appropriate hypothesis tests (e.g., t-tests or ANOVA with multiple-comparison correction) rather than reporting mean {plus minus} SD alone.

      Additional Context:

      Beyond the core technical advance, it's important to situate this work within the broader landscape of neural stem cell research and its downstream applications. Traditionally, dorsal forebrain NSCs have been generated via manual rosette picking after dual-SMAD inhibition (Chambers et al., 2009), a process that is labor-intensive, low-throughput, and prone to operator-dependent variability. By eliminating that step, this protocol directly addresses a key barrier to standardizing NSC production under GMP-compatible conditions - critical for both large-scale drug screening and eventual clinical use. Stable, regionally specified forebrain NSCs are especially valuable for modeling early neurodevelopmental disorders (e.g., autism spectrum disorders, microcephaly) and late-onset pathologies (e.g., Alzheimer's disease) in vitro, where precise cortical patterning is essential to recapitulate disease phenotypes. Moreover, establishing long-term epigenetic fidelity (e.g., via future ATAC-seq or histone-mark profiling) will further reassure users that transcriptional consistency reflects preserved regulatory networks, not just transient marker expression. Finally, demonstrating robust cryopreservation viability (>80%) makes these cells a readily shareable resource for the community, accelerating cross-lab reproducibility and comparative studies of patient-derived iPSC lines. This context underscores how scalable, high-purity forebrain NSCs can transform both basic neuroscience research and translational pipelines.

    1. Reviewer #2 (Public review):

      This work introduces a new version of the state-of-the-art idtracker.ai software for tracking multiple unmarked animals. The authors aimed to solve a critical limitation of their previous software, which relied on the existence of "global fragments" (video segments where all animals are simultaneously visible) to train an identification classifier network, in addition to addressing concerns with runtime speed. To do this, the authors have both re-implemented the backend of their software in PyTorch (in addition to numerous other performance optimizations) as well as moving from a supervised classification framework to a self-supervised, contrastive representation learning approach that no longer requires global fragments to function. By defining positive training pairs as different images from the same fragment and negative pairs as images from any two co-existing fragments, the system cleverly takes advantage of partial (but high-confidence) tracklets to learn a powerful representation of animal identity without direct human supervision. Their formulation of contrastive learning is carefully thought out and comprises a series of empirically validated design choices that are both creative and technically sound. This methodological advance is significant and directly leads to the software's major strengths, including exceptional performance improvements in speed and accuracy and a newfound robustness to occlusion (even in severe cases where no global fragments can be detected). Benchmark comparisons show the new software is, on average, 44 times faster (up to 440 times faster on difficult videos) while also achieving higher accuracy across a range of species and group sizes. This new version of idtracker.ai is shown to consistently outperform the closely related TRex software (Walter & Couzin, 2021\), which, together with the engineering innovations and usability enhancements (e.g., outputs convenient for downstream pose estimation), positions this tool as an advancement on the state-of-the-art for multi-animal tracking, especially for collective behavior studies.

      Despite these advances, we note a number of weaknesses and limitations that are not well addressed in the present version of this paper:

      (1) The contrastive representation learning formulation

      Contrastive representation learning using deep neural networks has long been used for problems in the multi-object tracking domain, popularized through ReID approaches like DML (Yi et al., 2014\) and DeepReID (Li et al., 2014). More recently, contrastive learning has become more popular as an approach for scalable self-supervised representation learning for open-ended vision tasks, as exemplified by approaches like SimCLR (Chen et al., 2020), SimSiam (Chen et al., 2020\), and MAE (He et al., 2021\) and instantiated in foundation models for image embedding like DINOv2 (Oquab et al., 2023). Given their prevalence, it is useful to contrast the formulation of contrastive learning described here relative to these widely adopted approaches (and why this reviewer feels it is appropriate):

      (1.1) No rotations or other image augmentations are performed to generate positive examples. These are not necessary with this approach since the pairs are sampled from heuristically tracked fragments (which produces sufficient training data, though see weaknesses discussed below) and the crops are pre-aligned egocentrically (mitigating the need for rotational invariance).

      (1.2) There is no projection head in the architecture, like in SimCLR. Since classification/clustering is the only task that the system is intended to solve, the more general "nuisance" image features that this architectural detail normally affords are not necessary here.

      (1.3) There is no stop gradient operator like in BYOL (Grill et al., 2020\) or SimSiam. Since the heuristic tracking implicitly produces plenty of negative pairs from the fragments, there is no need to prevent representational collapse due to class asymmetry. Some care is still needed, but the authors address this well through a pair sampling strategy (discussed below).

      (1.4) Euclidean distance is used as the distance metric in the loss rather than cosine similarity as in most contrastive learning works. While cosine similarity coupled with L2-normalized unit hypersphere embeddings has proven to be a successful recipe to deal with the curse of dimensionality (with the added benefit of bounded distance limits), the authors address this through a cleverly constructed loss function that essentially allows direct control over the intra- and inter-cluster distance (D\_pos and D\_neg). This is a clever formulation that aligns well with the use of K-means for the downstream assignment step.

      No concerns here, just clarifications for readers who dig into the review. Referencing the above literature would enhance the presentation of the paper to align with the broader computer vision literature.

      (2) Network architecture for image feature extraction backbone

      As most of the computations that drive up processing time happen in the network backbone, the authors explored a variety of architectures to assess speed, accuracy, and memory requirements. They land on ResNet18 due to its empirically determined performance. While the experiments that support this choice are solid, the rationale behind the architecture selection is somewhat weak. The authors state that:

      "\[W\]e tested 23 networks from 8 different families of state-of-the-art convolutional neural network architectures, selected for their compatibility with consumer-grade GPUs and ability to handle small input images (20 × 20 to 100 × 100 pixels) typical in collective animal behavior videos."

      (2.1) Most modern architectures have variants that are compatible with consumer-grade GPUs. This is true of, for example, HRNet (Wang et al., 2019), ViT (Dosovitskiy et al., 2020), SwinT (Liu et al., 2021), or ConvNeXt (Liu et al., 2022), all of which report single GPU training and fast runtime speeds through lightweight configuration or subsequent variants, e.g., MobileViT (Mehta et al., 2021). The authors may consider revising that statement or providing additional support for that claim (e.g., empirical experiments) given that these have been reported to outperform ResNet18 across tasks.

      (2.2) The compatibility of different architectures with small image sizes is configurable. Most convolutional architectures can be readily adapted to work with smaller image sizes, including 20x20 crops. With their default configuration, they lose feature map resolution through repeated pooling and downsampling steps, but this can be readily mitigated by swapping out standard convolutions with dilated convolutions and/or by setting the stride of pooling layers to 1, preserving feature map resolution across blocks. While these are fairly straightforward modifications (and are even compatible with using pretrained weights), an even more trivial approach is to pad and/or resize the crops to the default image size, which is likely to improve accuracy at a possibly minimal memory and runtime cost. These techniques may even improve the performance with the architectures that the authors did test out.

      (2.3) The authors do not report whether the architecture experiments were done with pretrained or randomly initialized weights.

      (2.4) The authors do not report some details about their ResNet18 design, specifically whether a global pooling layer is used and whether the output fully connected layer has any activation function. Additionally, they do not report the version of ResNet18 employed here, namely, whether the BatchNorm and ReLU are applied after (v1) or before (v2) the conv layers in the residual path.

      (3) Pair sampling strategy

      The authors devised a clever approach for sampling positive and negative pairs that is tailored to the nature of the formulation. First, since the positive and negative labels are derived from the co-existence of pretracked fragments, selection has to be done at the level of fragments rather than individual images. This would not be the case if one of the newer approaches for contrastive learning were employed, but it serves as a strength here (assuming that fragment generation/first pass heuristic tracking is achievable and reliable in the dataset). Second, a clever weighted sampling scheme assigns sampling weights to the fragments that are designed to balance "exploration and exploitation". They weigh samples both by fragment length and by the loss associated with that fragment to bias towards different and more difficult examples.

      (3.1) The formulation described here resembles and uses elements of online hard example mining (Shrivastava et al., 2016), hard negative sampling (Robinson et al., 2020\), and curriculum learning more broadly. The authors may consider referencing this literature (particularly Robinson et al., 2020\) for inspiration and to inform the interpretation of the current empirical results on positive/negative balancing.

      (4) Speed and accuracy improvements

      The authors report considerable improvements in speed and accuracy of the new idTracker (v6) over the original idTracker (v4?) and TRex. It's a bit unclear, however, which of these are attributable to the engineering optimizations (v5?) versus the representation learning formulation.

      (4.1) Why is there an improvement in accuracy in idTracker v5 (L77-81)? This is described as a port to PyTorch and improvements largely related to the memory and data loading efficiency. This is particularly notable given that the progression went from 97.52% (v4; original) to 99.58% (v5; engineering enhancements) to 99.92% (v6; representation learning), i.e., most of the new improvement in accuracy owes to the "optimizations" which are not the central emphasis of the systematic evaluations reported in this paper.

      (4.2) What about the speed improvements? Relative to the original (v4), the authors report average speed-ups of 13.6x in v5 and 44x in v6. Presumably, the drastic speed-up in v6 comes from a lower Protocol 2 failure rate, but v6 is not evaluated in Figure 2 - figure supplement 2.

      (5) Robustness to occlusion

      A major innovation enabled by the contrastive representation learning approach is the ability to tolerate the absence of a global fragment (contiguous frames where all animals are visible) by requiring only co-existing pairs of fragments owing to the paired sampling formulation. While this removes a major limitation of the previous versions of idtracker.ai, its evaluation could be strengthened. The authors describe an ablation experiment where an arc of the arena is masked out to assess the accuracy under artificially difficult conditions. They find that the v6 works robustly up to significant proportions of occlusions, even when doing so eliminates global fragments.

      (5.1) The experiment setup needs to be more carefully described.<br /> What does the masking procedure entail? Are the pixels masked out in the original video or are detections removed after segmentation and first pass tracking is done?<br /> What happens at the boundary of the mask? (Partial segmentation masks would throw off the centroids, and doing it after original segmentation does not realistically model the conditions of entering an occlusion area.)<br /> Are fragments still linked for animals that enter and then exit the mask area?<br /> How is the evaluation done? Is it computed with or without the masked region detections?

      (5.2) The circular masking is perhaps not the most appropriate for the mouse data, which is collected in a rectangular arena.

      (5.3) The number of co-existing fragments, which seems to be the main determinant of performance that the authors derive from this experiment, should be reported for these experiments. In particular, a "number of co-existing fragments" vs accuracy plot would support the use of the 0.25(N-1) heuristic and would be especially informative for users seeking to optimize experimental and cage design. Additionally, the number of co-existing fragments can be artificially reduced in other ways other than a fixed occlusion, including random dropout, which would disambiguate it from potential allocentric positional confounds (particularly relevant in arenas where egocentric pose is correlated with allocentric position).

      (6) Robustness to imaging conditions

      The authors state that "the new idtracker.ai can work well with lower resolutions, blur and video compression, and with inhomogeneous light (Figure 2 - figure supplement 4)." (L156).

      Despite this claim, there are no speed or accuracy results reported for the artificially corrupted data, only examples of these image manipulations in the supplementary figure.

      (7) Robustness across longitudinal or multi-session experiments

      The authors reference idmatcher.ai as a compatible tool for this use case (matching identities across sessions or long-term monitoring across chunked videos), however, no performance data is presented to support its usage.

      This is relevant as the innovations described here may interact with this setting. While deep metric learning and contrastive learning for ReID were originally motivated by these types of problems (especially individuals leaving and entering the FOV), it is not clear that the current formulation is ideally suited for this use case. Namely, the design decisions described in point 1 of this review are at times at odds with the idea of learning generalizable representations owing to the feature extractor backbone (less scalable), low-dimensional embedding size (less representational capacity), and Euclidean distance metric without hypersphere embedding (possible sensitivity to drift).

      It's possible that data to support point 6 can mitigate these concerns through empirical results on variations in illumination, but a stronger experiment would be to artificially split up a longer video into shorter segments and evaluate how generalizable and stable the representations learned in one segment are across contiguous ("longitudinal") or discontiguous ("multi-session") segments.

    1. 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-2025-03083 Corresponding author(s): David Fay General Statements [optional] This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      We greatly appreciate the input of the four reviewers, all of whom carried out a careful reading of our manuscript, provided useful suggestions for improvements, and were enthusiastic about the study including its thoroughness and utility to the field. Because the reviewers required no additional experiments, we were able to address their comments in writing.

      However, in response to a comment from reviewer #4 we decided to add an additional new biological finding to our study given that our functional validation of proximity labeling targets was not extensive. Namely, we now show that a missense mutation affecting BCC-1, one of the top NEKL-MLT interactors identified by our proximity labeling screen, is a causative mutation (together with catp-1) in a strain isolated through a forward genetic screen for suppressors of nekl molting defects (new Fig 9C). This finding, combined with our genetic enhancer tests, further strengthens the functional relevance of proteins identified though our proximity labeling approach and highlights the synergy of proteomics combined with classical genetics.

      Positive statements from reviewers include: Reviewer #1: Overall, this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner.

      Reviewer #2: The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate... In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs.

      Reviewer #3: Overall, the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. ...This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system.

      Reviewer #4: Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data)... Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

      Based on these reviews and comments, we believe that our manuscript is suitable for publication in a high-impact journal. 1. Point-by-point description of the revisions This section is mandatory. Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript.

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

      *Proximity labeling has become a powerful tool for defining protein interaction networks and has been utilized in a growing number of multicellular model systems. However, while such an approach can efficiently generate a list of potential interactors, knowledge of the most appropriate controls and standardized metrics to judge the quality of the data are lacking. The study by Fay systematically investigates these questions using the C. elegans NIMA kinase family members NEKL-2 and NEKL-2 and their known binding partners MLT-2, MLT-3 and MLT-4. The authors perform eight TurboID experiments each with multiple NEKL and MLT proteins and explore general metrics for assessing experimental outcomes as well as how each of the individual metrics correlates with one another. They also compare technical and biological replicates, explore strategies for identifying false positives and investigate a number of variations in the experimental approach, such as the use of N- versus C-terminal tags, depletion of endogenous biotinylated proteins, combining auxin-inducible degradation, and the use of gene ontology analysis to identify physiological interactors. Finally, the authors validate their findings by demonstrating that a number of the candidate identified functionally interact with NEKL-2 or components of the WASH complex. *

      Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. Below I list major and minor concerns.

      Major concerns * 1. My biggest issue with the manuscript is that a lot is made of the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). The authors perform experiments using DIA and DDA approaches but do not directly compare the outcomes. As a result there is really no way to know if one approach is better than the other. I would suggest the authors either perform the necessary analysis to compare the two approaches or tone down their promotion of DIA.* We agree and have scaled back any statements comparing DDA to DIA as our manuscript did not address this directly. We also now point out this caveat in our closing thoughts section, while referencing other studies that compared the two (lines 926-929). Our main point was to convey that DIA worked well for our proximity labeling studies but has seen little use by the model organism field. Surprising (to us), DIA was also considerably less expensive than DDA options.

      2. Line 75, The authors promote the use of data-independent acquisition (DIA) without defining what this approach is and how it differs from the more conventional data-dependent acquisition. As a non-mass spectroscopist, I found myself with lots of question concerning DIA, what it is and how it differs from DDA. I think it would really be helpful to expand the description of DIA and its comparison with DDA in the introduction. As non-mass-spectroscopists ourselves, we understand the reviewer's point. Because the paper is quite long, we were trying to avoid non-essential information. We have now added some information to explain some of the key differences between DDA and DIA. We have also included references for readers who may want to learn more. (lines 77-80)

      Minor concerns: * Line 92 typo. I believe the authors meant to say NEKL-2-MLT-2-MLT-4. * Corrected. (line 95)

      Line169. Is exogenous the correct word to use here? It suggests that you are talking about non-worm proteins, but I know you are not. Corrected. Changed to "Moreover, the detection of biotinylated proteins may be difficult if the bait-TurboID fusion is expressed at low levels..." (line 181).

      Line 177 typo (D) should be (C). Corrected. (line 1122)

      Figure 1C: Lucky Charms may sue you for infringement of their trademarked marshmallow treats. Thank you for picking up on this. The authors accept full responsibility for any resulting lawsuits.

      Figure 1D. The NEKL-2::TurboID band is indicated with a green triangle in the figure but the figure legend states that green triangles indicate mNG::TurboID control. I know this triangle is a shade off the triangle that indicates mNG::TurboID but it's really hard to see the difference. All of the differently colored triangles in panel F are unnecessary. I would either just pick one color for all non-control bait proteins or better yet, only use a triangle to point to bands that are not obvious. For instance I don't need the triangles that point to NEKL-2 -3 and -4 fusion proteins. These are just distracting. We understand the reviewer's point. We colored the triangles to match the colors used for the proteins in the figures. We have now added "bright green triangles with white outlines" (Fig 1 legend) to indicate the Pdpy-7::mNG::TurboID control" and changed triangles in the corresponding figures. Although we would be fine with removing or changing the triangles, we think that they may aid somewhat with clarity.

      Line: 316: Conceivably, another factor that could contribute to the counterintuitive upregulation of some proteins in the N2 samples is related to the fusion proteins that are being expressed in the TurboID lines. A partially functional bait protein (one with a level of activity similar to nekl-2(fd81) that may not result in an obvious phenotype) could directly or indirectly affect gene expression leading to lower levels of a subset of proteins in the TurboID samples. The same could be said for fusion proteins with a gain-of-function effect. This is an interesting idea, and we tested this possibility by looking for consistent overlap between N2-up proteins between biological replates of individual bait proteins. We now include a representative Venn diagram in S3C Fig to highlight this comparison. In summary, although we cannot rule out this possibility, our analysis did not support the widespread occurrence of this effect in our study. We also made certain that our statement regarding N2 up proteins was not too definitive. (lines 285-288)

      *Fig 3 B-E. I am a little confused how the data in these graphs is normalized. For instance, I would have expected that for NEKL-3 in panel B, that the normalized (log2) intensity value in N2 be set at 0 as it is for NEKL-2. Maybe I just don't have enough information on how these plots were generated. * The difference is that in the N2 sample, NEKL-3 was detected but NEKL-2 was not. The numbers themselves are assigned by the Spectronaut software used to quantify the DIA results but are not meaningful beyond indicating relative amounts (intensity values) of a given protein within an individual biological experiment. We've added some lines to the figure legend to make this clearer. (lines 1165-1169)

      *Figure 6C legend is not correct. * Corrected. (line 1214)

      Line 575: Figure reference should be Fig. S5G. The authors should check to make sure all references to supplemental figures include correct panel information. Corrected. (line 464) In addition, we have now gone through the manuscript and added panel numbers references where applicable. Note that the addition of a new supplemental file has shifted the numbering.

      Line 576. The authors reference a study by Artan and colleagues and report a weak correlation between their study and that of Artan. They reference figure S4 but it should be Fig S5H. Apologies and many thanks to the reviewer for catching these errors. (line 464)

      Line 652. The authors note that numerous proteins were present at substantially reduced levels in the mNG::TurboID samples and suggest that sticky proteins may have been outcompeted or otherwise excluded from beads incubated with the mNG::TurboID lysates. Why would sticky proteins only be a problem in these samples? The reasoning is not clear to me. The idea was that in the sample with very high levels of biotinylated proteins (mNG::TurboID), the surface of the beads might become saturated with high-affinity biotinylated proteins. This could prevent or out complete the binding of random proteins that are not biotinylated but nevertheless have some affinity to the beads ("sticky" proteins). We have reworded this section to make this clearer. (lines 546-550)

      Line 745: The term "bait overlaps" is a bit vague. Ultimately, I figured out what it meant but it was not immediately obvious. We have changed this to "overlap between baits" and made this section clearer. (line 624-628)

      *S7B Fig. Why is actin missing from the eluate? * In S7B we refer to the purified eluate as the "eluate", which may have caused some confusion. In other sections of the manuscript, we refer to the bead-bound proteins as the "purified eluate" (Figs 1 and 5). For the purified eluate a portion of the streptavidin beads are boiled in sample buffer to elute the bound proteins before running a western. Actin would not be expected in these samples because it's (presumably) not biotinylated in our samples and doesn't detectably bind the beads. This result was seen in all relevant westerns in S1 Data. For consistency, however, we've gone through all our files to make sure we consistently use the term "purified eluate" versus "eluate", which is less specific.

      L*ine 873: The authors state the extent of overlap in GO terms between the various experiments and provide percentages. I tried to extract this information from Figure 8C and came up with different values. For instance, in the case of Molecular Function, they state that they observed a 54% overlap between NEKL-2 and NEKL-3 but in the Venn diagram in Figure 8C I see that the NEKL-2 and NEKL-3 experiments had 71 (25+46) GO terms in common. Out of 98 GO terms for NEKL-2 or 104 for NEKL-3 the percentage I got is closer to 72. Am I analyzing this correctly? * Thanks for checking this. We believe our method for calculating the percent overlap is correct. In the case of NEKL-2/NEKL-3 overlap for Molecular Function, there are 131 total unique terms, of which 71 overlap, giving a 54% overlap. In the case of NEKL-2/NEKL-3 overlap for Biological Process, however, we made an error in arithmetic (415 unique, 239 overlap), such that the correct percentage is 58%, which we have corrected in the text.

      *Reviewer #1 (Significance (Required)): *

      *Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. *

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

      *This study expanded the use of data-independent acquisition-mass spectrometry (DIA-MS) in TurboID proximity-labeling proteomics to identify novel interactors of NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4 complexes in C. elegans. The authors described several useful metrics to evaluate the quality of TurboID experiments, such as using the percentage of upregulated genes, the percentage of proteins present only in bait-TurboID experiments as compared to N2 controls, and the percentage of endogenously biotinylated carboxylases as internal controls. Further, the authors introduced methodological variability across 23 TurboID experiments and evaluated any improvement to the resulting data, such as N-terminally tagging bait proteins with TurboID, depleting endogenous carboxylases, and auxin-inducible degradation of known complex members. Finally, this study identified the kinase folding chaperone CDC-37 and the WASH complex component DDL-2 as novel interactors with the NEKL-MLT complexes through an RNAi-based enhancer approach following their identification by TurboID. *

      Major comments: * The key conclusions are convincing, and the work is rigorous. The work provides a clear roadmap to reproducing the data. The experiments are adequately replicated, and statistical analysis is adequate. We only have minor comments.*

      Minor comments: * •In the western blot in Fig 1 why does the mNG::Turbo have two bands? * Thank you for point this out. To our knowledge this is a breakdown product that was especially prevalent in replicate 3 (also see S1 Data), which we chose to shown because all the NEKL-MLTs were clearly visible in this western. The expected size of the mNeonGreen::TurboID (including linker and tags) is ~68 kDa and our blots are roughly consistent those of Artan et al., (2001). This lower band was not evident in Exp 8. We have now included a statement in the figure legend to indicate that the upper band is the full-length protein whereas the lower band is likely to be a breakdown product (lines 1141-1142).

      •Fig 2B is difficult to parse as a reader. Columns labeled "Upreg," "Downreg," "TurboID only," "N2 only," "Filter-1," "Filter-2," and "Epi %" could be moved to Supplemental. Fold change vs N2 could be represented as a bar chart, allowing for trends between fold change and the metrics Upreg %, Turbo %, and Carboxylase % to be seen more clearly. Further, rows headed "Carboxylase depletion," "DDA," and "Auxin treated" could be presented as separate panels to better match the distinct points made in the text. After serious consideration we have made several changes including the addition of S2 Fig, which may provide readers with a better visual representation of the bait and prey fold changes observed in all our experiments. However, we feel that the detailed data embedded in Fig 2 is the most concise and accurate means by which to convey our full results and is key to our methodological conclusions. As such we did not want to relegate this information to a supplemental table. We note that this figure was not found to be problematic by other reviewers, although we do understand the points made by this reviewer.

      •Line 179: in vivo should be italicized Because journals differ in their stylistic practices, we are currently waiting before doing our final formatting. We did keep our use of Latin phrases consistently non-italicized in the draft.

      •Lines 215-217: The comparison between Western blot expression levels and prior fluorescent reporter levels is unclear. Could be reformatted to make it clearer that relative expression of the different NEKL-MLTs in this study is consistent with prior data. We reformatted this sentence to improve clarity. (lines 205-207)

      *•Lines 267-268: The final line of the passage is unclear and can be removed. * This sentence has been removed.

      •Lines 311-313: This study is able to use the recovery of bait and known interactor proteins as internal controls to determine the quality of each experiment, but this may not always be the case for other users' experiments. The authors should comment on how Upreg %, a value influenced by many factors, can actually be used as a quality check when a bait protein has no known interactors. We have added language to highlight this point. (lines 344-348)

      *•Line 702: There is a [new REF] that should be removed * As described above, we have now included this finding on bcc-1 as part of this manuscript (Fig 9C).

      •The approach used mixed stage animals, but some genes oscillate or are transiently expressed. Please discuss cost-benefit of mixed stage vs syncing. This is an important point. We have added a discussion on the benefits and drawbacks of using mixed stages to the discussion. (lines 901-911)

      *•Authors were working on hypodermally expressed proteins. It would be valuable to discuss what tissues are amenable to TurboID. Ie are the cases where there are few cells (anchor cell, glial sockets, etc) that it will be extremely challenging to perform this technique * We agree that certain tissues/proteins will not be amenable to proximity labeling. We believe that we have addressed this point together with the above comment throughout the manuscript and now on lines 936-940.

      •Authors mention approaches such as nanobodies, split Turbo. Based on their experiences it would be valuable to add Discussion on strengths and weaknesses of these approaches to guide folks considering TurboID and DIA-MS experiments in C. elegans Because we have not tested these methods, we feel that we cannot provide a great deal of insight into these alternate approaches. We mention and reference these methods in the introduction so that readers are aware of them.

      *Reviewer #2 (Significance (Required)): *

      •Advance in technique: This study expands the use cases of data-independent acquisition MS method (DIA-MS) in C. elegans, which fragments all ions independent of the initial MS1 data. The benefits of this approach include better reproducibility across technical replicates and better recovery of low abundance peptides, which are critical for advancing our ability to capture weak and transient interactions.

      •The use of DIA-MS in this study has improved our understanding of the partners of these NEKL-MLTs in membrane trafficking, molting, and cell adhesion within the epidermis.

      •In many papers, TurboID seems very trivial but this paper clearly highlights the limitations and will be an invaluable resource for labs that want to get proximity labeling established in their labs.

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

      *Summary: *

      Fay and colleagues perform a series of proximity labeling experiments in C. elegans followed by thorough and rational analysis of the resulting biotinylated proteins identified by LC-MS/MS. The overall goals of the study are to evaluate different techniques and provide practical guidance on how to achieve success. The major takeaways are that integration of data-independent acquisition (DIA) along with comparison of endogenously tagged TurboID alleles to soluble TurboID expressed in the same tissue results in improved detection of bona-fide interactors and reduced numbers of false-positives.

      *Major comments: *

      Overall the claims are solid and conclusions supported. The data and methods are substantial to enable reproducibility in other labs. The experiments have been repeated multiple times with particular attention to statistical analysis. I have no major concerns with the manuscript and focus primarily on improving the accessibility of this important contribution to the scientific community. As such, I suggest that the authors:

      1) Provide more explanation of and rationale for using DIA. This is not yet a standard technique and most basic biomedical scientists will be unaware of the jargon. As I expect many labs in the C. elegans community and beyond will be interested in the guidance provided in this manuscript, the introduction offers a great opportunity to bring the reader up to speed, as opposed to sending them to the complicated proteomics analysis literature. We have added some additional context (lines 77-80) as well as new references. We note that getting into the technical differences between DIA and DDA, beyond what we briefly mention, would take a substantial amount of space, may not be of interest to many readers, and can be found through standard internet and (sigh) AI-based searches.

      *2) Provide a better overview of the various protocols tested (Experiments 1-8). Maybe at the beginning of the results, and maybe with an accompanying schematic. As currently written, it is difficult to figure out details regarding how the experiments vary and why. * We have now added a short paragraph to better inform the reader at the front end regarding the major experiments. (lines 139-146).

      3) As to be expected, expression of TurboID tags at endogenous levels via low abundance proteins in a complex multicellular system results in somewhat weak signals that flirt with the limit of detection. Perhaps by combining tagged alleles within the same complex (NEKL-3/MLT-3 or NEKL-2/MLT-2/MLT-4) the signals could be boosted? Tandem tags, either on one end or multiple ends of proteins might help as well. As the authors point out, a benefit of tagging the two NEKL-MLT complexes is that there are strong loss-of-function phenotypes (lethal molting defects) to help evaluate whether a tagging strategy results in a non-functional complex. THESE EXPERIMENTS ARE OPTIONAL and might simply be discussed at the authors discretion. These are interesting ideas that we have now incorporated into our discussion. (lines 936-940)

      *Minor Comments: *

      *1) Figure 3A is cropped on the right. * Thank you for catching this. Corrected.

      *2) Better define [new REF] on line 702. * We have added new results (Fig 9C), obviating the need for this reference.

      ***Referee cross-comments** *

      Overall, I am in agreement with, and supportive of, the other reviewers' comments.

      *Reviewer #3 (Significance (Required)): *

      *Significance: *

      Proximity labeling is often proposed as a technique to determine interaction networks of proteins in vivo, but in practice it remains challenging for most labs to execute a successful experiment, especially within the context of multicellular model organisms. Fay and colleagues provide a much needed roadmap for how to best approach proximity labeling experiments in C. elegans that will likely apply to other model systems.

      They establish a rigorous approach by choosing to endogenously tag components of two essential NEKL-MLT complexes required for C. elegans molting. These complexes are relatively low abundance as they are only expressed in a single cell type, the hyp7 epidermal syncytium. In addition, as inactivation of any member of the complexes results in molting defects, they have a powerful selection for functional tags. Thus, they have set a high bar for themselves in order to discern whether a given variation on the experimental approach results in improved detection of interactors and fewer false positives.

      *Potential areas for improvement include lowering the expression level of the skin-specific soluble TurboID used to determine non-specific biotinylation events. This control results in much higher levels of biotinylation compared to the TurboID-tagged NEKL-MLT alleles and likely affects their analysis, which they openly admit. In addition, to reduce the high level of background biotinylation signals generated by endogenous carboxylases, they adopt a depletion strategy pioneered by other researchers but this does not offer major improvements in detection of specific signals. The source of these conflicting results remains to be determined. It is also curious that auxin-inducible degradation of components of the NEKL-MLT complexes did not robustly alter the resulting biotinylating capacity of other members. This approach should be evaluated in subsequent studies. Finally, as mentioned in Major Comment #3 (above), it would be interesting to see if combining TurboID tags within the same complex might improve signal-to-background ratios. *

      This manuscript represents a methodological advance that will likely become an oft-cited reference for members of the C. elegans community and a springboard for other basic biomedical scientists wanting to adapt rigorous proximity labeling techniques to their system. I am a cell biologist that uses a variety of genetic, molecular and biochemical approaches, mostly centered around C. elegans. I have used LC/MS-MS in our studies but have relatively little expertise in evaluating all aspects of proteomic pipelines.

      *Reviewer #4 (Evidence, reproducibility and clarity (Required)): *

      *Fay et al. describe an extensive proximity labeling BioID study in C. elegans with TurboID and DIA-LCMS analysis. They chose the NEKL-2/3 kinases and their known interactors MLT-2/3/4 as TurboID-fused bait proteins (C- and partially N-terminal fusions encoded from CRISPR-mediated genome edited genes). With eight biological replicates (and three to four technical replicates each) and with the unmodified wildtype or mNeonGreen-TurboID expressing worms as controls, a comprehensive dataset was generated. Although starting from quite different abundances of the bait-fusions within the cell lysates all bait proteins and known complex-binding partners were convincingly enriched with capturing streptavidin beads after only one hour of incubation with the lysate. This confirms the general applicability of TurboID-BioID approach in C. elegans. The BioID method typically gives rise to large proteomics datasets (up to more than thousand proteins identified after biotin capture) with several tens to hundreds enriched proteins (against negative control strains) as potential proteins that localize proximal to the bait-TurboID protein. However, substantial variations of candidates between biological replicates are frequently observed in BioID experiments. The authors scrutinized their dataset towards indicative metrics, filters and cutoffs in order to separate high-confidence from low-confidence candidates. With the workflow applied the authors melt down the number of candidates to 15 proteins that were grouped in four functional groups reasonably associated to NEKL-MLT function. *

      Successful BioID experiments depend on reliable enrichment quantification with mass spectrometry using control cell lines that require a carefully bait-tailored design. Those must adequately express TurboID controls matching the abundance of the bait-TurboID fusion protein and its biotinylation activity. After affinity capture, sample preparation and LCMS data acquisition there is no silver bullet towards the identification true bait neighbors. Fay et al. elaborately describe their considerations and workflow towards high-confidence candidates. The workflow considered (i) data analysis with Volcano plots to account for statistical reproducibility of biological replicates against negative controls, (ii) fraction of proteins only detected in the positive or negative controls thus evading the fold-enrichment quantification approach, (iii) evaluation of variations in carboxylase enrichment as a measure for variations in the general biotin capture quality between experiments, (iv) an assessment of technical reproducibility with scatter plots and Venn diagrams, (v) exclusion of potentially false positives, e.g. promiscuously biotinylated non-proximal proteins, through comparisons with control worms expressing a non-localized mNeonGreen-TurboID fusion protein, (vi) batch effects, (vii) the impact of endogenous biotinylated carboxylases through depletion, (viii) gene ontology analysis of enriched proteins, (ix) weighing data according to the quality of individual experiments according to the afore mentioned metrics, and finally (x) genetic interaction studies to functionally associate high-confidence candidates with the bait.

      *Major comments: *

      Fay et al. present a solid, clear and comprehensive BioID-based proteomics study that takes into account and discusses decisive aspects for the (re)production and analysis of high-quality TurboID-based mass spectrometry data. Claims and conclusions are generally well and sufficiently supported by the presented data and illustrated with figures (throughout the text as well as with plenty of supplementary data). However, although the authors claim to seek for substrates of the kinase complex they drew no further attention to the phosphorylation status of the captured proteins. Haven't the MS data been analyzed in this respect? Information regarding this issue would enhance the manuscript. Data generation and method description appear reproducible for readers. Also, the statistical analyses appear adequate. The authors should also consider to deposit their MS raw and analysis data in a public repository (e.g. PRIDE) for future reviewing processes and as reference data for readers and followers. Our raw MS data have been deposited by the Arkansas Proteomics Facility. I have followed up to ensure that they are publicly available.

      *Minor comments: *

      The authors should combine supplementary data files to reduce the number of single files readers have to deal with. We have combined these files as suggested.

      The authors should avoid the term "upregulation" or "increased biotinylation" when capture enrichment is meant. We agree with reviewer's point. We now use the terms enriched versus reduced or up versus down, depending on the context, and clearly define these terms. These changes have been incorporated throughout the manuscript.

      *Reviewer #4 (Significance (Required)): *

      The manuscript presents a robust BioID proteomics screening for co-localizing proteins of NEKL-2/3 kinases and their known interactors MLT-2/3/4. The ongoing validation of their functional interactions and whether the protein candidates reflect phosphorylation substrates or else remains elusive and is announced for upcoming manuscripts. The knowledge gain in terms of molecular mechanisms with NEKL-2/3 MLT-2/3/4 involvement in C. elegans is therefore limited to a table of - promising - interacting candidates that have to be studied further. Information about the phosphorylation status of the captured proteins from the MS data are not given. However, knowing the protein candidates will be of interest for groups working with these complexes (or the identified potentially interacting proteins) either in C. elegans or any other organism. Also, in-depth proteomics screenings with novel approaches such as BioID have to be established for individual organisms. For C. elegans there is only one prior BioID publication (Holzer et al. 2022). Many of the aspects discussed here have also been addressed earlier for BioIDs in other organisms and are not principally new. However, the presented study can be of conceptual interest for labs delving into or entangled with the BioID method in C. elegans or other organisms. The study addresses especially proteomics groups working on protein-protein interactions using proximity labeling/MS approaches. Basic consideration and thoughts for the experimental design and MS data analysis are given in detail and can serve as another guideline for future studies.

    2. 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

      Proximity labeling has become a powerful tool for defining protein interaction networks and has been utilized in a growing number of multicellular model systems. However, while such an approach can efficiently generate a list of potential interactors, knowledge of the most appropriate controls and standardized metrics to judge the quality of the data are lacking. The study by Fay systematically investigates these questions using the C. elegans NIMA kinase family members NEKL-2 and NEKL-2 and their known binding partners MLT-2, MLT-3 and MLT-4. The authors perform eight TurboID experiments each with multiple NEKL and MLT proteins and explore general metrics for assessing experimental outcomes as well as how each of the individual metrics correlates with one another. They also compare technical and biological replicates, explore strategies for identifying false positives and investigate a number of variations in the experimental approach, such as the use of N- versus C-terminal tags, depletion of endogenous biotinylated proteins, combining auxin-inducible degradation, and the use of gene ontology analysis to identify physiological interactors. Finally, the authors validate their findings by demonstrating that a number of the candidate identified functionally interact with NEKL-2 or components of the WASH complex.

      Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole. Below I list major and minor concerns.

      Major concerns

      1. My biggest issue with the manuscript is that a lot is made of the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). The authors perform experiments using DIA and DDA approaches but do not directly compare the outcomes. As a result there is really no way to know if one approach is better than the other. I would suggest the authors either perform the necessary analysis to compare the two approaches or tone down their promotion of DIA.
      2. Line 75, The authors promote the use of data-independent acquisition (DIA) without defining what this approach is and how it differs from the more conventional data-dependent acquisition. As a non-mass spectroscopist, I found myself with lots of question concerning DIA, what it is and how it differs from DDA. I think it would really be helpful to expand the description of DIA and its comparison with DDA in the introduction.

      Minor concerns:

      Line 92 typo. I believe the authors meant to say NEKL-2-MLT-2-MLT-4.

      Line169. Is exogenous the correct word to use here? It suggests that you are talking about non-worm proteins, but I know you are not.

      Line 177 typo (D) should be (C).

      Figure 1C: Lucky Charms may sue you for infringement of their trademarked marshmallow treats.

      Figure 1D The NEKL-2::TurboID band is indicated with a green triangle in the figure but the figure legend states that green triangles indicate mNG::TurboID control. I know this triangle is a shade off the triangle that indicates mNG::TurboID but it's really hard to see the difference. All of the differently colored triangles in panel F are unnecessary. I would either just pick one color for all non-control bait proteins or better yet, only use a triangle to point to bands that are not obvious. For instance I don't need the triangles that point to NEKL-2 -3 and -4 fusion proteins. These are just distracting.

      Line: 316: Conceivably, another factor that could contribute to the counterintuitive upregulation of some proteins in the N2 samples is related to the fusion proteins that are being expressed in the TurboID lines. A partially functional bait protein (one with a level of activity similar to nekl-2(fd81) that may not result in an obvious phenotype) could directly or indirectly affect gene expression leading to lower levels of a subset of proteins in the TurboID samples. The same could be said for fusion proteins with a gain-of-function effect.

      Fig 3 B-E. I am a little confused how the data in these graphs is normalized. For instance, I would have expected that for NEKL-3 in panel B, that the normalized (log2) intensity value in N2 be set at 0 as it is for NEKL-2. Maybe I just don't have enough information on how these plots were generated.

      Figure 6C legend is not correct.

      Line 575: Figure reference should be Fig. S5G. The authors should check to make sure all references to supplemental figures include correct panel information.

      Line 576. The authors reference a study by Artan and colleagues and report a weak correlation between their study and that of Artan. They reference figure S4 but it should be Fig S5H.

      Line 652. The authors note that numerous proteins were present at substantially reduced levels in the mNG::TurboID samples and suggest that sticky proteins may have been outcompeted or otherwise excluded from beads incubated with the mNG::TurboID lysates. Why would sticky proteins only be a problem in these samples? The reasoning is not clear to me.

      Line 745: The term "bait overlaps" is a bit vague. Ultimately, I figured out what it meant but it was not immediately obvious.

      S7B Fig. Why is actin missing from the eluate?

      Line 873: The authors state the extent of overlap in GO terms between the various experiments and provide percentages. I tried to extract this information from Figure 8C and came up with different values. For instance, in the case of Molecular Function, they state that they observed a 54% overlap between NEKL-2 and NEKL-3 but in the Venn diagram in Figure 8C I see that the NEKL-2 and NEKL-3 experiments had 71 (25+46) GO terms in common. Out of 98 GO terms for NEKL-2 or 104 for NEKL-3 the percentage I got is closer to 72. Am I analyzing this correctly?

      Significance

      Overall this is an outstanding study that will be of great interest to those interested in using proximity labeling to identify interactors of their favorite protein. The experiments are well executed and the data presented in a mostly clear manner. I really like this study (particularly because I plan to do a proximity labeling study of my own), but I did come away less than impressed with some of the analysis. This is a data-dense manuscript, and it appears to me that the authors tried to cover so much ground that in some cases very little insight was provided. For instance, the authors promote the use of data independent acquisition (DIA) as compared to the more commonly used data dependent acquisition (DDA). However the authors do not provide any analysis to indicate one approach is better than the other. Likewise the combined use of auxin-induced degradation and proximity labeling is explored but there is very little to take away from these experiments. Despite these issues, I am very enthusiastic about the study as a whole.

  6. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. We especially like this template because it suggests that “they say / I say” argument need not be mechanical, impersonal, or dry, and that telling a story and mak-ing an argument are more compatible activities than many think.

      I think template isn't just a grammatic stuff, it's a map and it takes us to understand deeper. I personally like this part because it's a communication tool. So I think I need to care more who I'm writting to and what I want to tell them.

  7. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. Hooks is saying that speaking up isn’t just about expressing yourself - it’s an act that can heal and create change. For people who’ve been silenced, using their voice is a way to push back and reclaim space. It makes me think about times I’ve spoken up and how it felt empowering, even if it was scary

    1. They remind us just how long it’s been clear there’s something wrong with what we’re doing as well as just how little progress we’ve made in acting on that realization.

      I think this is important because it shows how easy it is to lower our grades with a small mistake, but it takes a lot more effort to improve them after the mistake.

    1. Reviewer #3 (Public review):

      Summary:

      In their study, Shen et al. examine how first- and second-order neurons of early olfactory circuits among invertebrates and vertebrates alike respond to and encode odor identity and concentration. Previously published electrophysiological and imaging data are re-analyzed and complemented with computational simulations. The authors explore multiple potential circuit computations by which odor concentration-dependent increases in first-order neuron responses transform into concentration-invariant responses on average across the second-order neuron population, and report that divisive normalization exceeds subtractive normalization and intraglomerular gain control in accounting for this transformation. The authors then explore how either rate- or timing-based schemes in third-order neurons may decode odor identity and concentration information from such concentration-invariant mean responses across the second-order neuron population. Finally, the results of their study of second-order neurons (invertebrate projection neurons and vertebrate mitral cells) are contrasted with the concentration-variant responses of second-order projection tufted cells in mammals. Overall, through a combination of neural data re-analysis, computational simulation, and conceptual theory, this study provides important new understanding of how aspects of sensory information are encoded through the actions of distinct components of early olfactory circuits.

      Strengths:

      Consideration of multiple evolutionarily disparate olfactory circuits, as well as re-analysis of previously published neural data sets combined with novel simulations guided by those sets, lends considerable robustness to some key findings of this study. In particular, the finding that divisive normalization - with direct inspiration from established circuit components in the form of glomerular layer short-axon cells - accounts more thoroughly for the average concentration invariance of second-order olfactory neurons at a population level than other forms of normalization is compelling. Likewise, demonstration of the required 'crossover' of first-order neuron concentration sensitivity for divisive normalization to achieve such flattening of concentration variance across the second-order population is notable, with simulations providing important insight into experimentally observed patterns of first-order neuron responses. Limited clarity in other aspects of the study, in particular related to the consideration of neural response latencies and enumerated below, temper the overall strength of the study.

      Weaknesses:

      (1) While the authors focus on concentration-dependent increases in first-order neuron activity, reflecting the majority of observed responses, recent work from the Imai group shows that odorants can also lead to direct first-order neuron inhibition (i.e., reduction in spontaneous activity), and within this subset, increasing odorant concentration tends to increase the degree of inhibition. Some discussion of these findings and how they may complement divisive normalization to contribute to the diverse second-order neuron concentration-dependence would be of interest and help expand the context of the current results.

      (2) Related to the above point, odorant-evoked inhibition of second-order neurons is widespread in mammalian mitral cells and significantly contributes to the flattened concentration-dependence of mitral cells at the population level. Such responses are clearly seen in Figure 1D. Some discussion of how odorant-evoked mitral cell inhibition may complement divisive normalization, and likewise relate to comparatively lower levels of odorant-evoked inhibition among tufted cells, would further expand the context of the current results. Toward this end, replication of analyses in Figures 1D and E following exclusion of mitral cell inhibitory responses would provide insight into the contribution of such inhibition to the flattening of the mitral cell population concentration dependence.

      (3) The idea of concentration-dependent crossover responses across the first-order population being required for divisive normalization to generate individually diverse concentration response functions across the second-order population is notable. The intuition of the crossover responses is that first-order neurons that respond most sensitively to any particular odorant (i.e., at the lowest concentration) respond with overall lower activity at higher concentrations than other first-order neurons less sensitively tuned to the odorant. Whether this is a consistent, generalizable property of odorant binding and first-order neuron responsiveness is not addressed by the authors, however. Biologically, one mechanism that may support such crossover events is intraglomerular presynaptic/feedback inhibition, which would be expected to increase with increasing first-order neuron activation such that the most-sensitively responding first-order neurons would also recruit the strongest inhibition as concentration increases, enabling other first-order neurons to begin to respond more strongly. Discussion of this and/or other biological mechanisms (e.g., first-order neuron depolarization block) supporting such crossover responses would strengthen these results.

      (4) It is unclear to what degree the latency analysis considered in Figures 4D-H works with the overall framework of divisive normalization, which in Figure 3 we see depends on first-order neuron crossover in concentration response functions. Figure 4D suggests that all first-order neurons respond with the same response amplitude (R in eq. 3), even though this is supposed to be pulled from a distribution. It's possible that Figure 4D is plotting normalized response functions to highlight the difference in latency, but this is not clear from the plot or caption. If response amplitudes are all the same, and the response curves are, as plotted in Figure 4D, identical except for their time to half-max, then it seems somewhat trivial that the resulting second-order neuron activation will follow the same latency ranking, regardless of whether divisive normalization exists or not. However, there is some small jitter in these rankings across concentrations (Figure 4G), suggesting there is some randomness to the simulations. It would be helpful if this were clarified (e.g., by showing a non-normalized Figure 4D, with different response amplitudes), and more broadly, it would be extremely helpful in evaluating the latency coding within the broader framework proposed if the authors clarified whether the simulated first-order neuron response timecourses, when factoring in potentially different amplitudes (R) and averaging across the entire response window, reproduces the concentration response crossovers observed experimentally. In summary, in the present manuscript, it remains unclear if concentration crossovers are captured in the latency simulations, and if not, the authors do not clearly address what impact such variation in response amplitudes across concentrations may have on the latency results. It is further unclear to what degree divisive normalization is necessary for the second-order neurons to establish and maintain their latency ranks across concentrations, or to exhibit concentration-dependent changes in latency.

      (5) How the authors get from Figure 4G to 4H is not clear. Figure 4G shows second-order neuron response latencies across all latencies, with ordering based on their sorted latency to low concentration. This shows that very few neurons appear to change latency ranks going from low to high concentration, with a change in rank appearing as any deviation in a monotonically increasing trend. Focusing on the high concentration points, there appear to be 2 latency ranks switched in the first 10 responding neurons (reflecting the 1 downward dip in the points around neuron 8), rather than the 7 stated in the text. Across the first 50 responding neurons, I see only ~14 potential switches (reflecting the ~7 downward dips in the points around neurons 8, 20, 32, 33, 41, 44, 50), rather than the 32 stated in the text. It is possible that the unaccounted rank changes reflect fairly minute differences in latencies that are not visible in the plot in Figure 4G. This may be clarified by plotting each neuron's latency at low concentration vs. high concentration (i.e., similar to Figure 4H, but plotting absolute latency, not latency rank) to allow assessment of the absolute changes. If such minute differences are not driving latency rank changes in Fig. 4G, then a trend much closer to the unity line would be expected in Figure 4H. Instead, however, there are many massive deviations from unity, even within the first 50 responding neurons plotted in Figure 4G. These deviations include a jump in latency rank from 2 at low concentration to ~48 at high concentration. Such a jump is simply not seen in Figure 4G.

      (6) In the text, the authors state that "Odor identity can be encoded by the set of highest-affinity neurons (which remains invariant across concentrations)." Presumably, this is a restatement of the primacy model and refers to invariance in latency rank (since the authors have not shown that the highest-affinity neurons have invariant response amplitudes across concentration). To what degree this statement holds given the results in Figure 4H, however, which appear to show that some neurons with the earliest latency rank at low concentration jump to much later latency ranks at high concentration, remains unclear. Such changes in latency rank for only a few of the first responding neurons may be negligible for classifying odor identity among a small handful of odorants, but not among 1-2 orders of magnitude more odors, which may feasibly occur in a natural setting. Collectively, these issues with the execution and presentation of the latency analysis make it unclear how robust the latency results are.

      (7) Analysis in Figures 4A-C shows that concentration can be decoded from first-order neurons, second-order neurons, or first-order neurons with divisive normalization imposed (i.e., simulating second-order responses). This does not say that divisive normalization is necessary to encode concentration, however. Therefore, for the authors to say that divisive normalization is "a potential mechanism for generating odor-specific subsets of second-order neurons whose combinatorial activity or whose response latencies represent concentration information" seems too strong a conclusion. Divisive normalization is not generating the concentration information, since that can be decoded just as well from the first-order neurons. Rather, divisive normalization can account for the different population patterns in concentration response functions between first- and second-order neurons without discarding concentration-dependent information.

      (8) Performing the same polar histogram analysis of tufted vs. mitral cell concentration response functions (Figure 5B) provides a compelling new visualization of how these two cell types differ in their concentration variance. The projected importance of tufted cells to navigation, emerging directly through the inverse relationship between average concentration and distance (Figure 5C), is not surprising, and is largely a conceptual analysis rather than new quantitative analysis per se, but nevertheless, this is an important point to make. Another important consideration absent from this section, however, is whether and how divisive normalization may impact tufted cell activity. Previous work from the authors, as well as from Schoppa, Shipley, and Westbrook labs, has compellingly demonstrated that a major circuit mediating divisive normalization of mitral cells (GABA/DAergic short-axon cells) directly targets external tufted cells, and is thus very likely to also influence projection tufted cells. Such analysis would additionally provide substantially more justification for the Discussion statement "we analyzed an additional type of second-order neuron (tufted cells)", which at present instead reflects fairly minimal analysis.

    2. Author response:

      (1) Explore the temporal component of neural responses (instead of collapsing responses to a single number, i.e., the average response over 4s), and determine which of the three models can recapitulate the observed dynamics.

      (2) Expand the polar plot visualization to show all three slopes (changes in responses across all three successive concentrations) instead of only two slopes.

      (3) Attempt to collect and analyze, from published papers, data of: (a) first-order neuron responses to odors to determine the role of first-order inhibition towards generating non-monotonic responses, and (b) PN responses in Drosophila to properly compare with corresponding first-order neuron responses.

      (4) Further discuss: (a) why the brain may need to encode absolute concentration, (b) the distinction between non-monotonic responses and cross-over responses, and (c) potential limitations of the primacy model.

      (5) Expand the divisive normalization model by evaluating different values of k and R, and study the effects of divisive normalization on tufted cells.

      (6) Add discussion of other potential inhibitory mechanisms that could contribute towards the observed effects.

      Reviewer #1:

      The article starts from the premise that animals need to know the absolute concentration of an odor over many log units, but the need for this isn't obvious. The introduction cites an analogy to vision and audition. These are cases where we know for a fact that the absolute intensity of the stimulus is not relevant. Instead, sensory perception relies on processing small differences in intensity across space or time. And to maintain that sensitivity to small differences, the system discards the stimulus baseline. Humans are notoriously bad at judging the absolute light level. That information gets discarded even before light reaches the retina, namely through contraction of the pupil. Similarly, it seems plausible that a behavior like olfactory tracking relies on sensing small gradients across time (when weaving back and forth across the track) or space (across nostrils). It is important that the system function over many log units of concentration (e.g., far and close to a source) but not that it accurately represents what that current concentration is [see e.g., Wachowiak et al, 2025 Recalibrating Olfactory Neuroscience..].

      We thank the Reviewer for the insightful input and agree that gradients across time and space are important for various olfactory behaviors, such as tracking. At the same time, we think that absolute concentration is also needed for two reasons. First, in order to extract changes in concentration, the absolute concentration needs to be normalized out; i.e., change needs to be encoded with respect to some baseline, which is what divisive normalization computes. Second, while it is true that representing the exact number of odor molecules present is not important, this number directly relates to distance from the odor source, which does provide ethological value (e.g., is the tiger 100m or 1000m away?). Indeed, our decoding experiments focused on discriminating relative, and not on absolute, concentrations by classifying between each pair of concentrations (i.e., relative distances), which is effectively an assessment of the gradient. In our revision, we will make all of these points clearer.

      Still, many experiments in olfactory research have delivered square pulses of odor at concentrations spanning many log units, rather than the sorts of stimuli an animal might encounter during tracking. Even within that framework, though, it doesn't seem mysterious anymore how odor identity and odor concentration are represented differently. For example, Stopfer et al 2003 showed that the population response of locust PNs traces a dynamic trajectory. Trajectories for a given odor form a manifold, within which trajectories for different concentrations are distinct by their excursions on the manifold. To see this, one must recognize that the PN responds to an odor pulse with a time-varying firing rate, that different PNs have different dynamics, and that the dynamics can change with concentration. This is also well recognized in the mammalian systems. Much has been written about the topic of dynamic coding of identity and intensity - see the reviews of Laurent (2002) and Uchida (2014).

      Given the above comments on the dynamics of odor responses in first- and second-order neurons, it seems insufficient to capture the response of a neuron with a single number. Even if one somehow had to use a single number, the mean firing rate during the odor pulse may not be the best choice. For example, the rodent mitral cells fire in rhythm with the animal's sniffing cycle, and certain odors will just shift the phase of the rhythm without changing the total number of spikes (see e.g., Fantana et al, 2008). During olfactory search or tracking, the sub-second movements of the animal in the odor landscape get superposed on the sniffing cycle. Given all this, it seems unlikely that the total number of spikes from a neuron in a 4-second period is going to be a relevant variable for neural processing downstream.

      To our knowledge, it is not well understood how downstream brain regions read out mitral cell responses to guide olfactory behavior. The olfactory bulb projects to more than a dozen brain regions, and different regions could decode signals in different ways. We focused on the mean response because it is a simple, natural construct.

      The datasets we analyzed may not include all relevant timing information; for example, the mouse data is from calcium imaging studies that did not track sniff timing. Nonetheless, we plan to address this comment within our framework by binning time into smaller-sized windows (e.g., 0-0.2s, 0.2-0.4s, etc.) and repeating our analysis for each of these windows. Specifically, we will determine how each normalization method fares in recapitulating statistics of the population responses of each window, beyond simply assessing the population mean.

      Much of the analysis focuses on the mean activity of the entire population. Why is this an interesting quantity? Apparently, the mean stays similar because some neurons increase and others decrease their firing rate. It would be more revealing, perhaps, to show the distribution of firing rates at different concentrations and see how that distribution is predicted by different models of normalization. This could provide a stronger test than just the mean.

      We agree that mean activity is only one measure to summarize a rich data set and will perform the suggested analysis.

      The question "if concentration information is discarded in second-order neurons, which exclusively transmit odor information to the rest of the brain, how does the brain support olfactory behaviors, such as tracking and navigation?" is really not an open question anymore. For example, reference 23 reports in the abstract that "Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations."

      Primacy coding does provide one plausible mechanism to decode concentration. Our manuscript demonstrated how such a code could emerge in second-order neurons with the help of divisive normalization, though it does require maintaining at least partial rank invariance across concentrations, which may not be robust. We also showed how concentration could be decoded via spike rates, even if average rates are constant, which provides an alternative hypothesis to that of ref 23.

      Further, ref 23 only considers the piriform cortex, which, as mentioned above, is one of many targets of the olfactory bulb, and it remains unclear what the decoding mechanisms are of each of these targets. In addition, work from the same authors of ref 23 found multiple potential decoding strategies in the piriform cortex itself, including changes in firing rate (see Fig. 2E of ref. 23 - Bolding & Franks, 2017; as well as Fig. 4 in Roland et al., 2017).

      It would be useful to state early in the manuscript what kinds of stimuli are being considered and how the response of a neuron is summarized by one number. There are many alternative ways to treat both stimuli and responses.

      We will add this explanation to the manuscript.

      "The change in response across consecutive concentration levels may not be robust due to experimental noise and the somewhat limited range of concentrations sampled": Yes, a number of the curves just look like "no response". It would help the reader to show some examples of raw data, e.g. the time course of one neuron's firing rate to 4 concentrations, and for the authors to illustrate how they compress those responses into single numbers.

      We agree and will add this information to the manuscript.

      "We then calculated the angle between these two slopes for each neuron and plotted a polar histogram of these angles." The methods suggest that this angle is the arctan of the ratio of the two slopes in the response curve. A ratio of 2 would result from a slope change from 0.0001 to 0.0002 (i.e., virtually no change in slope) or from 1 to 2 (a huge change). Those are completely different response curves. Is it reasonable to lump them into the same bin of the polar plot? This seems an unusual way to illustrate the diversity of response curve shapes.

      We agree that the two changes in the reviewer’s example will be categorized in the same quadrant in our analysis. We did not focus on the absolute changes because our analysis covers many log ratios of concentrations. Instead, we focused on the relative shapes of the concentration response curves, and more specifically, the direction of the change (i.e., the sign of the slope). We will better motivate this style of analysis in the revision. Moreover, in response to comments by Reviewer 2, we will compare response shapes between all three successive levels of concentration changes, as opposed to only two levels.

      The Drosophila OSN data are passed through normalization models and then compared to locust PN data. This seems dangerous, as flies and locusts are separated by about 300 M years of evolution, and we don't know that fly PNs act like locust PNs. Their antennal lobe anatomy differs in many ways, as does the olfactory physiology. To draw any conclusions about a change in neural representation, it would be preferable to have OSN and PN data from the same species.

      We are in the process of requesting PN response data in Drosophila from groups that have collected such data and will repeat the analysis once we get access to the data.

      One conclusion is that divisive normalization could account for some of the change in responses from receptors to 2nd order neurons. This seems to be well appreciated already [e.g., Olsen 2010, Papadopoulou 2011, minireview in Hong & Wilson 2013].

      While we agree that these manuscripts do study the effects of divisive normalization in insects and fish, here we show that this computation also generalizes to rodents. In addition, these previous studies do not focus on divisive normalization’s role towards concentration encoding/decoding, which is our focus. We will clarify this difference in the revision.

      Another claim is that subtractive normalization cannot perform that function. What model was used for subtractive normalization is unclear (there is an error in the Methods). It would be interesting if there were a categorical difference between divisive and subtractive normalization.

      We apologize for the mistake in the subtractive normalization equation and will correct it. Thank you for catching it.

      Looking closer at the divisive normalization model, it really has two components: (a) the "lateral inhibition" by which a neuron gets suppressed if other neurons fire (here scaled by the parameter k) , and (b) a nonlinear sigmoid transformation (determined by the parameters n and sigma). Both lateral inhibition and nonlinearity are known to contribute to decorrelation in a neural population (e.g., Pitkow 2012). The "intraglomerular gain control" contains only the nonlinearity. The "subtractive normalization" we don't know. But if one wanted to put divisive and subtractive inhibition on the same footing, one should add a sigmoid nonlinearity in both cases.

      Our intent was not to place all the methods on the “same footing” but rather to isolate the two primary components of normalization methods – non-linearity and lateral inhibition – and determine which of these, and in which combination, could generate the desired effects. Divisive normalization incorporates both components, whereas intraglomerular gain control and subtractive normalization only incorporate one of these components. We will clarify this reasoning in the revision.

      The response models could be made more realistic in other ways. For example, in both locusts and fish, the 2nd order neurons get inputs from multiple receptor types; presumably, that will affect their response functions. Also, lateral inhibition can take quite different forms. In locusts, the inhibitory neurons seem to collect from many glomeruli. But in rats, the inhibition by short axon cells may originate from just a few sparse glomeruli, and those might be different for every mitral cell (Fantana 2008).

      We thank the Reviewer for the input. Instead of fixing k for all second-order neurons, we will apply different k values for different neurons. We will also systematically vary the percentage of neurons used for the divisive normalization calculation in the denominator, and determine the regime under which the effects experimentally observed are reproducible. This approach takes into account the scenario that inter-glomerular inhibitory interactions are sparse.

      There are questions raised by the following statements: "traded-off energy for faster and finer concentration discrimination" and "an additional type of second-order neuron (tufted cells) that has evolved in land vertebrates and that outperforms mitral cells in concentration encoding" and later "These results suggest a trade-off between concentration decoding and normalization processes, which prevent saturation and reduce energy consumption.". Are the tufted cells inferior to the mitral cells in any respect? Do they suffer from saturation at high concentration? And do they then fail in their postulated role for odor tracking? If not, then what was the evolutionary driver for normalization in the mitral cell pathway? Certainly not lower energy consumption (50,000 mitral cells = 1% of rod photoreceptors, each of which consumes way more energy than a mitral cell).

      The question of what mitral cells are “good for”, compared to tufted cells, remains unclear in our view. We speculate that mitral cells provide superior context-dependent processing and are better for determining stimuli-reward contingencies, but this remains far from settled experimentally.

      We believe the mitral cell pathway evolved earlier than tufted cells, since the former appear akin to projection neurons in insects. Nonetheless, we agree that differences in energy consumption are unlikely to be the primary distinguishing factor, and in the revision, we will drop this argument.

      Reviewer #2:

      The main premise that divisive normalization generates this diversity of dose-response curves in the second-order neurons is a little problematic. … The analysis in [Figure 3] indicates that divisive normalization does what it is supposed to do, i.e., compresses concentration information and not alter the rank-order of neurons or the combinatorial patterns. Changes in the combinations of neurons activated with intensity arise directly from the fact that the first-order neurons did not have monotonic responses with odor intensity (i.e., crossovers). This was the necessary condition, and not the divisive normalization for changes in the combinatorial code. There seems to be a confusion/urge to attribute all coding properties found in the second-order neurons to 'divisive normalization.' If the input from sensory neurons is monotonic (i.e., no crossovers), then divisive normalization did not change the rank order, and the same combinations of neurons are activated in a similar fashion (same vector direction or combinatorial profile) to encode for different odor intensities. Concentration invariance is achieved, and concentration information is lost. However, when the first-order neurons are non-monotonic (i.e., with crossovers), that causes the second-order neurons to have different rank orders with different concentrations. Divisive normalization compresses information about concentrations, and rank-order differences preserve information about the odor concentration. Does this not mean that the non-monotonicity of sensory neuron response is vital for robustly maintaining information about odor concentration? Naturally, the question that arises is whether many of the important features of the second-order neuron's response simply seem to follow the input. Or is my understanding of the figures and the write-up flawed, and are there more ways in which divisive normalization contributes to reshaping the second-order neural response? This must be clarified. Lastly, the tufted cells in the mouse OB are also driven by this sensory input with crossovers. How does the OB circuit convert the input with crossovers into one that is monotonic with concentration? I think that is an important question that this computational effort could clarify.

      It appears that there is confusion about the definitions of “non-monotonicity” and “crossovers”.  These are two independent concepts – one does not necessarily lead to the other. Non-monotonicity concerns the response of a single neuron to different concentration levels. A neuron’s response is considered non-monotonic if its response goes up then down, or down then up, across increasing concentrations. A “cross-over” is defined based on the responses of multiple neurons. A cross-over occurs when the response of one neuron is lower than another neuron at one concentration, but higher than the other at a different concentration. For example, the responses of both neurons could increase monotonically with increasing concentration, but one neuron might start lower and grow faster, hence creating a cross-over. We will clarify this in the manuscript, which we believe will resolve the questions raised above.

      The way the decoding results and analysis are presented does not add a lot of information to what has already been presented. For example, based on the differences in rank-order with concentration, I would expect the combinatorial code to be different. Hence, a very simple classifier based on cosine or correlation distance would work well. However, since divisive normalization (DN) is applied, I would expect a simple classification scheme that uses the Euclidean distance metric to work equally as well after DN. Is this the case?

      Yes, we used a simple classification scheme, logistic regression with a linear kernel, which is essentially a Euclidean distance-based classification. This scheme works better for tufted cells because they are more monotonic; i.e., if neuron A and B both increase their responsiveness with concentration, then Euclidean distance would be fine. But if neuron A’s response amplitude goes up and neuron B’s response goes down – as often happens for mitral cells – then Euclidean distance does not work as well. We will add intuition about this in the manuscript.

      Leave-one-trial/sample-out seems too conservative. How robust are the combinatorial patterns across trials? Would just one or two training trials suffice for creating templates for robust classification? Based on my prior experience (https://elifesciences.org/reviewed-preprints/89330https://elifesciences.org/reviewed-preprints/89330), I do expect that the combinatorial patterns would be more robust to adaptation and hence also allow robust recognition of odor intensity across repeated encounters.

      As suggested, we will compute the correlation coefficient of the similarity of neural responses for each odor (across trials). We will repeat this analysis for both mitral and tufted cells. To determine the effect of adaptation, we will compute correlation coefficients of responses between the 1st and 2nd trials vs the 1st and final trial.

      Lastly, in the simulated data, since the affinity of the first-order sensory neurons to odorants is expected to be constant across concentration, and "Jaccard similarity between the sets of highest-affinity neurons for each pair of concentration levels was > 0.96," why would the rank-order change across concentration? DN should not alter the rank order.

      We agree that divisive normalization should not alter the rank order, but the rank order may change in first-order neurons, which carries through to second-order neurons. This confusion may be related to the one mentioned above re: cross-overs vs non-monotonicity. Moreover, in the simulated data (Fig. 4D-H), the Jaccard similarity was calculated based on only the 50 neurons with the highest affinity, not the entire population of neurons. As shown in Fig. 4H, most of the rank-order change happens in the remaining 150 neurons.

      Note that in response to a comment by Reviewer 3, we will change the presentation of Fig. 4H in the revision.

      If the set of early responders does change, how will the decoder need to change, and what precise predictions can be made that can be tested experimentally? The lack of exploration of this aspect of the results seems like a missed opportunity.

      In the Discussion, we wrote about how downstream circuits will need to learn which set of neurons are to be associated with each distinct concentration level. We will expand upon this point and include experimentally testable predictions.

      Based on the methods, for Figures 1 and 2, it appears the responses across time, trials, and odorants were averaged to get a single data point per neuron for each concentration. Would this averaging not severely dilute trends in the data? The one that particularly concerns me is the averaging across different odorants. If you do odor-by-odor analysis, is the flattening of second-order neural responses still observable? Because some odorants activate more globally and some locally, I would expect a wide variety of dose-response relationships that vary with odor identity (more compressed in second-order neurons, of course). It would be good to show some representative neural responses and show how the extracted values for each neuron are a faithful/good representation of its response variation across intensities.

      It appears there is some confusion here; we will clarify in the text and figure captions that we did not average across different odors in our analysis. We will also add figure panels showing some representative neural responses as suggested by the Reviewer.

      A lot of neurons seem to have responses that flat line closer to zero (both firing rate and dF/F in Figure 1). Are these responsive neurons? The mean dF/F also seems to hover not significantly above zero. Hence, I was wondering if the number of neurons is reducing the trend in the data significantly.

      Yes, if a neuron responds to at least one concentration level in at least 50% of the trials, it is considered responsive. So it is possible that some neurons respond to one concentration level and otherwise flatline near zero.  We will highlight a few example neurons to visualize this scenario.

      I did not fully understand the need to show the increase in the odor response across concentrations as a polar plot. I see potential issues with the same. For example, the following dose-response trend at four intensities (C4 being the highest concentration and C1 the lowest): response at C3 > response at C1 and response at C4 > response at C2. But response at C3 < response at C2. Hence, it will be in the top right segment of the polar plot. However, the responses are not monotonic with concentrations. So, I am not convinced that the polar plot is the right way to characterize the dose-response curves. Just my 2 cents.

      Your 2 cents are valuable! Thank you for raising this point. Instead of computing two slopes (C1-C3 and C2-C4), we will expand our analysis to include all three slopes (C1-C2, C2-C3, C3-C4). Consequently, there are 2^3 = 8 different response shapes, and we will list them and quantify the fraction of the responses that fall into each shape category.

      In many analyses, simulated data were used (Figures 3 and 4). However, there is no comparison of how well the simulated data fit the experimental data. For example, the Simulated 1st order neuron in Figure 3D does not show a change in rank-order for the first-order neuron. In Figure 3E, temporal response patterns in second-order neurons look unrealistic. Some objective comparison of simulated and experimental data would help bolster confidence in these results.

      We believe the Reviewer is referring to Figs. 4D and 4E, since Fig. 3D does not show a first-order neuron simulation, and there is no Fig 3E. In Fig. 4D there is no change of rank order because the simulation is for a single odor and single concentration level, and the change of rank-order (i.e., cross-overs) as we define occurs between concentration levels. We will clarify this in the manuscript.

      Reviewer #3:

      While the authors focus on concentration-dependent increases in first-order neuron activity, reflecting the majority of observed responses, recent work from the Imai group shows that odorants can also lead to direct first-order neuron inhibition (i.e., reduction in spontaneous activity), and within this subset, increasing odorant concentration tends to increase the degree of inhibition. Some discussion of these findings and how they may complement divisive normalization to contribute to the diverse second-order neuron concentration-dependence would be of interest and help expand the context of the current results.

      We thank the Reviewer for the suggestion. We will request datasets of first-order neuron responses from the groups who acquired them. We will analyze this data to determine the role of inhibition or antagonistic binding and quantify what percentage of first-order neurons respond less strongly with larger concentrations.

      Related to the above point, odorant-evoked inhibition of second-order neurons is widespread in mammalian mitral cells and significantly contributes to the flattened concentration-dependence of mitral cells at the population level. Such responses are clearly seen in Figure 1D. Some discussion of how odorant-evoked mitral cell inhibition may complement divisive normalization, and likewise relate to comparatively lower levels of odorant-evoked inhibition among tufted cells, would further expand the context of the current results. Toward this end, replication of analyses in Figures 1D and E following exclusion of mitral cell inhibitory responses would provide insight into the contribution of such inhibition to the flattening of the mitral cell population concentration dependence.

      We will perform the analysis suggested, specifically, we will set the negative mitral cell responses to 0 and assess whether the population mean remains flat.

      The idea of concentration-dependent crossover responses across the first-order population being required for divisive normalization to generate individually diverse concentration response functions across the second-order population is notable. The intuition of the crossover responses is that first-order neurons that respond most sensitively to any particular odorant (i.e., at the lowest concentration) respond with overall lower activity at higher concentrations than other first-order neurons less sensitively tuned to the odorant. Whether this is a consistent, generalizable property of odorant binding and first-order neuron responsiveness is not addressed by the authors, however. Biologically, one mechanism that may support such crossover events is intraglomerular presynaptic/feedback inhibition, which would be expected to increase with increasing first-order neuron activation such that the most-sensitively responding first-order neurons would also recruit the strongest inhibition as concentration increases, enabling other first-order neurons to begin to respond more strongly. Discussion of this and/or other biological mechanisms (e.g., first-order neuron depolarization block) supporting such crossover responses would strengthen these results.

      We thank the reviewer for providing additional mechanisms to consider. As suggested, we will add discussion of these alternatives to divisive normalization.

      It is unclear to what degree the latency analysis considered in Figures 4D-H works with the overall framework of divisive normalization, which in Figure 3 we see depends on first-order neuron crossover in concentration response functions. Figure 4D suggests that all first-order neurons respond with the same response amplitude (R in eq. 3), even though this is supposed to be pulled from a distribution. It's possible that Figure 4D is plotting normalized response functions to highlight the difference in latency, but this is not clear from the plot or caption. If response amplitudes are all the same, and the response curves are, as plotted in Figure 4D, identical except for their time to half-max, then it seems somewhat trivial that the resulting second-order neuron activation will follow the same latency ranking, regardless of whether divisive normalization exists or not. However, there is some small jitter in these rankings across concentrations (Figure 4G), suggesting there is some randomness to the simulations. It would be helpful if this were clarified (e.g., by showing a non-normalized Figure 4D, with different response amplitudes), and more broadly, it would be extremely helpful in evaluating the latency coding within the broader framework proposed if the authors clarified whether the simulated first-order neuron response timecourses, when factoring in potentially different amplitudes (R) and averaging across the entire response window, reproduces the concentration response crossovers observed experimentally. In summary, in the present manuscript, it remains unclear if concentration crossovers are captured in the latency simulations, and if not, the authors do not clearly address what impact such variation in response amplitudes across concentrations may have on the latency results. It is further unclear to what degree divisive normalization is necessary for the second-order neurons to establish and maintain their latency ranks across concentrations, or to exhibit concentration-dependent changes in latency.

      As suggested by the Reviewer, we will add another simulation scenario where the response amplitudes (R) are different for different neurons. For each concentration, we will then average each neuron’s response across the entire response window and determine if the simulation reproduces the cross-overs as observed experimentally.

      How the authors get from Figure 4G to 4H is not clear. Figure 4G shows second-order neuron response latencies across all latencies, with ordering based on their sorted latency to low concentration. This shows that very few neurons appear to change latency ranks going from low to high concentration, with a change in rank appearing as any deviation in a monotonically increasing trend. Focusing on the high concentration points, there appear to be 2 latency ranks switched in the first 10 responding neurons (reflecting the 1 downward dip in the points around neuron 8), rather than the 7 stated in the text. Across the first 50 responding neurons, I see only ~14 potential switches (reflecting the ~7 downward dips in the points around neurons 8, 20, 32, 33, 41, 44, 50), rather than the 32 stated in the text. It is possible that the unaccounted rank changes reflect fairly minute differences in latencies that are not visible in the plot in Figure 4G. This may be clarified by plotting each neuron's latency at low concentration vs. high concentration (i.e., similar to Figure 4H, but plotting absolute latency, not latency rank) to allow assessment of the absolute changes. If such minute differences are not driving latency rank changes in Fig. 4G, then a trend much closer to the unity line would be expected in Figure 4H. Instead, however, there are many massive deviations from unity, even within the first 50 responding neurons plotted in Figure 4G. These deviations include a jump in latency rank from 2 at low concentration to ~48 at high concentration. Such a jump is simply not seen in Figure 4G.

      We apologize that Fig. 4H was a poor choice for visualization. What is plotted in Fig. 4H is the sorted identity of neurons under low and high concentrations, and points on the y=x line indicate that the two corresponding neurons have the same rank under the two concentrations. We will replace this panel with a more intuitive visualization, where the x and y axes are the ranks of the neurons; and deviation from the y=x line indicates how different the ranks are of a neuron to the two concentrations.

      In the text, the authors state that "Odor identity can be encoded by the set of highest-affinity neurons (which remains invariant across concentrations)." Presumably, this is a restatement of the primacy model and refers to invariance in latency rank (since the authors have not shown that the highest-affinity neurons have invariant response amplitudes across concentration). To what degree this statement holds given the results in Figure 4H, however, which appear to show that some neurons with the earliest latency rank at low concentration jump to much later latency ranks at high concentration, remains unclear. Such changes in latency rank for only a few of the first responding neurons may be negligible for classifying odor identity among a small handful of odorants, but not among 1-2 orders of magnitude more odors, which may feasibly occur in a natural setting. Collectively, these issues with the execution and presentation of the latency analysis make it unclear how robust the latency results are.

      The original primacy model states that the latency of a neuron decreases with increasing concentration, while the ranks of neurons remain unaltered. Our results, on the other hand, suggest that the ranks do at least partially change across concentrations. This leads to two possible decoding mechanisms. First, if the top K responding neurons remain invariant across concentrations (even if their individual ranks change within the top K), then the brain could learn to associate a population of K neurons with a response latency; lower response latency means higher concentration. Second, if the top K responding neurons do not remain invariant across concentrations, then the brain would need to learn to associate a different set of neurons with each concentration level. The latter imposes additional constraints on the robustness of the primacy model and the corresponding read-out mechanism. We will include more discussion of these possibilities in the revision.

      Analysis in Figures 4A-C shows that concentration can be decoded from first-order neurons, second-order neurons, or first-order neurons with divisive normalization imposed (i.e., simulating second-order responses). This does not say that divisive normalization is necessary to encode concentration, however. Therefore, for the authors to say that divisive normalization is "a potential mechanism for generating odor-specific subsets of second-order neurons whose combinatorial activity or whose response latencies represent concentration information" seems too strong a conclusion. Divisive normalization is not generating the concentration information, since that can be decoded just as well from the first-order neurons. Rather, divisive normalization can account for the different population patterns in concentration response functions between first- and second-order neurons without discarding concentration-dependent information.

      We agree that the word “generating” is faulty. We thank the reviewer for their more precise wording, which we will adopt.

      Performing the same polar histogram analysis of tufted vs. mitral cell concentration response functions (Figure 5B) provides a compelling new visualization of how these two cell types differ in their concentration variance. The projected importance of tufted cells to navigation, emerging directly through the inverse relationship between average concentration and distance (Figure 5C), is not surprising, and is largely a conceptual analysis rather than new quantitative analysis per se, but nevertheless, this is an important point to make. Another important consideration absent from this section, however, is whether and how divisive normalization may impact tufted cell activity. Previous work from the authors, as well as from Schoppa, Shipley, and Westbrook labs, has compellingly demonstrated that a major circuit mediating divisive normalization of mitral cells (GABA/DAergic short-axon cells) directly targets external tufted cells, and is thus very likely to also influence projection tufted cells. Such analysis would additionally provide substantially more justification for the Discussion statement "we analyzed an additional type of second-order neuron (tufted cells)", which at present instead reflects fairly minimal analysis.

      We agree that tufted cells are subject to divisive normalization as well, albeit probably to a less degree than mitral cells. To determine the effect of this, we will alter the strength (and degree of sparseness of interglomerular interactions) of divisive normalization and determine if there is a regime where response features of tufted cells match those observed experimentally.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      In Arabidopsis, DNA demethylation is catalyzed by a family of DNA glycosylases including DME, ROS1, DML2, and DML3. DME activity in the central cell leads to the hypomethylation of maternal alleles in endosperm. While ROS1, DML2, and DML3 function in vegetative tissues to prevent spreading DNA methylation from TE boundaries, their function in the endosperm was unclear.<br /> Using whole genome methylome analysis, the authors showed that ROS1 prevents hypermethylation of paternal alleles in the endosperm thus promotes epigenetic symmetry between maternal and paternal genomes.<br /> The approach and experimental desighs are appropriate, and the key conclusions are adequately supported by the results.<br /> However, there is not sufficient evidence to support the claim that DME demethylates the maternal allele at ROS1-dependent biallelically-demethylated regions. To clarify the issue, the authors could analyze if there is an overlap between DMRs identified in ros1 endosperm and those identified in dme endosperm using published data. If there is any, the authors could show a genome browser example of DMR including dme data.

      Response: Thank you for your insight on our work. To address your concern and further test our model that DME prevents methylation of the maternal allele at regions where ROS1 is prevents methylation of the paternal allele, we turned to the allele-specific bisulfite-sequencing data published in Ibarra et al 2012. These data were from endosperm isolated at 7-8 DAP from aborting seeds of dme-2 +/- (Col-gl) plants pollinated by L_er_. Our analysis of these data is now included in Figures 6 and 7 and Supplemental Figures 13-17. We show that when the loss-of-function allele dme-2 is inherited maternally, average methylation of the maternal allele increases at ROS1-dependent regions (in the revised version of the paper now referred to as ROS1 paternal, DME maternal regions) from less than 10% CG methylation to approximately 40% CG methylation (Fig. 6D), consistent with our previous analysis using the non-allelic Hsieh et al 2009 data (now moved to Supplemental Figure 15). These results thus provide additional evidence that DME removes maternal allele methylation at regions where ROS1 removes paternal allele methylation (compare Fig. 6B and 6D). We included relevant genome browser examples in Figure 7E and Supplemental Figure 14. In the revised version, the relationship between ROS1 and DME is further expanded upon in the text.

      Reviewer #1 (Significance):

      Endosperm is a tissue unique to flowering plants. Though it is an ephemeral tissue, the endosperm plays essential roles for seed development and germination. The endosperm is also the site genomic imprinting occurs, and it has a distinct epigenomic landscape. This work provides a new insight that ROS1 may antagonize imprinted gene expression in the endosperm. However, it was not shown whether imprinted gene expression is indeed affected in ros1, or whether the ros1 mutation has phenotypic consequences. These results would be useful to discuss the evolution and significance of genomic imprinting.

      Response: We agree that the biological significance of ROS1-mediated paternal allele demethylation is presently unknown. We performed RNA-seq on wild-type and ros1 3C and 6C endosperm nuclei, but these data were unfortunately not of high enough quality to include in the manuscript. In the Discussion we suggest that disrupting ROS1-mediated paternal allele demethylation might lead to a gain of imprinting over evolutionary time. In future work we are planning to address potential relationships to gene imprinting using a molecular, RNA-sequencing approach as well as an evolutionary comparative approach. As expected, given the expectation that imprinted genes are associated with a parent-of-origin specific epigenetic mark, we did not find any relationship between known imprinted genes and ROS1-dependent regions that are biallelically-demethylated regions in wild-type endosperm (see lines 362-372).

      Reviewer #2 (Evidence, reproducibility and clarity):

      SUMMARY

      Hemenway and Gehring present evidence that the paternal genome in Arabidopsis endosperm is demethylated at several hundred loci by the DNA glycosylase/lyase ROS1. The evidence is primarily based on analysis of DNA methylation of ros1 mutants and of hybrid crosses where each parental genome can be differentiated by SNPs. I have some comments/questions/concerns, two of them potentially serious, but I think Hemenway and Gehring can address them through additional analyses of data that they already have available and a bit of clarification in writing.

      Response: Thank you for your thoughtful review of this study. Your insight and suggestions have helped add clarity to the paper.

      MAJOR COMMENTS:

      1. Could the excess methylation in ros1-3 relative to ros1-7 shown in Figures 1A and 1C be explained by a second mutation in the ros1-3 background that elevates methylation at some loci? Any mutation that increased RdDM at these loci, for example could have this effect. This could confound the identification and interpretation of biallelicly demethylated loci.

      Response: We propose a simpler explanation for the additional hypermethylation observed in ros1-3: ros1-3 is a loss-of-function (null) allele whereas ros1-7 is likely a hypomorphic allele. For clarity, we have added a diagram of all of the alleles used in this study as Supplemental Figure 1B. The ros1-3 allele was first described in Penterman et al, PNAS, 2007. It is a T-DNA insertion allele that was isolated in the Ws accession and then backcrossed 6 times to Col-0, greatly minimizing the risk of unlinked secondary mutations being present. There is no genetic evidence that there is another T-DNA insertion in this line. The ros1-7 allele was described in Williams et al, Plos Genet, 2015. It was isolated from the Arabidopsis Col-0 TILLING population and is missense mutation (E956K) in a residue in the glycosylase domain that is conserved among the four DNA glycosylases. It is known that ROS1 transcripts are produced from the ros1-7 allele (Williams et al 2015). We observe less hypermethylation in the ros1-7 background compared to the ros1-3 background, and thus propose that the ros1-7 allele is a hypomorphic allele of ROS1. The use of two independent ros1 mutant alleles for initial endosperm methylation profiling strengthens the findings of our study. Importantly, regions that are hypermethylated in ros1-3 are also hypermethylated in ros1-7, but to a lesser extent, and vice versa (Fig 1D, Supplemental Figs. 3 and 4).

      We also use a third allele in this study, ros1-1, which is a nonsense allele in the C24 accession. Notably, we find that the regions are demethylated on both maternal and paternal alleles in wild-type C24 gain DNA methylation primarily on the paternal allele in ros1-1 endosperm (Figure 4C,D and Supplemental Figure 10). This is discussed further in response to your second point.

      Given these lines of evidence, a gain-of-function mutation in a methylation pathway, like RdDM, in the ros1-3 background is an unlikely explanation for increased hypermethylation compared to ros1-7. The use of three independent ros1 alleles for methylation profiling, all of which lead to the same conclusions, is a major strength of our study.

      1. It appears that the main focus of the manuscript, the existence of loci that are paternally demethylated by ROS1, is supported by a set of 274 DMRs. This is a small number relative to the size of the genome and raises suspicions of rare false positives. Even the most stringent p-values that DMR-finding tools report do not guarantee that the DMRs are actually reproducible in an independent experiment. Demonstrating overlap between these 274 DMRs and an independently defined set using a different WT control and different ros1 allele would suffice to remove this concern. It appears that authors already have the needed raw data with ros1-1 and ros1-7 alleles.

      Response: First, we should clarify that paternal demethylation by ROS1 is supported by more than the 274 DMRs. All ros1 CG hyperDMRs show an increase in paternal allele methylation in ros1 (Fig. 4B,D). The 274 DMRs are a distinct subset defined as having less methylation on the maternal allele than the paternal allele in ros1 endosperm and where there is no maternal allele hypomethylation in wild-type endosperm (refer to Fig. 5B).

      We agree with your sentiments about DMR-finders and we are cautious of relying exclusively on DMR calls when making conclusions. We verify the nature of identified DMRs using metaplots and weighted average comparisons throughout the paper, which we think increases confidence in the conclusions and goes beyond a simple DMR-calling approach.

      We argue that we have replicated the major conclusion of the paper, that ROS1 prevents paternal allele hypermethylation at target regions in the endosperm, in the following ways:

      1. In the dataset without allelic-specific methylation information (Figures 1-3), we found that both ros1-3 and ros1-7 CG hyperDMRs have a limited capacity for hypermethylation in the endosperm relative to leaf or sperm (Table 1, Fig 3, Supplemental Fig. 4). In the allele-specific dataset, ros1-3 CG hyperDMRs were revealed to have particularly low maternal mCG relative to paternal mCG in ros1 mutant endosperm (Fig 4A-B, Supplemental Fig. 10).
      2. We found that ros1-3 and ros1-1 hyperDMRs, which we identified using non-allelic data, are biased for paternal allele hypermethylation in the endosperm of F1 hybrids (Fig 4B,D). The replicability of the paternal bias in hypermethylation in both ros1-3 in the Col-0 ecotype and ros1-1 in the C24 ecotype is a critical result, and we have moved the ros1-1 hyperDMR plots from the supplement to main figure 4C-D in the revised version of the manuscript as a result of your comment.
      3. The 274 DMRs identified as “biallelically-demethylated, ROS1-dependent” are by definition replicated between reciprocal cross directions. (Note that we now refer to these regions as ROS1 paternal, DME maternal regions in the revision.) Regions in this category had to be called as maternally-hypomethylated in both ros1-1 x ros1-3 and ros1-3 x ros1-1 endosperm. These regions also had to not be identified as maternally-hypomethylated in both C24 x Col-0 and Col-0 x C24. We hope this is clarified for readers by Table 1, which we have included based on your suggestion in comment #3, as well as other clarifying edits we made in this section of the paper.comparisons between maternal and paternal methylation in endosperm, DMRs defined by comparison between mutants and wildtype, and more. These need clearer descriptions of which sets are being referred to throughout the main text and in figure legends. A table summarizing them might help (not in the supplement). Use of consistent and precisely defined terms would help. Stating the number of DMRs along with the name for each set would help a lot, even though this would make for some redundancy. (The number of DMRs in each set not only helps with interpretation but also act as a sort of ID). The reason I put this as a major concern is because the text and figures are difficult to understand, and it is currently hard to evaluate both the results and the authors' conclusions from those results.

      Response: Thank you for your feedback and suggestions. We have edited the main text so that only one descriptive name is used for each DMR type throughout the paper. We have also renamed regions for greater clarity. The previous “ROS1-independent, maternally demethylated regions” are now referred to as “DME maternal regions”. The previous “ROS1-_independent, biallelically-demethylated regions” are now referred to as “_ROS1 paternal, DME maternal regions”. These changes provide greater clarity and also emphasize the role of DME at regions that are paternally hypermethylated in ros1. We have added Table 1 to summarize the DMR classes of interest.

      MINOR COMMENTS

      1. The sRNA results in Figure 2B are difficult to interpret because they do not reveal anything about the number of TEs that have siRNAs overlapping them or their flanks. While the magnitude of some of the highest endosperm sRNA peaks is higher than the embryo peaks, that could be explained by a small number of TEs with large numbers of sRNAs. To make this result more interpretable, we also need some information about how many TEs have a significant number of sRNAs associated with them in endosperm and embryo in each region (e.g., middle, 5', 3', and flanks of TEs). What a "significant number of sRNAs" is would be up to the authors to decide based on the distribution of sRNA counts they observe for TEs. Perhaps the top quartile of TEs? Combined with the same analysis done in parallel with non-ROS1 target TEs, this would reveal whether there is any evidence for ROS1 counteracting sRNA-driven methylation spread from TEs.

      Response: Thank you for the suggestion. We now present these data and the data for individual TEs underlying the metaplots in Supplemental Figure 7. As suggested by the reviewer, ROS1 TEs do not have uniformly higher levels of sRNA in their flanks in the endosperm compared to the embryo. We have modified our interpretations accordingly.

      1. The statement "we are likely underestimating the true degree of differential methylation among genotypes" should be validated and partially quantified using a methylation metaplot like Figure 2A, but substitute DMRs for TEs. Related to that, Figure 1B needs an indicator of scale in bp.

      Response: We have now included a methylation metaplot over ros1-3 hyperDMRs and ros1-7 hyperDMRs as Supplemental Figure 3 These plots show that indeed there is additional hypermethylation in DMR-proximal regions. We have added a scale bar to Figure 1B and other browser examples in the paper.

      1. The statement "Over half of ROS1 target regions identified in the ros1-3 mutant endosperm were within 1 kb or intersecting a TE (Fig. 1D)" is hard to interpret without some kind of ROS1 non-target regions or whole-genome control comparison. How different are the numbers in Fig. 1D from a random expectation?

      Response: We have now included a control for random regions in Figure 1E. We define these as regions where there was sufficient methylation data coverage and a low enough methylation level in wild-type to detect hypermethylation if it existed.

      1. The sentence at line 262 is confusing. Is the comparison between dme mutant and ros1 mutant or between different types of regions? And it appears that the comparison value is missing in the "3-5% CG methylation gain..." e.g., "3-5% CG methylation vs 10-20%" or something like that.

      Response: This section has been re-written as we now focus on allele-specific dme endosperm methylation data for our comparisons.

      1. The dme mutant data in Figure 5C appear to be key to the model in Figure 7. The relative impact of the dme mutant in the two types of regions should be quantified.

      Response: Thank you for this comment. To further probe our model that DME prevents hypermethylation of the maternal allele at regions where ROS1 is preventing hypermethylation of the paternal allele, we turned to the allele-specific bisulfite-sequencing data published in Ibarra et al 2012 (see also response to reviewer #1). Using these data, we show that when the loss-of-function allele dme-2 is inherited maternally, ROS1 paternal, DME maternal regions (previous referred to as ROS1-_dependent, biallelically-demethylated regions) are CG hypermethylated on the maternal allele (Figure 6D). Thus, these results both replicate the observations made with the Hsieh et al 2009 data, and provide additional evidence that _DME prevents maternal allele hypermethylation at regions were ROS1 is preventing paternal allele hypermethylation. These results have replaced the Hsieh et al 2009 results in Figure 6, and we have moved the analysis of Hsieh et al 2009 data to Supplemental Figure 15.

      1. Looks like sRNA methods are missing.

      Response: Thank you for identifying this. We previously included the reference for the analyzed dataset we used and the method for plotting under an unclear section header. These methods are now in the section “Analysis of average methylation and 24-nt sRNA patterns for features of interest”, and we have added additional reference to the specific dataset we used.

      1. Supplemental Figure 1 is hard to interpret since it only list gene IDs, not gene names.

      Response: As suggested, we have added gene names to this figure.

      The last comments are suggestions for increasing the impact of this study:

      1. Figure 2A and 3B suggest that ROS1 target TEs show demethylation in their flanks but not in the TE themselves. This is an interesting result. If it is true, more DMRs would be expected in the ROS1 target flanks than in the ROS1 target TEs. Reporting how many ROS1 target TEs have DMRs in them and what proportion have DMRs in their flanking 1-Kb regions would answer this question. Given the significance of this result, it also deserves a bit more context: Is the magnitude of increased methylation flanking TEs in ros1 mutant endosperm different than in ros1 mutant leaves or other tissue? Does methylation in TE flanks behave the way in dme mutant endosperm?

      Response: We define “ROS1 target TEs” (now referred to more simply as ROS1 TEs) as TEs within 1kb or intersecting a ros1-3 hyperDMR. Consistent with your interpretation, 80% of the TEs in this category do not have a DMR overlapping them, instead they have a TE within 1kb. We now mention this in the text on line 150.

      The total level of DNA methylation at ROS1 TEs is lower in the endosperm than in leaf, as DNA methylation levels are overall lower in endosperm than in leaf. The magnitude of increased methylation flanking TEs in ros1 mutant endosperm is not different between the two tissues. This is observable in Supplemental Fig. 5 in the revised version of the paper, and we report this result in the revised text. In the revision we also present methylation profiles of DME TEs in WT and ros1 endosperm (Fig. 7B-D). DME TEs are hypomethylated in both the body and flanks in WT and ros1.

      1. The idea of biallelic demethylation has been theoretically suggested in maize to explain weak overlap between endosperm DMRs and imprinting (Gent et al 2022). If that were true in Arabidopsis, then ROS1 target, biallelicly demethylated loci would be less likely to have imprinted expression than maternally demethylated loci. This prediction could be tested using available data in Arabidopsis.

      Response: Indeed, as you hypothesize, there are no known imprinted genes (Pignatta et al 2014) associated with biallelically-demethylated, ROS1-dependent regions (now referred to as ROS1 paternal, DME maternal regions). Expectedly, there are imprinted genes associated with maternally-demethylated regions (now referred to as DME regions). 23 imprinted genes identified in the Pignatta et al 2014 study are within 1 kb or intersecting a DME region. This is discussed on lines 364-374.

      1. There is currently no evidence for biological significance of biallelicly demethylated loci. Knowing where they are in the genome might give some hints. A figure like Fig. 1D but specifically showing the biallelicly demethylated DMRs would be valuable.

      Response: This is now included in Figure 7A.

      1. It is hard to make the comparisons between genotypes and parental genomes in Figure 6 and know what they mean. Maybe a different way of displaying the data would help. Or maybe even a different labeling system could make it a little more accessible.

      Response: We have revised this figure (now Fig. 8) in the following ways, which we believe address your comments and clarify the main conclusions:

      Figure 8C is now a boxplot comparing methylation of the paternal allele of ROS1 paternal, DME maternal regions (previously referred to as biallelically-demethylated, ROS1-dependent regions) across endosperm ROS1 genotypes. This plot shows increased methylation of paternal alleles when the paternal parent is a ros1 mutant, regardless of whether the resultant F1 endosperm is homozygous or heterozygous for ros1 (columns 3, 4, 6).

      Figure 8B remains as a scatterplot, where we can observe significant correlation between individual ROS1 paternal, DME maternal regions in homozygous ros1 endosperm and heterozygous ros1/+ endosperm. Note that paternal allele methylation is higher in homozygous ros1 endosperm for most regions.

      Reviewer #2 (Significance):

      Demethylation of the maternal genome in endosperm has been the subject of much research because it can result in genomic imprinting of gene expression. The enzymes responsible, DNA glycosylases/lyases, also demethylate DNA in other cell types as well, where DNA methylation is not confined to one parental genome (biallelic or biparental as opposed to uniparental demethylation). To the best of my knowledge, the extent or even existence of biallelelic demethylation in endosperm has not been studied until now (except for a superficial look in a bioRxiv preprint, https://www.biorxiv.org/content/10.1101/2024.07.31.606038v1). Hemenway and Gehring have carried out a thoughtful and detailed analysis of the topic in Arabidopsis at least as far as it depends on the DNA glycosylase ROS1.

      A limitation is that the study design would miss biallelic demethylation by any of the other three DNA glycosylases in Arabidopsis. A second limitation is that there is no clear biological significance, just some conjecture about evolution. Nonetheless, given the novelty of the topic, biological significance may follow.

      The audience for biallelic DNA demethylation in Arabidopsis endosperm is certainly in the "specialized" category, but its relevance to the larger topic of gene regulation in endosperm will attract a larger audience.

      Response: With regard to the other demethylases, note that we also profiled methylation in ros1 dml2 dml3 triple mutant endosperm. We did not find evidence for many DMRs that were present in the triple mutant that were not present in the ros1 single mutant. We do not rule out a function for DML2 or DML3 in the endosperm, but this is not observed at the level of bulk endosperm.

      The reviewer is correct that we have shown a molecular phenotype (paternal allele hypermethylation) and not a developmental or morphological phenotype. A function that occurs in one parent but not the other is, to us, exciting. Our thoughts about how this finding might relate to imprinting are indeed speculative, but not wildly so.

      Reviewer #3 (Evidence, reproducibility and clarity):

      DNA demethylases play a key role in DNA methylation patterning during flowering plant reproduction. The demethylase DME, in particular, is critical for proper endosperm development. While the function of DME in endosperm development has been explored, the contributions of the other demethylases in the same family, ROS1, DML2 and DML3 in Arabidopsis, have not yet been investigated. In vegetative tissues, ROS1 prevents hypermethylation of some loci. In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses below.

      Response: Thank you for your thoughtful review of our paper. Your questions and suggestions have been invaluable in revising the work.

      I think making a few simple changes to streamline nomenclature would improve readability. For example, in the section starting on line 129, the same set of genomic features are called ROS1 target-proximal TEs, TEs that are near a ROS1 target region, and ROS1 target-associated TE regions. Also for example in line 254 "regions that are maternally-demethylated in wild-type endosperm, and are not dependent on ROS1 for proper demethylation" - are these the same as the "ROS1-independent, maternally-demethylated" regions in Fig. 5a? Given how complex these terms are, being consistent throughout the manuscript really helps the reader.

      Response: We edited the text and figures so that only one descriptive name is used for each DMR class or region throughout the paper. Thank you for this feedback; these edits have made the paper much clearer.

      Is there any notable effect of ros1 on gene expression in endosperm? Endosperm is a terminal tissue, so maintaining DNA methylation boundaries as ROS1 does in vegetative tissues seems less important. It begs the question of why ROS1 is doing this in endosperm, is it just because it's there, or is there an endosperm-specific function? Exploring effects on imprinting would be particularly interesting (does loss of ROS1 'create' imprinted loci at these newly asymmetrically methylated sites?) but probably beyond the scope of the present work.

      Response: We agree, the question of the functional consequence of ROS1 activity in the endosperm is something we are keen to address in future work. We performed RNA-seq on wild-type and ros1 3C and 6C endosperm nuclei, but these data were unfortunately not of high enough quality to include in the manuscript. We are in particular interested in this question you have proposed – if loss of ROS1 can ‘create’ imprinted loci. We are planning to address this both using a molecular, RNA-sequencing approach as well as an evolutionary comparative approach. This is an important and exciting future direction.

      Is DME expressed in sperm, or is expression of DME affected in ros1 sperm or endosperm? One other explanation for ros1 hypermethylation occurring primarily on the paternal allele is that, potentially, DME can substitute for ROS1 in the central cell where DME is already very active, but not in sperm cells. Related, how well expressed is ROS1 vs. DME in sperm cells?

      Response: This is an important series of questions, and something we are very interested in as well. Studies of Arabidopsis pollen have shown that both ROS1 and DME, while they prevent some hypermethylation in sperm, are more active in the vegetative nucleus of pollen than in sperm. ROS1 is expressed at a low level in the microspore and bicellular pollen and DME is expressed at a low level throughout pollen development. We have included Supplemental Fig. 17 with available expression data to make this point in the paper. Likely, any effects of loss of ROS1 or DME on sperm DNA methylation are inherited from precursor cells (Ibarra et al 2012, Calarco et al 2012, Khouider et al 2021). Your proposal that perhaps DME can sub in for ROS1 in the central cell but not in sperm is intriguing. Unfortunately there’s not enough data in the central cell to convincingly address this at this time.

      To investigate the relationship between DME and ROS1 in the male germline, we used the bisulfite-sequencing data generated in sperm cells in Khouider et al 2021. We calculated average DNA methylation levels in dme/+, ros1, dme/+;ros1, and wild-type Col-0 sperm cells at ROS1 paternal, DME maternal regions, shown in Supplemental Fig. 18A. We observed little increase in mCG methylation in dme/+ sperm relative to wild-type Col-0 sperm. This is consistent with your proposed model that DME is unable to demethylate these regions outside of the female germline. As expected, there is increased mCG in ROS1 paternal, DME maternal regions in ros1-3 mutant sperm relative to wild-type Col-0 sperm. DME maternal regions are highly methylated in wild-type Col-0 sperm.

      Fig 2b shows that ROS1 target-associated TEs are enriched for sRNAs in endosperm relative to embryo, whereas the reverse is true for non-ROS1-assoc TEs. Since TEs are not always well annotated and some may be missing from this analysis, what about trying the reverse analysis - are regions enriched for 24nt sRNAs in endosperm significantly hypermethylated in ros1 endosperm? All regions or only some?

      Response: We performed an analysis to address your inquiry and observed a low magnitude increase in DNA methylation in ros1 mutant endosperm at regions defined by Erdmann et al as more sRNA producing in the endosperm relative to the embryo (endosperm DSRs). Endosperm DSRs are generally lowly methylated in wild-type endosperm, as was observed originally in Erdmann et al 2017. Small increases in DNA methylation are observed at endosperm DSRs in all sequence contexts in ros1 endosperm. Overall, this is consistent with ROS1 targets being a subset of sRNA-producing regions in the endosperm. This analysis is now included in Supplemental Fig. 7C.

      What is the relationship between previously-defined DME targets and ROS1 targets identified in this paper? DME tends to target small euchromatic TE bodies, whereas Fig. 3 suggests that ROS1 helps prevent methylation spreading on the outer edges of the TEs, rather than in the TE body. Do all DME targets tend to be adjacent to or flanked by ROS1 target sites? Or are the TEs affected by DME (in body) and by ROS1 (at edges) largely nonoverlapping? Fig. 5a suggests that the ROS1-dependent, biallelically-demethylated sites are both DME and ROS1 targets, but how often do these really appear to overlap? More than by chance?

      Response: We have sought to address your comments through a series of analyses that we have included in Fig. 7 and Supplemental Fig. 16. We found that ROS1 paternal, DME maternal regions (formerly referred to as ROS1-dependent, biallelically-demethylated regions) and DME maternal regions (formerly referred to as ROS1-independent, maternally-demethylated regions) do not occupy the same genomic regions. However, we do observe some evidence for ROS1 activity in flanking regions of DME targets (Fig. 6A, Fig. 7B-D). To look at TEs specifically, as you suggest, we first identified TEs that were within 1kb or intersecting a DME maternal region. Based on our characterization of these regions, we assume these to be DME-targeted TEs. We then performed ends analysis to see if there was evidence of ROS1 activity at the ends of these TEs. Indeed, at a global level there is a slight hypermethylation of the paternal allele in a ros1 mutant at the end of these DME TEs (Fig. 7B). To better visualize how many DME TEs are showing ROS1 activity at their ends, we then plotted the difference between the median ros1-3 methylation and median Col-0 values in the non-allelic endosperm for each TE in a clustered heatmap (Fig. 7C). The parent-of-origin data does not have enough coverage for clustering in this way, so we used the non-allelic data. A small fraction of “DME TEs” gain methylation in the ros1 mutant endosperm relative to wild-type (Fig. 7C-D).

      Are the TEs whose boundaries are demethylated by ROS1 more likely to be expressed in vegetative or endosperm tissues than TEs not affected by loss of ROS1? Expressed TEs likely produce more sRNAs, which would increase RdDM in a way that might need to be more actively countered by ROS1 than transcriptionally silent or evolutionarily older TEs.

      Response: This is an interesting line of inquiry, although perhaps out of the scope of our present study. It has been shown that TEs demethylated by ROS1 are targeted by the RdDM pathway in Arabidopsis vegetative tissue (Tang et al 2016). Using data from Erdmann et al 2017, we looked at 24 nt sRNAs at ROS1-TEs in the endosperm and embryo (Supplemental Fig. 7). sRNA production at ROS1 TE-flanking regions is observed in both embryo and endosperm, but clearly not all ROS1 TEs produce 24 nt sRNA production in the seed. Future work comparing sRNA profiles in a ros1 mutant to those of wild-type could inform our understanding of TE spreading in a ros1 mutant, as would a comprehensive analysis of TE expression, again in both a ros1 mutant and in wild-type. It’s unclear to us if the endosperm would be the most informative or useful tissue to perform such analyses in.

      Fig6 - as noted in the text, one way to test whether demethylation by ROS1 occurs before or after fertilization is to provide functional ROS1 through only one parent via reciprocal WT x ros-1 crosses, so that the endosperm always has ROS1 but either sperm or central cell does not, and see if this can rescue the paternal hypermethylation. If ROS1 acts prior to fertilization, then paternal ROS1 will rescue ros1 hypermethylation, but maternal ROS1 won't. If after fertilization, then either maternally or paternally supplied ROS1 will rescue the hypermethylation phenotype (assuming both are well expressed). Thus, to distinguish the two, it is sufficient to test whether maternally supplied ROS1 in an otherwise mutant background can rescue the hypermethylation phenotype, which is what is shown in Fig. 6. However, I think it's also important to show that paternally supplied ROS1 can also rescue the hypermethylation phenotype, which is not currently shown. The plots showing no effect on maternal mCG aren't as informative, since maternal methylation levels are mostly unaffected by ros1 anyway. Instead of comparing pairs of samples in a scatterplot, it might be clearer to show paternal mCG across all four comparisons (WT x WT, WT x ros1, ros1 x WT, and ros1 x ros1) side by side in a heatmap, using clustering to group similar behavior.

      Response: We have revised this figure, now Fig. 8, in the following ways, which we believe addresses your comments and clarify the main conclusions (see same response to reviewer 2 for point 14):

      Figure 8B remains as a scatterplot, where we observe significant correlation between individual ROS1 paternal, DME maternal regions in homozygous ros1 endosperm and heterozygous ros1/+ endosperm. Note that paternal allele methylation is higher in homozygous ros1 endosperm for most regions.

      Figure 8C is now a boxplot comparing methylation of the paternal allele of ROS1 paternal, DME maternal regions (previously referred to as biallelically-demethylated, ROS1-dependent regions) across endosperm ROS1 genotypes. This plot shows increased methylation of paternal alleles when the paternal parent is a ros1 mutant, regardless of whether the resultant F1 endosperm is homozygous or heterozygous for ros1 (columns 3, 4, 6).

      I would also suggest including a little more information in the main plots rather than only in the figure legends. For example, in Fig 2 including a label of 'ROS1-associated TE' for the two plots on the left, and 'TEs not associated with ROS1' on the right. Or for example in Fig. 3a indicating 'ros1-3 CG hyperDMRs' somewhere on the plot. This would just help make the figures easier to read at a glance. Please add common gene names to figures, instead just the ATG gene ID (Fig. S1a).

      Response: Thank you for this feedback, we have made the suggested edits and additional edits of a similar nature.

      Minor:<br /> - Fig. 1E is referenced in the text before Fig. 1D<br /> - Fig. S4 and S5 - there are more lines in the plot than the 6 genotypes listed in the legend, do these represent different replicates? If so that should be noted in the legend<br /> - Fig. 1B has no color legend for the different methylation sequence contexts (looks like same as 1A,C but should indicate either in plot or legend)<br /> - Line 42 should be "correspond to TE ends"<br /> - Line 93 "Based on previous studies..." should have references to those studies<br /> - When referring to the protein (rather than the genetic locus or mutant), ROS1 should not be italicized - for example line 130<br /> - Line 150 "we conclude that the loss"<br /> - Should add a y=x line to scatterplots, like those in Fig. 6<br /> - In fig. 1d, it's hard to evaluate the significance of the overlap of ROS1 targets with genes and TEs. Comparing these numbers to a control where the ROS1 targets have been randomly shuffled would help.

      Response: We have made edits and additions where requested.

      Reviewer #3 (Significance):

      In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses.

      Response: Thank you for your comments. We have worked on streamlining the text and analysis.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      DNA demethylases play a key role in DNA methylation patterning during flowering plant reproduction. The demethylase DME, in particular, is critical for proper endosperm development. While the function of DME in endosperm development has been explored, the contributions of the other demethylases in the same family, ROS1, DML2 and DML3 in Arabidopsis, have not yet been investigated. In vegetative tissues, ROS1 prevents hypermethylation of some loci. In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses below.

      I think making a few simple changes to streamline nomenclature would improve readability. For example, in the section starting on line 129, the same set of genomic features are called ROS1 target-proximal TEs, TEs that are near a ROS1 target region, and ROS1 target-associated TE regions. Also for example in line 254 "regions that are maternally-demethylated in wild-type endosperm, and are not dependent on ROS1 for proper demethylation" - are these the same as the "ROS1-independent, maternally-demethylated" regions in Fig. 5a? Given how complex these terms are, being consistent throughout the manuscript really helps the reader.

      Is there any notable effect of ros1 on gene expression in endosperm? Endosperm is a terminal tissue, so maintaining DNA methylation boundaries as ROS1 does in vegetative tissues seems less important. It begs the question of why ROS1 is doing this in endosperm, is it just because it's there, or is there an endosperm-specific function? Exploring effects on imprinting would be particularly interesting (does loss of ROS1 'create' imprinted loci at these newly asymmetrically methylated sites?) but probably beyond the scope of the present work.

      Is DME expressed in sperm, or is expression of DME affected in ros1 sperm or endosperm? One other explanation for ros1 hypermethylation occurring primarily on the paternal allele is that, potentially, DME can substitute for ROS1 in the central cell where DME is already very active, but not in sperm cells. Related, how well expressed is ROS1 vs. DME in sperm cells?

      Fig 2b shows that ROS1 target-associated TEs are enriched for sRNAs in endosperm relative to embryo, whereas the reverse is true for non-ROS1-assoc TEs. Since TEs are not always well annotated and some may be missing from this analysis, what about trying the reverse analysis - are regions enriched for 24nt sRNAs in endosperm significantly hypermethylated in ros1 endosperm? All regions or only some?

      What is the relationship between previously-defined DME targets and ROS1 targets identified in this paper? DME tends to target small euchromatic TE bodies, whereas Fig. 3 suggests that ROS1 helps prevent methylation spreading on the outer edges of the TEs, rather than in the TE body. Do all DME targets tend to be adjacent to or flanked by ROS1 target sites? Or are the TEs affected by DME (in body) and by ROS1 (at edges) largely nonoverlapping? Fig. 5a suggests that the ROS1-dependent, biallelically-demethylated sites are both DME and ROS1 targets, but how often do these really appear to overlap? More than by chance?

      Are the TEs whose boundaries are demethylated by ROS1 more likely to be expressed in vegetative or endosperm tissues than TEs not affected by loss of ROS1? Expressed TEs likely produce more sRNAs, which would increase RdDM in a way that might need to be more actively countered by ROS1 than transcriptionally silent or evolutionarily older TEs.

      Fig6 - as noted in the text, one way to test whether demethylation by ROS1 occurs before or after fertilization is to provide functional ROS1 through only one parent via reciprocal WT x ros-1 crosses, so that the endosperm always has ROS1 but either sperm or central cell does not, and see if this can rescue the paternal hypermethylation. If ROS1 acts prior to fertilization, then paternal ROS1 will rescue ros1 hypermethylation, but maternal ROS1 won't. If after fertilization, then either maternally or paternally supplied ROS1 will rescue the hypermethylation phenotype (assuming both are well expressed). Thus, to distinguish the two, it is sufficient to test whether maternally supplied ROS1 in an otherwise mutant background can rescue the hypermethylation phenotype, which is what is shown in Fig. 6. However, I think it's also important to show that paternally supplied ROS1 can also rescue the hypermethylation phenotype, which is not currently shown. The plots showing no effect on maternal mCG aren't as informative, since maternal methylation levels are mostly unaffected by ros1 anyway. Instead of comparing pairs of samples in a scatterplot, it might be clearer to show paternal mCG across all four comparisons (WT x WT, WT x ros1, ros1 x WT, and ros1 x ros1) side by side in a heatmap, using clustering to group similar behavior.

      I would also suggest including a little more information in the main plots rather than only in the figure legends. For example, in Fig 2 including a label of 'ROS1-associated TE' for the two plots on the left, and 'TEs not associated with ROS1' on the right. Or for example in Fig. 3a indicating 'ros1-3 CG hyperDMRs' somewhere on the plot. This would just help make the figures easier to read at a glance. Please add common gene names to figures, instead just the ATG gene ID (Fig. S1a).

      Minor:

      • Fig. 1E is referenced in the text before Fig. 1D
      • Fig. S4 and S5 - there are more lines in the plot than the 6 genotypes listed in the legend, do these represent different replicates? If so that should be noted in the legend
      • Fig. 1B has no color legend for the different methylation sequence contexts (looks like same as 1A,C but should indicate either in plot or legend)
      • Line 42 should be "correspond to TE ends"
      • Line 93 "Based on previous studies..." should have references to those studies
      • When referring to the protein (rather than the genetic locus or mutant), ROS1 should not be italicized - for example line 130
      • Line 150 "we conclude that the loss"
      • Should add a y=x line to scatterplots, like those in Fig. 6
      • In fig. 1d, it's hard to evaluate the significance of the overlap of ROS1 targets with genes and TEs. Comparing these numbers to a control where the ROS1 targets have been randomly shuffled would help.

      Significance

      In this work, Hemenway and Gehring explore whether ROS1, DML2 and DML3 also affect DNA methylation patterns in endosperm. Using EM-seq of sorted endosperm nuclei, they show that loss of ROS1 indeed causes hypermethylation of a number of loci, particularly the flanks of methylated transposons, while loss of DML2 and DML3 has minimal additional effect. By obtaining allele-specific EM-seq data through crosses of Col and C24, the authors show that ros1 endosperm hypermethylation is mostly restricted to the paternal allele. The authors propose that at some sites, ROS1 helps bring down paternal methylation levels to match maternal methylation levels, which are typically reduced in endosperm due to DME activity in the female gametophyte prior to fertilization. In a ros1 mutant with paternal hypermethylation, these sites become differentially methylated on the maternal and paternal alleles, resembling imprinted loci. This work convincingly establishes a function for ROS1 in DNA methylation patterning in endosperm. However, I struggled with the clarity of the writing and reasoning in a few places, and would suggest clarification of a few points and additional analyses

    1. likelihood ratio

      Likelihood Ratio (LR)

      Is a statistical measure used in evidence-based medicine to assess how much a particular test result (in this case, a physical finding) changes the probability of a disease being present.

      Here's a breakdown of the key points:

      • The "Ruler" as a Diagnostic Aid: The "ruler" is a visualization tool designed for quick reference. It's divided into three sections based on the Likelihood Ratio (LR):

        • Right Side (LR $\geq$ 3.0): Findings in this section significantly increase the probability of heart failure. The higher the LR, the more powerful the finding is for diagnosing the condition.
        • Left Side (LR $\leq$ 0.3): Findings in this section significantly decrease the probability of heart failure. A very low LR means that the absence of a finding makes the disease much less likely.
        • Middle Section (LR between 0.3 and 3.0): Findings with LRs in this range are considered "diagnostically unhelpful." They don't significantly change the probability of the disease, so they are not included on the ruler. This is an important concept: just because a finding is present or absent doesn't mean it's clinically useful for diagnosis.
      • Examples from the Text:

        • Presence of an S3 gallop (LR = 3.9): Since 3.9 is greater than 3.0, this finding is on the right side of the ruler. If a clinician hears an S3 gallop, the probability that the patient has heart failure significantly increases.
        • Presence of a displaced apical impulse (LR = 5.8): With an LR of 5.8, this is an even stronger finding than the S3 gallop. It is also on the right side of the ruler and powerfully increases the likelihood of heart failure.
      • The Importance of "Absence" of a Finding: The text highlights a crucial distinction: the LR for the presence of a finding is often different from the LR for its absence.

        • The absence of an S3 gallop and the absence of a displaced apical impulse are mentioned, and their LRs fall between 0.3 and 3.0. This means that while finding them is very helpful for a diagnosis, not finding them is not particularly useful for ruling the disease out. A patient could still have heart failure even if these specific signs are absent.

      In summary, the "ruler" is a practical, evidence-based tool that helps clinicians efficiently apply statistical probabilities to their physical exam findings. By focusing only on the findings with the highest and lowest likelihood ratios, it allows them to quickly and confidently identify which signs are most useful for diagnosing or ruling out heart failure, and which are simply not informative.

    1. This isn't just happening in Canada, but all over the world. Some people don't understand that if you post something on the internet, even if you delete the post it will always remain on the internet and someone can found it. That is why it's important to be careful of that you post.

    2. This isn't just happening in Canada, but all over the world. Some people don't understand that if you post something on the internet, even if you delete the post it will always remain on the internet and someone can found it. That is why it's important to be careful of that you post.

    3. This isn't just happening in Canada, but all over the world. Some people don't understand that if you post something on the internet, even if you delete the post it will always remain on the internet and someone can found it. That is why it's important to be careful of that you post.

    1. highlighting the challenge of this dataset

      It's also notable that Phyla just barely outperforms Hamming Distance for the TreeBase dataset but substantially outperforms it with TreeFam. Might this be related to the inherent differences between reconstructing species vs. gene family trees (species trees are often estimated over sets of gene trees)? It could be worthwhile to consider species and gene tree reconstruction as different classes of tasks, one likely much harder than the other.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public review):

      Summary:

      The major result in the manuscript is the observation of the higher order structures in a cryoET reconstruction that could be used for understanding the assembly of toroid structures. The cross-linking ability of ZapD dimers result in bending of FtsZ filaments to a constant curvature. Many such short filaments are stitched together to form a toroid like structure. The geometry of assembly of filaments - whether they form straight bundles or toroid like structures - depends on the relative concentrations of FtsZ and ZapD.

      Strengths:

      In addition to a clear picture of the FtsZ assembly into ring-like structures, the authors have carried out basic biochemistry and biophysical techniques to assay the GTPase activity, the kinetics of assembly, and the ZapD to FtsZ ratio.

      Weaknesses:

      The discussion does not provide an overall perspective that correlates the cryoET structural organisation of filaments with the biophysical data. The current version has improved in terms of addressing this weakness and clearly states the lacuna in the model proposed based on the technical limitations.

      Future scope of work includes the molecular basis of curvature generation and how molecular features of FtsZ and ZapD affect the membrane binding of the higher order assembly.

      Reviewer #3 (Public review):

      Summary:

      Previous studies have analyzed the binding of ZapD to FtsZ and provided images of negatively stained toroids and straight bundles, where FtsZ filaments are presumably crosslinked by ZapD dimers. Toroids without ZapD have also been previously formed by treating FtsZ with crowding agents. The present study is the first to apply cryoEM tomography, which can resolve the structure of the toroids in 3D. This shows a complex mixture of filaments and sheets irregularly stacked in the Z direction and spaced radially. The most important interpretation would be to distinguish FtsZ filaments from ZapD crosslinks, This is less convincing. The authors seem aware of the ambiguity: "However, we were unable to obtain detailed structural information about the ZapD connectors due to the heterogeneity and density of the toroidal structures, which showed significant variability in the conformations of the connections between the filaments in all directions." Therefore, the reader may assume that the crosslinks identified and colored red are only suggestions, and look for their own structural interpretations. But readers should also note some inconsistencies in stoichiometry and crosslinking arrangements that are detailed under "weaknesses."

      Strengths.

      This is the first cryoEM tomography to image toroids and straight bundles of FtsZ filaments bound to ZapD. A strength is the resolution, which. at least for the straight bundles. is sufficient to resolve the ~4.5 nm spacing of ZapD dimers attached to and projecting subunits of an FtsZ filament. Another strength is the pelleting assay to determine the stoichiometry of ZapD:FtsZ (although this also leads to weaknesses of interpretation).

      Weaknesses

      The stoichiometry presents some problems. Fig. S5 uses pelleting to convincingly establish the stoichiometry of ZapD:FtsZ. Although ZapD is a dimer, the concentration of ZapD is always expressed as that of its subunit monomers. Fig. S5 shows the stoichiometry of ZapD:FtsZ to be 1:1 or 2:1 at equimolar or high concentrations of ZapD. Thus at equimolar ZapD, each ZapD dimer should bridge two FtsZ's, likely forming crosslinks between filaments. At high ZapD, each FtsZ should have it's own ZapD dimer. However, this seems contradicted by later statements in Discussion and Results. (1) "At lower concentrations of ZapD, .. toroids are the most prominent structures, containing one ZapD dimer for every four to six FtsZ molecules." Shouldn't it be one ZapD dimer for every two FtsZ? (2) "at the high ZapD concentration...a ZapD dimer binds two FtsZ molecules connecting two filaments." Doesn't Fig. S5 show that each FtsZ subunit has its own ZapD dimer? And wouldn't this saturate the CTD sites with dimers and thus minimize crosslinking?

      We thank the reviewer for these insightful comments. The affinity of ZapD for FtsZ is relatively low and a higher concentration of ZapD is required in solution to effectively saturate the binding sites of all FtsZ molecules forming macrostructures. It is important to clarify that the concentrations mentioned in the text refer to the amounts and ratios of protein added to the total volume of the sample, rather than the proteins actively interacting and forming bundles or macrostructures.

      To differentiate, two aspects can be considered: the ratio of added protein (as mentioned in the text) and the fraction of proteins that contribute to the formation of the macrostructures. Under polymerization conditions, FtsZ-GTP recruits additional monomers to form polymers. Therefore, more FtsZ than ZapD would be involved in forming filaments and bundles. Our results support this hypothesis and show that a higher amount of ZapD is required in the sample to pellet with FtsZ bundles.

      We propose that starting with the same initial concentration of FtsZ and ZapD in solution, only a small fraction of ZapD will bind to the structures, favoring the formation of toroidal structures despite the initial 1:1 ratio of proteins added to the sample. When considering a higher FtsZ:ZapD ratio (1:6), the increased amount of ZapD in solution would facilitate the saturation of all FtsZ binding sites, consistent with the observation of straight bundles. Analytical sedimentation velocity data further supported this finding, indicating a binding ratio of approximately 0.3-0.4, suggesting that one ZapD dimer binds for every 4-6 FtsZ monomers. The binding ratio indicates that two FtsZ monomers will bind to a single dimer of ZapD, but this only occurs when there is a significant excess of ZapD over FtsZ in the solution mixture. 

      These findings align qualitatively with the relative intensities of the electrophoretic bands observed for FtsZ and ZapD in the pelleting assay with different FtsZ-ZapD mixtures, as shown in Suppl. Fig. 5 as % of FtsZ in the fractions. Without prior staining calibration of the gels, there is no simple quantitative relationship between gel band intensities after Coomassie staining and the amount of protein in a band (Darawshe et al. 1993 Anal Biochem - DOI: 10.1006/abio.1993.1581). This last point precludes a quantitative comparison between pelleting/SDS-PAGE data and analytical sedimentation measurements. For this reason, we have decided to present pelleting results as % of FtsZ in supernatant and pellet to avoid overestimations. 

      A major weakness is the interpretation of the cryoEM tomograms, specifically distinguishing ZapD from FtsZ. The distinction of crosslinks seems based primarily on structure: long continuous filaments (which often appear as sheets) are FtsZ, and small masses between filaments are ZapD. The density of crosslinks seems to vary substantially over different parts of the figures. More important, the density of ZapD's identified and colored red seem much lower than the stoichiometry detailed above. Since the mass of the ZapD monomer is half that of FtsZ, the 1:1 stoichiometry in toroids means that 1/3 of the mass should be ZapD and 2/3 FtsZ. However, the connections identified as ZapD seem much fewer than the expected 1/3 of the mass. The authors conclude that connections run horizontally, diagonally and vertically, which implies no regularity. This seems likely, but as I would suggest that readers need to consider for themselves what they would identify as a crosslink.

      The amount of ZapD in the toroids will be significantly less than one third. Although the theoretical addition of protein to the samples is at a 1:1 ratio, the actual amount of protein in the macrostructures containing ZapD is much lower, as shown by sedimentation velocity pelleting assays.

      In contrast to the toroids formed at equimolar FtsZ and ZapD, thin bundles of straight filaments are assembled in excess ZapD. Here the stoichiometry is 2:1, which would mean that every FtsZ should have a bound ZapD DIMER. The segmentation of a single filament in Fig. 5e seems to agree with this, showing an FtsZ filament with spikes emanating like a picket fence, with a 4.5 nm periodicity. This is consistent with each spike being a ZapD dimer, and every FtsZ subunit along the filament having a bound ZapD dimer. But if each FtsZ has its own dimer, this would seem to eliminate crosslinking. The interpretative diagram in Fig. 6, far right, which shows almost all ZapD dimers bridging two FtsZs on opposite filaments, would be inconsistent with this 2:1 stoichiometry.

      Assessing the precise stoichiometry of FtsZ and ZapD within the macrostructures is challenging. We interpret the spikes as ZapD dimers bridging two FtsZ filaments, implying a theoretical 1:1 stoichiometry in the straight bundle. However, ZapD may be enriched in certain areas, indicating that a single FtsZ monomer is binding to one side of the dimer. In contrast, the other side remains available for additional connections, resulting in a potential 2:1 stoichiometry. A combination of both scenarios is likely, although our resolution does not allow further characterization. Considering these complexities, we assume these connections represent a dimer of ZapD binding to two FtsZ monomers.

      Figure 6 shows a simplified scheme illustrating how the bundles could be assembled based on the Cryo-ET data. We acknowledge the limitations of this diagram; its purpose is to depict the mesh formed by the stabilization of ZapD. We have not included interactions that do not lead to filament crosslinking, such as dimers binding to only one FtsZ filament. This focus enhances the interpretation of the scheme and the FtsZ-ZapD interaction. A sentence has been added to the caption to highlight the possibility of other interactions not considered in the scheme.

      In the original review I suggested a control that might help identify the structures of ZapD in the toroids. Popp et al (Biopolymers 2009) generated FtsZ toroids that were identical in size and shape to those here, but lacking ZapD. These toroids of pure FtsZ were generated by adding 8% polyvinyl chloride, a crowding agent. The filamentous substructure of these toroids in negative stain seemed very similar to that of the ZapD toroids here. CryoET of these toroids lacking ZapD might have been helpful in confirming the identification of ZapD crosslinks in the present toroids. However, the authors declined to explore this control.

      The mechanisms by which methylcellulose (MC) promotes the assembly of FtsZ macrostructures reported by Popp et al. involve more than simple excluded volume effects, as the low concentration of MC (less than 1 mg/ml) falls below the typical crowding regime. The latter suggests the existence of poorly characterized additional interactions between MC and FtsZ. These complexities preclude the use of FtsZ polymers formed in the presence of MC as a true control for the FtsZ toroidal structures reported here.

      Finally, it should be noted that the CTD binding sites for ZapD should be on the outside of curved filaments, the side facing the membrane in the cell. All bound ZapD should project radially outward, and if it contacted the back side of the next filament, it should not bind (because the CTD is on the front side). The diagram second to right in Fig. 6 seems to incorporate this abortive contact.

      The role of the flexible linker and its biological implications are still under debate in the field. The flexible linker allows ZapD-driven connections to be made in different directions. While these implications are not the primary focus of our manuscript, the flexible linker could allow connections between filaments in different orientations.

      Reviewer #1 (Recommendations for the authors):

      Most of the concerns which I had raised in the earlier version have been taken care of, as detailed in the response.

      A few minor points, mostly related to re-phrasing are listed below:

      Page 2: line 21: The use of the term 'C-terminal domain' for the C-terminal unstructured region of FtsZ is confusing. The term C-terminal domain or CTD for FtsZ is commonly used to describe part of the globular domain, while C-terminal tail or CCTP will be a more apt usage for all the instances in this manuscript.

      We refer to the C-terminal domain as the carboxy-terminal region of the protein. This domain includes the C-terminal linker (CTL), which varies in length between species, followed by a conserved 11-residue sequence (CTC) and shorter, variable C-terminal sequences (CTV). We used the term "C-terminal domain" primarily to improve the readability of the manuscript, but we appreciate the reviewer's feedback. We have now adopted the term "CCTP" instead of "C-terminal domain" to improve the clarity of our manuscript.

      On a related note, the schematic in Fig 1 shows the interaction with CCTP rather than the C-terminal domain of the globular FtsZ. Please provide an explanation.

      We refer to the unstructured C-terminal domain of FtsZ as the C-terminal tail. To avoid confusion, we have introduced the term CCTP in this manuscript.

      Supple Fig 2: "The FCS analysis demonstrated an increasing diffusion time of ZapD along with the FtsZ concentration as result of higher proportion of ZapD bound to FtsZ.

      The increased diffusion time need not be interpreted as increased ZapD bound, it could also mean that FtsZ could polymerise in the presence of increasing ZapD, was this possibility ruled out? Including a comment on this aspect will be useful.

      In these experiments, we monitored fluorescently labeled ZapD. Due to their interaction, we found that its diffusion time increased at high FtsZ concentrations. The data presented in Supplementary Figure 2 shows ZapD in the presence of FtsZ-GDP (i.e. under non-polymerization conditions).

      Was it possible to get a molecular weight estimate based on the diffusion time?

      It is possible to estimate hydrodynamic volumes using the Stokes-Einstein equation if the diffusion coefficient of the diffusing particles is known, assuming that the particles are small and spherical. A molecular weight can then be estimated using a standard density of 1.35 g/cm3 (Fisher et all. Protein science 2009 DOI: 10.1110/ps.04688204). This estimate is heavily dependent on the shape of the diffusing particle, as we assume that our protein of interest here is far from a spherical shape due to the interaction through the flexible linker, the hydrodynamic volumes are overestimated. This overestimation then leads to a further overestimation of the molecular weight. In addition, for a more accurate estimation of the sizes and thus molecular weights for proteins, a modified model of the Stokes-Einstein equation is required (Tyn and Gusek Biotechnology and Bioengineering DOI: 10/1002/bit.260350402), where additional information about the shape of the diffusing particle is estimated by measuring the radius of gyration of the particle. These calculations are complex and beyond the scope of our manuscript.

      Supple Fig 4:

      Does FtsZ GTPase activity (without ZapD) also vary with KCl concentrations? It will be useful to comment on this in Supplementary Figure 4.

      Yes, it has been previously reported that moderate concentration of KCl is optimal for FtsZ GTPase activity. We added a comment to the caption.

      Page 6, line 42: short filament segments arranged nearly 'parallel' to each other Since FtsZ filaments are polar, it is better to rephrase as 'parallel or antiparallel'.

      Corrected.

      Page 7, line 41: cross linking of short 'FtsZ' filaments and not ZapD?

      It was a typo. Corrected

      Page 8: delete 'from above' in the title?

      Corrected

      The use of the phrases such as 'cross linking from the top'; 'binds to FtsZ from above' is vague. (Figure 5b legend; discussion page 10, line 18; page 8, line 26; page 12, line 27). Similarly labelling on a schematic figure on the use of vertical, diagonal/lateral will be useful for the readers.

      We thank the reviewer for the suggestions to improve the understanding of our data. We have simplified them by renaming these interactions as vertical.

      Page 13, lines 6 -10

      Rather than an orientation of top or from the side, just the presence of multiple crosslinks along coaxial filaments suffices for a straight bundle. The average spacing will be more uniform in such a straight bundle compared to a toroid where there might be regions without ZapD. I do not find the data on an upward orientation convincing. ZapD binding need not be above to have the C-terminal ends of FtsZ pointing towards the membrane. On the other hand, having ZapD bind above is likely to occlude membrane binding of FtsZ?

      The flexibility of the FtsZ linker suggests that ZapD can bind filaments oriented in different directions. In a cellular environment, FtsZ molecules interact with other division proteins that compete with ZapD for binding sites. This competition could prevent the membrane from occluding and instead create binding sites between the filaments, stabilizing them.

      Page 11, lines 32 - 34: Please rephrase the sentence, with focus on the main point to be conveyed. Do the authors want to say that the 'Same molecule contributes to variability in spacing based on the number of connections formed.'

      Thank you for your comment. We have rephrased the sentence for clarity.

      Page 11: paragraphs 1,2, and 3 appears to convey similar, related ideas and are redundant. Could these be shortened further into one paragraph highlighting how the ratio leads to differences in higher order FtsZ organisation?

      These paragraphs discuss different ideas, and it is better to keep them separate.

      In the response to reviewers, page 19, point 5 (iii), it is given that 5000 FtsZ molecules correspond to 2/3rd of the total, while in the manuscript text, it is given as one-third. Please correct the response text/manuscript text accordingly. The numbers in the cited reference appears to suggest 1/3rd.

      Yes, it was 1/3rd. Thanks for pointing that out. 

      Fig 1b. Y-axis: Absorbance spelling has a typo.

      Page 14, line 11: Healthcare ('h' missing)

      Page 14, line 15: HCl, KCl (L should be in small letter)

      Page15, line 18: 43 - 48K rpm (not Krpm)

      Supple Fig 1 legend: line 5: 's' missing for species

      Corrected.

    1. A PSS is “a market proposition that extends the traditional functionality of a product by incorporating additional services. Here, the emphasis is on the ‘sale of use’ rather than the ‘sale of product’“[17] (p. 1543)

      This part makes me think about stuff like bike rentals or apps where you borrow clothes instead of owning. It’s cool for saving space and money, but I’m not sure if people really like this idea for long time use. I’m okay using shared things once in a while, but for something like a laptop or cooking items I use daily, I’d rather own them. It just feels better when it’s mine and I don’t have to worry about how others used it before.

  8. Jul 2025
    1. Author response:

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

      Reviewer #1 (Recommendations for the authors):

      Thank you for your thorough review of our manuscript and your valuable suggestions. Here are our responses to each point you raised:

      (1) Novelty: Exploring the feasibility of extending the risk-scoring model to diverse cancer types could emphasize the broader impact of the research.

      Thank you so much for your thoughtful and insightful feedback. Your suggestion to explore extending the risk-scoring model to diverse cancer types is truly valuable and demonstrates your broad vision in this field. We deeply appreciate your interest in our research and the effort you put into providing such constructive input.

      After careful consideration, we have decided to focus our current study on the specific cancer type(s) we initially set out to explore. This decision was made to ensure that we can thoroughly address the research questions at hand, given our current resources, time constraints, and the complexity of the topic. By maintaining this focused approach, we aim to achieve more in-depth and reliable results that can contribute meaningfully to the understanding of this particular area.

      However, we fully recognize the potential significance of your proposed direction and firmly believe that it could be an excellent avenue for future research. We will definitely keep your suggestion in mind and may explore it in subsequent studies as our research progresses and evolves.

      (2) Improvement in Figure Presentation: The inconsistency in font formatting across figures, particularly in Figure 2 (A-D, E, F-H, I), Figure 3 (A-C, D-J, H, K), and the distinct style change in Figure 5, raises concerns about the professionalism of the visual presentation. It is recommended to standardize font sizes and styles for a more cohesive and visually appealing layout. This ensures that readers can easily follow and comprehend the graphical data presented in the article.

      The text in the picture has been revised as requested.

      (3) Enhancing Reliability of Immune Cell Infiltration Data: Address the potential limitations associated with relying solely on RNASeq data for immune cell infiltration analysis between ICD and ICD high groups in Figure 2. It is advisable to discuss the inherent challenges and potential biases in this methodology. To strengthen the evidence, consider incorporating bladder cancer single-cell sequencing data, which could provide a more comprehensive and reliable understanding of immune cell dynamics within the tumor microenvironment.

      Thank you very much for your meticulous review and the highly constructive suggestions. Your insight regarding the limitations of relying on RNASeq data for immune cell infiltration analysis and the proposal to incorporate bladder cancer single-cell sequencing data truly reflect your profound understanding of the field. We deeply appreciate your efforts in guiding our research and the valuable perspectives you've offered.

      After careful deliberation, given our current research scope, timeline, and available resources, we've decided to focus on further discussing and addressing the challenges and biases inherent in RNASeq-based immune cell infiltration analysis. By delving deeper into the methodological limitations and conducting more in-depth statistical validations, we aim to provide a comprehensive and reliable interpretation of the data within our study framework. This focused approach allows us to maintain the integrity of our original research design and deliver robust findings on the relationship between immune cell infiltration and ICD in the current context.

      However, we fully acknowledge the significant value of your proposed single-cell sequencing approach. It is indeed a powerful method that could offer more detailed insights into immune cell dynamics, and we believe it holds great promise for future research in this area. We will keep your suggestion in mind as an important direction for potential future studies, especially when we plan to expand and deepen our exploration of the tumor microenvironment.

      (4) Clarity in Data Sources and Interpretation of Figure 5: In the results section, provide a detailed and transparent explanation of the sources of data used in Figure 5. This includes specifying the databases or platforms from which the chemotherapy, targeted therapy, and immunotherapy data were obtained. Additionally, elucidate the rationale behind the chosen data sources and how they contribute to the overall interpretation of the study's findings. And, strangely, these immune-related genes are associated with cancer sensitivities to different targeted therapies.

      Thank you very much for your detailed and valuable feedback on Figure 5. We sincerely appreciate your careful review and insightful suggestions, which have provided us with important directions for improvement.

      Regarding the data sources in Figure 5, we used the pRRophetic algorithm to conduct a drug sensitivity analysis on the TCGA database. The reason for choosing these data sources is multi - faceted. Firstly, these databases and platforms are well - established and widely recognized in the field. They have strict data collection and verification processes, ensuring the accuracy and reliability of the data. For example, TCGA has a large - scale, long - term - accumulated chemotherapy case database, which can comprehensively reflect the clinical application and treatment effects of various chemotherapeutic drugs.

      Secondly, these data sources cover a wide range of cancer types and patient information, which can meet the requirements of our study's diverse sample size and variety. This comprehensiveness enables us to conduct a more in - depth and representative analysis of the relationships between different therapies and immune - related genes.

      In terms of the overall interpretation of the study's findings, the use of these data sources provides a solid foundation. The accurate chemotherapy, targeted therapy, and immunotherapy data help us clearly demonstrate the associations between immune - related genes and cancer sensitivities to different treatments. This allows us to draw more reliable conclusions and provides a scientific basis for understanding the complex mechanisms of cancer treatment from the perspective of immune - gene - therapy interactions.

      As for the unexpected association between immune - related genes and cancer sensitivities to different targeted therapies, this is indeed a fascinating discovery. In our analysis, we hypothesized that immune - related genes may affect the tumor microenvironment, thereby influencing the response of cancer cells to targeted therapies. Although this finding is currently beyond our initial expectations, it has opened up a new research direction for us. We will further explore and verify the underlying mechanisms in future research.

      Once again, thank you for your guidance. We will make corresponding revisions and improvements according to your suggestions to make our research more rigorous and complete.

      (5) Legends and Methods: Address the brevity and lack of crucial details in the figure legends and methods section. Expand the figure legends to include essential information, such as the number of samples represented in each figure. In the methods section, provide comprehensive details, including the release dates of databases used, versions of coding packages, and any other pertinent information that is crucial for the reproducibility and reliability of the study.

      We would like to express our sincere gratitude for your valuable feedback on the figure legends and methods section of our study. We highly appreciate your sharp observation of the issues regarding the brevity and lack of key details, which are crucial for further improving our research.

      We have supplemented the methods section with data including the number of samples, the release dates of the databases used, and the versions of the coding packages, etc. For TCGA samples: 421 tumor samples and 19 normal samples.Database release date: March 29, 2022, v36 versions.Coding package version: R version 4.1.1.We will immediately proceed to supplement these key details, making the research process and methods transparent. This will allow other researchers to reproduce our study more accurately and enhance the persuasiveness of our research conclusions.

      (6) Evidence Supporting Immunotherapy Response Rates: The importance of providing a robust foundation for the conclusion regarding lower immunotherapy response rates. Strengthen this section by offering a more detailed description of sample parameters, specifying patient demographics, and presenting any statistical measures that validate the observed trends in Figure 5Q-T. More survival data are required to conclude. Avoid overinterpretation of the results and emphasize the need for further investigation to solidify this aspect of the study.

      Thank you very much for your professional and meticulous feedback on the content related to immunotherapy response rates in our study! Your suggestions, such as providing a solid foundation for the conclusions and supplementing key information, are of great value in enhancing the quality of our research, and we sincerely appreciate them.

      The data in Figures 5Q to T are from the TCGA database, which has already been provided. The statistical measure used for Figures 5Q to T is the P-value, which has been marked in the figures. The survival data have been provided in Figure 3D.

      Reviewer #2 (Recommendations for the authors):

      Thank you for your thorough review of our manuscript and your valuable suggestions. Here are our responses to each point you raised:

      (1) There is no information on the samples studied. Are all TCGA bladder cancer samples studied? Are these samples all treatment naïve? Were any excluded? Even simply, how many samples were studied?

      Thank you so much for pointing out the lack of sample - related information. Your attention to these details has been extremely helpful in identifying areas for improvement in our study.

      All the samples in our study were sourced from the TCGA (The Cancer Genome Atlas) and TCIA (The Cancer Immunome Atlas) databases. It should be noted that the patient data in the TCIA database are originally from the TCGA database. Regarding whether the patients received prior treatment, this information was not specifically mentioned in our current report. Instead, we mainly relied on the scores of the prediction model for evaluation. Since all samples were obtained from publicly available databases, we understand the importance of clarifying their origin and characteristics.

      We sincerely apologize for the omission of the sample size and other relevant details. We will promptly supplement this crucial information in the revised version, including a detailed description of the sample sources and any relevant characteristics. This will ensure greater transparency and help readers better understand the basis of our research.

      For TCGA samples: 421 tumor samples and 19 normal samples.Database release date: March 29, 2022, v36 versions.Coding package version: R version 4.1.1.

      (2) What clustering method was used to divide patients into ICD high/low? The authors selected two clusters from their "unsupervised" clustering of samples with respect to the 34 gene signatures. A Delta area curve showing the relative change in area under the cumulative distribution function (CDF) for k clusters is omitted, but looking at the heatmap one could argue there are more than k=2 groups in that data. Why was k=2 chosen? While "ICD-mid" may not fit the authors' narrative, how would k=3 affect their Figure1C KM curve and subsequent results?

      Thank you very much for raising these insightful and constructive questions, which have provided us with a clear direction for further improving our research.

      When dividing patients into ICD high and low groups, we used the unsupervised clustering method. This method was chosen because it has good adaptability and reliability in handling the gene signature data we have, and it can effectively classify the samples.

      Regarding the choice of k = 2, it is mainly based on the following considerations. Firstly, in the preliminary exploratory analysis, we found that when k = 2, the two groups showed significant and meaningful differences in key clinical characteristics and gene expression patterns. These differences are closely related to the core issues of our study and help to clearly illustrate the distinctions between the ICD high and low groups. At the same time, considering the simplicity and interpretability of the study, the division of k = 2 makes the results easier to understand and present. Although there may seem to be trends of more groups from the heatmap, after in-depth analysis, the biological significance and clinical associations of other possible groupings are not as clear and consistent as when k = 2.

      As for the impact of k = 3 on the KM curve in Figure 1C and subsequent results, we have conducted some preliminary simulation analyses. The results show that if the "ICD-mid" group is introduced, the KM curve in Figure 1C may become more complex, and the survival differences among the three groups may present different patterns. This may lead to a more detailed understanding of the response to immunotherapy and patient prognosis, but it will also increase the difficulty of interpreting the results. Since the biological characteristics and clinical significance of the "ICD-mid" group are relatively ambiguous, it may interfere with the presentation of our main conclusions to a certain extent. Therefore, in this study, we believe that the division of k = 2 is more conducive to highlighting the key research results and conclusions.

      Thank you again for your valuable comments. We will further improve the explanation and description of the relevant content in the paper to ensure the rigor and readability of the research.

      (3) The 'ICD' gene set contains a lot of immune response genes that code for pleiotropic proteins, as well as genes certainly involved in ICD. It is not convincing that the gene expression differences thus DEGs between the two groups, are not simply "immune-response high" vs "immune-response low". For the DEGS analysis, how many of the 34 ICD gene sets are DEGS between the two groups? Of those, which markers of ICD are DEGs vs. those that are related to immune activation?

      a. The pathway analysis then shows that the DEGs found are associated with the immune response.

      b. Are HMGB1, HSP, NLRP3, and other "ICD genes" and not just the immune activation ones, actually DEGs here?

      c. Figures D, I-J are not legible in the manus.

      We sincerely appreciate your profound insights and valuable questions regarding our research. These have provided us with an excellent opportunity to think more deeply and refine our study.

      We fully acknowledge and are grateful for your incisive observations on the "ICD" gene set and your valid concerns about the differential expression gene (DEG) analysis. During the research design phase, we were indeed aware of the complexity of gene functions within the "ICD" gene set and the potential confounding factors between immune responses and ICD. To distinguish the impacts of these two aspects as effectively as possible, we employed a variety of bioinformatics methods and validation strategies in our analysis.

      Regarding the DEG analysis, among the 34 ICD gene sets, 30 genes showed significant differential expression between the groups, excluding HMGB1, HSP90AA1, ATG5, and PIK3CA. We further conducted detailed classification and functional annotation analyses on these DEGs. The ICD gene set is from a previous article and is related to the process of ICD. Relevant literature is in the materials section. HMGB1: A damage-associated molecular pattern (DAMP) that activates immune cells (e.g., via TLR4) upon release, but its core function is to mediate the release of "danger signals" in ICD, with immune activation being a downstream effect.HSP90AA1: A heat shock protein involved in antigen presentation and immune cell function regulation, though its primary role is to assist in protein folding, with immune-related effects being auxiliary.NLRP3: A member of the NOD-like receptor family that forms an inflammasome, activating CASP1 and promoting the maturation and release of IL-1β and IL-18.Among the 34 DEGs, the majority are associated with immune activation, such as IL1B, IL6, IL17A/IL17RA, IFNG/IFNGR1, etc.

      (4) I may be missing something, but I cannot work out what was done in the paragraph reporting Figure 2I. Where is the ICB data from? How has this been analysed? What is the cohort? Where are the methods?

      The samples used in the analysis corresponding to Figure 2I were sourced from the TCGA (The Cancer Genome Atlas) and TCIA (The Cancer Immunome Atlas) databases. These databases are widely recognized in the field for their comprehensive and rigorously curated cancer - related data, ensuring the reliability and representativeness of our sample cohort.

      Regarding the data analysis, the specific methods employed are fully described in the "Methods" section of our manuscript.

      (5) How were the four genes for your risk model selected? It is not clear whether a multivariate model and perhaps LASSO regularisation was used to select these genes, or if they were selected arbitrarily.

      As you inquired about how the four genes for our risk model were selected, we'd like to elaborate based on the previous analysis steps. In the Cox univariate analysis, we systematically examined a series of ICD-related genes in relation to the overall survival (OS) of patients. Through this analysis, we successfully identified four ICD-related genes, namely CALR (with a p-value of 0.003), IFNB1 (p = 0.037), IFNG (p = 0.022), and IF1R1 (p = 0.047), that showed a significant association with OS, as illustrated in Figure 3A.

      Subsequently, to further refine and optimize the model for better prediction performance, we subjected these four genes to a LASSO regression analysis. In the LASSO regression analysis (as depicted in Figure 3B and C), we aimed to address potential multicollinearity issues among the genes and select the most relevant ones that could contribute effectively to the construction of a reliable predictive model. This process allowed us to confirm the significance of these four genes in predicting patient outcomes and incorporate them into our final predictive model.

      (6) How related are the high-risk and ICD-high groups? It is not clear. In the 'ICD-high' group in the 1A heatmap, patients typically have a z-score>0 for CALR, IL1R, IFNg, and some patients do also for IFNB1. However, in 3H, the 'high risk' group has a different expression pattern of these four genes.

      Patients were divided into ICD high-expression and low-expression groups based on gene expression levels. However, the relationship between these genes and patient prognosis is complex. As shown in Figure 3A, some genes such as IFNB1 and IFNG have an HR < 1, while CALR and IL1R1 have an HR > 1. Therefore, an algorithm was used to derive high-risk and low-risk groups based on their prognostic associations.

      (7) In the four-gene model, CALR is related to ICD, as outlined by the authors briefly in the discussion. IFNg, IL1R1, IFNB1 have a wide range of functions related to immune activity. The data is not convincing that this signature is related to ICD-adjuvancy. This is not discussed as a limitation, nor is it sufficiently argued, speculated, or referenced from the literature, why this is an ICD-signature, and why CALR-high status is related to poor prognosis.

      We acknowledge that the functions of these genes are indeed complex and extensive. In the current manuscript, we have included a preliminary discussion of their roles in the "Discussion" section. As demonstrated by the data presented earlier, these genes do exhibit associations with ICD, and we firmly believe in the validity of these findings.

      However, we are fully aware that our current discussion is not sufficient to fully elucidate the intricate relationships among these genes, ICD, and other biological processes. In response to your valuable feedback, we will conduct an in - depth review of the latest literature, aiming to gain a more comprehensive understanding of the underlying mechanisms.

      (8) Score is spelt incorrectly in Figures 3F-J.

      Figures 3F-J have been revised as requested.

      (9) The authors 'comprehensive analysis' in lines 165-173, is less convincing than the preceding survival curves associating their risk model with survival. Their 'correlations' have no statistics.

      We understand your concern regarding the persuasiveness of the content in this part, especially about the lack of statistical support for the correlations we presented. While we currently have our reasons for presenting the information in this way and are unable to make changes to the core data and descriptions at the moment, we deeply respect your perspective that it could be more convincing with proper statistical analysis.

      (10) The authors performed immunofluorescence imaging to "validate the reliability of the aforementioned results". There is no information on the imaging used, the panel (apart from four antibodies), the patient cohort, the number of images, where the 'normal' tissue is from, how the data were analysed etc. This data is not interpretable without this information.

      a. Is CD39 in the panel? CD8, LAG3? It's not clear what this analysis is.

      The color of each antibody has been marked in Fig 2B. The cohort information and its source have been supplemented. The staining experiment was carried out using a tissue microarray, and the analysis method can be found in the "Methods" section.Formalin-fixed, paraffin-embedded human tissue microarrays (HBlaU079Su01) were purchased from Shanghai Outdo Biotech Co., Ltd. (China), comprising a total of 63 cancer tissues and 16 adjacent normal tissues from bladder cancer patients. Detailed clinical information was downloaded from the company's website.The Remmele and Stegner’s semiquantitative immunoreactive score (IRS) scale was employed to assess the expression levels of each marker,as detailed inMethods2.5.CD39, CD8, and LAG3 were also stained, but the results were not presented.

      (11) The single-cell RNA sequencing analysis from their previous dataset is tagged at the end. CALR expression in most identified cells is interesting. Not clear what this adds to the work beyond 'we did scRNA-seq'. How were these data analysed? scRNA-seq analysis is complex and small nuances in pre-processing parameters can lead to divergent results. The details of such analysis are required!

      We understand your concern about the contribution of the single-cell RNA sequencing results. The main purpose of this analysis is to observe the expression changes of the four genes at the single-cell level. As you mentioned, single-cell RNA sequencing analysis is indeed complex, and we fully recognize the importance of detailed information. We performed the analysis using common analytical methods for single-cell sequencing.It has been supplemented in the Methods section.

    1. "one of the best critiques of modern AI design comes from a 1992 talk by the researcher Mark Weiser where he ranted against “copilot” as a metaphor for AI."

      • Weiser’s critique of the “copilot” metaphor for AI is foundational to this argument.

      "He gave this example: how should a computer help you fly a plane and avoid collisions?... The agentic option is a 'copilot' — a virtual human who you talk with to get help flying the plane. If you’re about to run into another plane it might yell at you 'collision, go right and down!'"

      • The “copilot” model is described as an interactive assistant giving explicit instructions or alerts.

      "design the cockpit so that the human pilot is naturally aware of their surroundings. In his words: 'You’ll no more run into another airplane than you would try to walk through a wall.'"

      • Weiser advocates for UIs that naturally augment human situational awareness, eliminating the need for explicit assistant intervention.

      "Weiser’s goal was an 'invisible computer'—not an assistant that grabs your attention, but a computer that fades into the background and becomes 'an extension of [your] body.'"

      • The ultimate aim: seamless, ambient support that integrates with human perception.

      "the Head-Up Display (HUD), which overlays flight info like the horizon and altitude on a transparent display directly in the pilot’s field of view."

      • The HUD is presented as a practical realization of Weiser’s concept: information is ambiently present, not actively disruptive.

      "A HUD feels completely different from a copilot! You don’t talk to it. It’s literally part invisible—you just become naturally aware of more things, as if you had magic eyes."

      • HUDs differ fundamentally from copilots by passively enhancing awareness instead of communicating via dialogue.

      "spellcheck isn’t designed as a 'virtual collaborator' talking to you about your spelling. It just instantly adds red squigglies when you misspell something! You now have a new sense you didn’t have before. It’s a HUD."

      • Spellcheck is given as a familiar analogy for AI-as-HUD: subtle, always-on augmentation of cognition.

      "use AI to build a custom debugger UI which visualizes the behavior of my program!... With the debugger, I have a HUD! I have new senses, I can see how my program runs."

      • Custom visual UIs for debugging exemplify HUD-like designs in AI-driven tools, enabling deeper understanding rather than focusing on transactional automation.

      "Both the spellchecker and custom debuggers show that automation / 'virtual assistant' isn’t the only possible UI. We can instead use tech to build better HUDs that enhance our human senses."

      • Non-agentic, perception-extending UI paradigms are positioned as powerful, sometimes preferable alternatives to assistant-like AI.

      "I don’t believe HUDs are universally better than copilots!... anyone serious about designing for AI should consider non-copilot form factors that more directly extend the human mind."

      • While not dismissing assistants, the author stresses the importance of exploring HUD-like, sense-extending UI for ambitious AI design.

      "routine predictable work might make sense to delegate to a virtual copilot / assistant. But when you’re shooting for extraordinary outcomes, perhaps the best bet is to equip human experts with new superpowers."

      • The conclusion is a nuanced tradeoff: assistants excel in predictable routines, but empowering human expertise requires HUD-style augmentation.
    1. A transcript of the performance is available if you prefer to read the text rather than watch and listen to the video.

      "It's not just universities it can be utilized by inquiring employers. You think they don't do their research, their quality control voyeurs. Something we view as harmless freedom of self-expression, from a professional perspective you'll be fired." This is a quote from the TED Talk where the "it" in the quote is referring to the digital footprint that is left by someone. Oftentimes, this can be overlooked by people being less private with themselves online, which can have horrendous consequences if something that is online is traced back to you which has legal or ethical consequences.

    1. Empathy of the text-based chatbot is positively related to consumers’ trust towardthe chatbot

      When a chatbot is empathetic, it's not just being nice for niceness' sake — it actually builds people's trust. This illustrates how emotional design isn't really about vibes; it affects how comfortable users are while interacting. That is, care = credibility in online convos.

    1. This op-ed addresses the issue with the exponential increase in publications and how this is leading to a lower quality of peer review which, in turn, is resulting in more bad science being published. It is a well-written article that tackles a seemingly eternal topic. This piece focussed more on the positives and potential actions which is nice to see as this is a topic that can become stuck in the problems. There are places throughout that would benefit from more clarity and at times there appears to be a bias towards publishers, almost placing blame on researchers. Very simple word changes or headings could immediately resolve any doubt here as I don't believe this is the intention of the article at all.

      Additionally, this article is very focussed on peer review (a positive) but I think that it would benefit from small additions throughout that zoom out from this and place the discussion in the context of the wider issues - for example you cannot change peer review incentives without changing the entire incentives around "service" activities including teaching, admin etc. This occurs to a degree with the discussion on other outputs, including preprints and data. Moreover, when discussing service type activities, there is data that reveals certain demographics deliberately avoid this work. Adding this element into the article would provide a much stronger argument for change (and do some good in the new current political climate).

      Overall, I thought this was a great piece when it was first posted online and does exactly what a good op-ed should - provoke thought and discussion. Below are some specific comments, in reading order. I do not believe that there are any substantial or essential changes required, particularly given that this is an op-ed article.

      -----

      Quote: "Academia is undergoing a rapid transformation characterized by exponential growth of scholarly outputs."

      Comment: There's an excellent paper providing evidence to this: https://direct.mit.edu/qss/article/5/4/823/124269/The-strain-on-scientific-publishing which would be a very positive addition

      Quote: "it’s challenging to keep up with the volume at which research publications are produced"

      Comment: Might be nice to add that this was a complaint dating back since almost the beginning of sharing research via print media, just to reinforce that this is a very old point.

      Quote: "submissions of poor-quality manuscripts"

      Comment: The use of "poor quality" here is unnecessary. Just because a submission is not accepted, it has no reflection on "quality". As such this does seem to needlessly diminish work rejected by one journal

      Quote: "Maybe there are too many poor quality journals too - responding to an underlying demand to publish low quality papers."

      Comment: This misses the flip side - poor quality journals encourage and actively drive low quality & outright fraudulent submissions due to the publisher dominance in the assessment of research and academics.

      Quote: "even after accounting for quality,"

      Comment: Quality is mentioned here but has yet to be clearly defined. What is "quality"? - how many articles a journal publishes? The "prestige" of a journal? How many people are citing the articles?

      Quote: "Researchers can – and do – respond to the availability by slicing up their work (and their data) into minimally publishable units"

      Comment: I fully agree that some researchers do exactly this. However, again, this seems to be blaming researchers for creating this firehose problem. I think this point could be reworded to not place so much blame or be substantiated with evidence that this is a widespread practice - my experience has been very mixed in that I've worked for people who do this almost to the extreme (and have very high self-citations) and also worked for people who focus on the science and making it as high quality and robust as possible. I agree many respond to the explosion of journals and varied quality in a negative manner but the journals, not researchers are the drivers here.

      Quote: "least important aspect of the expected contributions of scholars."

      Comment: I think it may be worth highlighting here that sometimes specific demographics (white males) actively avoid these kinds of service activities - there's a good study on this providing data in support of this. It adds an extra dimension into the argument for appropriate incentives and the importance & challenges of addressing this.

      Quote: "high quality peer review"

      Comment: Just another comment on the use of "quality'. This is not defined and I think when discussing these topics it is vital to be clear what one means by "high quality". For example, a high quality peer review that is designed as quality control would be detecting gross defects and fraud, preventing such work from being published (peer review does not reliably achieve this). In contrast, a high quality peer review designed to help authors improve their work and avoid hyperbole would be very detailed and collegial, not requesting large numbers of additional experiments.

      Quote: "conferring public trust in the oversight of science"

      Comment: I'm not convinced of this. Conveying peer review as a stamp of approval or QC leads to reduced trust when regular examples emerge with peer review failures - just look at Hydroxychloroquine and how peer review was used to justify that during COVID or the MMR/autism issues that are still on-going even after the work was retracted. I think this should be much more carefully worded, removed or expanded on to provide this perspective - this occurs slightly in the following sentence but it is very important to be clear on this point.

      Quote: "Researchers hold an incredible amount of market power in scholarly publishing"

      Comment: I like the next few paragraphs but, again, this seems to be blaming researchers when they in fact hold no/little power. I agree that researchers *could* use market pressure but this is entirely unrealistic when their careers depend on publishing X papers in X journal. An argument as to why science feels increasingly non-collaborative perhaps. Funders can have immediate and significant changes. Institutions adopting reward structures, such as teaching for example, would have significant impacts on researcher behaviour. Researchers are adapting to the demands the publication system creates - more journals, greater quantity and reduced quality whilst maintaining control over the assessment - eLife being removed from Wos/Scopus is a prime example of publishers (via their parent companies) preventing innovation or even rather basic improvements.

      Quote: "With preprint review, authors participate in a system that views peer review not as a gatekeeping hurdle to overcome to reach publication but as a participatory exercise to improve scholarship."

      Comment: This is framing that I really like; improving scholarship, not quality control.

      Quote: "buy"

      Comment: typo

      Quote: "adoption of preprint review can shift the inaccurate belief that all preprints lack review"

      Comment: Is this the right direction for preprints though? If we force all preprints to be reviewed and only value reviewed-preprints, then we effectively dismantle the benefits of preprints and their potential that we've been working so hard to build. A recent op-ed by Alice Fleerackers et al provided an excellent argument to this effect. More a question than a suggestion for anything to change.

      Quote: "between all of those stakeholders to work together without polarization"

      Comment: I disagree here - publishers have repeatedly shown that their only real interest is money. Working with them risks undermining all of the effort (financial, careers, reputation, time) that advocates for change put in. The OA movement should also highlight perfectly why this is such a bad route to go down (again). Publishers grip on preprint servers is a great example - those servers are hard to use as a reader, lack APIs and access to data, are not innovative or interacting with independent services. The community should make the rules and then publishers abide by and within them. Currently the publishers make all of the rules and dominate. Indeed, this is possibly the biggest ommision from this article - the total dominance of publishers across the entire ecosystem. You can't talk about change without highlighting that the publishers don't just own journals but the reference managers, the assessment systems, the databases etc. I may be an outlier on this point but for all of the people I interact with (often those at the bottom of the ladder) this is a strong feeling. Again, not a suggestion for anything to change and indeed the point of an op-ed is to stimulate thought and discussion so dissent is positive.

      Note that these annotations were made in hypothes.is and are available here, linked in-text for ease - comments are duplicated in this review.

    2. Summary of the essay

      In this essay, the author seeks to explain the ‘firehose’ problem in academic research, namely the rapid growth in the number of articles but also the seemingly concurrent decline in quality. The explanation, he concludes, lies in the ‘superstructure’ of misaligned incentives and feedback loops that primarily drive publisher and researcher behaviour, with the current publish or perish evaluation system at the core. On the publisher side, these include commercial incentives driving both higher acceptance rates in existing journals and the launch of new journals with higher acceptance rates. At the same time, publishers seek to retain reputational currency by maintaining consistency and therefore brand power of scarcer, legacy-prestige journals. The emergence of journal cascades (automatic referrals from one journal to another journal within the same publisher) and the introduction of APCs (especially for special issues) also contribute to commercial incentives driving article growth. On the researcher side, he argues that there is an apparent demand from researchers for more publishing outlets and simultaneous salami slicing by researchers because authors feel they have to distribute relatively more publications among journals that are perceived to be of lower quality (higher acceptance rates) in order to gain equivalent prestige to that of a higher impact paper. The state of peer review also impacts the firehose. The drain of PhD qualified scientists out of academia, compounded by a lack of recognition for peer review, further contributes to the firehose problem because there are insufficient reviewers in the system, especially for legitimate journals. Moreover, what peer review is done is no guarantee of quality (in highly selective journals as well as ‘predatory’). One of his conclusions is that there is not just a crisis in scholarly publishing but in peer review specifically and it is this crisis that will undermine science the most. Add AI into the mix of this publish or perish culture, and he predicts the firehose will burst.

      He suggests that the solution lies in researchers taking back power themselves by writing more but ‘publishing’ less. By writing more he means outputs beyond traditional journal publications such as policy briefs, blogs, preprints, data, code and so on, and that these should count as much as peer-reviewed publications. He places special emphasis on the potential role of preprints and on open and more collegiate preprint review acting as a filter upstream of the publishing firehouse. He ends with a call for more collegiality across all stakeholders to align the incentives and thus alleviate the pressure causing the firehose in the first place.

      General Comment

      I enjoyed reading the essay and think the author does a good job of exposing multiple incentives and competing interests in the system. Although discussion of perverse incentives has been raised in many articles and blog posts, the author specifically focuses on some of the key commercial drivers impacting publishing and the responses of researchers to those drivers. I found the essay compellingly written and thought provoking although it took me a while to work through the various layers of incentives.  In general, I agree with the incentives and drivers he has identified and especially his call for stakeholders to avoid polarization and work together to repair the system. Although I appreciate the need to have a focused argument I did miss a more in-depth discussion about the equally complex layers of incentives for institutions, funders and other organisations (such as Clarivate) that also feed the firehose.

      I note that my perspective comes from a position of being deeply embedded in publishing for most of my career. This will have also impacted what I took away from the essay and the focus of my comments below.

      Main comments

      1. I especially liked the idea of a ‘superstructure’ of incentives as I think that gives a sense of the size and complexity of the problem. At the same time, by focusing on publisher incentives and researchers’ response to them he has missed out important parts of the superstructure contributing to the firehose, namely the role of institutions and funders in the system. Although this is implicit, I think it would have been worth noting more, in particular:

        • He mentions institutions and the role of tenure and promotion towards the end but not the extent of the immense and immobilizing power this wields across the system (despite initiatives such as DORA and CoARA).

        • Most review panels (researchers) assessing grants for funders are also still using journal publications as a proxy for quality, even if the funder policy states journal name and rank should not be used

        • Many Institutions/Universities still rely on number and venue of publications. Although some notable institutions are moving away from this, the impact factor/journal rank is still largely relied on. This seems especially the case in China and India for example, which has shown a huge growth in research output. Although the author discusses the firehose, it would have been interesting to see a regional breakdown of this.

        • Libraries also often negotiate with publishers based on volume of articles – i.e they want evidence that they are getting more articles as they renegotiate a specific contract (e.g. Transformative agreements), rather than e.g. also considering the quality of service.

        • Institutions are also driven by rankings in a parallel way to researchers being assessed based on journal rank (or impact factor). How University Rankings are calculated is also often opaque (apart from the Leiden rankings) but publications form a core part. This further incentivises institutions to select researchers/faculty based on the number and venue of their publications in order to promote their own position in the rankings (and obtain funding)

      2. The essay is also about power dynamics and where power in the system lies. The implication in the essay is that power lies with the publishers and this can be taken back by researchers. Publishers do have power, especially those in possession of high prestige journals and yet publishers are also subject to the power of other parts of the system, such as funder and institutional evaluation policies. Crucially, other infrastructure organisations, such as Clarivate, that provide indexing services and citation metrics also exert a strong controlling force on the system, for example:

        • Only a subset of journals are ever indexed by Clarivate. And funders and Institutions also use the indexing status of a journal as a proxy of quality. A huge number of journals are thus excluded from the evaluation system (primarily in the arts and humanities but also many scholar-led journals from low and middle income countries and also new journals). This further exacerbates the firehose problem because researchers often target only indexed journals. I’d be interested to see if the firehose problem also exists in journals that are not traditionally indexed (although appreciate this is also likely to be skewed by discipline)

        • Indexers also take on the role of arbiters of journal quality and can choose to delist or list journals accordingly. Listing or delisting has a huge impact on the submission rates to journals that can be worth millions of dollars to a publisher, but it is often unclear how quality is assessed and there seems to be a large variance in who gets listed or not.

        • Clarivate are also paid large fees by publishers to use their products, which creates a potential conflict of interest for the indexer as delisting journals from major publishers could potentially cause a substantial loss of revenue if they withdraw their fees. Also Clarivate relies on publishers to create the journals on which their products are based which may also create a conflict if Clarivate wishes to retain the in-principle support of those publishers.

        • The delisting of elife recently, even though it is an innovator and of established quality, shows the precariousness of journal indexing.

      3. All the stakeholders in the system seem to be essentially ‘following the money’ in one way or another – it’s just that the currency for researchers, institutions, publishers and others varies. Publishers – both commercial and indeed most not-for profit -  follow the requirements of the majority of their ‘customers’  (and that’s what authors, institutions, subscribers etc are in this system) in order to ensure both sustainability and revenue growth. This may be a legacy of the commercialisation of research in the 20th Century but we should not be surprised that growth is a key objective for any company. It is likely that commercial players will continue to play an important role in science and science communication; what needs to be changed are the requirements of the customers.

      4. The root of the problem, as the author notes, is what is valued in the system, which is still largely journal publications. The author’s solution is for researchers to write more – and for value to be placed on this greater range of outputs by all stakeholders. I agree with this sentiment – I am an ardent advocate for Open Science. And yet, I also think the focus on outputs per se and not practice or services is always going to lead to the system being gamed in some way in order to increase the net worth of a specific actor in the system. Preprints and preprint review itself could be subject to such gaming if value is placed on e.g. the preprint server or the preprint-review platform as a proxy of preprint and then researcher quality.

      5. I think the only way to start to change the system is to start placing much more value on both the practices of researchers (as well as outputs) and on the services provided by publishers. Of course saying this is much easier than implementing it.

      Other comments

      1. A key argument is that higher acceptance rates actually create a perverse incentive for researchers to submit as many manuscripts as possible because they are more likely to get accepted in journals with higher acceptance rates. I disagree that higher acceptance rates per se are the main incentive for researchers to publish more. More powerful is the fact that those responsible for grants and promotion continue to use quantity of journal articles as a proxy for research quality.

      2. Higher acceptance rates are not necessarily an indicator of low quality or a bad thing if it means that null, negative and inconclusive results are also published

      3. The author states that Journal Impact Factors might have been an effective measure of quality in the past.  I take issue with this because the JIF has, as far as I know, always been driven by relatively few outliers (papers with very high citations) and I don’t know of evidence to show that this wasn’t also true in the past. It also makes the assumption that citations = quality.

      4. The author asks at one point “Why would field specialization need a lower threshold for publication if the merits of peer review are constant? ” I can see a case for lower thresholds, however, when the purpose of peer review is primarily to select for high impact, rather than rigour, of the science conducted. A similar case might be made for multidisciplinary research, where peer reviewers tend to assess an article from their discipline’s perspective and reject it because the part that is relevant to them is not interesting enough… Of course, this all points to the inherent problems with peer review (with which I agree with the author)

      5. The author puts his essay in appropriate context, drawing on a range of sources to support his argument. I particularly like that he tried to find source material that was openly available.

      6. He cites 2 papers by Bjoern Brembs to substantiate the claim that there is potentially poorer review in higher prestige journals than in lower ranked journals. These papers were published in 2013 and 2018 and the conclusions relied, in part, on the fact that higher ranked journals had more retractions. Apart from a potential reporting bias, given the flood of retractions across multiple journals in more recent years, I doubt this correlation now exists?

      7. The author works out submission rates from the published acceptance rates of journals. The author acknowledges this is only approximate and discusses several factors that could inflate or deflate it. I can add a few more variables that could impact the estimate, including: 1) the number of articles a publisher/journal rejects before articles are assigned to any editor (e.g. because of plagiarism, reporting issues or other research integrity issues), 2) the extent to which articles are triaged and rejected by editors before peer review (e.g. because it is out of scope or not sufficiently interesting to peer review); the number of articles rejected after peer review;  and 4) the extent to which authors independently withdraw an article at any stage of the process. When publishers publish acceptance rates, they don’t make it clear what goes into the numerator or the denominator and there are no community standards around this. The author rightly notes this process is too opaque.

      Catriona J. MacCallum

      As is my practice, I do not wish to remain anonymous. Please also note that I work for a large commercial publisher and am writing this review in an independent capacity such that this review reflects my own opinion, which are not necessarily those of my employer.

    3. This is a well written and clear enough piece that may be helpful for a reader new to the topic. To people familiar with the field there is not so much which is new here. The final recommendation is not well expressed. As currently put it is, I think, wrong. But it is a provocative idea. I comment section by section below.

      The first paragraphs repeat well established facts that there are too many papers. Seppelt et al’s contribution is missing here. It also reproduces the disengenuous claim, by a publisher’s employee, that publishers ‘only’ respond to demand. I do not think that is true. They create demand. They encourage authors to write and submit papers, as anyone who has been emailed by MDPI recently can testify. Why repeat something which is so inaccuate?

      The section on ‘upstream of the nozzle’ is rather confusing. I think the author is trying to establish if more work is being submitted. But this cannot be deduced from the data presented. No trends are given. Rejection rates will be a poor guide if the same paper is being rejected by several journals. I was also confused by the sources used to track growth in papers – why not just use Dimensions data? The final paragraph again repeats well known facts about the proliferation of outlets and salami slicing. Thus far the article has not introduced new arguments.

      Minor points in this section:

      • there are some unsupported claims. Eg ‘This is a practice that is often couched within the seemingly innocuous guise of field specialty journals.’

      • I also do not understand the logic of this rather long sentence: ‘The expansion of journals with higher acceptance rates alters the rational calculus for researchers - all things being equal higher acceptance rates create a perverse incentive to submit as many manuscripts as possible since the underlying probability of acceptance is simply higher than if those same publications were submitted to a journal with a lower acceptance rate, and hence higher prestige.’ I suggest it be rephrased

      The section on peer review (Who’s testing the water) is mostly a useful review of the issues. But there are some problems which need addressing. Bizarrely, when discussing whether there enough scientists, it fails to mention Hanson et al’s global study, despite linking to it’s preprint in the opening lines. Instead the author adopts a parochial North American approach and refers only to PhDs coming from the US. It is not adequate to take trends in one country to cannot explain an international publishing scene. These are not the ‘good data’ the author claims. Likewise the value of data on doctorates not going onto a post-doc hinges on how many post-docs there are. That trend is not supplied. This statement ‘Almost everyone getting a doctorate goes into a non-university position after graduation’ may be true, but no supporting data are supplied to justify it. Nor do we know what country, or countries, the author is referring to.

      The section ‘A Sip from the Spring’ makes the mistaken claim that researchers hold market power. This is not true. Researchers institutions, their libraries and governments are the main source of publisher income. It is here that the key proposal for improvement is made: researcher can write more and publish less. But if the problem is that there is too much poorly reviewed literature then this cannot be the solution. Removing all peer review, would mean there is even more material to read whose appearance is not slowed up by peer review at all. If peer review is becoming inadequate, evading it entirely is hardly a solution.

      This does not mean we should not release pre-prints. The author is right to advocate them, but the author is mistaken to think that this will reduce publishing pressures. The clue is in their name ‘pre-print’. Publication is intended.

      Missing from the author’s argument is recognition of the important role that communities of researchers form, and the roles that journals play in providing venues for conversation, disagreement and disucssion. They provide a filter. Yes researchers produce other material than publications as the author states: ‘grant proposals, editorials, policy briefs, blog posts, teaching curricula and lectures, software code and documentation, dataset curation, and labnotes and codebooks.’ I would add email and whatsapp messages to that list. But adding all that to our reading lists will not reduce the volume of things to be read. It must increase it. And it would make it harder to marshall and search all those words.

      But the idea is provocative nonetheless. Running through this paper, and occasionally made explicit, is the fact that publishers earn billions from their ‘service’ to academia. They have a strong commercial interest in our publishing more, and in competing with each other to produce a larger share of the market. If writing more, and publishing less, means we need to find ways of directing our thoughts so that they earn less money for publishers, then that could bring real change to the system.

      A minor point: the fire hose analogy is fully exploited and rather laboured in this paper. But it is a North American term and image, that does not travel so easily.

    4. A few months back, Upstream editor Martin Fenner suggested that I submit my Upstream blog post titled, Drinking from the Firehose? Write More and Publish Less, for peer-review as a sort of experiment for Upstream through MetaROR. MetaROR, a relative newcomer to the scholarly communication community, provides the review and curate steps in the "publish-review-curate" model for meta-research.

      While I do not consider myself a meta-researcher (scholars who conduct research on research) many of my positions on science policy have implications on the field (especially, those on transparency, openness, and reproducibility). I think the main call in my blog post for reform in scholarly communication – namely, to stop publishing in traditional journals as much and start rewarding a broader swath of scholarly activities like data sharing – is particularly appealing to meta-researchers who rely on non-publication outputs for their work. So, I submitted. The article was openly reviewed, and MetaROR provided an editorial assessment. Here, I reply to the reviewers and contribute to the curation of the original post.

      The reviews are very high-quality - in fact, they are some of the most well-reasoned reviews I've received in the 20 years I've been a scholar. If MetaROR represents the future of peer-review through the publish-review-curate model, scholarly communication is about to get a whole lot better. You can read the open reviews of my blog post here. The revised version of the editorial is here.

      Like traditional peer-review, each individual reviewer provided their feedback independently of the others and the handling editor did not curate the reviews. I prefer when editors do such curation since it helps to organize the response in a way that reduces redundancy. This is one of the main benefits of the group-based peer review systems - such as PREreview's Live Review. Also, there was no easy way (or at least not an obvious one) to export the reviews in plaintext from MetaROR so I could respond point-by-point in software of my choice. Below is an attempt to organize my response roughly around the major criticisms and suggestions in the review. Because this was an opinion piece and not research, I'm not going to respond to every point anyway – nearly all of which I would accept and revise accordingly had this been a research article.

      Too Easy on the Publishers, Too Hard on Researchers

      All three reviewers expressed some dismay over how light my criticism of the publishers was in my blog piece. I do not disagree. The reviewers rightfully point out that the publishers play outsized role in the inequity created in the scholarly communication space. However, I am choosing not to revise here much as the essay was already too long - it would have taken a tome to articulate my criticism of the publishers. That's out of scope. However, I revised the first paragraph in the conclusion to state:

      The publishers are incentivized to avoid any other form of reform - this is the rational option that publishers choose in response to the apparent demand from researchers - as Ciavarella rightly pointed out.

      Two of the reviewers also thought I was too harsh on researchers. I don't think that I was overly harsh. All three agree with me that researchers have some market role here but disagree with the extent to which they can exert influence. One reviewer claims researchers have no market power (to which I respectfully disagree). I've clarified in the paper that: 'the power any individual researcher has here is small. Collective action is needed.' I reject that researchers are blameless for the status quo - complacency empowers the publishers. Unfortunately, it's also baked into the superstructure of the reward system that is perpetuated by publisher-controlled market forces. I also added the following sentiment along these lines when discussing market-power of researchers:

      It's free to share and read research without the need for costly, anticompetitive gatekeeping. Leveraging that freedom is an untapped source of market power.

      Focus More on Institutions and Funders and Communities

      Two of the three reviewers thought I needed to draw more attention to the roles, demands, and influence that academic institutions, publisher consortia, libraries, indexing services, scholarly societies, and grassroots research organizations have in this ecosystem. I agree with all these points - and had Clarivate's irresponsible delisting of eLife in the Web of Science happened before I wrote the original piece, I would have highlighted that as one reviewer suggested.

      No New Arguments or Analysis

      The reviewers felt that, while well-articulated, the arguments I was espousing are not novel. First, I think it is worthy to renew the idea that we should be more selective in choosing what to publish in journals. Focusing on quality over quantity and valuing activities beyond journal publications should be repeated often until it's common practice.

      One comment called for more data and analysis, and another wanted some additional research cited. I think that's a great idea and I hope the reviewers can do that work or perhaps the open review will inspire others to do so.

      In response to the criticism that preprints themselves both presuppose an eventual traditional publication and that they could be gamed, I revised that section accordingly:

      There is risk of gaming preprints and preprint review just as there is in traditional publishing, such as by placing value on a paper for where it appears or how it was reviewed without considering its quality or contribution to science.

      One reviewer misunderstood my point about preprints altogether:

      Removing all peer review, would mean there is even more material to read whose appearance is not slowed up by peer review at all. If peer review is becoming inadequate, evading it entirely is hardly a solution. This does not mean we should not release pre-prints. The author is right to advocate them, but the author is mistaken to think that this will reduce publishing pressures. The clue is in their name ‘pre-print’. Publication is intended.

      I am absolutely not arguing for tossing out peer review. I strongly believe peer review is valuable but currently broken. Moreover, I reject that peer review needs to happen behind the gatekeeping of publishers. I revised to clarify here and added a footnote based on this reviewer's latter observation.

      Peer-review remains a critical check for pollutants in the waters - but the prevailing model needs significant reform. The traditional opaque, uncompensated system has eroded the quality, transparency, timeliness, and appropriateness of peer review due to competing priorities and a lack of appropriately aligned incentive structures. Novel models of peer review including, publish-review-curate and preprint review, and compensated review - ideally all done transparently and with conflicts of interest declared out in the open. At the same time, not all manuscripts need review to have value and most preprints with value (even those with reviews) should not be published in journals.

      New footnote: The term 'preprint' is evolving - what was once a moniker for non-peer reviewed manuscript intended to eventually become reviewed and published (or more likely, rejected) now scopes-in other forms including publish-curate-review and manuscripts with preprint reviews. A new labeling and metadata system is desperately needed to highlight the state of review of a particular manuscript in a record of versions. Version control systems and badging are ubiquitous in the open-source software community and could be easily adopted here.

      Volume is Volume is Volume

      Probably the most important critique among the set of reviews points out an apparent recursion in the logic of the thesis that I need to clarify: you can't solve the firehose problem by writing more, as that just adds more volume to the flow. My revision to the conclusion clarifies my intent: what I'm proposing is to stop sending so many papers to journals for publication and to choose preprints more often for reading, reviewing, and writing. At the same time the system should, maintain or increase non-publication scholarly outputs and reward those too.

      "Write-More" here is a placeholder for all the non-publication writing scholars do and should get credit for from their institutions and fields. Again, I happen to focus on writing because that's what I care about in this editorial and it would take volumes to pontificate on all the other services and activities that happen within the academy that are not properly rewarded.

      Summary

      Having my blog post peer-reviewed through MetaROR was a positive experience and I recommend the service. However, my post was still just an editorial – my opinions and thoughts – not research. Had this been a research article, however, the reviews as presented would have been a very good roadmap to improving the paper. For MetaROR, I have two suggestions: 1) the editorial assessment could be improved by organizing the key points and 2) create a way to have all reviews downloadable in plaintext for ease of importing into an editor.

      Acknowledgments

      Special thanks are owed to the reviewers, Catriona MacCallum, Dan Brockington, and Jonny Coates, the MetaROR handling editor Ludo Waltman, and to Upstream Editor and Front Matter founder Martin Fenner for the crazy idea to peer-review a blog post.

      Disclosure

      The opinions expressed here are my own and may not represent those of my employer, my associates, or the reviewers. I have no conflicts of interest to disclose.

      This author response was previously published on Upstream.

    1. The Utopian Vision of Smalltalk

      Smalltalk as a vision, not just a language:

      “Smalltalk is a vision of the world as it should be, not necessarily as it is but the way it's supposed to be.”

      A future imagined where Smalltalk has won:

      “As we all know, Smalltalk has won and a new age, a Utopia is upon us.”


      Language and System Design Philosophy

      Smalltalk wasn’t originally a full language, but a live object system:

      “Small Talk was never a programming language it was a programming system… there is no way to express a class as a linguistic construct.”

      Early Smalltalk used a clever hack for class definition:

      “Yes a class just happens to be this object that knows how to make other objects… but it is a hack and it's important to recognize that.”

      Textual syntax was later added to enable integration with tools like source control:

      “We came up with an actual textual Syntax for Smalltalk… interacting with Source Control Systems became really easy.”


      Resolving Dialect & Library Fragmentation

      Overcoming dialect fragmentation:

      “We realized all this and came together to define a Common Language.”

      Library standardization was harder but necessary:

      “The situation with the libraries was a bit of a mess… but we realized that the benefits of having a standard… outweighed [vendor-specific advantages].”


      Reflection and Mirrors

      Original reflection API mixed base and meta levels:

      “These class objects… are playing two roles… that architecture was replaced with mirrors.”

      Mirrors cleanly separate base-level and meta-level:

      “We clearly separated the base level and the meta level… that has all kinds of advantages for deployment, distribution, security.”


      Mobile and UI Integration

      Early design enabled seamless remote mobile dev:

      “We were already running images that could manipulate other images… fantastic development experiences almost immediately.”

      Binding to native UI was essential for adoption:

      “We realized early on that we should… bind to Native stuff… they run natively on all the platforms.”

      Tool evolution toward navigation-based UIs:

      “We wouldn’t dream of using the 40-year-old design of a browser… we evolved our tools.”


      Foreign Function Interfaces and “Alien” Objects

      Unified model for system integration:

      “These things outside of Smalltalk… they're second class objects… but they were objects.”

      Alien objects replaced clunky primitive syntax:

      “We didn’t need these hacky things like the Primitive syntax… everything was an object.”


      Modularity and Deployment

      Object ecosystems are difficult to transfer without structure:

      “Fish and objects live in an environment… if you want the fish to be transferred and survive you have to bring its friends.”

      Deployment as serialized object graphs:

      “We realized that an application… was also an object… we just had to serialize it.”

      Avoided “extraneous concepts like packages”:

      “We used the concepts of classes and objects to solve our distribution and modularity problems.”


      Optional Typing and Pluggable Type Systems

      Types as useful but not mandatory:

      “Types can be useful at times… we certainly don’t want them telling us how to live.”

      Static analyses used for optimization:

      “Extra information helps… we could have multiple analyses that didn’t conflict.”


      Web and Live Environments

      Ahead of the curve with web-based IDEs:

      “We built complete programming environments with all their features running in the browser long before anyone else.”

      True liveness beyond class browsers:

      “We never want to look at Dead code… environments evolved to show Exemplar values.”

      Integration of ML into the environment:

      “We started to incorporate [ML] in our live programming environments.”


      Performance and JIT

      Ahead of AOT compilation trend:

      “We had systems that would… have a database of compiled methods… ready to apply them instantly on Startup.”

      Addressed Apple’s no-JIT constraint early:

      “We already had techniques for that… just deploy that and turn off the jit.”


      Security through Object Capabilities

      Reinvention of capability-based security:

      “The capability you want is an object… the only damage you can do is through a message send.”

      Enforced encapsulation and access control:

      “We needed an access control model… public, protected, and private messages.”


      Education and Longevity

      Revolutionary ideas in education:

      “Smalltalk had these ideals of Education from day one.”

      Avoided student drop-off by staying dominant:

      “We short circuited [students leaving] because of all our previous successes.”

      Hypertext-based live documentation:

      “We could put live widgets embedded in text long before the worldwide web.”


      Satirical Punchline

      Entire talk is satirical utopia:

      “This is the world as it should be… imagine if the world wasn’t like the way I described it…”

      Ironic jab at real-world language use:

      “Think if one of the world’s largest… sites ran on PHP… we all know Facebook runs on Smalltalk.”

      • Core Concept: Missionary is a Clojure library for supervised data flow programming, providing a universal language for handling asynchronous event sources in both frontend and backend applications.

        "the missionary is a closure library for supervised data flow programming so this is a common language to manipulate asynchronous event sources um that you can use from anywhere in the stack"

      • Problem Addressed: The library tackles the difficulty of resource management in concurrent programming, where resources (like circuits, UI components, or threads) have a life cycle that must be explicitly managed.

        "this is in my "pinion the main reason why concurrency is hard a resource is an object with a Time dimension because it has a Time Dimension it also has a life cycle"

      • Limitations of Garbage Collection: Traditional garbage collection is insufficient for this problem due to bidirectional references between producers and consumers, leaving the producer unaware if a resource is still needed.

        "garbage collection doesn't help the reason why it doesn't fa is because um the reference to the resource is B directional there is it is referenced by the consumer and it is also referenced by the producer"

      • Proposed Solution: The solution is explicit structure, which enables automatic, demand-driven resource management by binding the resource's allocation lifespan to the period when its data is actually required.

        "what we want to achieve is to bind um the time span where the resource is allocated to the time span when the data produced by this resource is actually needed so it's demand WR resource maintenance"

      • DAG Supervision for Shared Resources: For complex scenarios with shared resources, missionary moves from a simple tree supervision model to a Directed Acyclic Graph (DAG) supervision model, where a resource can have multiple supervisors.

        "so now the supervision uh three is not a three anymore it's a dag has a shared resource here"

      • Core Supervision Policy: The policy for managing shared resources is "allocate on first use and dispose on last," elegantly summarized by the real-world analogy of "the last one turns of the lights."

        "the last one turns of the lights uh so this is a dck for the real world"

      • Improving on Functional Effect Systems: Missionary was conceived to improve upon functional effect systems like Rx by properly implementing continuous time, which requires synchronicity semantics to avoid event-ordering issues.

        "the IDE was uh continuous time is a good idea...the problem is um well at this time the popular FX system in Java was RX and RX doesn't properly Implement continuous time uh so the reason why it doesn't properly Implement continuous time is because it has no synchronicity semantics"

      • Glitch-Free Propagation: A key innovation is a bidirectional flow protocol and a propagation algorithm that uses a priority queue to traverse the dependency graph, ensuring atomic updates and preventing inconsistent intermediate states, known as "glitches."

        "to solve the glitch problem...the string protocol doesn't support that and so therefore the idea is can we have our and El two"

      • Bidirectional Flow Protocol: The protocol allows a producer to signal that its state is invalidated before a new value is computed, enabling a two-phase process of invalidating dependent nodes and then recomputing them in the correct order.

        "bidirectional IPC is what we need to implement uh back pressure and solve the git problem that I talk just before"

      • Successful Validation: The model has been battle-tested at scale through its use in Electric Clojure for serious business applications, proving its robustness and success in managing complex, dynamic, and massively concurrent UIs.

        "that's a success we've been validating that model at scale with electric closure we we've been working on that for more than two years now uh we've been using electric closure for serous business application and this algorithm just well we we experience really no problem with that"

    1. Bet on context engineering for rapid iteration.

      “Manus would bet on context engineering. This allows us to ship improvements in hours instead of weeks, and kept our product orthogonal to the underlying models: If model progress is the rising tide, we want Manus to be the boat, not the pillar stuck to the seabed.”

      Design around the KV-cache to optimize latency and cost.

      “If I had to choose just one metric, I'd argue that the KV-cache hit rate is the single most important metric for a production-stage AI agent.”

      Mask tool availability rather than removing tools mid-iteration.

      “Rather than removing tools, it masks the token logits during decoding to prevent (or enforce) the selection of certain actions based on the current context.”

      Use the file system as scalable, persistent context.

      “That's why we treat the file system as the ultimate context in Manus: unlimited in size, persistent by nature, and directly operable by the agent itself.”

      Manipulate attention through recitation of objectives.

      “By constantly rewriting the todo list, Manus is reciting its objectives into the end of the context. This pushes the global plan into the model's recent attention span, avoiding ‘lost-in-the-middle’ issues.”

      Keep errors in the context to enable adaptive behavior.

      “One of the most effective ways to improve agent behavior is deceptively simple: leave the wrong turns in the context. When the model sees a failed action—and the resulting observation or stack trace—it implicitly updates its internal beliefs.”

      Avoid over-few-shotting by injecting controlled diversity.

      “The fix is to increase diversity. Manus introduces small amounts of structured variation in actions and observations—different serialization templates, alternate phrasing, minor noise in order or formatting.”

      Context engineering defines agent performance and scalability.

      “Context engineering is still an emerging science—but for agent systems, it's already essential. … Engineer them well.”

    1. If any kind of misconduct appears while performing a core business by aprofessional, and if it’s connected not just to the business or to the legal aspects of ajob, but to its moral aspects as well, then some duties and obligations, ordinarilyimplicit, should be explicated. When a professional performs such an action, it is in facta process of social (ir)responsibility (CSI)

      There is a slippery slope fallacy implication that if one does not follow professional ethical conduct strictly, the result is CSI. While this may often be true, the text doesn't account for degrees of ethical failure or the possibility of correction before reaching irresponsibility.

    1. e asked where I had learned to speak English so well, and was confused when I said that Nigeria happened to have English as its official language. She asked if she could listen to what she called my "tribal music," and was consequently very disappointed when I produced my tape of Mariah Carey.

      It happens so I totally understnad what sh think and feel. It's just types of stereotype, but somtimes it hurts someone's feeling. I feel she is glad her room mate's asking, but asking these quetion creates another stereotype: American don't know outside of U.S. This is vice versa. So this part has so heavy meaning I think.

    1. measured by the effects oftheir work on student learning

      It's so frustrating that this is a measurement. I think it really depends on so many factors and that combination of factors is what should be used, it's just hard that some of the factors scream a little louder than others.

    1. reply to u/VampySiren on https://reddit.com/r/typewriters/comments/1m7yz1m/silveretti_ribbon_vibrator_getting_stuck_in/

      On most machines, the ribbon vibrator is meant to slide up and down freely and it typically returns with just gravity. 9 times out of 10 the reason that the vibrator doesn't go back down because it's either dirty/gummy or has been slightly bent. Put a few drops of mineral spirits or similar degreaser on it and give it a light scrub with a toothbrush. If it doesn't move freely after a round or two of this, is it bent and hanging on something? If so, bend it so that it moves freely.

      The other 1 of 10 times, it's the ribbon that's been installed correctly.

    1. Be clear about the consequences of using AI to generate pornographic images. Tell students that they may see apps to create nude pictures advertised on platforms like TikTok. Though they may be curious or think it's funny (because the pictures aren't "real"), using AI to generate nude pictures of someone is harassment and illegal. It doesn't just harm the victim—law enforcement could get involved. Victims should tell a trusted adult, report to authorities, and can also report the incident to CyberTipline.org.

      This part really stands out as a necessary and urgent conversation. With how normalized AI tools have become on platforms like TikTok, I can see how some students might not fully grasp the seriousness of using them to generate explicit content. It’s not a harmless joke—it’s a form of harassment with real legal and emotional consequences. As someone who spends a lot of time online, I think we all need to take more responsibility in calling out this kind of behavior and making sure people know where to get help,

    2. Make students aware that deepfakes exist online already, so it's good to be skeptical about audio and video that's especially shocking or shared widely to get an emotional response. People can still spot some imperfections, but they're getting more difficult to identify.

      This is such an important reminder, especially for students who often trust what they see online. I’ve seen deepfake videos shared on social media that looked incredibly real at first glance. It worries me how easily misinformation can spread, especially when it's designed to trigger strong emotions. As educators and students, we need to build digital literacy skills—not just for reading and writing, but also for recognizing manipulated media. Critical thinking is more essential than ever.

    1. Figure 8.1).

      The fact that there is a step dedicated to just researching strategies/programs/interventions is giving me so much buy-in. As a special education teacher, it's so important for me to think from that perspective and it can feel like I need to remind other people of that difference too, but this step makes it to where everyone is explicitly thinking about it.

    1. I would agree it's freedom to actually use your devices in such ways (otherwise you are not owning it, just leasing it from a somebody who still tells you what you can and cannot do). It does not surprise me, that criminals also use this. Problem is: again the act of emancipation and its potential is criminalized instead of actual criminal behavior.

    1. Author response:

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

      Reviewer #1 (Public review):

      Summary:

      Phytophathogens including fungal pathogens such as F. graminearum remain a major threat to agriculture and food security. Several agriculturally relevant fungicides including the potent Quinofumelin have been discovered to date, yet the mechanisms of their action and specific targets within the cell remain unclear. This paper sets out to contribute to addressing these outstanding questions.

      We appreciate the reviewer's accurate summary of our manuscript.

      Strengths:

      The paper is generally well-written and provides convincing data to support their claims for the impact of Quinofumelin on fungal growth, the target of the drug, and the potential mechanism. Critically the authors identify an important pyrimidine pathway dihydroorotate dehydrogenase (DHODH) gene FgDHODHII in the pathway or mechanism of the drug from the prominent plant pathogen F. graminearum, confirming it as the target for Quinofumelin. The evidence is supported by transcriptomic, metabolomic as well as MST, SPR, molecular docking/structural biology analyses.

      We appreciate the reviewer's recognition of the strengths of our manuscript.

      Weaknesses:

      Whilst the study adds to our knowledge about this drug, it is, however, worth stating that previous reports (although in different organisms) by Higashimura et al., 2022 https://pmc.ncbi.nlm.nih.gov/articles/PMC9716045/ had already identified DHODH as the target for Quinofumelin and hence this knowledge is not new and hence the authors may want to tone down the claim that they discovered this mechanism and also give sufficient credit to the previous authors work at the start of the write-up in the introduction section rather than in passing as they did with reference 25? other specific recommendations to improve the text are provided in the recommendations for authors section below.

      We appreciate the reviewer's suggestion. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of previous work on quinofumelin by Higashimura et al., 2022 in the discussion section to more effectively contextualize their contributions. Moreover, we have made revisions and provided responses in accordance with the recommendations.

      Reviewer #2 (Public review):

      Summary:

      In the current study, the authors aim to identify the mode of action/molecular mechanism of characterized a fungicide, quinofumelin, and its biological impact on transcriptomics and metabolomics in Fusarium graminearum and other Fusarium species. Two sets of data were generated between quinofumelin and no treatment group, and differentially abundant transcripts and metabolites were identified. The authors further focused on uridine/uracil biosynthesis pathway, considering the significant up- and down-regulation observed in final metabolites and some of the genes in the pathways. Using a deletion mutant of one of the genes and in vitro biochemical assays, the authors concluded that quinofumelin binds to the dihydroorotate dehydrogenase.

      We appreciate the reviewer's accurate summary of our manuscript.

      Strengths:

      Omics datasets were leveraged to understand the physiological impact of quinofumelin, showing the intracellular impact of the fungicide. The characterization of FgDHODHII deletion strains with supplemented metabolites clearly showed the impact of the enzyme on fungal growth.

      We appreciate the reviewer's recognition of the strengths of our manuscript.

      Weaknesses:

      Some interpretation of results is not accurate and some experiments lack controls. The comparison between quinofumelin-treated deletion strains, in the presence of different metabolites didn't suggest the fungicide is FgDHODHII specific. A wild type is required in this experiment.

      Potential Impact: Confirming the target of quinofumelin may help understand its resistance mehchanism, and further development of other inhibitory molecules against the target.

      The manuscript would benefit more in explaining the study rationale if more background on previous characterization of this fungicide on Fusarium is given.

      We appreciate the reviewer's suggestion. Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil. In our previous study, quinofumelin not only exhibited excellent antifungal activity against the mycelial growth and spore germination of F. graminearum, but also inhibited the biosynthesis of deoxynivalenol (DON). We have added this part to the introduction section.

      Reviewer #3 (Public review):

      Summary:

      The manuscript shows the mechanism of action of quinofumelin, a novel fungicide, against the fungus Fusarium graminearum. Through omics analysis, phenotypic analysis, and in silico approaches, the role of quinofumelin in targeting DHODH is uncovered.

      We appreciate the reviewer's accurate summary of our manuscript.

      Strengths:

      The phenotypic analysis and mutant generation are nice data and add to the role of metabolites in bypassing pyrimidine biosynthesis.

      We appreciate the reviewer's recognition of the strengths of our manuscript.

      Weaknesses:

      The role of DHODH in this class of fungicides has been known and this data does not add any further significance to the field. The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

      There is no mention of the other fungicide within this class ipflufenoquin, as there is ample data on this molecule.

      We appreciate the reviewer's suggestion. We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions. The information regarding action mechanism of ipflufenoquin against filamentous fungi was added in discussion section.

      Reviewer #1 (Recommendations for the authors):

      (1) Given that the DHODH gene had been identified as a target earlier, could the authors perform blast experiments with this gene instead and let us know the percentage similarity between the FgDHODHII gene and the Pyricularia oryzae class II DHODH gene in the report by Higashimura et al., 2022.

      BLAST experiment revealed that the percentage similarity between the FgDHODHII gene and the class II DHODH gene of P. oryzae was 55.41%. We have added the description ‘Additionally, the amino acid sequence of the FgDHODHII exhibits 55.41% similarity to that of DHODHII from Pyricularia oryzae, as previously reported (Higashimura et al., 2022)’ in section Results.

      (2) Abstract:

      The authors started abbreviating new terms e.g. DEG, DMP, etc but then all of a sudden stopped and introduced UMP with no full meaning of the abbreviation. Please give the full meaning of all abbreviations in the text, UMP, STC, RM, etc.

      We have provided the full meaning for all abbreviations as requested.

      (3) Introduction section:

      The introduction talks very little about the work of other groups on quinofumelin. Perhaps add this information in and reference them including the work of Higashimura et al., 2022 which has done quite significant work on this topic but is not even mentioned in the background

      We have added the work of other groups on quinofumelin in section introduction.

      (4) General statements:

      Please show a model of the pyrimidine pathway that quinofumelin attacks to make it easier for the reader to understand the context. They could just copy this from KEGG

      We have added the model (Fig. 7).

      (5) Line 186:

      The authors did a great job of demonstrating interactions with the Quinofumelin and went to lengths to perform MST, SPR, molecular docking, and structural biology analyses yet in the end provide no details about the specific amino acid residues involved in the interaction. I would suggest that site-directed mutagenesis studies be performed on FgDHODHII to identify specific amino acid residues that interact with Quinofumelin and show that their disruption weakens Quinofumelin interaction with FgDHODHII.

      Thank you for this insightful suggestion. We fully agree with the importance of elucidating the interaction mechanism. At present, we are conducting site-directed mutagenesis studies based on interaction sites from docking results and the mutation sites of FgDHODHII from the resistant mutants; however, due to the limitations in the accuracy of existing predictive models, this work remains ongoing. Additionally, we are undertaking co-crystallization experiments of FgDHODHII with quinofumelin to directly and precisely reveal their interaction pattern

      (6) Line 76:

      What is the reference or evidence for the statement 'In addition, quinofumelin exhibits no cross-resistance to currently extensively used fungicides, indicating its unique action target against phytopathogenic fungi.

      If two fungicides share the same mechanism of action, they will exhibit cross resistance. Previous studies have demonstrated that quinofumelin retains effective antifungal activity against fungal strains resistant to commercial fungicides, indicating that quinofumelin does not exhibit cross-resistance with other commercially available fungicides and possesses a novel mechanism of action. Additionally, we have added the relevant inference.

      (7) Line 80-82:

      Again, considering the work of previous authors, this target is not newly discovered. Please consider toning down this statement 'This newly discovered selective target for antimicrobial agents provides a valuable resource for the design and development of targeted pesticides.'

      We have rewritten the description of this sentence.

      (8) Line 138: If the authors have identified DHODH in experimental groups (I assume in F. graminearum), what was the exact locus tag or gene name in F. graminearum, and why not just continue with this gene you identified or what is the point of doing a blast again to find the gene if the DHODH gene if it already came up in your transcriptomic or metabolic studies? This unfortunately doesn't make sense but could be explained better.

      The information of FgDHODHII (gene ID: FGSG_09678) has been added. We have revised this part.

      Reviewer #2 (Recommendations for the authors):

      (1) Line 40:

      Please add a reference.

      We have added the reference

      (2) Line 47:

      Please add a reference.

      We have added the reference.

      (3) Line 50:

      The lack of target diversity in existing fungicides doesn't necessarily serve as a reason for discovering new targets being more challenging than identifying new fungicides within existing categories, please consider adjusting the argument here. Instead, the authors can consider reasons for the lack of new targets in the field.

      We have revised the description.

      (4) Line 63:

      Please cite your source with the new technology.

      We have added the reference.

      (5) Line 68:

      What are you referring to for "targeted medicine", do you have a reference?

      We have revised the description and the reference.

      (6) Line 74:

      One of the papers referred to "quinoxyfen", what are the similarities and differences between the two? Please elaborate for the readership.

      Quinoxyfen, similar to quinofumelin, contains a quinoline ring structure. It inhibits mycelial growth by disrupting the MAP kinase signaling pathway in fungi (https://www.frac.info). In addition, quinoxyfen still exhibits excellent antifungal activity against the quinofumelin-resistant mutants (the findings from our group), indicating that action mechanism for quinofumelin and quinoxyfen differ.

      (7) Line 84:

      Please introduce why RNA-Seq was designed in the study first. What were the groups compared? How was the experiment set up? Without this background, it is hard to know why and how you did the experiment.

      According to your suggestions, we have added the description in Section Results. In addition, the experimental process was described in Section Materials and methods as follows: A total of 20 mL of YEPD medium containing 1 mL of conidia suspension (1×105 conidia/mL) was incubated with shaking (175 rpm/min) at 25°C. After 24 h, the medium was added with quinofumelin at a concentration of 1 μg/mL, while an equal amount of dimethyl sulfoxide was added as the control (CK). The incubation continued for another 48 h, followed by filtration and collection of hyphae. Carry out quantitative expression of genes, and then analyze the differences between groups based on the results of DESeq2 for quantitative expression.

      (8) Figures:

      The figure labeling is missing (Figures 1,2,3 etc). Please re-order your figure to match the text

      The figures have been inserted.

      (9) Line. 97:

      "Volcano plot" is a common plot to visualize DEGs, you can directly refer to the name.

      We have revised the description.

      (10) Figure 1d, 1e:

      Can you separate down- and up-regulated genes here? Does the count refer to gene number?

      The expression information for down- and up-regulated genes is presented in Figure 1a and 1b. However, these bubble plots do not distinguish down- and up-regulated genes. Instead, they only display the significant enrichment of differentially expressed genes in specific metabolic pathways. To more clearly represent the data, we have added the detailed counts of down- and up-regulated genes for each metabolic pathway in Supplementary Table S1 and S2. Here, the term "count" refers to differentially expressed genes that fall within a certain pathway.

      (11) Line 111:

      Again, no reasoning or description of why and how the experiment was done here.

      Based on the results of KEGG enrichment analysis, DEMs are associated with pathways such as thiamine metabolism, tryptophan metabolism, nitrogen metabolism, amino acid sugar and nucleotide sugar metabolism, pantothenic acid and CoA biosynthesis, and nucleotide sugar production compounds synthesis. To specifically investigate the metabolic pathways involved action mechanism of quinofumelin, we performed further metabolomic experiments. Therefore, we have added this description according the reviewer’s suggestions.

      (12) Figure 2a:

      It seems many more metabolites were reduced than increased. Is this expected? Due to the antifungal activity of this compound, how sick is the fungus upon treatment? A physiological study on F. graminearum (in a dose-dependent manner) should be done prior to the omics study. Why do you think there's a stark difference between positive and negative modes in terms of number of metabolites down- and up-regulated?

      Quinofumelin demonstrates exceptional antifungal activity against Fusarium graminearum. The results indicate that the number of reduced metabolites significantly exceeds the number of increased metabolites upon quinofumelin treatment. Mycelial growth is markedly inhibited under quinofumelin exposure. Prior to conducting omics studies, we performed a series of physiological and biochemical experiments (refer to Qian Xiu's dissertation https://paper.njau.edu.cn/openfile?dbid=72&objid=50_49_57_56_49_49&flag=free). Upon quinofumelin treatment, the number of down-regulated metabolites notably surpasses that of up-regulated metabolites compared to the control group. Based on the findings from the down-regulated metabolites, we conducted experiments by exogenously supplementing these metabolites under quinofumelin treatment to investigate whether mycelial growth could be restored. The results revealed that only the exogenous addition of uracil can restore mycelial growth impaired by quinofumelin.

      Quinofumelin exhibits an excellent antifungal activity against F. graminearum. At a concentration of 1 μg/mL, quinofumelin inhibits mycelial growth by up to 90%. This inhibitory effect indicates that life activities of F. graminearum are significantly disrupted by quinofumelin. Consequently, there is a marked difference in down- and up-regulated metabolites between quinofumelin-treated group and untreated control group. The detailed results were presented in Figures 1 and 2.

      (13) Figure 2e:

      This is a good analysis. To help represent the data more clearly, the authors can consider representing the expression using fold change with a p-value for each gene.

      To more clearly represent the data, we have incorporated the information on significant differences in metabolites in the de novo pyrimidine biosynthesis pathway, as affected by quinofumelin, in accordance with the reviewer’s suggestions.

      (14) Line 142:

      Please indicate fold change and p-value for statistical significance. Did you validate this by RT-qPCR?

      We validated the expression level of the DHODH gene under quinofumelin treatment using RT-qPCR. The results indicated that, upon treatment with the EC50 and EC90 concentrations of quinofumelin, the expression of the DHODH gene was significantly reduced by 11.91% and 33.77%, respectively (P<0.05). The corresponding results have been shown in Figure S4.

      (15) Line 145:

      It looks like uracil is the only metabolite differentially abundant in the samples - how did you conclude this whole pathway was impacted by the treatment?

      The experiments involving the exogenous supplementation of uracil revealed that the addition of uracil could restore mycelial growth inhibited by quinofumelin. Consequently, we infer that quinofumelin disrupts the de novo pyrimidine biosynthesis pathway. In addition, as uracil is the end product of the de novo pyrimidine biosynthesis pathway, the disruption of this pathway results in a reduction in uracil levels.

      (16) Figure 3:

      What sequence was used as the root of the tree? Why were the species chosen? Since the BLAST query was Homo sapiens sequence, would it be good to use that as the root?

      FgDHODHII sequence was used as the root of the tree. These selected fungal species represent significant plant-pathogenic fungi in agriculture production. According to your suggestion, we have removed the BLAST query of Homo sapiens in Figure 3.

      (17) Figure 4:

      How were the concentrations used to test chosen?

      Prior to this experiment, we carried out concentration-dependent exogenous supplementation experiments. The results indicated that 50 μg/mL of uracil can fully restore mycelial growth inhibited by quinofumelin. Consequently, we chose 50 μg/mL as the testing concentration.

      (18) Line 164:

      Why do you hypothesize supplementing dihydroorotate would restore resistance? The metabolite seemed accumulated in the treatment condition, whereas downstream metabolites were comparable or even depleted. The DHODH gene expression was suppressed. Would accumulation of dihydroorotate be associated with growth inhibition by quinofumelin? Please include the hypothesis and rationale for the experimental setup.

      DHODH regulates the conversion of dihydroorotate to orotate in the de novo pyrimidine biosynthesis pathway. The inhibition of DHODH by quinofumelin results in the accumulation of dihydroorotate and the depletion of the downstream metabolites, including UMP, uridine and uracil. Consequently, downstream metabolites were considered as positive controls, while upstream metabolite dihydroorotate served as a negative control. This design further demonstrates DHODH as action target of quinofumelin against F. graminearum. In addition, the accumulation of dihydroorotate is not associated with growth inhibition by quinofumelin; however, but the depletion of downstream metabolites in the de novo pyrimidine biosynthesis pathway is closely associated with growth inhibition by quinofumelin.

      (19) Line 168:

      I'm not sure if this conclusion is valid from your results in Figure 4 showing which metabolites restore growth.

      o minimize the potential influence of strain-specific effects, five strains were tested in the experiments shown in Figure 4. For each strain, the first row (first column) corresponds to control condition, while second row (first column) represents treatment with 1 μg/mL of quinofumelin, which completely inhibits mycelial growth. The second row (second column) for each strain represents the supplementation with 50 μg/mL of dihydroorotate fails to restore mycelial growth inhibited by quinofumelin. In contrast, the second row (third column, fourth column, fifth colomns) for each strain demonstrated that the supplementation of 50 μg/mL of UMP, uridine and uracil, respectively, can effectively restore mycelial growth inhibited by quinofumelin.

      (20) Figure 5a:

      The fact you saw growth of the deletion mutant means it's not lethal. However, the growth was severely inhibited.

      Our experimental results indicate that the growth of the deletion mutant is lethal. The mycelial growth observed originates from mycelial plugs that were not exposed to quinofumelin, rather than from the plates amended with quinofumelin.

      (21) Figure 5b:

      Would you expect different restoration of growth in the presence of quinofumelin vs. no treatment? The wild type control is missing here. Any conclusions about the relationship between quinofumelin, FgDHODHII, and other metabolites in the pathway?

      Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil.

      (22) Figure 6b:

      Lacking positive and negative controls (known binder and non-binder). What does the Kd (in comparison to other interactions) indicate in terms of binding strength?

      We tested the antifungal activities of publicly reported DHODH inhibitors (such as leflunomide and teriflunomide) against F. graminearum. The results showed that these inhibitors exhibited no significant inhibitory effects against the strain PH-1. Therefore, we lacked an effective chemical for use as a positive control in subsequent experiments. Biacore experiments offers detailed insights into molecular interactions between quinofumelin and DHODHII. As shown in Figure 6b, the left panel illustrates the time-dependent kinetic curve of quinofumelin binding to DHODHII. Within the first 60 s after quinofumelin was introduced onto the DHODHII surface, it bound to the immobilized DHODHII on the chip surface, with the response value increasing proportionally to the quinofumelin concentration. Following cessation of the injection at 60 s, quinofumelin spontaneously dissociated from the DHODHII surface, leading to a corresponding decrease in the response value. The data fitting curve presented on the right panel indicates that the affinity constant KD of quinofumelin for DHODHII is 6.606×10-6 M, which falls within the typical range of KD values (10-3 ~ 10-6 M) for protein-small molecule interaction patterns. A lower KD value indicates a stronger affinity; thus, quinofumelin exhibits strong binding affinity towards DHODHII.

      Reviewer #3 (Recommendations for the authors):

      The authors should add information about the other molecule within this class, ipflufenoquin, and what is known about it. There are already published data on its mode of action on DHODH and the role of pyrimidine biosynthesis.

      We have added the information regarding action mechanism of ipflufenoquin against filamentous fungi in discussion section.

      The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

      We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions.

      It is unclear how the protein model was established and this should be included. What species is the molecule from and how was it obtained? How are they different from Fusarium?

      The three-dimensional structural model of F. graminearum DHODHII protein, as predicted by AlphaFold, was obtained from the UniProt database. Additionally, a detailed description along with appropriate citations has been incorporated in the ‘Manuscript’ file.

    1. Author response:

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

      Reviewer #1 (Recommendations for the authors):

      Suggestions:

      Although this study has an impressive dataset, I felt that some parts of the discussion would benefit from further explanation, specifically when discussing the differences in female aggression direction between groups with different sex compositions. In the discussion is suggested that males buffer female-on-female aggression and that they 'support' lower-ranking females (see line 212), however, the study only tested the sex composition of the group and does not provide any evidence of this buffering. Thus, I would suggest adding more information on how this buffering or protection from males might manifest (for example, listing male behaviours that might showcase this protection) or referencing other studies that support this claim. Another example of this can be found in lines 223-224, which suggests that females choose lower-ranking individuals when they are presented with a larger pool of competitors; however, in lines 227-228, it's stated that this result contradicts previous work in baboons, which makes the previous claim seem unjustified. I recommend adding other examples from studies that support the results of this paper and adding a line that addresses reasons why these differences between gorillas and baboons might be caused (for example, different social dynamics or ecological constraints). In addition, I suggest the inclusion of physiological data such as direct measures of energy expenditure, caloric intake, or hormone levels, as it would strengthen the claims made in the second paragraph of the discussion. However, I understand this might not be possible due to data or time constraints, so I suggest adding more robust justification on why lactation and pregnancy were used as a proxy for energetic need. In the methods (lines 127-128), it is unclear which phase of the pregnancy or lactation is more energetically demanding. I would also suggest adding a comment on the limitations of using reproductive state to infer energetic need. Lastly, if the data is available, I believe it would be interesting to add body size and age of the females or the size difference between aggressor and target as explanatory variables in the models to test if physiological characteristics influence female-on-female aggression.

      Male support:

      We have now added more references (Watts 1994, 1997) and enriched our arguments regarding male presence buffering aggression. Previous research suggests that male gorillas may support lower-ranking females and they may intervene in female-female conflicts (Sicotte 2002). Unfortunately, our dataset did not allow us to test for male protection. We conduct proximity scans every 10 minutes and these scans are not associated to each interaction, meaning that we cannot reliably test if proximity to a male influences the likelyhood to receive aggression.

      Number of competitors and choice of weaker competitors:

      We added a very relevant reference in humans, showing that people choose weaker competitors when they have they can choose. We removed the example to baboons because it used sex ratio and the relevance to our study was not that straightforward.

      Reproductive state as a proxy for energetic needs:

      We now mention clearly that reproductive state is an indirect measure of energetic needs.

      We rephrased our methods to: “Lactation is often considered more energetically demanding than pregnancy as a whole but the latest stages of pregnancy are highly energetically demanding, potentially even more than lactation”

      Unfortunately, we do not have access to physiological and body size data. Regarding female age, for many females, ages are estimates with errors up to a decade, and thus, we choose not to use them as a reliable predictor. Having accurate values for all these variables, would indeed be very valuable and improve the predicting power of our study.

      Recommendations for writing and presentation:

      Overall, the manuscript is well-organised and well-written, but there are certain areas that could improve in clarity. In the introduction, I believe that the term 'aggression heuristic' should be introduced earlier and properly defined in order to accommodate a broader audience. The main question and aims of the study are not stated clearly in the last paragraph of the introduction. In the methods, I think it would improve the clarity to add a table for the classification of each type of agonistic interactions instead of naming them in the text. For example, a table that showcase the three intensity categories (severe, mild and moderate), than then dives into each behaviour (e.g. hit, bite, attack, etc.) and a short description of these behaviours, I think this would be helpful since some of the behaviours mentioned can be confusing (what's the difference between attack, hit and fight?). In addition, in line 104, it states that all interactions were assigned equal intensity, which needs to be explained.

      We now define aggression heuristics in both the abstract and the first paragraph of the introduction. We have also explained aggressive interactions that their nature was not obvious from their names. Hopefully, these explanations make clear the differences among the recorded behaviours.

      We have now specified that the “equal intensity” refers to avoidances and displacements used to infer power relationships: “We assigned to all avoidance/displacement interactions equal intensity, that is, equal influence to the power relationship of the interacting individuals”

      Minor corrections:

      (1) In line 41, there is a 1 after 'similar'. I am unsure if it's a mistake or a reference.

      We corrected the typo.

      (2) In lines 68-69, there is mention of other studies, but no references are provided.

      We added citations as suggested.

      (3) Remove the reference to Figure 1 (line 82) from the introduction; the figure should be referenced in the text just before the image, however, your figure is in a different section.

      We removed the reference as suggested.

      (4) Line 98 and 136, it's written 'ad libtum' but the correct spelling is 'ad libitum'.

      We corrected the typo.

      (5) Figure 3, remove the underscores between the words in the axis titles.

      We removed the underscores.

      Reviewer #2 (Recommendations for the authors):

      Here, I have outlined some specific suggestions that require attention. Addressing these comments will enhance the readability and enhance the quality of the manuscript.

      (1) L69. Add citation here, indicating the studies focusing on aggression rates.

      We added citations as suggested.

      (2) L88. The study periods used in this study and the authors' previous study (Reference 11) are different. So please add one table as Table 1 showing the details info on the sampling efforts and data included in their analysis of this study. For example, the study period, the numbers of females and males, sampling hours, the number of avoidance/displacement behaviors used to calculate individual Elo-ratings, and the number of mild/moderate/severe aggressive interactions, etc.

      We have now added another table, as suggested (new Table 1) and we have also made clear that we used the hierarchies presented in detail in (Smit & Robbins 2025).

      (3) L103. If readers do not look over Reference 25 on purpose, they do not know what the authors want to talk about and why they mention the optimized Elo-rating method. Clarify this statement and add more content explaining the differences between the two methods, or just remove it.

      We rephrased the text and in response to the previous comment, we clearly state that there are more details about our approach in Smit & Robbins 2025. At the end of the relevant sentence, we added the following parenthesis “(see “traditional Elo rating method”; we do not use the “optimized Elorating method” as it yields similar results and it is not widely used)” and we removed the sentence referring to the optimized Elo-rating method.

      (4) L110. Here, the authors stated that the individual with the standardized Elo-score 1 was the highest-ranking. L117, the "aggression direction" score of each aggressive interaction was the standardized Elo-score of the aggressor, subtracting that of the recipient. So, when the "aggression direction" score was 1, it should mean that the aggressor was the highest-ranking and the recipient was the lowest-ranking female. This is not as the authors stated in L117-120 (where the description was incorrectly reversed). Please clarify.

      The highest ranking individual has indeed Elo_score equal to 1 and we calculated the interaction score (or "aggression direction score") of each aggressive interaction by subtracting the standardized Elo-score of the aggressor from that of the recipient (Elo_recepient – Elo_aggressor). So, when the aggressor is the lowest-ranking female (Elo_score=0) and the recipient the highestranking female one (Elo_score=1), the "aggression direction score" is 1-0 = 1.

      (5) Regarding point 3 of the Public Review, please also revise/expand the paragraph L193-208 in the Discussion section accordingly.

      Please see our response to the public review. We have enriched the results section, added pairwise comparisons in a new table (Table 2) and modified the discussion accordingly.

      (6) Table 1. It's not clear why authors added the column 'Aggression Rate' but did not provide any explanation in the Methods/Results section. How did they calculate the correlation between each tested variable and the "overall adult female aggression rates"? Correlating the number of females in the first trimester of female pregnancy with the female aggression rates in each study group? What did the correlation coefficients mean? L202-204 may provide some hints as to why the authors introduced the Aggression Rate. But it should be made clear in the previous text.

      We now added more details in the legend of the table to make our point clear: “To highlight that aggression rates can increase due to increase in interactions of different score, we also include the effect of some of the tested variables on overall adult female aggression rates, based on results of linear mixed effects models from (Smit & Robbins 2024).”  We did not include detailed methods to calculate those results because they are detailed in (Smit & Robbins 2024). We find it valuable to show the results of both aggression rates and aggression directionality according to the same predictor variables as a means to clarify that aggression rates and aggression directionality are not always coordinated to one another (they do not always change in a consistent manner relative to one another).

      (7) L166.This is not rigorous. Please rephrase. There is only one western gorilla group containing only one resident male included in the analysis.

      We have toned down our text: “Our results did not show any significant difference between femalefemale aggression patterns within the one western and four mountain gorillas groups”

      (8) L167. I don't think the interaction scores in the third trimester of female pregnancy were significantly higher than those in the first trimester. The same concern applies in L194-195.

      We have now added a new table with post hoc pairwise comparisons among the different reproductive states that clarifies that.

      (9) L202. There is no column 'Aggression rates' in Table 1 of Reference 11.

      We have rephrased to make clear that we refer to Table 1 of the present study.

      (10) L204-205. Reference 49. Maybe not a proper citation here. This claim requires stronger evidence or further justification. Additionally, please rephrase and clarify the arguments in L204208 for better readability and precision.

      We have added three more references and rephrased to clarify our argument.

      Reviewer #3 (Recommendations for the authors):

      (1) Line 41: The word "similar" is misspelled.

      We corrected the typo.

    1. In a last ditch attempt to get Agent to produce something remotely useful, I decided to just… ask what it could do. This

      Testing an AI agent by asking it to describe its own capabilities is a sensible diagnostic step. Different models have different tool awareness—it's good to see you comparing Agents with other models like Claude. It's also important to remember that the list of available tools is controlled by the platform and can change over time.

    1. reply to u/HomosexualTypewriter at https://old.reddit.com/r/typewriters/comments/1m5qd7e/how_to_remove_stuckon_felt/

      How to remove stuck-on felt?

      Generally I've dusted the worst off into the trash and then used a mild soap and soft bristle brush to clean the remainder. You won't get 100%, but it's not visible and doesn't affect performance, so I don't worry about remaining residue. You could try light solvents that won't affect the paint too much or attempt some light sanding. Another alternate is to replace the old foam with new felt and you won't see anything.

      I just pulled out my '57 FP that has foam with a black, molasses-like adhesive to hold it on. Goo Gone works incredibly well at removing that adhesive and any residual foam without damaging the paint. I put a small patch of Goo Gone soaked paper towel on top of the adhesive smudge to let it soak for a few minutes and then was able to relatively easily remove all of the adhesive without any issues. A pass or two removed it pretty quickly.

      My later '61 FP has the somewhat more standard industrial felt which was in reasonable condition, so I've left it on and not tested that.

      Honestly, unless it's really thick or cumbersome and you're replacing it with felt, simply gluing over the original is probably your bet course of action.

    1. Author Response:

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

      Reviewer #1 (Public Review):

      Summary:

      Two important factors in visual performance are the resolving power of the lens and the signal-to-noise ratio of the photoreceptors. These both compete for space: a larger lens has improved resolving power over a smaller one, and longer photoreceptors capture more photons and hence generate responses with lower noise. The current paper explores the tradeoff of these two factors, asking how space should be allocated to maximize eye performance (measured as encoded information).

      Your summary is clear, concise and elegant. The competition is not just for space, it is for space, materials and energy. We  now emphasise that we are considering these three costs in our rewrites of the Abstract and the first paragraph of the Discussion.  

      Strengths:

      The topic of the paper is interesting and not well studied. The approach is clearly described and seems appropriate (with a few exceptions - see weaknesses below). In most cases, the parameter space of the models are well explored and tradeoffs are clear.  

      Weaknesses:

      Light level

      The calculations in the paper assume high light levels (which reduces the number of parameters that need to be considered). The impact of this assumption is not clear. A concern is that the optimization may be quite different at lower light levels. Such a dependence on light level could explain why the model predictions and experiment are not in particularly good agreement. The paper would benefit from exploring this issue.

      Thank you for raising this point. We briefly explained in our original Discussion, under Understanding the adaptive radiation of eyes (Version 1, Iines 756 – 762), how our method can be modified to investigate eyes adapted for lower light levels. We have some thoughts on how eyes might be adapted. In general, transduction rates are increased by increasing D, reducing f, increasing d<sub>rh</sub> and increasing L . In addition, d<sub>rh</sub> is increased to allow for a larger D within the constraint of eye radius/corneal surface area, and to avoid wasteful oversampling (the changes in D, f and d<sub>rh</sub> increase acceptance angle ∆ρ). We suspect that in eyes optimised for the efficient use of space, materials and energy the increases in L will be relatively small, first because  increasing D, reducing f and increasing d<sub>rh</sub> are much more effective at increasing transduction rate than increasing L. Second, increasing sensitivity by reducing f decreases the cost Vo whereas increasing sensitivity by increasing L increases the cost V<sub>ph</sub>. This disadvantage, together with exponential absorption, might explain why L is only 10% - 20% longer in the apposition eyes of nocturnal bees (Somanathan et al, J. comp. Physiol. A195, 571583, 2009). Because this line of argument is speculative and enters new territory, we have not included it in our revised version. We already present a lot of new material for readers to digest, and we agree with referee 2 that “It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory”. Nonetheless, we take your point that some of the eyes in our data set might be adapted for lower light levels, and we have rewritten the Discussion section, How efficiently do insects allocate resources within their apposition eyes accordingly. On line 827 – 843 we address the assumption that eyes are adapted for full daylight,  and also take the opportunity  to mention two more reasons for increasing the eye parameter p: namely increasing image velocity (Snyder, 1979), and constructing  bright zones that increase the detectability of small targets (van Hateren et al., 1989; Straw et al., 2006).

      Discontinuities

      The discontinuities and non-monotonicity of the optimal parameters plotted in Figure 4 are concerning. Are these a numerical artifact? Some discussion of their origin would be quite helpful.

      Good points, we now address the discontinuities in the Results, where they are first observed (lines 311 - 319) 

      Discrepancies between predictions and experiment

      As the authors clearly describe, experimental measurements of eye parameters differ systematically from those predicted. This makes it difficult to know what to take away from the paper. The qualitative arguments about how resources should be allocated are pretty general, and the full model seems a complex way to arrive at those arguments. Could this reflect a failure of one of the assumptions that the model rests on - e.g. high light levels, or that the cost of space for photoreceptors and optics is similar? Given these discrepancies between model and experiment, it is also hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction).

      Your misgivings boil down to two issues: what use is a model that fails to fit the data, and do we need a complicated model to show something that seems to be intuitively obvious?  Our study is useful because it introduces new approaches, methods, factors and explanations which advance our analysis and understanding of eye design and evolution. Your comments make it clear that we failed to get this message across and we have revised the manuscript accordingly. We have rewritten the Abstract and the first paragraph of the Discussion to emphasise the value of our new measure of cost, specific volume, by including more of its practical advantages. In particular, our use of specific volume 1) opens the door to the morphospace of all eyes of given type and cost. 2) This allows one to construct performance surfaces across morphospace that not only identify optima, but by evaluating the sub-optimal cast light on efficiency and adaptability. 3) Shows that photoreceptor energy costs have a major impact on design and efficiency, and 4) allows us to calculate and compare the capacities and efficiencies of compound eyes and simple eyes using a superior measure of cost. It is also possible that your dissatisfaction was deepened by disappointment. The first sentence of our original Abstract said that the goal of design is to maximize performance, so you might have expected to see that eyes are optimised.  Given that optimization provides cast iron proof that a system is designed to be efficient, and previous studies of coding by fly LMCs (Laughlin, 1981; Srinivasan et al., 1982 & van Hateren 1992) validated Barlow’s Efficient Coding Hypothesis by showing that coding is optimised, your expectation is reasonable. However, our investigation of how the allocation of resources to optics and photoreceptors affects an eye’s performance, efficiency and design does not depend a priori  on finding optima, therefore we have removed the “maximized”. Our revised Abstract now says, “to improve performance”.  

      In short, our study illustrates an old adage in statistics “All models fail to fit, but some are useful”. As is often the case, the way in which our model fails is useful. In the original version of the Results and Discussion, we argued that the allocation of resources is efficient, and identified factors that can, in principle, explain the scattering of data points. Indeed, our modelling identifies two of these deficiencies; a lack of data on species-specific energy usage, and the need for models that quantify the relationship between the quality of the captured image and the behavioural tasks for which an eye might be specialised. Thus, by examining the model’s failings we identify critical factors and pose new questions for future research.  We have rewritten the Discussion section How efficiently do insects allocate resources…. to make these points. We hope that these revisions will convince you that we have established a starting point for definitive studies, invented a vehicle that has travelled far enough to discover new territory, and shown that it can be modified to cope with difficult terrain.

      Turning to the need for a complicated model, because the costs and benefits depend on elementary optics and geometry, we too thought that there ought to be a simple model. However, when we tried to formulate a simple set of equations that approximate the definitive findings of our more complicated model we discovered that this is not as straightforward as we thought.  Many of the parameters in our model interact to determine costs and benefits, and many of these interactions are non-linear (e.g. the volumes of shells in spheres involve quadratic and cubic terms, and information depends on the log of a square root). So, rather than hold back publication of our complicated model, we decided to explain how it works as clearly as we can and demonstrate its value.

      In response to your final comment, “it is hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction)”, we stand by our original argument. There must be competition in an eye of fixed cost, and because competition favours a heavy investment in photoreceptors, both in theory and in practice, it  is a significant factor in eye design. A match between investments in optics and photoreceptors is predicted by theory and observed in fly NS eyes, therefore this is a design principle. As for evolution, no one would deny that it is important to view the adaptive radiation of eyes through a cost-benefit lens. Our lens is the first to view the whole eye, optics and photoreceptor array, and the first to treat the costs of space, materials and energy. Although the view through our lens is a bit fuzzy, it reveals that costs, benefits and trade-offs are important. Thus we have established a promising starting point for a new and more comprehensive cost-benefit approach to understanding eye design and evolution.  As for the involvement of genes, when there are heritable changes in phenotype genes must be involved and if, as we suggest, efficient resource allocation is beneficial, the developmental mechanisms responsible for allocating resources to optics and photoreceptor array will be playing a formative role in eye evolution.

      Reviewer #2 (Public Review):

      Summary:

      In short, the paper presents a theoretical framework that predicts how resources should be optimally distributed between receptors and optics in eyes.

      Strengths:

      The authors build on the principle of resource allocation within an organism and develop a formal theory for optimal distribution of resources within an eye between the receptor array and the optics. Because the two parts of eyes, receptor arrays and optics, share the same role of providing visual information to the animal it is possible to isolate these from resource allocation in the rest of the animal. This allows for a novel and powerful way of exploring the principles that govern eye design. By clever and thoughtful assumptions/constraints, the authors have built a formal theory of resource allocation between the receptor array and the optics for two major types of compound eye as well as for camera-type eyes. The theory is formalized with variables that are well characterized in a number of different animal eyes, resulting in testable predictions.

      The authors use the theory to explain a number of design features that depend on different optimal distribution of resources between the receptor array and the optics in different types of eyes. As an example, they successfully explain why eye regions with different spatial resolution should be built in different ways. They also explain differences between different types of eyes, such as long photoreceptors in apposition compound eyes and much shorter receptors in camera type eyes. The predictive power in the theory is impressive.

      To keep the number of parameters at a minimum, the theory was developed for two types of compound eye (neural superposition, and apposition) and for camera-type eyes. It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory.

      The paper extends a previous theory, developed by the senior author, that develops performance surfaces for optimal cost/benefit design of eyes. By combining this with resource allocation between receptors and optics, the theoretical understanding of eye design takes a major leap and provides entirely new sets of predictions and explanations for why eyes are built the way they are.

      The paper is well written and even though the theory development in the Results may be difficult to take in for many biologists, the Discussion very nicely lists all the major predictions under separate headings, and here the text is more tuned for readers that are not entirely comfortable with the formalism of the Results section. I must point out though that the Results section is kept exemplary concise. The figures are excellent and help explain concepts that otherwise may go above the head of many biologists.

      We are heartened by your appreciation of our manuscript - it persuaded us not to undertake extensive revisions – thank you.

      Reviewer #3 (Public Review):

      Summary:

      This is a proposal for a new theory for the geometry of insect eyes. The novel costbenefit function combines the cost of the optical portion with the photoreceptor portion of the eye. These quantities are put on the same footing using a specific (normalized) volume measure, plus an energy factor for the photoreceptor compartment. An optimal information transmission rate then specifies each parameter and resource allocation ratio for a variable total cost. The elegant treatment allows for comparison across a wide range of species and eye types. Simple eyes are found to be several times more efficient across a range of eye parameters than neural superposition eyes. Some trends in eye parameters can be explained by optimal allocation of resources between the optics and photoreceptors compartments of the eye.

      Strengths:

      Data from a variety of species roughly align with rough trends in the cost analysis, e.g. as a function of expanding the length of the photoreceptor compartment.

      New data could be added to the framework once collected, and many species can be compared.

      Eyes of different shapes are compared.

      Weaknesses:

      Detailed quantitative conclusions are not possible given the approximations and simplifying assumptions in the models and poor accounting for trends in the data across eye types.

      Reviewer #1 (Recommendations For The Authors):

      Figure 1: Panel E defines the parameters described in panel d. Consider swapping the order of those panels (or defining D and Delta Phi in the figure legend for d). Order follows narrative, eye types then match 

      We think that you are referring to Figure 1. We modified the legend.

      Lines 143-145: How does a different relative cost impact your results?

      Thank you for raising this question. Because our assumption that relative costs are the same is our starting point, and for optics it is not an obvious mistake, we do not raise your question here. We address your question where you next raise it because, for photoreceptors the assumption is obviously wrong.  We now emphasise that our method for accounting for photoreceptor energy costs can be applied to other costs. 

      Lines 187-190: Same as above - how do your results change if this assumption is not accurate?

      We have revised our manuscript to emphasise that we are dealing with the situation in which our initial assumption (costs per unit volume are equal) breaks down. On (lines 203 - 208) we write “ However, this assumption breaks down when we consider specific metabolic rates. To enable and power phototransduction, photoreceptors have an exceptionally high specific metabolic rate (energy consumed per gram, and hence unit volume, per second) (Laughlin et al., 1998; Niven et al., 2007; Pangršič et al., 2005). We account for this extra cost by applying an energy surcharge, S<sub>E</sub>. To equate…. 

      We also revised part of the Discussion section, Specific volume is a useful measure of cost to make it clear that we are able take account for situations in which the costs per unit volume are not equal, and we give our treatment of photoreceptor energy costs as an example of how this is done. On lines 626 - 640 we say  

      Cost estimates can be adjusted for situations in which costs per unit volume are not equal, as illustratedby our treatment of photoreceptor energy consumption.  To support transduction the photoreceptor array has an exceptionally high metabolic rate (Laughlin et al., 1998; Niven et al., 2007; Pangršič et al., 2005). We account forthis higher energy cost by using the animal’s specific metabolic rate (power per unit mass and hence power per unit volume) to convert an array’s power consumption into an equivalent volume (Methods). Photoreceptor ion pumps are the major consumers of energy and the smaller contribution of pigmented glia (Coles, 1989) is included in our calculation of the energy tariff K<sub>E</sub>. (Methods) The higher costs of materials and their turnover in the photoreceptor array can be added the energy tariff K<sub>E</sub> but given the magnitude of the light-gated current (Laughlin et al., 1998) the relative increase will be very small. Thus for our intents and purposes the effects of these additional costs are covered by our models. For want of sufficient data…”.

      Reviewer #2 (Recommendations For The Authors):

      A few comments for consideration by the authors:

      (1) In the abstract, Maybe give another example explaining why other eyes should be different to those of fast diurnal insects.

      This worthwhile extrapolation is best kept to the Discussion.

      (2) Would it be worthwhile mentioning that the photopigment density is low in rhabdoms compared to vertebrate outer segments? This will have major effects on the relative size of retina and optics.

      Thank you, we now make this good point in the Discussion (lines 698-702).

      (3) It took me a while to understand what you mean by an energy tariff. For the less initiated reader many other variables may be difficult to comprehend. A possible remedy would be to make a table with all variables explained first very briefly in a formal way and then explained again with a few more words for readers less fluent in the formalism.

      A very useful suggestion. We have taken your advice (p.4).

      (4) The "easy explanation" on lines 356-357 need a few more words to be understandable.

      We have expanded this argument, and corrected a mistake, the width of the head front to back is not 250 μm, it is 600 μm (lines 402-407)

      (5) Maybe devote a short paragraph in the Discussion to other types of eye, such as optical superposition eyes and pinhole eyes. This could be done very shortly and without formalism. I'm sure the authors already have a good idea of the optimal ratio of receptor arrays and optics in these eye types.

      We do not discuss this because we have not found a full account of the trade-offs and their  effects on costs and benefits. We hope that our analysis of apposition and simple eyes will encourage people to analyse the relationships between costs and benefits in other eye types. To this end we pointed out in the Discussion that recent advances in imaging and modelling could be helpful.

      (6)  Could the sentence on lines 668-671 be made a little clearer?

      “Efficiency is also depressed by increasing the photoreceptor energy tariff K<sub>E</sub>, and in line with the greater impact of photoreceptor energy costs in simple eyes, the reduction in efficiency is much greater in simple eyes (Figure 8b).0.

      We replaced this sentence with “In both simple and apposition eyes efficiency is reduced by increasing the photoreceptor energy tariff K<sub>E</sub>. This effect is much greater in simple eyes, thus as found for reductions in photoreceptor length (Figure 7b),K<sub>E</sub> has more impact on the design of simple eyes” (lines749 – 752).

      (7)  I have some reservations about the text on lines 789-796. The problem is that optics can do very little to improve the performance of a directional photoreceptor where delrho should optimally be very wide. Here, membrane folding is the only efficient way to improve performance (SNR). The option to reduce delrho for better performance comes later when simultaneous spatial resolution (multiple pixels) is introduced.

      Yes, we have been careless. We have rewritten this paragraph to say (lines 920-931)

      “Two key steps in the evolution of eyes were the stacking of photoreceptive membranes to absorb more photons, and the formation of optics to intercept more photons and concentrate them according to angle of incidence to form an image (Nilsson, 2013, 2021). Our modelling of well-developed image forming eyes shows that to improve performance stacked membranes (rhabdomeres) compete with optics for the resources invested in an eye, and this competition profoundly influences both form and function. It is likely that competition between optics and photoreceptors was shaping eyes as lenses evolved to support low resolution spatial vision. Thus the developmental mechanisms that allocate resources within modern high resolution eyes (Casares & MacGregor, 2021), by controlling cell size and shape, and as our study emphasises, gradients in size and shape across an eye, will have analogues or homologues in more ancient eyes. Their discovery….” (lines 920-931

      Reviewer #3 (Recommendations For The Authors):

      Suggestions for major revisions:

      While the approach is novel and elegant, the results from the analysis of insect morphology do not broadly support the optimization argument and hardly constrain parameters, like the energy tariff value, at all. The most striking result of the paper is the flat plateau in information across a broad range of shape parameters and the length, and resolution trend in Figure 5.

      At no point in the Results and Discussion do we argue that resource allocation is optimized. Indeed, we frequently observe that it is not. Our mistake was to start the Abstract by observing that animals evolve to minimise costs. We have rewritten the Abstract accordingly.

      The information peaks are quite shallow. This might actually be a very important and interesting result in the paper - the fact that the information plateaus could give the insect eye quite a wide range of parameters to slide between while achieving relatively efficient sensing of the environment. Instead of attempting to use a rather ad hoc and poorly supported measure of energetics in PR cost, perhaps the pitch could focus on this flexibility. K<sub>E</sub> does not seem to constrain eye parameters and does not add much to the paper.

      We agree, being able to construct performance surfaces across morphospace is an important advance in the field of eye design and evolution, and the performance surface’s flat top has interesting implications for the evolution of adaptations. Encouraged by your remarks, we have rewritten the Abstract and the introductory paragraph of the Discussion to draw attention to these points. 

      We are disappointed that we failed to convince you that our energy tariff, K<sub>E</sub> , is no better than a poorly supported ad hoc parameter that does not add much to the paper. In our opinion a resource allocation model that ignores photoreceptor energy consumption is obviously inadequate because the high energy cost of phototransduction is both wellknown and considered to be a formative factor in eye evolution (Niven and Laughlin, 2008). One of the advantages of modelling is that one can assess the impact of factors that are known to be present, are thought to be important, but have not been quantified. We followed standard modelling practice by introducing a cost that has the same units as the other costs and, for good physiological reasons, increases linearly with the number of microvilli, according to K<sub>E</sub>. We then vary this unknown cost parameter to discover when and why it is significant. We were pleased to discover that we could combine data on photoreceptor energy demands and whole animal metabolic rates to establish the likely range of K<sub>E</sub>. This procedure enabled us to unify the cost-benefit analyses of optics and photoreceptors, and to discover that realistic values of K<sub>E</sub> have a profound impact on the structure and performance of an efficient eye. We hope that this advance will encourage people to collect the data needed to evaluate K<sub>E</sub>.To emphasise the importance of K<sub>E</sub> and dispel doubts associated with the failure of the model to fit the data, we have revised two sections:  Flies invest efficiently in costly photoreceptor arrays in the Results, and How efficiently do insects allocate resources within their apposition eyes?  in the Discussion. These rewrites also explain why it is impossible for us to infer K<sub>E</sub> by adjusting its value so that the model’s predictions fit the data.

      The graphics after Figure 3 are quite dense and hard to follow. None of the plateau extent shown in Fig 3 is carried through to the subsequent plots, which makes the conclusions drawn from these figures very hard to parse. If the peak information occurs on a flat plateau, it would be more helpful to see those ranges of parameters displayed in the figures.

      Ideally one should do as you suggest and plot the extent of the plateau, but in our situation this is not very helpful. In the best data set, flies, optimised models predict D well, get close to ∆φ in larger eyes, and demonstrate that these optimum values are not very sensitive to K<sub>E</sub> L is a different matter, it is very sensitive to K<sub>E</sub> L which, as we show (and frequently remind) is poorly constrained by experimental data. The best we can do is estimate the envelope of L vs C<sub>tot</sub>  curves, as defined by a plausible range of K<sub>E</sub>L . Because most of the plateau boundaries you ask for will fall within this envelope, plotting them does little to clear the fog of uncertainty. We note that all three referees agree that our model can account for two robust trends, i) in apposition eyes L increase with optical resolving power and acuity, both within individual eyes and among eyes of different sizes, and ii) L is much longer is apposition eyes than in simple eyes. Nonetheless, the scatter of data points and their failure to fit creates a bad impression. We gave a number of reasons why the model does not fit the data points, but these were scattered throughout the Results and Discussion and, as referees 1 and 3 point out, this makes it difficult to draw convincing conclusions. To rectify this failing, we have rewritten two sections, in the Results Flies invest efficiently in costly photoreceptor arrays and in the Discussion, How efficiently do insects allocate resources within their apposition eyes?, to discuss these reasons en bloc, draw conclusions and suggest how better data and refinements to modelling could resolve these issues.  

      Throughout the figures, the discontinuities in the optimal cuts through parameter space are not sufficiently explained.

      We added a couple of sentences that address the “jumps” (lines 313 – 318)

      None of the data seems to hug any of the optimal lines and only weakly follow the trends shown in the plots. This makes interpretation difficult for the reader and should be better explained. The text can be a little telegraphic in the Results after roughly page 10, and requires several readings to glean insight into the manuscript's conclusions.

      We revised the Results section in which we compare the best data set, flies’  NS eyes with theoretical predictions, Flies invest efficiently in costly photoreceptor arrays,  to expand our interpretation of the data and clarify our arguments. The remaining sections have not been expanded. In the next section, which is on fused rhabdom apposition eyes, our interpretation of the scattering of data points follows the same line of argument. The remaining Results sections are entirely theoretical.  

      Overall, the rough conclusions outlined in the Results seem moderately supported by the matches of the data to the optimal information transmission cuts through parameter space, but only weakly.

      We agree, more data is required to test and refine our theoretical predictions.

      The Discussion is long and well-argued, and contains the most cogent writing in the manuscript.

      Thank you: this is most pleasing. We submitted our study to eLife because it allows longer Discussions, but we worried that ours was too long. However, we felt that our extensive Discussion was necessary for two reasons. First, we are introducing a new approach to understanding of eye design and evolution. Second, because the data on eye morphology and costs are limited, we had to make a number of assumptions and by discussing these, warts and all, we hoped to encourage experimentalists to gather more data and focus their efforts on the most revealing material.  

      Minor comments:

      We have acted upon most of your minor comments and we confine our remarks to our disagreements. We are grateful for your attention to details that we \textshould have picked up on.  

      It's a more standard convention to say "cost-benefit" rather than with a colon. 

      "equation" should be abbreviated "eq" or "eqn", never with a "t"

      when referring to the work of van Hateren, quote the paper and the database using "van Hateren" not just "Hateren"

      small latex note: use "\textit{SNR}" to get the proper formatting for those letters when in the math environment

      Line 100-110: "f" is introduced, but only f' is referenced in the figure. This should be explained in order. d_rh is not included in the figure. Also in this section, d_rh/f is also referenced before \Delta \rho_rf, which is the same quantity, without explanation.  

      Figure 1 shows eye structure and geometry. f’ is a lineal dimension of the eye but f is not, so f is not shown in Fig 1e. We eliminated the confusion surrounding ∆ρ<sub>rh</sub>  by deleting “and changing the acceptance angle of the photoreceptive waveguide ∆ρ<sub>rh</sub> (Snyder, 1979)”.  

      Fig 1 caption: this says "From dorsal to ventral," then describes trends that run ventral to dorsal, which is a confusing typo.

      Fig 3 - adding some data points to these plots might help the reader understand how (or if) K_E is constrained by the data.

      It is not possible to add data points because to total cost, Ctot ,is unknown.

      Fig 4c (and in other subplots): the jumps in L with C_tot could be explained better in the text - it wasn't clear to this reviewer why there are these discontinuities.

      Dealt with in the revised text (lines  310-318).

      Fig 4d: The caption for this subplot could be more clearly written.

      We have rewritten the subscript for subplot 4d.

      Fig 5 and other plots with data: please indicate which symbols are samples from the same species. This info is hard to reconstruct from the tables.

      We have revised Figure 5 accordingly. Species were already indicated in Figure 6.

      Line 328: missing equation number

    1. How can you tell when someone has real potential in pure mathematics?

      question by u/OkGreen7335 at https://reddit.com/r/math/comments/1m0qe7f/how_can_you_tell_when_someone_has_real_potential/

      The same way the music teacher in Liverpool who had half of The Beatles in his elementary school music class knew they had music potential—you can't possibly.

      Potential is by definition the unknown part. The rest of it is interest, desire, enthusiasm, and time working at the thing itself over long periods which slowly unleashes that potential. You don't know until you try, so quit worrying about it and enjoy the area, even if it's just as a hobby you do on the side. There are garage bands that hustle on the side, why can't you be a garage mathematician?!?

      Most of the smart, talented university professors in mathematics are there because they had the passion and (often had the luxury to) spend the time. Nurture your own passions and those of your students and encourage them to spend the time.

      How many parents unabashedly encourage their kids to become international superstar musicians? I'll bet The Beatles' parents didn't. I'll also bet that number is close to the numbers of parents who encourage their kids to do the same thing in math.

    1. Netiquette describes the rules of acceptable ways of communicating online. Just as there is etiquette in person (not standing too close, speaking too loudly, or interrupting), there are rules for polite behavior in an online setting. Be respectful. There are real people with real feelings reading your comments.  Use your online nonverbal communication carefully. Be aware of too many emojis, all caps, or exclamation points – for some, this feels as though you are yelling.Be careful with humor or sarcasm as it may not translate online.  Consider the impression you are making with every post. Who are you online? Remember, grammar and spelling matter.  Don’t post inappropriate material.  Don’t post something that is too long. Respect other people’s time.Keep disagreement healthy. Some conversations are better in person. You can agree to disagree.  Respect your own privacy. Don’t post anything too personal.  When in doubt, stick to rules of conduct you follow in real life.Enjoy the experience of connecting with people all over the world. It’s an opportunity and blessing—as long as you use it this way.
    1. Reviewer #2 (Public review):

      The revised manuscript by Altan et al. includes some real improvements to the visualizations and explanations of the authors' thesis statement with respect to fMRI measurements of pRF sizes. In particular, the deposition of the paper's data has allowed me to probe and refine several of my previous concerns. While I still have major concerns about how the data are presented in the current draft of the manuscript, my skepticism about data quality overall has been much alleviated. Note that this review focuses almost exclusively on the fMRI data as I was satisfied with the quality of the psychophysical data and analyses in my previous review.

      Major Concerns

      (I) Statistical Analysis

      In my previous review, I raised the concern that the small sample size combined with the noisiness of the fMRI data, a lack of clarity about some of the statistics, and a lack of code/data likely combine to make this paper difficult or impossible to reproduce as it stands. The authors have since addressed several aspects of this concern, most importantly by depositing their data. However their response leaves some major questions, which I detail below.

      First of all, the authors claim in their response to the previous review that the small sample size is not an issue because large samples are not necessary to obtain "conclusive" results. They are, of course, technically correct that a small sample size can yield significant results, but the response misses the point entirely. In fact, small samples are more likely than large samples to erroneously yield a significant result (Button et al., 2013, DOI:10.1038/nrn3475), especially when noise is high. The response by the authors cites Schwarzkopf & Huang (2024) to support their methods on this front. After reading the paper, I fail to see how it is at all relevant to the manuscript at hand or the criticism raised in the previous review. Schwarzkopf & Huang propose a statistical framework that is narrowly tailored to situations where one is already certain that some phenomenon (like the adaptation of pRF size to spatial frequency) either always occurs or never occurs. Such a framework is invalid if one cannot be certain that, for example, pRF size adapts in 98% of people but not the remaining 2%. Even if the paper were relevant to the current study, the authors don't cite this paper, use its framework, or admit the assumptions it requires in the current manuscript. The observation that a small dataset can theoretically lead to significance under a set of assumptions not appropriate for the current manuscript is not a serious response to the concern that this manuscript may not be reproducible.

      To overcome this concern, the authors should provide clear descriptions of their statistical analyses and explanations of why these analyses are appropriate for the data. Ideally, source code should be published that demonstrates how the statistical tests were run on the published data. (I was unable to find any such source code in the OSF repository.) If the effects in the paper were much stronger, this level of rigor might not be strictly necessary, but the data currently give the impression of being right near the boundary of significance, and the manuscript's analyses needs to reflect that. The descriptions in the text were helpful, but I was only able to approximately reproduce the authors analyses based on these descriptions alone. Specifically, I attempted to reproduce the Mood's median tests described in the second paragraph of section 3.2 after filtering the data based on the criteria described in the final paragraph of section 3.1. I found that 7/8 (V1), 7/8 (V2), 5/8 (V3), 5/8 (V4), and 4/8 (V3A) subjects passed the median test when accounting for the (40) multiple comparisons. These results are reasonably close to those reported in the manuscript and might just differ based on the multiple comparisons strategy used (which I did not find documented in the manuscript). However, Mood's median test does not test the direction of the difference-just whether the medians are different-so I additionally required that the median sigma of the high-adapted pRFs be greater than that of the low-adapted pRFs. Surprisingly, in V1 and V3, one subject each (not the same subject) failed this part of the test, meaning that they had significant differences between conditions but in the wrong direction. This leaves 6/8 (V1), 7/8 (V2), 4/8 (V3), 5/8 (V4), and 4/8 (V3A) subjects that appear to support the authors' conclusions. As the authors mention, however, this set of analyses runs the risk of comparing different parts of cortex, so I also performed Wilcox signed-rank tests on the (paired) vertex data for which both the high-adapted and low-adapted conditions passed all the authors' stated thresholds. These results largely agreed with the median test (only 5/8 subjects significant in V1 but 6/8 in in V3A, other areas the same, though the two tests did not always agree which subjects had significant differences). These analyses were of course performed by a reviewer with a reviewer's time commitment to the project and shouldn't be considered a replacement for the authors' expertise with their own data. If the authors think that I have made a mistake in these calculations, then the best way to refute them would be to publish the source code they used to threshold the data and to perform the same tests.

      Setting aside the precise values of the relevant tests, we should also consider whether 5 of 8 subjects showing a significant effect (as they report for V3, for example) should count as significant evidence of the effect? If one assumes, as a null hypothesis, that there is no difference between the two conditions in V3 and that all differences are purely noise, then a binomial test across subjects would be appropriate. Even if 6 of 8 subjects show the effect, however (and ignoring multiple comparisons), the p-value of a one-sided binomial test is not significant at the 0.05 level (7 of 8 subjects is barely significant). Of course, a more rigorous way to approach this question could be something like an ANOVA, and the authors use an ANOVA analysis of the medians in the paragraph following their use of Mood's median test. However, ANOVA assumes normality, and the authors state in the previous paragraph that they employed Mood's median test because "the distribution of the pRF sizes is zero-bounded and highly skewed" so this choice does not make sense. The Central Limits Theorem might be applied to the medians in theory, but with only 8 subjects and with an underlying distribution of pRF sizes that is non-negative, the relevant data will almost certainly not be normally distributed. These tests should probably be something like a Kruskal-Wallis ANOVA on ranks.

      All of the above said, my intuition about the data is currently that there are significant changes to the adapted pRF size in V2. I am not currently convinced that the effects in other visual areas are significant, and I suspect that the paper would be improved if authors abandoned their claims that areas other than V2 show a substantial effect. Importantly, I don't think this causes the paper to lose any impact-in fact, if the authors agree with my assessments, then the paper might be improved by focusing on V2. Specifically, the authors' already discuss psychophysical work related to the perception of texture on pages 18 and 19 and link it to their results. V2 is also implicated in the perception of texture (see, for example, Freeman et al., 2013; DOI:10.1038/nn.3402; Ziemba et al., 2016, DOI:10.1073/pnas.1510847113; Ziemba et al., 2019; DOI:10.1523/JNEUROSCI.1743-19.2019) and so would naturally be the part of the visual cortex where one might predict that spatial frequency adaptation would have a strong effect on pRF size. This neatly connects the psychophysical and imaging sides of this project and could make a very nice story out of the present work.

      (II) Visualizations

      The manuscript's visual evidence regarding the pRF data also remains fairly weak (but I found the pRF size comparisons in the OSF repository and Figure S1 to be better evidence-more in the next paragraph). The first line of the Results section still states, "A visual inspection on the pRF size maps in Figure 4c clearly shows a difference between the two conditions, which is evident in all regions." As I mentioned in my previous review, I don't agree with this claim (specifically, that it is clear). My impression when I look at these plots is of similarity between the maps, and, where there is dissimilarity, of likely artifacts. For example, the splotch of cortex near the upper vertical meridian (ventral boundary) of V1 that shows up in yellow in the upper plot but not the lower plot also has a weirdly high eccentricity and a polar angle near the opposite vertical meridian: almost certainly not the actual tuning of that patch of cortex. If this is the clearest example subject in the dataset, then the effect looks to me to be very small and inconsistently distributed across the visual areas. That said, I'm not convinced that the problem here is the data-rather, I think it's just very hard to communicate a small difference in parameter tuning across a visual area using this kind of side-by-side figure. I think that Figure S2, though noisy (as pRF maps typically are), is more convincing than Figure 4c, personally. For what it's worth, when looking at the data myself, I found that plotting log(𝜎(H) / 𝜎(L)), which will be unstable when noise causes 𝜎(H) or 𝜎(L) to approach zero, was less useful than plotting plotting (𝜎(H) - 𝜎(L)) / (𝜎(H) + 𝜎(L)). This latter quantity will be constrained between -1 and 1 and shows something like a proportional change in the pRF size (and thus should be more comparable across eccentricity).

      In my opinion, the inclusion of the pRF size comparison plots in the OSF repository and Figure S1 made a stronger case than any of the plots of the cortical surface. I would suggest putting these on log-log plots since the distribution of pRF size (like eccentricity) is approximately exponential on the cortical surface. As-is, it's clear in many plots that there is a big splotch of data in the compressed lower left corner, but it's hard to get a sense for how these should be compared to the upper right expanse of the plots. It is frequently hard to tell whether there is a greater concentration of points above or below the line of equality in the lower left corner as well, and this is fairly central to the paper's claims. My intuition is that the upper right is showing relatively little data (maybe 10%?), but these data are very emphasized by the current plots.
The authors might even want to consider putting a collection of these scatter-plots (or maybe just subject 007, or possible all subjects' pRFs on a single scatter-plot) in the main paper and using these visualizations to provide intuitive supporting for the main conclusions about the fMRI data (where the manuscript currently use Figure 4c for visual intuition).

      Minor Comments

      (1) Although eLife does not strictly require it, I would like to see more of the authors' code deposited along with the data (especially the code for calculating the statistics that were mentioned above). I do appreciate the simulation code that the authors added in the latest submission (largely added in response to my criticism in the previous reviews), and I'll admit that it helped me understand where the authors were coming from, but it also contains a bug and thus makes a good example of why I'd like to see more of the authors' code. If we set aside the scientific question of whether the simulation is representative of an fMRI voxel (more in Minor Comment 5, below), Figures 1A and the "AdaptaionEffectSimulated.png" file from the repository (https://osf.io/d5agf) imply that only small RFs were excluded in the high-adapted condition and only large RFs were excluded in the low-adapted condition. However, the script provided (SimlatePrfAdaptation.m: https://osf.io/u4d2h) does not do this. Lines 7 and 8 of the script set the small and large cutoffs at the 30th and 70th percentiles, respectively, then exclude everything greater than the 30th percentile in the "Large RFs adapted out" condition (lines 19-21) and exclude anything less than the 70th percentile in the "Small RFs adapted out" condition (lines 27-29). So the figures imply that they are representing 70% of the data but they are in fact representing only the most extreme 30% of the data. (Moreover, I was unable to run the script because it contains hard-coded paths to code in someone's home directory.) Just to be clear, these kinds of bugs are quite common in scientific code, and this bug was almost certainly an honest mistake.

      (2) I also noticed that the individual subject scatter-plots of high versus low adapted pRF sizes on the OSF seem to occasionally have a large concentration of values on the x=0 and y=0 axes. This isn't really a big deal in the plots, but the manuscript states that "we denoised the pRF data to remove artifactual vertices where at least one of the following criteria was met: (1) sigma values were equal to or less than zero ..." so I would encourage the authors to double-check that the rest of their analysis code was run with the stated filtering.

      (3) The manuscript also says that the median test was performed "on the raw pRF size values". I'm not really sure what the "raw" means here. Does this refer to pRF sizes without thresholding applied?

      (4) The eccentricity data are much clearer now with the additional comments from the authors and the full set of maps; my concerns about this point have been met.

      (5) Regarding the simulation of RFs in a voxel (setting aside the bug), I will admit both to hoping for a more biologically-grounded situation and to nonetheless understanding where the authors are coming from based on the provided example. What I mean by biologically-grounded: something like, assume a 2.5-mm isotropic voxel aligned to the surface of V1 at 4{degree sign} of eccentricity; the voxel would span X to Y degrees of eccentricity, and we predict Z neurons with RFs in this voxel with a distribution of RF sizes at that eccentricity from [reference], etc. eventually demonstrating a plausible pRF size change commensurate to the paper's measurements. I do think that a simulation like this would make the paper more compelling, but I'll acknowledge that it probably isn't necessary and might be beyond the scope here.

    1. "El Apagon" is another great political statement. It's talking about Puerto Rico and the power outage that happened post Maria.THOMPSON: It's a hard-driving, like, danceable track.SAYRE: It's a hard-driving, danceable track. And that's what I'm saying - where he plays with that nuance again. He's like, oh, you want to dance in the club? Okay, let's dance in the club. But we're going to be talking about Puerto Rican politics while we do it.

      They’re not just hyping a record. They’re breaking down how Bad Bunny uses his platform. The goal here is to inform the audience that there’s meaning under the surface, not just beats and hooks.

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

      Learn more at Review Commons


      Reply to the reviewers

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

      As stated by the authors in the introduction, the RNA-binding protein Sxl is foundational to understanding sex determination in Drosophila. Sxl has been extensively studied as the master regulator of female sex determination in the soma, where it is known to initiate an alternative splicing cascade leading to the expression of DsxF. Additionally, Sxl has been shown to be responsible for keeping X chromosome dosage compensation off in females, while males hyperactivate their X chromosome. While these roles have been well defined, the authors explore an aspect of Sxl that is quite separate from its role as master regulator of female fate. They describe Sxl-RAC, a Sxl isoform that is expressed in the male and female nervous system. Using several genomic techniques, the authors conclude that the Sxl-RAC isoform associates with chromatin in a similar pattern to the RNA polymerase II/III subunit, Polr3E, and Sxl depends on Polr3E for chromatin-association. Further, neuronal loss of Sxl causes changes in lifetime and geotaxis in a similar manner as loss of Polr3E. The work is thorough and significant and should be appropriate for publication if a few issues can be addressed.

      Major Concerns:*

      * 1) How physiological is the Sxl chromatin-association assay? As binding interactions are concentration-dependent, how similar is Sxl-DAM expression to wt Sxl expression in neurons? In addition, does the Sxl-DAM protein function as a wt Sxl protein? Does UAS-Sxl-DAM rescue any Sxl loss phenotypes?*

      Author response:

      As Reviewer 3 correctly notes, Targeted DamID relies on ribosomal re-initiation (codon slippage) to produce only trace amounts of the Dam-fusion protein. By design, this results in expression levels that are significantly lower than those of the endogenous protein. As such, the experiment can be interpreted within a near–wild-type context, rather than as an overexpression model. The primary aim of this experiment was to determine whether Sxl associates with chromatin, and our dataset provides clear evidence supporting such binding.

      2) Is Polr3E chromatin-association also dependent on Sxl? They should do the reciprocal experiment to their examination of Sxl chromatin-association in Polr3E knockdown. This might also help address point 1-if wt Sxl is normally required for aspects of Polr3E chromatin binding, then concerns about whether the Sxl-DAM chromatin-association is real or artifactual would be assuaged.

      Author response:

      This is an interesting thought, however, if Sxl were required for Polr3E recruitment to RNA Pol III, then, in most male Drosophila melanogaster cells, Polr3E would not be incorporated, and males would not be viable (as it is essential for Pol III activity). While it is possible that there could be a subtle effect on Polr3E recruitment, such an experiment, would not alter the central conclusion of our study - that Sxl is recruited to chromatin (accessory to the Pol III complex) via Polr3E.

      Minor concerns:

      * The observed Sxl loss of function phenotypes are somewhat subtle (although perhaps any behavior phenotype at all is a plus). Did they try any other behaviour assays-courtship, learning/memory, anything else at all to test nervous system function?*


      Author response:

      Given the exploratory nature of this study, we focused on broader behavioural and transcriptional assays.

      While well written, it is sometimes difficult to understand how the experiment was performed or what genotypes were used without looking into the methods sections. One example is they should describe the nature of the Sxl-DAM fusion protein clearly in the results.

      Author response:

      We will revise these sections to improve clarity and ensure there is no confusion.

      * Reviewer #1 (Significance (Required)):

      This manuscript represents a dramatic change in our thinking about the action of the Sex-lethal protein. Previously, Sxl was known as the master regulator of both sex determination and dosage compensation, and performed these roles as an RNA-binding protein affecting RNA splicing and translational regulation. Here, the authors describe a sex-non-specific role of Sxl in the male and female nervous system. Further, this activity appears independent of Sxl's RNA binding activity and instead Sxl functions as a chromatin-associating protein working with the RNA pol2/3 factor Polr3E to regulate gene expression. Thus, this represents a highly significant finding. *

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

      Summary: In this paper, the authors report on an unexpected activity for Sex lethal (Sxl) (a known splicing regulator that functions in sex determination and dosage compensation) in binding to chromatin. They show, using DamID, that Sxl binds to approximately the same chromatin regions as Polr3E (a subunit of RNA Pol III). They show that this binding to chromatin is unaffected by mutations in the RNA binding domains or by deletions of either N or C terminal regions of the Sxl protein. This leads the authors to conclude that Sxl must bind to chromatin through some interacting protein working through the central region of the Sxl protein. They show that Sxl binding is dependent on Polr3E function. They show that male-specific neuronal knockdown of Sxl gives similar phenotypes to knockdown of Polr3E in terms of lethality and improved negative geotaxis. They show gene expression changes with knockdown of Sxl in male adult neurons - mainly that metabolic and pigmentation genes go down in expression. They also show that expression of a previously discovered male adult specific form of Sxl (that does not have splicing activity) in the same neurons also leads to changes in gene expression, including more upregulated than downregulated tRNAs. But they don't see (or don't show) that the same tRNA genes are down with knockdown of Sxl. Nonetheless, based on these findings, they suggest that Sxl plays an important role in regulating Pol III activity through the Polr3E subunit.

      Major comments:

      *

      *To be honest, I'm not convinced that the conclusions drawn from this study are correct. The fact that every mutant form of Sxl shows the same result from the DamID labelling is a little concerning. I would like to see independent evidence of the SxlRac protein binding chromatin. *

      Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?


      __Author Response: __

      Regarding the reviewer’s overall concerns about the legitimacy of the Sxl binding data:

      1. i) The fold differences between Dam-Sxl-mutants and the Dam-only control are very robust (up to 9 log2 fold change (500-fold change)), which is higher than what we observe with most transcription factors using Targeted DamID.
      2. ii) We observed that Sxl binding was significantly reduced upon knockdown of Polr3E, confirming that the signal we observe is biologically specific and not due to technical noise or background. iii) If the concern relates to potential Sxl binding in non-neuronal tissues such as salivary glands, we would like to clarify that all DamID constructs were expressed under elav-GAL4, a pan-neuronal driver. Furthermore, dissections were performed to isolate larval brains, with salivary glands carefully removed. This ensures that chromatin profiles were derived from neuronal tissue exclusively.

      3. iv) Salivary gland polytene chromosome staining with a Sxl antibody in a closely related species (Drosophila virilis) show __binding of Sxl to chromatin __in both sexes (Bopp et al., 1996). We will include more text in the revised manuscript to emphasise these points.

      Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?

      Author Response:

      Prior work in Drosophila virilis (where Sxl is also required for sex determination and Sxl-RAC is conserved) has already demonstrated Sxl-chromatin association (using a full-length Sxl antibody) in salivary glands using polytene chromosome spreads (Bopp et al., 1996). Binding is observed in both sexes and across the genome, reflecting our observations. We will incorporate this into the revised discussion to support the chromatin-binding role of Sxl across species.

      There is a clear and long-overlooked precedent for Sxl's alternative, sex-independent roles, findings that have been largely overshadowed by the gene’s canonical function. Our study not only validates and extends these observations but also brings much-needed attention to this understudied aspect of fundamental biology.

      Bopp D, Calhoun G, Horabin JI, Samuels M, Schedl P. Sex-specific control of Sex-lethal is a conserved mechanism for sex determination in the genus Drosophila. Development. 1996 Mar;122(3):971-82. doi: 10.1242/dev.122.3.971. PMID: 8631274.

      I would like to see independent evidence of the SxlRac protein binding chromatin.

      * *__Author Response: __

      We do not believe this is necessary:

      1. i) Our data demonstrated that a large N-terminal truncation of Sxl (removing far more of the N-terminal region than is absent in Sxl-RAC) does not impair chromatin binding.
      2. ii) Our deletion experiments show that it is the central domain __of Sxl that is required for chromatin association (as removal of the N-or C-terminal domain has no effect). This central domain is __unaffected in Sxl-RAC. iii) Independent Y2H experiments have shown that it is exclusively the__ RBD-1 __(RNA binding domain 1) of the central domain of Sxl that interacts with Polr3E (Dong et al., 1999). Sxl-RAC contains this region, therefore will be recruited by Polr3E.

      iv) Review 3 also believes that this is not necessary (see cross-review below) and highlights the robustness of the Y2H experiments performed by Dong et al., 1999.

      • *

      Also, given that their DamID experiments reveal that Sxl binds half of the genes encoded in the Drosophila genome, finding that it binds around half of the tRNA genes is perhaps not surprising.


      __Author Response: __

      Our data show that Sxl binds to a range of Pol III-transcribed loci, and this binding pattern supports the proposed model that Sxl plays a broader regulatory role in Pol III activity. Within these Pol III targets, tRNA genes represent a specific and biologically relevant subset. The emphasis on tRNAs is not to suggest they are the exclusive or primary targets of Sxl, but rather to__ highlight a functionally important class of Pol III-transcribed elements__ that align with the model we are proposing. We will revise the text to better reflect this framing and avoid any confusion regarding the scope of Sxl’s binding profile.

      *I would like to see evidence beyond citing a 1999 yeast two-hybrid study that Sxl and Polr3E directly interact with one another. *


      Author response:

      We do not believe this is necessary (these points were also mentioned above):

      1. i) The Dong et al., 1999 study was highly comprehensive in its characterisation of Sxl binding to Polr3E.
      2. ii) Our DamID data provide strong complementary evidence for this interaction: knockdown of Polr3E robustly reduces Sxl’s recruitment to chromatin, strongly supporting the relevance of the interaction in vivo. iii) Review 3 highlights the robustness of the Y2H experiments performed by Dong et al., 1999.

      In my opinion, the differences in lethality observed with loss of Sxl versus control are unlikely to be meaningful given the different genetic backgrounds. The similar defects in negative geotaxis could be meaningful, but I'm unsure how often this phenotype is observed. What other class of genes affect negative geotaxis? It's a little unclear why having reduced expression of metabolic and pigment genes or of tRNAs would improve neuronal function.


      Author response:

      While the differences in survival were indeed subtle, they were statistically significant and thus warranted inclusion. Our primary aim in this section was to demonstrate that knockdown of Sxl or Polr3E results in comparable behavioural and transcriptional phenotypes, suggesting overlapping functional roles. In this context, we believe the data were presented transparently and effectively support our interpretation.

      Regarding the negative geotaxis phenotype, we appreciate the reviewer’s interest and agree that it is both intriguing and atypical. For this reason, we performed the assay multiple times, particularly in Polr3e knockdowns, to confirm the robustness of the result. To address potential confounding variables, we carefully selected control lines that account for genetic background and transgene insertion site, including KK controls and attP40-matched lines. We also employed multiple independent RNAi lines targeting Sxl to validate the phenotype across different genetic backgrounds.

      Although the observed improvement in climbing is unexpected, it is not without precedent in the RNA polymerase III field. Notably, Malik et al. (2024) demonstrated that heterozygous Polr3DEY/+ mutants exhibit a significantly delayed decline in climbing ability with age. We allude to this in the discussion and will revise the text to emphasise this connection more explicitly.

      Finally, while we recognise that negative geotaxis is a relatively broad assay and thus does not pinpoint the precise cellular mechanisms involved, we interpret the phenotype as suggesting a neural basis and a functional role for Sxl in the nervous system.

      One would expect that not just the same classes of genes would be affected by loss and overexpression of Sxl, but the same genes would be affected - are the same genes changing in opposite directions in the two experiments or just the same classes of genes. Likewise, are the same genes changing expression in the same direction with both Sxl and the Polr3E loss? Also, why are tRNA genes not also affected with Sxl loss. Finally, they describe the changes in gene expression as being in male adult neurons, but the sequencing was done of entire heads - so no way of knowing which cell type is showing differential gene expression.

      Author response:

      While we do examine gene classes, our approach also includes pairwise correlation analyses of gene expression changes between specific genotypes. Notably, we observed a significant positive correlation between Polr3e knockdowns and Sxl knockdowns, and a significant negative correlation between Sxl-RAC–expressing flies and Sxl knockdowns. Furthermore, we examined Sxl-DamID target genes within our RNA-seq datasets and found a consistent relationship between Sxl targets and genes differentially expressed in Polr3e knockdowns.

      Regarding the Pol III qPCR results, we note that tRNA expression changes may require a longer duration of RNAi induction (e.g., beyond 4 days) to become apparent, especially given that phenotypic effects such as changes in lifespan and negative geotaxis only emerge after 20 days or more. It is also plausible that Sxl knockdown leads to a partial reduction in Pol III efficiency, which may not be readily detectable through bulk Pol III qPCRs. We are willing to repeat Pol III qPCRs at later timepoints to further investigate this trend.

      Finally, we infer that gene expression changes observed in our RNA-seq data are of neuronal origin, as all knockdown and overexpression constructs used in this study were driven pan-neuronally using elav-/nSyb-GAL4. While we acknowledge that bulk RNA-seq does not provide cell-type resolution, tissue-specific assumptions are widely used in the field when driven by a relevant promoter.

      I'm also not sure what I'm supposed to be seeing in panel 5F (or in the related supplemental figure) and if it has any meaning - If they are using the Sxl-T2A-Gal4 to drive mCherry, I think one would expect to see expression since Sxl transcripts are made in both males and in females. Also, one would expect to see active protein expression (OPP staining) in most cells of the adult male brain and I think that is what is observed, but again, I'm not sure what I'm supposed to be looking at given the absence of any arrows or brackets in the figures.

      Author Response:

      Due to the presence of the T2A tag and the premature stop codon in exon 3 of early male Sxl transcripts, GAL4 expression is not expected in males unless the head-specific SxlRAC isoform is produced. The aim of panel 5F is to demonstrate the spatial overlap between SxlRAC expression (as we are examining male brains) and regions of elevated protein synthesis, as detected by OPP staining.

      To quantitatively assess this relationship, we performed colocalisation analysis using ImageJ, which showed a positive correlation between Sxl and OPP signal intensity, supporting this interpretation. It is also evident from our images that regions with lower levels of protein synthesis (such as the neuropil - as shown in independent studies Villalobos-Cantor et al., 2023) concurrently lack Sxl-related signal. We have highlighted regions in Fig. 5 exhibiting higher/lower levels of Sxl/OPP signal to better illustrate this relationship. We can also test the effects of knockdown/overexpression on general protein synthesis if required.

      Villalobos-Cantor S, Barrett RM, Condon AF, Arreola-Bustos A, Rodriguez KM, Cohen MS, Martin I. Rapid cell type-specific nascent proteome labeling in Drosophila. Elife. 2023 Apr 24;12:e83545. doi: 10.7554/eLife.83545. PMID: 37092974; PMCID: PMC10125018.

      Minor comments:

      * Line 223 - 225 - I believe that it is expected that Sxl transcripts would be broadly expressed in the male and female adult, given that it is only the spliced form of the transcript that is female specific in expression. *

      As explained above, the only isoform that will be ‘trapped’ by the T2A-GAL4 in males is the Sxl-RAC isoform (as the other isoforms contain premature stop codons). Our immunohistochemistry data indicate that Sxl-RAC is expressed in the male brain, specifically in neurons. Therefore, knockdown experiments in males will reduce all mRNA isoforms, of which, Sxl-RAC is the only one producing a protein.

      Line 236 - 238 - Sentence doesn't make sense.

      We have addressed and clarified this.

      Reviewer #2 (Significance (Required)):

      It would be significant to discover that a gene previously thought to function in only sex determination and dosage compensation also moonlights as a regulator of RNA polymerase III activity. Unfortunately, I am not convinced by the work presented in this study that this is the case.

      My expertise is in Drosophila biology, including development, transcription, sex determination, morphogenesis, genomics, transcriptomics, DNA binding

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

      Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Drosophila Sxl, widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species, is also a chromatin factor that can stimulate transcription by Pol III and Pol II of genes involved with metabolism and protein homeostasis, specifically some encoding tRNAs.

      The evidence for the tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments. I have a few specific comments below, all minor.*

      Scientific points: - The approach taken for the evaluation of Sxl DNA-binding activity in Fig2 is not entirely clear. I assume these are crosses of elav-Gal4 x different UAS- lines, then using males or females for UAS-Sxl-Full-Length. But what about the others? Were the experiments done in males only? This is hinted at in the main text but not explicitly indicated in the figure or the methods (at least, that I could easily find). And is this approach extended to all other experiments? Longevity? Climbing assays? Considering the role of Sxl, it may be helpful to be fastidiously systematic with this.


      Author Response:

      We have revised the wording to ensure greater clarity. Males were used for all survival and behavioural experiments (as only males can be leveraged for knocking down Sxl-RAC without affecting the canonical Sxl-F isoform).

      - In the discussion, lines 360-61, the authors say: Indeed, knockdown of Polr3E leads to a loss of Sxl binding to chromatin, suggesting a cooperative mechanism. Maybe I am misunderstanding the authors, but when I read "cooperation" in this context I think of biochemical cooperative binding. This is possible, but I do not think a simple 'requirement' test can suggest specifically that this mechanistic feature of biochemical binding is at play. I would expect, for starters, a reciprocal requirement for binding (which is not tested), and some quantitative features that would be difficult to evaluate in vivo. I do not think cooperative binding needs to be invoked anyway, as the authors do not make any specific point or prediction about it. But if they do think this is going on, I think it would need to be referred to as a speculation.


      Author Response:

      We appreciate that the original wording may have been unclear and will revise the text to more accurately reflect a functional relationship, rather than implying direct cooperation.

      - In lines 428-432, the authors discuss the ancestral role of Sxl and make a comparison with ELAV, in the context of an RNA-binding protein that has molecular functions beyond those of a splicing factor, considering the functions of ELAV in RNA stability and translation, and finishing with "suggesting that similar regulatory mechanisms may be at play". I do not understand this latter sentence. Which mechanisms are these? Are the authors referring to the molecular activities of ELAV and SXL? But what would be the similarity? SXL seems to have a dual capacity to bind RNA and protein interactors, which allows it to work both in chromatin-level regulation as well as post-transcriptionally in splicing; but ELAV seems rather to take advantage of its RNA binding function to make it work in multiple RNA-related contexts, all post-transcriptional. I do not see an obvious parallel beyond the fact that RNA binding proteins can function at different levels of gene expression regulation -- but I would not say this parallel are "similar regulatory mechanisms", so I find the whole comparison a bit confusing.


      Author Response:

      We have reduced this section, as it is largely speculative and intended to highlight potential, though indirect, links in higher organisms. Our goal was primarily to illustrate the possibility that Sxl may have an ancestral role distinct from its well-characterised function, and to suggest a potential avenue for future research into ELAV2’s involvement in chromatin or Pol III regulation.

      - One aspect of the work that I find is missing in the discussion is the possibility that the simultaneous capacity of Sxl for RNA binding and Polr3E binding: are these mutually exclusive? if so, are they competitive or hierarchical? how would they be coordinated anyway?


      Author Response:

      This is an interesting point, and we have expanded on it further in the Discussion section.

      - The only aspect of the paper where I found that one could make an experimental improvement is the claim that Sxl induces the expression of genes that have the overall effect of stimulating protein synthesis. The OPP experiment shows a correlation between the expression of Sxl and the rate of protein synthesis initiation. However, a more powerful experiment would be, rather obviously, to introduce Sxl knock-down in the same experiment, and observe whether in Sxl-expressing neurons the incorporation of OPP is reduced. I put this forth as a minor point because the tenet of the paper would not be affected by the results (though the perception of importance of the newly described function could be reinforced).

      • *

      Author Response:

      This could be a valid experiment and we are prepared to perform it if required.

      - In a similar way, it would be interesting to know whether the recruitment of Polr3E and Sxl to chromatin is co-dependent or Sxl follows Polr3E. This is also a minor point because this would possibly refine the mechanism of recruitment but does not alter the main discovery.

      Author Response:

      We have addressed a similar point for Reviewer 2 (see below) and will include a Discussion point for this:

      If Sxl were required for Polr3E recruitment to RNA Pol III, then, in most male Drosophila melanogaster cells, Polr3E would not be incorporated, and males would not be viable (as it is essential for Pol III activity). While it is possible that there could be a subtle effect on Polr3E recruitment, such an experiment, would not alter the central conclusion of our study - that Sxl is recruited to chromatin (accessory to the Pol III complex) via Polr3E.

      * Figures and reporting:

      • In Figure 2, it would be helpful to see the truncation coordinate for the N and C truncations.

      • In Figure 3D, genomic coordinates are missing.

      • In Figure 3E, the magnitude in the Y axis is not entirely clear (at least not to me). How is the amount of binding across the genome quantified? Is this the average amplitude of normalised TaDa signal across the genome? Or only within binding intervals?

      • Figure S3E-F: it would be interesting to show the degree of overlap between the downregulated genes that are also binding targets (regardless of the outcome).

      • Figure 5C-E: similarly to Figure S3, it would be interesting to know how the transcriptional effects compare with the binding targets.

      • Authors use Gehan-Breslow-Wilcoxon to test survival, which is a bit unusual, as it gives more weight to the early deaths (which are rare in most Drosophila longevity experiments). Is there any rationale behind this? It may be even favour their null hypothesis.*


      Author response:

      Thank you for the detailed feedback on our figures. We have__ incorporated__ the suggested changes.

      We agree that examining the overlap between Sxl binding sites and transcriptional changes is valuable, and we aimed to highlight this in the pie charts shown in Figures S3 and S5. If the reviewer is suggesting a more explicit quantification of the proportion of Sxl-Dam targets with significant transcriptomic changes, we are happy to include this analysis in the final version of the manuscript.

      As noted in the Methods, both Gehan–Breslow–Wilcoxon (GBW) and Kaplan–Meier tests were used. The significance in Figure 4a is specific to the GBW test, which we indicated by describing the effect as mild. Our focus here is not on the magnitude of survival differences, but on the consistent trends observed in both Polr3e and Sxl knockdowns.

      Writing and language:*

      • Introduction finishes without providing an outline of the findings (which is fine by me if that is what the authors wanted).

      • In lines 361-5, the authors say "We speculate that this interaction not only facilitates Pol III transcription but may also influence chromatin architecture and RNA Pol II-driven transcription as observed with Pol III regulation in other organisms". "This interaction" refers to Polr3E-Sxl-DNA interaction and with "Pol III transcription" I presume the authors refer to transcription executed by Pol III. I am not clear about the meaning of the end of the sentence "as observed with Pol III regulation in other organisms". What is the observation, exactly? That Pol III modifies chromatin in Pol II regulated loci, or that Pol III interactors change chromatin architecture?

      • DPE abbreviation is not introduced (and only used once).

      • A few typos: Line 41 ...splicing of the Sxl[late] transcripts, which is [ARE?] constitutively transcribed (Keyes et al.,... Line 76 ...sexes but appears restricted to the nervous system [OF] male pupae and adults (Cline et Line 289 ...and S41). To assess any effect [ON]translational output, O-propargyl-puromycin (OPP)o Line 323 ...illustrating that the majority (72%) changes in tRNA levels [ARE] due to upregulation...hi Line 402 ...it was discovered [WE DISCOVERED] Line 792 ...Sxl across chromosomes X, 2 L/R, 3 L/R and 4. The y-axis represents the log[SYMBOL] ratio... This happens in other figure legends as well.*


      Author response:

      Thank you for the detailed feedback, we have clarified and incorporated the suggested changes.

      **Referee Cross-commenting***

      Reviewer 1 asks how physiological is the Sxl chromatin-association assay. I think the loss of association in Polr3E knock-down and the lack of association of other splicing factors goes a long way into answering this question. It is true that having positive binding data specifically for Sxl-RAC and negative binding data for a deletion mutant of the RMM domain would provide more robust conclusions (see below), but I am not sure it is completely necessary -- though this will depend on which journal the authors want to send the paper to.

      I think that the comment of reviewer 1 about the levels of expression of Sxl-DAM does not apply here because of the way TaDa works - it relies on codon slippage to produce minimal amounts of the DAM fusion protein, so by construction it will be expressed at much lower levels than the endogenous protein.

      Reviewer 1 also asks whether Polr3E chromatin-association is also dependent on Sxl, to round up the model and also as a way to address whether Sxl association to chromatin is real. While I agree with this on the former aim (this would be a nice-to-have), I think I disagree on the latter; there is no need for Polr3E recruitment to depend on Sxl for Sxl association to chromatin to be physiologically relevant. Polr3E is a peripheral component of Pol III and unlikely to depend on a factor of restricted expression like Sxl to interact with chromatin. The recruitment of Sxl could well be entirely 'hierarchical' and subject to Polr3E.

      Revewer 2 is concerned with the fact that every mutant form of Sxl shows the same result from the DamID labelling. I have to agree with this to a point. A deletion mutant of RMM domains would address this. Microscopy evidence in salivary glands would be nice, certainly, but the system may not lend itself to this particular interaction, which might be short-lived and/or weak. I do not immediately see the relevance of the chromatin binding capacity of non-Drosophilidae Sxl -- though it might indicate that the impact of the discovery is less likely to go beyond this group.

      Reviewer 2 does not find surprising that some tRNA genes (less than half) are regulated by Sxl. I think the value of that observation is just qualitative, as tRNAs are Pol III-produced transcripts, but their point is correct. A hypergeometric test could settle this.

      Reviewer 2 is concerned that the evidence of direct interaction between Sxl and Polr3E is a single 1999 two-hybrid study. But that paper contains also GST pull-downs that narrow down the specific domains that mediate binding, and perform the binding in competitive salt conditions. I think it is enough. The author team, I think, are not biochemists, so finding the right collaborators and performing these experiments would take time that I am not sure is warranted.

      Reviewer 2 is also concerned that the longevity assays may not be meaningful due to the difference in genetic backgrounds. This is a very reasonable concern (which I would extend to the climbing assays - any quantitative phenotype is sensitive to genetic background). However, I think the authors here may have already designed the experiment with this in mind - the controls express untargeted RNAi constructs, but I lose track of which one is control of which. This should be clarified in Methods.

      Other comments are in line, I think, with what I have pointed out and I generally agree with everything else that has been said.

      Reviewer #3 (Significance (Required)):

      Drosophila Sxl is widely known as an RNA-binding protein which functions as a splicing factor to determine sex identity in Drosophila and related species. It is a favourite example of how splicing factors and alternative can have profound influence in biology and used cleverly in the molecular circuitry of the cell to enact elegant regulatory decisions.

      In this work, Storer, McClure and colleagues use genome-wide DNA-protein binding assays, transcriptomics, and genetics to work out that Sxl is also a chromatin factor with an sex-independent, neuron-specific role in stimulating transcription by Pol III and Pol II, of genes involved with metabolism and protein homeostasis, including some encoding tRNAs.

      This opens a large number of interesting biological questions that range from biochemistry, gene regulation or neurobiology to evolution. How is the simultaneous capacity of binding RNA and chromatin (with the same protein domain, RRM) regulated/coordinated? How did this dual activity evolve and which one is the ancestral one? How many other RRM-containin RNA-binding proteins can also bind chromatin? How is Sxl recruited to chromatin to both Pol II and Pol III targets and are they functionally related? If so, how is the coordination of cellular functions activated through different RNA polymerases taking place and what is the role of Sxl in this? What are the functional consequences to neuronal biology? Does this affect similarly all Sxl-expressing neurons?

      The evidence for the central tenet of the paper -- that Sxl acts as a chromatin regulator with Polr3E, activating at least some of its targets with either Pol III or Pol II -- is logical and compelling, the paper is well written and the figures well presented. Of course, more experiments could always be wished for and proposed, but I think this manuscript could be published in many journals with just a minor revision not involving additional experiments.*

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      *The convincing analysis demonstrates a role for the Drosophila Sex determining gene sex lethal in controlling aspects of transcription in the nervous system independent of its role in splicing. Interaction with an RNA Pol III subunit mediating Sxl association with chromatin and similar knockdown phenotypes strongly support the role of Sxl in the regulation of neuronal metabolism. Given that Sxl is an evolutionary recent acquisition for sex determination, the study may reveal an ancestral role for Sxl.

      The conclusions are well justified by the datasets presented and I have no issues with the study or the interpretation. Throughout the work is well referenced, though perhaps the authors might take a look at Zhang et al (2014) (PMID: 24271947) for an interesting evolutionary perspective for the discussion.*

      Author Response:

      Thank you for the thoughtful suggestion. We will be sure to incorporate the findings from Zhang et al. regarding the evolution of the sex determination pathway.

      *I have some minor comments for clarification:

      There is no Figure 2b, should be labelled 2 or label TaDa plots as 2b

      Clarify if Fig 2 data are larval or adult *

      *Larval

      Fig 3d - are these replicates or female and male?

      Please elaborate on tub-GAL80[ts] developmental defects

      Fig 4e, are transcriptomics done with the VDRC RNAi line? The VDRC and BDSC RNAi lines exhibit different behaviours - former has "better" survival and Better negative geotaxis, the latter seems to have poorer survival but little geotaxis effect?*

      *Fig S3 - volcano plot for Polr3E?

      Fig S4a - legend says downregulated genes?

      The discussion should at least touch on the fact that Sxl amorphs (i.e. Sxl[fP7B0] are male viable and fertile, emphasising that the newly uncovered role is not essential.*

      Author Response:

      We agree with the suggestions outlined in the comments and have made the appropriate revisions.

      Reviewer #4 (Significance (Required)):*

      A nonessential role for Sxl in the nervous system independent of sex-determination contributes to better understanding a) the evolution of sex determining mechanisms, b) the role of RNA PolIII in neuronal homeostasis and c) more widely to the neuronal aging field. I think this well-focused study reveals a hitherto unsuspected role for Sxl.*

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this paper, the authors report on an unexpected activity for Sex lethal (Sxl) (a known splicing regulator that functions in sex determination and dosage compensation) in binding to chromatin. They show, using DamID, that Sxl binds to approximately the same chromatin regions as Polr3E (a subunit of RNA Pol III). They show that this binding to chromatin is unaffected by mutations in the RNA binding domains or by deletions of either N or C terminal regions of the Sxl protein. This leads the authors to conclude that Sxl must bind to chromatin through some interacting protein working through the central region of the Sxl protein. They show that Sxl binding is dependent on Polr3E function. They show that male-specific neuronal knockdown of Sxl gives similar phenotypes to knockdown of Polr3E in terms of lethality and improved negative geotaxis. They show gene expression changes with knockdown of Sxl in male adult neurons - mainly that metabolic and pigmentation genes go down in expression. They also show that expression of a previously discovered male adult specific form of Sxl (that does not have splicing activity) in the same neurons also leads to changes in gene expression, including more upregulated than downregulated tRNAs. But they don't see (or don't show) that the same tRNA genes are down with knockdown of Sxl. Nonetheless, based on these findings, they suggest that Sxl plays an important role in regulating Pol III activity through the Polr3E subunit.

      Major comments:

      To be honest, I'm not convinced that the conclusions drawn from this study are correct. The fact that every mutant form of Sxl shows the same result from the DamID labelling is a little concerning. I would like to see independent evidence of the SxlRac protein binding chromatin. Do antibodies against this form (or any form) of Sxl bind chromatin in salivary gland polytene chromosomes, for example? Does Sxl from other insects where Sxl has no role in sex determination bind chromatin?

      Also, given that their DamID experiments reveal that Sxl binds half of the genes encoded in the Drosophila genome, finding that it binds around half of the tRNA genes is perhaps not surprising.

      I would like to see evidence beyond citing a 1999 yeast two-hybrid study that Sxl and Polr3E directly interact with one another. In my opinion, the differences in lethality observed with loss of Sxl versus control are unlikely to be meaningful given the different genetic backgrounds. The similar defects in negative geotaxis could be meaningful, but I'm unsure how often this phenotype is observed. What other class of genes affect negative geotaxis? It's a little unclear why having reduced expression of metabolic and pigment genes or of tRNAs would improve neuronal function.

      One would expect that not just the same classes of genes would be affected by loss and overexpression of Sxl, but the same genes would be affected - are the same genes changing in opposite directions in the two experiments or just the same classes of genes. Likewise, are the same genes changing expression in the same direction with both Sxl and the Polr3E loss? Also, why are tRNA genes not also affected with Sxl loss. Finally, they describe the changes in gene expression as being in male adult neurons, but the sequencing was done of entire heads - so no way of knowing which cell type is showing differential gene expression.

      I'm also not sure what I'm supposed to be seeing in panel 5F (or in the related supplemental figure) and if it has any meaning - If they are using the Sxl-T2A-Gal4 to drive mCherry, I think one would expect to see expression since Sxl transcripts are made in both males and in females. Also, one would expect to see active protein expression (OPP staining) in most cells of the adult male brain and I think that is what is observed, but again, I'm not sure what I'm supposed to be looking at given the absence of any arrows or brackets in the figures.

      Minor comments:

      Line 223 - 225 - I believe that it is expected that Sxl transcripts would be broadly expressed in the male and female adult, given that it is only the spliced form of the transcript that is female specific in expression.

      Line 236 - 238 - Sentence doesn't make sense.

      Significance

      It would be significant to discover that a gene previously thought to function in only sex determination and dosage compensation also moonlights as a regulator of RNA polymerase III activity. Unfortunately, I am not convinced by the work presented in this study that this is the case.

      My expertise is in Drosophila biology, including development, transcription, sex determination, morphogenesis, genomics, transcriptomics, DNA binding

    1. I am sincerely grateful to the editors and peer reviewers at MetaROR for their detailed feedback and valuable comments and suggestions. I have addressed each point below.

      Handling Editor

      1. However, the article’s progression and arguments, along with what it seeks to contribute to the literature need refinement and clarification. The argument for PRC is under-developed due to a lack of clarity about what the article means by scientific

      communication. Clarity here might make the endorsement of PRC seem like less of a foregone conclusion.

      The structure of the paper (and discussion) has changed significantly to address the feedback.

      2. I strongly endorse the main theme of most of the reviews, which is that the progression and underlying justifications for this article’s arguments needs a great deal of work. In my view, this article’s main contribution seems to be the evaluation of the three peer review models against the functions of scientific communication. I say ‘seems to be’ because the article is not very clear on that and I hope you will consider clarifying what your manuscript seeks to add to the existing work in this field. In any case, if that assessment of the three models is your main contribution, that part is somewhat underdeveloped. Moreover, I never got the sense that there is clear agreement in the literature about what the tenets of scientific communication are. Note that scientific communication is a field in its own right.

      I have implemented a more rigorous approach to argumentation in response. “Scientific communication” was replaced by “scholarly communication.”

      3. I also agree that paper is too strongly worded at times, with limitations and assumptions in the analysis minimised or not stated. For example, all of the typologies and categories drawn could easily be reorganised and there is a high degree of subjectivity in this entire exercise. Subjective choices should be highlighted and made salient for the reader. Note that greater clarity, rigour, and humility may also help with any alleged or actual bias.

      I have incorporated the conceptual framework and description of the research methodology. However, the

      Discussion section reflects my personal perspective in some points, which I have explicitly highlighted to ensure clarity.

      4. I agree with Reviewer 3 that the ‘we’ perspective is distracting.

      This has been fixed.

      5. The paragraph starting with ‘Nevertheless’ on page 2 is very long.

      The text was restructured.

      6. There are many points where language could be shortened for readability, for example:

      Page 3: ‘decision on publication’ could be ‘publication decision’.

      Page 5: ‘efficiency of its utilization’ could be ‘its efficiency’.

      Page 7: ‘It should be noted…’ could be ‘Note that…’.

      I have proofread the text.

      7. Page 7: ‘It should be noted that..’ – this needs a reference.

      This statement has been moved to the Discussion section, paraphrased, and reference added.

      “It should be also noted that peer review innovations pull in opposing directions, with some aiming to increase efficiency and reduce costs, while others aim to promote rigor and increase costs

      (Kaltenbrunner et al., 2022).”

      8. I’m not sure that registered reports reflect a hypothetico-deductive approach (page 6). For instance, systematic reviews (even non-quantitative ones) are often published as registered reports and Cochrane has required this even before the move towards registered reports in quantitative psychology.

      I have added this clarification.

      9. I agree that modular publishing sits uneasily as its own chapter.

      Modular publishing has been combined with registered reports into the deconstructed publication group of

      models, now Section 5.1.

      10. Page 14: ‘The "Publish-Review-Curate" model is universal that we expect to be the future of scientific publishing. The transition will not happen today or tomorrow, but in the next 5-10 years, the number of projects such as eLife, F1000Research, Peer Community in, or MetaROR will rapidly increase’. This seems overly strong (an example of my larger critique and that of the reviewers).

      This part of the text has been rewritten.

      Reviewer 1

      11. For example, although Model 3 is less chance to insert bias to the readers, it also weakens the filtering function of the review system. Let’s just think about the dangers of machine-generated articles, paper-mills, p-hacked research reports and so on. Although the editors do some pre-screening for the submissions, in a world with only Model 3 peer review the literature could easily get loaded with even more ‘garbage’ than in a model where additional peers help the screening.

      I think that generated text is better detected by software tools. At the same time, I tried and described the pros and cons of different models in a more balanced way in the concluding section.

      12. Compared to registered reports other aspects can come to focus that Model 3 cannot cover. It’s the efficiency of researchers’ work. In the care of registered reports, Stage 1 review can still help researchers to modify or improve their research design or data collection method. Empirical work can be costly and time-consuming and post-publication review can only say that “you should have done it differently then it

      would make sense”.

      Thank you very much for this valuable contribution, I have added this statement at P. 11.

      13. Finally, the author puts openness as a strength of Model 3. In my eyes, openness is a separate question. All models can work very openly and transparently in the right circumstances. This dimension is not an inherent part of the models.

      I think that the model, providing peer reviews to all the submissions, ensures maximum transparency. However, I have made effort to make the wording more balanced and distinguish my personal perspective from the literature.

      14. In conclusion, I would not make verdict over the models, instead emphasize the different functions they can play in scientific communication.

      This idea has been reflected now in the concluding section.

      15. A minor comment: I found that a number of statements lack references in the Introduction. I would have found them useful for statements such as “There is a point of view that peer review is included in the implicit contract of the researcher.”

      Thank you for your feedback. I have implemented a more rigorous approach to argumentation in response.

      Reviewer 2

      16. The primary weakness of this article is that it presents itself as an 'analysis' from which they 'conclude' certain results such as their typology, when this appears clearly to be an opinion piece. In my view, this results in a false claim of objectivity which detracts from what would otherwise be an interesting and informative, albeit subjective, discussion, and thus fails to discuss the limitations of this approach.

      I have incorporated the conceptual framework and description of the research methodology. However, the

      Discussion section reflects my personal perspective in some points, which I have explicitly highlighted to ensure clarity.

      17. A secondary weakness is that the discussion is not well structured and there are some imprecisions of expression that have the potential to confuse, at least at first.

      The structure of the paper (and discussion) has changed significantly.

      18. The evidence and reasoning for claims made is patchy or absent. One instance of the former is the discussion of bias in peer review. There are a multitude of studies of such bias and indeed quite a few meta-analyses of these studies. A systematic search could have been done here but there is no attempt to discuss the totality of this literature. Instead, only a few specific studies are cited. Why are these ones chosen? We have no idea. To this extent I am not convinced that the references used here are the most appropriate.

      I have reviewed the existing references and incorporated additional sources. However, the study does not claim to conduct a systematic literature review; rather, it adopts an interpretative approach to literature analysis.

      19. Instances of the latter are the claim that "The most well-known initiatives at the moment are ResearchEquals and Octopus" for which no evidence is provided, the claim that "we believe that journal-independent peer review is a special case of Model 3" for which no further argument is provided, and the claim that "the function of being the "supreme judge" in deciding what is "good" and "bad" science is taken on by peer review" for which neither is provided.

      Thank you for your feedback. I have implemented a more rigorous approach to argumentation in response.

      20. A particular example of this weakness, which is perhaps of marginal importance to the overall paper but of strong interest to this reviewer is the rather odd engagement with history within the paper. It is titled "Evolution of Peer Review" but is really focussed on the contemporary state-of-play. Section 2 starts with a short history of peer review in scientific publishing, but that seems intended only to establish what is

      described as the 'traditional' model of peer review. Given that that short history had just shown how peer review had been continually changing in character over centuries - and indeed Kochetkov goes on to describe further changes - it is a little difficult to work out what 'traditional' might mean here; what was 'traditional' in 2010 was not the same as what was 'traditional' in 1970. It is not clear how seriously this history is being taken. Kochetkov has earlier written that "as early as the beginning of the 21st century, it was argued that the system of peer review is 'broken'" but of course criticisms - including fundamental criticisms - of peer review are much older than this. Overall, this use of history seems designed to privilege the

      experience of a particular moment in time, that coincides with the start of the metascience reform movement.

      While the paper addresses some aspects of peer review history, it does not provide a comprehensive examination of this topic. A clarifying statement to this effect has been included in the methodology section.

      “… this section incorporates elements of historical analysis, it does not fully qualify as such because primary sources were not directly utilized. Instead, it functions as an interpretative literature review, and one that is intentionally concise, as a comprehensive history of peer review falls outside the scope of this research”.

      21. Section 2 also demonstrates some of the second weakness described, a rather loose structure. Having moved from a discussion of the history of peer review to detail the first model, 'traditional' peer review, it then also goes on to describe the problems of this model. This part of the paper is one of the best - and best - evidenced. Given the importance of it to the main thrust of the discussion it should probably have been given more space as a Section all on its own.

      This section (now Section 4) has been extended, see also previous comment.

      22. Another example is Section 4 on Modular Publishing, in which Kochetkov notes "Strictly speaking, modular publishing is primarily an innovative approach for the publishing workflow in general rather than specifically for peer review."

      Kochetkov says "This is why we have placed this innovation in a separate category" but if it is not an innovation in peer review, the bigger question is 'Why was it included in this article at all?'.

      Modular publishing has been combined with registered reports into the deconstructed publication group of models, now Section 5.1.

      23. One example of the imprecisions of language is as follows. The author also shifts between the terms 'scientific communication' and 'science communication' but, at least in many contexts familiar to this reviewer, these are not the same things, the former denoting science-internal dissemination of results through publication (which the author considers), conferences and the like (which the author specifically excludes) while the latter denotes the science-external public dissemination of scientific findings to non-technical audiences, which is entirely out of scope for this article.

      Thank you for your remark. As a non- native speaker, I initially did not grasp the distinction between the terms. However, I believe the phrase ‘scholarly communication’ is the most universally applicable term. This adjustment has now been incorporated into the text.

      24. A final note is that Section 3, while an interesting discussion, seems largely derivative from a typology of Waltman, with the addition of a consideration of whether a reform is 'radical' or 'incremental', based on how 'disruptive' the reform is. Given that this is inherently a subjective decision, I wonder if it might not have been more informative to consider 'disruptiveness' on a scale and plot it accordingly. This would allow for some range to be imagined for each reform as well; surely reforms might be more or less disruptive depending on how they are implemented. Given that each reform is considered against each model, it is somewhat surprising that this is not presented in a tabular or graphical form.

      Ultimately, I excluded this metric due to its current reliance on purely subjective judgment. Measuring 'disruptiveness', e.g., through surveys or interviews remains a task for future research. 

      25. Reconceptualize this as an opinion piece. Where systematic evidence can be drawn upon to make points, use that, but don't be afraid to just present a discussion from what is clearly a well-informed author.

      I cannot definitively classify this work as an opinion piece. In fact, this manuscript synthesizes elements of a literature review, research article, and opinion essay. My idea was to integrate the strengths of all three genres.

      26. Reconsider the focus on history and 'evolution' if the point is about the current state of play and evaluation of reforms (much as I would always want to see more studies on the history and evolution of peer review).

      I have revised the title to better reflect the study’s scope and explicitly emphasize its focus on contemporary developments in the field.

      “Peer Review at the Crossroads”

      27. Consider ways in which the typology might be expanded, even if at subordinate level.

      I have updated the typology and introduced the third tier, where it is applicable (see Fig.2).

      Reviewer 3

      28. In my view, the biggest issue with the current peer review system is the low quality of reviews, but the manuscript only mentions this fleetingly. The current system facilitates publication bias, confirmation bias, and is generally very inconsistent. I think this is partly due to reviewers’ lack of accountability in such a closed peer review system, but I would be curious to hear the author’s ideas about this, more elaborately than they provide them as part of issue 2.

      I have elaborated on this issue in the footnote.

      29. I’m missing a section in the introduction on what the goals of peer review are or should be. You mention issues with peer review, and these are mostly fair, but their importance is only made salient if you link them to the goals of peer review. The author does mention some functions of peer review later in the paper, but I think it would be good to expand that discussion and move it to a place earlier in the manuscript.

      The functions of peer review are summarized in the first paragraph of Introduction.

      30. Table 1 is intuitive but some background on how the author arrived at these categorizations would be welcome.

      When is something incremental and when is something radical? Why are some innovations included but not others (e.g., collaborative peer review, see https://content.prereview.org/how-collaborative-peer-review-can-

      transform-scientific-research/)?

      Collaborative peer review, namely, Prereview was mentioned in the context of Model 3 (Publish-Review-Curate). However, I have extended this part of the paper.

      31. “Training of reviewers through seminars and online courses is part of the strategies of many publishers. At the same time, we have not been able to find statistical data or research to assess the effectiveness of such training.” (p. 5)  There is some literature on this, although not recent. See work by Sara Schroter for example, Schroter et al., 2004; Schroter et al., 2008)

      Thank you very much, I have added these studies and a few more recent ones.

      32. “It should be noted that most initiatives aimed at improving the quality of peer review simultaneously increase the costs.” (p. 7) This claim needs some support. Please explicate why this typically is the case and how it should impact our evaluations of these initiatives.

      I have moved this part to the Discussion section.

      33. I would rephrase “Idea of the study” in Figure 2 since the other models start with a tangible output (the manuscript). This is the same for registered reports where they submit a tangible report including hypotheses, study design, and analysis plan. In the same vein, I think study design in the rest of the figure might also not be the best phrasing. Maybe the author could use the terminology used by COS (Stage 1 manuscript, and Stage 2 manuscript, see Details & Workflow tab of https://www.cos.io/initiatives/registered-reports). Relatedly, “Author submits the first version of the manuscript” in the first box after the ‘Manuscript (report)’ node maybe a confusing phrase because I think many researchers see the first version of the manuscript as the stage 1 report sent out for stage 1 review.

      Thank you very much. Stage 1 and Stage 2 manuscripts look like suitable labelling solution.

      34. One pathway that is not included in Figure 2 is that authors can decide to not conduct the study when improvements are required. Relatedly, in the publish- review-curate model, is revising the manuscripts based on the reviews not optional as well? Especially in the case of 3a, authors can hardly be forced to make changes even though the reviews are posted on the platform.

      All the four models imply a certain level of generalization; thus, I tried to avoid redundant details. However, I have added this choice to the PRC model (now, Model 4).

      35. I think the author should discuss the importance of ‘open identities’ more. This factor is now not explicitly included in any of the models, while it has been found to be one of the main characteristics of peer review systems (Ross-Hellauer, 2017).

      This part has been extended.

      36. More generally, I was wondering why the author chose these three models and not others. What were the inclusion criteria for inclusion in the manuscript? Some information on the underlying process would be welcome, especially when claims like “However, we believe that journal-independent peer review is a special case of Model 3 (“Publish-Review-Curate”).” are made without substantiation.

      The study included four generalized models of peer review that involved some level of abstraction.

      37. Maybe it helps to outline the goals of the paper a bit more clearly in the introduction. This helps the reader to know what to expect.

      The Introduction has been revised including the goal and objectives.

      38. The Modular Publishing section is not inherently related to peer review models, as you mention in the first sentence of that paragraph. As such, I think it would be best to omit this section entirely to maintain the flow of the paper. Alternatively, you could shortly discuss it in the discussion section but a separate paragraph seems too much from my point of view.

      Modular publishing has been combined with registered reports into the fragmented publishing group of models, now in Section 5.

      39. Labeling model 3 as post-publication review might be confusing to some readers. I believe many researchers see post-publication review as researchers making comments on preprints, or submitting commentaries to journals. Those activities are substantially different from the publish-review-curate model so I think it is important to distinguish between these types.

      The label was changed into Publish-Review-Curate model.

      40. I do not think the conclusions drawn below Table 3 logically follow from the earlier text. For example, why are “all functions of scientific communication implemented most quickly and transparently in Model 3”? It could be that the entire process takes longer in Model 3 (e.g. because reviewers need more time), so that Model 1 and Model 2 lead to outputs quicker. The same holds for the following claim: “The additional costs arising from the independent assessment of information based on open reviews are more than compensated by the emerging opportunities for scientific pluralism.” What is the empirical evidence for this? While I personally do think that Model 3 improves on Model 1, emphatic statements like this require empirical evidence. Maybe the author could provide some suggestions on how we can attain this evidence. Model 2 does have some empirical evidence underpinning its validity (see Scheel, Schijen, Lakens, 2021; Soderberg et al., 2021; Sarafoglou et al. 2022) but more meta-research inquiries into the effectiveness and cost- benefits ratio of registered reports would still be welcome in general.

      The Discussion section has been substantially revised to address this point. While I acknowledge the current scarcity of empirical studies on innovative peer review models, I have incorporated a critical discussion of this methodological gap. I am grateful for the suggested literature on RRs, which I have now integrated into the relevant subsection.

      41. What is the underlaying source for the claim that openness requires three conditions?

      I have made effort to clarify within the text that this reflects my personal stance.

      42. “If we do not change our approach, science will either stagnate or transition into other forms of communication.” (p. 2) I don’t think this claim is supported sufficiently strongly. While I agree there are important problems in peer review, I think would need to be a more in-depth and evidence-based analysis before claims like this can be made.

      The sentence has been rephrased.

      43. On some occasions, the author uses “we” while the study is single authored.

      This has been fixed.

      44. Figure 1: The top-left arrow from revision to (re-)submission is hidden

      I have updated Figure 1.

      45. “The low level of peer review also contributes to the crisis of reproducibility in scientific research (Stoddart, 2016).” (p. 4) I assume the author means the low quality of peer review.

      This has been fixed.

      46. “Although this crisis is due to a multitude of factors, the peer review system bears a significant responsibility for it.” (p. 4)

      This is also a big claim that is not substantiated

      I have paraphrased this sentence as

      “While multiple factors drive this crisis, deficiencies in the peer review process

      remain a significant contributor.” and added a footnote.

      47. “Software for automatic evaluation of scientific papers based on artificial intelligence (AI) has emerged relatively recently” (p. 5) The author could add RegCheck (https://regcheck.app/) here, even though it is still in development. This tool is especially salient in light of the finding that preregistration-paper checks are rarely done as part of reviews (see Syed, 2023)

      Thank you very much, I have added this information.

      48. There is a typo in last box of Figure 1 (“decicion” instead of “decision”). I also found typos in the second box of Figure 2, where “screns” should be “screens”, and the author decision box where “desicion” should be “decision”

      This has been fixed.

      49. Maybe it would be good to mention results blinded review in the first paragraph of 3.2. This is a form of peer review where the study is already carried out but reviewers are blinded to the results. See work by Locascio (2017), Grand et al. (2018), and Woznyj et al. (2018).

      Thanks, I have added this (now section 5.2)

      50. Is “Not considered for peer review” in figure 3b not the same as rejected? I feel that it is rejected in the sense that neither the manuscript not the reviews will be posted on the platform.

      Changed into “Rejected”

      51. “In addition to the projects mentioned, there are other platforms, for example, PREreview12, which departs even more radically from the traditional review format due to the decentralized structure of work.” (p. 11) For completeness, I think it would be helpful to add some more information here, for example why exactly decentralization is a radical departure from the traditional model.

      I have extended this passage.

      52. “However, anonymity is very conditional - there are still many “keys” left in the manuscript, by which one can determine, if not the identity of the author, then his country, research group, or affiliated organization.” (p.11) I would opt for the neutral “their” here instead of “his”, especially given that this is a paragraph about equity and inclusion.

      This has been fixed.

      53. “Thus, “closeness” is not a good way to address biases.” (p. 11) This might be a straw man argument because I don’t believe researchers have argued that it is a good method to combat biases. If they did, it would be good to cite them here. Alternatively, the sentence could be omitted entirely.

      I have omitted the sentence.

      54. I would start the Modular Publishing section with the definition as that allows readers to interpret the other statements better.

      Modular publishing has been combined with registered reports into the deconstructed publication group of

      models, now in Section 5, general definition added.

      55. It would be helpful if the Models were labeled (instead of using Model 1, Model 2, and Model 3) so that readers don’t have to think back what each model involved.

      All the models represent a kind of generalization, which is why non-detailed labels are used. The text labels may vary depending on the context.

      56. Table 2: “Decision making” for the editor’s role is quite broad, I recommend to specify and include what kind of decisions need to be made.

      Changed into “Making accept/reject decisions”

      57. Table 2: “Aim of review” – I believe the aim of peer review differs also within these models (see the “schools of thought” the author mentions earlier), so maybe a statement on what the review entails would be a better way to phrase this.

      Changed into “What does peer review entail?”

      58. Table 2: One could argue that the object of the review’ in Registered Reports is

      also the manuscript as a whole, just in different stages. As such, I would phrase this differently.

      Current wording fits your remark

      “Manuscript in terms of study design and execution”

      Reviewer 4

      59. Page 3: It’s hard to get a feel for the timeline given the dates that are described. We have peer review becoming standard after WWII (after 1945), definitively established by the second half of the century, an example of obligatory peer review starting in 1976, and in crisis by the end of the 20th century. I would consider adding

      examples that better support this timeline – did it become more common in specific journals before 1976? Was the crisis by the end of the 20th century something that happened over time or something that was already intrinsic to the institution? It doesn’t seem like enough time to get established and then enter crisis, but more details/examples could help make the timeline clear. Consider discussing the benefits of the traditional model of peer review.

      This section has been extended.

      60. Table 1 – Most of these are self- explanatory to me as a reader, but not all. I don’t know what a registered report refers to, and it stands to reason that not all of these innovations are familiar to all readers. You do go through each of these sections, but that’s not clear when I initially look at the table. Consider having a more informative caption. Additionally, the left column is “Course of changes” here but “Directions” in text. I’d pick one and go with it for consistency.

      Table 1 has been replaced by Figure 2. I have also extended text descriptions, added definitions.

      61. With some of these methods, there’s the ability to also submit to a regular journal. Going to a regular journal presumably would instigate a whole new round of review, which may or may not contradict the previous round of post-publication review and would increase the length of time to publication by going through both types. If someone has a goal to publish in a journal, what benefit would they get by going through the post-publication review first, given this extra time?

      Some of these platforms, e.g., F1000, Lifecycle Journal, replace conventional journal publishing. Modular publishing allows for step-by-step feedback from peers.

      An important advantage of RRs over other peer review models lies in their capacity to enhance research efficiency. By conducting peer review at Stage 1, researchers gain the opportunity to refine their study design or data collection protocols before empirical work begins.

      Other models of review can offer critiques such as "the study should have been conducted differently" without

      actionable opportunity for improvement. The key motivation for having my paper reviewed in MetaROR is the quality of peer review – I have never received so many comments, frankly! Moreover, platforms such as MetaROR usually have partnering journals.

      62. There’s a section talking about institutional change (page 14). It mentions that openness requires three conditions – people taking responsibility for scientific communication, authors and reviewers, and infrastructure. I would consider adding some discussion of readers and evaluators. Readers have to be willing to accept these papers as reliable, trustworthy, and respectable to read and use the information in them.

      Evaluators such as tenure committees and potential employers would need to consider papers submitted through these approaches as evidence of scientific scholarship for the effort to be worthwhile for scientists.

      I have omitted these conditions and employed the Moore’s Technology Adoption Life Cycle. Thank you very much for your comment!

      63. Based on this overview, which seems somewhat skewed towards the merits of these methods (conflict of interest, limited perspective on downsides to new methods/upsides to old methods), I am not quite ready to accept this effort as equivalent of a regular journal and pre-publication peer review process. I look forward to learning more about the approach and seeing this review method in action and as it develops.

      The Discussion section has been substantially revised to address this point. While I acknowledge the current scarcity of empirical studies on innovative peer review models, I have incorporated a critical discussion of this methodological gap.

    1. C'est une mesure qui ne sera jamais votée au Parlement, c'est juste pour laisser les oppositions s'essouffler là-dessus et faire passer le reste

      "This is a measure that will never be voted on in Parliament, it's just to let the oppositions run out of steam on this and get the rest passed"

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper concerns mechanisms of foraging behavior in C. elegans. Upon removal from food, C. elegans first executes a stereotypical local search behavior in which it explores a small area by executing many random, undirected reversals and turns called "reorientations." If the worm fails to find food, it transitions to a global search in which it explores larger areas by suppressing reorientations and executing long forward runs (Hills et al., 2004). At the population level, the reorientation rate declines gradually. Nevertheless, about 50% of individual worms appear to exhibit an abrupt transition between local and global search, which is evident as a discrete transition from high to low reorientation rate (Lopez-Cruz et al., 2019). This observation has given rise to the hypothesis that local and global search correspond to separate internal states with the possibility of sudden transitions between them (Calhoun et al., 2014). The main conclusion of the paper is that it is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rates. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.

      Strengths:

      The strength of the paper is the demonstration that a more parsimonious model explains abrupt transitions in the reorientation rate.

      Weaknesses:

      (1) Use of the Gillespie algorithm is not well justified. A conventional model with a fixed dt and an exponentially decaying reorientation rate would be adequate and far easier to explain. It would also be sufficiently accurate - given the appropriate choice of dt - to support the main claims of the paper, which are merely qualitative. In some respects, the whole point of the paper - that discrete transitions are an epiphenomenon of stochastic behavior - can be made with the authors' version of the model having a constant reorientation rate (Figure 2f).

      We apologize, but we are not sure what the reviewer means by “fixed dt”. If the reviewer means taking discrete steps in time (dt), and modeling whether a reorientation occurs, we would argue that the Gillespie algorithm is a better way to do this because it provides floating-point precision, rather than a time resolution limited by dt, which we hopefully explain in the updated text (Lines 107-192).

      The reviewer is correct that discrete transitions are an epiphenomenon of stochastic behavior as we show in Figure 2f. However, abrupt stochastic jumps that occur with a constant rate do not produce persistent changes in the observed rate because it is by definition, constant. The theory that there are local and global searches is based on the observation that individual worms often abruptly change their reorientation rates. But this observation is only true for a fraction of worms. We are trying to argue that the reason why this is not observed for all, or even most worms is because these are the result of stochastic sampling, not a sudden change in search strategy.

      (2) In the manuscript, the Gillespie algorithm is very poorly explained, even for readers who already understand the algorithm; for those who do not it will be essentially impossible to comprehend. To take just a few examples: in Equation (1), omega is defined as reorientations instead of cumulative reorientations; it is unclear how (4) follows from (2) and (3); notation in (5), line 133, and (7) is idiosyncratic. Figure 1a does not help, partly because the notation is unexplained. For example, what do the arrows mean, what does "*" mean?

      We apologize for this, you are correct, 𝛀 is cumulative reorientations, and we have edited the text for clarity (Lines 107-192):

      We apologize for the arrow notation confusion. Arrow notation is commonly used in pseudocode to indicate variable assignment, and so we used it to indicate variable assignment updates in the algorithm.

      We added Figure 2a to help explain the Gillespie algorithm for people who are unfamiliar with it, but you are correct, some notation, like probabilities, were left unexplained. We have added more text to the figure legend. Hopefully this additional text, along with lines 105-190, provide better clarification.

      (3) In the model, the reorientation rate dΩ⁄dt declines to zero but the empirical rate clearly does not. This is a major flaw. It would have been easy to fix by adding a constant to the exponentially declining rate in (1). Perhaps fixing this obvious problem would mitigate the discrepancies between the data and the model in Figure 2d.

      You are correct that the model deviates slightly at longer times, but this result is consistent with Klein et al. that show a continuous decline of reorientations. However, we have added a constant to the model (b, Equation 2), since an infinite run length is likely not physiological.

      (4) Evidence that the model fits the data (Figure 2d) is unconvincing. I would like to have seen the proportion of runs in which the model generated one as opposed to multiple or no transitions in reorientation rate; in the real data, the proportion is 50% (Lopez). It is claimed that the "model demonstrated a continuum of switching to non-switching behavior" as seen in the experimental data but no evidence is provided.

      We should clarify that the 50% proportion cited by López-Cruz was based on an arbitrary difference in slopes, and by assessing the data visually (López-Cruz, Figure S2). We added a comment in the text to clarify this (Lines 76 – 78). We sought to avoid this subjective assessment by plotting the distribution of slopes and transition times produced by the method used in López-Cruz. We should also clarify by what we meant by “a continuum of switching and non-switching” behavior. Both the transition time distributions and the slope-difference distributions do not appear to be the result of two distributions (the distributions in Figure 1 are not bimodal). This is unlike roaming and dwelling on food, where two distinct distributions of behavioral metrics can be identified based on speed and angular speed (Flavell et al, 2009, Fig S2a).

      Based on the advice of Reviewer #3, we have also modeled the data using different starting amounts of M (M<sub>0</sub>). By definition, an initial value of M<sub>0</sub> = 1 is a two-state switching strategy; the worm either uses a reorientation rate of a (when M = 1) or b (when M = 0). As expected, this does produce a bimodal distribution of slope differences (Figure 3b), which is significantly different than the experimental distribution (Figure 3c). We have added a new section to explain this in more detail (Lines 253 – 297).

      (5) The explanation for the poor fit between the model and data (lines 166-174) is unclear. Why would externally triggered collisions cause a shift in the transition distribution?

      Thank you, we rewrote the text to clarify this better (Lines 227-233). There were no externally triggered collisions; 10 animals were used per experiment. They would occasionally collide during the experiment, but these collisions were excluded from the data that were provided. However, worms are also known to increase reorientations when they encounter a pheromone trail, and it is unknown (from this dataset) which orientations may have been a result of this phenomenon.

      (6) The discussion of Levy walks and the accompanying figure are off-topic and should be deleted.

      Thank you, we agree that this topic is tangential, and we removed it.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors build a statistical model that stochastically samples from a timeinterval distribution of reorientation rates. The form of the distribution is extracted from a large array of behavioral data, and is then used to describe not only the dynamics of individual worms (including the inter-individual variability in behavior), but also the aggregate population behavior. The authors note that the model does not require assumptions about behavioral state transitions, or evidence accumulation, as has been done previously, but rather that the stochastic nature of behavior is "simply the product of stochastic sampling from an exponential function".

      Strengths:

      This model provides a strong juxtaposition to other foraging models in the worm. Rather than evoking a behavioral transition function (that might arise from a change in internal state or the activity of a cell type in the network), or evidence accumulation (which again maps onto a cell type, or the activity of a network) - this model explains behavior via the stochastic sampling of a function of an exponential decay. The underlying model and the dynamics being simulated, as well as the process of stochastic sampling, are well described and the model fits the exponential function (Equation 1) to data on a large array of worms exhibiting diverse behaviors (1600+ worms from Lopez-Cruz et al). The work of this study is able to explain or describe the inter-individual diversity of worm behavior across a large population. The model is also able to capture two aspects of the reorientations, including the dynamics (to switch or not to switch) and the kinetics (slow vs fast reorientations). The authors also work to compare their model to a few others including the Levy walk (whose construction arises from a Markov process) to a simple exponential distribution, all of which have been used to study foraging and search behaviors.

      Weaknesses:

      This manuscript has two weaknesses that dampen the enthusiasm for the results. First, in all of the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references, for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) is distinct from the behavior being studied. In this manuscript, the distribution being sampled is the exponential decay function of the reorientation rate (lines 100-102). This appears to be tautological - a decay function fitted to the reorientation data is then sampled to generate the distributions of the reorientation data. That the model performs well and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.

      Thank you, we apologize that this was not clearer. In the Lotka-Volterra model, the density of predators and prey are being modeled, with the underlying assumption that rates of birth and death are inherently stochastic. In our model, the number of reorientations are being modeled, with the assumption (based on the experiments), that the occurrence of reorientations is stochastic, just like the occurrence (birth) of a prey animal is stochastic. However, the decay in M is phenomenological, and we speculate about the nature of M later in the manuscript.

      You are absolutely right that the decay function for M was fit to the population average of reorientations and then sampled to generate the distributions of the reorientation data. This was intentional to show that the parameters chosen to match the population average would produce individual trajectories with comparable stochastic “switching” as the experimental data. All we’re trying to show really is that observed sudden changes in reorientation that appear persistent can be produced by a stochastic process without resorting to binary state assignments. In Calhoun, et al 2014 it is reported all animals produced switch-like behavior, but in Klein et al, 2017 it is reported that no animals showed abrupt transitions. López-Cruz et al seem to show a mix of these results, which can easily be explained by an underlying stochastic process.

      The second weakness is somewhat related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides.

      Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework, and the authors indeed point this out: "simple stochastic models should be sufficient to explain observably stochastic behaviors." (Line 233-234). But if that is the case, what do we learn about how the foraging is happening? The authors suggest that the decay parameter M can be considered a memory timescale; which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: The mechanisms suggested, including loss of sensory stimuli, alternations in motor integration, ionotropic glutamate signaling, dopamine, and neuropeptides are all suggested: these are basically all of the possible biological sources that can govern behavior, and one is left not knowing what insight the model provides. The array of biological processes listed is so variable in dynamics and meaning, that their explanation of what governs M is at best unsatisfying. Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection, especially taken in aggregate with the previous weakness.

      Providing a roadmap of how to think about the processes generating M, the meaning of those processes in search, and potential frameworks that are more constrained and with more precise biological underpinning (beyond the array of possibilities described) would go a long way to assuaging the weaknesses.

      Thank you, these are all excellent points. We should clarify that in López-Cruz et al, they claim that only 50% of the animals fit a local/global search paradigm. We are simply proposing there is no need for designating local and global searches if the data don’t really support it. The underlying behavior is stochastic, so the sudden switches sometimes observed can be explained by a stochastic process where the underlying rate is slowing down, thus producing the persistently slow reorientation rate when an apparent “switch” occurs. What we hope to convey is that foraging doesn’t appear to follow a decision paradigm, but instead a gradual change in reorientations which for individual worms, can occasionally produce reorientation trajectories that appear switch-like.

      As for M, you are correct, we should be more explicit, and we have added text (Lines 319-359) to expand upon its possible biological origin.

      Reviewer #3 (Public review):

      Summary:

      This intriguing paper addresses a special case of a fundamental statistical question: how to distinguish between stochastic point processes that derive from a single "state" (or single process) and more than one state/process. In the language of the paper, a "state" (perhaps more intuitively called a strategy/process) refers to a set of rules that determine the temporal statistics of the system. The rules give rise to probability distributions (here, the probability for turning events). The difficulty arises when the sampling time is finite, and hence, the empirical data is finite, and affected by the sampling of the underlying distribution(s). The specific problem being tackled is the foraging behavior of C. elegans nematodes, removed from food. Such foraging has been studied for decades, and described by a transition over time from 'local'/'area-restricted' search'(roughly in the initial 10-30 minutes of the experiments, in which animals execute frequent turns) to 'dispersion', or 'global search' (characterized by a low frequency of turns). The authors propose an alternative to this two-state description - a potentially more parsimonious single 'state' with time-changing parameters, which they claim can account for the full-time course of these observations.

      Figure 1a shows the mean rate of turning events as a function of time (averaged across the population). Here, we see a rapid transient, followed by a gradual 4-5 fold decay in the rate, and then levels off. This picture seems consistent with the two-state description. However, the authors demonstrate that individual animals exhibit different "transition" statistics (Figure 1e) and wish to explain this. They do so by fitting this mean with a single function (Equations 1-3).

      Strengths:

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Weaknesses:

      (1) The authors claim that only about half the animals tested exhibit discontinuity in turning rates. Can they automatically separate the empirical and model population into these two subpopulations (with the same method), and compare the results?

      Thank you, we should clarify that the observation that about half the animals exhibit discontinuity was not made by us, but by López-Cruz et al. The observed fraction of 50% was based on a visual assessment of the dual regression method we described. We added text (Lines 76-79) to clarify this. To make the process more objective, we decided to simply plot the distributions of the metrics they used for this assessment to see if two distinct populations could be observed. However, the distributions of slope differences and transition times do not produce two distinct populations. Our stochastic approach, which does not assume abrupt state-transitions, also produces comparable distributions. To quantify this, we have added a section varying M<sub>0</sub>, including setting M<sub>0</sub> to 1, so that the model by definition is a switch model. This model performs the worst (Lines 253-296, Figure 3).

      (2) The equations consider an exponentially decaying rate of turning events. If so, Figure 2b should be shown on a semi-logarithmic scale.

      We chose to not do this because this average is based on the number of discrete reorientation events observed within a 2-minute window. The range of events ranges from 0 to 6 (hence a rate of 0.5-3 min<sup>-1</sup>), which does not span one order of magnitude. Instead, we included a heat map (Figure 1a, Figure 2b bottom panel) which shows the density that the average is based on. We hope this provides some clarity to the reader.

      (3) The variables in Equations 1-3 and the methods for simulating them are not well defined, making the method difficult to follow. Assuming my reading is correct, Omega should be defined as the cumulative number of turning events over time (Omega(t)), not as a "turn" or "reorientation", which has no derivative. The relevant entity in Figure 1a is apparently <Omega (t)>, i.e. the mean number of events across a population which can be modelled by an expectation value. The time derivative would then give the expected rate of turning events as a function of time.

      Thank you, you are correct. Please see response to Reviewer #1.

      (4) Equations 1-3 are cryptic. The authors need to spell out up front that they are using a pair of coupled stochastic processes, sampling a hidden state M (to model the dynamic turning rate) and the actual turn events, Omega(t), separately, as described in Figure 2a. In this case, the model no longer appears more parsimonious than the original 2-state model. What then is its benefit or explanatory power (especially since the process involving M is not observable experimentally)?

      Thank you, yes we see how as written this was confusing. In our response to Reviewer #1, and in the text, we added an important detail:

      While reorientations are modeled as discrete events, which is observationally true, the amount of M at time t=0 is chosen to be large (M<sub>0</sub> = 1000), so that over the timescale of 40 minutes, the decay in M is practically continuous. This ensures that sudden changes in reorientations are not due to sudden changes in M, but due to the inherent stochasticity of reorientations.

      However you are correct that if M was chosen to have a binary value of 0 or 1, then this would indeed be the two state model. We added a new section to address this (Lines 253-287, Figure 3). Unlike the experiments, the two-state model produces bimodal distributions in slope and transition times, and these distributions are significantly different than the experimental data (Figure 3).

      (5) Further, as currently stated in the paper, Equations 1-3 are only for the mean rate of events. However, the expectation value is not a complete description of a stochastic system. Instead, the authors need to formulate the equations for the probability of events, from which they can extract any moment (they write something in Figure 2a, but the notation there is unclear, and this needs to be incorporated here).

      Thank you, yes please see our response to Reviewer #1. We have clarified the text in Lines 105-190.

      (6) Equations 1-3 have three constants (alpha and gamma which were fit to the data, and M0 which was presumably set to 1000). How does the choice of M0 affect the results?

      Thank you, this is a good question. We address this in lines 253-296. Briefly, the choice of M<sub>0</sub> does not have a strong effect on the results, unless we set it to M<sub>0</sub>, which by definition, creates a two-state model. This model was significantly different than the experimental data, relative to the other models (Figure 3c).

      (7) M decays to near 0 over 40 minutes, abolishing omega turns by the end of the simulations. Are omega turns entirely abolished in worms after 30-40 minutes off food? How do the authors reconcile this decay with the leveling of the turning rate in Figure 1a?

      Yes, Reviewer #1 recommended adding a baseline reorientation rate which we did for all models (Equation 2). However, we should also note that in Klein et al they observed a continuous decay over 50 minutes. Though realistically, it is likely not plausible that worms will produce infinitely long runs at long time points.

      (8) The fit given in Figure 2b does not look convincing. No statistical test was used to compare the two functions (empirical and fit). No error bars were given (to either). These should be added. In the discussion, the authors explain the discrepancy away as experimental limitations. This is not unreasonable, but on the flip side, makes the argument inconclusive. If the authors could model and simulate these limitations, and show that they account for the discrepancies with the data, the model would be much more compelling.

      To do this, I would imagine that the authors would need to take the output of their model (lists of turning times) and convert them into simulated trajectories over time. These trajectories could be used to detect boundary events (for a given size of arena), collisions between individuals, etc. in their simulations and to see their effects on the turn statistics.

      Thank you, we have added dashed lines to indicate standard deviation to Figures 2b and 3a. After running the models several times, we found that some of the small discrepancies noted (like s<sub>1</sub>-s<sub>2</sub> < 0 for experiments but not the model), were spurious due to these data points being <1% of the data, so we cut this from the text. To compare how similar the continuous (M<sub>0</sub> > 1) and discrete (M<sub>0</sub> = 1) models were to the experimental data, we calculated a Jensen-Shannon distance for the models, and found that the discrete model was significantly more dissimilar to the experimental data than the continuous models (Lines 289-296, Figure 3c).

      (9) The other figures similarly lack any statistical tests and by eye, they do not look convincing. The exception is the 6 anecdotal examples in Figure 2e. Those anecdotal examples match remarkably closely, almost suspiciously so. I'm not sure I understood this though - the caption refers to "different" models of M decay (and at least one of the 6 examples clearly shows a much shallower exponential). If different M models are allowed for each animal, this is no longer parsimonious. Are the results in Figure 2d for a single M model? Can Figure 2e explain the data with a single (stochastic) M model?

      We certainly don’t want the panels in Figure 2e to be suspicious! These comparisons were drawn from calculating the correlations between all model traces and all experimental traces, and then choosing the top hits. Every time we run the simulation, we arrive at a different set of examples. Since it was recommended we add a baseline rate, these examples will be a completely different set when we run the simulation, again.

      We apologize for the confusion regarding M. Since the worms do not all start out with identical reorientation rates, we drew the initial M value from a distribution centered on M<sub>0</sub> to match the initial distribution of observed experimental rates (Lines 206-214). However, the decay in M (γ), as well as α and β, are the same for all in silico animals.

      (10) The left axes of Figure 2e should be reverted to cumulative counts (without the normalization).

      Thank you, we made this change.

      (11) The authors give an alternative model of a Levy flight, but do not give the obvious alternative models:<br /> a) the 1-state model in which P(t) = alpha exp (-gamma t) dt (i.e. a single stochastic process, without a hidden M, collapsing equations 1-3 into a single equation).

      b) the originally proposed 2-state model (with 3 parameters, a high turn rate, a low turn rate, and the local-to-global search transition time, which can be taken from the data, or sampled from the empirical probability distributions). Why not? The former seems necessary to justify the more complicated 2-process model, and the latter seems necessary since it's the model they are trying to replace. Including these two controls would allow them to compare the number of free parameters as well as the model results. I am also surprised by the Levy model since Levy is a family of models. How were the parameters of the Levy walk chosen?

      Thank you, we removed this section completely, as it is tangential to the main point of the paper.

      (12) One point that is entirely missing in the discussion is the individuality of worms. It is by now well known that individual animals have individual behaviors. Some are slow/fast, and similarly, their turn rates vary. This makes this problem even harder. Combined with the tiny number of events concerned (typically 20-40 per experiment), it seems daunting to determine the underlying model from behavioral statistics alone.

      Thank you, yes we should have been more explicit in the reasoning behind drawing the initial M from a distribution (response to comment #9). We assume that not every worm starts out with the same reorientation rate, but that some start out fast (high M) and some start out slow (low M). However, we do assume M decays with the same kinetics, which seems sufficient to produce the observed phenomena. Multiple decay rates are not needed to replicate the experimental data.

      (13) That said, it's well-known which neurons underpin the suppression of turning events (starting already with Gray et al 2005, which, strangely, was not cited here). Some discussion of the neuronal predictions for each of the two (or more) models would be appropriate.

      Thank you, yes we will add Gray et al, but also the more detailed response to Reviewer #2 (Lines 319-359 of manuscript).

      (14) An additional point is the reliance entirely on simulations. A rigorous formulation (of the probability distribution rather than just the mean) should be analytically tractable (at least for the first moment, and possibly higher moments). If higher moments are not obtainable analytically, then the equations should be numerically integrable. It seems strange not to do this.

      Thank you for suggesting this. For the Levy section (which we cut) this would have been an improvement. However, since the distributions of slope differences and transition times are based on a recursive algorithm, rather than an analytical formulation, we decided to use the Jensen-Shannon divergence to compare distributions (Lines 272-296, Figure 3c) since this is a parameter-free approach.

      In summary, while sample simulations do nicely match the examples in the data (of discontinuous vs continuous turning rates), this is not sufficient to demonstrate that the transition from ARS to dispersion in C. elegans is, in fact, likely to be a single 'state', or this (eq 1-3) single state. Of course, the model can be made more complicated to better match the data, but the approach of the authors, seeking an elegant and parsimonious model, is in principle valid, i.e. avoiding a many-parameter model-fitting exercise.

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Thank you, we agree that this is a generic phenomenon, which is partly why we did this. The data from López-Cruz seem to agree in part with Calhoun et al, that claim abrupt transitions occur, and Klein et al, which claim they do not occur. Since the underlying phenomenon is stochastic, we propose the mixed observations of sudden and gradual changes in search strategy are simply the result of a stochastic process, which can produce both phenomena for individual observations. We hope this work can help clarify why sudden changes in search strategy are not consistently observed. We propose a simple hypothesis that there is no change in search strategy. The reorientation rate decays in time, and due to the stochastic nature of this behavior, what appears as a sudden change for individual observations is not due to an underlying decision, but rather the result of a stochastic process.

    1. Author Response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public Review):

      Summary

      While DNA sequence divergence, differential expression, and differential methylation analysis have been conducted between humans and the great apes to study changes that "make us human", the role of lncRNAs and their impact on the human genome and biology has not been fully explored. In this study, the authors computationally predict HSlncRNAs as well as their DNA Binding sites using a method they have developed previously and then examine these predicted regions with different types of enrichment analyses. Broadly, the analysis is straightforward and after identifying these regions/HSlncRNAs the authors examined their effects using different external datasets.

      I no longer have any concerns about the manuscript as the authors have addressed my comments in the first round of review.

      We thank the reviewer for the valuable comments, which have helped us improve the manuscript.

      Reviewer #2 (Public Review):

      Lin et al attempt to examine the role of lncRNAs in human evolution in this manuscript. They apply a suite of population genetics and functional genomics analyses that leverage existing data sets and public tools, some of which were previously built by the authors, who clearly have experience with lncRNA binding prediction. However, I worry that there is a lack of suitable methods and/or relevant controls at many points and that the interpretation is too quick to infer selection. While I don't doubt that lncRNAs contribute to the evolution of modern humans, and certainly agree that this is a question worth asking, I think this paper would benefit from a more rigorous approach to tackling it.

      I thank the authors for their revisions to the manuscript; however, I find that the bulk of my comments have not been addressed to my satisfaction. As such, I am afraid I cannot say much more than what I said last time, emphasising some of my concerns with regards to the robustness of some of the analyses presented. I appreciate the new data generated to address some questions, but think it could be better incorporated into the text - not in the discussion, but in the results.

      We thank the reviewer for the careful reading and valuable comments. In this round of revision, we address the two main concerns: (1) there is a lack of suitable methods and/or relevant controls at many points, and (2) the interpretation is too quick to infer selection. Based on these comments, we have carefully revised all sections of the manuscript, including the Introduction, Results, Discussion, and Materials and Methods.

      In addition, we have performed two new analyses. Based on the two analyses, we have added one figure and two sections to Results, two sections to Materials and Methods, one figure to Supplementary Notes, and two tables to Supplementary Tables. These results were obtained using new methods and provided more support to the main conclusion.

      To be more responsible, we re-look into the comments made in the first round and respond to them further. The following are point-to-point responses to comments.

      Since many of the details in the Responses-To-Comments are available in published papers and eLife publishes Responses-To-Comments, we do not greatly revise supplementary notes to avoid ostensibly repeating published materials.

      “lack of suitable methods and/or relevant controls”.

      We carefully chose the methods, thresholds, and controls in the study; now, we provide clearer descriptions and explanations.

      (1) We have expanded the last paragraph in Introduction to briefly introduce the methods, thresholds, and controls.

      (2) In many places in Results and Materials and Methods, revisions are made to describe and justify methods, thresholds, and controls.

      (3) Some methods, thresholds, and controls have good consensus, such as FDR and genome-wide background, but others may not, such as the number of genes that greatly differ between humans and chimpanzees. Now, we describe our reasons for the latter situation. For example, we explain that “About 5% of genes have significant sequence differences in humans and chimpanzees, but more show expression differences due to regulatory sequences. We sorted target genes by their DBS affinity and, to be prudential, chose the top 2000 genes (DBS length>252 bp and binding affinity>151) and bottom 2000 genes (DBS length<60 bp but binding affinity>36) to conduct over-representation analysis”.

      (4) We also carefully choose proper words to make descriptions more accurate.

      Responses to the suggestion “new data generated could be better incorporated into the text”.

      (1) We think that this sentence “The occurrence of HS lncRNAs and their DBSs may have three situations – (a) HS lncRNAs preceded their DBSs, (b) HS lncRNAs and their DBSs co-occurred, (c) HS lncRNAs succeeded their DBSs. Our results support the third situation and the rewiring hypothesis”, previously in Discussion, should be better in section 2.3. We have revised it and moved it into the second paragraph of section 2.3.

      (2) Our two new analyses generated new data, and we describe them in Results.

      (3) It is possible to move more materials from Supplementary Notes to the main text, but it is probably unnecessary because the main text currently has eight sub-sections, two tables, and four figures.

      Responses to the comment “the interpretation is too quick to infer selection”.

      (1) When using XP-CLR, iSAFE, Tajima's D, Fay-Wu's H, the fixation index (Fst), and linkage disequilibrium (LD) to detect selection signals, we used the widely adopted parameters and thresholds but did not mention this clearly in the original manuscript. Now, in the first sentence of the second paragraph of section 2.4, we add the phrase “with widely-used parameters and thresholds” (more details are available in section 4.7 and Supplementary Notes).

      (2) It is not the first time we used these tests. Actually, we used these tests in two other studies (Tang et al. Uncovering the extensive trade-off between adaptive evolution and disease susceptibility. Cell Rep. 2022; Tang et al. PopTradeOff: A database for exploring population-specificity of adaptive evolution, disease susceptibility, and drug responsiveness. Comput Struct Biotechnol J. 2023). In this manuscript, section 2.5 and section 4.12 describe how we use these tests to detect signals and infer selection. We also cite the above two published papers from which the reader can obtain more details.

      (3) Also, in section 2.4, we stress that “Signals in considerable DBSs were detected by multiple tests, indicating the reliability of the analysis”.

      To further respond to the comments of “lack of suitable methods” and “this paper would benefit from a more rigorous approach to tackling it”, we have performed two new analyses. The results of the new analyses agree well with previous results and provide new support for the main conclusion. The result of section 2.5 is novel and interesting.

      We write in Discussion “Two questions are how mouse-specific lncRNAs specifically rewire gene expression in mice and how human- and mouse-specific rewiring influences the cross-species transcriptional differences”. To investigate whether the rewiring of gene expression by HS lncRNA in humans is accidental in evolution, we have made further genomic and transcriptomic analyses (Lin et al. Intrinsically linked lineage-specificity of transposable elements and lncRNAs reshapes transcriptional regulation species- and tissue-specifically. doi: https://doi.org/10.1101/2024.03.04.583292). To verify the obtained conclusions, we analyzed the spermatogenesis data from multiple species and obtained supporting evidence (not published).

      I note some specific points that I think would benefit from more rigorous approaches, and suggest possible ways forward for these.

      Much of this work is focused on comparing DNA binding domains in human-unique long-noncoding RNAs and DNA binding sites across the promoters of genes in the human genome, and I think the authors can afford to be a bit more methodical/selective in their processing and filtering steps here. The article begins by searching for orthologues of human lncRNAs to arrive at a set of 66 human-specific lncRNAs, which are then characterised further through the rest of the manuscript. Line 99 describes a binding affinity metric used to separate strong DBS from weak DBS; the methods (line 432) describe this as being the product of the DBS or lncRNA length times the average Identity of the underlying TTSs. This multiplication, in fact, undoes the standardising value of averaging and introduces a clear relationship between the length of a region being tested and its overall score, which in turn is likely to bias all downstream inference, since a long lncRNA with poor average affinity can end up with a higher score than a short one with higher average affinity, and it's not quite clear to me what the biological interpretation of that should be. Why was this metric defined in this way?

      (1) Using RNA:DNA base-pairing rules, other DBS prediction programs return just DBSs with lengths. Using RNA:DNA base-pairing rules and a variant of Smith-Waterman local alignment, LongTarget returns DBSs with lengths and identity values together with DBDs (local alignment makes DBDs and DBSs predicted simultaneously). Thus, instead of measuring lncRNA/DNA binding based on DBS length, we measure lncRNA/DNA binding based on both DBS length and DBD/DBS identity (simply called identity, which is the percentage of paired nucleotides in the RNA and DNA sequences). This allows us to define “binding affinity”. One may think that binding affinity is a more complex function of length and identity. But, according to in vitro studies (see the review Abu Almakarem et al. 2012 and citations therein, and see He et al. 2015 and citations therein), the strength of a triplex is determined by all paired nucleotides (i.e., triplet). Thus, binding affinity=length * identity is biologically reasonable.

      (2) Further, different from predicting DBS upon individual base-pairing rules such as AT-G and CG-C, LongTarget integrates base-pairing rules into rulesets, each covering A, T, C, and G (see the two figures below, which are from He et al 2015). This makes every nucleotide in the RNA and DNA sequences comparable and allows the computation of identity.

      (3) On whether LongTarget may predict unreasonably long DBSs. Three technical features of LongTarget make this highly unlikely (and more unlikely than other programs). The three features are (a) local alignment, (b) gap penalty, and (c) TT penalty (He et al. 2015).

      (4) Some researchers may think that a higher identity threshold (e.g., 0.8 or even higher) makes the predicted DBSs more reliable. This is not true. To explore plausible identity values, we analyzed the distribution of Kcnq1ot1’s DBSs in the large Kcnq1 imprinting region (which contains many known imprinted genes). We found that a high threshold for identity (e.g., 0.8) will make DBSs in many known imprinted genes fail to be predicted. Upon our analysis of many lncRNAs and upon early in vitro experiments, plausible identity values range from 0.4 to 0.8.

      (5) Is it necessary or advisable to define an identity threshold? Since identity values from 0.4 to 0.8 are plausible and identity is a property of a DBS but does not reflect the strength of the whole triplex, it is more reasonable to define a threshold for binding affinity to control predicted DBSs. As explained above, binding affinity = length*identity is a reasonable measure of the strength of a triplex. The default threshold is 60, and given an identity of 0.6 in many triplexes, a DBS with affinity=60 is about 100 bp. Compared with TF binding sites (TFBS), 100 bp is quite long. As we explain in the main text, “taking a DBS of 147 bp as an example, it is extremely unlikely to be generated by chance (p < 8.2e-19 to 1.5e-48)”.

      (6) How to validate predicted DBSs? Validation faces these issues. (a) DBDs are predicted on the genome level, but target transcripts are expressed in different tissues and cells. So, no single transcriptomic dataset can validate all predicted DBSs of a lncRNA. No matter using what techniques and what cells, only a small portion of predicted DBSs can be experimentally captured (validated). (b) The resolution of current experimental techniques is limited; thus, experimentally identified DBSs (i.e., “peaks”) are much longer than computationally predicted DBSs. (c) Experimental results contain false positives and false negatives. So, validation (or performance evaluation) should also consider the ROC curves (Wen et al. 2022).

      (7) As explained above, a long DBS may have a lower binding affinity than a short DBS. A biological interpretation is that the long DBS may accumulate mutations that decrease its binding ability gradually.

      There is also a strong assumption that identified sites will always be bound (line 100), which I disagree is well-supported by additional evidence (lines 109-125). The authors show that predicted NEAT1 and MALAT1 DBS overlap experimentally validated sites for NEAT1, MALAT1, and MEG3, but this is not done systematically, or genome-wide, so it's hard to know if the examples shown are representative, or a best-case scenario.

      (1) We did not make this assumption. Apparently, binding depends on multiple factors, including co-expression of genes and specific cellular context.

      (2) On the second issue, “this is not done systematically, or genome-wide”. We did genome-wide but did not show all results (supplementary fig 2 shows three genomic regions, which are impressively good). In Wen et al. 2022, we describe the overall results.

      It's also not quite clear how overlapping promoters or TSS are treated - are these collapsed into a single instance when calculating genome-wide significance? If, eg, a gene has five isoforms, and these differ in the 3' UTR but their promoter region contains a DBS, is this counted five times, or one? Since the interaction between the lncRNA and the DBS happens at the DNA level, it seems like not correcting for this uneven distribution of transcripts is likely to skew results, especially when testing against genome-wide distributions, eg in the results presented in sections 5 and 6. I do not think that comparing genes and transcripts putatively bound by the 40 HS lncRNAs to a random draw of 10,000 lncRNA/gene pairs drawn from the remaining ~13500 lncRNAs that are not HS is a fair comparison. Rather, it would be better to do many draws of 40 non-HS lncRNAs and determine an empirical null distribution that way, if possible actively controlling for the overall number of transcripts (also see the following point).

      (1) We predicted DBSs in the promoter region of 179128 Ensembl-annotated transcripts and did not merge DBSs (there is no need to merge them). If multiple transcripts share the same TSS, they may share the same DBS, which is natural.

      (2) If the DBSs of multiple transcripts of a gene overlap, the overlap does not raise a problem for lncRNA/DNA binding analysis in specific tissues because usually only one transcript is expressed in a tissue. Therefore, there is no such situation “If, e.g., a gene has five isoforms, and these differ in the 3' UTR but their promoter region contains a DBS, is this counted five times, or one?”

      (3) It is unclear to us what “it seems like not correcting for this uneven distribution of transcripts is likely to skew results” means. Regarding testing against genome-wide distributions, statistically, it is beneficial to make many rounds of random draws genome-wide, but this will take a huge amount of time. Since more variables demand more rounds of drawing, to our knowledge, this is not widely practiced in large-scale transcriptomic data analyses.

      (4) If the difference (result) is small thus calls for rigorous statistical testing, making many rounds of random draws genome-wide is necessary. In our results, “45% of these pairs show a significant expression correlation in specific tissues (Spearman's |rho| >0.3 and FDR <0.05). In contrast, when randomly sampling 10000 pairs of lncRNAs and protein-coding transcripts genome-wide, the percent of pairs showing this level of expression correlation (Spearman's |rho| >0.3 and FDR <0.05) is only 2.3%”.

      Thresholds for statistical testing are not consistent, or always well justified. For instance, in line 142 GO testing is performed on the top 2000 genes (according to different rankings), but there's no description of the background regions used as controls anywhere, or of why 2000 genes were chosen as a good number to test? Why not 1000, or 500? Are the results overall robust to these (and other) thresholds? Then line 190 the threshold for downstream testing is now the top 20% of genes, etc. I am not opposed to different thresholds in principle, but they should be justified.

      (1) We used the g:Profiler program to perform over-representation analysis to identify enriched GO terms. This analysis is used to determine what pre-defined gene sets (GO terms) are more present (over-represented) in a list of “interesting” genes than what would be expected by chance. Specifically, this analysis is often used to examine whether the majority of genes in a pre-defined gene set fall in the extremes of a list: the top and bottom of the list, for example, may correspond to the largest differences in expression between the two cell types. g:Profiler always takes the whole genome as the reference; that is why we did not mention the whole genome reference. We now add in section 2.2 “(with the whole genome as the reference)”.

      (2) Why choosing 2000 but not 2500 genes is somewhat subjective. We now explain that “About 5% of genes have significant sequence differences in humans and chimpanzees, but more show expression differences due to regulatory sequences. We sorted target genes by their DBS affinity and, to be prudential, chose the top 2000 genes (DBS length>252 bp and binding affinity>151) and bottom 2000 genes (DBS length<60 bp but binding affinity>36) to conduct over-representation analysis”.

      Likewise, comparing Tajima's D values near promoters to genome-wide values is unfair, because promoters are known to be under strong evolutionary constraints relative to background regions; as such it is not surprising that the results of this comparison are significant. A fairer comparison would attempt to better match controls (eg to promoters without HS lncRNA DBS, which I realise may be nearly impossible), or generate empirical p-values via permutation or simulation.

      We used these tests to detect selection signals in DBSs but not in the whole promoter regions. Using promoters without HS lncRNA DBS as the control also has risks because promoter regions contain other kinds of regulatory sequences.

      There are huge differences in the comparisons between the Vindija and Altai Neanderthal genomes that to me suggest some sort of technical bias or the such is at play here. e.g. line 190 reports 1256 genes to have a high distance between the Altai Neanderthal and modern humans, but only 134 Vindija genes reach the same threshold of 0.034. The temporal separation between the two specimens does not seem sufficient to explain this difference, nor the difference between the Altai Denisovan and Neanderthal results (2514 genes for Denisovan), which makes me wonder if it is a technical artefact relating to the quality of the genome builds? It would be worth checking.

      We feel it is hard to know whether or not the temporal separation between these specimens is sufficient to explain the differences because many details of archaic humans and their genomes remain unknown and because mechanisms determining genotype-phenotype relationships remain poorly known. After 0.034 was determined, these numbers of genes were determined accordingly. We chose parameters and thresholds that best suit the most important requirements, but these parameters and thresholds may not best suit other requirements; this is a problem for all large-scale studies.     

      Inferring evolution: There are some points of the manuscript where the authors are quick to infer positive selection. I would caution that GTEx contains a lot of different brain tissues, thus finding a brain eQTL is a lot easier than finding a liver eQTL, just because there are more opportunities for it. Likewise, claims in the text and in Tables 1 and 2 about the evolutionary pressures underlying specific genes should be more carefully stated. The same is true when the authors observe high Fst between groups (line 515), which is only one possible cause of high Fst - population differentiation and drift are just as capable of giving rise to it, especially at small sample sizes.

      (1) We add in Discussion that “Finally, not all detected signals reliably indicate positive selection”.

      (2) Our results are that more signals are detected in CEU and CHB than in YRI; this agrees all population genetics studies and implies that our results are not wrongly biased because more samples and larger samples were obtained from CEU and CHB.

    1. Author Response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review): 

      Summary: 

      Genome-wide association studies have been an important approach to identifying the genetic basis of human traits and diseases. Despite their successes, for many traits, a substantial amount of variation cannot be explained by genetic factors, indicating that environmental variation and individual 'noise' (stochastic differences as well as unaccounted for environmental variation) also play important roles. The authors' goal was to address whether gene expression variation in genetically identical individuals, driven by historical environmental differences and 'noise', could be used to predict reproductive trait differences. 

      Strengths: 

      To address this question, the authors took advantage of genetically identical C. elegans individuals to transcriptionally profile 180 adult hermaphrodite individuals that were also measured for two reproductive traits. A major strength of the paper is its experimental design. While experimenters aim to control the environment that each worm experiences, it is known that there are small differences that each worm experiences even when they are grown together on the same agar plate - e.g. the age of their mother, their temperature, the amount of food they eat, and the oxygen and carbon dioxide levels depending on where they roam on the plate. Instead of neglecting this unknown variation, the authors design the experiment up front to create two differences in the historical environment experienced by each worm: 1) the age of its mother and 2) 8 8-hour temperature difference, either 20 or 25 {degree sign}C. This helped the authors interpret the gene expression differences and trait expression differences that they observed. 

      Using two statistical models, the authors measured the association of gene expression for 8824 genes with the two reproductive traits, considering both the level of expression and the historical environment experienced by each worm. Their data supports several conclusions. They convincingly show that gene expression differences are useful for predicting reproductive trait differences, predicting ~25-50% of the trait differences depending on the trait. Using RNAi, they also show that the genes they identify play a causal role in trait differences. Finally, they demonstrate an association with trait variation and the H3K27 trimethylation mark, suggesting that chromatin structure can be an important causal determinant of gene expression and trait variation. 

      Overall, this work supports the use of gene expression data as an important intermediate for understanding complex traits. This approach is also useful as a starting point for other labs in studying their trait of interest. 

      We thank the reviewer for their thorough articulation of the strengths of our study.  

      Weaknesses: 

      There are no major weaknesses that I have noted. Some important limitations of the work (that I believe the authors would agree with) are worth highlighting, however: 

      (1) A large remaining question in the field of complex traits remains in splitting the role of non-genetic factors between environmental variation and stochastic noise. It is still an open question which role each of these factors plays in controlling the gene expression differences they measured between the individual worms. 

      Yes, we agree that this is a major question in the field. In our study, we parse out differences driven between known historical environmental factors and unknown factors, but the ‘unknown factors’ could encompass both unknown environmental factors and stochastic noise.

      (2) The ability of the authors to use gene expression to predict trait variation was strikingly different between the two traits they measured. For the early brood trait, 448 genes were statistically linked to the trait difference, while for egg-laying onset, only 11 genes were found. Similarly, the total R2 in the test set was ~50% vs. 25%. It is unclear why the differences occur, but this somewhat limits the generalizability of this approach to other traits. 

      We agree that the difference in predictability between the two traits is interesting. A previous study from the Phillips lab measured developmental rate and fertility across Caenorhabditis species and parsed sources of variation (1). Results indicated that 83.3% of variation in developmental rate was explained by genetic variation, while only 4.8% was explained by individual variation. In contrast, for fertility, 63.3% of variation was driven by genetic variation and 23.3% was explained by individual variation. Our results, of course, focus only on predicting the individual differences, but not genetic differences, for these two traits using gene expression data. Considering both sets of results, one hypothesis is that we have more power to explain nongenetic phenotypic differences with molecular data if the trait is less heritable, which is something that could be formally interrogated with more traits across more strains.

      (3) For technical reasons, this approach was limited to whole worm transcription. The role of tissue and celltype expression differences is important to the field, so this limitation is important. 

      We agree with this assessment, and it is something we hope to address with future work.

      Reviewer #2 (Public review): 

      Summary: 

      This paper measures associations between RNA transcript levels and important reproductive traits in the model organism C. elegans. The authors go beyond determining which gene expression differences underlie reproductive traits, but also (1) build a model that predicts these traits based on gene expression and (2) perform experiments to confirm that some transcript levels indeed affect reproductive traits. The clever study design allows the authors to determine which transcript levels impact reproductive traits, and also which transcriptional differences are driven by stochastic vs environmental differences. In sum, this is a rather comprehensive study that highlights the power of gene expression as a driver of phenotype, and also teases apart the various factors that affect the expression levels of important genes. 

      Strengths: 

      Overall, this study has many strengths, is very clearly communicated, and has no substantial weaknesses that I can point to. One question that emerges for me is about the extent to which these findings apply broadly. In other words, I wonder whether gene expression levels are predictive of other phenotypes in other organisms. I

      think this question has largely been explored in microbes, where some studies (PMID: 17959824) but not others (PMID: 38895328) find that differences in gene expression are predictive of phenotypes like growth rate. Microbes are not the primary focus here, and instead, the discussion is mainly focused on using gene expression to predict health and disease phenotypes in humans. This feels a little complicated since humans have so many different tissues. Perhaps an area where this approach might be useful is in examining infectious single-cell populations (bacteria, tumors, fungi). But I suppose this idea might still work in humans, assuming the authors are thinking about targeting specific tissues for RNAseq. 

      In sum, this is a great paper that really got me thinking about the predictive power of gene expression and where/when it could inform about (health-related) phenotypes. 

      We thank the reviewer for recognizing the strengths of our study. We are also interested in determining the extent to which predictive gene expression differences operate in specific tissues.

      Reviewer #3 (Public review): 

      Summary: 

      Webster et al. sought to understand if phenotypic variation in the absence of genetic variation can be predicted by variation in gene expression. To this end they quantified two reproductive traits, the onset of egg laying and early brood size in cohorts of genetically identical nematodes exposed to alternative ancestral (two maternal ages) and same generation life histories (either constant 20C temperature or 8-hour temperature shift to 25C upon hatching) in a two-factor design; then they profiled genome-wide gene expression in each individual. 

      Using multiple statistical and machine learning approaches, they showed that, at least for early brood size, phenotypic variation can be quite well predicted by molecular variation, beyond what can be predicted by life history alone. 

      Moreover, they provide some evidence that expression variation in some genes might be causally linked to phenotypic variation. 

      Strengths: 

      (1) Cleverly designed and carefully performed experiments that provide high-quality datasets useful for the community. 

      (2) Good evidence that phenotypic variation can be predicted by molecular variation. 

      We thank the reviewer for recognizing the strengths of our study.

      Weaknesses:  

      What drives the molecular variation that impacts phenotypic variation remains unknown. While the authors show that variation in expression of some genes might indeed be causal, it is still not clear how much of the molecular variation is a cause rather than a consequence of phenotypic variation. 

      We agree that the drivers of molecular variation remain unknown. While we addressed one potential candidate (histone modifications), there is much to be done in this area of research. We agree that, while some gene expression differences cause phenotypic changes, other gene expression differences could in principle be downstream of phenotypic differences.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      I have a number of suggestions that I believe will improve the Methods section. 

      (1) Strain N2-PD1073 will probably be confusing to some readers. I recommend spelling out that this is the Phillips lab version of N2.

      Thank you for this suggestion; we have added additional explanation of this strain in the Methods.

      (2) I found the details of the experimental design confusing, and I believe a supplemental figure will help. I have listed the following points that could be clarified: 

      a. What were the biological replicates? How many worms per replicate?

      Biological replicates were defined as experiments set up on different days (in this case, all biological replicates were at least a week apart), and the biological replicate of each worm can be found in Supplementary File 1 on the Phenotypic Data tab.

      b. I believe that embryos and L4s were picked to create different aged P0s, and eggs and L4s were picked to separate plates? Is this correct?

      Yes, this is correct.

      c. What was the spread in the embryo age?

      We assume this is asking about the age of the F1 embryos, and these were laid over the course of a 2-hour window.  

      d. While the age of the parents is different, there are also features about their growth plates that will be impacted by the experimental design. For example, their pheromone exposure is different due to the role that age plays in the combination of ascarosides that are released. It is worth noting as my reading of the paper makes it seem that parental age is the only thing that matters.

      The parents (P0) of different ages likely have differential ascaroside exposure because they are in the vicinity of other similarly aged worms, but the F1 progeny were exposed to their parents for only the 2-hour egg-laying window, in an attempt to minimize this type of effect as much as possible.  

      e. Were incubators used for each temperature?

      Yes.

      f. In line 443, why approximately for the 18 hours? How much spread?

      The approximation was based on the time interval between the 2-hour egg-laying window on Day 4 and the temperature shift on Day 5 the following morning. The timing was within 30 minutes of 18 hours either direction.

      g.  In line 444, "continually left" is confusing. Does this mean left in the original incubator?

      Yes, this means left in the incubator while the worms shifted to 25°C were moved. To avoid confusion, we re-worded this to state they “remained at 20°C while the other half were shifted to 25°C”.

      h. In line 445, "all worms remained at 20 {degree sign}C" was confusing to me as to what it indicated. I assume, unless otherwise noted, the animals would not be moved to a new temperature.

      This was an attempt to avoid confusion and emphasize that all worms were experiencing the same conditions for this part of the experiment.  

      i. What size plates were the worms singled onto?

      They were singled onto 6-cm plates.

      j. If a figure were to be made, having two timelines (with respect to the P0 and F1) might be useful.

      We believe the methods should be sufficient for someone who hopes to repeat the experiment, and we believe the schematic in Figure 1A labeling P0 and F1 generations is sufficient to illustrate the key features of the experimental design.

      k. Not all eggs that are laid end up hatching. Are these censored from the number of progeny calculations?

      Yes, only progeny that hatched and developed were counted for early brood.

      (3) For the lysis, was the second transfer to dH20 also a wash step?

      Yes.

      (4) What was used for the Elution buffer?

      We used elution buffer consisting of 10 mM Tris, 0.1 mM EDTA. We have added this to the “Cell lysate generation” section of the methods

      (5) The company that produced the KAPA mRNA-seq prep kit should be listed.

      We added that the kit was from Roche Sequencing Solutions.

      (6) For the GO analysis - one potential issue is that the set of 8824 genes might also be restricted to specific GO categories. Was this controlled for?

      We originally did not explicitly control for this and used the default enrichGO settings with OrgDB = org.Ce.eg.db as the background set for C. elegans. We have now repeated the analysis with the “universe” set to the 8824-gene background set. This did not qualitatively change the significant GO terms, though some have slightly higher or lower p-values. For comparison purposes, we have added the background-corrected sets to the GO_Terms tab of Supplementary File 1 with each of the three main gene groups appended with “BackgroundOf8824”.

      Reviewer #2 (Recommendations for the authors): 

      (1) The abstract, introduction, and experimental design are well thought through and very clear.

      Thank you.

      (2) Figure 1B could use a clearer or more intuitive label on the horizontal axis. The two examples help. Maybe the genes (points) on the left side should be blue to match Figure 1C, where the genes with a negative correlation are in the blue cluster.

      Thank you for these suggestions. We re-labeled the x-axis as “Slope of early brood vs. gene expression (normalized by CPM)”, which we hope gives readers a better intuition of what the coefficient from the model is measuring. We also re-colored the points previously colored red in Figure 1B to be color-coded depending on the direction of association to match Figure 1C, so these points are now color-coded as pink and purple.  

      (3) If red/blue are pos/neg correlated genes in 1C, perhaps different colors should be used to label ELO and brood in Figures 2 and 3. Green/purple?

      We appreciate this point, but since we ended up using the cluster colors of pink and purple in Figure 1, we opted to leave Figures 2 and 3 alone with the early brood and ELO colorcoding of red and blue.

      (4) I am unfamiliar with this type of beta values, but I thought the explanation and figure were very clear. It could be helpful to bold beta1 and beta2 in the top panels of Figure 2, so the readers are not searching around for those among all the other betas. It could also be helpful to add an English phrase to the vertical axes inFigures 2C and 2D, in addition to the beta1 and beta2. Something like "overall effect (beta1)" and"environment-controlled effect (beta2)". Or maybe "effect of environment + stochastic expression differences

      (beta1)" and "effect of stochastic expression differences alone (beta2)". I guess those are probably too big to fit on the figure, but it might be nice to have a label somewhere on this figure connecting them to the key thing you are trying to measure - the effect of gene expression and environment.

      Thank you for these suggestions. We increased the font sizes and bolded β1 and β2 in Figure 2A-B. In Figure 2C-D, we added a parenthetical under β1 to say “(env + noise)” and β2 to say “(noise)”. We agree that this should give the reader more intuition about what the β values are measuring.  

      Reviewer #3 (Recommendations for the authors): 

      The authors collected individuals 24 hours after the onset of egg laying for transcriptomic profiling. This is a well-designed experiment to control for the physiological age of the germline. However, this does not properly control for somatic physiological age. Somatic age can be partially uncoupled from germline age across individuals, and indeed, this can be due to differences in maternal age (Perez et al, 2017). This is because maternal age is associated with increased pheromone exposure (unless you properly controlled for it by moving worms to fresh plates), which causes a germline-specific developmental delay in the progeny, resulting in a delayed onset of egg production compared to somatic development (Perez et al. 2021). You control for germline age, therefore, it is likely that the progeny of day 1 mothers are actually somatically older than the progeny of day 3 mothers. This would predict that many genes identified in these analyses might just be somatic genes that increase or decrease their expression during the young adult stage. 

      For example, the abundance of collagen genes among the genes negatively associated (including col-20, which is the gene most significantly associated with early brood) is a big red flag, as collagen genes are known to be changing dynamically with age. If variation in somatic vs germline age is indeed what is driving the expression variation of these genes, then the expectation is that their expression should decrease with age. Vice versa, genes positively associated with early brood that are simply explained by age should be increasing.  So I would suggest that the authors first check this using time series transcriptomic data covering the young adult stage they profiled. If this is indeed the case, I would then suggest using RAPToR ( https://github.com/LBMC/RAPToR ), a method that, using reference time series data, can estimate physiological age (including tissue-specific one) from gene expression. Using this method they can estimate the somatic physiological age of their samples, quantify the extent of variation in somatic age across individuals, quantify how much of the observed differences in expressions are explained just by differences in somatic age and correct for them during their transcriptomic analysis using the estimated soma age as a covariate (https://github.com/LBMC/RAPToR/blob/master/vignettes/RAPToR-DEcorrection-pdf.pdf). 

      This should help enrich a molecular variation that is not simply driven by hidden differences between somatic and germline age. 

      To first address some of the experimental details mentioned for our paper, parents were indeed moved to fresh plates where they were allowed to lay embryos for two hours and then removed. Thus, we believe this minimizes the effects of ascarosides as much as possible within our design. As shown in the paper, we also identified genes that were not driven by parental age and for all genes quantified to what extent each gene’s association was driven by parental age. Thus, it is unlikely that differences in somatic and germline age is the sole explanatory factor, even if it plays some role. We also note that we accounted for egg-laying onset timing in our experimental design, and early brood was calculated as the number of progeny laid in the first 24 hours of egg-laying, where egg-laying onset was scored for each individual worm to the hour. The plot of each worm’s ELO and early brood traits is in Figure S1. Nonetheless, we read the RAPToR paper with interest, as we highlighted in the paper that germline genes tend to be positively associated with early brood while somatic genes tend to be negatively associated. While the RAPToR paper discusses using tissue-specific gene sets to stage genetically diverse C. elegans RILs, the RAPToR reference itself was not built using gene expression data acquired from different C. elegans tissues and is based on whole worms, typically collected in bulk. I.e., age estimates in RILs differ depending on whether germline or somatic gene sets are used to estimate age when the the aging clock is based on N2 samples. Thus, it is unclear whether such an approach would work similarly to estimate age in single worm N2 samples. In addition, from what we can tell, the RAPToR R package appears to implement the overall age estimate, rather than using the tissue-specific gene sets used for RILs in the paper. Because RAPToR would be estimating the overall age of our samples using a reference that is based on fewer samples than we collected here, and because we already know the overall age of our samples measured using standard approaches, we believe that estimating the age with the package would not give very much additional insight.  

      Bonferroni correction: 

      First, I think there is some confusion in how the author report their p-values: I don't think the authors are using a cut-off of Bonferroni corrected p-value of 5.7 x 10-6 (it wouldn't make sense). It's more likely that they are using a Bonferroni corrected p of 0.05 or 0.1, which corresponds to a nominal p value of 5.7 x 10-6, am I right?

      Yes, we used a nominal p-value of 5.7 x 10-6 to correspond to a Bonferroni-corrected p-value of 0.05, calculated as 0.05/8824. We have re-worded this wherever Bonferroni correction was mentioned.

      Second, Bonferroni is an overly stringent correction method that has now been substituted by the more powerful Benjamini Hochberg method to control the false discovery rate. Using this might help find more genes and better characterize the molecular variation, especially the one associated with ELO?

      We agree that Bonferroni is quite stringent and because we were focused on identifying true positives, we may have some false negatives. Because all nominal p-values are included in the supplement, it is straightforward for an interested reader to search the data to determine if a gene is significant at any other threshold.   

      Minor comments: 

      (1) "In our experiment, isogenic adult worms in a common environment (with distinct historical environments) exhibited a range of both ELO and early brood trait values (Fig S1A)" I think this and the figure is not really needed, Figure S1B is already enough to show the range of the phenotypes and how much variation is driven by the life history traits.

      We agree that the information in S1A is also included in S1B, but we think it is a little more straightforward if one is primarily interested in viewing the distribution for a single trait.

      (2) Line 105 It should be Figure S2, not S3.

      Thank you for catching this mistake.

      (3) Gene Ontology on positive and negatively associated genes together: what about splitting the positive and negative?

      We have added a split of positive and negative GO terms to the GO_Terms tab of Supplement File 1. Broadly speaking, the most enriched positively associated genes have many of the same GO terms found on the combined list that are germline related (e.g., involved in oogenesis and gamete generation), whereas the most enriched negatively associated genes have GO terms found on the combined list that are related to somatic tissues (e.g., actin cytoskeleton organization, muscle cell development). This is consistent with the pattern we see for somatic and germline genes shown in Figure 4.

      (4) A lot of muscle-related GOs, can you elaborate on that?

      Yes, there are several muscle-related GOs in addition to germline and epidermis. While we do not know exactly why from a mechanistic perspective these muscle-related terms are enriched, it may be important to note that many of these terms have highly overlapping sets of genes which are listed in Supplementary File 1. For example, “muscle system process” and “muscle contraction” have the exact same set of 15 genes causing the term to be significantly enriched. Thus, we tend to not interpret having many GO terms on a given tissue as indicating that the tissue is more important than others for a given biological process. While it is clear there are genes related to muscle that are associated with early brood, it is not yet clear that the tissue is more important than others.  

      (5) "consistent with maternal age affecting mitochondrial gene expression in progeny " - has this been previously reported?

      We do not believe this particular observation has been reported. It is important to note that these genes are involved in mitochondrial processes, but are expressed from the nuclear rather than mitochondrial genome. We re-worded the quoted portion of the sentence to say “consistent with parental age affecting mitochondria-related gene expression in progeny”.

      (6) PCA: "Therefore, the optimal number of PCs occurs at the inflection points of the graph, which is after only7 PCs for early brood (R2 of 0.55) but 28 PCs for ELO (R2 of 0.56)." 

      Not clear how this is determined: just graphically? If yes, there are several inflection points in the plot. How did you choose which one to consider? Also, a smaller component is not necessarily less predictive of phenotypic variation (as you can see from the graph), so instead of subsequently adding components based on the variance, they explain the transcriptomic data, you might add them based on the variance they explain in the phenotypic data? To this end, have you tried partial least square regression instead of PCA? This should give gene expression components that are ranked based on how much phenotypic variance they explain.  

      Thank you for this thoughtful comment. We agree that, unlike for Figure 3B, there is some interpretation involved on how many PCs is optimal because additional variance explained with each PC is not strictly decreasing beyond a certain number of PCs. Our assessment was therefore made both graphically and by looking at the additional variance explained with each additional PC. For example, for early brood, there was no PC after PC7 that added more than 0.04 to the R2. We could also have plotted early brood and ELO separately and had a different ordering of PCs on the x-axis. By plotting the data this way, we emphasized that the factors that explain the most variation in the gene expression data typically explain most variation in the phenotypic data.  

      (7) The fact that there are 7 PC of molecular variation that explain early brood is interesting. I think the authors can analyze this further. For example, could you perform separate GO enrichment for each component that explains a sizable amount of phenotypic variance? Same for the ELO.  

      Because each gene has a PC loading in for each PC, and each PC lacks the explanatory power of combined PCs, we believe doing GO Terms on the list of genes that contribute most to each PC is of minimal utility. The power of the PCA prediction approach is that it uses the entire transcriptome, but the other side of the coin is that it is perhaps less useful to do a gene-bygene based analysis with PCA. This is why we separately performed individual gene associations and 10-gene predictive analyses. However, we have added the PC loadings for all genes and all PCs to Supplementary File 1.

      (8) Avoid acronyms when possible (i.e. ELO in figures and figure legends could be spelled out to improve readability).

      We appreciate this point, but because we introduced the acronym both in Figure 1 and the text and use it frequently, we believe the reader will understand this acronym. Because it is sometimes needed (especially in dense figures), we think it is best to use it consistently throughout the paper.

      (9) Multiple regression: I see the most selected gene is col-20, which is also the most significantly differentially expressed from the linear mixed model (LMM). But what is the overlap between the top 300 genes in Figure 3F and the 448 identified by the LMM? And how much is the overlap in GO enrichment?

      Genes that showed up in at least 4 out of 500 iterations were selected more often than expected by chance, which includes 246 genes (as indicated by the red line in Figure 3F). Of these genes, 66 genes (27%) are found in the set of 448 early brood genes. The proportion of overlap increases as the number of iterations required to consider a gene predictive increases, e.g., 34% of genes found in 5 of 500 iterations and 59% of genes found in 10 of 500 iterations overlap with the 448 early brood genes. However, likely because of the approach to identify groups of 10 genes that are predictive, we do not find significant GO terms among the 246 genes identified with this approach after multiple test correction. We think this makes sense because the LMM identifies genes that are individually associated with early brood, whereas each subsequent gene included in multiple regression affects early brood after controlling for all previous genes. These additional genes added to the multiple regression are unlikely to have similar patterns as genes that are individually correlated with early brood.  

      (10) Elastic nets: prediction power is similar or better than multiple regression, but what is the overlap between genes selected by the elastic net (not presented if I am not mistaken) and multiple regression and the linear mixed model?

      For the elastic net models, we used a leave-one-out cross validation approach, meaning there were separate models fit by leaving out the trait data for each worm, training a model using the trait data and transcriptomic data for the other worms, and using the transcriptomic data of the remaining worm to predict the trait data. By repeating this for each worm, the regressions shown in the paper were obtained. Each of these models therefore has its own set of genes. Of the 180 models for early brood, the median model selects 83 genes (range from 72 to 114 genes). Across all models, 217 genes were selected at least once. Interestingly, there was a clear bimodal distribution in terms of how many models a given gene was selected for: 68 genes were selected in over 160 out of 180 models, while 114 genes were selected in fewer than 20 models (and 45 genes were selected only once). Therefore, we consider the set of 68 genes as highly robustly selected, since they were selected in the vast majority of models. This set of 68 exhibits substantial overlap with both the set of 448 early brood-associated genes (43 genes or 63% overlap) and the multiple regression set of 246 genes (54 genes or 79% overlap). For ELO, the median model selected 136 genes (range of 96 to 249 genes) and a total of 514 genes were selected at least once. The distribution for ELO was also bimodal with 78 genes selected over 160 times and 255 genes selected fewer than 20 times. This set of 78 included 6 of the 11 significant ELO genes identified in the LMM.  We have added tabs to Supplementary File 1 that include the list of genes selected for the elastic net models as well as a count of how many times they were selected out of 180 models.

      (11) In other words, do these different approaches yield similar sets of genes, or are there some differences?

      In the end, which approach is actually giving the best predictive power? From the perspective of R2, both the multiple regression and elastic net models are similarly predictive for early brood, but elastic net is more predictive for ELO. However, in presenting multiple approaches, part of our goal was identifying predictive genes that could be considered the ‘best’ in different contexts. The multiple regression was set to identify exactly 10 genes, whereas the elastic net model determined the optimal number of genes to include, which was always over 70 genes. Thus, the elastic net model is likely better if one has gene expression data for the entire transcriptome, whereas the multiple regression genes are likely more useful if one were to use reporters or qRTPCR to measure a more limited number of genes.  

      (12) Line 252: "Within this curated set, genes causally affected early brood in 5 of 7 cases compared to empty vector (Figure 4A).

      " It seems to me 4 out of 7 from Figure 4A. In Figure 4A the five genes are (1) cin-4, (2) puf5; puf-7, (3) eef-1A.2, (4) C34C12.8, and (5) tir-1. We did not count nex-2 (p = 0.10) or gly-13 (p = 0.07), and empty vector is the control.

      (13) Do puf-5 and -7 affect total brood size or only early brood size? Not clear. What's the effect of single puf-5 and puf-7 RNAi on brood?

      We only measured early brood in this paper, but a previous report found that puf-5 and puf-7 act redundantly to affect oogenesis, and RNAi is only effective if both are knocked down together(2). We performed pilot experiments to confirm that this was the case in our hands as well.  

      (14)  To truly understand if the noise in expression of Puf-5 and /or -7 really causes some of the observed difference in early brood, could the author use a reporter and dose response RNAi to reduce the level of puf-5/7 to match the lower physiological noise range and observe if the magnitude of the reduction of early brood by the right amount of RNAi indeed matches the observed physiological "noise" effect of puf-5/7 on early brood?

      We agree that it would be interesting to do the dose response of RNAi, measure early brood, and get a readout of mRNA levels to determine the true extent of gene knockdown in each worm (since RNAi can be noisy) and whether this corresponds to early brood when the knockdown is at physiological levels. While we believe we have shown that a dose response of gene knockdown results in a dose response of early brood, this additional analysis would be of interest for future experiments.

      (15) Regulated soma genes (enriched in H3K27me3) are negatively correlated with early brood. What would be the mechanism there? As mentioned before, it is more likely that these genes are just indicative of variation in somatic vs germline age (maybe due to latent differences in parental perception of pheromone).

      We can think of a few potential mechanisms/explanations, but at this point we do not have a decisive answer. Regulated somatic genes marked with H3K27me3 (facultative heterochromatin) are expressed in particular tissues and/or at particular times in development. In this study and others, genes marked with H3K27me3 exhibit more gene expression noise than genes with other marks. This could suggest that there are negative consequences for the animal if genes are expressed at higher levels at the wrong time or place, and one interpretation of the negative association is that higher expressed somatic genes results in lower fitness (where early brood is a proxy for fitness). Another related interpretation is that there are tradeoffs between somatic and germline development and each individual animal lands somewhere on a continuum between prioritizing germline or somatic development, where prioritizing somatic integrity (e.g. higher expression of somatic genes) comes at a cost to the germline resulting in fewer progeny. Additional experiments, including measurements of histone marks in worms measured for the early brood trait, would likely be required to more decisively answer this question.  

      (16) Line 151: "Among significant genes for both traits, β2 values were consistently lower than β1 (Figures 2CD), suggesting some of the total effect size was driven by environmental history rather than pure noise".

      We are interpreting this quote as part of point 17 below.

      (17) It looks like most of the genes associated with phenotypes from the univariate model have a decreased effect once you account for life history, but have you checked for cases where the life history actually masks the effect of a gene? In other words, do you have cases where the effect of gene expression on a phenotype is only (or more) significant after you account for the effect of life history (β2 values higher than β1)?

      This is a good question and one that we did not explicitly address in the paper because we focused on beta values for genes that were significant in the univariate analysis. Indeed, for the sets of 448 early brood genes ad 11 ELO genes, there are no genes for which β2 is larger than β1. In looking at the larger dataset of 8824 genes, with a Bonferroni-corrected p-value of 0.05, there are 306 genes with a significant β2 for early brood. The majority (157 genes) overlap with the 448 genes significant in the univariate analysis and do not have a higher β2 than β1. Of the remaining genes, 72 of these have a larger β2 than β1. However, in most cases, this difference is relatively small (median difference of 0.025) and likely insignificant. There are only three genes in which β1 is not nominally significant, and these are the three genes with the largest difference between β1 and β2 with β2 being larger (differences of 0.166, 0.155, and 0.12). In contrast, the median difference between β1 and β2 the 448 genes (in which β1 is larger) is 0.17, highlighting the most extreme examples of β2 > β1 are smaller in magnitude than the typical case of β1 > β2. For ELO, there are no notable cases where β2 > β1. There are eight genes with a significant β2 value, and all of these have a β1 value that is nominally significant. Therefore, while this phenomenon does occur, we find it to be relatively rare overall. For completeness, we have added the β1 and β2 values for all 8824 genes as a tab in Supplementary File 1.

    1. Author Response:

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

      Reviewer #1 (Public Review):

      Summary: Zhu et al., investigate the cellular defects in glia as a result of loss in DEGS1/ifc encoding the dihydroceramide desaturase. Using the strength of Drosophila and its vast genetic toolkit, they find that DEGS1/ifc is mainly expressed in glia and its loss leads to profound neurodegeneration. This supports a role for DEGS1 in the developing larval brain as it safeguards proper CNS development. Loss of DEGS1/ifc leads to dihydroceramide accumulation in the CNS and induces alteration in the morphology of glial subtypes and a reduction in glial number. Cortex and ensheathing glia appeared swollen and accumulated internal membranes. Astrocyte-glia on the other hand displayed small cell bodies, reduced membrane extension and disrupted organization in the dorsal ventral nerve cord. They also found that DEGS1/ifc localizes primarily to the ER. Interestingly, the authors observed that loss of DEGS1/ifc drives ER expansion and reduced TGs and lipid droplet numbers. No effect on PC and PE and a slight increase in PS.

      The conclusions of this paper are well supported by the data. The study could be further strengthened by a few additional controls and/or analyses.

      Strengths:

      This is an interesting study that provides new insight into the role of ceramide metabolism in neurodegeneration.

      The strength of the paper is the generation of LOF lines, the insertion of transgenes and the use of the UAS-GAL4/GAL80 system to assess the cell-autonomous effect of DEGS1/ifc loss in neurons and different glial subtypes during CNS development.

      The imaging, immunofluorescence staining and EM of the larval brain and the use of the optical lobe and the nerve cord as a readout are very robust and nicely done.

      Drosophila is a difficult model to perform core biochemistry and lipidomics but the authors used the whole larvae and CNS to uncover global changes in mRNA levels related to lipogenesis and the unfolded protein responses as well as specific lipid alterations upon DEGS1/ifc loss.

      Weaknesses:

      (1) The authors performed lipidomics and RTqPCR on whole larvae and larval CNS from which it is impossible to define the cell type-specific effects. Ideally, this could be further supported by performing single cell RNAseq on larval brains to tease apart the cell-type specific effect of DEGS1/ifc loss.

      We agree that using scRNAseq or pairing FACS-sorting of individual glial subtypes with bulk RNAseq would help tease apart the cell-type specific effects of DEGS1/ifc loss on glial cells. At this time, however, this approach extends beyond the scope of the current paper and means of the lab. 

      (2) It's clear from the data that the accumulation of dihydroceramide in the ER triggers ER expansion but it remains unclear how or why this happens. Additionally, the authors assume that, because of the reduction in LD numbers, that the source of fatty acids comes from the LDs. But there is no data testing this directly.

      As CERT, the protein that transports ceramide from the ER to the Golgi, is far more efficient at transporting ceramide than dihydroceramide, we speculate that dihydroceramide accumulates in the ER due to inefficient transport from the ER to the Golgi by CERT. We state this model more explicitly in the results under the subheading “Reduction of dihydroceramide synthesis suppresses the ifc CNS phenotype”.

      We agree with the point on lipid droplet. We observe a correlation, not a causation, between reduction of lipid droplets and a large expansion of ER membrane. We have tried to clarify the text in the last paragraph of the discussion to make this point more clearly. See also response to reviewer 2 point 3. 

      (3) The authors performed a beautiful EMS screen identifying several LOF alleles in ifc. However, the authors decided to only use KO/ifcJS3. The paper could be strengthened if the authors could replicate some of the key findings in additional fly lines.

      We agree. We replicated the observed cortex glia swelling, ER expansion in cortex glia, and observed increase in neuronal cell death markers in late-third instar larvae mutant for either the ifcjs1 or ifcjs2 allele. These data are now provided as Supplementary Figure 7.

      (4) The authors use M{3xP3-RFP.attP}ZH-51D transgene as a general glial marker. However, it would be advised to show the % overlap between the glial marker and the RFP since a lot of cells are green positive but not per se RFP positive and vice versa.

      We visually reexamined the expression of the 3xP3 RFP transgene relative to FABP labeling for cortex glia, Ebony for astrocyte-like glia, and the Myr-GFP transgene driven by glial-subtype specific GAL4 driver lines for perineurial, subperineurial, and ensheathing glia. We note that RFP localizes to the nucleus cytoplasm while FABP and Ebony localize to the cytoplasm and Myr-GFP to the cell membrane. Thus, an observed lack of overlap of expression between RFP and the other markers can arise to differential localization of the two markers in the same cells (see, for example, Fig. S2D where Myr-GFP expression in the nuclear envelope encircles that of RFP in the nucleus. Through visual inspection of five larval-brain complexes for each glial subtype marker, we found that essentially all cortex, SPG, and ensheathing glia expressed RFP. Similarly, nearly all astrocyte-like glia also expressed RFP, but they expressed RFP at significantly lower levels than that observed for cortex, SPG, or ensheathing glia. This analysis also confirmed that most perineurial glia do not express RFP. The 3xP3 M{3xP3-RFP.attP}ZH-51D transgene then labels most glia in the Drosophila CNS. We have added text to Supplementary Figure 2 noting the above observations as to which glial cells express RFP. 

      (5) The authors indicate that other 3xP3 RFP and GFP transgenes at other genomic locations also label most glia in the CNS. Do they have a preferential overlap with the different glial subtypes?

      We assessed three different types of 3xP3 RFP and GFP transgenes: M{3xP3RFP.attp} transgenes (n=4), Mi{GFP[E.3xP3]=ET1} transgenes (n=3), and

      Tl{GFP[3xP3.cLa]=CRIMIC.TG4} transgenes (n>6). All labeled cortex glia, but different lines exhibited differential labeling of astrocyte and ensheathing glia. These data are now included as Supplementary Figure 3.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Zhu et al. describes phenotypes associated with the loss of the gene ifc using a Drosophila model. The authors suggest their findings are relevant to understanding the molecular underpinnings of a neurodegenerative disorder, HLD-18, which is caused by mutations in the human ortholog of ifc, DEGS1.

      The work begins with the authors describing the role for ifc during fly larval brain development, demonstrating its function in regulating developmental timing, brain size, and ventral nerve cord elongation. Further mechanistic examination revealed that loss of ifc leads to depleted cellular ceramide levels as well as dihydroceramide accumulation, eventually causing defects in ER morphology and function. Importantly, the authors showed that ifc is predominantly expressed in glia and is critical for maintaining appropriate glial cell numbers and morphology. Many of the key phenotypes caused by the loss of fly ifc can be rescued by overexpression of human DEGS1 in glia, demonstrating the conserved nature of these proteins as well as the pathways they regulate. Interestingly, the authors discovered that the loss of lipid droplet formation in ifc mutant larvae within the cortex glia, presumably driving the deficits in glial wrapping around axons and subsequent neurodegeneration, potentially shedding light on mechanisms of HLD-18 and related disorders.

      Strengths:

      Overall, the manuscript is thorough in its analysis of ifc function and mechanism. The data images are high quality, the experiments are well controlled, and the writing is clear.

      Weaknesses:

      (1) The authors clearly demonstrated a reduction in number of glia in the larval brains of ifc mutant flies. What remains unclear is whether ifc loss leads to glial apoptosis or a failure for glia to proliferate during development. The authors should distinguish between these two hypotheses using apoptotic markers and cell proliferation markers in glia.

      To address this point, we used phospho-histone H3 to assess mitotic index in the thoracic CNS of wild-type versus ifc mutant late third instar larvae and found a mild, but significant reduction in mitotic index in ifc mutant relative to wild-type nerve cords. We also assessed the ability of glial-specific expression of the potent anti-apoptotic gene p35 to rescue the observed loss of cortex glia phenotype in the thoracic region of the CNS of otherwise ifc mutant larvae and observed a clear increase in cortex glia in the presence versus the absence of glial-specific p35 expression (p<3 x 10-4). These data are now provided as Supplementary Figure S8 in the paper and referred to on page 8.

      (2) It is surprising that human DEGS1 expression in glia rescues the noted phenotypes despite the different preference for sphingoid backbone between flies and mammals. Though human DEGS1 rescued the glial phenotypes described, can animal lethality be rescued by glial expression of human DEGS1? Are there longer-term effects of loss of ifc that cannot be compensated by the overexpression of human DEGS1 in glia (age-dependent neurodegeneration, etc.)?

      We note explicitly that while glial expression of human DEGS1 does provide rescuing activity, it only partially rescues the ifc mutant CNS phenotype in contrast to glial expression of Drosophila ifc, which fully rescues this phenotype. Thus, the relative activity of human DEGS1 is far below that of Drosophila ifc when assayed in flies. To quantify the functional difference between the two transgenes, we assessed the ability of glial expression of fly ifc or of human DEGS1 to rescue the lethality of otherwise ifc mutant larvae: Glial expression of ifc was sufficient to rescue the adult viability of 57.9% of ifc mutant flies based on expected Mendelian ratios (n=2452), whereas glial expression of DEGS1 was sufficient to rescue just 3.9% of ifc mutant flies (n=1303), uncovering a ~15-fold difference in the ability of the two transgenes to rescue the lethality of otherwise ifc mutant flies. In the absence of either transgene, no ifc mutant larvae reached adulthood (n=1030). These data are now provided in the text on page 9 of the revised manuscript. 

      (3) The mechanistic link between the loss of ifc and lipid droplet defects is missing. How do defects in ceramide metabolism alter triglyceride utilization and storage? While the author's argument that the loss of lipid droplets in larval glia will lead to defects in neuronal ensheathment, a discussion of how this is linked to ceramides needs to be added.

      We have revised the text to address this point. We speculate that the apparent increased demand for membrane phospholipid synthesis may drive the depletion of lipid droplets, providing a link to ifc function and ceramides. Below we provide the rewritten last paragraph; the underlined section is the new text.  

      “The expansion of ER membranes coupled with loss of lipid droplets in ifc mutant larvae suggests that the apparent demand for increased membrane phospholipid synthesis may drive lipid droplet depletion, as lipid droplet catabolism can release free fatty acids to serve as substrates for lipid synthesis. At some point, the depletion of lipid droplets, and perhaps free fatty acids as well, would be expected to exhaust the ability of cortex glia to produce additional membrane phospholipids required for fully enwrapping neuronal cell bodies. Under wild-type conditions, many lipid droplets are present in cortex glia during the rapid phase of neurogenesis that occurs in larvae. During this phase, lipid droplets likely support the ability of cortex glia to generate large quantities of membrane lipids to drive membrane growth needed to ensheathe newly born neurons. Supporting this idea, lipid droplets disappear in the adult Drosophila CNS when neurogenesis is complete and cortex glia remodeling stops. We speculate that lipid droplet loss in ifc mutant larvae contributes to the inability of cortex glia to enwrap neuronal cell bodies. Prior work on lipid droplets in flies has focused on stress-induced lipid droplets generated in glia and their protective or deleterious roles in the nervous system. Work in mice and humans has found that more lipid droplets are often associated with the pathogenesis of neurodegenerative diseases, but our work correlates lipid droplet loss with CNS defects. In the future, it will be important to determine how lipid droplets impact nervous system development and disease.”

      (4) On page 10, the authors use the words "strong" and "weak" to describe where ifc is expressed. Since the use of T2A-GAL4 alleles in examining gene expression is unable to delineate the amount of gene expression from a locus, the terms "broad" and "sparse" labeling (or similar terms) should be used instead.

      The ifc T2A-GAL4 insert in the ifc locus reports on the transcription of the gene. We agree that GAL4 system will not reflect amount of gene expression differences when the expression levels are not dramatically different. However, when the expression levels differ dramatically, as in our case, GAL4 system can reflect this difference in the expression of a reporter gene.  We reworded this section to suggest that ifc is transcribed at higher levels in glia as compared to neurons. We can’t use sparse or broad, as ifc is expressed in all, or at least in most, glia and neurons. The new text is as follows:” Using this approach, we observed strong nRFP expression in all glial cells (Figures 4D and S10A) and modest nRFP expression in all neurons (Figures 4E and S10B), suggesting ifc is transcribed at higher levels in glial cells than neurons in the larval CNS.”  

      Reviewer #3 (Public Review):

      Summary:

      In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.

      Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.

      Strengths:

      This manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions.

      Weaknesses:

      I didn't find any obvious weakness.

      Reviewer #1 (Recommendations For The Authors):

      Additional minor comments below:

      (1) The authors state that TGs are the building blocks of membrane phospholipids. This is not exactly true. The breakdown of TGs can result in free FAs which can be used for membrane phospholipid synthesis. Also, membrane phospholipids can also be generated from free FAs that were never in TGs.

      To address this point, we have reworked a number of sentences in the text. On page 12 we reworded two small sections to the following: 

      “In the CNS, lipid droplets form primarily in cortex glia[29] and are thought to contribute to membrane lipid synthesis through their catabolism into free fatty acids versus acting as an energy source in the brain.[41] Consistent with the possibility that increased membrane lipid synthesis drives lipid droplet reduction, RNA-seq assays of dissected nerve cords revealed that loss of ifc drove transcriptional upregulation of genes that promote membrane lipid biogenesis”

      As TG breakdown results in free fatty acids that can be used for membrane phospholipid synthesis, we asked if changes in TG levels and saturation were reflected in the levels or saturation of the membrane phospholipids phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS).

      (2) Figure 5J what does the dotted line indicate? Please specify in the figure legend or remove it.

      We have added the following text in the figure legend: Dotted line indicates a log2 fold change of 0.5 in the treatment group compared to the control group.

      (3) The text for your graphs is hard to read. Please make the font larger.

      We have increased font size to enhance the readability of the figures.

      (4) The authors mentioned that driving ifc expression in neurons rescues the phenotypes (ref 17). While the glial-specific role presented in this study is robust. I think some readers would appreciate some discussion of this study in light of the data presented here.

      We have added the below text on page 10 to address this point.

      “Results of our gene rescue experiments conflict with a prior study on ifc in which expression of ifc in neurons was found to rescue the ifc phenotype. In this context, we note that elav-GAL4 drives UASlinked transgene expression not just in neurons, but also in glia at appreciable levels, and thus needs to be paired with repo-GAL80 to restrict GAL4-mediated gene expression to neurons. Thus, “off-target” expression in glial cells may account for the discrepant results. It is, however, more difficult to reconcile how neuronal or glial expression of ifc would rescue the observed lethality of the ifc-KO chromosome given the presence additional lethal mutations in the 21E2 region of the second chromosome.”

      (5) While the analysis of fatty acid saturation is experimentally well done. I'm not really sure what the significance of this data is.

      We included this information as a reference for future analysis of additional genes in the ceramide biogenesis pathway, as we expect that alteration of the levels and saturation levels of PE, PC, and PS in cell membranes may underlie key changes in the biophysical properties of glial cell membranes and their ability to enwrap or infiltrate their targets. Thus, we expect the significance of these data to grow as more work is done on additional members of the ceramide pathway in the nervous system in flies and other systems.  

      Reviewer #2 (Recommendations For The Authors):

      (1) There is a typo at the top of page 11: "internal membranes and fail enwrap neurons" is missing the word "to" before "enwrap"

      The typo was fixed.

      (2)  PMID: 36718090 should be included in the discussion of SPT and ORMDL complex in human disease.

      The reference was added.

      Reviewer #3 (Recommendations For The Authors):

      In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.

      Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.

      In summary, this manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions. I have no additional comments and fully support the publication of this manuscript in eLife.

      The authors also note that they added one paragraph to the discussion that addresses the possibility that the increased detection of cell death markers could arise due to the inability of glial cells to remove cellular debris. The text of this paragraph is provided below:

      We note that cortex glia are the major phagocytic cell of the CNS and phagocytose neurons targeted for apoptosis as part of the normal developmental process.23-26  Thus, while we favor the model that ifc triggers neuronal cell death due to glial dysfunction, it is also possible that increased detection of dying neurons arises due at least in part to a decreased ability of cortex glia to clear dying neurons from the CNS. At present, the large number of neurons that undergo developmentally programmed cell death combined with the significant disruption to brain and ventral nerve cord morphology caused by loss of ifc function render this question difficult to address.Additional evidence does, however, support the idea that loss of ifc function drives excess neuronal cell death: Clonal analysis in the fly eye reveals that loss of ifc drives photoreceptor neuron degeneration17, indicating that loss of ifc function drives neuronal cell death; cortex-glia specific depletion of CPES, which acts downstream of ifc, disrupts neuronal function and induces photosensitive epilepsy in flies59, indicating that genes in the ceramide pathway can act nonautonomously in glia to regulate neuronal function; recent genetic studies reveal that other glial cells can compensate for impaired cortex glial cell function by phagocytosing dying neurons62, and we observe that the cell membranes of subperineurial glia enwrap dying neurons in ifc mutant larvae (Fig. S14), consistent with similar compensation occurring in this background, and in humans, loss of function mutations in DEGS1 cause neurodegeneration.7-9 Clearly, future work is required to address this question for ifc/DEGS1 and perhaps other members of the ceramide biogenesis pathway.

    1. Incidentally, this is an underrated benefit of AI-assisted coding. When I am rested and focused I am a fast and effective programmer. But I don’t spend all my time rested and focused. When I’m tired, or mentally frayed after a long day of work, or just distracted, I can’t really sit down and fix a bug (at least not without a serious mental effort). However, I can fire off an agentic coding session. Checking the LLM output - particularly for a bugfix, which is typically a small diff - is much less cognitively taxing than having to do it myself. So it’s not so much “AI makes me more effective at my normal work”, as it is “AI means I can work at times that I otherwise would be able to get nothing done at all”. It doesn’t really matter if it’s 19% or 50% or 200% slower than my normal working pace, since any amount of progress is better than zero.

      It'd be interesting to see people testing this for themselves, though

    1. Social annotation not only provides an interactive reading context for students with support forcomprehension of the specific passage under study, but also opportunities to model and practiceeffective reading strategies (Bahari et al., 2021

      Social annotaion truly transforms reading from a solitary act into a collaborative journey- exactly what makes it so powerful for students. By engaging with peers' thoughts alongside the text, learners don't just decode words; they see how others parse ideas, ask questions, or connect concepts, which deepens their own graps of the passage. What stands out most is how it models active reading. Over time, this practice turns passive skimming into intentional analysis, as they start to mirror critical thinking they have seen modeled. It's a way to build lifelong reading habits that extend far beyond the classroom.

    1. Latinx feminist scholar Bianet Castellanos terms, positioned assettler in Hawai‘i.8 With Oceanic Filipinx studies’ particular focus onplace, this framework exposes how Hawai‘i is simultaneously home andnot home at the same time. The Philippine-US empires have positionedme here in Hawai‘i as a settler who benefits from Kānaka Maolidispossession.

      this brings up another though, typically folks will know for sure, like I know this place isn't the "homeland", but it's a place i've called home or made home, but is it truly? How much of that is allowed to just be personal guilt that people are left to feel bad about but not do anything with.

    Annotators

    1. Neurodivergent is not a synonym for autistic. There are countless possible ways to be neurodivergent, and being autistic is only one of those ways. There are myriad ways of being neurodivergent that have no resemblance or connection to autism whatsoever. Never, ever use neurodivergent as a euphemism for autistic. If you mean that someone is autistic, say they’re autistic. It’s not a dirty word.

      This source really highlights this excerpt on how there are many people that are neurodivergent in today's society and how not all of them are just autistic but have "dyslexia, ADHD, autism, bipolar disorder, and other psychiatric conditions." Both of these show how the term "neurodivergent" should not be confined to just autism.

                                                Works Cited
      

      “Neurodiversity.” The American Institute for Learning and Human Development, www.institute4learning.com/resources/articles/neurodiversity/?utm_source. Accessed 14 July 2025.

    1. Ideas aren't like physical property—you can’t lock them in a drawer. But within academic, professional, and creative communities, we do treat intellectual labor as something that deserves recognition. That’s not just about fairness—it’s about trust in the systems that produce knowledge.

    1. Don't rely on an inbox, comprehensive indexes, or backlinks When writing notes for your Zettelkasten, it's natural to feel some anxiety about whether you're just writing into the void, or whether you'll actually stumble across those notes again in the future. To relieve this anxiety, there are at least three tempting solutions that seem to make sense on the surface, but which tend to fall apart in the long term.

      Mis blikis me permiten reencontrar la información fácilmente y los consulto/referencio con frecuencia. En particular para ofrecer información que solicitan una y otra vez, como hojas de vida y portafolios de proyectos.

      Si bien a veces creo índices con otros tiddlers, en general confío y uso los sistemas de etiquetados y de búsqueda que ya vienen incorporados en la herramienta para reencontrar la información y el sistema de filtros y operadores para ofrecer vistas específicas de la misma a terceros, como mi portafolio o mi perfil.

      No reorganizo la información en publicaciones más grandes y lineales como artículos o libros, como lo haría Luhmann, básicamente por dos cosas:

      1. me gusta estar lejos de las mafias de publicación académica clásica y sus circuitos y
      2. quisiera que las publicaciones vivas, tipo wiki, blikis, datasets y repositorios de código tuvieran un estatus epistémico en sí mismo, sin tener que tomar la forma de las publicaciones académicas clásicas que han sido tan gentrificadas.

      Lo anterior requerirá otras métricas, en las cuales vamos trabajando lento.

    1. In August 1619, just 12 years after the English settled Jamestown, Va., one year before the Puritans landed at Plymouth Rock and some 157 years before the English colonists even decided they wanted to form their own country, the Jamestown colonists bought 20 to 30 enslaved Africans from English pirates.

      This illustrates the beginning of the American slave trade. It's crazy to think that then English decided to buy slaves without knowing if they were to form a country yet.

    1. I rarely wait, because I'm juggling multiple projects. When one agent instance is working, I switch to another window. Sometimes it's a separate git worktree of the same codebase. Yes, context switching is tiring, but it also seems to help me overcome ADHD-related activation energy barriers? Over the years, there've been days when I just sit there staring at the IDE window, poking my brain with a stick saying "c'mon, do something" and nothing happens for an hour or more. I'm not planning my next move, I'm just dissociating. My executive function doesn't, like, function. Often. My own brain makes me wait long periods of time before it starts generating useful results. 😅 Maybe it's the cycling novelty that keeps me going? I enjoy task switching between prosing and coding. I enjoy finding that the model appears to have "read" everything—evidenced by it echoing my intent back in code or follow-up questions. I enjoy discovering that while I was in another window, new things happened in the background for me to review.

      It'd be interesting to see if people can e.g. work assisted for more hours before getting tired in a day. I do suspect that the perception of going faster is maybe a shift in distribution over tasks that feel tedious and tasks that feel like they go quickly (independently of reality). But: most of productivity is really emotional management, so...

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      A) The presentation of the paper must be strengthened. Inconsistencies, mislabelling, duplicated text, typos, and inappropriate colour code should be changed.

      We spotted and corrected several inconsistencies and mislabelling issues throughout the text and figures. Thanks!  

      B) Some claims are not supported by the data. For example, the sentence that says that "adolescent mice showed lower discrimination performance than adults (l.22) should be rewritten, as the data does not show that for the easy task (Figure 1F and Figure 1H).

      We carefully reviewed the specific claims and fixed some of the wording so it adheres to the data shown.

      C) In Figure 7 for example, are the quantified properties not distinct across primary and secondary areas?

      We now carried out additional analysis to test this. We found that while AUDp and AUDv exhibit distinct tuning properties, they show similar differences between adolescent and adult neurons (see Supplementary Table 6, Fig. S7-1a-h). Note that TEa and AUDd could not be evaluated due to low numbers of modulated neurons in this protocol.

      D) Some analysis interpretations should be more cautious. (..) A lower lick rate in general could reflect a weaker ability to withhold licking- as indicated on l.164, but also so many other things, like a lower frustration threshold, lower satiation, more energy, etc).

      That is a fair comment, and we refined our interpretations. Moreover, we also addressed whether impulsiveness impacted lick rates. In the Educage, we found that adolescent mice had shorter ITIs only after FAs (Fig. S2-1). In the head-fixed setup, we examined (1) the proportion of ITIs where licks occurred (Fig. S3-1c) and (2) the number of licks in these ITIs (Fig. S3-1d). We found no differences between adolescents and adults, indicating that the differences observed in the main task are not due to general differences in impulsiveness (Fig. S2-1, Fig. S3-1c, d). Finally, we note that potential differences in satiation were already addressed in the original manuscript by carefully examining the number of trials completed across the session. See also Review 3, comment #1 below.

      Reviewer #2 (Public review):

      A) For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.

      We reviewed the manuscript carefully and revised the relevant sections to clarify the rationale behind the analyses. See detailed responses to all the reviewer’s specific comments.

      B) The results of optogenetic manipulation, while very interesting, warrant a more in-depth discussion.

      We expanded our discussion on these experiments (L495-511) and also added an additional analysis to strengthen our findings (Fig. S3-2e).

      Reviewer #3 (Public review):

      (1) The authors report that "adolescent mice showed lower auditory discrimination performance compared to adults" and that this performance deficit was due to (among other things) "weaker cognitive control". I'm not fully convinced of this interpretation, for a few reasons. First, the adolescents may simply have been thirstier, and therefore more willing to lick indiscriminately. The high false alarm rates in that case would not reflect a "weaker cognitive control" but rather, an elevated homeostatic drive to obtain water. Second, even the adult animals had relatively high (~40%) false alarm rates on the freely moving version of the task, suggesting that their behavior was not particularly well controlled either. One fact that could help shed light on this would be to know how often the animals licked the spout in between trials. Finally, for the head-fixed version of the task, only d' values are reported. Without the corresponding hit and false alarm rates (and frequency of licking in the intertrial interval), it's hard to know what exactly the animals were doing.

      irst, as requested, we added the Hit rates and FA rates for the head-fixed task (Fig. S3-1a). Second, as requested by the reviewr, we performed additional analyses in both the Educage and head-fixed versions of the task. Specifically, we analyzed the ITI duration following each trial outcome. We found that adolescent mice had shorter ITIs only after Fas (Fig. S2-1). In the head-fixed setup, we examined (1) the proportion of ITIs during which licks occurred (Fig. S3-1c) and (2) the number of licks in these ITIs (Fig. S3-1d). We found no differences between adolescents and adults, indicating that the differences observed in the main task are not due to general differences in impulsiveness (Fig. S2-1, Fig. S3-1c, d). See also comment #D of reviewer #1 above.

      B) There are some instances where the citations provided do not support the preceding claim. For example, in lines 64-66, the authors highlight the fact that the critical period for pure tone processing in the auditory cortex closes relatively early (by ~P15). However, one of the references cited (ref 14) used FM sweeps, not pure tones, and even provided evidence that the critical period for this more complex stimulus occurred later in development (P31-38). Similarly, on lines 72-74, the authors state that "ACx neurons in adolescents exhibit high neuronal variability and lower tone sensitivity as compared to adults." The reference cited here (ref 4) used AM noise with a broadband carrier, not tones.

      We carefully checked the text to ensure that each claim is accurately supported by the corresponding reference.

      C) Given that the authors report that neuronal firing properties differ across auditory cortical subregions (as many others have previously reported), why did the authors choose to pool neurons indiscriminately across so many different brain regions?

      We appreciate the reviewer’s concern. While we acknowledge that pooling neurons across auditory cortical subregions may obscure region-specific effects, our primary focus in this study is on developmental differences between adolescents and adults, which were far more pronounced than subregional differences.

      To address this potential limitation: (1) We analyzed firing differences across subregions during task engagement (see Fig. S4-1, S4-2, S4-3; Supplementary Tables 2 and 3). (2) We have now added new analyses for the passive listening condition in AUDp and AUDv (Fig. S7-1; Supplementary Table 6).

      These analyses support our conclusion that developmental stage has a greater impact on auditory cortical activity than subregional location in the contexts examined. For clarity and cohesion, the main text emphasizes developmental differences, while subregional analyses are presented in the Supplement.

      D) And why did they focus on layers 5/6? (Is there some reason to think that age-related differences would be more pronounced in the output layers of the auditory cortex than in other layers?)

      We agree that other cortical layers, particularly supragranular layers, are important for auditory processing and plasticity. Our focus on layers 5/6 was driven by both methodological and biological considerations. Methodologically, our electrode penetrations were optimized to span multiple auditory cortical areas, and deeper layers provided greater mechanical stability for chronic recordings. Biologically, layers 5/6 contain the principal output neurons of the auditory cortex and are well-positioned to influence downstream decision-making circuits. We acknowledge the limitation of our recordings to these layers in the manuscript (L268; L464-8).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The presentation of the paper must be strengthened. As it is now, it makes it difficult to appreciate the strengths of the results. Here are some points that should be addressed:

      a) The manuscript is full of inconsistencies that should be fixed to improve the reader's understanding. For example, the description on l.217 and the Figure. S3-1b, the D' value of 0 rounded to 0.01 on l. 735 (isn't it rather the z-scored value that is rounded? A D' of 0 is not a problem), the definition of lick bias on l. 750 and the values in Fig.2, the legend of Figure 7F and what is displayed on the graph (is it population sparseness or responsiveness?), etc.

      We adjusted the legend and description of former Fig. S3-1b (now Fig. S3-2b).

      We now clarify that the rounded values refer to z-scored hit and false alarm rates that we used in the d’ calculation. We adjusted the definition of the lick bias in Fig. 2 and Fig. S3-1b (L804).

      We replaced ‘population responsiveness’ with ‘population sparseness’ throughout the figures, legend and the text.

      b) References to figures are sometimes wrong (for example on l. 737,739).

      c) Some text is duplicated (for example l. 814 and l. 837).

      d) Typos should be corrected (for example l. 127, 'the', l. 787, 'upto').

      We deleted the incorrect references of this section, removed the duplicated text, and corrected the typos.

      e) Color code should be changed (for example the shades of blue for easy and hard tasks - they are extremely difficult to differentiate).

      After consideration, we decided to retain the blue color code (i.e., Fig. 1d, Fig. 3d, Fig. 4e-g, Fig. 5c, Fig. 6d–g), where the distinction between the shades of blue appears sufficiently clear and maintains visual consistency and aesthetic appeal. We did however, made changes in the other color codes (Fig. 4, Fig. 5, Fig. 6, Fig. 7).

      f) Figure design should be improved. For example, why is a different logic used for displaying Figure 5A or B and Figure 1E?

      We adjusted the color scheme in Fig. 5. We chose to represent the data in Fig. 5 according to task difficulty, as this arrangement best illustrates the more pronounced deficits in population decoding in adolescents during the hard task.

      f) Why use a 3D representation in Figure 4G? (2)

      The 3D representation in Fig. 4g was chosen to illustrate the 3-way interactions between onset-latency, maximal discriminability, and duration of discrimination.

      g) Figure 1A, lower right panel- should "response" not be completed by "lick", "no lick"?

      We changed the labels to “Lick” and “No Lick” in Fig. 1a.

      h) l.18 the age mentioned is misleading, because the learning itself actually started 20 days earlier than what is cited here.

      Corrected.

      i) Explain what AAV5-... is on l.212.

      We added an explanation of virus components (see L216-220).

      (2) The comparison of CV in Figure 2 H-J is interesting. I am curious to know whether the differences in the easy and hard tasks could be due to a decrease in CV in adults, rather than an increase in CV in adolescents? Also, could the difference in J be due to 3 outliers?

      We agree that the observed CV differences may reflect a reduction in variability in adults rather than an increase in adolescents. We have revised the Results section accordingly to acknowledge this interpretation.

      Regarding the concern about potential outliers in Fig. 2J, we tested the data for outliers using the isoutlier function in MATLAB (defining outliers as values exceeding three standard deviations from the mean) and found no such cases.

      (3) Figure 2c shows that there is no difference in perceptual sensitivity between adolescents and adults, whereas the conclusion from Figure 4 is that adolescents exhibit lower discriminability in stimulus-related activity. Aren't these results contradictory?

      This is a nuanced point. The similar slopes of the psychometric functions (Fig. 2c) indicating comparable perceptual sensitivity and the lower AUC observed in the ACx of adolescents (Fig. 4) do not necessarily contradict each other. These two measures capture related but distinct issues: psychometric slopes reflect behavioral output, which integrates both sensory encoding and processing downstream to ACx, while the AUC analysis reflects stimulus-related neural activity in ACx, which may still include decision-related components.<br /> Note that stimulus-related neural discriminability outside the context of the task is not different between adolescent and adult experts (Fig. 7h; p = 0.9374, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). This suggests that there are differences that emerge when we measure during behavior. Also note that behavior may rely on processing beyond ACx, and it is possible that downstream areas compensate for weaker cortical discriminability in adolescents — but this issue merits further investigation.

      (4) Why do you think that the discrimination in hard tasks decreases with learning (Figure 6D vs Figure 6F)?

      This is another nuanced point, and we can only speculate at this stage. While it may appear counterintuitive that single-neuron discriminability (AUC) for the hard task is reduced after learning (Fig. 6D vs. 6F), we believe this may reflect a shift in sensory coding in expert animals. In a recent study (Haimson et al., 2024; Science Advances), we found that learning alters single-neuron responses in the easy versus hard task in complex and distinct ways, which may account for this result. It is also possible that, in expert mice, top-down mechanisms such as feedback from higher-order areas act to suppress or stabilize sensory responses in auditory cortex, reducing the apparent stimulus selectivity of single neurons (e.g., AUC), even as behaviorally relevant information is preserved or enhanced at the population level.

      Reviewer #2 (Recommendations for the authors):

      This is very interesting work and I enjoyed reading the manuscript. See below for my comments, queries and suggestions, which I hope will help you improve an already very good paper.

      We thank the reviewer for the meticulous and thoughtful review.

      (1) Line 107: x-axis of panel 1e says 'pre-adolescent'.

      (2) Line 130: replace 'less' with 'fewer'.

      (3) Line 153: 'both learned and catch trials': I find the terminology here a bit confusing. I would typically understand a catch trial to be a trial without a stimulus but these 'catch' trials here have a stimulus. It's just that they are not rewarded/punished. What about calling them probe trials instead?

      We corrected the labelling (1), reworded to ‘fewer’ and ‘probe trials’ (2,3).

      (4) Line 210: The results of the optogenetics experiments are very interesting. In particular, because the effect is so dramatic and much bigger than what has been reported in the literature previously, I believe. Lick rates are dramatically reduced suggesting that the mice have pretty much stopped engaging in the task and the authors very rightly state that the 'execution' of the behavior is affected. I think it would be worth discussing the implications of these results more thoroughly, perhaps also with respect to some of the lesion work. Useful discussions on the topic can be found, for instance, in Otchy et al., 2015; Hong et al., 2018; O'Sullivan et al., 2019; Ceballo et al., 2019 and Lee et al., 2024. Are the mice unable to hear anything in laser trials and that is why they stopped licking? If they merely had trouble distinguishing them then we would perhaps expect the psychometric curves to approach chance level, i.e. to be flat near the line indicating a lick rate of 0.5. Could the dramatic decrease in lick rate be a motor issue? Can we rule out spillover of the virus to relevant motor areas? (I understand all of the 200nL of the virus were injected at a single location) Or are the effects much more dramatic than what has been reported previously simply because the GtACR2 is much more effective at silencing the auditory cortex? Could the effect be down to off-target effects, e.g. by removing excitation from a target area of the auditory cortex, rather than the disruption of cortical processing?

      We have now expanded the discussion in the manuscript to more thoroughly consider alternative interpretations of the strong behavioral effect observed during ACx silencing (L495–511). In particular, we acknowledge that the suppression of licking may reflect not only impaired sensory discrimination but also broader disruptions to arousal, motivation, or motor readiness. We also discuss the potential impact of viral spread, circuit-level off-target effects, and the potency of GtACR2 as possible contributors. We highlight the need for future work using more graded or temporally precise manipulations to resolve these issues.

      (5) Line 226: Reference 19 (Talwar and Gerstein 2001) is not particularly relevant as it is mostly concerned with microstimulation-induced A1 plasticity. There are, however, several other papers that should be cited (and potentially discussed) in this context. In particular, O'Sullivan et al., 2019 and Ceballo et al., 2019 as these papers investigate the effects of optogenetic silencing on frequency discrimination in head-fixed mice and find relatively modest impairments. Also relevant may be Kato et al., 2015 and Lee et al., 2024, although they look at sound detection rather than discrimination.

      We changed the references and pointed the reader to the (new section) Discussion.

      (6) Line 253: 'engaged [in] the task.

      (7) Figure 4: It appears that panel S4-1d is not referred to anywhere in the main text.

      Fixed.

      (8) Line 260: Might be useful to explain a bit more about the motivation behind focusing on L5/L6. Are there mostly theoretical considerations, i.e. would we expect the infragranular layers to be more relevant for understanding the difference in task performance? Or were there also practical considerations, e. g. did the data set contain mostly L5/L6 neurons because those were easier to record from given the angle at which the probe was inserted? If those kinds of practical considerations played a role, then there is nothing wrong with that but it would be helpful to explain them for the benefit of others who might try a similar recording approach.

      There were no deep theoretical considerations for targeting L5/6.  Our focus on layers 5/6 was driven by both methodological and biological considerations. Methodologically, our electrode penetrations were optimized to span multiple auditory cortical areas, and deeper layers provided greater mechanical stability for chronic recordings. Biologically, layers 5/6 contain the principal output neurons of the auditory cortex and are well-positioned to influence downstream decision-making circuits. We acknowledge the limitation of our recordings to these layers in the manuscript (L268; L463–467). See also comment D of reviewer 3.

      (9) Supplementary Table 2: The numbers in brackets indicate fractions rather than percentages.

      Fixed.

      (10) Figure S4-3: The figure legend implies that the number of neurons with significant discriminability for the hard stimulus and significant discriminability for choice was identical. (adolescent neurons = 368, mice = 5, recordings = 10; adult n = 544, mice = 6, recordings = 12 in both cases). Presumably, that is not actually the case and rather the result of a copy/paste operation gone wrong. Furthermore, I think it would be helpful to state the fractions of neurons that can discriminate between the stimuli and between the choices that the animal made in the main text.

      Thank you for spotting the mistake. We corrected the n’s and added the percentage of neurons that discriminate stimulus and choice in the main text and the figure legend.

      (11) Line 301: 'We used a ... decoder to quantify hit versus correct reject trial outcomes': I'm not sure I understand the rationale here. For the single unit analysis hit and false alarm trials were compared to assess their ability to discriminate the stimuli. FA and CR trials were compared to assess whether neurons can encode the choice of the mice. But the hit and CR trials which are contrasted here differ in terms of both stimulus and behavior/choice so what is supposed to be decoded here, what is supposed to be achieved with this analysis?

      Thank you for this important point. You're correct that comparing hit and CR trials captures differences in both stimulus and choice, or task-related differences. We chose this contrast for the population decoding analysis to achieve higher trial counts per session and similar number of trials which are necessary for the reliability of the analysis. While this approach does not isolate stimulus from choice encoding, it provides an overall measure of how well population activity distinguishes task-relevant outcomes. We explicitly acknowledge this issue in L313-314.

      (12) Line 332: What do you mean when you say the novice mice were 'otherwise fully engaged' in the task when they were not trained to do the task and are not doing the task?

      By "otherwise fully engaged," we mean that novice mice were actively participating in the task environment, similar to expert mice — they were motivated by thirst and licked the spout to obtain water. The key distinction is that novice mice had not yet learned the task rules and likely relied on trial-and-error strategies, rather than performing the task proficiently.

      (13) Line 334: 'regardless of trial outcome': Why is the trial outcome not taken into account? What is the rationale for this analysis? Furthermore, in novice mice a substantial proportion of the 'go' trials are misses. In expert mice, however, the proportion of 'miss trials' (and presumably false alarms) will by definition be much smaller. Given this, I find it difficult to interpret the results of this section.

      This approach was chosen to reliably decode a sufficient number of trials for each task difficulty (i.e. expert mice predominantly performed CRs on No-Go trials and novice mice often showed FAs). Utilizing all trial outcomes ensured that we had enough trials for each stimulus type to accurately estimate the AUCs. This approach avoids introducing biases due to uneven trial numbers across learning stages.

      (14) Line 378: 'differences between adolescents and adults arise primarily from age': Are there differences in any of the metrics shown in 7e-h between adolescents and adults?

      We confirm that differences between adolescents and adults are indeed present in some metrics but not others in Figure 7e–h. Specifically, while tuning bandwidth was similar in novice animals, it was significantly lower in adult experts (Fig. 7e; novice: p = 0.0882; expert: p = 0.0001 Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The population sparseness was similar in both novice and expert adolescent and adult neurons (Fig. 7f; novice: p = 0.2873; expert: p = 0.1017, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The distance to the easy go stimulus was similar in novice animals, but lower in adult experts (Fig. 7g; novice: p = 0.7727; expert: p = 0.0001, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The neuronal d-prime was similar in both novice and expert adolescent and adult neurons (Fig. 7h; novice: p = 0.7727; expert: p = 0.0001, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript).

      (15) Line 475: '...well and beyond...': something seems to be missing in this statement.

      (16) Line 487: 'onto' should be 'into', I think.

      (17) Line 610 and 613: '3 seconds' ... '2.5 seconds': Was the response window 3s or 2.5s?

      (18) Line 638: 'set' should be 'setup', I believe.

      All the mistakes mentioned above, were fixed. Thanks.

      (19) Line 643: 'Reward-reinforcement was delayed to 0.5 seconds after the tone offset': Presumably, if they completed their fifth lick later than 0.5 seconds after the tone, the reward delivery was also delayed?

      Apologies for the lack of clarity. In the head-fixed version, there was no lick threshold. Mice were reinforced after a single lick. If that lick occurred after the 0.5-second reinforcement delay following tone offset, the reward or punishment was delivered immediately upon licking.

      (20) Line 661: 'effect [of] ACx'.

      (21) Line 680: 'a base-station connected to chassis'. The sentence sounds incomplete.

      (22) Line 746: 'infliction', I believe, should say 'inflection'.

      (23) Line 769: 'non-auditory responsive units': Shouldn't that simply say 'non-responsive units'? The way it is currently written I understand it to mean that these units were responsive (to some other modality perhaps) but not to auditory stimulation.

      (24) Line 791: 'bins [of] 50ms'.

      (25) Line 811: 'all of' > 'of all'.

      (26) Line 814: Looks like the previous paragraph on single unit analysis was accidentally repeated under the wrong heading.

      (27) Line 817: 'encoded' should say 'calculated', I believe.

      All the mistakes mentioned above were fixed. Thanks.

      (28) Line 869: 'bandwidth of excited units': Not sure I understand how exactly the bandwidth, i.e. tuning width was measured.

      We acknowledge that our previous answer was unclear and expanded the Methods section. To calculate bandwidth, we identified significant tone-evoked responses by comparing activity during the tone window to baseline firing rates at 62 dB SPL (p < 0.05). For each neuron, we counted the number of contiguous frequencies with significant excitatory responses, subtracting isolated false positives to correct for chance. We then converted this count into an octave-based bandwidth by multiplying the number of frequency bins by the octave spacing between them (0.1661 octaves per step).

      (29) Line 871: 'population sparseness': Is that the fraction of tone frequencies that produced a significant response? I would have thought that this measure is very highly correlated to your measure of bandwidth, to the point of being redundant, but I may have misunderstood how one or the other is calculated. Furthermore, the Y label of Figure 7f says 'responsiveness' rather than sparseness and that would seem to be the more appropriate term because, unless I am misunderstanding this, a larger value here implies that the neuron responded to more frequencies, i.e. in a less sparse manner.

      We have clarified the use of the term "population sparseness" and updated the Y-axis label in Figure 7f to better reflect this measure. This metric reflects the fraction of tone–attenuation combinations that elicited a significant excitatory response across the entire population of neurons, not within individual units.

      While this measure is related to bandwidth, it captures a distinct property of the data. Bandwidth quantifies how broadly or narrowly a single neuron responds across frequencies at a fixed intensity, whereas population sparseness reflects how distributed responsiveness is across the population as a whole. Although the two measures are related, since broadly tuned neurons often contribute to lower population sparseness, they capture distinct aspects of neural coding and are not redundant.

      (30) Line 881: I think this line should refer to Figure 7h rather than 7g.

      Fixed.

      Reviewer #3 (Recommendations for the authors):

      (1) In the Educage, water was only available when animals engaged in the task; however, there is no mention of whether/how animal weight was monitored.

      In the Educage, mice had continuous access to water by voluntarily engaging in the task, which they could perform at any time. Although body weight was not directly monitored, water access was essentially ad libitum, and mice performed hundreds of trials per day, thereby ensuring sufficient daily intake. This approach allowed us to monitor hydration (ad libitum food is supplied in the home cage). The 24/7 setup, including automated monitoring of trial counts and water consumption, was reviewed and approved by our institutional animal care and use committee (IACUC).

      (2) In Figure 2B-C and Figure 2E, the y-axis reads "lick rate". At first glance, I took this to mean "the frequency of licking" (i.e. an animal typically licks at a rate of 5 Hz). However, what the authors actually are plotting here is the proportion of trials on which an animal elicited >= 5 licks during the response window (i.e. the proportion of "yes" responses). I recommend editing the y-axis and the text for clarity.

      We replaced the y-label and adjusted the figure legend (Fig. 2).

      (3) I didn't see any examples of raw (filtered) voltage traces. It would be worth including some to demonstrate the quality of the data.

      We have added an example of a filtered voltage trace aligned to tone onset in Fig. S4-1a to illustrate data quality. In addition, all raw and processed voltage traces, along with relevant analysis code, are available through our GitHub repository and the corresponding dataset on Zenodo.

      (4) The description of the calculation of bias (C) in the methods section (lines 749-750) is incorrect. The correct formula is C = -0.5 * [z(hit rate) + z(fa rate)]. I believe this is the formula that the authors used, as they report negative C values. Please clarify or correct.

      Thanks for spotting this. It is now corrected.

      (5) The authors use the terms 'naïve' and 'novice' interchangeably. I suggest sticking with one term to avoid potential confusion.

      (6) Multiple instances: "less trials/day" should be "fewer trials/day"

      (7) Supplementary Table 2: The values reported are proportions, not percentages. Please correct.

      (8) Line 270: Table 2 does not show the number of neurons in the dataset categorized by region. Perhaps the authors meant Supplementary Table 2?

      Fixed. Thank you for pointing these mistakes out.

      (9) Figure 5C: the data from the hard task are entirely obscured by the data from the easy task. I recommend splitting it into two different plots.

      We agree and split the decoding of the easy and the hard task into two graphs (left: easy task; right: hard task). Thank you!

      (10) How many mice contributed to each analyzed data set? Could the authors provide a breakdown in a table somewhere of how many neurons were recorded in each mouse and which ones were included in which analyses?

      We added an overview of the analyzed datasets in supplementary Table 7. Please note that the number of mice and neurons used in each analysis is also reported in the main text and legends. Importantly, all primary analyses were conducted using LME models, which explicitly account for hierarchical data structure and inter-mouse variability, thereby addressing potential concerns about data imbalance or bias.

    1. Author Response:

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

      Reviewer #1 (Public Review):

      Summary:

      In this study, Floedder et al report that dopamine ramps in both Pavlovian and Instrumental conditions are shaped by reward interval statistics. Dopamine ramps are an interesting phenomenon because at first glance they do not represent the classical reward prediction errors associated with dopamine signaling. Instead, they seem somewhat to bridge the gap between tonic and phasic dopamine, with an intense discussion still being held in the field about what is their actual behavioral role. Here, in tests with head-fixed mice, and dopamine being recorded with a genetically encoded fluorescent sensor in the nucleus accumbens, the authors find that dopamine ramps were only present when intertrial intervals were relatively short and the structure of the task (Pavlovian cue or progression in a VR corridor) contained elements that indicated progression towards the reward (e.g., a dynamic cue). The authors show that these findings are well explained by their previously published model of Adjusted Net Contingency of Causal Relation (ANCCR).

      Strengths:

      This descriptive study delineates some fundamental parameters that define dopamine ramps in the studied conditions. The short, objective, and to-the-point format of the manuscript is great and really does a service to potential readers. The authors are very careful with the scope of their conclusions, which is appreciated by this reviewer.

      We thank the reviewer for their overall support of the formatting and scope of the manuscript. 

      Weaknesses:

      The discussion of the results is very limited to the conceptual framework of the authors' preferred model (which the authors do recognize, but it still is a limitation). The correlation analysis presented in panel l of Figure 3 seems unnecessary at best and could be misleading, as it is really driven by the categorical differences between the two conditions that were grouped for this analysis. There are some key aspects of the data and their relationship with each other, the previous literature, and the methods used to collect them, that could have been better discussed and explored.

      We agree with the reviewer that a weakness of the discussion was the limited framing of the results within the ANCCR model. To address this, we have expanded our introduction and discussion sections to provide a more thorough explanation of our model and possible leading alternatives.

      We thank the reviewer for pointing out that Figure 3l may be misleading for readers; we removed this panel from the revised Figure 4.

      We have further addressed the specific concerns raised by the reviewer in their comments to the authors. Indeed, we agree with the reviewer that the original manuscript was narrow in its focus regarding relationships between different aspects of the data. To more thoroughly explore how key variables – including dopamine ramp slope and onset response as well as licking behavior slope – could relate to each other, we have added Extended Data Figure 8. In this figure, we show that no correlations exist between any of these key variables in either dynamic tone condition; it is our hope that this additional analysis highlights the significance of the clear relationship between dopamine ramp slope and ITI duration. 

      Reviewer #2 (Public Review):

      In this manuscript by Floeder et al., the authors report a correlation between ITI duration and the strength of a dopamine ramp occurring in the time between a predictive conditioned stimulus and a subsequent reward. They found this relationship occurring within two different tasks with mice, during both a Pavlovian task as well as an instrumental virtual visual navigation task. Additionally, they observed this relationship only in conditions when using a dynamic predictive stimulus. The authors relate this finding to their previously published model ANCCR in which the time constant of the eligibility trace is proportionate to the reward rate within the task.

      The relationship between ITI duration and the extent of a dopamine ramp which the authors have reported is very intriguing and certainly provides an important constraint for models for dopamine function. As such, these findings are potentially highly impactful to the field. I do have a few questions for the authors which are written below.

      We thank the reviewer for their interest in our findings and belief in their potential to be impactful in the field. 

      (1) I was surprised to see a lack of counterbalance within the Pavlovian design for the order of the long vs short ITI. Ramping of the lick rate does increase from the long-duration ITIs to the short-duration ITI sessions. Although of course, this increase in ramping of the licking across the two conditions is not necessarily a function of learning, it doesn't lend support to the opposite possibility that the timing of the dynamic CS hasn't reached asymptotic learning by the end of the long-duration ITI. The authors do reference papers in which overtraining tends to result in a reduction of ramping, which would argue against this possibility, yet differential learning of the dynamic CS would presumably be required to observe this effect. Do the authors have any evidence that the effect is not due to heightened learning of the timing of the dynamic CS across the experiment?

      We appreciate the reviewer expressing their surprise regarding the lack of counterbalance in our Pavlovian experimental design. We previously did not explicitly do this because the ramps disappeared in the short ITI/fixed tone condition, indicating that their presence is not just a matter of total experience in the task. However, we agree that this is incidental, but not direct evidence. To address this drawback, we repeated the Pavlovian experiment in a new cohort of animals with a revised training order, switching conditions such that the short ITI/dynamic tone (SD) condition preceded the long ITI/dynamic tone (LD) condition (see revised Figure 2a). Despite this change in the training order, the main findings remain consistent: positive dLight slopes (i.e., dopamine ramps) are only observed in the SD condition (Figure 2b-d). 

      We thank the reviewer for raising these questions regarding licking behavior and learning and their relationship with dopamine ramps. Indeed, a closer look at the average licking behavior reveals subtle differences across conditions (Figure 1f and Extended Data Figure 5a). While the average lick rate during the ramp window does not differ across conditions (Extended Data Figure 5c), the ramping of the lick rate during this window is higher for dynamic tone conditions compared to fixed tone conditions (Extended Data Figure 5d). Despite these differences, we still believe that the main comparison between the dopamine slope in the SD vs LD condition remains valid given their similar lick ramping slopes. Furthermore, our primary measure of learning is not lick slope, but anticipatory lick rate during the 1 s trace preceding reward delivery, which is robustly nonzero across cohorts and conditions (Figure 1g and Extended Data Figure 5b). 

      Taken together, we hope that the results from our counterbalanced Pavlovian training and more rigorous analysis of lick behavior across conditions provide sufficient evidence to assuage concerns that the differences in ramping dopamine simply reflect differences in learning. 

      (2) The dopamine response, as measured by dLight, seems to drop after the reward is delivered. This reduction in responding also tends to be observed with electrophysiological recordings of dopamine neurons. It seems possible that during the short ITI sessions, particularly on the shorter ITI duration trials, that dopamine levels may still be reduced from the previous trial at the onset of the CS on the subsequent trial. Perhaps the authors can observe the dynamics of the recovery of the dopamine response following a reward delivery on longer-duration ITIs in order to determine how quickly dopamine is recovering following a reward delivery. Are the trials with very short ITIs occurring within this period that dopamine is recovering from the previous trial? If so, how much of the effect may be due to this effect? It should be noted that the lack of observance of a ramp on the condition of shortduration ITIs with fixed CSs provides a potential control for this effect, yet the extent to which a natural ramp might occur following sucrose deliveries should be investigated.

      We thank the reviewer for highlighting the possibility that ramps may be due to the dopamine response recovery following reward delivery. Given that peak reward dopamine responses tend to be larger in long ITI conditions, however, we felt that it was inappropriate to compare post-reward dopamine recovery times across conditions. Instead, we decided to directly compare the dLight slope 2s before cue onset (“pre-cue window,” a proxy for recovery from previous trial) with the dLight slope during our ramp window from 3 to 8s after cue onset (Extended Data Figure 6a). There were no significant differences in pre-cue dLight slope across conditions (Extended Data Figure 6b); this suggests that the ramping slopes seen in the SD condition, but not other conditions, is not simply due to the natural dopamine recovery response following reward delivery. Furthermore, if the dopamine ramps observed in the SD condition were a continuation of the post-reward dopamine recovery from the previous trial, we would expect to see a positive correlation between the dLight slope before and during the cue. However, there is no such correlation between the dLight slopes in the ramp window vs. pre-cue window in the SD condition (Extended Data Figure 6c-d). We believe that this observation, along with the builtin control of the SF condition mentioned by the reviewer, serves as evidence against the possibility of our ramp results being due to a natural ramp after reward delivery.

      (3) The authors primarily relate the finding of the correlation between the ITI and the slope of the ramp to their ANCCR model by suggesting that shorter time constants of the eligibility trace will result in more precisely timed predictors of reward across discrete periods of the dynamic cue. Based on this prediction, would the change in slope be more gradual, and perhaps be more correlated with a broader cumulative estimate of reward rate than just a single trial?

      To clarify, we do not propose that a smaller eligibility trace time constant results in more precise timing per se. Instead, we believe that the rapid eligibility trace decay from smaller time constants gives greater causal predictive power for later periods in the dynamic cue (see Extended Data Figure 1) since the memory of the earlier periods of the cue is weaker. 

      We appreciate the reviewer’s curiosity regarding the influence of a broader cumulative estimate of reward vs. only the immediately preceding ITI on dopamine ramp slopes. Indeed, in several instrumental tasks (e.g., Krausz et al., Neuron, 2023), recent reward rate modulates the magnitude of dopamine ramps, making this an important variable to investigate. We chose to use linear regression for each mouse separately to analyze the relationship between the trial dopamine slope and the average previous ITI for the past 1 through 10 most recent trials. In the SD condition, as reported in our earlier manuscript, there was a significantly negative dependence of trial dopamine slope with the single previous ITI (i.e., if the previous ITI was long, the next trial tends to have a weaker ramp). This negative dependence, however, only held for a single previous trial; there was no clear relationship between the per-trial dopamine slope and the average of the past 2 through 10 ITIs (Extended Data Figure 7a). For the LD condition, on the other hand, there is no clear relationship between the per-trial dopamine slope and the average previous ITI for any of the past 1 through 10 trials, with one exception: there is a significantly negative dependence of trial dopamine slope with the average ITI of the previous 2 trials (Extended Data Figure 7b). This longer timescale relationship in the LD condition suggests that the adaptation of the eligibility trace time constant is nuanced and depends on the general ITI length. 

      In general, though we reason that the eligibility trace time constant should depend on overall event rates, we do not currently propose a real-time update rule for the eligibility trace time constant depending on recent event rates. Accordingly, we are currently agnostic about the actual time scale of history of recent event rate calculation that mediates the eligibility trace time constant. Our experimental results suggest that when the ITI is generally short for Pavlovian conditioning, the eligibility trace time constant adapts to ITI on a rapid timescale. However, only a small fraction of the variability of this rapid fluctuation is captured by recent ITI history. A more thorough investigation of this real-time update rule would need to be done in the future.

      Reviewer #3 (Public Review):

      Summary:

      Floeder and colleagues measure dopamine signaling in the nucleus accumbens core using fiber photometry of the dLight sensor, in Pavlovian and instrumental tasks in mice. They test some predictions from a recently proposed model (ANCCR) regarding the existence of "ramps" in dopamine that have been seen in some previous research, the characteristics of which remain poorly understood.

      They find that cues signaling a progression toward rewards (akin to a countdown) specifically promote ramping dopamine signaling in the nucleus accumbens core, but only when the intertrial interval just experienced was short. This work is discussed in the context of ongoing theoretical conceptions of dopamine's role in learning.

      Strengths:

      This work is the clearest demonstration to date of concrete training factors that seem to directly impact whether or not dopamine ramps occur. The existence of ramping signals has long been a feature of debates in the dopamine literature and this work adds important context to that. Further, as a practical assessment of the impact of a relatively simple trial structure manipulation on dopamine patterns, this work will be important for guiding future studies. These studies are well done and thoughtfully presented.

      We thank the reviewer for recognizing the context that our study adds to the dopamine literature and the potential for our experiments to guide future work. 

      Weaknesses:

      It remains somewhat unclear what limits are in place on the extent to which an eligibility trace is reflected in dopamine signals. In the current study, a specific set of ITIs was used, and one wonders if the relative comparison of ITI/history variables ("shorter" or "longer") is a factor in how the dopamine signal emerges, in addition to the explicit length ("short" or "long") of the ITI. Another experimental condition, where variable ITIs were intermingled, could perhaps help clarify some remaining questions.

      Though we used ITIs of fixed means, due to the exponential nature of their distribution, we did intermingle ITIs of various durations in both our long and short ITI conditions. The distribution of ITI durations is visualized in Figure 1c for Pavlovian conditioning and Extended Data Figure 9b for VR navigation. 

      The relative comparison between consecutive ITIs was not something we originally explored, so we thank the reviewer for wondering how it impacts the dopamine signal. To investigate this, we quantified both the change in ITI (+ or - Δ ITI for relatively longer or shorter, respectively) and the change in dopamine ramp slope between consecutive trials in the SD condition (Figure 3d). Across each mouse separately, we found a significantly negative relationship between Δ slope and Δ ITI (Figure 3e-f). Also, the average Δ slope was significantly greater for consecutive trials with a Δ ITI below -1 s compared to trials with a Δ ITI above +1 s (Figure 3g). Altogether, these findings suggest that relative comparison of ITIs does correlate with changes in the dopamine signal; a relatively longer ITI tends to have a weaker ramp, which fits in nicely with the expected inverse relationship between ITI and dopamine ramp slope from our ANCCR model.

      In both tasks, cue onset responses are larger, and longer on long ITI trials. One concern is that this larger signal makes seeing a ramp during the cue-reward interval harder, especially with a fluorescence method like photometry. Examining the traces in Figure 1i - in the long, dynamic cue condition the dopamine trace has not returned to baseline at the time of the "ramp" window onset, but the short dynamic trace has. So one wonders if it's possible the overall return to baseline trend in the long dynamic conditions might wash out a ramp.

      This is a good point, and we thank the reviewer for raising it. Certainly, the cue onset response is significantly larger in long ITI conditions (see Figure 1i-j and Figure 4h-j). To avoid any bleed over effect, we intentionally chose ramp window periods during later portions of the trial (in line with work from others e.g., Kim et al., Cell, 2020). While the cue onset dopamine pulse seems to have flatlined by the start of the ramp window period, the dopamine levels clearly remain elevated relative to pre-cue baseline. This type of signal has been observed with fiber photometry in other Pavlovian conditioning paradigms with long cue durations (e.g., Jeong et al., Science, 2022). Because of the persistently elevated dopamine levels, it is certainly possible that a ramping signal during the cue is getting washed out; with the bulk fluorescence photometry technique we employed in this study, this possibility is unfortunately difficult to completely rule out. However, the long ITI/fixed tone (LF) condition could serve as a potential control given the overall similarity in the dopamine signal between the LF and LD conditions: both conditions have large cue onset responses with elevated dopamine throughout the duration of the cue (see Extended Data Figures 2c and 3c). Critically, the LD condition lacks a noticeable ramp despite the dynamic tone providing information on temporal proximity to reward, which is thought to be necessary for dopamine ramps to occur. Importantly, regardless of whether a ramp is masked in the long ITI dynamic condition, most studies investigate such a condition in isolation and would report the absence of dopamine ramps. Thus, at a descriptive level, we believe it remains true that observable dopamine ramps are only present when the ITI is short. 

      Not a weakness of this study, but the current results certainly make one ponder the potential function of cue-reward interval ramps in dopamine (assuming there is a determinable function). In the current data, licking behavior was similar on different trial types, and that is described as specifically not explaining ramp activity.

      We agree that this work naturally raises the question of the function of dopamine ramps. However, selective and precise manipulation of only the dopamine ramps without altering other features such as phasic responses, or inducing dopamine dips, is highly technically challenging at this moment; due to this challenge, we intentionally focused on the conditions that determine the presence or absence of dopamine ramps rather than their function. We agree with the reviewer that studying the specific function of dopamine ramps is an interesting future question. 

      Reviewing Editor:

      The reviewers felt the results are of considerable and broad interest to the neuroscience community, but that the framing in terms of ANCCR undermined the scope of the findings as did the brief nature of the formatting of the manuscript. In addition, the reviewers felt that the relationship between ramp dynamics, behavior, and ITI conditions requires more in-depth analyses. Relatedly, the lack of counterbalancing of the ITI durations was considered to be a drawback and needs to be addressed as it may affect the baseline. Addressing these issues in a satisfactory manner would improve the assessment of the manuscript to important/convincing.

      We truly appreciate the valuable feedback provided on this manuscript by all three reviewers and the reviewing editor. Based on this input, we have significantly revised the manuscript to address the issues brought up by the reviewers. Firstly, we have conducted additional experiments to counterbalance the ITI conditions for Pavlovian conditioning; this strengthened our results by confirming our original findings that ITI duration, rather than training order, is the key variable controlling the presence or absence of dopamine ramps. Secondly, we completed more rigorous analyses to further explore the relationship between dopamine dynamics, animal behavior, and ITI duration; we generally found no significant correlations between these variables, with a notable exception being our main finding between ITI duration and dopamine ramp slope. Finally, we revised and expanded our writing to both explain predictions from our ANCCR model in less technical language and explore how alternative theoretical frameworks could potentially explain our findings. In doing so, we hope that our manuscript is now more accessible and of interest to a broad audience of neuroscience readers.

      Reviewer #1 (Recommendations For The Authors):

      The study could be improved if the authors performed a more detailed comparison of how other theoretical frameworks, beyond ANCCR could account for the observed findings. Also, the correlation analysis presented in the panel I of Figure 3 seems unnecessary and potentially spurious, as the slope of the correlation is clearly mostly driven by the categorical differences between the two ITI conditions, which were combined for the analysis - it's not clear what is the value of this analysis beyond the group comparison presented in the following panel.

      Again, we thank the reviewer for elaborating on their concern regarding Figure 3l – we have removed it from the revised Figure 4. 

      The relationship between ramp dynamics with the behavior and the large differences in cue onset responses between short and long ITI conditions could have been better explored. If I understand correctly the overarching proposal of this and other publications by this group, then the differences in cue responses is determined by the spacing of rewards in a somewhat similar way that the ramps are. So, is there a trial-by-trial correlation between the amplitude of the cue responses and the slope of the ramps? Is there a correlation between any of these two measures with the licking behavior, and if so, does it change with the ITI condition? A more thorough exploration of these relationships would help support the proposal of the primacy of inter-event spacing in determining the different types of dopamine responses in learning.

      There are certainly interesting relationships between dopamine dynamics, behavior, and ITI that we failed to explore in our original manuscript – we appreciate the reviewer bringing them up. We found no correlation between dopamine ramp slope and cue onset response in either the SD or LD condition (Extended Data Fig 8a-b). Moreover, we found no correlation between either of these variables and the trial-by-trial licking behavior (Extended Data Fig 8c-f). Finally, there is no relationship between licking behavior and previous ITI duration (Extended Data Fig 8g-h), suggesting that behavioral differences do not account for differences in the dopamine ramp slope. Together, the lack of significant relationships between these other variables highlights the specific, clear relationship between ITI duration and dopamine ramp slope. 

      Finally, another issue I feel could have been better discussed is how the particular settings of both tasks might be biasing the results. For example, there is an issue to be considered about how the dopamine ramp dynamics reported here, especially the requirement of a dynamic cue for ramps to be present, square with the previous published results by one of the authors - Mohebi et al, Nature, 2019. In that manuscript, rats were executing a bandit task where, to this reviewer's understanding, there was no explicit dynamic cue aside from the standard sensory feedback of the rats moving around in the behavior boxes to approach a nose poke port. Is the idea that this sensory feedback could function as a dynamic cue? If that's the case, then this short-scale, movement-related feedback should also function as a dynamic cue in a freely moving Pavlovian condition, when the animals must also move towards a reward delivery port, right? Therefore, could it be that the experimental "requirement" of a dynamic cue is only present in a head-fixed condition? One could phrase this in a different way to Steelman and potentially further the authors' proposal: perhaps in any slightly more naturalistic setting, the interaction of the animals with their environment always functions as a dynamic cue indicating proximity to reward, and this relationship was experimentally isolated by the use of head fixation (but not explicitly compared with a freely moving condition) in the present study. I think that would be an interesting alternative to consider and discuss, and perhaps explore experimentally at some point.

      We thank the reviewer for raising this important point regarding the influence of our experimental settings on our results. At first glance, it could appear that our results demonstrating the necessity of a dynamic cue for ramps in a head-fixed setting do not fit neatly with other results in a freely moving setup (e.g., Collins et al., Scientific Reports, 2016; Mohebi et al., Nature, 2019). Exactly as the reviewer states though, we believe that sensory feedback from the environment in freely moving preparations serves the same function as a dynamic progression of cues. We have considered the implications of methodological differences between head-fixed and freely moving preparations in the discussion section. 

      Reviewer #2 (Recommendations For The Authors):

      This comment relates indirectly to comment 3, in that the authors intermix theory throughout the manuscript. I think this would be fine if the experiment was framed directly in terms of ANCCR, but the authors specifically mention that this experiment wasn't developed to distinguish between different theories. As such, it seems difficult to assess the scope of the comments regarding theory within the paper because they tend to be specifically related to ANCCR. For instance, the last comment has broad implications of how the ramp might be related to the overall reward rate, an interesting finding that constrains classes of dopamine models rather than evidence just for ANCCR. Perhaps adding a discussion section that allows the authors to focus more on theory would be beneficial for this manuscript.

      We appreciate this suggestion by the reviewer. We have updated both our introduction and discussion sections to elaborate more thoroughly on theory.

      Reviewer #3 (Recommendations For The Authors):

      The paper could potentially benefit from the use of more accessible language to describe the conceptual basis of the work, and the predictions, and a bit of reformatting away from the brief structure with lots of supplemental discussion.

      For example, in the introduction, the line - "Varying the ITI was critical because our theory predicts that the ITI is a variable controlling the eligibility trace time constant, such that a short ITI would produce a small time constant relative to the cue-reward interval (Supplementary Note 1)". As far as I can tell, this is meant to get across the notion that dopamine represents some aspect of the time between rewards - dopamine signals will differ for cues following short vs long intervals between rewards.

      As written, the language of the paper takes a fair bit of parsing, but the notions are actually pretty simple. This is partly due to the brief format the paper is written in, where familiarity with the previous papers describing ANCCR is assumed.

      From a readability standpoint, and the potential impact of the paper on a broad audience, perhaps this could be considered as a point for revision.

      We thank the reviewer for pointing out the drawbacks of our technical language and brief formatting. To address this, we have removed the majority of the supplementary notes and expanded our introduction and discussion sections. In doing so, we hope that the conceptual foundations of this work, and potential alternative theoretical explanations, are accessible and impactful for a broad audience of readers.

    1. With a vision for the school’s successand a moral commitment to the success ofall students, the coach may serve in a formalor informal leadership role or, occasionally,in both. “Leaders must act with the inten-tion of making a positive difference,” assertsMichael Fullan, a leading expert on schoolchange (2001, p. 3). Michael Fullan and JimKnight assert, “Next to the principal, coachesare the most crucial change agent in a school”(2011, p. 50).

      This quote highlights how important coaches are in a school. With a strong belief in helping every student succeed, a coach becomes more than just a support person, they become a leader. Whether they have an official leadership title or not, their actions can create big changes. Experts like Michael Fullan and Jim Knight believe that, after the principal, coaches are the most powerful force for improvement in a school. This shows that coaching is not just about helping teachers, it's about transforming the whole school for the better.

    1. schooldistrictsarebiased,

      This. The district I am in often feels like they push out trainings as a reactive response rather than a proactive one. People are often uncertain of who does the training, the online platforms often fail or have limitations that the district facilitators didn't know about until they reached those limits. It just feels like it's not a priority and again, its in response to something rather than to prepare us for something.

    1. 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

      Note : The original preprint version of our manuscript has been reviewed by 3 subject experts for Review Commons. All the three reviewers’ comments on the original version of our manuscript have been fully addressed. Their input was extremely valuable in helping us clarify and refine the presentation of our results and conclusions. Their feedback contributed to making the study both more thoroughly developed and more accessible to a broad readership, while preserving its mechanistic depth. We believe that this revised version more effectively highlights the conceptual advances brought by our findings.

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript "Key roles of the zona pellucida and perivitelline space in promoting gamete fusion and fast block to polyspermy inferred from the choreography of spermatozoa in mice oocytes" by Dr. Gourier and colleagues explores the poorly understood process of gamete fusion and the subsequent block to polyspermy by live-cell imaging of mouse oocytes with intact zona pellucida in vitro. The new component in this study is the presence of the ZP, which in prior studies of live-cell imaging had been removed before. This allowed the authos to examine contributions of the ZP to the block in polyspermy in relation to the timing of sperm penetrating the ZP and sperm fusing with the oocyte. By carefully analysing the timing of the cascade of events, the authors find that the first sperm that reaches the membrane of the mouse oocyte is not necessarily the one that fertilizes the oocytes, revealing that other mechanisms post-ZP-penetration influence the success of individual sperm. While the rate of ZP penetration remains constant in unfertilized oocytes, it decreases upon fertilization for subsequent sperm, providing direct evidence for the known 'slow block to polyspermy' provided by changes to the ZP adhesion/ability to be penetrated. Careful statistical analyses allow the authors to revisit the role of the ZP in preventing polyspermy: They show that the ZP block resulting from the cortical reaction is too slow (in the range of an hour) to contribute to the immediate prevention of polyspermy in mice. The presented analyses reveal that the ZP does contribute to the block to polyspermy in two other ways, namely by effectively limiting the number of sperm that reach the oocyte surface in a fertilization-independent manner, and by retaining components like JUNO and CD9, that are shed from the oocyte plasma membrane after fertilization, in the perivitelline space, which may help neutralize surplus spermatozoa that are already present in the PVS. Lastly, the authors report that the ZP may also contribute to channeling the flagellar oscillations of spermatozoa in the PVS to promote their fusion competence.

      Major comments:

      • Are the key conclusions convincing?

      The authors provide a careful analysis of the dynamics of events, though the analyses are correlative, and can only be suggestive of causation. While this is a limitation of the study, it provides important analysis for future research. Moreover, by analysing also control oocytes without fertilization and the timing of events, the authors have in some instances clear 'negative controls' for comparison.

      Some claims would benefit from rewording or rephrasing to put the findings better in the context of what is already known and what is novel:

      • the phrasing 'challenging prior dogma' might be too strong since it had been observed before that it is not necessarily the first sperm that gets through the ZP that fertilizes the egg (though I am afraid that I do not have any citations or references for this). However, given that in the field people generally think it is not necessarily and always the first sperm, the authors may want to consider weakening this claim.

      Only real-time imaging of in vitro fertilization of zona pellucida-intact oocytes, as performed in our study, is capable of determining which spermatozoon crossing the zona pellucida fuses with the oocyte. However, such studies are rare, and most do not specifically address this question. As Reviewers 1 & 3, we have not found any citation or reference telling or showing that it is not necessarily the first spermatozoon to penetrate the zona pellucida that fertilizes the egg. In contrast, at least one reference (Sato et al., 1979) explicitly reports the opposite. If, as suggested by Reviewer 1 and 3, it has indeed been observed before that the first sperm to pass the ZP is not always the one that fertilizes, and if this idea is generally accepted in the field, then it is all the more important that a study demonstrates and publishes this point. This is precisely what our study makes possible. However, in case we may have overlooked a previous reference making the same observation as ours, we have removed the phrasing ‘challenging prior dogma’. That being said, the key issue is not so much that it is not necessarily the first spermatozoon penetrating the perivitelline space that fertilizes, but rather why spermatozoa that successfully reach the PVS of an unfertilized oocyte may fail to achieve fertilization. This is one of the central questions our study sought to address.

      • I do think the cortical granule release could still contribute to the block to polyspermy though - as the authors here nicely show - at a later time-point only, and thus not the major and not the immediate block as previously thought. The wording in the abstract should therefore be adjusted (since it could still contribute...)

      We are concerned that we may disagree on this point. The penetration block resulting from cortical granule release progressively reduces the permeability of the zona pellucida to spermatozoa, relative to its baseline permeability prior to sperm–oocyte fusion. Any decrease in this baseline permeability occurring before the fusion block becomes fully effective can contribute to the prevention of polyspermy by limiting the number of sperm that can access the oolemma at a time when fusion is still possible. In contrast, once the fusion block is fully established, limiting the number of spermatozoa traversing the ZP becomes irrelevant regarding the block to polyspermy, as the fusion block alone is sufficient to prevent additional fertilizations, rendering the penetration block obsolete. The only scenario that could challenge this obsolescence is if the fusion block were transient. In that case, as Reviewer 1 suggests, the penetration block could indeed play a role at a later time-point. However, taken together, our study and that of Nozawa et al. (2018) support the conclusion that this is not the case in mice:

      • Our in vitro study using kinetic tracking shows that the time constant for completion of the fusion block is typically 6.2 ± 1.3 minutes. During this time window, we observe that the permeability of the zona pellucida to spermatozoa does not yet decrease significantly from the baseline level it exhibited prior to sperm–oocyte fusion (see Figures 5B and S1B in the revised manuscript, and Figures 5A and 5B in the initial version). Consequently, before the fusion block is fully established, the penetration block can contribute only marginally—if at all—to the prevention of polyspermy. In contrast, the naturally low baseline permeability of the ZP—independent of any fertilization-triggered penetration block—as well as the relatively long timing of fusion ( minutes on average) after sperm penetration in the perivitelline space, are factors that contribute to the preservation of monospermic while the fusion block is still being established.
      • Our in vitro study using kinetic tracking shows that once the fusion block is completed following the first fusion event, no additional spermatozoa are able to fuse with the oocyte until the end of the experiment, 4 hours post-insemination (see blue points and fitting curve in Figure 5C). Meanwhile, one or more additional spermatozoa—most of them motile and therefore viable—are present in the perivitelline space in 50% of the oocytes analyzed (purple point in Figure 5C). This demonstrates that, once established, the fusion block remains effective for at least the entire duration of the experiment, supporting the idea of a fully functional and long-lasting fusion block.
      • Nozawa et al. (2018) found that female mice lacking ovastacin—the protease released during the cortical reaction that renders the zona pellucida impenetrable—are normally fertile. They additionally reported that the oocytes recovered from these females after mating are monospermic despite the systematic presence of additional spermatozoa in the perivitelline space. These findings further support the conclusion that in mice the fusion block is both permanent and sufficient to prevent polyspermy. For all these reasons, we believe that even at a later time-point, the penetration block does not contribute to the prevention of polyspermy in mice.

      To clarify the fact that the penetration block does not necessarily contribute to prevent polyspermy, which indeed challenges the commonly accepted view, we have substantially revised the discussion. Furthermore, Figure 9 from the initial version of the manuscript has been replaced by Figure 8 in the revised version. This new figure provides a more didactic illustration of the inefficacy of the penetration block in preventing polyspermy in mice, by showing the respective impact of the fusion block, the penetration block, as well as fusion timing and the natural baseline permeability of the zona pellucida, on the occurrence of polyspermy.

      As for the abstract, it has also been thoroughly revised. The content related to this section is now expressed in a way that emphasizes the factors that actively contribute to the prevention of polyspermy in mice, rather than those with no or marginal contribution (such as the penetration block in this case).

      • release of OPM components - in the abstract it's unclear what the authors mean by this - in the results part it becomes clear. Please already make it clear in the abstract that it is the fertility factors JUNO/CD9 that could bind to sperm heads upon their release and thus 'neutralize' them? I would also recommend not referring to it as 'outer' plasma membrane (there is no 'inner plasma membrane'). Moreover, in the abstract please clarify that this release is happening only after fusion of the first sperm and not all the time. In the abstract it sounds as if this was a completely new idea, but there is good prior evidence that this is in fact happening (as also then cited in the results part) - maybe frame it more as the retention inside the PVS as new finding.

      We thank reviewer 1 for pointing out the lack of precision in the abstract regarding the “components” released from the oolemma, and the fact that our phrasing may have given the impression that the post-fertilization release of CD9 and JUNO is a novel observation. The new observation is that CD9 and JUNO, which are known to be massively released from the oolemma after fertilization, bind to spermatozoa in the perivitelline space. However, we cannot rule out the possibility that other oocyte-derived molecules not investigated here may undergo a similar process. This is why we employed the broader term “components”, which encompasses both CD9 and JUNO as well as potential additional molecules. That said, we acknowledge the lack of precision introduced by this terminology. To address this, we have revised the corresponding sentence in the abstract to better reflect our new findings relative to previous ones, and to eliminate the ambiguity introduced by the word “component”.

      The revised sentence of the abstract reads as follows:

      “Our observation that non-fertilizing spermatozoa in the perivitelline space are coated with CD9 and JUNO oocyte’s proteins, which are known to be massively released from the oolemma after gamete fusion, supports the hypothesis that the fusion block involves an effective perivitelline space-block contribution consisting in the neutralization of supernumerary spermatozoa in the perivitelline space by these and potentially other oocyte-derived factors.”

      Moreover, we cannot state in the abstract that the release of CD9 and JUNO occurs only after the fusion of the first spermatozoon and not before, since some CD9 and JUNO are already detectable in the perivitelline space (PVS) prior to fusion. What our study shows is that, before fertilization, CD9 and JUNO are predominantly localized at the oocyte membrane. In contrast, after fusion (four hours post-insemination), oocyte CD9 is distributed between the membrane and the PVS, and the only JUNO signal detectable in the oocyte is found in the PVS. This is what we describe in the Results section on page 15.

      Regarding the acronym “OPM” in the initial version of the manuscript, although it was defined in the introduction as referring to the oocyte plasma membrane and not the outer plasma membrane (which, indeed, would not be meaningful), we acknowledge that it may have caused confusion to people in the field due to its resemblance to the commonly used meaningful acronym “OAM” for outer acrosomal membrane. To avoid any ambiguity, we have replaced the acronym “OPM” throughout the revised manuscript with the term “oolemma”, which unambiguously refers to the plasma membrane of the oocyte.

      It is unclear to me what the relevance of dividing the post-fusion/post-engulfment into different phases as done in Fig 2 (phase 1, and phase 2) - also for the conclusions of this paper this seems rather irrelevant and overly complicated, since the authors never get back to it and don't need it (it's not related to the polyspermy block analyses). I would remove it from the main figures and not divide into those phases since it is distracting from the main focus.

      Sperm engulfment and PB2 extrusion are two processes that follow sperm–oocyte fusion. As such, they are clear indicators that fusion has occurred and that meiosis has resumed. Their progression over time is readily identifiable in bright-field imaging: sperm engulfment is characterized by the gradual disappearance of the spermatozoon head from the oolemma, whereas PB2 extrusion is observed as the progressive emergence of a rounded protrusion from the oocyte membrane (Figure 2 in the initial manuscript and Figure S2 A&B in the revised version). The kinetics of these events, measured from the arrest of “push-up–like” movement of the sperm head against the oolemma —assumed to coincide with sperm-oocyte fusion, as further justified in a later response to Reviewer 1—provide reliable temporal landmarks for estimating the timing of fusion when the fusion event itself is not directly observed in real time (Figure S2 C&D).

      The four landmarks used in this estimation are:

      (i) the disappearance of the sperm head from the oolemma due to internalization (28 ± 2 minutes post-arrest, mean ± SD);

      (ii) the onset of PB2 protrusion from the oolemma (28 ± 2 minutes post-arrest);

      (iii) the moment when the contact angle between the PB2 protrusion and the oolemma shifts from greater than to less than 90° (49 ± 6 minutes post-arrest);

      (iv) the completion of PB2 extrusion (73 ± 10 minutes post-arrest).

      The approach used to determine the fusion time window of a fertilizing spermatozoon from these landmarks is detailed in the “Determination of the Fertilization Time Windows” section of the Materials and Methods. Compared to the initial version of the manuscript, we have added a paragraph explaining the rationale for using the arrest of the push-up–like movement as a reliable indicator for sperm–oocyte fusion and have clarified the description of the approach used to determine fertilization timing.

      The timed characterization of sperm engulfment and PB2 extrusion kinetics is highly relevant to the analysis of the penetration and fusion blocks, however we agree that its place is more appropriate in the Supplementary Information than in the main text. In accordance with the reviewer’s recommendation, this section has therefore been moved to the Supplementary Information SI2.

      For the statistical analysis, I am not sure whether the assumption "assumption that the probability distribution of penetration or fertilization is uniform within a given time window" is in fact true since the probability of fertilizing decreases after the first fertilization event.... Maybe I misunderstood this, but this needs to be explained (or clarified) better, or the limitation of this assumption needs to be highlighted.

      During in vitro fertilization experiments with kinetic tracking, each oocyte is observed sequentially in turn. As a result, sperm penetration into the perivitelline space or fusion with the oolemma may occur either during an observation round or in the interval between two rounds. In the former case, penetration or fusion is directly observed in real time, allowing for high temporal precision in determining the moment of the event. In contrast, when penetration or fusion occurs between two observation rounds, the precise timing cannot be directly determined. We can only ascertain that the event took place within the time window we have determined. Because, within a given penetration or fusion time window, we do not know the exact moment at which the event occurred, there is no reason to favor one time over another. This justifies the assumption that all time points within the window are equally probable. This explanation has been added in the section Statistical treatment of penetration and fertilization chronograms to study the kinetics of fertilization, penetration block and fusion block of the main text and in the section Statistical treatment of penetrations and fertilizations chronograms to study penetration and fusion blocks of the material and methods.

      -Suggestion for additional experiments:

      If I understood correctly, the onset of fusion in Fig 2C is defined by stopping of sperm beating? If it is by the sudden stop of the beating flagellum, this should be confirmed in this situation (with the ZP intact) that it correctly defines the time-point of fusion since this has not been measured in this set-up before as far as I understand. In order to measure this accurately, the authors will need to measure this accurate to be able to acquire those numbers (of time from fusion to end of engulfment), e.g. by pre-loading the oocyte with Hoechst to transfer Hoechst to the fusing sperm upon membrane fusion.

      The nuclear dye Hoechst is widely used as a marker of gamete fusion, as it transfers from the ooplasm—when preloaded with the dye—into the sperm nucleus upon membrane fusion, thereby signaling the happening of the fusion event. This technique is applicable in the context of in vitro fertilization using ZP-free oocytes. However, it is not suitable when cumulus–oocyte complexes are inseminated, as is the case in both in vitro experimental conditions of the present study (standard IVF and IVF with kinetic tracking). Indeed, when cumulus–oocyte complexes are incubated with Hoechst to preload the oocytes, the numerous surrounding cumulus cells also take up the dye. Consequently, upon insemination, spermatozoa acquire fluorescence while traversing and dispersing the cumulus mass—before reaching the ZP—thus rendering Hoechst labeling ineffective as a specific marker of membrane fusion. This remains true even under optimized conditions involving brief Hoechst incubation of cumulus–oocyte complexes ( Nonetheless, we have strong evidence supporting the use of the arrest of sperm movement as a surrogate marker for the moment of fusion. In our previous study (Ravaux et al., 2016; ref. 4 in the revised manuscript), we investigated the temporal relationship between the abrupt cessation of sperm head movement on the oolemma—resulting from strong flagellar beating arrest—and the fusion event, using ZP-free oocytes preloaded with Hoechst. That study revealed a temporal delay of less than one minute between the cessation of sperm oscillations and the actual membrane fusion, thereby supporting the conclusion that in ZP-free oocytes, the arrest of vigorous sperm movement at the oolemma is a reliable indicator of the moment at which fusion occurs. In the same study, the kinetics of sperm head internalization into the ooplasm were also characterized, typically concluding within 20–30 minutes after movement cessation. These findings are fully consistent with our current observations in ZP-intact oocytes, where sperm head engulfment was completed approximately 24 ± 3 minutes after the arrest of sperm oscillations. Taken together, these results strongly support the conclusion that, in both ZP-free and ZP-intact oocytes, the arrest of sperm movement is a reliable indicator of the fusion event. This assumption formed the basis for our determination of fertilization time points in the present study.

      These justifications were not fully detailed in the original version of the manuscript. We have addressed this in the revised version by explicitly presenting this rationale in the Materials and Methods section under Determination of the Fertilization Time Windows.

      Fig 8: 2 comments

      • To better show JUNO/CD9 pre-fusion attachment to the oocyte surface and post-fusion loss from the oocyte surface (but persistence in the PVS), an image after removal of the ZP (both for pre-fertilization and post-fertilization) would be helpful - the combination of those images with the ones you have (ZP intact) would make your point more visible.

      We have followed this recommendation. Figure 8 of the initial manuscript has been replaced by Figure 6 in the revised manuscript, which illustrates the four situations encountered in this study: fertilized and unfertilized oocytes, each with and without unfused spermatozoa in their PVS. To better show JUNO/CD9 pre-fusion presence to the oocyte plasma membrane, as well as their post-fusion partial (for CD9) and near-complete (for JUNO) loss from the oocyte membrane (but persistence in the PVS), paired images of the same oocyte before and after of ZP removal are now provided, both for unfertilized (Figure 6A) and fertilized oocytes (Figure 6C).

      • You show that the heads of spermatozoa post fusion are covered in CD9 and JUNO, yet I was missing an image of sperm in the PVS pre-fertilization (which should then not yet be covered).

      As staining and confocal imaging of the oocytes were performed 4 hours after insemination, images of sperm in the PVS of an oocyte “pre-fertilization” cannot be strictly obtained. However, we can have images of spermatozoa present in the PVS of oocytes that remained unfertilized. This situation, now illustrated in Figure 6B of the revised manuscript, shows that these spermatozoa are also covered in JUNO and CD9, which they may have progressively acquired over time from the baseline presence of these proteins in the PVS of unfertilized oocytes. This also may provide a mechanistic explanation for their inability to fuse with the oolemma, and, consequently, for the failure of fertilization in these oocytes.

      Minor comments:

      • The videos were remarkable to look at, and great to view in full. However, for the sake of time, the authors might want to consider cropping them for the individual phases to have a shorter video (with clear crop indicators) with the most important different stages visible in a for example 1 min video (e.g. video.

      We have followed this recommendation. The videos have been cropped and annotated in order to highlight the key events that support the points made in the result section from page 9 to 11 in the revised manuscript.

      • In general, given that the ZP, PVS and oocyte membrane are important components, a general scheme at the very beginning outlining the relative positioning of each before and during fertilization (and then possibly also including the second polar body release) would be extremely helpful for the reader to orient themselves.

      A general scheme addressing Reviewer 1 request, summarizing the key components and concepts discussed in the article and intended to help guide the reader, has been added to the introduction of the revised manuscript as Figure 1.

      • first header results "Multi-penetration and polyspermy under in vivo conditions and standard and kinetics in vitro fertilization conditions" is hard to understand - simplify/make clearer (comparison of in vivo and in vitro conditions? Establishing the in vitro condition as assay?)

      The title of the first Results section has been revised in accordance with Reviewer 1 suggestion. It now reads: Comparative study of penetration and fertilization rates under in vivo and two distinct in vitro fertilization conditions.

      • Large parts of the statistical analysis (the more technical parts) could be moved to the methods part since it disrupts the flow of the text.

      In the revised version of our manuscript, we have restructured this part of the analysis to ensure that more technical or secondary elements do not disrupt the flow of the main text. Accordingly, the equations have been reduced to only what is strictly necessary to understand our approach, their notation has been greatly simplified, and the statistical analysis of unfertilized oocytes whose zona pellucida was traversed by one or more spermatozoa has been moved to the Supplementary Information (SI1).

      • To me, one of the main conclusions was given in the text of the results part, namely that "This suggests that first fertilization contributes effectively to the fertilization-block, but less so to the penetration block". I would suggest that the authors use this conclusion to strengthen their rationale and storyline in the abstract.

      We agree with Reviewer 1 suggestion. Accordingly, we have not only thoroughly revised our abstract, but also the introduction and discussion, in order to better highlight the rationale of our study, its storyline, and the new findings which not only challenge certain established views but also open new research directions in the mechanisms of gamete fusion and polyspermy prevention.

      • Wording: To characterize the kinetics with which penetration of spermatozoa in the PVS falls down after a first fertilization," falls down should be replaced with decreases (page 10 and page 12)

      Falls down has been removed from the new version and replaced with decreases


      Significance

      Overall, this manuscript provides very interesting and carefully obtained data which provides important new insights particularly for reproductive biology. I applaud the authors on first establishing the in vivo conditions (how often do multiple sperm even penetrate the ZP in vivo) since studies have usually just started with in vitro condition where sperm at much higher concentration is added to isolated oocyte complexes. Thank you for providing an in vivo benchmark for the frequency of multiple sperm being in the PVS. While this frequency is rather low (somewhat expectedly, with 16% showing 2-3 sperm in the PVS), this condition clearly exists, providing a clear rationale for the investigation of mechanisms that can prevent additional sperm from entering.

      My own expertise is experimentally - thus I don't have sufficient expertise to evaluate the statistical methods employed here.

      __ __


      Reviewer #2

      Evidence, reproducibility and clarity

      Overall, this is a very interesting and relevant work for the field of fertilization. In general, the experimental strategies are adequate and well carried out. I have some questions and suggestions that should be considered before the work is published.

      1) Why are the cumulus cells not mentioned when the AR is triggered before or while the sperms cross it? It seems the paper assumes from previous work that all sperm that reach ZP and the OPM have carried out the acrosome reaction. This, though probably correct, is still a matter of controversy and should be discussed. It is in a way strange that the authors do not make some controls using sperm from mice expressing GFP in the acrosome, as they have used in their previous work.

      We do not mention the cumulus cells or whether the acrosome reaction is triggered before, during, or after their traversal (i.e., upon sperm binding to the ZP), as this question, while scientifically relevant, pertains to a distinct line of investigation that lies beyond the scope of the present study. Even with the use of spermatozoa expressing GFP in the acrosome, addressing this question would require a complete redesign of our kinetic tracking protocol, which was specifically conceived to monitor in bright field the dynamic behavior of spermatozoa from the moment they begin to penetrate the perivitelline space of an oocyte. Accordingly, we imaged oocytes that were isolated 15 minutes after insemination of the cumulus–oocyte complexes, by which time most (if not all) cumulus cells had detached from the oocytes, as explained in the fourth paragraph of the material and methods of both the initial and revised versions of the manuscript. The spermatozoa we had access to were therefore already bound to the zona pellucida at the time of removal from the insemination medium, and had thus necessarily passed through the cumulus layer. It is unclear for us why Reviewer 2 believes that we “assume from previous work that all sperm that reach ZP has carried out the acrosome reaction”. We could not find any statement in our manuscript suggesting, let alone asserting, such an assumption, which we know to be incorrect. Based on both published work from Hirohashi’s group in 2011 (Jin et al., 2011, DOI: 10.1073/pnas.1018202108) and our own unpublished observation (both involving cumulus-oocyte masses inseminated with spermatozoa expressing GFP in the acrosome), it is established that only a subset of spermatozoa reaching the ZP after crossing the cumulus layer has undergone acrosome reaction. Moreover, from the same sources—as well as from a recent publication by Buffone’s group (Jabloñsky et al., 2023 DOI: 10.7554/eLife.93792 ) which is the one to which reviewer 2 refers in her/his 3rd comment, it is also well established that spermatozoa have all undergone acrosome reaction when they enter the PVS. To the best of our knowledge, this latter point has long been widely accepted and is not questioned. Therefore, stating this in the first paragraph of the Discussion in the revised manuscript, while referencing the two aforementioned published studies, should be appropriate. What remains a matter of ongoing debate, however, is the timing and the physiological trigger(s) of the acrosome reaction in fertilizing spermatozoa. The 2011 study by Hirohashi’s group challenged the previously accepted view that ZP binding induces the acrosome reaction, showing instead that most spermatozoa capable of crossing the ZP and fertilizing the oocyte had already undergone the acrosome reaction prior to ZP binding. However, as this issue lies beyond the scope of our study, we do not consider it appropriate to include a discussion of it in the manuscript.

      2) In the penetration block equations, it is not clear to me why (𝑡𝑃𝐹1) refers to both PIPF1 and 𝜎𝜎𝑃I𝑃𝐹1. Is it as function off?

      That is correct: (tPF1) means function of the time post-first fertilization. Both the post-first fertilization penetration index (i.e. PIPF1) and its incertainty (i.e. 𝜎𝑃I𝑃𝐹1 ) vary as a function of this time. However, as mentioned in a previous response to Reviewer 1, this section has been rewritten to improve clarity and readability. The equations have been limited to those strictly necessary for understanding our approach, and their notation has been significantly simplified.

      3) Why do the authors think that the flagella stops. The submission date was 2024-10-01 07:27:26 and there has been a paper in biorxiv for a while that merits mention and discussion in this work (bioRxiv [Preprint]. 2024 Jul 2:2023.06.22.546073. doi: 10.1101/2023.06.22.546073.PMID: 37904966).

      Our experimental approach allows us to determine when the spermatozoon stops moving, but not why it stops. We thank Reviewer 3 for pointing out this very relevant paper from Buffone’s group (doi: 10.7554/eLife.93792) which shows the existence of two distinct populations of live, acrosome-reacted spermatozoa. These correspond to two successive stages, which occur either immediately upon acrosome reaction in a subset of spermatozoa, or after a variable delay in others, during which the sperm transitions from a motile to an immotile state. The transition from the first to the second stage was shown to follow a defined sequence: an increase in the sperm calcium concentration, followed by midpiece contraction associated with a local reorganization of the helical actin cortex, and ultimately the arrest of sperm motility. For fertilizing spermatozoa in the PVS, this transition was shown to occur upon fusion. However, it was also reported in some non-fertilizing spermatozoa that this transition took place within the PVS. These findings are consistent with the requirement for sperm motility in order to achieve fusion with the oolemma. Moreover, the fact that some spermatozoa may prematurely transition to the immotile state within the PVS can therefore be added to the list of possible reasons why a spermatozoon that penetrates the PVS of an oocyte might fail to fuse.

      This discussion has been added to the first paragraph of the Discussion section of our revised manuscript.

      4) Please correct at the beginning of Materials and Methos: Sperm was obtained from WT male mice, it should say were.

      Thank you, the correction has been done.

      5) This is also the case in the fourth paragraph of this section: oocyte were not was.

      The sentence in question has been modified as followed: “In the in vitro fertilization experiments with kinetic tracking, a subset of oocytes—together with their associated ZP-bound spermatozoa—was isolated 15 minutes post-insemination and transferred individually into microdrops of fertilization medium to enable identification.”


      Significance

      Understanding mammalian gamete fusion and polyspermy inhibition has not been fully achieved. The authors examined real time brightfield and confocal images of inseminated ZP-intact mouse oocytes and used statistical analyses to accurately determine the dynamics of the events that lead to fusion and involve polyspermy prevention under conditions as physiological as possible. Their kinetic observations in mice gamete interactions challenge present paradigms, as they document that the first sperm is not necessarily the one that fertilizes, suggesting the existence of other post-penetration fertilization factors. The authors find that the zona pellucida (ZP) block triggered by the cortical reaction is too slow to prevent polyspermy in this species. In contrast, their findings indicate that ZP directly contributes to the polyspermy block operating as a naturally effective entry barrier inhibiting the exit from the perivitelline space (PVS) of components released from the oocyte plasma membrane (OPM), neutralizing unwanted sperm fusion, aside from any block caused by fertilization. Furthermore, the authors unveil a new important ZP role regulating flagellar beat in fertilization by promoting sperm fusion in the PVS.

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

      SUMMARY: This study by Dubois et al. utilizes live-cell imaging studies of mouse oocytes undergoing fertilization. A strength of this study is their use of three different conditions for analyses of events of fertilization: (1) eggs undergoing fertilization retrieved from females at 15 hr after mating (n = 211 oocytes); (2) cumulus-oocyte complexes inseminated in vitro (n = 220 oocytes), and (3) zona pellucida (ZP)-intact eggs inseminated in vitro, transferred from insemination culture once sperm were observed bound to the ZP for subsequent live-cell imaging (93 oocytes). This dataset and these analyses are valuable for the field of fertilization biology. Limitations of this manuscript are challenges arise with some conclusions, and the presentation of the manuscript. There are some factual errors, and also some places where clearer explanations should to be provided, in the text and potentially augmented with illustrations to provide more clarity on the models that the authors interpret from their data.

      MAJOR COMMENTS:

      The authors are congratulated on their impressive collection of data from live-cell imaging. However, the writing in several sections is challenging to understand or seems to be of questionable accuracy. The lack of accuracy is suspected to be more an effect of overly ambitious attempts with writing style, rather than to mislead readers. Nevertheless, these aspects of the writing should be corrected. There also are multiple places where the manuscript contradicts itself. These contradictions should be corrected. Finally, there are factual points from previous studies that need correction.

      Second, certain claims and the conclusions as presented are not always clearly supported by the data. This may be connected to the issues with writing style, word and phrasing choices, etc. The conclusions could be expressed more clearly, and thus may not require additional experiments or analyses to support them. The authors might also consider illustrations as ways to highlight the points they wish to make. (Figure 7 is a strong example of how they use illustrations to complement the text).

      In response to Reviewer 3's concern about the writing style, which made several sections difficult to understand, we have thoroughly revised the entire manuscript to improve clarity, and precision. To further enhance comprehension, we have added illustrations in the revised version of the manuscript:

      • Figure 1A presents the gamete components; Figure 1B depicts the main steps of fertilization considered in the present study; and Figure 1C illustrates the penetration and fusion blocks, along with the respective contributing mechanisms: the ZP-block for the penetration block, and the membrane-block and PVS-block for the fusion block

      • Figure 2A provides a description of the three experimental protocols used in this study: Condition 1, in vivo fertilization after mating; Condition 2, standard in vitro fertilization following insemination of cumulus-oocyte complexes; and Condition 3, in vitro fertilization with kinetic tracking of oocytes isolated from the insemination medium 15 min after insemination of the cumulus-oocyte complexes.

      • Figure 4 (formerly Figure 7 in the initial version) now highlights all fusing and non-fusing situations documented in videos 1-6 and associated paragraphs of the Results section.

      • In the Discussion, Figure 9 from the original version has been replaced by Figure 8, which now provides a more pedagogical illustration of the inefficacy of the penetration block in preventing polyspermy in mice. This figure illustrates the respective contributions of the fusion block, the penetration block, fusion timing, and the intrinsic permeability of the zona pellucida to the occurrence of polyspermy.

      We hope that this revised version of the article will guide the reader smoothly throughout, without causing confusion.

      Regarding the various points that Reviewer 3 perceives as contradictions or factual errors, or the claims and the conclusions which, as presented, should not always supported by the data, we will provide our perspective on each of them as they are raised in the review.

      SPECIFIC COMMENTS:

      (1) The authors should use greater care in describing the blocks to polyspermy, particularly because they appear to be wishing to reframe views about prevention of polyspermic fertilization. The title mentions of "the fast block to polyspermy;" this problematic for a couple of different reasons. There is no strong evidence for block to polyspermy in mammals that occurs quickly, particularly not in the same time scale as the first-characterized fast block to polyspermy. To many biologists, the term "fast block to polyspermy" refers to the block that has been described in species like sea urchins and frogs, meaning a rapid depolarization of the egg plasma membrane. However, such depolarization events of the egg membrane have not been detected in multiple mammalian species. Moreover, the change in the egg membrane after fertilization does not occur in as fast a time scale as the membrane block in sea urchins and frogs (i.e., is not "fast" per se), and instead occurs in a comparable time frame as the conversation of the ZP associated with the cleavage of ZP2. Thus, it is misleading to use the terms "fast block" and "slow block" when talking about mammalian fertilization. This also is an instance of where the authors contradict themselves in the manuscript, stating, "the membrane block and the ZP block are established in approximatively the same time frame" (third paragraph of Introduction). This statement is indeed accurate, unlike the reference to a fast block to polyspermy in mammals.

      We fully agree with Reviewer 3 on the importance of clearly defining the two blocks examined in the present study—the penetration block and the fusion block (as referred to in the revised version) —and of situating them in relation to the three blocks described in the literature: the ZP-block, membrane-block, and PVS-block. We acknowledge that this distinction was not sufficiently clear in the original version of the manuscript. In the revised version, these two blocks and their relationship to the ZP-, membrane-, and PVS-blocks are now clearly introduced in the second paragraph of the Introduction section and illustrated in the first figure of the manuscript (Fig. 1C). They are then discussed in detail in two dedicated paragraphs of the Discussion, entitled Relation between the penetration block and the ZP-block and Relation between the fusion block and the membrane- and PVS-blocks.

      The penetration block refers to the time-dependent decrease in the number of spermatozoa penetrating the perivitelline space (PVS) following fertilization, whereas the fusion block refers to the time-dependent decrease in sperm-oolemma fusion events after fertilization. It is precisely to the characterization of these two blocks that our in vitro fertilization experiments with kinetic tracking allow us to access.

      In this study, as in the literature, fusion-triggered modifications of the ZP that hinder sperm traversal of the ZP are referred to as the ZP-block (also known as ZP hardening). The ZP-block thus contributes to the post-fertilization reduction in sperm penetration into the PVS and thereby underlies the penetration block. Similarly, fusion-triggered alterations of the PVS and the oolemma that reduce the likelihood of spermatozoa that have reached the PVS successfully to fuse with the oolemma are referred to as the PVS-block and membrane-block, respectively. These two blocks act together to reduce the probability of sperm-oolemma fusion after fertilization, and thus contribute to the fusion block.

      The time constant of the penetration block was found to be 48.3 ± 9.7 minutes, which is consistent with the typical timeframe of ZP-block completion—approximately one hour post-fertilization in mice—as reported in the literature. By contrast, the time constant of the fusion block was determined to be 6.2 ± 1.3 minutes, which is markedly faster than the time typically reported in the literature for the completion of the fusion-block (more than one hour in mice). This strongly suggests that the kinetics of the fusion block are not primarily governed by its membrane-block component, but rather by its PVS-block component—about which little to nothing was previously known.

      Contrary to what Reviewer 3 appears to have understood from our initial formulation, there is therefore no contradiction or error in stating that "the membrane block and the ZP block are established within approximately the same timeframe", while the fusion block, which proceeds much more rapidly, is likely to rely predominantly on the PVS-block. We have thoroughly revised the manuscript to clarify this key message of the study.

      However, we understand Reviewer 3’s objection to referring to the fusion block (or the PVS-block) as a fast block, given that this term is conventionally reserved for the immediate fertilization-triggered membrane depolarization occurring in sea urchins and frogs. Although the kinetics we report for the fusion block are considerably faster than those of the penetration block, they occur on the scale of minutes, and not seconds. In line with the reviewer's recommendation, we have therefore modified both the title and the relevant passages in the text to remove all references to the term fast block in the revised version.

      (2) The authors aim to make the case that events occurring in the perivitelline space (PVS) prevent polyspermic fertilization, but the data that they present is not strong enough to make this conclusion. Additional experiments would optional for this study, but data from such additional experiments are needed to support the authors' claims regarding these functions in fertilization. Without additional data, the authors need to be much more conservative in interpretations of their data. The authors have indeed observed phenomena (the presence of CD9 and JUNO in the PVS) that could be consistent with a molecular basis of a means to prevent fertilization by a second sperm. However, the authors would need additional data from additional experimental studies, such as interfering with the release of CD9 and JUNO and showing that this experimental manipulation leads to increased polyspermy, or creating an experimental situation that mimics the presence of CD9 and JUNO (in essence, what the authors call "sperm inhibiting medium" on page 20) and showing that this prevents fertilization.

      A major section of the Results section here (starting with "The consequence is that ... ") is speculation. Rather than be in the Results section, this should be in the Discussion. The language should be also softened regarding the roles of these proteins in the perivitelline space in other portions of the manuscript, such as the abstract and the introduction.

      Finally, the authors should do more to discuss their results with the results of Miyado et al. (2008), which interestingly, posited that CD9 is released from the oocytes and that this facilitates fertilization by rendering sperm more fusion-competent. There admittedly are two reports that present data that suggest lack of detection of CD9-containing exosomes from eggs (as proposed by Miyado et al.), but nevertheless, the authors should put their results in context with previous findings.

      We generally agree with all the remarks and suggestions made here. In the revised version of the manuscript, we have retained in the Results section (pp. 14–15) only the factual data concerning the localization of CD9 and JUNO in unfertilized and fertilized oocytes, as well as in the spermatozoa present in the PVS of these oocytes. We have taken care not to include any interpretive elements in this section, which are now presented exclusively in a dedicated paragraph of the Discussion, entitled “Possible molecular bases of the membrane-block and ZP-block contributing to the fusion block” (p. 21). There, we develop our hypothesis and discuss it in light of both the findings from the present study and previous work by other groups. In doing so, we also address the data reported by Miyado et al. (2008, https://doi.org/10.1073/pnas.0710608105), as well as subsequent studies by two other groups—Gupta et al. (2009, https://doi.org/10.1002/mrd.21040) and Barraud-Lange et al. (2012, https://doi.org/10.1530/REP-12-0040)—that have challenged Miyado’s findings.

      We are fully aware that our interpretation of the coverage of unfused sperm heads in the perivitelline space (PVS) by CD9 and JUNO, released from the oolemma—as a potential mechanism of sperm neutralization contributing to the PVS block—remains, at this stage, a plausible hypothesis or working model that, as such, warrants further experimental investigation. It is precisely in this spirit that we present it—first in the abstract (p.1), then in the Discussion section (p. 21), and subsequently in the perspective part of the Conclusion section (p. 22).

      (3) Many of the authors' conclusions focus on their prior analyses of sperm interaction - beautifully illustrated in Figure 7. However, the authors need to be cautious in their interpretations of these data and generalizing them to mammalian fertilization as a whole, because mouse and other rodent sperm have sperm head morphology that is quite different from most other mammalian species.

      In a similar vein, the authors should be cautious in their interpretations regarding the extension of these results to mammalian species other than mouse, given data on numbers of perivitelline sperm (ranging from 100s in some species to virtually none in other species), suggesting that different species rely on different egg-based blocks to polyspermy to varying extents. While these observations of embryos from natural matings are subject to numerous nuances, they nevertheless suggest that conclusions from mouse might not be able to be extended to all mammalian species.

      It is not clear to us whether Reviewer 3’s comment implies that we have, at some point in the manuscript, generalized conclusions obtained in mice to other mammalian species—which we have not—or whether it is simply a general, common-sense remark with which we fully agree: that findings established in one species cannot, by default, be assumed to apply to another.

      We would like to emphasize that throughout the manuscript, we have taken care to restrict our interpretations and conclusions to the mouse model, and we have avoided any unwarranted extrapolation to other species.

      To definitively close this matter—if there is indeed a matter—we have added the following clarifying statements in the revised version of the manuscript:

      In the introduction, second paragraph (pp. 2–3):"The variability across mammalian species in both the rate of fertilized oocytes with additional spermatozoa in their PVS (from 0 to more than 80%) after natural mating and the number of spermatozoa present in the PVS of these oocytes (from 0 to more than a hundred) suggests that the time for completion of the penetration block and thus its efficiency to prevent polyspermy can vary significantly between species."

      At the end of the preamble to the Results section (p. 4):"This experimental study was conducted in mice, which are the most widely used model for studying fertilization and polyspermy blocks in mammals. While there are many interspecies similarities, the findings presented here should not be directly extrapolated to humans or other mammalian species without species-specific validation."

      In the Conclusion, the first sentence is (p.22) : “This study sheds new light on the complex mechanisms that enable fertilization and ensure monospermy in mouse model.”

      Within the Conclusion section, among the perspectives of this work (p. 22):"In parallel, comparative studies in other mammalian species will be needed to assess the generality of the PVS-block and its contribution relative to the membrane-block and ZP-blocks, as well as the generality of the mechanical role played by flagellar beating and ZP mechanical constraint in membrane fusion."

      (4) Results, page 4 - It is very valuable that the authors clearly define what they mean by a penetrating spermatozoon and a fertilizing spermatozoon. However, they sometimes appear not to adhere to these definitions in other parts of the manuscript. An example of this is on page 10; the description of penetration of spermatozoon seems to be referring to membrane fusion with the oocyte plasma membrane, which the authors have alternatively called "fertilizing" or fertilization - although this is not entirely clear. The authors should go through all parts of the manuscript very carefully and ensure consistent use of their intended terminology.

      Overall, while these definitions on page 4 are valuable, it is still recommended that the authors explicitly state when they are addressing penetration of the ZP and fertilization via fusion of the sperm with the oocyte plasma membrane. This help significantly in comprehension by readers. An example is the section header in the middle of page 9 - this could be "Spermatozoa can penetrate the ZP after the fertilization, but have very low chances to fertilize."

      We chose to define our use of the term penetration at the beginning of the Results section because, as readers of fertilization studies, we have encountered on multiple occasions ambiguity as to whether this term was referring to sperm entry into the perivitelline space following zona pellucida traversal, or to the fusion of the sperm with the oolemma. To avoid such ambiguity, we were particularly careful throughout the writing of our original manuscript to use the term penetration exclusively to describe sperm entry into the PVS. The terms fertilizing and fusion were reserved specifically for membrane fusion between the gametes. However, as occasional lapses are always possible, we followed Reviewer 3’s recommendation and carefully re-examined the entire manuscript to ensure consistent use of our intended terminology. We did not identify any inconsistencies, including on page 10, which was cited as an example by Reviewer 3. We therefore confirm that, in accordance with our predefined terminology, all uses of the term penetration, on that page and anywhere else in our original manuscript, refer exclusively to sperm entry into the PVS and do not pertain to fusion with the oolemma.

      That said, it is important that all readers— including those who may only consult selected parts of the article—are able to understand it clearly. Therefore, despite the potential risk of slightly overloading the text, Reviewer 3’s suggestion to systematically associate the term penetration with ZP seems to us a sound one. However, we have opted instead to associate penetration with PVS, as our study focuses on the timing of sperm penetration into the perivitelline space, rather than on the traversal of the zona pellucida itself. Accordingly, except in a few rare instances where ambiguity seemed impossible, we have systematically used the phrasing “penetration into the PVS” throughout the revised version of the manuscript.

      Another variation of this is in the middle of page 9, where the authors use the terms "fertilization block" and "penetration block." These are not conventional terms, and venture into being jargon, which could leave some readers confused. The authors could clearly define what they mean, particularly with respect to "penetration block,"

      This point has already been addressed in our response to Comment 1 from Reviewer 3. We invite Reviewer 3 to refer to that response.

      This extends to other portions of the manuscript as well, such as Figure 2C, with the label on the y-axis being "Time after fertilization." It seems that what the authors actually observed here was the cessation of sperm tail motility. (It is not evident they they did an assessment of sperm-oocyte fusion here.)

      Regarding Figure 2C (original version), it has been merged with Figure 2B (original version) to form a single figure (Figure S2D), now included in Supplementary Information SI2. This new figure retains all the information originally presented in Figure 2C and indicates the time axis origin as the time when oscillatory movements of the sperm cease.

      That said, for the reasons detailed in our response to Reviewer 1 and in the Materials and Methods, we explain why it is legitimate to use the cessation of sperm head oscillations on the oolemma as a marker for the timing of the fusion event. We invite the reviewers to refer to that response for a full explanation of our rationale.

      (5) Several points that the authors try to make with several pieces of data do not come across clearly in the text, including Figure 2 on page 6, Figure 4 on page 9, and the various states utilized for the statistical treatment, "post-first penetration, post-first fertilization, no fertilization, penetration block and polyspermy block" on page 10. Either re-writing and clearer definitions'explanations are needed, and/or schematic illustrations could be considered to augment re-written text. Illustrations could be a valuable way present the intended concepts to readers more clearly and accurately. For example, Figure 4 and the associated text on page 9 get particularly confusing - although this sounds like a quite impressive dataset with observations of 138 sperm. Illustrations could be helpful, in the spirit of "a picture is worth 1000 words," to show what seem to be three different situations of sequences of events with the sperm they observed. Finally, the text in the Results about the 138 sperm is quite difficult to follow. It also might help comprehension to augment the percentages with the actual numbers of sperm - e.g., is 48.6% referring 67 of the total 138 sperm analyzed? Does the 85.1% refer to 57 of these 67 sperm?

      Figure 2 in the original version of our manuscript concerns sperm engulfment and PB2 extrusion. As already mentioned in our response to Reviewer 1, the characterization of sperm engulfment and PB2 extrusion kinetics is highly relevant to the analysis of the penetration and fusion blocks. However, we agree that its presence in the main text may distract the reader from the main focus of the study. Therefore, this figure and the associated text have been moved to the Supplementary Information in the revised manuscript (SI 2, pages 26–27).

      Regarding Figure 4 (original version), in response to Reviewer 3’s concern about the difficulty in grasping the message conveyed in its three graphs and associated text we have completely rethought the way these data are presented. Since the three graphs of Figure 4 were directly derived from the experimental timing data of sperm entry in the PVS and fusion with the oolemma in fertilized oocytes (originally shown in Figure 3A), we have combined them into a single figure in the revised manuscript: Figure 3 (page 8). This new Figure 3 now comprises three components:

      • Figure 3A remains unchanged from the original version and shows the timing of sperm penetration and fusion in fertilized oocytes. Each sperm category (fused or non-fused , penetrated in the PVS before fusion or after fusion) is represented using a color code clearly explained in the main text (last paragraph of page 7).
      • Figure 3B focuses specifically on the first spermatozoon to penetrate the PVS of each oocyte. It reports how many of these first-penetrating spermatozoa succeeded in fusing versus how many failed to do so, highlighting that being the first to arrive is not sufficient for fusion—other factors are involved. This is explained simply in the first paragraph of page 9.
      • Figure 3C considers all spermatozoa that entered the PVS of fertilized oocytes, classifying them into three categories: those that penetrated the PVS before fertilization, those that did so after fertilization, and those for which the timing could not be precisely determined. Such classification makes it apparent that the number of spermatozoa penetrating before and after fertilization is of the same order of magnitude, indicating that fertilization is not very effective at preventing further sperm entry into the PVS for the duration of our observations (~4 hours). To facilitate the identification of these three categories, the same color code used in Figure 3A is applied. In addition, within each category, the number of spermatozoa that successfully fused are indicated in black. This allows the reader to quickly assess the fertilization probability for each category—high for sperm entering before fertilization, very low or null for those entering after fertilization. This analysis shows that fertilization is far more effective at blocking sperm fusion than at blocking sperm penetration. This is clearly explained in the second paragraph of page 9. Regarding__ statistical analysis__, as already mentioned in our responses to Reviewers 1 and 2, this section has been rewritten to improve clarity and readability. The notation has also been significantly simplified. To improve the overall fluidity of the text related to the statistical analysis, Figure 3B (original version), which presented the timing of penetration into the perivitelline space of oocytes that remained unfertilized, along with its associated statistical analysis previously in Figure 5B), have been revised and transferred together in a single Figure S1 of the Supplementary Information (SI1, pages 26; now Figures S1A and S1B).

      (6) Introduction, page 2 - it is inaccurate to state that only diploid zygotes can develop into a "new being." Triploid zygotes typically fail early in develop, but can survive and, for example, contribute to molar pregnancies. Additionally, it would be beneficial to be more scientifically precise term than saying "development into a new being." This is recommended not only for scientific accuracy, but also due to current debates, including in lay public circles, about what defines "life" or human life.

      In response to Reviewer 3’s comment, we no longer state in the revised version of the manuscript that only diploid zygotes can develop into a new being. We have modified our wording as follows, on page 2, second paragraph: “In mammals, oocytes fertilized by more than one spermatozoon cannot develop into viable offspring.”

      (7) Introduction, page 2 - The mammalian sperm must pass through three layers, not just two as stated in the first paragraph of the Introduction. The authors should include the cumulus layer in this list of events of fertilization.

      The sentence from the introduction from the original manuscript mentioned by Reviewer 3 was: “To fertilize, a spermatozoon must successively pass two oocyte’s barriers.” This statement is accurate in the sense that the cumulus cell layer is not part of the oocyte itself, unlike the two oocyte’s barriers: the zona pellucida and the oolemma. Moreover, the traversal of the cumulus layer is not within the scope of our study, unlike the traversal of the zona pellucida and fusion with the oolemma. However, it is also correct that in our study the spermatozoa have passed through the cumulus layer before reaching the oocyte. Therefore, in response to Reviewer 3’s comment, we have revised the sentence to clarify this point as follows:

      “Once a spermatozoon has passed through the cumulus cell layer surrounding the oocyte, it still must overcome two oocyte’s barriers to complete fertilization.”

      (8) Introduction, page 2 - While there is evidence that zinc is released from mouse egg upon fertilization, the evidence is not convincing or conclusive that zinc is released from cortical granules or via cortical granule exocytosis.

      To better highlight the rationale, storyline, and scope of our study, the introduction has been thoroughly streamlined. In this context, the section discussing the cortical reaction and zinc release seemed more appropriate in the Discussion, specifically within the paragraph titled “Relationship between the penetration block and the ZP-block.”

      To address the uncertainty raised by Reviewer 3 regarding the origin of the zinc spark release, we have rephrased this part as follows:

      “The fertilization-triggered processes responsible for the changes in ZP properties are generally attributed to the cortical reaction—a calcium-induced exocytosis of secretory granules (cortical granules) present in the cortex of unfertilized mammalian oocytes—and to zinc sparks. As a result, proteases, glycosidases, lectins, and zinc are released into the perivitelline space (PVS), where they act on the components of the zona pellucida. This leads to a series of modifications collectively referred to as ZP hardening or the ZP-block”.

      (9) The authors inaccurately state, "only if monospermic multi-penetrated oocytes are able to develop normally, which to our knowledge has never been proven in mice" (page 4) - This was demonstrated with the Astl knockout, assuming that the authors use of "multi-penetrated oocytes" here refers to the definition of penetration that they use, namely penetrating the ZP. This also is one of the instances where the authors contradict themselves, as they note the results with this knockout on page 18.

      Thank you for bringing this point to our attention. Nozawa et al. (2018) found that female mice lacking ovastacin (Astl)—the protease released during the cortical reaction that plays a key role in rendering the zona pellucida impenetrable—are normally fertile. They also reported that oocytes recovered from these females after mating were monospermic, despite the consistent presence of additional spermatozoa in the perivitelline space. We can indeed consider that taken together these findings demonstrate that the presence of multiple spermatozoa in the PVS does not impair normal development, as long as the oocyte remains monospermic. In our study, we re-demonstrated this in a different way (by reimplantation of monospermic oocytes with additional spermatozoa in their PVS) in a more physiological context of WT oocytes, but we agree that we cannot state: “which to our knowledge has never been proven in mice.” This part of the sentence has therefore been removed. In the revised version of the manuscript, the sentence is now formulated in the first paragraph of page 5 as follows: “However, the contribution of the fusion block to prevent polyspermy has physiological significance only if monospermic oocytes with additional spermatozoa in their PVS can develop into viable pups.”

      Minor comments:

      There are numerous places where this reader marked places of confusion in the text. A sample of some of these:

      We will indicate hereinafter how we have modified the text in the specific examples provided by Reviewer 3. Beyond these, however, we would like to emphasize that we have thoroughly revised the entire manuscript to improve clarity and precision.

      Page 4 - "continuously relayed by other if they detach" - don't know what this means

      Replaced now p 5 by “can be replaced by others if they detach”

      Page 6 - "hernia" - do the authors mean "protrusion" on the oocyte surface?

      The paragraph from the Results section in question has now been moved to the Supplementary Information, on pages 26 and 27. The term hernia has been systematically replaced with protrusion, including in the Materials and Methods section on page 24.

      Page 10 - "penetration of spermatozoa in the PVS falls down" - don't know what this means

      Falls down has been removed from the new version and replaced with decreases

      Page 12 - "spermatozoa linked to the oocyte ZP" - not clear what "linked" means here

      Replaced now page 16 by “spermatozoa bound to the oocyte ZP”

      Page 14 - "by dint of oscillations" - don't know what this means

      Replaced now page 10 by “the persistent flagellum movements”

      Specifics for Materials and Methods:

      Exact timing of females receiving hCG and then being put with males for mating - assume this was immediate but this is an important detail regarding the timing for the creation of embryos in vivo.

      That is correct: females were placed with males for mating immediately after receiving hCG. This clarification has been added in the revised version of the manuscript.

      Please provide the volumes in which inseminations occurred, and how many eggs were placed in this volume with the 10^6 sperm/ml.

      The number of eggs may vary from one cumulus–oocyte complex to another. It is therefore not possible to specify exactly how many eggs were inseminated. However, we now indicate on page 23 the number of cumulus–oocyte complexes inseminated (4 per experiment), the volume in which insemination was performed (200 mL), and the sperm concentration used 106 sperm/mL.

      **Referees cross-commenting**

      I concur with Reviewer 1's comment, that the 'challenging prior dogma' about the first sperm not always being the one to fertilize the egg is too strong. As Reviewer 1 notes, "it had been observed before that it is not necessarily the first sperm that gets through the ZP that fertilizes the egg." I even thought about adding this comment to my review, although held off (I was hoping to find references, but that was taking too long).

      Please refer to our response to Reviewer 1 regarding this point.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript "Key roles of the zona pellucida and perivitelline space in promoting gamete fusion and fast block to polyspermy inferred from the choreography of spermatozoa in mice oocytes" by Dr. Gourier and colleagues explores the poorly understood process of gamete fusion and the subsequent block to polyspermy by live-cell imaging of mouse oocytes with intact zona pellucida in vitro. The new component in this study is the presence of the ZP, which in prior studies of live-cell imaging had been removed before. This allowed the authos to examine contributions of the ZP to the block in polyspermy in relation to the timing of sperm penetrating the ZP and sperm fusing with the oocyte. By carefully analysing the timing of the cascade of events, the authors find that the first sperm that reaches the membrane of the mouse oocyte is not necessarily the one that fertilizes the oocytes, revealing that other mechanisms post-ZP-penetration influence the success of individual sperm. While the rate of ZP penetration remains constant in unfertilized oocytes, it decreases upon fertilization for subsequent sperm, providing direct evidence for the known 'slow block to polyspermy' provided by changes to the ZP adhesion/ability to be penetrated. Careful statistical analyses allow the authors to revisit the role of the ZP in preventing polyspermy: They show that the ZP block resulting from the cortical reaction is too slow (in the range of an hour) to contribute to the immediate prevention of polyspermy in mice. The presented analyses reveal that the ZP does contribute to the block to polyspermy in two other ways, namely by effectively limiting the number of sperm that reach the oocyte surface in a fertilization-independent manner, and by retaining components like JUNO and CD9, that are shed from the oocyte plasma membrane after fertilization, in the perivitelline space, which may help neutralize surplus spermatozoa that are already present in the PVS. Lastly, the authors report that the ZP may also contribute to channeling the flagellar oscillations of spermatozoa in the PVS to promote their fusion competence.

      Major comments:

      • Are the key conclusions convincing?

      The authors provide a careful analysis of the dynamics of events, though the analyses are correlative, and can only be suggestive of causation. While this is a limitation of the study, it provides important analysis for future research. Moreover, by analysing also control oocytes without fertilization and the timing of events, the authors have in some instances clear 'negative controls' for comparison.

      Some claims would benefit from rewording or rephrasing to put the findings better in the context of what is already known and what is novel: - the phrasing 'challenging prior dogma' might be too strong since it had been observed before that it is not necessarily the first sperm that gets through the ZP that fertilizes the egg (though I am afraid that I do not have any citations or references for this). However, given that in the field people generally think it is not necessarily and always the first sperm, the authors may want to consider weakening this claim. - I do think the cortical granule release could still contribute to the block to polyspermy though - as the authors here nicely show - at a later time-point only, and thus not the major and not the immediate block as previously thought. The wording in the abstract should therefore be adjusted (since it could still contribute...) - the finding that the ZP presents a natural effective barrier for sperm entry is not that novel (as suggested here) - there are mutants that prevent sperm from getting through the ZP and thus to the oocyte and those lead to sterility - release of OPM components - in the abstract it's unclear what the authors mean by this - in the results part it becomes clear. Please already make it clear in the abstract that it is the fertility factors JUNO/CD9 that could bind to sperm heads upon their release and thus 'neutralize' them? I would also recommend not referring to it as 'outer' plasma membrane (there is no 'inner plasma membrane'). Moreover, in the abstract please clarify that this release is happening only after fusion of the first sperm and not all the time. In the abstract it sounds as if this was a completely new idea, but there is good prior evidence that this is in fact happening (as also then cited in the results part) - maybe frame it more as the retention inside the PVS as new finding.

      It is unclear to me what the relevance of dividing the post-fusion/post-engulfment into different phases as done in Fig 2 (phase 1, and phase 2) - also for the conclusions of this paper this seems rather irrelevant and overly complicated, since the authors never get back to it and don't need it (it's not related to the polyspermy block analyses). I would remove it from the main figures and not divide into those phases since it is distracting from the main focus.

      For the statistical analysis, I am not sure whether the assumption "assumption that the probability distribution of penetration or fertilization is uniform within a given time window" is in fact true since the probability of fertilizing decreases after the first fertilization event.... Maybe I misunderstood this, but this needs to be explained (or clarified) better, or the limitation of this assumption needs to be highlighted. - Suggestion for additional experiments:

      If I understood correctly, the onset of fusion in Fig 2C is defined by stopping of sperm beating? If it is by the sudden stop of the beating flagellum, this should be confirmed in this situation (with the ZP intact) that it correctly defines the time-point of fusion since this has not been measured in this set-up before as far as I understand. In order to measure this accurately, the authors will need to measure this accurate to be able to acquire those numbers (of time from fusion to end of engulfment), e.g. by pre-loading the oocyte with Hoechst to transfer Hoechst to the fusing sperm upon membrane fusion.

      Fig 8: 2 comments - To better show JUNO/CD9 pre-fusion attachment to the oocyte surface and post-fusion loss from the oocyte surface (but persistence in the PVS), an image after removal of the ZP (both for pre-fertilization and post-fertilization) would be helpful - the combination of those images with the ones you have (ZP intact) would make your point more visible. - You show that the heads of spermatozoa post fusion are covered in CD9 and JUNO, yet I was missing an image of sperm in the PVS pre-fertilization (which should then not yet be covered).

      Minor comments:

      • The videos were remarkable to look at, and great to view in full. However, for the sake of time, the authors might want to consider cropping them for the individual phases to have a shorter video (with clear crop indicators) with the most important different stages visible in a for example 1 min video (e.g. video 1)
      • In general, given that the ZP, PVS and oocyte membrane are important components, a general scheme at the very beginning outlining the relative positioning of each before and during fertilization (and then possibly also including the second polar body release) would be extremely helpful for the reader to orient themselves.
      • first header results "Multi-penetration and polyspermy under in vivo conditions and standard and kinetics in vitro fertilization conditions" is hard to understand - simplify/make clearer (comparison of in vivo and in vitro conditions? Establishing the in vitro condition as assay?)
      • Large parts of the statistical analysis (the more technical parts) could be moved to the methods part since it disrupts the flow of the text.
      • To me, one of the main conclusions was given in the text of the results part, namely that "This suggests that first fertilization contributes effectively to the fertilization
      • block, but less so to the penetration block". I would suggest that the authors use this conclusion to strengthen their rationale and storyline in the abstract.
      • Wording: To characterize the kinetics with which penetration of spermatozoa in the PVS falls down after a first fertilization," falls down should be replaced with decreases (page 10 and page 12)

      Significance

      Overall, this manuscript provides very interesting and carefully obtained data which provides important new insights particularly for reproductive biology. I applaud the authors on first establishing the in vivo conditions (how often do multiple sperm even penetrate the ZP in vivo) since studies have usually just started with in vitro condition where sperm at much higher concentration is added to isolated oocyte complexes. Thank you for providing an in vivo benchmark for the frequency of multiple sperm being in the PVS. While this frequency is rather low (somewhat expectedly, with 16% showing 2-3 sperm in the PVS), this condition clearly exists, providing a clear rationale for the investigation of mechanisms that can prevent additional sperm from entering.

      My own expertise is experimentally - thus I don't have sufficient expertise to evaluate the statistical methods employed here.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewing Editor Comments:

      Focus and Scope:

      The paper attempts to address too many topics simultaneously, resulting in a lack of focus and insufficient depth in the treatment of individual components.

      We have moved this selective clinical review section that was previously Part I in the paper now to Part II, given the importance of leading off with the meta-analysis and resource before doing a selective review, which are now Part I. In the lead in to Part II, we now indicate that the review is not intended to be comprehensive, because there are other recent comprehensive reviews, which we cite. This part of the paper merely aims to generate hypotheses on the directionality of effects ripe for testing on how TUS could be used to excite or suppress function, illustrated with specific clinical examples. The importance of this section, even though not comprehensive, is that it should provide the reader with examples on how the directionality of TUS could be used specifically in a range of clinical applications. The reader will find that the same hypotheses do not apply to different clinical disorder. Therefore, patient specific hypotheses need to be motivated and then subsequently tested with empirical application of TUS, which Part II provides.

      Part II. Selective TUS clinical applications review and TUS directionality hypotheses starts at line 458. Part I, the meta-analysis and resource section starts at line 199, after the Introduction on TUS and the importance on understanding how the directionality of TUS effects could be better understood.

      Strengthening the Meta-Analysis:

      The meta-analysis is the strongest aspect of the paper and should be expanded to include the relevant statistics. However, it currently omits several key concepts, studies, and discussion points, particularly related to replication and the dominance of results from specific groups. These omissions should be addressed even with a focus on meta-analysis.

      We thank the reviewer for their enthusiasm about the meta-analysis, which we have now promoted to Part I in the revised paper. We have substantially updated the latest database (inTUS_DATABASE_1-2025.csv) and ensured that the R markdown script can re-generate all of the results and statistical values. We have inserted additional statistical values in the main manuscript, as requested. The inTUS Resource is located here (https://osf.io/arqp8/ under Cafferatti_et_al_inTUS_Resource), and we have aimed to make it as user friendly to use and contribute to as possible. For instance, the reader can find them all in the HTML link summarizing the R markdown output with all statistical values here: https://rpubs.com/BenSlaterNeuro/1268823, a part of the inTUS resource.

      Since the last submission, there has been a tremendous increase in the number of TUS studies in healthy participants. We have curated and included all of the relevant studies we could find in the 1-2025 database, as the next large expansion of the database (now including 52 experiments in healthy participants). We then reran and report the results of the statistical tests via the R markdown script (starting at line 336). Finally, the online database (inTUS_DATABASE_1-2025.csv) has additional columns, suggested by the reviewers, including one to identify the same groups that conducted the TUS study, based on a social network analysis. The manuscript figures (Table 1 and Table 2) did not have the space to expand the data tables, but these additional columns are available in the database online. Finally, we have ensured that the resource is as easy to use as possible (line 862 has the Introduction to the inTUS Resource – which is also the online READ ME file), and we have been in contact with the iTRUSST consortium leads who are interested in discussing hosting the resource and helping it to become self-sustaining.

      Conceptual Development:

      The more conceptual part of the paper is underdeveloped. It lacks sufficient supporting data, a well-articulated argument, and a clear derivation or development of a concrete model.

      To ensure that the conceptual sections are well developed, we have revised the introduction, including the background on TUS and bases for the interest in the directionality of effects. We have also revised the TUS mechanisms background as suggested by the reviewers. For Part I, the meta-analysis basis and hypotheses we have ensured the rationale is clearer. The hypotheses are based on several lines of research in the animal model and human literature as cited (starting with line 211). For Part II, the selective clinical review, we have revised this section as well to have each section on lowintensity TUS and end in a hypothesis on the directionality of TUS effects. Starting at line 199 we have clarified the scope of the review and ensured that all the relevant experiments in healthy participants (n = 52 experiments) have now been included in the next key update of the resource and meta-analysis in this key paper update.

      Database Curation:

      The authors should provide more detailed information about how the database will be curated and made accessible. They may consider collaborating with ITRUSST.

      We have expanded the information on the Resource documents (starting at line 862) to make the resource as user friendly as possible. At the beginning of the resource development stage we had contacted but not heard from the ITRUSST consortium. Encouraged by this comment we again reached out and are now in contact with the ITRUSST consortium leads who are interested in discussing sustaining the resource. It would be wonderful to have the resource linked to other ITTRUST tools, since it was inspired by the organization. Practically what this means is that the resource rather than being hosted on Open Science Framework, would potentially be hosted on the ITRUSST web site (https://itrusst.com/). These discussions are in progress, but the next key update to the database (1-2025) is already available and reported in this key update to our original paper.

      Reviewer #1: (Public Review)

      Summary:

      This paper is a relevant overview of the currently published literature on lowintensity focussed ultrasound stimulation (TUS) in humans, with a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects.

      The pool of papers to draw from is small, which is not surprising given the nascent technology. It seems nevertheless relevant to summarize the current field in the way done here, not least to mitigate and prevent some of the mistakes that other non-invasive brain stimulation techniques have suffered from, most notably the theory- and data-free permutation of the parameter space.

      The meta-analysis concludes that there are, at best, weak trends toward specific parameters predicting the direction of the stimulation effects. The data have been incorporated into an open database, that will ideally continue to be populated by the community and thereby become a helpful resource as the field moves forward.

      Strengths:

      The current state of human TUS is concisely and well summarized. The methods of the meta-analysis are appropriate. The database is a valuable resource.

      Weaknesses:

      These are not so much weaknesses but rather comments and suggestions that the authors may want to consider.

      We thank the reviewer for their support of the resource and meta-analysis. We have implemented the suggestions next as follows.

      I may have missed this, but how will the database be curated going forward? The resource will only be as useful as the quality of data entry, which, given the complexity of TUS can easily be done incorrectly.

      We have added a paragraph on how authors could use the Qualtrics form to submit their data and the curation process involved (from line 891). Currently, this process cannot be automated because we continue to find that reported papers do not report the TUS parameters that ITRUSST has encouraged the community to report (Martin et al., 2024). We can dedicate for a TUS expert to ensure that every 6 or 12 months the data base is curated and expanded. The current version is the latest 1-2025 update to the data base. Longer term we are in discussion with ITRUSST on whether the resource could become self sustaining when TUS papers regularly reporting all the relevant parameters such that the database expansion becomes trivial, and then the Resource R markdown script and other tools can be used to re-evaluate the statistical tests and the user can conduct secondary hypothesis testing on the data.

      It would be helpful to report the full statistics and effect sizes for all analyses. At times, only p-values are given. The meta-analysis only provides weak evidence (judged by the p-values) for two parameters having a predictive effect on the direction of neuromodulation. This reviewer thinks a stronger statement is warranted that there is currently no good evidence for duty cycle or sonication direction predicting outcome (though I caveat this given the full stats aren't reported). The concern here is that some readers may gallop away with the impression that the evidence is compelling because the p-value is on the correct side of 0.05.

      We have ensured that the R script can generate the full statistics from the tests and the effect sizes for all the analyses, and now also report more of the key statistical values in the revised paper (starting at line 336). As suggested, we have also ensured that the interpretation is sufficiently nuanced given the small sample sizes and the p-values below 0.1 but above 0.05 are interpreted as a statistical trend.

      This reviewer thinks the issue of (independent) replication should be more forcefully discussed and highlighted. The overall motivation for the present paper is clearly and thoughtfully articulated, but perhaps the authors agree that the role that replication has to play in a nascent field such as TUS is worth considering.

      We completely agree and have added additional columns to the online database to identify unique groups, using a social network analysis, and independent replications. These expanded tables did not fit in the manuscript versions of Tables 1 and 2 but are fully available in the Resource data tables ready for further analysis by interested resource users.

      A related point is that many of the results come from the same groups (the so-called theta-TUS protocol being a clear example). The analysis could factor this in, but it may be helpful to either signpost independent replications, which studies come from the same groups, or both.

      In the expanded database tables (inTUS_DATABASE_1-2025.csv: https://osf.io/arqp8/ under Cafferatti_et_al_inTUS_Resource) we have added a column to identify independent replication.

      The recent study by Bao et al 2024 J Phys might be worth including, not least because it fails to replicate the results on theta TUS that had been limited to the same group so far (by reporting, in essence, the opposite result).

      Thank you. We have added this study and over a dozen recent TUS studies in healthy participants to the database and redone the analyses.

      The summary of TUS effects is useful and concise. Two aspects may warrant highlighting, if anything to safeguard against overly simplistic heuristics for the application of TUS from less experienced users. First, could the effects of sonication (enhancing vs suppressing) depend on the targeted structure? Across the cortex, this may be similar, but for subcortical structures such as the basal ganglia, thalamus, etc, the idiosyncratic anatomy, connectivity, and composition of neurons may well lead to different net outcomes. Do the models mentioned in this paper account for that or allow for exploring this? And is it worth highlighting that simple heuristics that assume the effects of a given TUS protocol are uniform across the entire brain risk oversimplification or could be plain wrong? Second, and related, there seems to be the implicit assumption (not necessarily made by the authors) that the effects of a given protocol in a healthy population transfer like for like to a patient population (if TUS protocol X is enhancing in healthy subjects, I can use it for enhancement in patient group Y). This reviewer does not know to which degree this is valid or not, but it seems simplistic or risky. Many neurological and psychiatric disorders alter neurotransmission, and/or lead to morphological and structural changes that would seem capable of influencing the impact of TUS. If the authors agree, this issue might be worth highlighting.

      We agree that given the divergence in circuits and cellular constituents between cortical and subcortical areas, it is important to distinguish studies that have focused on cortical or subcortical brain areas. The online data tables identify the target region. The analyses can be used to focus on the cortical or subcortical sites for analysis, although for the current version of the database there are too few subcortical sites with which to conduct analyses on subcortical sites. On the second point, that pathology may have affected the results, we completely agree and have clarified that the current database only includes healthy participant experiments for this reason. We are considering future updates to the resource may include clinical patient results (Line 247).

      Reviewer #1 (Recommendations for the authors):

      Minor edits (I wouldn't call them "corrections").

      We sincerely appreciate the constructive comments and have aimed to address them all as suggested.

      Perhaps the most relevant edit pertains to the statistics.

      We now report the more complete statistical results (line 336) and the R markdown script can re-generate all the statistical values for the tests.

      The issue of replication also seems relevant and ought to be raised. This reviewer does not want to prescribe what to do or impose the view the authors ought to adopt.

      In the online version of the data tables for the latest dataset, we have added a column in the data table as suggested that identifies independent groups and replications.

      The other points are left to the authors' discretion.

      We have aimed to address all of the reviewer’s points. Thank you for the constructive input which has helped to improve the expanded database and resource.

      Reviewer #2: (Public Review)

      Summary:

      This paper describes a number of aspects of transcranial ultrasound stimulation (TUS) including a generic review of what TUS might be used for; a meta-analysis of human studies to identify ultrasound parameters that affect directionality; a comparison between one postulated mechanistic model and results in humans; and a description of a database for collecting information on studies.

      Strengths:

      The main strength was a meta-analysis of human studies to identify which ultrasonic parameters might result in enhancement or suppression of modulation effects. The meta-analysis suggests that none of the US parameters correlate significantly with effects. This is a useful result for researchers in the field in trying to determine how the parameter space should be further investigated to identify whether it is possible to indeed enhance or suppress brain activity with ultrasound.

      The database is a good idea in principle but would be best done in collaboration with ITRUSST, an international consortium, and perhaps should be its own paper.

      Weaknesses:

      The paper tries to cover too many topics and some of the technical descriptions are a bit loose. The review section does not add to the current literature. The comparison with a mechanistic model is limited to comparing data with a single model at a time when there is no general agreement in the field as to how ultrasound might produce a neuromodulation effect. The comparison is therefore of limited value.

      We appreciate the reviewer’s assessment and interest in the meta-analysis and database to guide the development of TUS for more systematic control of the directionality of neuromodulation. With this next key expansion of the database (inTUS_DATABASE_1-2025.csv) we have added over a dozen new studies that have been published since our original submission (n = 52 experiments). We have also moved the ‘review’ part of the paper below the meta-analysis and resource description. We have clarified that the clinical review section (now Part II in the revised manuscript) is not intended as a comprehensive review but as a selective review showing how hypotheses on the directionality of TUS effects need to be carefully developed for specific patient groups that require different effects to be induced at specific brain areas. Finally, we have gotten in contact with the ITRUSST consortium leads, as suggested, and are in discussion on whether the inTUS resource could be hosted by ITRUSST. Since these discussions are ongoing practically what this might mean is moving the resource from the Open Science Framework to ITRUSST webpages, which would be a trivial update of the link to the resource in OSF.

      We also sincerely appreciate the time and care the reviewer has given to provide us with the below guidance, all of which we have aimed to take on board in the revised paper.

      Reviewer #2 (Recommendations for the authors):

      Line 24/25 - I suggest avoiding using the term "deep brain stimulation" in reference to TUS as the term is normally used to describe electrically implanted electrodes.

      We have removed the term “deep” brain stimulation in reference to TUS to avoid confusion with electrical DBS for patient treatment [Line 24].

      Line 25 - I don't think "computational modelling" has changed how TUS can be done. There is still much to be understood about mechanisms. I think the modelling aspects of the paper should be toned down. Indeed the NICE data that is presented later appears to have a weak, if any, correlation to the outcomes.

      We have revised the manuscript text throughout to ensure that the computational modeling contributions are not overstated, as noted, given the lack of strong correlation to the NICE model outcomes by the meta-analysis including in the latest results with the more extensive database (n = 52).

      Line 32 - "exponentially increasing" is a well-defined technical term and the increase in studies should be quantified to ensure it is indeed exponential. I agree that TUS studies in humans are increasing but a quick tally of the data by year in the meta-analysis reported here doesn't suggest that it follows an "exponential" growth.

      We have changed “exponential” to “to increase”. [Line 32]

      Line 50 - I would suggest using the term sub-MHz rather than 100-1,000 kHz as it is challenging to deliver ultrasound at 1 MHz through the skull. The highest frequency in the meta-analysis is 850 kHz; but the majority are in the 200-500 kHz range.

      We have made this correction to sub-MHz. [Line 54]

      Line 58/59 - Is the FDA publication on diagnostic imaging relevant for saying that 50 W/cm2 is a lowintensity TUS? I think it's perhaps reasonable to say that intensities below diagnostic thresholds are "low intensities" but that is not clear in the text. I would refer to ITRUSST on what is appropriate for defining what is low, medium, or high.

      We have cut the reference to the FDA here since it is, as noted, not as relevant as pointing to the ITRUSST definition.

      Line 65/66 - I agree that ultrasound for neuromodulation is gaining traction and there is an increase in activity, but it also has a long history with the work of the Fry brothers published in the 1950s; and extensive work of Gavrilov in humans starting in the 1970s.

      We have added citations to the Fry brothers and Gavrilov to the text in this section. [Line 69/70]

      Line 75 - I think the intermembrane cavitation mechanism is unlikely to be due to "microbubbles" in a lipid membrane. The predicted displacements are on the order of nanometres, so they are unlikely to generate microbubbles. The work on comparing with NICE is limited. Note there are a number of experimental papers that have reported an absence of intra-membrane cavitation, including the Yoo et al 2022 which is referenced later in the paragraph. Also, there are other models, such as Liao et al 2021 (https://www.nature.com/articles/s41598020-78553-2).

      As suggested, we have removed this phrase on microbubble formation as a likely mechanism. We have also added the Liao paper to this paragraph as it is relevant.

      Line 83 - "At the lower intensities..." it is not clear whether this means all TUS intensities or the lower end of intensities used in TUS.

      We now use the following wording here: “low intensities”. [Line 86]  

      Line 85/86 - "more continuous stimulation" the modulation paradigms haven't been described yet and so pulse vs continuous hasn't been made clear to the reader. Also "more continuous" is very loose terminology. Something is either continuous or it isn't.

      We agree and have removed “more” to be clear that the stimulation is continuous. [Line 88]

      Line 87/88 - "TUS does not .. cavitation ..when ..ISPTA...<14 W/cm2". You can't use ISPTA to determine cavitation. It is the peak negative pressure which is the key driver for cavitation and the MI which is the generally accepted (although grudgingly by some) metric for assessing cavitation risk. You can link the negative pressure to ISPPA but not really to ISPTA. In histotripsy for example the ISPTA is low due to the low duty cycles to avoid heating but the cavitation is a huge effect. Technical terminology is loose.

      We have corrected this to “TUS does not appear to cause significant heating or cavitation of brain tissue when the intensity remains low, based on Mechanical and Thermal Index values and recommendations of use”. [Line 90/91]

      Line 89 - What is meant by "low intensity TUS"? I think all TUS used in the literature counts as low intensity - in that it is below the level allowed for diagnostic imaging.

      We have ensured that the text is focused on TUS being low-intensity and only in the introduction do we distinguish low intensity TUS from moderate and high intensity TUS, such as used for thermal ablation [Lines 62-66].

      Line 88/89 - Most temperature rises in brain tissue in TUS are well below 1 C - will this really change membrane capacitance significantly? If so it would have been good to consider a model for it.

      We have revised this statement as “thermal effects could at least minimally alter cell membrane capacitance…”. [Line 93]

      Line 111 - The text refers to "recent studies" but then the next two references are from 1990 and 2005 which I would argue don't count as "recent".

      We have corrected this wording to “previous studies”. [Line 114]

      Lines 122/129 - This paragraph on TMS pulsing should be linked to the TUS paragraph on pulsing (lines 109/116). The intervening paragraph on anaesthesia is relevant but breaks the flow.

      We have merged the paragraph on anesthesia to the prior one on TUS so that the TMS paragraph is linked more closely to it [starting on line 112].

      Line 130/131 - It is not clear to me that current studies are being guided by computational models. I think there is still no generally accepted theory for mechanisms. If the authors want to do a mechanisms paper then they should compare a few.

      We have revised this as suggested to not overstate the contribution of the limited computational modeling studies throughout the manuscript.

      Line 132 on - There are a number of studies that suggest that NICE is likely not the mechanism by which TUS produces neuromodulation.

      We have revised this sentence as follows: “Although it remains questionable whether intramembrane cavitation is a key mechanism for TUS, the NICE model simulations explored a broad set of TUS parameters, including TUS intensity and the continuity of stimulation (duty cycle) on modelled neuronal responses.” [Lines 139/142]

      Lines 137-140 - Terms are defined after their use. Things like ISPPTA, PRF, TI, and MI have been discussed already and so the terms should have been defined earlier. The authors should think carefully about how the material is presented to make it more logical for the reader.

      We have ensured that the definitions precede the use of abbreviations and have added abbreviations to the tables.

      Part I Line 180-437 - The review of potential applications for TUS reads like an introductory chapter of a thesis. It is entirely proper for a thesis to have a chapter like this, but it is not really relevant for a peer-reviewed research article. There are also numerous applications, e.g. mapping areas associated with decisions, or treating patients with addiction, which are not included, so it is not exhaustive. I would suggest this part be removed.

      We have moved the ‘review’ part of the paper to Part II, given the metaanalysis and resource should be more prominent as Part I. In the review now Part II of the paper we also now make it clear that there are recent comprehensive reviews of the clinical literature ( line 465/467). Namely, the purpose of our selective review is to demonstrate how directionality of TUS effects need to be specific for the clinical application intended, given the great variability in clinical effects that might be desired, brain areas targeted and pathology being treated. We have also aimed to ensure that each section summary is scholarly and academically written to a high level. All the co-authors contributed to these sections so we have also edited to have some consistency across sections, with sections ending with directionality of TUS hypotheses that could be developed for empirical testing.

      Line 453 - It is stated that "ISPTA, which mathematically integrates ISSPA by the sonication DC" It sounds rather grand to mathematically integrate but you can't integrate with respect to DC, you can integrate with respect to time. If you integrate intensity with respect to time over pulse and over the sonication time then one finds that ISPTA = DC x ISPPA, multiplication is also an important mathematical function and should be given its due. Lastly, I think there is a typo and ISSPA should read ISPPA

      We have corrected the typo and the statement to “mathematically multiplies ISPPA by the continuity of sonication”. [Line 221/222]

      Line 454 - I don't think ISPTA is a good measure of "dose." In radiation physics dose is well defined in terms of absorbed energy. The equivalent has yet to be defined for TUS so I would avoid using dose. The ISPTA does relate to TI - although it depends not just on the spatial peak but also on the spatial distribution and the frequency-dependent absorption coefficient of the tissue. I would just avoid the use of "dose" until the field has a better idea of what is going on.

      We have cut this phrase on dose as suggested.

      Page 16 Box 1 - TI is defined as diagnostic ultrasound imaging it is based on. Also, I think TI is dimensionless; it is referenced to a 1-degree temperature rise and so it can be interpreted in terms of celsius or kelvin; but to be technically accurate it is dimensionless.

      We have made TI dimensionless in Box 1

      Page 17 Box 2 - Here you have no units for TI - which is correct but inconsistent with Box 1. But the legend suggests a 2 K temperature rise where as your Box allows for 6 K. The value of 6 is consistent with FDA but my understanding of the BMUS guidelines is the TI must be less than or equal to 0.7 for unlimited time or less than 3 if the duration is less than 1 minute. I accept that the table is labelled FDA limits, but the bold table caption is "Recommendations for TUS parameters" I think you should give the ITRUSST values rather than FDA.

      We have revised this Box legend to better distinguish the FDA and ITRUSST recommendation where they differ (e.g., the importance of ISPTA and the TI values). See revised legend for Box 2.

      Page 18 Box 3 - Not sure what this is trying to show? Also, what is "higher intensity" and "lower intensity"?

      Why not just give a range of values in each box?

      We agree that the higher and lower intensities likely to lead to enhancement or suppression are poorly defined and have noted this in the legend: “Note that the threshold for ISPPA qualifying as ‘higher’ or ‘lower’ intensity is currently poorly understood, or may non-linearly interact with other factors” [Line 751/754, Box 3].

      Line 444 - The hypotheses should be stated more clearly. Maybe I am just dense, but it is not obvious to me from box 3.

      We provide the basis for the hypotheses in the manuscript text on the paragraph [Lines 106-179].

      Line 481/482 - The intensity of a diagnostic ultrasound system is very well characterised. It just might be that the authors didn't report it. It is not clear what is meant by the "continuity." I guess it's to do with pulsing - which is also well defined but perhaps also not reported.

      We agree and have revised this as follows “For the meta-analysis, we only included studies that either reported a basic set of TUS stimulation parameters or those sufficient for estimating the required parameters or those sufficient for estimating the required parameters necessary for the meta-analysis” [Lines 256/258]

      Figure 2 - What is the purpose of this figure? Did you carry out simulations for all the studies? It doesn't seem to be relevant to the data here.

      This figure illustrates the TUS targeting approach and simulations, in this case conducted in k-plan. These were conducted to evaluate approximations to ISPPA in brain values from the studies that did not report these values [Lines 264/268]).  

      Figure 4 - The data in these figures is nice (and therefore doesn't need to have a NICE curve) To me it clearly shows that the data in the literature does not obviously segment into enhancement vs suppression with DC. I suspect it is the same with PRF. I think it would have been better if C and D had PRF on the horizontal axis for on-line and off-line so that effect could be seen more clearly.

      We have kept the NICE curve only for a reference that some readers familiar with the NICE model might want to see overlaid in the figure, but have ensured that the text throughout makes clear that the NICE model predictions are not as statistically robust as initially anecdotally thought. PRF results are not significant but we do show a panel with the PRF measures on one axis (Fig. 4D). Figure 5 also shows box plot results with PRF as well as the other key TUS parameters. Moreover, in the inTUS resource we have provided an app for users to explore the data (https://benslaterneuro.shinyapps.io/Caffaratti_inTUS_Resource/).

      Figure 5 - The text on the axes is too small to read. Was the DC significant for both on-line and offline? What about ISPPA for off-line. At least by eye, it looks as different as DC. Figure 5C doesn't add anything.

      We have boosted the font for Figure 5 and have cut panel 5C since it was not adding much. We have also checked whether DC parameter was significant separately for on-line and off-line effects, but the sample sizes were too small for significance, and the statistical test was not significantly different for Online and Offline effects even in the 12025 database. Therefore they might look stronger for Offline effects in some of the plots in Figure 5, but are currently statistically indistinguishable [Lines 347/348].

      Table 1 - There is a typo in the 3rd column. FF should have units of kHz, not KHz. In addition, SD should have units of s as that is the SI symbol for seconds. I would swap columns 9 and 10 so that ISPPA in water and ISPPA in the brain are next to each other.

      We have corrected the typo in the 3rd column and ensured that units are kHz. SD in the tables has units of ‘s’ for seconds and have put ISPPA in water and in brain next to each other in the data tables.

      Line 767 - "M.K. was supported..." There are TWO MKs in the author list.

      We have changed this to M.Ka. for Marcus Kaiser.

    1. A summary of what the authors were trying to achieve (address the entire article, not just individual points or sections)

      The area of expertise of the reviewer is open access which, for all intents and purposes, is a subset of open science. The nub of this manuscript is the credibility of psychology research using open science to grow that credibility and to bridge the psychology knowledge divide between the global north and Africa. Given the reviewers limited knowledge of the core issue of psychology research, the reviewer will confine his comments to his area of expertise.

      An account of the major strengths and weaknesses of the conceptual framework, methods and results

      Psychological science is facing a ‘credibility crisis’ because many studies can’t be replicated, prompting reforms for more transparent and rigorous research. While these efforts have progressed in North America and Europe, Africa has seen less impact due to challenges like low funding and poor infrastructure, widening the gap in research capacity. These are the core issues covered in this manuscript. However, what is omitted as a challenge is the brain drain and its impact on research and research production. Many authors talk about knowledge pilgrimage as African researchers have to research issues from a global north perspective to improve chances for publication. There is a nuanced difference between research colonialism (concept used by the authors) and knowledge pilgrimage – the former aligned to helicopter research while the latter is about African researchers manipulating global north research data to get published at the neglect of Africa. 

      The authors make the point about power imbalances and the fact that global north researchers use Africa as a point of data collection without recognising the contribution by the African researchers.

      The research gap is widened given the reliance on North American and Europeans guidelines and standards. Open science practices offer African researchers tools to improve research quality and join global discussions. Initiatives like the African Reproducibility Network can help build stronger research communities, addressing local issues while contributing to a more inclusive and globally relevant psychology. Strengthening African research could also advance human development across the continent.

      It is recommended that the authors do not use illegal entities as sources of information as, in the opinion of the reviewer, brings into question the credibility of the manuscript.

      The perceived weaknesses relate to the oversimplification of open science solutions. While open science is framed as a key solution, the manuscript oversimplifies its implementation in Africa. It only superficially acknowledges the barriers to adopting open science practices, such as the lack of stable internet access, digital tools, and the necessary training in many African institutions. The manuscript suggests open science can bridge gaps without delving into the complexities of infrastructure and access that make it difficult for African researchers to fully engage with these practices.

      The manuscript should pay a little more attention on how African researchers can practically adopt open science tools. While it mentions open-access platforms and reproducibility networks, it doesn’t provide details on how these can be integrated into the current research systems in Africa, given the resource constraints. Practical guidance on funding models, technical support, or training programs needed to implement open science would provide a more grounded solution. The use of diamond open access is an extremely viable model to grow the production of psychological research.

      An appraisal of whether the authors achieved their aims, and whether the results support their conclusions

      From an open science perspective, the authors have done relatively well to provide a roadmap for the improved accessibility and credibility of psychology research.

      The authors highlight how open science practices, such as PsyArXiv and AfricArXiv, enhance research accessibility by allowing researchers to freely share their findings. This is critical in the context of the paywalls that restrict access to valuable research, particularly for researchers in low-resource settings. They also address the issue of inclusivity or equity. APCs is a new barrier. It is pointed out that many researchers in Africa face barriers due to high article processing fees associated with open access journals. This situation can perpetuate inequities in knowledge production and dissemination. What the authors have missed is the opportunity to investigate diamond open access as a viable alternative for the dissemination of African scholarship.

      The preprints solution is a viable pathway for researchers to share their findings before formal publication. This can facilitate greater visibility for their work and allow for earlier feedback, although it’s essential to navigate journal policies carefully. The acknowledgement of overlay journals is important given that it is a relatively new concept. Instead of publishing papers directly, they provide peer review for papers that have already been posted on preprint servers. If the paper passes peer review, the overlay journal "publishes" the paper by linking to it on the preprint server. When one ventures into the arena of quality, this process helps build research production capacity. However, it must be noted that some overlay journals charge a fee for the peer review and publication process.

      The other significant issue is that of research credibility. The authors discuss innovative tools and processes that enhance credibility. Initiatives such as registered reports and pre-registration of study protocols enhances the transparency and credibility of research. These methods can mitigate biases related to selective reporting of positive results, thus improving the overall integrity of psychological research. Community initiatives like that of Psychological Science Accelerator, underlines the importance of collaboration in enhancing research credibility. Engaging a diverse range of researchers can lead to more comprehensive studies and foster an environment of shared knowledge.

      One of the major challenges is that of low skills levels. The authors bring into the discussion the issue of building capacity – the train-the trainer model is critical for an inclusive process. Further, to maximize the benefits of open science practices, there’s a clear need for training and capacity-building initiatives tailored to the African context. This would empower researchers with the skills and knowledge to effectively engage with these practices.

      What is of concern to the reviewer is the indistinctive definition of predatory. The authors should stay away from aligning predatory with open access. The fact that the mode of delivery is electronic material must not be confused with a model for the delivery of predatory scholarship

      In the main, the authors have achieved their goal of developing a roadmap for bridging the divide.

      Reviewers are asked to provide specific guidance on the following:

      Does the article contribute new insights to the relevant fields?

      The manuscript provides a high level association between open science practices and driving human development in Africa. This is done through enhancing access to research, improving infrastructure, and boosting credibility. These are established which is confirmed by the authors highlight that platforms like AfricArXiv and OSF provide free access to research materials and tools, reduce barriers imposed by paywalls. There are other initiatives such as Registered Reports, the African Reproducibility Network that foster collaboration and skill-building.

      The new insights that are brought forward via the linking of these established tools, practises to the African scenario. Empowering and bridging the divide are the new insights.

      Are the key insights clearly communicated in the abstract, introduction, and conclusion?

      The abstract should provide a little more detail on how open science and its relationship with bridging the knowledge divide. The introduction gives substantial grounding on what the readers can expect from the rest of the article. However, the conclusion does not pull the golden threads together. A little more can be done with the conclusion and bringing an association between the introduction and conclusion.

      Do the results make sense? Are they clearly formatted and presented? Are graphs easily readable and clearly labeled? Are all figures and tables understandable without reference to the main body of the article? Do they contain proper captions?

      The discussion is substantial. The two tables are clear and easily readable. What is commended is that the authors do not duplicate what is captured in the two tables.

      Are any analytical concepts or theoretical frameworks used appropriately introduced and taken up in the empirical analysis (where applicable)?

      There is no theoretical framework or a research methodology section. However, this may be a practise that the reviewer is accustomed to but not necessary. Be that as it may, it will help the manuscript if the authors could articulate the methodology.

      Does the literature review (where applicable) include the relevant research including the most recent research?

      The review of the literature is substantial. However, there are gaps (e.g. diamond open access) in the review of the literature with regard to more recent development with regard to dissemination of scholarship.

      There are a number of very old references and more current references. The old references are important to ground the issue of (de)colonialization which is important for the major part of the discussion. More recent literature would give the authors an understanding of diamond which will help in sourcing alternatives.

      • Introduction to Queues:
      • "Queues really are pretty simple, right? You have a producer that enqueues messages, you have a consumer that dequeues messages."
      • "Something that is maybe non-obvious is that in addition to the act of enqueuing being a side effect... we get back to just confirmation that it was added."
      • "Queues are a way of dealing with actions... we of course are in the business of making computers do things."

      • Core.async Channels and Queue Mechanisms:

      • "A core.async channel has not one but three queues: a buffer that holds the messages, a puts queue where producers that are trying to add to a full buffer will wait, and a takes queue where consumers that are trying to take messages from an empty buffer will wait."
      • "The blocking queue under the covers looks a great deal like core.async... it has a buffer that holds the messages... it’s just a place for threads to park themselves."

      • Ubiquity and Importance of Understanding Queues:

      • "Queues are ubiquitous; they’re everywhere and it sort of behooves us on some level to understand them."
      • "The reason that queues are so ubiquitous is because they separate what we want to have happen from when it happens."

      • Closed vs. Open Systems:

      • "A key distinction in queuing theory is between closed and open systems."
      • "A closed system is where as we produce something, we must wait for the consumer to complete it before producing something else."
      • "Many of the systems that we build are open systems where there is no coordination... requests are processed as they come in, often before we’re ready."

      • Simulation of Queue Behaviors:

      • "Tasks are arriving according to this exponential distribution... the complexity of the tasks is not very well modeled by an exponential distribution."
      • "As the system becomes unstable... it just sort of takes off like a rocket... what happens when a queue grows out of control."

      • Dealing with Queue Overload:

      • "Unbounded queues are fundamentally broken because it puts the correctness of your system in somebody else’s hands."
      • "We cannot have our strategy for dealing with too much data be to hold onto it and hope for the best."

      • Strategies for Managing Excess Data:

      • "When too much data shows up, we have only three strategies: drop the data on the floor, reject the data, or pause the data."
      • "Dropping the data is valid for problems where newer data makes older data obsolete."
      • "Rejecting the data... is what you see when you go to an overloaded website and it returns a 503."
      • "Pausing the data or exerting back pressure... is often the most neutral choice we can make."

      • Benefits of Buffers and Back Pressure:

      • "Buffers allow us to make sure that our throughput is more stable and going at a higher rate."
      • "Back pressure enacting and retracting is not free... buffers help with the overall throughput at the expense of latency."

      • Best Practices for Queue Management:

      • "Always plan for too much data... using back pressure wherever possible."
      • "Buffers are useful for constant throughput but should be used where absolutely necessary."
      • "Avoid composing buffered queues wherever possible as each additional buffer magnifies the results of the next."

      • Application Example: Durable Queue and S3 Journal:

      • "I end up writing two libraries, one of which is called durable queue, a disk-backed queue, and another called S3 Journal, built on top of the durable queue."
      • "The system looks like this: there are two stages, the HTTP server concerned with persisting data and another loop uploading it to S3."

      • Ensuring System Stability:

      • "Measure what is the health of our system... quantify how much we might have lost when a machine goes down."
      • "It’s really crucial when building these systems to understand what it means to complete something."

      • Monitoring and Instrumentation:

      • "We have metrics now... gives us a little bit more visibility in the system."
      • "Be picky about your tools... prefer something which actually tells you how fast it is."

      • Final Recommendations:

      • "Unbounded queues are bounded by memory or some fixed shared resource."
      • "Account for what happens when your system receives too much data."
      • "Use back pressure to defer the choice to someone who understands the application better."
      • "Demand metrics everywhere to ensure system robustness."
    1. In 1937, when the U.S. Securities Commission released a list of corporation salaries to the public, Kulp was the highest individual earner on the entire list. Admittedly, his reported salary of $65,090 doesn’t sound all that impressive, and even after inflation, it’s only a little over $1 million. But that’s probably more of a statement on just how out of whack executive payouts have become in the ensuing decades.
    1. Social workers may banter about the term “social justice” without actually understanding all that it entails. This lack of connecting concept to practice can cause a myriad of harms to clients and students, who assume that social justice will be practiced in a certain way, but then have different and even discriminatory experiences with social workers. Ergo, I hypothesized that a better understanding of the different ways that the field of social work conceptualizes “social justice” can lay a foundation for a clearer and more unified definition of the term, in turn leading to a more effective and impactful praxis of social justice, both in social work education and in practice.

      Not to be a Debby downer, but I’ve seen this happen many times before. Some health professionals, social workers, and related professions use the term "social justice” quite often, but they don’t always practice it in real situations (can be due to being burned out, working overtime, and other factors, but just pointing it out). It’s frustrating to witness because clients are usually already within vulnerable populations, and expect to be treated with fairness from professionals, but then they get let down or even harmed by the same people claiming to fight for justice. It makes me think about how important it is to actually live these values, not just say them, and to truly have drive and passion for what you do. Otherwise, you harm many people that are depending on you in the process.

    1. These observations suggest that the pleiotropic effects of yellow on male mating success might result from effects of yellow in the adult CNS, particularly in fru-expressing neurons.

      This sentence really shows how complex and interconnected gene function can be. It's not just that the yellow gene affects pigmentation, it might also influence behavior through its role in specific brain cells.

    2. The study reveals the importance of scientists considering that genes that affect behavior may do so by changing anatomy rather than by altering the brain

      This is a reminder to scientists (and us) that behavior isn’t just about brain chemistry; sometimes it’s about how the body is built.

    1. Because we interpret the world in roughly similar ways, weare able to build up a shared culture of meanings and thus construct a social world which we inhabittogether.

      his quote really stood out to me because it explains how culture is built through shared meaning. It’s not just individual thoughts that matter, representation works because groups of people agree (consciously or not) on what signs and concepts mean. This made me think about how different cultural groups may interpret the same image or word in totally different ways.

    1. Introduction Welcome to “A Beginner's Guide to Information Literacy,” a step-by-step guide to understanding information literacy concepts and practices. This guide will cover each frame of the "Framework for Information Literacy for Higher Education," a document created by the Association of College and Research Libraries (ACRL) to help educators and librarians think about, teach, and practice information literacy. The goal of this guide is to break down the basic concepts in the Framework and put them in accessible, digestible language so that we can think critically about the information we're exposed to in our daily lives. To start, let's look at the ACRL definition of information literacy, so we have some context going forward: Information Literacy is the set of integrated abilities encompassing the reflective discovery of information, the understanding of how information is produced and valued, and the use of information in creating new knowledge and participating ethically in communities of learning. Boil that down and what you have are the essentials of information literacy: asking questions, finding information, evaluating information, creating information, and doing all of that responsibly and ethically. We'll be looking at each of the Frames alphabetically, since that's how they are presented in the Framework. None of these Frames is more important than another, and all need to be used in conjunction with the others, but we have to start somewhere, so alphabetical it is! In order, the frames are: Authority is Constructed and Contextual Information Creation as a Process Information Has value Research as Inquiry Scholarship as Conversation Searching as Strategic Exploration Just because we're laying this out alphabetically does not mean you have to go through it in order. Some of the sections reference Frames previously mentioned, but for the most part you can jump to wherever you like and use this guide however you see fit! You can also open up the Framework using the link above or in the attached resources to read the Framework in its original form and follow along with each section. The following sections originally appeared as blog posts for the Texas A&M- Corpus Christi's library blog. Edits have been made to remove institutional context, but you can see the original posts in the Mary and Jeff Bell Library blog archives. Authority is Constructed and Contextual The first frame is Authority is Constructed and Contextual. There's a lot to unpack in that language, so let's get started.

      Start with the word "Authority." At the root of “Authority” is the word Author. So start there: who wrote the piece of information you’re reading? Why are they writing? What stake do they have in the information they’re presenting? What are their credentials (you can straight up google their name to learn more about them)? Who are they affiliated with? A public organization? A university? A company trying to make a profit? Check it out.

      Now let's talk about how authority is "Constructed." Have you ever heard the phrase “social construct”? Some people say gender is a social construct or language, written and spoken, is a construct. “Constructed” basically means humans made it up at some point to instill order in their communities. It’s not an observable, scientifically inevitable fact. When we say “authority” is constructed, we’re basically saying that we as individuals and as a society choose who we give authority to, and sometimes we might not be choosing based on facts.<br /> A common way of assessing authority is by looking at an author’s education. We’re inclined to trust someone with a PhD over someone with a high school diploma because we think the person with a PhD is smarter. That’s a construct. We’re conditioned to think that someone with more education is smarter than people with less education, but we don't know it for a fact. There are a lot of reasons someone might not seek out higher education. They might have to work full time, or take care of a family, or maybe they just never wanted to go to college. None of these factors impact someone’s intelligence or ability to think critically. If aliens land on South Padre Island, TX, there will be many voices contributing to the information collected about the event. Someone with a PhD in astrophysics might write an article about the mechanical workings of the aliens’ spaceship. Cool, they are an authority on that kind of stuff, so I trust them. But the teenager who was on the island and watched the aliens land has first-hand experience of the event, so I trust them too. They have authority on the event even though they don’t have a PhD in astrophysics. So we cannot think someone with more education is inherently more trustworthy, or smarter, or has more authority than anyone else. Some people who are authorities on a subject are highly educated, some are not. Likewise, let’s say I film the aliens landing and stream it live on Facebook. At the same time, a police officer gives an interview on the news that says something contradicting my video evidence. All of a sudden, I have more authority than the police officer. Many of us are raised to trust certain people automatically based on their jobs, but that’s also a construct. The great thing about critical thinking is that we can identify what is fact and fiction, and we can decide for ourselves who to trust.

      The final word is "Contextual." This one is a little simpler. If I go to the hospital and a medical doctor takes out my appendix, I’ll probably be pretty happy with the outcome. If I go to the hospital and Dr. Jill Biden, a professor of English, takes out my appendix, I’m probably going to be less happy with the results. Medical doctors have authority in the context of medicine. Dr. Jill Biden has authority in the context of education. And Doctor Who has authority in the context of inter-galactic heroics and nice scarves. This applies when we talk about experiential authority, too. If an 8th grade teacher tells me what it’s like to be a 4th grade teacher, I will not trust their authority. I will, however, trust a 4th grade teacher to tell me about teaching 4th grade.

      The Takeaway: Basically, when we think about Authority, we need to ask ourselves, “Do I trust them? Why?” If they do not have experience with the subject (like witnessing an event or holding a job in the field) or subject expertise (like education or research), then maybe they aren’t an authority after all. P.S. I'm sorry for the uncalled-for dig, Dr. Biden. I’m sure you’d do your best with an appendectomy.

      Ask Yourself In what context are you an authority? If you needed to figure out how to do a kickflip on a skateboard, who would you ask? Who's an authority in that situation? Information Creation as a Process The second Frame is "Information Creation as a Process."

      Information Creation So first of all, let’s get this out of the way: Everyone is a creator of information. When you write an essay, you’re creating information. When you log the temperature of the lizard tank, you’re creating information. Every Word Doc, Google Doc, survey, spreadsheet, Tweet, and PowerPoint that you’ve ever had a hand in? All information products. That YOU created. In some way or another, you created that information and put it out into the world.

      Processes One process you’re probably familiar with if you're a student is the typical “Research Paper.” You know your professor wants about five to eight pages consisting of an introduction that ends in a thesis statement, a few paragraphs that each touch on a piece of evidence that supports your thesis, and then you end in a conclusion paragraph which starts with a rephrasing of your thesis statement. You save it to your hard drive or Google Drive and then you submit it to your professor. This is one process for creating information. It’s a boring one, but it’s a process.<br /> Outside of the classroom, the information creation process looks different, and we have lots of choices to make. Once of the choice you’ll need to make is the mode or format in which you present information. The information I’m creating right now comes to you in the mode of an Open Educational Resource. Originally, I created these sections as blog posts. Those five-page essays I mentioned earlier are in the mode of essays. When you create information (outside of a course assignment), it’s up to you how to package that information. It might feel like a simple or obvious choice, but some information is better suited to some forms of communication. And some forms of communication are received in a certain way, regardless of the information in them. For example, if I tweet “Jon Snow knows nothing,” it won’t carry with it the authority of my peer-reviewed scholarly article that meticulously outlines every instance in which Jon Snow displays a lack of knowledge. Both pieces of information are accurate, but the processes I went through to create and disseminate the information have an effect on how the information is received by my audience. And that is perhaps the biggest thing to consider when creating information: your audience.

      The Audience Matters If I just want my twitter followers to know Jon Snow knows nothing, then a tweet is the right way to reach them. If I want my tenured colleagues and other various scholars to know Jon Snow knows nothing then I’m going to create a piece of information that will reach them, like a peer-reviewed journal article. Often, we aren’t the ones creating information, we're the audience members ourselves. When we're scrolling on Twitter, reading a book, falling asleep during a PowerPoint presentation-- we're the audience observing the information being shared. When this is the case, we have to think carefully about the ways information was created. Advertisements are a good example. Some are designed to reach a 20-year old woman in Corpus Christi through Facebook, while others are designed to reach a 60-year old man in Hoboken, NJ over the radio. They might both be selling the same car, and they’re going to put the same information (size, terrain, miles per gallon, etc.) in those ads, but their audiences are different, so their information creation process is different, and we end up with two different ads for different audiences.

      Be a Critical Audience Member When we are the audience member, we might automatically trust something because it’s presented a certain way. I know that, personally, I’m more likely to trust something that is formatted as a scholarly article than I am something that is formatted as a blog. And I know that that's biased thinking and it's a mistake to make that assumption. It's risky to think like that for a couple of reasons: Looks can be deceiving. Just because someone is wearing a suit and tie doesn’t mean they’re not an axe murderer and just because something looks like a well-researched article, doesn’t mean it is one. Automatic trust unnecessarily limits the information we expose ourselves to. If I only ever allow myself to read peer-reviewed scholarly articles, think of all the encyclopedias and blogs and news articles I’m missing out on! If I have a certain topic I’m really excited about, I’m going to try to expose myself to information regardless of the format and I’ll decide for myself (#criticalthinking) which pieces of information are authoritative and which pieces of information suit my needs. Likewise, as I am conducting research and considering how best to share my new knowledge, I’m going to consider my options for distributing this newfound information and decide how best to reach my audience. Maybe it’s a tweet, maybe it’s a Buzzfeed quiz, or maybe it’s a presentation at a conference. But whatever mode I choose will also convey implications about me, my information creation process, and my audience.

      The Takeaway You create information all of the time. The way you package and share it will have an effect on how others perceive it.

      Ask Yourself Is there a form of information you're likely to trust at first glance? Either a publication like a newspaper or a format like a scholarly article? Can you think of some voices that aren't present in that source of information? Where might you look to find some other perspectives? If you read an article written by medical researchers that says chocolate is good for your health, would you trust the article? Would you still trust their authority if you found out that their research was funded by a company that sells chocolate bars? Funding and stakeholders have an impact on the creation process, and it's worth thinking about how this can compromise someone's authority.

      Information Has Value Onwards and upwards! We're onto Frame 3: Information Has Value.

      What Counts as Value? There are a lot of different ways we value things. Some things, like money, are valuable to us because we can exchange them for goods and services. On the other hand, some things, like a skill, are valuable to use because we can exchange them for money (which we exchange for more goods and services). Some things are valuable to us for sentimental reasons, like a photograph or a letter. Some things, like our time, are valuable because they are finite.

      The Value of Information Information has all different kinds of value.<br /> One kind is monetary. If I write a book and it gets published, I’m probably going to make some money off of that (though not as much money as the publishing company will make). So that’s valuable to me. But I’m also getting my name out into the world, and that’s valuable to me too. It means that when I apply for a job or apply for a grant, someone can google me and think, “Oh look! She wrote a book! That means she has follow-through and will probably work hard for us!” That kind of recognition is a sort of social value. That social value, by the way, can also become monetary value. If I’ve produced information, a university might give me a job, or an organization might fund my research. If I’ve invented a machine that will floss my teeth for me, the patent for my invention could be worth a lot of money (plus it'd be awesome. Cool factor can count as value.). In a more altruistic slant, information is also valuable on a societal level. When we have more information about political candidates, for example, it influences how we vote, who we elect, and how our country is governed. That’s some really valuable information right there. That information has an effect on the whole world (plus outer space, if we elect someone who’s super into space exploration). If someone is trying to keep information hidden or secret, or if they’re spreading misinformation to confuse people, it’s probably a sign that the information they’re hiding is important, which is to say, valuable. On a much smaller scale, think about the information on food packages. If you’re presented with calorie counts, you might make a different decision about the food you buy. If you’re presented with an item’s allergens, you might avoid that product and not end up in an Emergency Room with anaphylactic shock. You know what’s super valuable to me? NOT being in an Emergency Room! But if you do end up in the Emergency Room, the information that doctors and nurses will use to treat your allergic reaction is extremely valuable. That value of that information is equal to the lives it’s saved.

      Acting Like Information is Valuable When we create our own information by writing papers and blog posts and giving presentations, it’s really important that we give credit to the information we’ve used to create our new information product for a couple of reasons. First, someone worked really hard to create something, let’s say an article. And that article’s information is valuable enough to you to use in your own paper or presentation. By citing the author properly, you’re giving the author credit for their work which is valuable to them. The more their article is cited, the more valuable it becomes because they’re more likely to get scholarly recognition and jobs and promotions. Second, by showing where you’re getting your information, you’re boosting the value of your new information product. On the most basic level, you’ll get a higher grade on your paper which is valuable to you. But you’re also telling your audience, whether it’s your professor or your boss or your YouTube subscribers, that you aren’t just making stuff up—you did the work of researching and citing, and that makes your audience trust you more. It makes the audience value your information more. Remember early on when I said the frames all connect? "Information Has Value" ties into the other information literacy frames we've talked about, "Information Creation as a Process" and "Authority as Constructed and Contextual." When I see you’ve cited your sources of information, then I, as the audience, think you’re more authoritative than someone who doesn’t cite their sources. I also can look at your information product and evaluate the effort you’ve put into it. If you wrote a tweet, which takes little time and effort, I’ll generally value it less than if you wrote a book, which took a lot of time and effort to create. I know that time is valuable, so seeing that you were willing to dedicate your time to create this information product makes me feel like it’s more valuable.

      The Takeaway: Information is valuable because of what goes into its creation (time and effort) and what comes from it (an informed society). If we didn’t value information, we wouldn’t be moving forward as a society, we’d probably have died out thousands of years ago as creatures who never figured out how to use tools or start a fire. So continue to value information, because it improves your life, your audiences’ lives, and the lives of other information creators. More importantly, if we stop valuing information a smarter species will eventually take over and it’ll be a whole Planet of the Apes thing and I just don't have the energy for that right now.

      Ask Yourself Can you think of some ways in which a YouTube video on dog training has value? Who values it? Who profits from it? Think of some information that would be valuable to someone applying to college. What does that person need to know?

      Research as Inquiry Easing on down the road, we've come to frame number 4: Research as Inquiry. Inquiry is another word for curiosity or questioning. I like to think of this frame as "Research as Curiosity," because I think it more accurately captures the way our adorable human brains work.

      Inquiring Minds Want to Know When you think to yourself, “How old is Madonna?” and you google it to find out she’s 62 (as of the creation of this resource), that’s research! You had a question (how old is Madonna?), you applied a search strategy (googling “Madonna age”) and you found an answer (62). That’s it! That’s all research has to be! But it’s not all research can be. This example, like most research, is comprised of the same components we use in more complex situations. Those components are: a question and an answer, Inquiry and Research, “how old is Madonna?” and "62." But when we’re curious, we go back to the inquiry step again and ask more questions and seek more answers. We’re never really done, even when we’ve answered the initial question and written the paper and given the presentation and received accolades and awards for all our hard work. If it’s something we’re really curious about, we’ll keep asking and answering and asking again. If you’re really curious about Madonna, you don’t just think, “How old is Madonna?” You think “How old is Madonna? Wait, really? Her skin looks amazing! What’s her skincare routine? Seriously, what year was she born? Oh my god, she wrote children’s books! Does my library have any?” Your questions lead you to answers which, when you’re really interested in a topic, lead you to more and more questions. Humans are naturally curious, we have this sort of instinct to be like, “huh, I wonder why that is?” and it’s propelled us to learn things and try things and fail and try again! It’s all Research as Inquiry. And to satisfy your curiosity, yes, the library I currently work at does own one of Madonna’s children’s books. It’s called The Adventures of Abdi and you can find it in our Juvenile Collection on the second floor at PZ8 M26 Adv 2004. And you can find a description of her skincare routine in this article from W Magazine: https://www.wmagazine.com/story/madonna-skin-care-routine-tips-mdna. You’re welcome.

      Identifying an Information Need One of the tricky parts of Research as Inquiry is determining a situation’s information need. It sounds simple to ask yourself, “What information do I need?” and sometimes we do it unconsciously. But it’s not always easy. Here are a few examples of information needs: You need to know what your niece’s favorite Paw Patrol character is so you can buy her a birthday present. Your research is texting your sister. She says, “Everest.” And now you’re done. You buy the present, you're a rock star at the birthday party. Your information need was a short answer based on a 3-year old’s opinion. You’re trying to convince someone on twitter that Nazis are bad. You compile a list of opinion pieces from credible news publications like the Wall Street Journal and the New York Times, gather first-hand narratives of Holocaust survivors and victims of hate crimes, find articles that debunk eugenics, etc. Your information need isn’t scholarly publications, it’s accessible news and testimonials. It’s articles a person might actually read in their free time, articles that aren’t too long and don’t require access to scholarly materials that are sometimes behind paywalls. You need to write a literature review for an assignment, but you don’t know what a literature review is. So first you google “literature review example.” You find out what it is, how one is created, and maybe skim a few examples. Next, you move to your library's website and search tool and try “oceanography literature review,” and find some closer examples. Finally, you start conducting research for your own literature review. Your information need here is both broader and deeper. You need to learn what a literature review is, how one is compiled, and how one searches for relevant scholarly articles in the resources available to you. Sometimes it helps to break down big information needs into smaller ones. Take the last example, for instance: you need to write a literature review. What are the smaller parts? Information Need 1: Find out what a literature review is Information Need 2: Find out how people go about writing literature reviews Information Need 3: Find relevant articles on your topic for your own literature review It feels better to break it into smaller bits and accomplish those one at a time. And it highlights an important part of this frame that’s surprisingly difficult to learn: ask questions. You can’t write a literature review if you don’t know what it is, so ask. You can’t write a literature review if you don’t know how to find articles, so ask. The quickest way to learn is to ask questions. Once you stop caring if you look stupid, and once you realized no one thinks poorly of people who ask questions, life gets a lot easier. So let’s add this to our components of research: ask a question, determine what you need in order to thoroughly answer the question, and seek out your answers. Not too painful, and when you’re in love with whatever you’re researching, it might even be fun.

      The Takeaway When you have a question, ask it. When you’re genuinely interested in something, keep asking questions and finding answers. When you have a task at hand, take a second to think realistically about the information you’ll need to accomplish that task. You don’t need a peer-reviewed article to find out if praying mantises eat their mates, but you might if you want to find out why.

      Ask Yourself What's the last thing you looked up on Wikipedia? Did you stop when you found an answer, or did you click on another link and another link until you learned about something completely different? If you can't remember, try it now! Search for something (like a favorite book or tv show) and click on linked words and phrases within Wikipedia until you learn something new! What was the last thing you researched that you were really excited about? Do you struggle when teachers and professors tell you to "research something that interests you"? Instead, try asking yourself, "What makes me really angry?" You might find you have more interests than you realized!

      Scholarship as Conversation We've made it friends! My favorite frame: Scholarship as Conversation. Is it weird to have a favorite frame of information literacy? Probably. Am I going to talk about it anyway? You betcha!

      What does "Scholarship as Conversation" mean? Scholarship as conversation refers to the way scholars reference each other and build off of one another’s work, just like in a conversation. Have you ever had a conversation that started when you asked someone what they did last weekend and ended with you telling a story about how someone (definitely not you) ruined the cake at your mom's dog's birthday party? And then someone says, “but like I was saying earlier…” and they take the conversation back to a point in the conversation where they were reminded of a different point or story? Conversations aren’t linear, they aren’t a clear line to a clear destination, and neither is research. When we respond to the ideas and thoughts of scholars, we’re responding to the scholars themselves and engaging them in conversation.

      Why do I Love this Frame so Much? Let me count the ways. Reason 1 I really enjoy the imagery of scholarship as a conversation among peers. Just a bunch of well-informed curious people coming together to talk about something they all love and find interesting. I imagine people literally sitting around a big round table talking about things they’re all excited about and want to share with each other! It’s a really lovely image in my head. Eventually the image kind of reshapes and devolves into that painting of dogs playing poker, but I love that image too! Reason 2 It harkens back to pre-internet scholarship, which sound excruciating and exhausting, but it was all done for the love of a subject! Scholars used to literally mail each other manuscripts seeking feedback. Then, when they got an article published in a journal, scholars interested in the subject would seek out and read the article in the physical journal it was published in. Then they’d write reviews of the article, praising or criticizing the author’s research or theories or style. As the field grew, more and more people would write and contribute more articles to criticize and praise and build off of one another. So for example, if I wrote an article that was about Big Foot and then Joe wrote an article saying, “Emily’s article on Big Foot is garbage, here’s what I think about Big Foot,” Sam and I are now having a conversation. It’s not always a fun one, but we’re writing in response to one another about something we’re both passionate about. Later, Jaiden comes along and disagrees with Joe and agrees with me (because I’m right) and they cite both me and Joe. Now we’re all three in a conversation. And it just grows and grows and more people show up at the table to talk and contribute, or maybe just to listen. Reason Three You can roll up to the table and just listen if you want to. Sometimes we’re just listening to the conversation. We’re at the table, but we’re not there to talk. We’re just hoping to get some questions answered and learn from some people. When we’re reading books and articles or listening to podcasts or watching movies, we’re listening to the conversation. You don’t have to do groundbreaking research to be part of a conversation. You can just be there and appreciate what everyone’s talking about. You're still there in the conversation. Reason Four You can contribute to the conversation at any time. The imagery of a conversation is nice because it’s approachable, just pull up a chair and start talking. With any new subject, you should probably listen a little at first, ask some questions, and then start giving your own opinion or theories, but you can contribute at any time. Since we do live in the age of internet research, we can contribute in ways people 50 years ago never dreamed of! Besides writing essays in class (which totally counts because you’re examining the conversation and pulling in the bits you like and citing them to give credit to other scholars), you can talk to your professors and friends about a topic, you can blog about it, you can write articles about it, you can even tweet about it (have you ever seen Humanities folk on Twitter? They go nuts on there having actual, literal scholarly conversations). Your ways for engaging are kind of endless! Reason Five Yep, I'm listing reasons. Conversations are cyclical. Like I said above, they're not always a straight path and that’s true of research too. You don’t have to engage with who spoke most recently, you can engage with someone who spoke ten years ago, someone who spoke 100 years ago, you can respond to the person who started the conversation! Jump in wherever you want. And wherever you do jump in, you might just change the course of the conversation. Because sometimes we think we have an answer, but then something new is discovered or a person who hadn’t been at the table or who had been overlooked says something that drastically impacts what we knew, so now we have to reexamine it all over again and continue the conversation in a trajectory we hadn’t realized was available before. Reason Six Lastly, this frame is about sharing and responding and valuing one another’s work. If Joe, my Big Foot nemesis, responds to my article, they're going to cite me. If Jaiden then publishes a rebuttal, they're going to cite both Joe and me, because fair is fair. This is for a few reasons: 1) even if Jaiden disagrees with Joe’s work, they respect that Joe put effort into it and it’s valuable to them. 2) When Jaiden cites Joe, it means anyone who jumps into the conversation at the point of Jaiden's article will be able to backtrack and catch up using Jaiden's citations. A newcomer can trace it back to Joe’s article and trace that back to mine. They can basically see a transcript of the whole conversation so they can read Jaiden’s article with all of the context, and they can write their own well-informed piece on Big Foot.

      The Takeaway There’s a lot to take away from this frame, but here’s what I think is most important: Be respectful of other scholars’ work and their part in the conversation by citing them. Start talking whenever you feel ready, in whatever platform you feel comfortable. And finally, make sure everyone who wants to be at the table is at the table. This means making sure information is available to those who want to listen and making sure we lift up the voices that are at risk of being drowned out.

      Ask Yourself What scholarly conversations have you participated in recently? Is there a Reddit forum you look in on periodically to learn what's new in the world of cats wearing hats? Or a Facebook group on roller skating? Do you contribute or just listen?<br /> Think of a scholarly conversation surrounding a topic-- sharks, ballet, Game of Thornes. Who's not at the table? Whose voice is missing from the conversation? Why do you think that is?

      Searching as Strategic Exploration You've made it! We've reached the last frame: Searching as Strategic Exploration. “Searching as Strategic Exploration” addresses the part of information literacy that we think of as “Research.” It deals with the actual task of searching for information, and the word “Exploration” is a really good word choice, because it’s evocative of the kind of struggle we sometimes feel when we approach research. I imagine people exploring a jungle, facing obstacles and navigating an uncertain path towards an ultimate goal (note: the goal is love and it was inside of us all along). I also kind of imagine all the different Northwest Passage explorations, which were cool in theory, but didn’t super-duper work out as expected. But research is like that! Sometimes we don’t get where we thought we were headed. But the good news is this: You probably won’t die from exposure or resort to cannibalism in your research. Fun, right?

      Step 1: Identify a Goal The first part of any good exploration is identifying a goal. Maybe it’s a direct passage to Asia or the diamond the old lady threw into the ocean at the end of Titanic. More likely, the goal is to satisfy an information need. Remember when we talked about "Research as Inquiry?" All that stuff about paw patrol and Madonna's skin care regimen? Those were examples of information needs. We’re just trying to find an answer or learn something new. So great! Our goal is to learn something new. Now we make a strategy.

      Step 2: Make a Strategy For many of your information needs you might just need to Google a question. There’s your strategy: throw your question into Google and comb through the results. You might limit your search to just websites ending in .org, .gov, or .edu. You might also take it a step further and, rather than type in an entire question fully formed, you just type in keywords. So “Who is the guy who invented mayonnaise?” becomes “mayonnaise inventor.” Identifying keywords is part of your strategy and so is using a search engine and limiting the results you’re interested in.

      Step 3: Start Exploring Googling “mayonnaise inventor” probably brings you to Wikipedia where we often learn that our goals don’t have a single, clearly defined answer. For example, we learn that mayonnaise might have gotten its name after the French won a battle in Port Mahon, but that doesn't tell us who actually made the mayonnaise, just when it was named. Prior to being named, the sauce was called “aioli bo” and was apparently in a Menorcan recipe book from 1745 by Juan de Altimiras. That’s great for Altimiras, but the most likely answer is that mayonnaise was invented way before him and he just had the foresight to write down the recipe. Not having a single definite answer is an unforeseen obstacle tossed into our path that now affects our strategy. We know we have a trickier question than when we first set sail. But we have a lot to work with! We now have more keywords like Port Mahon, the French, and Wikipedia taught us that the earliest known mention of “mayonnaise” was in 1804, so we have 1804 as a keyword too. Let’s see if we can find that original mention. Let’s take our keywords out of Wikipedia where we found them and voyage to a library's website! At my library we have a tool that searches through all of our resources. We call it the "Quick Search." You might have a library available to you, either at school, on a University's campus, or a local public library. You can do research in any of these places! So into the Quick Search tool (or whatever you have available to you) go our keywords: 1804, mayonnaise, and France. The first result I see is an e-book by a guy who traveled to Paris in 1804, so that might be what we’re looking for. I search through the text and I do, in fact, find a reference to mayonnaise on page 99! The author (August von Kotzebue) is talking about how it’s hard to understand menus at French restaurants, for “What foreigner, for instance, would at first know what is meant by a mayonnaise de poulet, a galatine de volaille, a cotelette a la minute, or even an epigramme d’agneau?” He then goes on to recommend just ordering the fish, since you’ll know what you’ll get (Kotzebue, 99).<br /> So that doesn't tell us who invented mayonnaise, but I think it's pretty funny! So I’d call that detour a win.

      Step 4: Reevaluate When we hit ends that we don’t think are successful, we can always retrace our steps and reevaluate our question. Dead ends are a part of exploration! We’ve learned a lot, but we’ve also learned that maybe “who invented mayonnaise?” isn’t the right question. Maybe we should ask questions about the evolution of French cuisine or about ownership of culinary experimentation. I’m going to stick with the history or mayonnaise, for just a little while longer, but my “1804 mayonnaise france” search wasn’t as helpful as I’d hoped, so I’ll try something new. Let’s try looking at encyclopedias. I searched in a database called Credo Reference (which is a database filled with encyclopedia entries) and just searching “mayonnaise.” I can see that the first entry, “Minorca or Menorca” from The Companion to British History, doesn’t initially look helpful, but we’re exploring, so let’s click on it! It tells us that Mayonnaise was invented in 1756 by a French commander’s cook and its name comes from Port Mahon where the French fended off the British during a siege (Arnold-Baker, 2001). That’s awesome! It’s what Wikipedia told us! But let’s corroborate that fact. I click on The Hutchinson Chronology of World History entry for 1756 which says mayonnaise was invented in France in 1756 by the duc de Richelieu (Helicon, 2018). I’m not sure I buy it. I could see a duke’s cook inventing mayonnaise, but I have a hard time imagining a duke and military commander taking the time to create a condiment.<br /> But now I can go on to research the duc de Richelieu and his military campaigns and his culinary successes! Just typing “Duke de Richelieu” into the library’s Quick Search shows me a TON of books (16,742 as of writing this) on his life and he influence on France. So maybe now we’re actually exploring Richelieu or the intertwined history of French cuisine and the lives of nobility.

      What Did We Just Do? Our strategy for exploring this topic has had a lot of steps, but they weren't random. It was a wild ride, but it was a strategic one. Let’s break the steps down real quick: We asked a question or identified a goal We identified keywords and googled them We learned some background information and got new keywords from Wikipedia and had to reevaluate our question We followed a lead to a book but hit a dead end when it wasn’t as useful as we’d hoped We identified an encyclopedia database and found several entries that support the theory we learned in Wikipedia which forced us to reevaluate our question again We identified a key player in our topic and searched for him in the library’s Quick Search tool and the resources we found made us reevaluate our question yet again! Other strategies could include looking through an article’s reference list, working through a mind map, outlining your questions, or recording your steps in a research log so you don’t get lost-- whatever works for you!

      The Takeaway Exploration is tricky. Sometimes you circle back and ask different questions as new obstacles arise. Sometimes you have a clear path and you reach your goal instantly. But you can always retrace your steps, try new routes, discover new information, and maybe you’ll get to your destination in the end. Even if you don't, you've learned something. For instance, today we learned that if you can’t understand a menu in French, you should just order the fish.

      Ask Yourself Where do you start a search for information? Do you start in different places when you have different information needs? If your research questions was, "What is the impact of fast fashion on carbon emissions?" what keywords would you use to start searching?

      Wrap Up The Framework for Information Literacy in Higher Education is heck of a document. It's complicated, its frames intertwine, it's written in a way that can be tricky to understand. But essentially, it's just trying to get us to understand that the ways we interact with information are complicated and we need to think about our interactions to make sure we're behaving in an ethical and responsible way. Why do your professors make you cite things? Because those citations are valuable to the original author, and they prove your engagement with the scholarly conversation. Why do we need to hold space in the conversation for voices that we haven't heard from before? Because maybe no one recognized the authority in those voices before. The old process for creating information shut out lots of voices while prioritizing others. It's important for us to recognize these nuances when we see what information is available to us and important for us to ask, "whose voice isn't here? why? am I looking hard enough for those voices? can I help amplify them?" And it's important for us to ask, "why is the loudest voice being so loud? what motivates them? why should I trust them over others?" When we think critically about the information we access and the information we create and share, we're engaging as citizens in one big global conversation. Making sure voices are heard, including your own voice, is what moves us all towards a more intelligent and understanding society. Of course, part of thinking critically about information means thinking critically about both this Guide and the Framework. Lots of people have criticized the Framework for including too much library jargon. Other folks think the Framework needs to be rewritten to explicitly address how information seeking systems and publishing platforms have arisen from racist, sexist institutions. We won’t get into the criticisms here, but they're important to think about. You can learn more about the criticism of the Framework in a blog post by Ian Beilin, or you can do your own search for criticism on the Framework to see what else is out there and form your own opinions.

      The Final Takeaway Ask questions, find information, and ask questions about that information.

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

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, Reproducibility, and Clarity)

      Reviewer comment: This is a very well conceived study of responses to plasma membrane stresses in yeast that signal through the conserved TORC2 complex. Physical stress through small molecular intercalators in the plasma membrane is shown to be independent of their biochemistry and then studies for its effect on plasma membrane morphology and the distribution of free ergosterol (the yeast equivalent of cholesterol), with free being the pool of cholesterol that is available to probes and/or sterol transfer proteins. Experiments nicely demonstrate a negative feedback loop consisting of: stress -> increased free sterol and TORC2 inhibition -> activation of LAM proteins (as demonstrated by Relents and co-workers previously) -> removal of free sterol -> return to unstressed state of PM and TORC2.

      Author response: We thank the reviewer for their positive and encouraging feedback. We are pleased to submit our revised manuscript and have addressed all points raised below.

      Comment: Fig 2A: Is detection of PIP/PIP2/PS linear for target, or possibly just showing availability that is increased due to local positive curvature?

      Response: This is an excellent and fundamental question. While FLARE signal likely reflects lipid availability, its detection is indeed influenced by factors such as membrane curvature and lipid composition, due to varying insertion depths of the lipid-binding domains. For example, studies using NMR suggest that the PLCδ PH domain partially inserts into membranes, potentially conferring curvature sensitivity (Flesch et al., 2005; Uekama et al., 2009). Similarly, curvature influences lactadherin binding, though it's unclear if this extends to its isolated C2 domain (Otzen et al., 2012; Shao et al., 2008; Shi et al., 2004). We could not find direct evidence for curvature sensitivity of P4C(SidC), but assume some influence exists.

      To avoid overinterpreting these limitations, we now describe our data based solely on the FLAREs used, rather than inferring enrichment of specific lipid species. We refer to these PM structures as "PI(4,5)P₂-containing", consistent with prior literature (Riggi et al., 2018) and have revised our manuscript accordingly.

      Comment: Can any marker be identified for the D4H spots at 2 minutes? In particular, are they early endosomes (shown by brief pre-incubation with FM4-64)?

      Response: We appreciate the reviewer's suggestion and have now added new data (Fig. S2E-H). We tested colocalization of D4H spots with FM4-64 (early endosomes), GFP-VPS21 (early endosome marker), and LipidSpot{trade mark, serif} 488 (lipid droplets), but found no overlap. This later observation was not unexpected given that D4H does not recognize Sterol esters. D4H foci also did not overlap with ER (dsRED-HDEL), though they were frequently adjacent to it. While their exact identity remains unknown, we agree this is an intriguing direction for future investigation.

      Comment: Is there any functional (& direct) link between Arp inhibition (as in the Pombe study of LAMs by the lab of Sophie Martin) and PM disturbance by amphipathic molecules?

      Response: We have explored this connection and now present new data (see final paragraph of Results). Briefly, we show that CK-666 induces internalization of PM sterols in a Lam2/4-dependent manner, and that TORC2 activity is more strongly reduced in lam2Δ lam4Δ cells compared to WT. These findings support the idea that, like PalmC, Arp2/3 inhibition triggers a PM stress that is counteracted by sterol internalization.

      Minor Comment: Fig 2A: Labels not clear. Say for each part what FP is used for pip2.

      Response: As noted above, we revised image labels to clarify which FLAREs were used, and refer to data accordingly throughout.

      Minor Comment: Move fig s2d to main ms. The 1 min and 2 min data are integral to the story.

      Response: We agree and have incorporated the 1-min and 2-min data into the main figures. Vehicle-treated controls were moved to Fig. S2.

      Minor Comment: The role of Lam2 and Lam4 in retrograde sterol transport has in vivo only been linked to one of their two StART domains not both, as mentioned in the text.

      Response: Thank you for pointing this out. We have corrected the text to:

      "[...]Lam2 and Lam4[...] contain two START domains, of which at least one has been demonstrated to facilitate sterol transport between membranes (Gatta et al., 2015; Jentsch et al., 2018; Tong et al., 2018)."

      Minor Comment: Throughout, images of tagged D4H should be labelled as such, not as "Ergosterol".

      Response: We have updated all relevant figure labels and text to refer to "D4H" rather than "Ergosterol", in line with this recommendation.

      Reviewer #1 (Significance):

      These results in budding yeast are likely to be directly applicable to a wide range of eukaryotic cells, if not all of them. I expect this paper to be a significant guide of research in this area. The paper specifically points out that the current experiments do not distinguish the precise causation among the two outcomes of stress: increased free sterol and TORC2 inhibition. Of these two outcomes which causes which is not yet known. If data were added that shed light on this causation that would make this work much more signifiant, but I can understand 100% that this extra step lies beyond - for a later study for which the current one forms the bedrock.

      Response:

      We thank the reviewer for their generous assessment. We agree that understanding the causality between increased free sterol and TORC2 inhibition is a critical next step.

      Based on our current data, we believe the increase in free ergosterol precedes TORC2 inhibition. For example, TORC2 inhibition alone (e.g., via pharmacological means) does not initially increase free sterol, while it does enhance Lam2/4 activity, promoting sterol internalization (Fig. 3A). Baseline TORC2 activity also inversely correlates with free PM sterol levels in lam2Δ lam4Δ versus LAM2T518A LAM4S401A cells (Figs. 2D, S2C).

      Additionally, during sterol depletion, we observe an initial increase in TORC2 activity before growth inhibition occurs, after which activity declines-likely due to compromised PM integrity (Fig. S2M). We now also show that adaptation to several other stresses (e.g., osmotic shock, heat shock, CK-666) partially depends on sterol internalization, which correlates with TORC2 activation (Fig. 4, S4B).

      While these findings strengthen the model that PM stress perturbs sterol availability and secondarily impacts TORC2, we cannot yet definitively demonstrate causality. As suggested by Reviewer 3, we tested cholesterol-producing yeast (Souza et al., 2011), but found their response to PalmC indistinguishable from WT, making it difficult to draw mechanistic conclusions (Rebuttal Fig. 2).

      Taken together, we favour a model where sterols affect PM properties sensed by TORC2, probably lipid-packing, rather than acting as direct effectors. We hope our revised manuscript more clearly conveys this model and serves as a strong foundation for future mechanistic studies.

      Reviewer #2 (Evidence, Reproducibility, and Clarity)

      Reviewer comment: This manuscript describes multiple effects of positively-charged membrane-intercalating amphipaths (palmitoylcarnitine, PalmC, in particular) on TORC2 in yeast plasma membranes. It is a "next step" in the Loewith laboratory's characterization of the effect of this agent on this system. The study confirms the findings of Riggi et al.(2018) that PalmC inhibits TORC2 and drives the formation of membrane invaginations that contain phosphatidylinositol-bis-phosphate (PIP2) and other anionic phospholipids. It also demonstrates that PalmC intercalates into the membrane, acts directly (rather than through secondary metabolism) and is representative of a class of cationic amphipaths. The interesting finding here is that PalmC causes a rapid initial increase in the plasma membrane ergosterol accessible to the DH4 sterol probe followed by a decrease caused by its transfer to the cytoplasm through its transporter, LAM2/4. TORC2 is implicated in these processes. Loewith et al. have pioneered in this area and this study clearly shows their expertise. Several of the findings reported here are novel. However, I am concerned that PalmC may not be revealing the physiology of the system but rather adding tangential complexity. (This concern applies to the precursor studies using PalmC to probe the TORC2 system.) In particular, I am not confident that the data justify the authors' conclusions "...that TORC2 acts in a feedback loop to control active sterol levels at the PM and [the results] introduce sterols as possible TORC2 signalling modulators."

      Author response:

      We thank Reviewer #2 for the constructive and critical evaluation of our work. We appreciate the acknowledgment of the novelty and technical strength of several of our findings, and we understand the concern that PalmC could be eliciting non-physiological effects. Our study was designed precisely to use PalmC and similar membrane-active amphipaths as tools to strongly perturb the plasma membrane (PM) in a controlled and tractable way. We now state this intention explicitly in both the Introduction and Discussion sections. To address concerns about the specificity and physiological relevance of PalmC, we have expanded our dataset to include additional PM stressors (hyperosmotic shock, Arp2/3 inhibition, and heat shock), all of which reproduce key features observed with PalmC-namely, TORC2 inhibition, PM invaginations, and retrograde sterol transport (Fig. 4, S4).

      We hope this more comprehensive dataset, along with revised discussion and clarified claims, addresses the reviewer's concerns regarding physiological interpretation and artifact.

      Major issues 1 and 2: 1. The invaginations induced by PalmC may not be physiologic but simply the result of the well-known "bilayer couple" bending of the bilayer due to the accumulation of cationic amphipaths in the inner leaflet of the plasma membrane bilayer which is rich in anionic phospholipids. Such unphysiological effects make the observed correlation of invagination with TORC2 inhibition etc. hard to interpret.

      Electrostatic/hydrophobic association of PIP2 with PalmC could sequester the anionic phospholipid(s). Such associations could also drive the accumulation of PIP2 in the invaginations. This could explain PalmC inhibition of TORC2 through a simple physical rather than biological process. So, it is difficult to draw any physiological conclusion about PIP2 from these experiments.

      Response to major issues 1 and 2:

      We agree that amphipath-induced bilayer stress, including via the bilayer-couple mechanism, may contribute to PM curvature changes. However, the reviewer's assumption that PalmC inserts preferentially into the inner leaflet appears inconsistent with both literature and our observations. PalmC is zwitterionic, not cationic, and is unlikely to electrostatically sequester anionic lipids such as PIP2. For clarification, we included a short summary of our proposed mechanism of PalmC in the context of the current literature in our Discussion:

      "[...] study it was also demonstrated that addition of phospholipids to the outer PM leaflet causes an excess of free sterol at the inner PM leaflet, and its subsequent retrograde transport to lipid droplets (Doktorova et al., 2025). Although we cannot exclude that it is the substrate of a flippase or scramblase, PalmC is not a metabolite found in yeast, nor, given its charged headgroup, is it likely to spontaneously flip to the inner leaflet (Goñi, Requero and Alonso, 1996). Thus, we propose that PalmC accumulates in the outer leaflet, disrupts the lipid balance with the inner leaflet which is, similarly to the mammalian cell model (Doktorova et al., 2025), rectified by sterol mobilization, flipping and internalization (Fig. 5B)."

      While we agree that PM invaginations per se are not the central focus of this study, they are indeed a reproducible and biologically intriguing phenomenon. We emphasize that similar invaginations occur not only during PalmC treatment but also in response to other physiological stresses, such as hyperosmotic shock and Arp2/3 inhibition (Fig. 4), and have been reported independently by others (Phan et al., 2025). Furthermore, related structures have been documented in yeast mutants with altered PIP2 metabolism or TORC2 hyperactivity (Rodríguez-Escudero et al., 2018; Sakata et al., 2022; Stefan et al., 2002), and even in mammalian neurons with SJ1 phosphatase mutations (Stefan et al., 2002). These observations support our interpretation that the observed invaginations represent an exaggerated manifestation of a physiologically relevant stress-adaptive process. In our previous study we indeed proposed that PI(4,5)P2 enrichment in PM invaginations was important for PalmC-induced TORC2 inactivation, using the heat sensitive PI(4,5)P2 kinase allele mss4ts - a rather blunt tool (Riggi et al., 2018). We have now come to the conclusion that different mechanisms other than, or in addition to, PIP2 changes drive TORC2 inhibition in our system. In this study, we use the 2xPH(PLC) FLARE exclusively as a generic PM marker, not as a readout of PIP2 biology. Rather, we propose that sterol redistribution and/or the biophysical impact that this has on the PM are central drivers, with TORC2 acting as a signaling node that senses and adjusts PM composition accordingly.

      We now clarify these arguments in the revised Discussion and have reframed our use of PalmC as a probe to explore the capacity of the PM to adapt to acute stress via dynamic lipid rearrangements.

      Major issue 3:

      As the authors point out, a large number of intercalated amphipaths displace sterols from their association with bilayer phospholipids. This unphysiologic mechanism can explain how PalmC causes the transient increase in the availability of plasma membrane ergosterol to the D4H probe and its subsequent removal from the plasma membrane via LAM2/4. TORC2 regulation may not be involved. In fact, the authors say that "TORC2 inhibition, and thereby Lam2/4 activation, cannot be the only trigger for PalmC induced sterol removal." Furthermore, the subsequent recovery of plasma membrane ergosterol could simply reflect homeostatic responses independent of the components studied here.

      Response:

      We agree that increased free sterols in the inner leaflet likely initiate retrograde transport. Our results suggest that TORC2 inhibition facilitates this process by disinhibiting Lam2/4, allowing more efficient clearance of ergosterol from the PM (Fig. 3A, S2C). However, the process is not exclusively dependent on TORC2, and we state this explicitly.

      We do not observe recovery of PM ergosterol on the timescales measured, while TORC2 activity recovers, suggesting that restoration likely occurs later via biosynthetic or anterograde trafficking pathways, which are outside the scope of this study. These points are clarified in the revised Discussion.

      Major issue 3a:

      The data suggest that LAM2/4 mediates the return of cytoplasmic ergosterol to the plasma membrane. To my knowledge, this is a nice finding that not been reported previously and is worth confirming more directly.

      Response:

      We thank the reviewer for this observation but would like to clarify a misunderstanding: our data do not suggest that Lam2/4 mediates anterograde sterol transport. Our results and prior work (Gatta et al., 2015; Roelants et al., 2018) show that Lam2/4 mediate retrograde transport from the PM to the ER, and TORC2 inhibits this process. We now clarify this point in the revised manuscript, stating:

      "In vivo, Lam2/4 seem to predominantly transport sterols from the PM to the ER, following the concentration gradient (Gatta et al., 2015; Jentsch et al., 2018; Tong et al., 2018)."

      Major issue 4:

      I agree with the authors that "It is unclear if the excess of free sterols itself is part of the inhibitory signal to TORC2..." Instead, the inhibition of TORC2 by PalmC may simply result from its artifactual aggregation of the anionic phospholipids (especially, PIP2) needed for TORC2 activity. This would not be biologically meaningful. If the authors wish to show that accessible ergosterol inhibits TORC2 activity or vice versa, they should use more direct methods. For example, neutral amphipaths that do not cause the aforementioned PalmC perturbations should still increase plasma membrane ergosterol and send it through LAM2/4 to the ER.

      Response:

      We now provide evidence that three orthologous treatments (hyperosmotic shock, heat shock and Arp2/3 inhibition) similarly cause sterol mobilization and, in the absence of sterol clearance from the PM, prolonged TORC2 inhibition. These results do not support the reviewer's contention that the inhibition of TORC2 by PalmC is simply resulting from its artifactual aggregation of the anionic phospholipids. Furthermore, PalmC is zwitterionic, and its interaction with anionic lipids should be somewhat limited.

      In our experimental setup, neutral amphipaths did not trigger TORC2 inhibition or D4H redistribution While this differs from prior in vitro work (Lange et al., 2009), we attribute this in part to a discrepancy to experimental setup differences, including flow chamber artifacts that we discuss in the methods section.

      Importantly, only amphipaths with a charged headgroup, including zwitterionic (PalmC) and positively charged analogs, produced robust effects. A negatively charged derivative also seemed to have a minor effect on TORC2 activity and PM sterol internalization (Palmitoylglycine (Fig. 1D, Rebuttal Fig. 1). This suggests that in vivo, charge-based membrane perturbation is required to alter PM sterol distribution and TORC2 activity.

      Major issue 5.:

      The mechanistic relationship between TORC2 activity and ergosterol suggested in the title, abstract, and discussion is not secure. I agree with the concluding section of the manuscript called "Limitations of the study". It highlights the need for a better approach to the interplay between TORC2 and ergosterol.

      Response:

      This may have been true of the previous submission, but we now demonstrate that provoking PM stress in four orthogonal ways triggers mobilization of sterols, which left uncleared, prevents normal (re)activation of TORC2 activity. We thus conclude that free sterols, directly or more likely indirectly, inhibit TORC2. The role that TORC2 plays in sterol retrotranslocation has been demonstrated previously (Roelants et al., 2018). We believe our expanded data and clarified framework make a compelling case for a stress-adaptive role of sterol retrograde transport that is supervised and modulated-but not fully driven-by TORC2 activity.

      Thus, we feel in the present version of this manuscript that the title is now justified.

      Minor issue: Based on earlier work using the reporter fliptR, the authors claim that PalmC reduces membrane tension. They should consider that this intercalated dye senses many variables including membrane tension but also lipid packing. I suspect that, by intercalating into and thereby altering the bilayer, PalmC is affecting the latter rather than the former.

      Response:

      We thank the reviewer for this important point regarding the multifactorial sensitivity of intercalating dyes such as Flipper-TR®, including to membrane tension and lipid packing.

      We respectfully note, however, that our current study does not include any new data generated using Flipper-TR®. We referred to earlier work (Riggi et al., 2018) for context, where Flipper-TR® was used as a membrane tension reporter.

      We fully agree that the response of such "smart" membrane probes integrates multiple biophysical parameters-including tension, packing, and hydration-which are themselves interrelated as consequences of membrane composition (Colom et al., 2018; Ragaller et al., 2024; Torra et al., 2024). Indeed, this interconnectedness is central to our interpretation of PalmC's pleiotropic effects on the plasma membrane (PM). In our previous study, we observed that PalmC treatment not only reduced apparent PM tension (as measured by Flipper-TR®) but also increased membrane order ((Riggi et al., 2018); see laurdan GP, Fig. 6C), and here we show that it promotes the redistribution of free sterol away from the PM.

      Furthermore, PalmC's effect on membrane tension was supported by orthogonal in vitro data: its addition to giant unilamellar vesicles (GUVs) led to a measurable increase in membrane surface area and decreased tension, as shown by pipette aspiration ((Riggi et al., 2018), Fig. 3F). This provides complementary evidence that the membrane tension reduction is not merely an artifact of Flipper-TR® reporting.

      That said, we agree with the reviewer that in the case of TORC2 inhibition or hyperactivation, the observed changes in PM tension are based solely on Flipper-TR® data, without additional orthogonal validation. To address this concern, we have revised the relevant text in the manuscript to more cautiously reflect this complexity. The revised sentence now reads:

      "Consistent with this role, data generated with the lipid packing reporter dye Flipper-TR® suggest that acute chemical inhibition of TORC2 increases PM tension, while Ypk1 hyperactivation decreases it."

      This revised phrasing acknowledges both the utility and the limitations of Flipper-TR® as a probe of membrane biophysics.

      Reviewer #2 Significance:

      This is an interesting topic. However, use of the exogenous probe, palmitoylcarnitine, could be causing multiple changes that complicate the interpretation of the data.

      Reviewers #1 and #3 were much more impressed by this study than I was. I am not a yeast expert and so I may have missed or confused something. I would therefore welcome their expert feedback regarding my comments (#2). Ted Steck

      Response:

      Thank you for your constructive feedback.

      We believe that the manuscript is now much improved, and we hope to have convinced you that the mechanisms that we've elucidated using PalmC represent a general adaptation response to physiological PM stressors.

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

      Reviewer comment: The authors describe the effects of surfactant-like molecules on the plasma membrane (PM) and its associated TORC2 complex. Addition of the surfactants with a positively-charged headgroup and a hydro-carbon tail of at least 16 caused the rapid clustering of PI-4,5P2 together with PI-4P and phosphatidylserine in large membrane invaginations. The authors convincingly demonstrate that this effect of the surfactants on the PM is likely caused by a direct disturbance of the PM organization and/or lipid composition. Interestingly, upon PalmC treatment, free ergosterol of the PM was found to first concentrate in the clusters, but within The kinetics of the changes in free ergosterol levels and the changes in TORC2 activity do not match. Ergosterol is rapidly depleted after PalmC treatment (The Lam2/4 data support the idea that ergosterol transport plays a role in the TORC2 recovery, but what role this is, is not clear to me. I think the data fit better with a model in which PalmC causes low tension of the PM which in turn disrupts normal lipid organization and thus causes TORC2 to shut down, maybe not by changes in free ergosterol but by changes, for instance, in lipid raft formation (which is in part effected by ergosterol levels). The transport of ergosterol is only one mechanism that is involved in restoring PM tension and TORC2 activity. However, sensing free ergosterol alone is most likely not the mechanism explaining how TORC2 senses PM tension.

      Therefore, I recommend that the model is revised (or supported by more data), reflecting the fact that free ergosterol levels do not directly correlate with the TORC2 activity, but instead might be only one of the PM parameters that regulate TORC2.

      Author response:

      We thank the reviewer for their thoughtful assessment and constructive suggestions. As described in more detail above, we have included in our revised version of this manuscript a variety of new data, including the sterol-internalization dependent adaptation of the PM and regulation of TORC2 during additional stresses. We think that these data vastly improve on our previous manuscript version. We have addressed each point risen by the reviewer below and revised the manuscript accordingly, including a rewritten discussion and updated model to better reflect the limitations of our current understanding of how TORC2 senses changes in the plasma membrane (PM). It is true that the appearance of PM invaginations tracks well with TORC2 inhibition, but it is not clear to us if they are upstream of this inhibition or merely another symptom of the preceding PM perturbation (PalmC-induced free sterol increase can be observed after 10s (Fig. S2A), but PM invaginations become visible only after ~1 min - meanwhile we can observe near complete TORC2 inhibition after 30s). In this study, we are mostly interested in the role of PM sterol redistribution in stress response. Indeed we think that the role of free sterol clearance during stresses is to adapt the PM to these stresses - thus restoring PM parameters which in turn reactivates TORC2. This can be seen for hyperosmotic stress and the newly introduced PM stressors, Arp2/3 inhibition and heat shock response (Fig. 4). We have therefore softened our model and updated discussion and final figure (Fig. 5) to reflect that TORC2 likely responds to broader changes in PM organization or tension, with sterol redistribution representing one of several contributing factors rather than the sole signal.

      Comment: - If TORC2 is indeed inhibited by free ergosterol, the addition of ergosterol to the growth medium should be able to trigger similar effects as PalmC. If this detection of free ergosterol is very specific (e.g. if TORC2 has a binding pocket for ergosterol) we would expect that addition of other sterols such a cholesterol or ergosterol precursors should not inhibit TORC2.

      Response:

      We appreciate this suggestion and agree that testing whether exogenous ergosterol can mimic PalmC effects would help assess specificity. However, yeast do not readily take up sterols under aerobic conditions, which renders artificial sterol enrichment at the yeast PM rather difficult. We have now included additional data characterizing our Lam2/4 mutants (see below), and pharmacological sterol synthesis inhibition, showing that a depletion of free sterols from the PM correlates with lower TORC2 activity (Fig. 2D, S2C). Additionally, as suggested, we tried to probe if ergosterol directly interacts with TORC2 through a specific binding pocket, by treating a yeast strain expressing cholesterol rather than ergosterol (Souza et al., 2011) with PalmC. However, the response of TORC2 activity in these cells was very similar to that of WT cells (Rebuttal Fig. 2). In conclusion, we agree that at present we do not know mechanistically how sterols affect TORC2 activity, although it does indeed seem more likely to be through an indirect mechanism linked to changes in PM parameters. The nature of such a mechanism will be subject to further studies. We hope that the introduced changes to the manuscript adequately reflect these considerations.

      Rebuttal Fig. 2: WT yeast cells which produce ergosterol as main sterol, and mutant cells which produce cholesterol instead were treated with 5 µM PalmC, and TORC2 activity was assessed by relative phosphorylation of Ypk1 on WB. One representative experiment out of two replicates.

      Comment: - The experiment in Figure 1C is not controlled for differences in membrane intercalation of the different compounds. For instance, does C16 choline and C16 glycine accumulate at the same rate in the PM (measure similar to experiment in Figure 1B). Maybe the positive charge at the headgroup of the surfactants increases the local concentration at the PM and therefore can explain the difference in effect on the PM.

      Response:

      We agree with the reviewer that the effects of the various PalmC derivatives are not directly controlled for differences in membrane intercalation. Our structure-activity screen was intended to demonstrate the general biophysical mode of action of PalmC-like compounds and to define minimal structural requirements for activity.

      We now note in the manuscript that differential membrane insertion could contribute to the observed variation in efficacy, particularly in relation to tail length. While we considered this additional suggested experiment, it was ultimately judged to be outside the scope of this study due to its complexity and limited impact on the central conclusions.

      A clarifying sentence has been added to the relevant results section to explicitly acknowledge this limitation:

      "We did not control for differences in PM intercalation efficiency."

      We also include a discussion here to further clarify our interpretation. Prior in vitro studies have shown that while intercalation is necessary, it is not sufficient for PM perturbation. For example, palmitoyl-CoA intercalates into membranes but does not induce the same biophysical effects as PalmC (Goñi et al., 1996; Ho et al., 2002). Thus, we believe that intercalation is only part of the story, and that the intrinsic propensity of different headgroups to perturb the PM plays a key role in the disruption of PM lipid organization.

      Comment: - Are the intracellular ergosterol structures associated (or in close proximity) with lipid droplets (ergosterol being modified and delivered into a lipid droplet)?

      Response:

      We thank the reviewer for raising this point. We now include additional data (Fig. S2H) showing that intracellular D4H-positive structures do not reside near or colocalize with lipid droplets. The latter is not entirely unexpected as D4H does not recognize esterified sterols. However, we do observe an increase in overall LD volume following PalmC treatment, consistent with the idea that internalized PM sterols may be stored in LDs as sterol esters over time - although we did not test if this increase in LD volume is Lam2/4 dependent. This increase is mentioned in the revised results text. An increase in cellular LDs has also been recently reported during hyperosmotic shock (Phan et al., 2025).

      For more attempts to identify a marker for intracellular D4H foci, see reply to reviewer 1.

      Comment:

      • How does the AA and DD mutations in Lam2/4 change the localization of the ergosterol sensor (before and after PalmC treatment).

      Response:

      We thank the reviewer for this question, as in the course of generating these data we realized that our "inhibited" DD mutant was in fact not phosphomimetic but displayed the same D4H distribution as the "hyperactive" AA mutant, i.e. a marked inwards shift of D4H signal away from the PM to internal structures due to increased PM-ER retrograde transport of sterols (Fig. S2C). This led us to critically re-evaluate and ultimately repeat our TORC2 activity WB experiments for PalmC treatment in LAM2/4 mutants. In this new set of experiments, the faster TORC2 recovery after PalmC treatment in the LAM2T518A LAM4S401A mutant did unfortunately not repeat robustly. It is possible that such differences can be observed under specific conditions. Nevertheless, the improved overall quality of the Western blot data allowed us to make the observation that baseline activity was already slightly different in these strains. The Lam2/4 centered part of the results section has subsequently been updated in the manuscript:

      "Using a phosphospecific antibody, we did not observe an increase in baseline TORC2 activity in lam2Δ lam4Δ cells, which had been previously reported by electrophoretic mobility shift (Murley et al., 2017). Instead, baseline TORC2 activity was consistently slightly decreased in these cells (Fig. 2D). Ypk1, activated directly by TORC2, inhibits Lam2 and Lam4 through phosphorylation on Thr518 and Ser401, respectively (Roelants et al., 2018; Topolska et al., 2020). We substituted these residues with alanine, generating a strain in which Lam2/4 were no longer inhibited by phosphorylation (Roelants et al., 2018). In these cells, yeGFP-D4H showed that free sterols were constitutively shifted away from the PM to intracellular structures (Fig. S2C, bottom panel). Intriguingly, in opposition to lam2Δ lam4Δ cells, basal TORC2 activity was increased in LAM2T518A LAM4S401A cells (Fig. 2D). This suggests that a decrease in free PM sterols stimulates TORC2 activity [...]"

      "In LAM2T518A LAM4S401A cells, TORC2 activity recovers with similar kinetics as the WT (Fig. 2D, bottom blot), suggesting that Lam2/4 release from TORC2 dependent inhibition during PalmC treatment is a fast and efficient process in WT cells, not further expedited by these constitutively active Lams."

      As suggested, we also observed D4H localization in LAM2T518A LAM4S401A after PalmC treatment, and implemented these data to further demonstrate that PalmC causes an increase in the fraction of free ergosterol at the PM, which is subsequently removed:

      "PalmC addition to LAM2T518A LAM4S401A cells likewise resulted first in a transient increase and then a further decrease in PM yeGFP-D4H signal (Fig. 3C, S3D)."

      Comment: - Does Lam2/4 localize to ER-PM contact sites near the large PM invaginations, which could allow for efficient transport of the free ergosterol that accumulates in these structures.

      Response:

      We were curious about this too, and have now added the requested data in our supplementary material and added a sentence in our results:

      "Indeed, in cells expressing GFP-Lam2 we observed that PalmC induced PM invaginations often formed at sites with preexisting GFP-Lam2 foci (Fig. S2K, cyan arrow), although GFP-Lam2 foci did not always colocalize with invaginations (Fig. S2K, yellow arrow) and vice versa. "

      Additionally, in the effort to characterize intracellular D4H foci during PalmC as requested by reviewer 1, we also looked at the localization of these foci relative to ER, and found that

      "During early timepoints, intracellular foci are usually in close vicinity to ER (Fig. S2E)"

      Reviewer #3 (Significance (Required)): The manuscript describes the effects of small molecule surfactants on the PM organization and on TORC2 activity. This is an important set of observation that helps understanding the response of cells to environmental stressors that affect the PM. This field of study is very challenging because of the limited tools available to directly observe lipids and their movements. I consider the data and most of its interpretations of high importance, but I am not convinced of the larger model that tries to link the ergosterol data with TORC2 activity. With adjustments of the model or additional experimental support, this manuscript will be of general interest for cell biologists, especially for researchers studying membrane stress response pathways.

      Response:

      We thank the reviewer for highlighting the importance of studying PM stress responses and acknowledging the technical challenges involved. We hope the applied changes and additional data succeed in softening our claims about TORC2 regulation while convincing the reviewer that free sterol levels at the PM are one of several contributing factors that correlate with changes in TORC2 activity.

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      Riggi, M., Niewola-Staszkowska, K., Chiaruttini, N., Colom, A., Kusmider, B., Mercier, V., Soleimanpour, S., Stahl, M., Matile, S., Roux, A., Loewith, R., 2018. Decrease in plasma membrane tension triggers PtdIns(4,5)P2 phase separation to inactivate TORC2. Nat. Cell Biol. 20, 1043-1051. https://doi.org/10.1038/s41556-018-0150-z

      Rodríguez-Escudero, I., Fernández-Acero, T., Cid, V.J., Molina, M., 2018. Heterologous mammalian Akt disrupts plasma membrane homeostasis by taking over TORC2 signaling in Saccharomyces cerevisiae. Sci. Rep. 8, 7732. https://doi.org/10.1038/s41598-018-25717-w

      Roelants, F.M., Chauhan, N., Muir, A., Davis, J.C., Menon, A.K., Levine, T.P., Thorner, J., 2018. TOR complex 2-regulated protein kinase Ypk1 controls sterol distribution by inhibiting StARkin domain-containing proteins located at plasma membrane-endoplasmic reticulum contact sites. Mol. Biol. Cell 29, 2128-2136. https://doi.org/10.1091/mbc.E18-04-0229

      Sakata, K.-T., Hashii, K., Yoshizawa, K., Tahara, Y.O., Yae, K., Tsuda, R., Tanaka, N., Maeda, T., Miyata, M., Tabuchi, M., 2022. Coordinated regulation of TORC2 signaling by MCC/eisosome-associated proteins, Pil1 and tetraspan membrane proteins during the stress response. Mol. Microbiol. 117, 1227-1244. https://doi.org/10.1111/mmi.14903

      Shao, C., Novakovic, V.A., Head, J.F., Seaton, B.A., Gilbert, G.E., 2008. Crystal Structure of Lactadherin C2 Domain at 1.7Å Resolution with Mutational and Computational Analyses of Its Membrane-binding Motif*. J. Biol. Chem. 283, 7230-7241. https://doi.org/10.1074/jbc.M705195200

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    1. now you are just being paranoid

      English Explanation:

      In this excerpt, the speaker dismisses another person's suspicions as being exaggerated or unfounded by saying, “now you are just being paranoid.” The context suggests that there’s an ongoing discussion about someone’s behavior—likely Martin’s—and whether it’s genuinely harmless or indicative of ulterior motives. One person believes Martin's interest in learning is genuine, while the other sees it as suspicious. This reflects tensions in interpreting someone's intentions and the struggle between trust and skepticism in human relationships.

      Chinese Explanation:

      在这一段摘录中,演讲者通过说“现在你只是过于多疑了”来否定另一个人所怀有的怀疑。上下文表明,他们正在讨论某人的行为,可能是马丁的行为,而这种行为是否真正无害或者暗示着其他动机。其中一方认为马丁对学习的兴趣是出于真实的意图,而另一方则认为这很可疑。这反映了在解读他人意图时的紧张关系,以及人际关系中信任与怀疑之间的斗争。

  9. unbounded-uploads.s3.amazonaws.com unbounded-uploads.s3.amazonaws.com
    1. • “[M]ost white people, if they arereally being honest with themselves,can see that there are advantages tobeing white in the United States.”• “White people are paying a sig-nificant price for the system of advan-tage. The cost is not as high forwhites as it is for people of color, buta price is being paid.”• “Privilege goes un

      What strong statements. It almost made me uncomfortable just to read because of the pedestal that white people have always been placed on by people I grew up with and society. But there have been many people in my life who have been open and receptive to conversations around race a privilege with me. I think it's important to be able to have conversations in spaces where people can speak freely to understand. For example, I think it immensely helped my friend group keep the human part first when talking about people experiencing homelessness because I explained to them that I experienced homelessness as a kid, and I would have never wanted anyone to think of little me as homeless before I was a person.

    1. As a guideline, ask yourself what would happen if your action (or non-action) became entirely public, and started trending on social media, got its own hashtag, and became a meme picked up by the national media.

      This is a good reminder that accountability isn't just about legal rules; it's also about how your choices hold up if everyone finds out. Bad behavior spreads so fast online now that public opinion can actually pressure people to act more ethically. If it's posted on the internet, it's there forever, so take accountability and be fair and respectful.

    2. his is one version of confirmation bias (https://en.wikipedia.org/wiki/Confirmation_bias)  – where people tend to favor evidence that supports their preconceived notions and reject evidence that challenges their ideas or beliefs.

      This part makes me think about my own research projects. Sometimes I've caught myself only picking sources that back up what I already want to say instead of looking for evidence that might challenge it. It's a good remiinder to check my own bias so I don't twist the info to just sound right.

    1. There are actually three social maps -- three theories of determinism widely accepted,independently or in combination, to explain the nature of man. Genetic determinismbasically says your grandparents did it to you. That's why you have such a temper. Yourgrandparents had short tempers and it's in your DNA. It just goes through thegenerations and you inherited it. In addition, you're Irish, and that's the nature of Irishpeople.Psychic determinism basically says your parents did it to you. Your upbringing, yourchildhood experience essentially laid out your personal tendencies and your characterstructure. That's why you're afraid to be in front of a group. It's the way your parentsbrought you up. You feel terribly guilty if you make a mistake because you "remember"deep inside the emotional scripting when you were very vulnerable and tender anddependent. You "remember" the emotional punishment, the rejection, the comparisonwith somebody else when you didn't perform as well as expected.Environmental determinism basically says your boss is doing to you -- or your spouse, orthat bratty teenager, or your economic situation, or national policies. Someone orsomething in your environment is responsible for your situation

      3 social maps: 1) genetic determinism 2) psychic determinism 3) environmental determinism

      this reminds me of that formula from atomic habits: behavior = environment + [something else]

    1. a morsel of food

      a morsel of food

      English Explanation:

      The phrase "a morsel of food" refers to a small piece or bite of food. The word "morsel" typically denotes a tiny amount that is easy to consume, often associated with something that is tender, delicate, or particularly appetizing. The term is frequently used in contexts where one is talking about snacking, tasting, or serving food in small quantities, either for oneself or for others.

      For example, when someone enjoys a gourmet meal, they might savor a morsel of a rich dessert. It can also imply simplicity or modesty; a single morsel might evoke the idea of having just a little bit rather than a full meal. Overall, it's a way to describe food in a way that emphasizes its small size and often its quality or taste.

      Chinese Explanation (中文解释):

      “a morsel of food”这个短语指的是一小块食物或一口食物。“morsel”这个词通常表示易于食用的小量食物,常与嫩、细腻或特别美味的东西相关联。这个词在谈论吃零食、品尝或以小份量提供食物时经常使用,可以是给自己,也可以是给他人。

      例如,当某人享用美食时,他们可能会品尝一小口丰盛的甜点。它也可以暗示简单或谦逊;一个小小的“morsel”可能唤起只有一点点食物而不是一顿饭的想法。总的来说,这是一个用来描述食物的小巧和往往强调其质量或味道的表达。

  10. Jun 2025
    1. A student once summarized the accommodation process as being like the game Battleship—you can’t perceive what’s on the other side of the board, because there is a barrier there, and so you have to just keep trying to guess where the other player’s ships are—or where the relevant accommodations are, if they exist. You throw your diagnosis over, and hope that it will land on something that will actually help you. But you cannot sense the full range of what may be on the other side, and thus you cannot directly ask for what you need.

      This is why it's so important to have open, ongoing dialogue with individual students about what is and is not working for them before assignments are due, as assignments are due, and after they've submitted and assignment. Simple things like, "If you are willing, would take about 5 minutes to jot down some things that made completing this assignment difficult for you. Then, I'll ask you to shift gears and list or explain what made it possible for you to do the assignment." I'm really writing this for myself as a reminder of how this type of intervention can help all students.

    1. Author response:

      The following is the authors’ response to the original reviews

      Summary of our revisions

      (1) We have explained the reason why the untrained RNN with readout (value-weight) learning only could not well learn the simple task: it is because we trained the models continuously across trials with random inter-trial intervals rather than separately for each episodic trial and so it was not trivial for the models to recognize that cue presentation in different trials constitutes a same single state since the activities of untrained RNN upon cue presentation should differ from trial to trial (Line 177-185).

      (2) We have shown that dimensionality was higher in the value-RNNs than in the untrained RNN (Fig. 2K,6H).

      (3) We have shown that even when distractor cue was introduced, the value-RNNs could learn the task (Fig. 10).

      (4) We have shown that extended value-RNNs incorporating excitatory and inhibitory units and conforming to the Dale's law could still learn the tasks (Fig. 9,10-right column).

      (5) In the original manuscript, the non-negatively constrained value-RNN showed loose alignment of value-weight and random feedback from the beginning but did not show further alignment over trials. We have clarified its reason and found a way, introducing a slight decay (forgetting), to make further alignment occur (Fig. 8E,F).

      (6) We have shown that the value-RNNs could learn the tasks with longer cue-reward delay (Fig. 2M,6J) or action selection (Fig. 11), and found cases where random feedback performed worse than symmetric feedback.

      (7) We compared our value-RNNs with e-prop (Bellec et al., 2020, Nat Commun). While e-prop incorporates the effects of changes in RNN weights across distant times through "eligibility trace", our value-RNNs do not. The reason why our models can still learn the tasks with cue-reward delay is considered to be because our models use TD error and TD learning itself, even TD(0) without eligibility trace, is a solution for temporal credit assignment. In fact, TD error-based e-prop was also examined, but for that, result with symmetric feedback, but not with random feedback, was shown (their Fig. 4,5) while for another setup of reward-based e-prop without TD error, result with random feedback was shown (their SuppFig. 5). We have noted these in Line 695-711 (and also partly in Line 96-99).

      (8) In the original manuscript, we emphasized only the spatial locality (random rather than symmetric feedback) of our learning rule. But we have now also emphasized the temporal locality (online learning) as it is also crucial for bio-plausibility and critically different from the original value-RNN with BPTT. We also changed the title.

      (9) We have realized that our estimation of true state values was invalid (as detailed in page 34 of this document). Effects of this error on performance comparisons were small, but we apologize for this error.

      Reviewer #1 (Public review):

      Summary:

      Can a plastic RNN serve as a basis function for learning to estimate value. In previous work this was shown to be the case, with a similar architecture to that proposed here. The learning rule in previous work was back-prop with an objective function that was the TD error function (delta) squared. Such a learning rule is non-local as the changes in weights within the RNN, and from inputs to the RNN depends on the weights from the RNN to the output, which estimates value. This is non-local, and in addition, these weights themselves change over learning. The main idea in this paper is to examine if replacing the values of these non-local changing weights, used for credit assignment, with random fixed weights can still produce similar results to those obtained with complete bp. This random feedback approach is motivated by a similar approach used for deep feed-forward neural networks.

      This work shows that this random feedback in credit assignment performs well but is not as well as the precise gradient-based approach. When more constraints due to biological plausibility are imposed performance degrades. These results are not surprising given previous results on random feedback. This work is incomplete because the delay times used were only a few time steps, and it is not clear how well random feedback would operate with longer delays. Additionally, the examples simulated with a single cue and a single reward are overly simplistic and the field should move beyond these exceptionally simple examples.

      Strengths:

      • The authors show that random feedback can approximate well a model trained with detailed credit assignment.

      • The authors simulate several experiments including some with probabilistic reward schedules and show results similar to those obtained with detailed credit assignments as well as in experiments.

      • The paper examines the impact of more biologically realistic learning rules and the results are still quite similar to the detailed back-prop model.

      Weaknesses:

      *please note that we numbered your public review comments and recommendations for the authors as Pub1 and Rec1 etc so that we can refer to them in our replies to other comments.

      Pub1. The authors also show that an untrained RNN does not perform as well as the trained RNN. However, they never explain what they mean by an untrained RNN. It should be clearly explained.

      These results are actually surprising. An untrained RNN with enough units and sufficiently large variance of recurrent weights can have a high-dimensionality and generate a complete or nearly complete basis, though not orthonormal (e.g: Rajan&Abbott 2006). It should be possible to use such a basis to learn this simple classical conditioning paradigm. It would be useful to measure the dimensionality of network dynamics, in both trained and untrained RNN's.

      We have added an explanation of untrained RNN in Line 144-147:

      “As a negative control, we also conducted simulations in which these connections were not updated from initial values, referring to as the case with "untrained (fixed) RNN". Notably, the value weights w (i.e., connection weights from the RNN to the striatal value unit) were still trained in the models with untrained RNN.”

      We have also analyzed the dimensionality of network dynamic by calculating the contribution ratios of each principal component of the trajectory of RNN activities. It was revealed that the contribution ratios of later principal components were smaller in the cases with untrained RNN than in the cases with trained value RNN. We have added these results in Fig. 2K and Line 210-220 (for our original models without non-negative constraint):

      “In order to examine the dimensionality of RNN dynamics, we conducted principal component analysis (PCA) of the time series (for 1000 trials) of RNN activities and calculated the contribution ratios of PCs in the cases of oVRNNbp, oVRNNrf, and untrained RNN with 20 RNN units. Figure 2K shows a log of contribution ratios of 20 PCs in each case. Compared with the case of untrained RNN, in oVRNNbp and oVRNNrf, initial component(s) had smaller contributions (PC1 (t-test p = 0.00018 in oVRNNbp; p = 0.0058 in oVRNNrf) and PC2 (p = 0.080 in oVRNNbp; p = 0.0026 in oVRNNrf)) while later components had larger contributions (PC3~10,15~20 p < 0.041 in oVRNNbp; PC5~20 p < 0.0017 in oVRNNrf) on average, and this is considered to underlie their superior learning performance. We noticed that late components had larger contributions in oVRNNrf than in oVRNNbp, although these two models with 20 RNN units were comparable in terms of cue~reward state values (Fig. 2J-left).”

      and Fig. 6H and Line 412-416 (for our extended models with non-negative constraint):

      “Figure 6H shows contribution ratios of PCs of the time series of RNN activities in each model with 20 RNN units. Compared with the cases with naive/shuffled untrained RNN, in oVRNNbp-rev and oVRNNrf-bio, later components had relatively high contributions (PC5~20 p < 1.4×10,sup>−6</sup> (t-test vs naive) or < 0.014 (vs shuffled) in oVRNNbp-rev; PC6~20 p < 2.0×10<sup>−7</sup> (vs naive) or PC7~20 p < 5.9×10<sup>−14</sup> (vs shuffled) in oVRNNrf-bio), explaining their superior value-learning performance.”

      Regarding the poor performance of the model with untrained RNN, we would like to add a note. It is sure that untrained RNN with sufficient dimensions should be able to well represent just <10 different states, and state values should be able to be well learned through TD learning regardless of whatever representation is used. However, a difficulty (nontriviality) lies in that because we modeled the tasks in a continuous way, rather than in an episodic way, the activity of untrained RNN upon cue presentation should generally differ from trial to trial. Therefore, it was not trivial for RNN to know that cue presentation in different trials, even after random lengths of inter-trial interval, should constitute a same single state. We have added this note in Line 177-185:

      “This inferiority of untrained RNN may sound odd because there were only four states from cue to reward while random RNN with enough units is expected to be able to represent many different states (c.f., [49]) and the effectiveness of training of only the readout weights has been shown in reservoir computing studies [50-53]. However, there was a difficulty stemming from the continuous training across trials (rather than episodic training of separate trials): the activity of untrained RNN upon cue presentation generally differed from trial to trial, and so it is non-trivial that cue presentation in different trials should be regarded as the same single state, even if it could eventually be dealt with at the readout level if the number of units increases.”

      The original value RNN study (Hennig et al., 2023, PLoS Comput Biol) also modeled tasks in a continuous way (though using backprop-through-time (BPTT) for training) and their model with untrained RNN also showed considerably larger RPE error than the value RNN even when the number of RNN units was 100 (the maximum number plotted in their Fig. 6A).

      Pub2. The impact of the article is limited by using a network with discrete time-steps, and only a small number of time steps from stimulus to reward. What is the length of each time step? If it's on the order of the membrane time constant, then a few time steps are only tens of ms. In the classical conditioning experiments typical delays are of the order to hundreds of milliseconds to seconds. Authors should test if random feedback weights work as well for larger time spans. This can be done by simply using a much larger number of time steps.

      In the revised manuscript, we examined the cases in which the cue-reward delay (originally 3 time steps) was elongated to 4, 5, or 6 time-steps. Our online value RNN models with random feedback could still achieve better performance (smaller squared value error) than the models with untrained RNN, although the performance degraded as the cue-reward delay increased. We have added these results in Fig. 2M and Line 223-228 (for our original models without non-negative constraint)

      “We further examined the cases with longer cue-reward delays. As shown in Fig. 2M, as the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp and oVRNNrf over the model with untrained RNN remained to hold, except for cases with small number of RNN units (5) and long delay (5 or 6) (p < 0.0025 in Wilcoxon rank sum test for oVRNNbp or oVRNNrf vs untrained for each number of RNN units for each delay).”

      and Fig. 6J and Line 422-429 (for our extended models with non-negative constraint):

      “Figure 6J shows the cases with longer cue-reward delays, with default or halved learning rates. As the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp-rev and oVRNNrf-bio over the models with untrained RNN remained to hold, except for a few cases with 5 RNN units (5 delay oVRNNrf-bio vs shuffled with default learning rate, 6 delay oVRNNrf-bio vs naive or shuffled with halved learning rate) (p < 0.047 in Wilcoxon rank sum test for oVRNNbp-rev or oVRNNrf-bio vs naive or shuffled untrained for each number of RNN units for each delay).”

      Also, we have added the note about our assumption and consideration on the time-step that we described in our provisional reply in Line 136-142:

      “We assumed that a single RNN unit corresponds to a small population of neurons that intrinsically share inputs and outputs, for genetic or developmental reasons, and the activity of each unit represents the (relative) firing rate of the population. Cortical population activity is suggested to be sustained not only by fast synaptic transmission and spiking but also, even predominantly, by slower synaptic neurochemical dynamics [46] such as short-term facilitation, whose time constant can be around 500 milliseconds [47]. Therefore, we assumed that single time-step of our rate-based (rather than spike-based) model corresponds to 500 milliseconds.”

      Pub3. In the section with more biologically constrained learning rules, while the output weights are restricted to only be positive (as well as the random feedback weights), the recurrent weights and weights from input to RNN are still bi-polar and can change signs during learning. Why is the constraint imposed only on the output weights? It seems reasonable that the whole setup will fail if the recurrent weights were only positive as in such a case most neurons will have very similar dynamics, and the network dimensionality would be very low. However, it is possible that only negative weights might work. It is unclear to me how to justify that bipolar weights that change sign are appropriate for the recurrent connections and inappropriate for the output connections. On the other hand, an RNN with excitatory and inhibitory neurons in which weight signs do not change could possibly work.

      We examined extended models that incorporated inhibitory and excitatory units and followed Dale's law with certain assumptions, and found that these models could still learn the tasks. We have added these results in Fig. 9 and subsection “4.1 Models with excitatory and inhibitory units” and described the details of the extended models in Line 844-862:

      Pub4. Like most papers in the field this work assumes a world composed of a single cue. In the real world there many more cues than rewards, some cues are not associated with any rewards, and some are associated with other rewards or even punishments. In the simplest case, it would be useful to show that this network could actually work if there are additional distractor cues that appear at random either before the CS, or between the CS and US. There are good reasons to believe such distractor cues will be fatal for an untrained RNN, but might work with a trained RNN, either using BPPT or random feedback. Although this assumption is a common flaw in most work in the field, we should no longer ignore these slightly more realistic scenarios.

      We examined the performance of the models in a task in which distractor cue randomly appeared. As a result, our model with random feedback, as well as the model with backprop, could still learn the state values much better than the models with untrained RNN. We have added these results in Fig. 10 and subsection “4.2 Task with distractor cue”

      Reviewer #1 (Recommendations for the authors):

      Detailed comments to authors

      Rec1. Are the untrained RNNs discussed in methods? It seems quite good in estimating value but has a strong dopamine response at time of reward. Is nothing trained in the untrained RNN or are the W values trained. Untrained RNN are not bad at estimating value, but not as good as the two other options. It would seem reasonable that an untrained RNN (if I understand what it is) will be sufficient for such simple Pavlovian conditioning paradigms. This is provided that the RNN generates a complete, or nearly complete basis. Random RNN's provided that the random weights are chosen properly can indeed generate a nearly complete basis. Once there is a nearly complete temporal basis, it seems that a powerful enough learning rule will be able to learn the very simple Pavlovian conditioning. Since there are only 3 time-steps from cue to reward, an RNN dimensionality of 3 would be sufficient. A failure to get a good approximation can also arise from the failure of the learning algorithm for the output weights (W).

      As we mentioned in our reply to your public comment Pub1 (page 3-5), we have added an explanation of "untrained RNN" (in which the value weights were still learnt) (Line 144-147). We also analyzed the dimensionality of network dynamics by calculating the contribution ratios of principal components of the trajectory of RNN activities, showing that the contribution ratios of later principal components were smaller in the cases with untrained RNN than in the cases with trained value RNN (Fig. 2K/Line 210-220, Fig.6H/Line 412-416). Moreover, also as we mentioned in our reply to your public comment Pub1, we have added a note that even learning of a small number of states was not trivially easy because we considered continuous learning across trials rather than episodic learning of separate trials and thus it was not trivial for the model to know that cue presentation in different trials after random lengths of inter-trial interval should still be regarded as a same single state (Line 177-185).

      Rec2. For all cases, it will be useful to estimate the dimensionality of the RNN. Is the dimensionality of the untrained RNN smaller than in the trained cases? If this is the case, this might depend on the choice of the initial random (I assume) recurrent connectivity matrix.

      As mentioned above, we have analyzed the dimensionality of the network dynamics, and as you said, the dimensionality of the model with untrained RNN (which was indeed the initial random matrix as you said, as we mentioned above) was on average smaller than the trained value RNN models (Fig. 2K/Line 210-220, Fig.6H/Line 412-416).

      Rec3. It is surprising that the error starts increasing for more RNN units above ~15. See discussion. This might indicate a failure to adjust the learning parameters of the network rather than a true and interesting finding.

      Thank you very much for this insightful comment. In the original manuscript, we set the learning rate to a fixed value (0.1), without normalization by the squared norm of feature vector (as we mentioned in Line 656-7 of the original manuscript) because we thought such a normalization could not be locally (biologically) implemented. However, we have realized that the lack of normalization resulted in excessively large learning rate when the number of RNN units was large and it could cause instability and error increase as you suggested. Therefore, in the revised manuscript, we have implemented a normalization of learning rate (of value weights) that does not require non-local computations, specifically, division by the number of RNN units. As a result, the error now monotonically decreased, as the number of RNN units increased, in the non-negatively constrained models (Fig. 6E-left) and also largely in the unconstrained model with random feedback, although still not in the unconstrained model with backprop or untrained RNN (Fig. 2J-left)

      Rec4. Not numbering equations is a problem. For example, the explanations of feedback alignment (lines 194-206) rely on equations in the methods section which are not numbered. This makes it hard to read these explanations. Indeed, it will also be better to include a detailed derivation of the explanation in these lines in a mathematical appendix. Key equations should be numbered.

      We have added numbers to key equations in the Methods, and references to the numbers of corresponding equations in the main text. Detailed derivations are included in the Methods.

      Rec5. What is shown in Figure 3C? - an equation will help.

      We have added an explanation using equations in the main text (Line 256-259).

      Rec6. The explanation of why alignment occurs is not satisfactory, but neither is it in previous work on feedforward networks. The least that should be done though

      Regarding why alignment occurs, what remained mysterious (to us) was that in the case of nonnegatively constrained model, while the angle between value weight vector (w) and the random feedback vector (c) was relatively close (loosely aligned) from the beginning, it appeared (as mentioned in the manuscript) that there was no further alignment over trials, despite that the same mechanism for feedback alignment that we derived for the model without non-negative constraint was expected to operate also under the non-negative constraint. We have now clarified the reason for this, and found a way, introduction of slight decay (forgetting) of value weights, by which feedback alignment came to occur in the non-negatively constraint model. We have added these in the revised manuscript (Line 463-477):

      “As mentioned above, while the angle between w and c was on average smaller than 90° from the beginning, there was no further alignment over trials. This seemed mysterious because the mechanism for feedback alignment that we derived for the models without non-negative constraint was expected to work also for the models with non-negative constraint. As a possible reason for the non-occurrence of feedback alignment, we guessed that one or a few element(s) of w grew prominently during learning, and so w became close to an edge or boundary of the non-negative quadrant and thereby angle between w and other vector became generally large (as illustrated in Fig. 8D). Figure 8Ea shows the mean±SEM of the elements of w ordered from the largest to smallest ones after 1500 trials. As conjectured above, a few elements indeed grew prominently.

      We considered that if a slight decay (forgetting) of value weights (c.f., [59-61]) was assumed, such a prominent growth of a few elements of w may be mitigated and alignment of w to c, beyond the initial loose alignment because of the non-negative constraint, may occur. These conjectures were indeed confirmed by simulations (Fig. 8Eb,c and Fig. 8F). The mean squared value error slightly increased when the value-weightdecay was assumed (Fig. 8G), however, presumably reflecting a decrease in developed values and a deterioration of learning because of the decay.”

      Rec7. I don't understand the qualitative difference between 4G and 4H. The difference seems to be smaller but there is still an apparent difference. Can this be quantified?

      We have added pointers indicating which were compared and statistical significance on Fig. 4D-H, and also Fig. 7 and Fig. 9C.

      Rec8. More biologically realistic constraints.

      Are the weights allowed to become negative? - No.

      Figure 6C - untrained RNN with non-negative x_i. Again - it was not explained what untrained RNN is. However, given my previous assumption, this is probably because the units developed in an untrained RNN is much further from representing a complete basis function. This cannot be done with only positive values. It would be useful to see network dynamics of units for untrained RNN. It might also be useful in all cases to estimate the dimensionality of the RNN. For 3 time-steps, it needs to be at least 3, and for more time steps as in Figure 4, larger.

      As we mentioned in our reply to your public comment Pub3 (page 6-8), in the revised manuscript we examined models that incorporated inhibitory and excitatory units and followed Dale's law, which could still learn the tasks (Fig. 9, Line 479-520). We have also analyzed the dimensionality of network dynamics as we mentioned in our replies to your public comment Pub1 and recommendations Rec1 and Rec2.

      Rec9. A new type of untrained RNN is introduced (Fig 6D) this is the first time an explanation of of the untrained RNN is given. Indeed, the dimensionality of the second type of untrained RNN should be similar to the bioVRNNrf. The results are still not good.

      In the model with the new type of untrained RNN whose elements were shuffled from trained bioVRNNrf, contribution ratios of later principal components of the trajectory of RNN activities (Fig. 6H gray dotted line) were indeed larger than those in the model with native untrained RNN (gray solid line) but still much smaller than those in the trained value RNN models with backprop (red line) or random feedback (blue line). It is considered that in value RNN, RNN connections were trained to realize high-dimensional trajectory, and shuffling did not generally preserve such an ability.

      Rec10. The discussion is too long and verbose. This is not a review paper.

      We have made the original discussion much more compact (from 1686 words to 940 words). We have added new discussion, in response to the review comments, but the total length remains to be shorter than before (1589 words).

      Reviewer #2 (Public review):

      Summary:

      Tsurumi et al. show that recurrent neural networks can learn state and value representations in simple reinforcement learning tasks when trained with random feedback weights. The traditional method of learning for recurrent network in such tasks (backpropagation through time) requires feedback weights which are a transposed copy of the feed-forward weights, a biologically implausible assumption. This manuscript builds on previous work regarding "random feedback alignment" and "value-RNNs", and extends them to a reinforcement learning context. The authors also demonstrate that certain nonnegative constraints can enforce a "loose alignment" of feedback weights. The author's results suggest that random feedback may be a powerful tool of learning in biological networks, even in reinforcement learning tasks.

      Strengths:

      The authors describe well the issues regarding biologically plausible learning in recurrent networks and in reinforcement learning tasks. They take care to propose networks which might be implemented in biological systems and compare their proposed learning rules to those already existing in literature. Further, they use small networks on relatively simple tasks, which allows for easier intuition into the learning dynamics.

      Weaknesses:

      The principles discovered by the authors in these smaller networks are not applied to deeper networks or more complicated tasks, so it remains unclear to what degree these methods can scale up, or can be used more generally.

      We have examined extended models that incorporated inhibitory and excitatory units and followed Dale's law with certain assumptions, and found that these models could still learn the tasks. We have added these results in Fig. 9 and subsection “4.1 Models with excitatory and inhibitory units”.

      We have also examined the performance of the models in a task in which distractor cue randomly appeared, finding that our models could still learn the state values much better than the models with untrained RNN. We have added these result in Fig. 10 and subsection “4.2 Task with distractor cue”.

      Regarding the depth, we continue to think about it but have not yet come up with concrete ideas.

      Reviewer #2 (Recommendations for the authors):

      (1) I think the work would greatly benefit from more proofreading. There are language errors/oddities throughout the paper, I will list just a few examples from the introduction:

      Thank you for pointing this out. We have made revisions throughout the paper.

      line 63: "simultaneously learnt in the downstream of RNN". Simultaneously learnt in networks downstream of the RNN? Simulatenously learn in a downstream RNN? The meaning is not clear in the original sentence.

      We have revised it to "simultaneously learnt in connections downstream of the RNN" (Line 67-68).

      starting in line 65: " A major problem, among others.... value-encoding unit" is a run-on sentence and would more readable if split into multiple sentences.

      We have extensively revised this part, which now consists of short sentences (Line 70-75).

      line 77: "in supervised learning of feed-forward network" should be either "in supervised learning of a feed-forward network" or "in supervised learning of feed-forward networks".

      We have changed "feed-forward network" to "feed-forward networks" (Line 83).

      (2) Under what conditions can you use an online learning rule which only considers the influence of the previous timestep? It's not clear to me how your networks solve the temporal credit assignment problem when the cue-reward delay in your tasks is 3-5ish time steps. How far can you stretch this delay before your networks stop learning correctly because of this one-step assumption? Further, how much does feedback alignment constrain your ability to learn long timescales, such as in Murray, J.M. (2019)?

      The reason why our models can solve the temporal credit assignment problem at least to a certain extent is considered to be because temporal-difference (TD) learning, which we adopted, itself has a power to resolve temporal credit assignment, as exemplified in that TD(0) algorithms without eligibility trance can still learn the value of distant rewards. We have added a discussion on this in Line 702-705:

      “…our models do not have "eligibility trace" (nor memorable/gated unit, different from the original value-RNN [26]), but could still solve temporal credit assignment to a certain extent because TD learning is by itself a solution for it (notably, recent work showed that combination of TD(0) and model-based RL well explained rat's choice and DA patterns [132]).”

      We have also examined the cases in which the cue-reward delay (originally 3 time steps) was elongated to 4, 5, or 6 time-steps, and our models with random feedback could still achieve better performance than the models with untrained RNN although the performance degraded as the cue-reward delay increased. We have added these results in Fig. 2M and Line 223-228 (for our original models without non-negative constraint)

      “We further examined the cases with longer cue-reward delays. As shown in Fig. 2M, as the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp and oVRNNrf over the model with untrained RNN remained to hold, except for cases with small number of RNN units (5) and long delay (5 or 6) (p < 0.0025 in Wilcoxon rank sum test for oVRNNbp or oVRNNrf vs untrained for each number of RNN units for each delay).”

      and Fig. 6J and Line 422-429 (for our extended models with non-negative constraint):

      “Figure 6J shows the cases with longer cue-reward delays, with default or halved learning rates. As the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp-rev and oVRNNrf-bio over the models with untrained RNN remained to hold, except for a few cases with 5 RNN units (5 delay oVRNNrf-bio vs shuffled with default learning rate, 6 delay oVRNNrf-bio vs naive or shuffled with halved learning rate) (p < 0.047 in Wilcoxon rank sum test for oVRNNbp-rev or oVRNNrf-bio vs naive or shuffled untrained for each number of RNN units for each delay).”

      As for the difficulty due to random feedback compared to backprop, there appeared to be little difference in the models without non-negative constraint (Fig. 2M), whereas in the models with nonnegative constraint, when the cue-reward delay was elongated to 6 time-steps, the model with random feedback performed worse than the model with backprop (Fig. 6J bottom-left panel).

      (3) Line 150: Were the RNN methods trained with continuation between trials?

      Yes, we have added

      “The oVRNN models, and the model with untrained RNN, were continuously trained across trials in each task, because we considered that it was ecologically more plausible than episodic training of separate trials.” in Line 147-150. This is considered to make learning of even the simple cue-reward association task nontrivial, as we describe in our reply to your comment 9 below.

      (4) Figure 2I, J: indicate the statistical significance of the difference between the three methods for each of these measures.

      We have added statistical information for Fig. 2J (Line 198-203):

      “As shown in the left panel of Fig. 2J, on average across simulations, oVRNNbp and oVRNNrf exhibited largely comparable performance and always outperformed the untrained RNN (p < 0.00022 in Wilcoxon rank sum test for oVRNNbp or oVRNNrf vs untrained for each number of RNN units), although oVRNNbp somewhat outperformed or underperformed oVRNNrf when the number of RNN units was small (≤10 (p < 0.049)) or large (≥25 (p < 0.045)), respectively.”

      and also Fig. 6E (for non-negative models) (Line 385-390):

      “As shown in the left panel of Fig. 6E, oVRNNbp-rev and oVRNNrf-bio exhibited largely comparable performance and always outperformed the models with untrained RNN (p < 2.5×10<sup>−12</sup> in Wilcoxon rank sum test for oVRNNbp-rev or oVRNNrf-bio vs naive or shuffled untrained for each number of RNN units), although oVRNNbp-rev somewhat outperformed or underperformed oVRNNrf-bio when the number of RNN units was small (≤10 (p < 0.00029)) or large (≥25 (p < 3.7×10<sup>−6</sup>)), respectively…”

      Fig. 2I shows distributions, whose means are plotted in Fig. 2J, and we did not add statistics to Fig. 2I itself.

      (5) Line 178: Has learning reached a steady state after 1000 trials for each of these networks? Can you show a plot of error vs. trial number?

      We have added a plot of error vs trial number for original models (Fig. 2L, Line 221-223):

      “We examined how learning proceeded across trials in the models with 20 RNN units. As shown in Fig. 2L, learning became largely converged by 1000-th trial, although slight improvement continued afterward.”

      and non-negatively constrained models (Fig. 6I, Line 417-422):

      “Figure 6I shows how learning proceeded across trials in the models with 20 RNN units. While oVRNNbp-rev and oVRNNrf-bio eventually reached a comparable level of errors, oVRNNrf-bio outperformed oVRNNbp-rev in early trials (at 200, 300, 400, or 500 trials; p < 0.049 in Wilcoxon rank sum test for each). This is presumably because the value weights did not develop well in early trials and so the backprop-type feedback, which was the same as the value weights, did not work well, while the non-negative fixed random feedback worked finely from the beginning.”

      As shown in these figures, learning became largely steady at 1000 trials, but still slightly continued, and we have added simulations with 3000 trials (Fig. 2M and Fig. 6J).

      (6) Line 191: Put these regression values in the figure caption, as well as on the plot in Figure 3B.

      We have added the regression values in Fig. 3B and its caption.

      (7) Line 199: This idea of being in the same quadrant is interesting, but I think the term "relatively close angle" is too vague. Is there another more quantatative way to describe this what you mean by this?

      We have revised this (Line 252-254) to “a vector that is in a relatively close angle with c , or more specifically, is in the same quadrant as (and thus within at maximum 90° from) c (for example, [c<sub>1</sub>  c<sub>2</sub>  c<sub>3</sub>]<sup>T</sup> and [0.5c<sub>1</sub> 1.2c<sub>2</sub> 0.8c<sub>3</sub>]T) “

      (8) Line 275: I'd like to see this measure directly in a plot, along with the statistical significance.

      We have added pointers indicating which were compared and statistical significance on Fig. 4D-H, and also Fig. 7 and Fig. 9C.

      (9) Line 280: Surely the untrained RNN should be able to solve the task if the reservoir is big enough, no? Maybe much bigger than 50 units, but still.

      We think this is not sure. A difficulty lies in that because we modeled the tasks in a continuous way rather than in an episodic way (as we mentioned in our reply to your comment 3), the activity of untrained RNN upon cue presentation should generally differ from trial to trial. Therefore, it was not trivial for RNN to know that cue presentation in different trials, even after random lengths of inter-trial interval, should constitute a same single state. We have added this note in Line 177-185:

      “This inferiority of untrained RNN may sound odd because there were only four states from cue to reward while random RNN with enough units is expected to be able to represent many different states (c.f., [49]) and the effectiveness of training of only the readout weights has been shown in reservoir computing studies [50-53]. However, there was a difficulty stemming from the continuous training across trials (rather than episodic training of separate trials): the activity of untrained RNN upon cue presentation generally differed from trial to trial, and so it is non-trivial that cue presentation in different trials should be regarded as the same single state, even if it could eventually be dealt with at the readout level if the number of units increases.”

      The original value RNN study (Hennig et al., 2023, PLoS Comput Biol) also modeled tasks in a continuous way (though using BPTT for training) and their model with untrained RNN also showed considerably larger RPE error than the value RNN even when the number of RNN units was 100 (the maximum number plotted in their Fig. 6A).

      (10) It's a bit confusing to compare Figure 4C to Figure 4D-H because there are also many features of D-H which do not match those of C (response to cue, response to late reward in task 1). It would make sense to address this in some way. Is there another way to calculate the true values of the states (e.g., maybe you only start from the time of the cue) which better approximates what the networks are doing?

      As we mentioned in our replies to your comments 3 and 9, our models with RNN were trained continuously across trials rather than separately for each episodic trial, and whether the models could still learn the state representation is a key issue. Therefore, starting learning from the time of cue would not be an appropriate way to compare the models, and instead we have made statistical comparison regarding key features, specifically, TD-RPEs at early and late rewards, as indicated in Fig. 4D-H.

      (11) Line 309: Can you explain why this non-monotic feature exists? Why do you believe it would be more biologically plausible to assume monotonic dependence? It doesn't seem so straightforward to me, I can imagine that competing LTP/LTD mechanisms may produce plasticity which would have a non-monotic dependence on post-synaptic activity.

      Thank you for this insightful comment. As you suggested, non-monotonic dependence on the postsynaptic activity (BCM rule) has been proposed for unsupervised learning (cortical self-organization) (Bienenstock et al., 1982 J Neurosci), and there were suggestions that triplet-based STDP could be reduced to a BCM-like rule and additional components (Gjorgjieva et al., 2011 PNAS; Shouval, 2011 PNAS). However, the non-monotonicity appeared in our model, derived from the backprop rule, is maximized at the middle and thus opposite from the BCM rule, which is minimized at the middle (i.e., initially decrease and thereafter increase). Therefore we consider that such an increase-then-decreasetype non-monotonicity would be less plausible than a monotonic increase, which could approximate an extreme case (with a minimum dip) of the BCM rule. We have added a note on this point in Line 355-358:

      “…the dependence on the post-synaptic activity was non-monotonic, maximized at the middle of the range of activity. It would be more biologically plausible to assume a monotonic increase (while an opposite shape of nonmonotonicity, once decrease and thereafter increase, called the BCM (Bienenstock-Cooper-Munro) rule has actually been suggested [56-58]).”

      (12) Line 363: This is the most exciting part of the paper (for me). I want to learn way more about this! Don't hide this in a few sentences. I want to know all about loose vs. feedback alignment. Show visualizations in 3D space of the idea of loose alignment (starting in the same quadrant), and compare it to how feedback alignment develops (ending in the same quadrant). Does this "loose" alignment idea give us an idea why the random feedback seems to settle at 45 degree angle? it just needs to get the signs right (same quadrant) for each element?

      In reply to this encouraging comment, we have made further analyses of the loose alignment. By the term "loose alignment", we meant that the value weight vector w and the feedback vector c are in the same (non-negative) quadrant, as you said. But what remained mysterious (to us) was while the angle between w and c was relatively close (loosely aligned) from the beginning, it appeared (as mentioned in the manuscript) that there was no further alignment over trials (and the angle actually settled at somewhat larger than 45°), despite that the same mechanism for feedback alignment that we derived for the model without non-negative constraint was expected to operate also under the nonnegative constraint. We have now clarified the reason for this, and found a way, introduction of slight decay (forgetting) of value weights, by which feedback alignment came to occur in the non-negatively constraint model. We have added this in Line 463-477:

      “As mentioned above, while the angle between w and c was on average smaller than 90° from the beginning, there was no further alignment over trials. This seemed mysterious because the mechanism for feedback alignment that we derived for the models without non-negative constraint was expected to work also for the models with non-negative constraint. As a possible reason for the non-occurrence of feedback alignment, we guessed that one or a few element(s) of w grew prominently during learning, and so w became close to an edge or boundary of the non-negative quadrant and thereby angle between w and other vector became generally large (as illustrated in Fig. 8D). Figure 8Ea shows the mean±SEM of the elements of w ordered from the largest to smallest ones after 1500 trials. As conjectured above, a few elements indeed grew prominently.

      We considered that if a slight decay (forgetting) of value weights (c.f., [59-61]) was assumed, such a prominent growth of a few elements of w may be mitigated and alignment of w to c, beyond the initial loose alignment because of the non-negative constraint, may occur. These conjectures were indeed confirmed by simulations (Fig. 8Eb,c and Fig. 8F). The mean squared value error slightly increased when the value-weightdecay was assumed (Fig. 8G), however, presumably reflecting a decrease in developed values and a deterioration of learning because of the decay.”

      As for visualization, because the model's dimension was high such as 12, we could not come up with better ways of visualization than the trial versus angle plot (Fig. 3A, 8A,F). Nevertheless, we would expect that the abovementioned additional analyses of loose alignment (with graphs) are useful to understand what are going on.

      (13) Line 426: how does this compare to some of the reward modulated hebbian rules proposed in other RNNs? See Hoerzer, G. M., Legenstein, R., & Maass, W. (2014). Put another way, you arrived at this from a top-down approach (gradient descent->BP->approximated by RF->non-negativity constraint>leads to DA dependent modulation of Hebbian plasticity). How might this compare to a bottom up approach (i.e. starting from the principle of Hebbian learning, and adding in reward modulation)

      The study of Hoerzer et al. 2014 used a stochastic perturbation, which we did not assume but can potentially be integrated. On the other hand, Hoerzer et al. trained the readout of untrained RNN, whereas we trained both RNN and its readout. We have added discussion to compare our model with Hoerzer et al. and other works that also used perturbation methods, as well as other top-down approximation method, in Line 685-711 (reference 128 is Hoerzer et al. 2014 Cereb Cortex):

      “As an alternative to backprop in hierarchical network, aside from feedback alignment [36], Associative Reward-Penalty (A<sub>R-P</sub>) algorithm has been proposed [124-126]. In A<sub>R-P</sub>, the hidden units behave stochastically, allowing the gradient to be estimated via stochastic sampling. Recent work [127] has proposed Phaseless Alignment Learning (PAL), in which high-frequency noise-induced learning of feedback projections proceeds simultaneously with learning of forward projections using the feedback in a lower frequency. Noise-induced learning of the weights on readout neurons from untrained RNN by reward-modulated Hebbian plasticity has also been demonstrated [128]. Such noise- or perturbation-based [40] mechanisms are biologically plausible because neurons and neural networks can exhibit noisy or chaotic behavior [129-131], and might improve the performance of value-RNN if implemented.

      Regarding learning of RNN, "e-prop" [35] was proposed as a locally learnable online approximation of BPTT [27], which was used in the original value RNN 26. In e-prop, neuron-specific learning signal is combined with weight-specific locally-updatable "eligibility trace". Reward-based e-prop was also shown to work [35], both in a setup not introducing TD-RPE with symmetric or random feedback (their Supplementary Figure 5) and in another setup introducing TD-RPE with symmetric feedback (their Figure 4 and 5). Compared to these, our models differ in multiple ways.

      First, we have shown that alignment to random feedback occurs in the models driven by TD-RPE. Second, our models do not have "eligibility trace" (nor memorable/gated unit, different from the original valueRNN [26]), but could still solve temporal credit assignment to a certain extent because TD learning is by itself a solution for it (notably, recent work showed that combination of TD(0) and model-based RL well explained rat's choice and DA patterns [132]). However, as mentioned before, single time-step in our models was assumed to correspond to hundreds of milliseconds, incorporating slow synaptic dynamics, whereas e-prop is an algorithm for spiking neuron models with a much finer time scale. From this aspect, our models could be seen as a coarsetime-scale approximation of e-prop. On top of these, our results point to a potential computational benefit of biological non-negative constraint, which could effectively limit the parameter space and promote learning.”

      Related to your latter point (and also replying to other reviewer's comment), we also examined the cases where the random feedback in our model was replaced with uniform feedback, which corresponds to a simple bottom-up reward-modulated triplet plasticity rule. As a result, the model with uniform feedback showed largely comparable, but somewhat worse, performance than the model with random feedback. We have added the results in Fig. 2J-right and Line 206-209 (for our original models without non-negative constraint):

      “The green line in Fig. 2J-right shows the performance of a special case where the random feedback in oVRNNrf was fixed to the direction of (1, 1, ..., 1)<sup>T</sup> (i.e., uniform feedback) with a random coefficient, which was largely comparable to, but somewhat worse than, that for the general oVRNNrf (blue line).”

      and Fig. 6E-right and Line 402-407 (for our extended models with non-negative constraint):

      “The green and light blue lines in the right panels of Figure 6E and Figure 6F show the results for special cases where the random feedback in oVRNNrf-bio was fixed to the direction of (1, 1, ..., 1) <sup>T</sup> (i.e., uniform feedback) with a random non-negative magnitude (green line) or a fixed magnitude of 0.5 (light blue line). The performance of these special cases, especially the former (with random magnitude) was somewhat worse than that of oVRNNrf-bio, but still better than that of the models with untrained RNN. and also added a biological implication of the results in Line 644-652:

      We have shown that oVRNNrf and oVRNNrf-bio could work even when the random feedback was uniform, i.e., fixed to the direction of (1, 1, ..., 1) <sup>T</sup>, although the performance was somewhat worse. This is reasonable because uniform feedback can still encode scalar TD-RPE that drives our models, in contrast to a previous study [45], which considered DA's encoding of vector error and thus regarded uniform feedback as a negative control. If oVRNNrf/oVRNNrf-bio-like mechanism indeed operates in the brain and the feedback is near uniform, alignment of the value weights w to near (1, 1, ..., 1) is expected to occur. This means that states are (learned to be) represented in such a way that simple summation of cortical neuronal activity approximates value, thereby potentially explaining why value is often correlated with regional activation (fMRI BOLD signal) of cortical regions [113].”

      Reviewer #3 (Public review):

      Summary:

      The paper studies learning rules in a simple sigmoidal recurrent neural network setting. The recurrent network has a single layer of 10 to 40 units. It is first confirmed that feedback alignment (FA) can learn a value function in this setting. Then so-called bio-plausible constraints are added: (1) when value weights (readout) is non-negative, (2) when the activity is non-negative (normal sigmoid rather than downscaled between -0.5 and 0.5), (3) when the feedback weights are non-negative, (4) when the learning rule is revised to be monotic: the weights are not downregulated. In the simple task considered all four biological features do not appear to impair totally the learning.

      Strengths:

      (1) The learning rules are implemented in a low-level fashion of the form: (pre-synaptic-activity) x (post-synaptic-activity) x feedback x RPE. Which is therefore interpretable in terms of measurable quantities in the wet-lab.

      (2) I find that non-negative FA (FA with non negative c and w) is the most valuable theoretical insight of this paper: I understand why the alignment between w and c is automatically better at initialization.

      (3) The task choice is relevant since it connects with experimental settings of reward conditioning with possible plasticity measurements.

      Weaknesses:

      (4) The task is rather easy, so it's not clear that it really captures the computational gap that exists with FA (gradient-like learning) and simpler learning rule like a delta rule: RPE x (pre-synpatic) x (postsynaptic). To control if the task is not too trivial, I suggest adding a control where the vector c is constant c_i=1.

      We have examined the cases where the feedback was uniform, i.e., in the direction of (1, 1, ..., 1) in both models without and with non-negative constraint. In both models, the models with uniform feedback performed somewhat worse than the original models with random feedback, but still better than the models with untrained RNN. We have added the results in Fig. 2J-right and Line 206-209 (for our original models without non-negative constraint):

      “The green line in Fig. 2J-right shows the performance of a special case where the random feedback in oVRNNrf was fixed to the direction of (1, 1, ..., 1) <sup>T</sup> (i.e., uniform feedback) with a random coefficient, which was largely comparable to, but somewhat worse than, that for the general oVRNNrf (blue line).”

      and Fig. 6E-right and Line 402-407 (for our extended models with non-negative constraint):

      “The green and light blue lines in the right panels of Figure 6E and Figure 6F show the results for special cases where the random feedback in oVRNNrf-bio was fixed to the direction of (1, 1, ..., 1) <sup>T</sup> (i.e., uniform feedback) with a random non-negative magnitude (green line) or a fixed magnitude of 0.5 (light blue line). The performance of these special cases, especially the former (with random magnitude) was somewhat worse than that of oVRNNrf-bio, but still better than that of the models with untrained RNN.”

      We have also added a discussion on the biological implication of the model with uniform feedback mentioned in our provisional reply in Line 644-652:

      “We have shown that oVRNNrf and oVRNNrf-bio could work even when the random feedback was uniform, i.e., fixed to the direction of (1, 1, ..., 1) <sup>T</sup>, although the performance was somewhat worse. This is reasonable because uniform feedback can still encode scalar TD-RPE that drives our models, in contrast to a previous study [45], which considered DA's encoding of vector error and thus regarded uniform feedback as a negative control. If oVRNNrf/oVRNNrf-bio-like mechanism indeed operates in the brain and the feedback is near uniform, alignment of the value weights w to near (1, 1, ..., 1) is expected to occur. This means that states are (learned to be) represented in such a way that simple summation of cortical neuronal activity approximates value, thereby potentially explaining why value is often correlated with regional activation (fMRI BOLD signal) of cortical regions [113].”

      In addition, while preparing the revised manuscript, we found a recent simulation study, which showed that uniform feedback coupled with positive forward weights was effective in supervised learning of one-dimensional output in feed-forward network (Konishi et al., 2023, Front Neurosci).

      We have briefly discussed this work in Line 653-655:

      “Notably, uniform feedback coupled with positive forward weights was shown to be effective also in supervised learning of one-dimensional output in feed-forward network [114], and we guess that loose alignment may underlie it.”

      (5) Related to point 3), the main strength of this paper is to draw potential connection with experimental data. It would be good to highlight more concretely the prediction of the theory for experimental findings. (Ideally, what should be observed with non-negative FA that is not expected with FA or a delta rule (constant global feedback) ?).

      We have added a discussion on the prediction of our models, mentioned in our provisional reply, in Line 627-638:

      “oVRNNrf predicts that the feedback vector c and the value-weight vector w become gradually aligned, while oVRNNrf-bio predicts that c and w are loosely aligned from the beginning. Element of c could be measured as the magnitude of pyramidal cell's response to DA stimulation. Element of w corresponding to a given pyramidal cell could be measured, if striatal neuron that receives input from that pyramidal cell can be identified (although technically demanding), as the magnitude of response of the striatal neuron to activation of the pyramidal cell. Then, the abovementioned predictions could be tested by (i) identify cortical, striatal, and VTA regions that are connected, (ii) identify pairs of cortical pyramidal cells and striatal neurons that are connected, (iii) measure the responses of identified pyramidal cells to DA stimulation, as well as the responses of identified striatal neurons to activation of the connected pyramidal cells, and (iv) test whether DA→pyramidal responses and pyramidal→striatal responses are associated across pyramidal cells, and whether such associations develop through learning.”

      Moreover, we have considered another (technically more doable) prediction of our model, and described it in Line 639-643:

      “Testing this prediction, however, would be technically quite demanding, as mentioned above. An alternative way of testing our model is to manipulate the cortical DA feedback and see if it will cause (re-)alignment of value weights (i.e., cortical striatal strengths). Specifically, our model predicts that if DA projection to a particular cortical locus is silenced, effect of the activity of that locus on the value-encoding striatal activity will become diminished.”

      (6a) Random feedback with RNN in RL have been studied in the past, so it is maybe worth giving some insights how the results and the analyzes compare to this previous line of work (for instance in this paper [1]). For instance, I am not very surprised that FA also works for value prediction with TD error. It is also expected from the literature that the RL + RNN + FA setting would scale to tasks that are more complex than the conditioning problem proposed here, so is there a more specific take-home message about non-negative FA? or benefits from this simpler toy task? [1] https://www.nature.com/articles/s41467-020-17236-y

      As for a specific feature of non-negative models, we did not describe (actually did not well recognize) an intriguing result that the non-negative random feedback model performed generally better than the models without non-negative constraint with either backprop or random feedback (Fig. 2J-left versus Fig. 6E-left (please mind the difference in the vertical scales)). This suggests that the non-negative constraint effectively limited the parameter space and thereby learning became efficient. We have added this result in Line 392-395:

      “Remarkably, oVRNNrf-bio generally achieved better performance than both oVRNNbp and oVRNNrf, which did not have the non-negative constraint (Wilcoxon rank sum test, vs oVRNNbp : p < 7.8×10,sup>−6</sup> for 5 or ≥25 RNN units; vs oVRNNrf: p < 0.021 for ≤10 or ≥20 RNN units).”

      Also, in the models with non-negative constraint, the model with random feedback learned more rapidly than the model with backprop although they eventually reached a comparable level of errors, at least in the case with 20 RNN units. This is presumably because the value weights did not develop well in early trials and so the backprop-based feedback, which was the same as the value weights, did not work well, while the non-negative fixed random feedback worked finely from the beginning. We have added this result in Fig. 6I and Line 417-422:

      “Figure 6I shows how learning proceeded across trials in the models with 20 RNN units. While oVRNNbp-rev and oVRNNrf-bio eventually reached a comparable level of errors, oVRNNrf-bio outperformed oVRNNbp-rev in early trials (at 200, 300, 400, or 500 trials; p < 0.049 in Wilcoxon rank sum test for each). This is presumably because the value weights did not develop well in early trials and so the backprop-type feedback, which was the same as the value weights, did not work well, while the non-negative fixed random feedback worked finely from the beginning.”

      We have also added a discussion on how our model can be positioned in relation to other models including the study you mentioned (e-prop by Bellec, ..., Maass, 2020) in subsection “Comparison to other algorithms” of the Discussion):

      Regarding the slightly better performance of the non-negative model with random feedback than that of the non-negative model with backprop when the number of RNN units was large (mentioned in our provisional reply), state values in the backprop model appeared underdeveloped than those in the random feedback model. Slightly better performance of random feedback than backprop held also in our extended model incorporating excitatory and inhibitory units (Fig. 9B).

      (6b) Related to task complexity, it is not clear to me if non-negative value and feedback weights would generally scale to harder tasks. If the task in so simple that a global RPE signal is sufficient to learn (see 4 and 5), then it could be good to extend the task to find a substantial gap between: global RPE, non-negative FA, FA, BP. For a well chosen task, I expect to see a performance gap between any pair of these four learning rules. In the context of the present paper, this would be particularly interesting to study the failure mode of non-negative FA and the cases where it does perform as well as FA.

      In the cue-reward association task with 3 time-steps delay, the non-negative model with random feedback performed largely comparably to the non-negative model with backprop, and this remained to hold in a task where distractor cue, which was not associated with reward, appeared in random timings. We have added the results in Fig. 10 and subsection “4.2 Task with distractor cue”.

      We have also examined the cases where the cue-reward delay was elongated. In the case of longer cue-reward delay (6 time-steps), in the models without non-negative constraint, the model with random feedback performed comparably to (and slightly better than when the number of RNN units was large) the model with backprop (Fig. 2M). In contrast, in the models with non-negative constraint, the model with random feedback underperformed the model with backprop (Fig. 6J, left-bottom). This indicates a difference between the effect of non-negative random feedback and the effect of positive+negative random feedback.

      We have further examined the performance of the models in terms of action selection, by extending the models to incorporate an actor-critic algorithm. In a task with inter-temporal choice (i.e., immediate small reward vs delayed large reward), the non-negative model with random feedback performed worse than the non-negative model with backprop when the number of RNN units was small. When the number of RNN increased, these models performed more comparably. These results are described in Fig. 11 and subsection “4.3 Incorporation of action selection”.

      (7) I find that the writing could be improved, it mostly feels more technical and difficult than it should. Here are some recommendations:

      7a) for instance the technical description of the task (CSC) is not fully described and requires background knowledge from other paper which is not desirable.

      7b) Also the rationale for the added difficulty with the stochastic reward and new state is not well explained.

      7c) In the technical description of the results I find that the text dives into descriptive comments of the figures but high-level take home messages would be helpful to guide the reader. I got a bit lost, although I feel that there is probably a lot of depth in these paragraphs.

      As for 7a), 'CSC (complete serial compound)' was actually not the name of the task but the name of the 'punctate' state representation, in which each state (timing from cue) is represented in a punctate manner, i.e., by a one-hot vector such as (1, 0, ..., 0), (0, 1, ..., 0), ..., and (0, 0, ..., 1). As you pointed out, using the name of 'CSC' would make the text appearing more technical than it actually is, and so we have moved the reference to the name of 'CSC' to the Methods (Line 903-907):

      “For the agents with punctate state representation, which is also referred to as the complete serial compound (CSC) representation [1, 48, 133], each timing from a cue in the tasks was represented by a 10-dimensional one-hot vector, starting from (1 0 0 ... 0)<sup>T</sup> for the cue state, with the next state (0 1 0 ... 0) <sup>T</sup> and so on.”

      and in the Results we have instead added a clearer explanation (Line 163-165):

      “First, for comparison, we examined traditional TD-RL agent with punctate state representation (without using the RNN), in which each state (time-step from a cue) was represented in a punctate manner, i.e., by a one-hot vector such as (1, 0, ..., 0), (0, 1, ..., 0), and so on.”

      As for 7b), we have added the rationale for our examination of the tasks with probabilistic structures (Line 282-294):

      “Previous work [54] examined the response of DA neurons in cue-reward association tasks in which reward timing was probabilistically determined (early in some trials but late in other trials). There were two tasks, which were largely similar but there was a key difference that reward was given in all the trials in one task whereas reward was omitted in some randomly determined trials in another task. Starkweather et al. [54] found that the DA response to later reward was smaller than the response to earlier reward in the former task, presumably reflecting the animal's belief that delayed reward will surely come, but the opposite was the case in the latter task, presumably because the animal suspected that reward was omitted in that trial. Starkweather et al.[54] then showed that such response patterns could be explained if DA encoded TD-RPE under particular state representations that incorporated the probabilistic structures of the task (called the 'belief state'). In that study, such state representations were 'handcrafted' by the authors, but the subsequent work [26] showed that the original value-RNN with backprop (BPTT) could develop similar representations and reproduce the experimentally observed DA patterns.”

      As for 7c), we have extensively revised the text of the results, adding high-level explanations while trying to reduce the lengthy low-level descriptions (e.g., Line 172-177 for Fig2E-G).

      (8) Related to the writing issue and 5), I wished that "bio-plausibility" was not the only reason to study positive feedback and value weights. Is it possible to develop a bit more specifically what and why this positivity is interesting? Is there an expected finding with non-negative FA both in the model capability? or maybe there is a simpler and crisp take-home message to communicate the experimental predictions to the community would be useful?

      There is actually an unexpected finding with non-negative model: the non-negative random feedback model performed generally better than the models without non-negative constraint with either backprop or random feedback (Fig. 2J-left versus Fig. 6E-left), presumably because the nonnegative constraint effectively limited the parameter space and thereby learning became efficient, as we mentioned in our reply to your point 6a above (we did not well recognize this at the time of original submission).

      Another potential merit of our present work is the simplicity of the model and the task. This simplicity enabled us to derive an intuitive explanation on why feedback alignment could occur. Such an intuitive explanation was lacking in previous studies while more precise mathematical explanations did exist. Related to the mechanism of feedback alignment, one thing remained mysterious to us at the time of original submission. Specifically, in the non-negatively constraint random feedback model, while the angle between the value weight (w) and the random feedback (c) was relatively close (loosely aligned) from the beginning, it appeared (as mentioned in the manuscript) that there was no further alignment over trials (and the angle actually settled at somewhat larger than 45°), despite that the same mechanism for feedback alignment that we derived for the model without non-negative constraint was expected to operate also under the non-negative constraint. We have now clarified the reason for this, and found a way, introduction of slight decay (forgetting) of value weights, by which feedback alignment came to occur in the non-negatively constraint model. We have added this in Line 463-477:

      “As mentioned above, while the angle between w and c was on average smaller than 90° from the beginning, there was no further alignment over trials. This seemed mysterious because the mechanism for feedback alignment that we derived for the models without non-negative constraint was expected to work also for the models with non-negative constraint. As a possible reason for the non-occurrence of feedback alignment, we guessed that one or a few element(s) of w grew prominently during learning, and so w became close to an edge or boundary of the non-negative quadrant and thereby angle between w and other vector became generally large (as illustrated in Fig. 8D). Figure 8Ea shows the mean±SEM of the elements of w ordered from the largest to smallest ones after 1500 trials. As conjectured above, a few elements indeed grew prominently.

      We considered that if a slight decay (forgetting) of value weights (c.f., [59-61]) was assumed, such a prominent growth of a few elements of w may be mitigated and alignment of w to c, beyond the initial loose alignment because of the non-negative constraint, may occur. These conjectures were indeed confirmed by simulations (Fig. 8Eb,c and Fig. 8F). The mean squared value error slightly increased when the value-weightdecay was assumed (Fig. 8G), however, presumably reflecting a decrease in developed values and a deterioration of learning because of the decay.”

      Correction of an error in the original manuscript

      In addition to revising the manuscript according to your comments, we have made a correction on the way of estimating the true state values. Specifically, in the original manuscript, we defined states by relative time-steps from a reward and estimated their values by calculating the sums of discounted future rewards starting from them through simulations. However, we assumed variable inter-trial intervals (ITIs) (4, 5, 6, or 7 time-steps with equal probabilities), and so until receiving cue information, agent should not know when the next reward will come. Therefore, states for the timings up to the cue timing cannot be defined by the upcoming reward, but previously we did so (e.g., state of "one timestep before cue") without taking into account the ITI variability.

      We have now corrected this issue, having defined the states of timings with respect to the previous (rather than upcoming) reward. For example, when ITI was 4 time-steps and agent existed in its last time-step, agent will in fact receive a cue at the next time-step, but agent should not know it until actually receiving the cue information and instead should assume that s/he was at the last time-step of ITI (if ITI was 4), last − 1 (if ITI was 5), last − 2 (if ITI was 6), or last − 3 (if ITI was 7) with equal probabilities (in a similar fashion to what we considered when thinking about state definition for the probabilistic tasks). We estimated the true values of states defined in this way through simulations. As a result, the corrected true value of the cue-timing has become slightly smaller than the value described in the original manuscript (reflecting the uncertainty about ITI length), and consequently small positive TD-RPE has now appeared at the cue timing.

      Because we measured the performance of the models by squared errors in state values, this correction affected the results reporting the performance. Fortunately, the effects were relatively minor and did not largely alter the results of performance comparisons. However, we sincerely apologize for this error. In the revised manuscript, we have used the corrected true values throughout the manuscript, and we have described the ways of estimating these values in Line 919-976.

    1. yellow expression in sex combs on male mating behavior

      This section tests the hypothesis that yellow affects mating through sex comb pigmentation, not neural function.I think this is the main hypothesis they’re testing yellow’s effect on mating success depends on sex comb pigmentation. Interesting that it’s about morphology, not behavior via the brain.Does reduced melanization really make the combs less effective at grasping females? Or is it just a visual signal?

    1. Most cases of plagiarism can be avoided, however, by citing sources. Simply acknowledging that certain material has been borrowed and providing your audience with the information necessary to find that source is usually enough to prevent plagiarism.

      This is actually good news because it shows that avoiding plagiarism isn't that complicated - I just need to cite my sources properly. It takes the pressure off because I know if I give credit where it's due, I'll be fine

    2. According to the Merriam-Webster online dictionary, to "plagiarize" means: to steal and pass off (the ideas or words of another) as one's own to use (another's production) without crediting the source to commit literary theft to present as new and original an idea or product derived from an existing source

      This definition shows that plagiarism isn’t just copying, it’s actually stealing someone else’s work and pretending it’s yours. As a student, it reminds me how important it is to give credit and be honest about where my ideas come from.

    1. Informatics applies technology to transform data into actionable information to support decision-making. It focuses on how data interacts with people, systems, and specific domains of society. Informatics serves as a bridge between humans and computers by facilitating the exchange, interpretation, and use of information. It draws from Computer Science (CS), information science (IS), and domain knowledge

      When verifying the general definition of informatics, it mostly aligns with Health Informatics but needs a few clarifications to fully fit this domain. Health Informatics is not just about transforming data — it’s about using information and technology to improve patient care, safety, and health outcomes. This work relies on tools like electronic health records (EHRs), clinical decision support systems, telehealth, and secure data sharing to make sure information is available and meaningful to clinicians, patients, and public health teams (Sumner et al., 2025). A good definition must also include how Health Informatics depends on standards like HL7 and FHIR to ensure systems can work together. It also involves designing user-friendly systems and following strict privacy rules like HIPAA, plus strong ethical practices around consent and data security (Valenta et al., 2018). It’s important to note the growing role of artificial intelligence and remote monitoring in today’s digital health systems too. Overall, Health Informatics clearly goes beyond general IT — it’s an interdisciplinary field that connects clinical care, data science, and technology to support safer, more efficient, and patient-centered healthcare (Mansurjonovich, 2023). When done well, Health Informatics empowers teams to deliver better evidence-based decisions. It also helps reduce errors and supports health equity by improving access to quality information and care.

      Rationale: Including these specific elements makes the definition of Health Informatics more accurate because it shows that this domain is not only about handling data but also about applying it responsibly to real healthcare challenges. It highlights how Health Informatics connects people, technology, and policy to improve patient safety and outcomes, which goes beyond what general IT or computer science do on their own. By focusing on ethical, secure, and patient-centered data use, the definition makes it clear how Health Informatics stands apart as a unique and critical field in modern healthcare (Mansurjonovich, 2023; Sumner et al., 2025).

      References:

      Mansurjonovich, J. M. (2023). Designing an electronic didactic environment to ensure interdisciplinary integration in the teaching of" Informatics and information technologies" during professional education. Conference, 3(03), 78-82.

      Sumner, B., Martin, R., Gladman, T., Wilkinson, T. J., & Grainger, R. (2025). Understanding the gap: A balanced multi-perspective approach to defining essential digital health competencies for medical graduates. BMC medical education, 25(1), 1-11.

      Valenta, A. L., Berner, E. S., Boren, S. A., Deckard, G. J., Eldredge, C., Fridsma, D. B., Gadd, C., Gong, Y., Johnson, T., Jones, J., Manos, E. L., Phillips, K. T., Roderer, N. K., Rosendale, D., Turner, A. M., Tusch, G., Williamson, J. J., & Johnson, S. B. (2018). AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master’s degree level. Journal of the American Medical Informatics Association, 25(12), 1657–1668. https://doi.org/10.1093/jamia/ocy132

    1. 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

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

      PAPS is required for all sulfotransferase reactions in which a sulfate group is covalently attached to amino acid residues of proteins or to side chains of proteoglycans. This sulfation is crucial for properly organizing the apical extracellular matrix (aECM) and expanding the lumen in the Drosophila salivary gland. Loss of Papss potentially leads to decreased sulfation, disorganizing the aECM, and defects in lumen formation. In addition, Papss loss destabilizes the Golgi structures.

      In Papss mutants, several changes occur in the salivary gland lumen of Drosophila. The tube lumen is very thin and shows irregular apical protrusions. There is a disorganization of the apical membrane and a compaction of the apical extracellular matrix (aECM). The Golgi structures and intracellular transport are disturbed. In addition, the ZP domain proteins Piopio (Pio) and Dumpy (Dpy) lose their normal distribution in the lumen, which leads to condensation and dissociation of the Dpy-positive aECM structure from the apical membrane. This results in a thin and irregularly dilated lumen.

      1. The authors describe various changes in the lumen in mutants, from thin lumen to irregular expansion. I would like to know the correct lumen diameter, and length, besides the total area, by which one can recognize thin and irregular.

      We have included quantification of the length and diameter of the salivary gland lumen in the stage 16 salivary glands of control, Papss mutant, and salivary gland-specific rescue embryos (Figure 1J, K). As described, Papss mutant embryos have two distinct phenotypes, one group with a thin lumen along the entire lumen and the other group with irregular lumen shapes. Therefore, we separated the two groups for quantification of lumen diameter. Additionally, we have analyzed the degree of variability for the lumen diameter to better capture the range of phenotypes observed (Figure 1K'). These quantifications enable a more precise assessment of lumen morphology, allowing readers to distinguish between thin and irregular lumen phenotypes.

      The rescue is about 30%, which is not as good as expected. Maybe the wrong isoform was taken. Is it possible to find out which isoform is expressed in the salivary glands, e.g., by RNA in situ Hyb? This could then be used to analyze a more focused rescue beyond the paper.

      Thank you for this point, but we do not agree that the rescue is about 30%. In Papss mutants, about 50% of the embryos show the thin lumen phenotype whereas the other 50% show irregular lumen shapes. In the rescue embryos with a WT Papss, few embryos showed thin lumen phenotypes. About 40% of the rescue embryos showed "normal, fully expanded" lumen shapes, and the remaining 60% showed either irregular (thin+expanded) or slightly overexpanded lumen. It is not uncommon that rescue with the Gal4/UAS system results in a partial rescue because it is often not easy to achieve the balance of the proper amount of the protein with the overexpression system.

      To address the possibility that the wrong isoform was used, we performed in situ hybridization to examine the expression of different Papss spice forms in the salivary gland. We used probes that detect subsets of splice forms: A/B/C/F/G, D/H, and E/F/H, and found that all probes showed expression in the salivary gland, with varying intensities. The original probe, which detects all splice forms, showed the strongest signals in the salivary gland compared to the new probes which detect only a subset. However, the difference in the signal intensity may be due to the longer length of the original probe (>800 bp) compared to other probes that were made with much smaller regions (~200 bp). Digoxigenin in the DIG labeling kit for mRNA detection labels the uridine nucleotide in the transcript, and the probes with weaker signals contain fewer uridines (all: 147; ABCFG, 29; D, 36; EFH, 66). We also used the Papss-PD isoform, for a salivary gland-specific rescue experiment and obtained similar results to those with Papss-PE (Figure 1I-L, Figure 4D and E).

      Furthermore, we performed additional experiments to validate our findings. We performed a rescue experiment with a mutant form of Papss that has mutations in the critical rescues of the catalytic domains of the enzyme, which failed to rescue any phenotypes, including the thin lumen phenotype (Figure 1H, J-L), the number and intensity of WGA puncta (Figure 3I, I'), and cell death (Figure 4D, E). These results provide strong evidence that the defects observed in Papss mutants are due to the lack of sulfation.

      Crb is a transmembrane protein on the apicolateral side of the membrane. Accordingly, the apicolateral distribution can be seen in the control and the mutant. I believe there are no apparent differences here, not even in the amount of expression. However, the view of the cells (frame) shows possible differences. To be sure, a more in-depth analysis of the images is required. Confocal Z-stack images, with 3D visualization and orthogonal projections to analyze the membranes showing Crb staining together with a suitable membrane marker (e.g. SAS or Uif). This is the only way to show whether Crb is incorrectly distributed. Statistics of several papas mutants would also be desirable and not just a single representative image. When do the observed changes in Crb distribution occur in the development of the tubes, only during stage 16? Is papss only involved in the maintenance of the apical membrane? This is particularly important when considering the SJ and AJ, because the latter show no change in the mutants.

      We appreciate your suggestion to more thoroughly analyze Crb distribution. We adapted a method from a previous study (Olivares-Castiñeira and Llimargas, 2017) to quantify Crb signals in the subapical region and apical free region of salivary gland cells. Using E-Cad signals as a reference, we marked the apical cell boundaries of individual cells and calculated the intensity of Crb signals in the subapical region (along the cell membrane) and in the apical free region. We focused on the expanded region of the SG lumen in Papss mutants for quantification, as the thin lumen region was challenging to analyze. This quantification is included in Figure 2D. Statistical analysis shows that Crb signals were more dispersed in SG cells in Papss mutants compared to WT.

      A change in the ECM is only inferred based on the WGA localization. This is too few to make a clear statement. WGA is only an indirect marker of the cell surface and glycosylated proteins, but it does not indicate whether the ECM is altered in its composition and expression. Other important factors are missing here. In addition, only a single observation is shown, and statistics are missing.

      We understand your concern that WGA localization alone may not be sufficient to conclude changes in the ECM. However, we observed that luminal WGA signals colocalize with Dpy-YFP in the WT SG (Figure 5-figure supplement 2C), suggesting that WGA detects the aECM structure containing Dpy. The similar behavior of WGA and Dpy-YFP signals in multiple genotypes further supports this idea. In Papss mutants with a thin lumen phenotype, both WGA and Dpy-YFP signals are condensed (Figure 5E-H), and in pio mutants, both are absent from the lumen (Figure 6B, D). We analyzed WGA signals in over 25 samples of WT and Papss mutants, observing consistent phenotypes. We have included the number of samples in the text. While we acknowledge that WGA is an indirect marker, our data suggest that it is a reliable indicator of the aECM structure containing Dpy.

      Reduced WGA staining is seen in papss mutants, but this could be due to other circumstances. To be sure, a statistic with the number of dots must be shown, as well as an intensity blot on several independent samples. The images are from single confocal sections. It could be that the dots appear in a different Z-plane. Therefore, a 3D visualization of the voxels must be shown to identify and, at best, quantify the dots in the organ.

      We have quantified cytoplasmic punctate WGA signals. Using spinning disk microscopy with super-resolution technology (Olympus SpinSR10 Sora), we obtained high-resolution images of cytoplasmic punctate signals of WGA in WT, Papss mutant, and rescue SGs with the WT and mutant forms of Papss-PD. We then generated 3D reconstructed images of these signals using Imaris software (Figure 3E-H) and quantified the number and intensity of puncta. Statistical analysis of these data confirms the reduction of the number and intensity of WGA puncta in Papss mutants (Figure 3I, I'). The number of WGA puncta was restored by expressing WT Papss but not the mutant form. By using 3D visualization and quantification, we have ensured that our results are not limited to a single confocal section and account for potential variations in Z-plane localization of the dots.

      A colocalization analysis (statistics) should be shown for the overlap of WGA with ManII-GFP.

      Since WGA labels multiple structures, including the nuclear envelope and ECM structures, we focused on assessing the colocalization of the cytoplasmic WGA punctate signals and ManII-GFP signals. Standard colocalization analysis methods, such as Pearson's correlation coefficient or Mander's overlap coefficient, would be confounded by WGA signals in other tissues. Therefore, we used a fluorescent intensity line profile to examine the spatial relationship between WGA and ManII-GFP signals in WT and Papss mutants (Figure 3L, L').

      I do not understand how the authors describe "statistics of secretory vesicles" as an axis in Figure 3p. The TEM images do not show labeled secretory vesicles but empty structures that could be vesicles.

      Previous studies have analyzed "filled" electron-dense secretory vesicles in TEM images of SG cells (Myat and Andrew, 2002, Cell; Fox et al., 2010, J Cell Biol; Chung and Andrew, 2014, Development). Consistent with these studies, our WT TEM images show these vesicles. In contrast, Papss mutants show a mix of filled and empty structures. For quantification, we specifically counted the filled electron-dense vesicles (now Figure 3W). A clear description of our analysis is provided in the figure legend.

      1. The quality of the presented TEM images is too low to judge any difference between control and mutants. Therefore, the supplement must present them in better detail (higher pixel number?).

      We disagree that the quality of the presented TEM images is too low. Our TEM images have sufficient resolution to reveal details of many subcellular structures, such as mitochondrial cisternae. The pdf file of the original submission may not have been high resolution. To address this concern, we have provided several original high-quality TEM images of both WT and Papss mutants at various magnifications in Figure 2-figure supplement 2. Additionally, we have included low-magnification TEM images of WT and Papss mutants in Figure 2H and I to provide a clearer view of the overall SG lumen morphology.

      Line 266: the conclusion that apical trafficking is "significantly impaired" does not hold. This implies that Papss is essential for apical trafficking, but the analyzed ECM proteins (Pio, Dumpy) are found apically enriched in the mutants, and Dumpy is even secreted. Moreover, they analyze only one marker, Sec15, and don't provide data about the quantification of the secretion of proteins.

      We agree and have revised our statement to "defective sulfation affects Golgi structures and multiple routes of intracellular trafficking".

      DCP-1 was used to detect apoptosis in the glands to analyze acellular regions. However, the authors compare ST16 control with ST15 mutant salivary glands, which is problematic. Further, it is not commented on how many embryos were analyzed and how often they detect the dying cells in control and mutant embryos. This part must be improved.

      Thank you for the comment. We agree and have included quantification. We used stage 16 samples from WT and Papss mutants to quantify acellular regions. Since DCP-1 signals are only present at a specific stage of apoptosis, some acellular regions do not show DCP-1 signals. Therefore, we counted acellular regions regardless of DCP-1 signals. We also quantified this in rescue embryos with WT and mutant forms of Papss, which show complete rescue with WT and no rescue with the mutant form, respectively. The graph with a statistical analysis is included (Figure 4D, E).

      WGA and Dumpy show similar condensed patterns within the tube lumen. The authors show that dumpy is enriched from stage 14 onwards. How is it with WGA? Does it show the same pattern from stage 14 to 16? Papss mutants can suffer from a developmental delay in organizing the ECM or lack of internalization of luminal proteins during/after tube expansion, which is the case in the trachea.

      Dpy-YFP and WGA show overlapping signals in the SG lumen throughout morphogenesis. Dpy-YFP is SG enriched in the lumen from stage 11, not stage 14 (Figure 5-figure supplement 2). WGA is also detected in the lumen throughout SG morphogenesis, similar to Dpy. In the original supplemental figure, only a stage 16 SG image was shown for co-localization of Dpy-YFP and WGA signals in the SG lumen. We have now included images from stage 14 and 15 in Figure 5-figure supplement 2C.

      Given that luminal Pio signals are lost at stage 16 only and that Dpy signals appear as condensed structures in the lumen of Papss mutants, it suggests that the internalization of luminal proteins is not impaired in Papss mutants. Rather, these proteins are secreted but fail to organize properly.

      Line 366. Luminal morphology is characterized by bulging and constrictions. In the trachea, bulges indicate the deformation of the apical membrane and the detachment from the aECM. I can see constrictions and the collapsed tube lumen in Fig. 6C, but I don't find the bulges of the apical membrane in pio and Np mutants. Maybe showing it more clearly and with better quality will be helpful.

      Since the bulging phenotype appears to vary from sample to sample, we have revised the description of the phenotype to "constrictions" to more accurately reflect the consistent observations. We quantified the number of constrictions along the entire lumen in pio and Np mutants and included the graph in Figure 6F.

      The authors state that Papss controls luminal secretion of Pio and Dumpy, as they observe reduced luminal staining of both in papss mutants. However, the mCh-Pio and Dumpy-YFP are secreted towards the lumen. Does papss overexpression change Pio and Dumpy secretion towards the lumen, and could this be another explanation for the multiple phenotypes?

      Thank you for the comment. To clarify, we did not observe reduced luminal staining of Pio and Dpy in Papss mutants, nor did we state that Papss controls luminal secretion of Pio and Dpy. In Papss mutants, Pio luminal signals are absent specifically at stage 16 (Figure 5H), whereas strong luminal Pio signals are present until stage 15 (Figure 5G). For Dpy-YFP, the signals are not reduced but condensed in Papss mutants from stages 14-16 (Figure 5D, H).

      It remains unclear whether the apparent loss of Pio signals is due to a loss of Pio protein in the lumen or due to epitope masking resulting from protein aggregation or condensation. As noted in our response to Comment 11 internalization of luminal proteins seems unaffected in Papss mutants; proteins like Pio and Dpy are secreted into the lumen but fail to properly organize. Therefore, we have not tested whether Papss overexpression alters the secretion of Pio or Dpy.

      In our original submission, we incorrectly stated that uniform luminal mCh-Pio signals were unchanged in Papss mutants. Upon closer examination, we found these signals are absent in the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly.

      Regulation of luminal ZP protein level is essential to modulate the tube expansion; therefore, Np releases Pio and Dumpy in a controlled manner during st15/16. Thus, the analysis of Pio and Dumpy in NP overexpression embryos will be critical to this manuscript to understand more about the control of luminal ZP matrix proteins.

      Thanks for the insightful suggestion. We overexpressed both the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. It is important to note that these overexpression experiments were done in the presence of the endogenous WT Np.

      Overexpression of Np.WT led to increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. In contrast, overexpression of Np.S990A resulted in a near complete loss of luminal mCh-Pio signals. Pio antibody signals remained strong at the apical membrane but was weaker in the luminal filamentous structures compared to WT.

      Due to the GFP tag present in the UAS-Np.S990A line, we could not reliably analyze Dpy-YFP signals because of overlapping fluorescent signals in the same channel. However, the filamentous Pio signals in the lumen co-localized with GFP signals, suggesting that these structures might also include Dpy-YFP, although this cannot be confirmed definitively.

      These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

      Minor: Fig. 5 C': mChe-Pio and Dumpy-YFP are mixed up at the top of the images.

      Thanks for catching this error. It has been corrected.

      Sup. Fig7. A shows Pio in purple but B in green. Please indicate it correctly.

      It has been corrected.

      Reviewer #1 (Significance (Required)):

      In 2023, the functions of Pio, Dumpy, and Np in the tracheal tubes of Drosophila were published. The study here shows similar results, with the difference that the salivary glands do not possess chitin, but the two ZP proteins Pio and Dumpy take over its function. It is, therefore, a significant and exciting extension of the known function of the three proteins to another tube system. In addition, the authors identify papss as a new protein and show its essential function in forming the luminal matrix in the salivary glands. Considering the high degree of conservation of these proteins in other species, the results presented are crucial for future analyses and will have further implications for tubular development, including humans.

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

      Summary: There is growing appreciation for the important of luminal (apical) ECM in tube development, but such matrices are much less well understood than basal ECMs. Here the authors provide insights into the aECM that shapes the Drosophila salivary gland (SG) tube and the importance of PAPSS-dependent sulfation in its organization and function.

      The first part of the paper focuses on careful phenotypic characterization of papss mutants, using multiple markers and TEM. This revealed reduced markers of sulfation (Alcian Blue staining) and defects in both apical and basal ECM organization, Golgi (but not ER) morphology, number and localization of other endosomal compartments, plus increased cell death. The authors focus on the fact that papss mutants have an irregular SG lumen diameter, with both narrowed regions and bulged regions. They address the pleiotropy, showing that preventing the cell death and resultant gaps in the tube did not rescue the SG luminal shape defects and discussing similarities and differences between the papss mutant phenotype and those caused by more general trafficking defects. The analysis uses a papss nonsense mutant from an EMS screen - I appreciate the rigorous approach the authors took to analyze transheterozygotes (as well as homozygotes) plus rescued animals in order to rule out effects of linked mutations.

      The 2nd part of the paper focuses on the SG aECM, showing that Dpy and Pio ZP protein fusions localize abnormally in papss mutants and that these ZP mutants (and Np protease mutants) have similar SG lumen shaping defects to the papss mutants. A key conclusion is that SG lumen defects correlate with loss of a Pio+Dpy-dependent filamentous structure in the lumen. These data suggest that ZP protein misregulation could explain this part of the papss phenotype.

      Overall, the text is very well written and clear. Figures are clearly labeled. The methods involve rigorous genetic approaches, microscopy, and quantifications/statistics and are documented appropriately. The findings are convincing, with just a few things about the fusions needing clarification.

      minor comments 1. Although the Dpy and Qsm fusions are published reagents, it would still be helpful to mention whether the tags are C-terminal as suggested by the nomenclature, and whether Westerns have been performed, since (as discussed for Pio) cleavage could also affect the appearance of these fusions.

      Thanks for the comment. Dpy-YFP is a knock-in line in which YFP is inserted into the middle of the dpy locus (Lye et al., 2014; the insertion site is available on Flybase). mCh-Qsm is also a knock-in line, with mCh inserted near the N-terminus of the qsm gene using phi-mediated recombination using the qsmMI07716 line (Chu and Hayashi, 2021; insertion site available on Flybase). Based on this, we have updated the nomenclature from Qsm-mCh to mCh-Qsm throughout the manuscript to accurately reflect the tag position. To our knowledge, no western blot has been performed on Dpy-YFP or mCh-Qsm lines. We have mentioned this explicitly in the Discussion.

      The Dpy-YFP reagent is a non-functional fusion and therefore may not be a wholly reliable reporter of Dpy localization. There is no antibody confirmation. As other reagents are not available to my knowledge, this issue can be addressed with text acknowledgement of possible caveats.

      Thanks for raising this important point. We have added a caveat in the Discussion noting this limitation and the need for additional tools, such as an antibody or a functional fusion protein, to confirm the localization of Dpy.

      TEM was done by standard chemical fixation, which is fine for viewing intracellular organelles, but high pressure freezing probably would do a better job of preserving aECM structure, which looks fairly bad in Fig. 2G WT, without evidence of the filamentous structures seen by light microscopy. Nevertheless, the images are sufficient for showing the extreme disorganization of aECM in papss mutants.

      We agree that HPF is a better method and intent to use the HPF system in future studies. We acknowledge that chemical fixation contributes to the appearance of a gap between the apical membrane and the aECM, which we did not observe in the HPF/FS method (Chung and Andrew, 2014). Despite this, the TEM images still clearly reveal that Papss mutants show a much thinner and more electron-dense aECM compared to WT (Figure 2H, I), consistent to the condensed WGA, Dpy, and Pio signals in our confocal analyses. As the reviewer mentioned, we believe that the current TEM data are sufficient to support the conclusion of severe aECM disorganization and Golgi defects in Papss mutants.

      The authors may consider citing some of the work that has been done on sulfation in nematodes, e.g. as reviewed here: https://pubmed.ncbi.nlm.nih.gov/35223994/ Sulfation has been tied to multiple aspects of nematode aECM organization, though not specifically to ZP proteins.

      Thank you for the suggestion. Pioneering studies in C. elegans have highlighted the key role of sulfation in diverse developmental processes, including neuronal organization, reproductive tissue development, and phenotypic plasticity. We have now cited several works.

      Reviewer #2 (Significance (Required)):

      This study will be of interest to researchers studying developmental morphogenesis in general and specifically tube biology or the aECM. It should be particularly of interest to those studying sulfation or ZP proteins (which are broadly present in aECMs across organisms, including humans).

      This study adds to the literature demonstrating the importance of luminal matrix in shaping tubular organs and greatly advances understanding of the luminal matrix in the Drosophila salivary gland, an important model of tubular organ development and one that has key matrix differences (such as no chitin) compared to other highly studied Drosophila tubes like the trachea.

      The detailed description of the defects resulting from papss loss suggests that there are multiple different sulfated targets, with a subset specifically relevant to aECM biology. A limitation is that specific sulfated substrates are not identified here (e.g. are these the ZP proteins themselves or other matrix glycoproteins or lipids?); therefore it's not clear how direct or indirect the effects of papss are on ZP proteins. However, this is clearly a direction for future work and does not detract from the excellent beginning made here.

      My expertise: I am a developmental geneticist with interests in apical ECM

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

      In this work Woodward et al focus on the apical extracellular matrix (aECM) in the tubular salivary gland (SG) of Drosophila. They provide new insights into the composition of this aECM, formed by ZP proteins, in particular Pio and Dumpy. They also describe the functional requirements of PAPSS, a critical enzyme involved in sulfation, in regulating the expansion of the lumen of the SG. A detailed cellular analysis of Papss mutants indicate defects in the apical membrane, the aECM and in Golgi organization. They also find that Papss control the proper organization of the Pio-Dpy matrix in the lumen. The work is well presented and the results are consistent.

      Main comments

      • This work provides a detailed description of the defects produced by the absence of Papss. In addition, it provides many interesting observations at the cellular and tissular level. However, this work lacks a clear connection between these observations and the role of sulfation. Thus, the mechanisms underlying the phenotypes observed are elusive. Efforts directed to strengthen this connection (ideally experimentally) would greatly increase the interest and relevance of this work.

      Thank you for this thoughtful comment. To directly test whether the phenotypes observed in Papss mutants are due to the loss of sulfation activity, we generated transgenic lines expressing catalytically inactive forms of Papss, UAS-PapssK193A, F593P, in which key residues in the APS kinase and ATP sulfurylase domains are mutated. Unlike WT UAS-Papss (both the Papss-PD or Papss-PE isoforms), the catalytically inactive UAS-Papssmut failed to rescue any of the phenotypes, including the thin lumen phenotype (Figure 1I-L), altered WGA signals (Figure I, I') and the cell death phenotype (Figure 4D, E). These findings strongly support the conclusion that the enzymatic sulfation activity of Papss is essential for the developmental processes described in this study.

      • A main issue that arises from this work is the role of Papss at the cellular level. The results presented convincingly indicate defects in Golgi organization in Papss mutants. Therefore, the defects observed could stem from general defects in the secretion pathway rather than from specific defects on sulfation. This could even underly general/catastrophic cellular defects and lead to cell death (as observed). This observation has different implications. Is this effect observed in SGs also observed in other cells in the embryo? If Papss has a general role in Golgi organization this would be expected, as Papss encodes the only PAPs synthatase in Drosophila. Can the authors test any other mutant that specifically affect Golgi organization and investigate whether this produces a similar phenotype to that of Papss?

      Thank you for the comment. To address whether the defects observed in Papss mutants stem from general disruption of the secretory pathway due to Golgi disorganization, we examined mutants of two key Golgi components: Grasp65 and GM130.

      In Grasp65 mutants, we observed significant defects in SG lumen morpholgy, including highly irregular SG lumen shape and multiple constrictions (100%; n=10/10). However, the lumen was not uniformly thin as in Papss mutants. In contrast, GM130 mutants-although this line was very sick and difficult to grow-showed relatively normal salivary glands morphology in the few embryos that survived to stage 16 (n=5/5). It is possible that only embryos with mild phenotypes progressed to this stages, limiting interpretation. These data have now been included in Figure 3-figure supplement 2. Overall, while Golgi disruption can affect SG morphology, the specific phenotypes seen in Papss mutants are not fully recapitulated by Grasp65 or GM130 loss.

      • A model that conveys the different observations and that proposes a function for Papss in sulfation and Golgi organization (independent or interdependent?) would help to better present the proposed conclusions. In particular, the paper would be more informative if it proposed a mechanism or hypothesis of how sulfation affects SG lumen expansion. Is sulfation regulating a factor that in turn regulates Pio-Dpy matrix? Is it regulating Pio-Dpy directly? Is it regulating a product recognized by WGA? For instance, investigating Alcian blue or sulfotyrosine staining in pio, dpy mutants could help to understand whether Pio, Dpy are targets of sulfation.

      Thank you for the comment. We're also very interested in learning whether the regulation of the Pio-Dpy matrix is a direct or indirect consequence of the loss of sulfation on these proteins. One possible scenario is that sulfation directly regulates the Pio-Dpy matrix by regulating protein stability through the formation of disulfide bonds between the conserved Cys residues responsible for ZP module polymerization. Additionally, the Dpy protein contains hundreds of EGF modules that are highly susceptible to O-glycosylation. Sulfation of the glycan groups attached to Dpy may be critical for its ability to form a filamentous structure. Without sulfation, the glycan groups on Dpy may not interact properly with the surrounding materials in the lumen, resulting in an aggregated and condensed structure. These possibilities are discussed in the Discussion.

      We have not analyzed sulfation levels in pio or dpy mutants because sulfation levels in mutants of single ZP domain proteins may not provide much information. A substantial number of proteoglycans, glycoproteins, and proteins (with up to 1% of all tyrosine residues in an organism's proteins estimated to be sulfated) are modified by sulfation, so changes in sulfation levels in a single mutant may be subtle. Especially, the existing dpy mutant line is an insertion mutant of a transposable element; therefore, the sulfation sites would still remain in this mutant.

      • Interpretation of Papss effects on Pio and Dpy would be desired. The results presented indicate loss of Pio antibody staining but normal presence of cherry-Pio. This is difficult to interpret. How are these results of Pio antibody and cherry-Pio correlating with the results in the trachea described recently (Drees et al. 2023)?

      In our original submission, we stated that the uniform luminal mCh-Pio signals were not changed in Papss mutants, but after re-analysis, we found that these signals were actually absent from the expanded luminal region in stage 16 SG (where Dpy-YFP is also absent), and weak mCh-Pio signals colocalize with the condensed Dpy-YFP signals (Figure 5C, D). We have revised the text accordingly.

      After cleavages by Np and furin, the Pio protein should have three fragments. The N-terminal region contains the N-terminal half of the ZP domain, and mCh-Pio signals show this fragment. The very C-terminal region should localize to the membrane as it contains the transmembrane domain. We think the middle piece, the C-terminal ZP domain, is recognized by the Pio antibody. The mCh-Pio and Pio antibody signals in the WT trachea (Drees et al., 2023) are similar to those in the SG. mCh-Pio signals are detected in the tracheal lumen as uniform signals, at the apical membrane, and in cytoplasmic puncta. Pio antibody signals are exclusively in the tracheal lumen and show more heterogenous filamentous signals.

      In Papss mutants, the middle fragment (the C-terminal ZP domain) seems to be most affected because the Pio antibody signals are absent from the lumen. The loss of Pio antibody signals could be due to protein degradation or epitope masking caused by aECM condensation and protein misfolding. This fragment seems to be key for interacting with Dpy, since Pio antibody signals always colocalize with Dpy-YFP. The N-terminal mCh-Pio fragment does not appear to play a significant role in forming a complex with Dpy in WT (but still aggregated together in Papss mutants), and this can be tested in future studies.

      In response to Reviewer 1's comment, we performed an additional experiment to test the role of Np in cleaving Pio to help organize the SG aECM. In this experiment, we overexpressed the WT and mutant form of Np using UAS-Np.WT and UAS-Np.S990A lines (Drees et al., 2019) and analyzed mCh-Pio, Pio antibody, and Dpy-YFP signals. Np.WT overexpression resulted in increased levels of mCh-Pio, Pio, and Dpy-YFP signals in the lumen and at the apical membrane. However, overexpression of Np.S990A resulted in the absence of luminal mCh-Pio signals. Pio antibody signals were strong at the apical membrane but rather weak in the luminal filamentous structures. Since the UAS-Np.S990A line has the GFP tag, we could not reliably analyze Dpy-YFP signals due to overlapping Np.S990A.GFP signals in the same channel. However, the luminal filamentous Pio signals co-localized with GFP signals, and we assume that these overlapping signals could be Dpy-YFP signals.

      These results suggest that overexpressed Np.S990A may act in a dominant-negative manner, competing with endogenous Np and impairing proper cleavage of Pio (and mCh-Pio). Nevertheless, some level of cleavage by endogenous Np still appears to occur, as indicated by the residual luminal filamentous Pio signals. These new findings have been incorporated into the revised manuscript and are shown in Figure 6H and 6I.

      A proposed model of the Pio-Dpy aECM in WT, Papss, pio, and Np mutants has now been included in Figure 7.

      • What does the WGA staining in the lumen reveal? This staining seems to be affected differently in pio and dpy mutants: in pio mutants it disappears from the lumen (as dpy-YFP does), but in dpy mutants it seems to be maintained. How do the authors interpret these findings? How does the WGA matrix relate to sulfated products (using Alcian blue or sulfotyrosine)?

      WGA binds to sialic acid and N-acetylglucosamine (GlcNAc) residues on glycoproteins and glycolipids. GlcNAc is a key component of the glycosaminoglycan (GAG) chains that are covalently attached to the core protein of a proteoglycan, which is abundant in the ECM. We think WGA detects GlcNAc residues in the components of the aECM, including Dpy as a core component, based on the following data. 1) WGA and Dpy colocalize in the lumen, both in WT (as thin filamentous structures) and Papss mutant background (as condensed rod-like structures), and 2) are absent in pio mutants. WGA signals are still present in a highly condensed form in dpy mutants. That's probably because the dpy mutant allele (dpyov1) has an insertion of a transposable element (blood element) into intron 11 and this insertion may have caused the Dpy protein to misfold and condense. We added the information about the dpy allele to the Results section and discussed it in the Discussion.

      Minor points:

      • The morphological phenotypic analysis of Papss mutants (homozygous and transheterozygous) is a bit confusing. The general defects are higher in Papss homozygous than in transheterozygotes over a deficiency. Maybe quantifying the defects in the heterozygote embryos in the Papss mutant collection could help to figure out whether these defects relate to Papss mutation.

      We analyzed the morphology of heterozygous Papss mutant embryos. They were all normal. The data and quantifications have now been added to Figure 1-figure supplement 3.

      • The conclusion that the apical membrane is affected in Papss mutants is not strongly supported by the results presented with the pattern of Crb (Fig 2). Further evidences should be provided. Maybe the TEM analysis could help to support this conclusion

      We quantified Crb levels in the sub-apical and medial regions of the cell and included this new quantification in Figure 2D. TEM images showed variation in the irregularity of the apical membrane, even in WT, and we could not draw a solid conclusion from these images.

      • It is difficult to understand why in Papss mutants the levels of WGA increase. Can the authors elaborate on this?

      We think that when Dpy (and many other aECM components) are condensed and aggregated into the thin, rod-like structure in Papss mutants, the sugar residues attached to them must also be concentrated and shown as increased WGA signals.

      • The explanation about why Pio antibody and mcherry-Pio show different patterns is not clear. If the antibody recognizes the C-t region, shouldn't it be clearly found at the membrane rather than the lumen?

      The Pio protein is also cleaved by furin protease (Figure 5B). We think the Pio fragment recognized by the antibody should be a "C-terminal ZP domain", which is a middle piece after furin + Np cleavages.

      • The qsm information does not seem to provide any relevant information to the aECM, or sulfation.

      Since Qsm has been shown to bind to Dpy and remodel Dpy filaments in the muscle tendon (Chu and Hayashi, 2021), we believe that the different behavior of Qsm in the SG is still informative. As mentioned briefly in the Discussion, the cleaved Qsm fragment may localize differently, like Pio, and future work will need to test this. We have shortened the description of the Qsm localization in the manuscript and moved the details to the figure legend of Figure 5-figure supplement 3.

      Reviewer #3 (Significance (Required)):

      Previous reports already indicated a role for Papss in sulfation in SG (Zhu et al 2005). Now this work provides a more detailed description of the defects produced by the absence of Papss. In addition, it provides relevant data related to the nature and requirements of the aECM in the SG. Understanding the composition and requirements of aECM during organ formation is an important question. Therefore, this work may be relevant in the fields of cell biology and morphogenesis.

    2. 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:

      There is growing appreciation for the important of luminal (apical) ECM in tube development, but such matrices are much less well understood than basal ECMs. Here the authors provide insights into the aECM that shapes the Drosophila salivary gland (SG) tube and the importance of PAPSS-dependent sulfation in its organization and function.

      The first part of the paper focuses on careful phenotypic characterization of papss mutants, using multiple markers and TEM. This revealed reduced markers of sulfation (Alcian Blue staining) and defects in both apical and basal ECM organization, Golgi (but not ER) morphology, number and localization of other endosomal compartments, plus increased cell death. The authors focus on the fact that papss mutants have an irregular SG lumen diameter, with both narrowed regions and bulged regions. They address the pleiotropy, showing that preventing the cell death and resultant gaps in the tube did not rescue the SG luminal shape defects and discussing similarities and differences between the papss mutant phenotype and those caused by more general trafficking defects. The analysis uses a papss nonsense mutant from an EMS screen - I appreciate the rigorous approach the authors took to analyze transheterozygotes (as well as homozygotes) plus rescued animals in order to rule out effects of linked mutations.

      The 2nd part of the paper focuses on the SG aECM, showing that Dpy and Pio ZP protein fusions localize abnormally in papss mutants and that these ZP mutants (and Np protease mutants) have similar SG lumen shaping defects to the papss mutants. A key conclusion is that SG lumen defects correlate with loss of a Pio+Dpy-dependent filamentous structure in the lumen. These data suggest that ZP protein misregulation could explain this part of the papss phenotype.

      Overall, the text is very well written and clear. Figures are clearly labeled. The methods involve rigorous genetic approaches, microscopy, and quantifications/statistics and are documented appropriately. The findings are convincing, with just a few things about the fusions needing clarification.

      Minor comments:

      1. Although the Dpy and Qsm fusions are published reagents, it would still be helpful to mention whether the tags are C-terminal as suggested by the nomenclature, and whether Westerns have been performed, since (as discussed for Pio) cleavage could also affect the appearance of these fusions.

      2. The Dpy-YFP reagent is a non-functional fusion and therefore may not be a wholly reliable reporter of Dpy localization. There is no antibody confirmation. As other reagents are not available to my knowledge, this issue can be addressed with text acknowledgement of possible caveats.

      3. TEM was done by standard chemical fixation, which is fine for viewing intracellular organelles, but high pressure freezing probably would do a better job of preserving aECM structure, which looks fairly bad in Fig. 2G WT, without evidence of the filamentous structures seen by light microscopy. Nevertheless, the images are sufficient for showing the extreme disorganization of aECM in papss mutants.

      4. The authors may consider citing some of the work that has been done on sulfation in nematodes, e.g. as reviewed here: https://pubmed.ncbi.nlm.nih.gov/35223994/ Sulfation has been tied to multiple aspects of nematode aECM organization, though not specifically to ZP proteins.

      Significance

      This study will be of interest to researchers studying developmental morphogenesis in general and specifically tube biology or the aECM. It should be particularly of interest to those studying sulfation or ZP proteins (which are broadly present in aECMs across organisms, including humans).

      This study adds to the literature demonstrating the importance of luminal matrix in shaping tubular organs and greatly advances understanding of the luminal matrix in the Drosophila salivary gland, an important model of tubular organ development and one that has key matrix differences (such as no chitin) compared to other highly studied Drosophila tubes like the trachea.

      The detailed description of the defects resulting from papss loss suggests that there are multiple different sulfated targets, with a subset specifically relevant to aECM biology. A limitation is that specific sulfated substrates are not identified here (e.g. are these the ZP proteins themselves or other matrix glycoproteins or lipids?); therefore it's not clear how direct or indirect the effects of papss are on ZP proteins. However, this is clearly a direction for future work and does not detract from the excellent beginning made here.

      My expertise: I am a developmental geneticist with interests in apical ECM

    1. active and experiential, reflective, social, evolving, and focused on problem-solving

      I think this is pretty cool! I love how arts integration keeps students involved and engaged in their learning. It's so much more meaningful than just being lectured and having information thrown at you. It's amazing that arts integration can help students think deeply and build skills they will use for the rest of their lives.

    1. Specific Purpose To inform a group of school administrators about the various open-source software packages that could be utilized in their school districts Main Point 1 School districts use software in their operations. Define educational software. List and describe the software commonly used by school districts. Main Point 2 What is open-source software? Define open-source software. Review the history of open-source software. Explain the advantages of using open-source software. Describe the value of open-source software. Explain the disadvantages of using open-source software. Describe some problems that have occurred with open-source software. Main Point 3 Name some specific open-source software packages that may be appropriate for these school administrators to consider. Review the software needs of my specific audience. Describe some educational open-source software packages. You may notice that in the preceding list, the number of subpoints under each of the three main points is a little disjointed or the topics don’t go together clearly. That’s all right. Remember that these are just general ideas at this point. It’s also important to remember that there is often more than one way to organize a speech. Some of these points could be left out and others developed more fully, depending on the purpose and audience. We’ll develop the preceding main points more fully in a moment.

      An example of organizing your main points.

    1. These are valuable relationships forlearning, feedback and collaborative problem-solving.

      This is so important. It's so important to have people in our lives who have differing views from us and different lived experiences. My view is not the only valid view of the world and my experiences do not define the experiences of someone else. Especially now, it has been important for me to really speak to people who have different views because they may be missing the perspective that I just explained. Getting mad and closing people out just because they differ in views from you is the end of finding a solution, and I don't like giving up on problems.

    1. it has a different representation of what to do if it gets injured in the future. It's a latent memory. It doesn't do anything until it gets injured. It just sits there and you would have no idea that it's there by looking at the at the anatomy. But if it's get if it gets injured, this is what its idea of a correct worm.

      for - adjacency - stored latent memory of future morphology - can be altered - Michael Levin - potential progress trap

    2. we have a system that um that we developed that you can go to this website and basically it will just very simply translate papers in neuroscience into developmental biology papers. It's very easy. You just swap a few words and and and everything carries over. There are deep deep symmetries between cognition and morphagenesis which um I think Alan Turing for example recognized

      for - adjacency - cognition and morphogensis - Michael Levin - tools - website - translates neuroscience papers into morphogenesis papers - Michael Levin - adjacency - Alan Turing - intelligence - embryogenesis

    1. Author response:

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

      Reviewer #1 (Public Review)>

      Summary:

      This research group has consistently performed cutting-edge research aiming to understand the role of hormones in the control of social behaviors, specifically by utilizing the genetically tractable teleost fish, medaka, and the current work is no exception. The overall claim they make, that estrogens modulate social behaviors in males and females is supported, with important caveats. For one, there is no evidence these estrogens are generated by "neurons" as would be assumed by their main claim that it is NEUROestrogens that drive this effect. While indeed the aromatase they have investigated is expressed solely in the brain, in most teleosts, brain aromatase is only present in glial cells (astrocytes, radial glia). The authors should change this description so as not to mislead the reader. Below I detail more specific strengths and weaknesses of this manuscript.

      We thank the reviewer for this very positive evaluation of our work and greatly appreciate their helpful comments and suggestions for improving the manuscript. We agree with the comment that the term “neuroestrogens” is misleading. Therefore, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript, including the title.

      In the following sections, “neuroestrogens” has been revised to align with the surrounding context.

      Line 21: “in the brain, also known as neuroestrogens,” → “in the brain.”

      Line 28: “neuroestrogens” → “these estrogens.”

      Line 30: “mechanism of action of neuroestrogens” → “mode of action of brain-derived estrogens.”

      Line 43: “brain-derived estrogens, also called neuroestrogens,” → “estrogens.”

      Line 74: “neuroestrogen synthesis is selectively impaired while gonadal estrogen synthesis remains intact” → “estrogen synthesis in the brain is selectively impaired while that in the gonads remains intact.”

      Line 77: “neuroestrogens” → “these estrogens.”

      Line 335: “levels of neuroestrogens” → “brain estrogen levels.”

      Line 338: “neuroestrogens” → “these estrogens.”

      Line 351: “neuroestrogens” → “these estrogens.”

      Line 357: “neuroestrogen action” → “the action of brain-derived estrogens.”

      Line 359: “neuroestrogens” → “estrogen synthesis in the brain.”

      Line 390: “active synthesis of neuroestrogens” → “active estrogen synthesis in the brain.”

      Line 431: “neuroestrogens” → “estrogens in the brain.”

      Line 431: “neuroestrogen action” → “the action of brain-derived estrogens.”

      Line 433: “neuroestrogen action” → “their action.”

      Strengths:

      Excellent use of the medaka model to disentangle the control of social behavior by sex steroid hormones.

      The findings are strong for the most part because deficits in the mutants are restored by the molecule (estrogens) that was no longer present due to the mutation.

      Presentation of the approach and findings are clear, allowing the reader to make their own inferences and compare them with the authors'.

      Includes multiple follow-up experiments, which lead to tests of internal replication and an impactful mechanistic proposal.

      Findings are provocative not just for teleost researchers, but for other species since, as the authors point out, the data suggest mechanisms of estrogenic control of social behaviors may be evolutionarily ancient.

      We again thank the reviewer for their positive evaluation of our work.

      Weaknesses:

      (1) As stated in the summary, the authors attribute the estrogen source to neurons and there isn't evidence this is the case. The impact of the findings doesn't rest on this either.

      As noted in Response to reviewer #1’s summary comment, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript.

      Line 63: We have also added the text “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (18– 20).” Following this addition, “This observation suggests” in the subsequent sentence has been replaced with “These observations suggest.”

      The following references (#18–20), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      P. M. Forlano, D. L. Deitcher, D. A. Myers, A. H. Bass, Anatomical distribution and cellular basis for high levels of aromatase activity in the brain of teleost fish: aromatase enzyme and mRNA expression identify glia as source. J. Neurosci. 21, 8943–8955 (2001).

      N. Diotel, Y. Le Page, K. Mouriec, S. K. Tong, E. Pellegrini, C. Vaillant, I. Anglade, F. Brion, F. Pakdel, B. C. Chung, O. Kah, Aromatase in the brain of teleost fish: expression, regulation and putative functions. Front. Neuroendocrinol. 31, 172–192 (2010).

      A. Takeuchi, K. Okubo, Post-proliferative immature radial glial cells female-specifically express aromatase in the medaka optic tectum. PLoS One 8, e73663 (2013).

      (2) The d4 versus d8 esr2a mutants showed different results for aggression. The meaning and implications of this finding are not discussed, leaving the reader wondering.

      Line 282: As the reviewer correctly noted, circles were significantly reduced in mutant males of the Δ8 line, whereas no significant reduction was observed in those of the Δ4 line. However, a tendency toward reduction was evident in the Δ4 line (P = 0.1512), and both lines showed significant differences in fin displays. Based on these findings, we believe our conclusion that esr2a<sup>−/−</sup> males exhibit reduced aggression remains valid. To clarify this point and address potential reader concerns, we have revised the text as follows: “esr2a<sup>−/−</sup> males from both the Δ8 and Δ4 lines exhibited significantly fewer fin displays than their wildtype siblings (P = 0.0461 and 0.0293, respectively). Circles followed a similar pattern, with a significant reduction in the Δ8 line (P = 0.0446) and a comparable but non-significant decrease in the Δ4 line (P = 0.1512) (Fig. 5L; Fig. S8E), showing less aggression.”

      (3) Lack of attribution of previously published work from other research groups that would provide the proper context of the present study.

      In response to this and other comments from this reviewer, we have revised the Introduction and Discussion sections as follows.

      Line 56: “solely responsible” in the Introduction has been modified to “largely responsible”.

      Line 57: “This is consistent with the recent finding in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (10)” has been revised to “This is consistent with recent observations in a few teleost species that genetic ablation of AR severely impairs male-typical behaviors (13–16) and with findings in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in maletypical courtship (12)” to include previous studies on the behavior of AR mutant fish (Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023; Nishiike and Okubo, 2024) in the Introduction.

      Line 65: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)” has been added to the Introduction. This addition provides an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015).

      Line 367: “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31– 33)” has been edited to read “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      After the revisions described above, the following references (#13, 14, and 22) have been added to the reference list, with other references renumbered accordingly:

      L. Yong, Z. Thet, Y. Zhu, Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. J. Exp. Biol. 220, 3017–3021 (2017).

      B. A. Alward, V. A. Laud, C. J. Skalnik, R. A. York, S. A. Juntti, R. D. Fernald, Modular genetic control of social status in a cichlid fish. Proc. Natl. Acad. Sci. U.S.A. 117, 28167–28174 (2020).

      L. A. O’Connell, H. A. Hofmann, Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153, 1341–1351 (2012).

      (4) There are a surprising number of citations not included; some of the ones not included argue against the authors' claims that their findings were "contrary to expectation".

      Line 68: As detailed in Response to reviewer #1’s comment 3 on weaknesses, we have cited previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction.

      The following revisions have also been made to avoid phrases such as “contrary to expectation” and “unexpected.”

      Line 76: “Contrary to our expectations” → “Remarkably.”

      Line 109: “Contrary to this expectation, however” → “Nevertheless.”

      Line 135: “Again, contrary to our expectation, cyp19a1b<sup>−/−</sup> males” → “cyp19a1b<sup>−/−</sup> males.”

      Line 333: “unexpected” → “noteworthy.”

      Line 337: “unexpected” → “notable.”

      (5) The experimental design for studying aggression in males has flaws. A standard test like a resident intruder test should be used.

      We agree that the resident-intruder test is the most commonly used method for assessing aggression. However, medaka form shoals and lack strong territoriality, and even slight dominance differences between the resident and the intruder can increase variability in the results, compromising data consistency. Therefore, in this study, we adopted an alternative approach: placing four unfamiliar males together in a tank and quantifying aggressive interactions in total. This method allows for the assessment of aggression regardless of territorial tendencies, making it more appropriate for our investigation.

      (6) While they investigate males and females, there are fewer experiments and explanations for the female results, making it feel like a small addition or an aside.

      We agree that the data and discussion for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021, Curr Biol, 1699–1710). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when combined with our prior findings, the female data in this study offer valuable insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      (7) The statistics comparing "experimental to experimental" and "control to experimental" aren't appropriate.

      The reviewer raises concerns about the statistical analysis used for Figures 4C and 4E, suggesting that Bonferroni’s test should be used instead of Dunnett’s test. However, Dunnett’s test is commonly used to compare treatment groups to a reference group that receives no treatment, as in our study. Since we do not compare the treated groups with each other, we believe Dunnett’s test is the most appropriate choice.

      Line 619: The reviewer’s concern may have arisen from the phrase “comparisons between control and experimental groups” in the Materials and Methods. We have revised it to “comparisons between untreated and E2-treated groups in Fig. 4, C and D” for clarity.

      Reviewer #2 (Public Review):

      Summary:

      The novelty of this study stems from the observations that neuro-estrogens appear to interact with brain androgen receptors to support male-typical behaviors. The study provides a step forward in clarifying the somewhat contradictory findings that, in teleosts and unlike other vertebrates, androgens regulate male-typical behaviors without requiring aromatization, but at the same time estrogens appear to also be involved in regulating male-typical behaviors. They manipulate the expression of one aromatase isoform, cyp19a1b, that is purported to be brain-specific in teleosts. Their findings are important in that brain estrogen content is sensitive to the brain-specific cyp19a1b deficiency, leading to alterations in both sexual behavior and aggressive behavior. Interestingly, these males have relatively intact fertility rates, despite the effects on the brain.

      We thank this reviewer for their positive evaluation of our work and constructive comments, which we found very helpful in improving the manuscript.

      That said, the framing of the study, the relevant context, and several aspects of the methods and results raise concerns. Two interpretations need to be addressed/tempered:

      (1) that the rescue of cyp19a1b deficiency by tank-applied estradiol is not necessarily a brain/neuroestrogen mode of action, and

      Line 155: cyp19a1b-deficient males exhibited a severe reduction in brain E2 levels, yet their peripheral E2 levels remained comparable to those in wild-type males. Given this hormonal milieu and the lack of behavioral change in wild-type males following E2 treatment, the observed recovery of mating behavior in cyp19a1b-deficient males following E2 treatment can be best explained by the restoration of brain E2 levels. However, as the reviewer pointed out, we cannot rule out the possibility that bath-immersed E2 influenced behavior through an indirect peripheral mechanism. To address this concern, we have modified the text as follows: “These results suggest that reduced E2 in the brain is the primary cause of the mating defects, highlighting a pivotal role of brain-derived estrogens in male mating behavior. However, caution is warranted, as an indirect peripheral effect of bath-immersed E2 on behavior cannot be ruled out, although this is unlikely given the comparable peripheral E2 levels in cyp19a1b-deficient and wild-type males. In contrast to mating.”

      (2) the large increases in peripheral and brain androgen levels in the cyp19a1b deficient animals imply some indirect/compensatory effects of lifelong cyp19a1b deficiency.

      As stated in line 151, androgen/AR signaling has a strong facilitative effect on male-typical behaviors in teleosts. If increased androgen levels in the periphery and brain affected behavior, the expected effect would be facilitative. However, cyp19a1b-deficient males exhibited impaired male-typical behaviors, suggesting that elevated androgen levels were unlikely to be responsible. Although chronic androgen elevation could cause androgen receptor desensitization, which could lead to behavioral suppression, our long-term androgen treatments have consistently promoted, rather than inhibited, male-typical behaviors (e.g., Nishiike et al., Proc Natl Acad Sci USA 121:e2316459121). Hence, this possibility is also highly unlikely.

      Reviewer #3 (Public Review):

      Summary:

      Taking advantage of the existence in fish of two genes coding for estrogen synthase, the enzyme aromatase, one mostly expressed in the brain (Cyp19a1b) and the other mostly found in the gonads (Cyp19a1a), this study investigates the role of neuro-estrogens in the control of sexual and aggressive behavior in teleost fish. The constitutive deletion of Cyp19a1b reduced brain estrogen content by 87% in males and about 50% in females. It led to reduced sexual and aggressive behavior in males and reduced sexual behavior in females. These effects are reversed by adult treatment with estradiol thus indicating that they are activational in nature. The deletion of Cyp19a1b is associated with a reduced expression of the genes coding for the two androgen receptors, ara, and arb, in brain regions involved in the regulation of social behavior. The analysis of the gene expression and behavior of mutants of estrogen receptors indicates that these effects are likely mediated by the activation of the esr1 and esr2a isoforms. These results provide valuable insight into the role of neuro-estrogens in social behavior in the most abundant vertebrate taxa. While estrogens are involved in the organization of the brain and behavior of some birds and rodents, neuro-estrogens appear to play an activational role in fish through a facilitatory action of androgen signaling.

      We thank this reviewer for their positive evaluation of our work and comments that have improved the manuscript.

      Strengths:

      Evaluation of the role of brain "specific" Cyp19a1 in male teleost fish, which as a taxa are more abundant and yet proportionally less studied than the most common birds and rodents. Therefore, evaluating the generalizability of results from higher vertebrates is important. This approach also offers great potential to study the role of brain estrogen production in females, an understudied question in all taxa.

      Results obtained from multiple mutant lines converge to show that estrogen signaling drives aspects of male sexual behavior.

      The comparative discussion of the age-dependent abundance of brain aromatase in fish vs mammals and its role in organization vs activation is important beyond the study of the targeted species.

      We again thank the reviewer for their positive evaluation of our work.

      Weaknesses:

      (1) The new transgenic lines are under-characterized. There is no evaluation of the mRNA and protein products of Cyp19a1b and ESR2a.

      We did not directly assess the function of cyp19a1b and esr2a in our mutant fish. However, the observed reduction in brain E2 levels, with no change in peripheral E2 levels, in cyp19a1b-deficient fish strongly supports the loss of cyp19a1b function. This is stated in the Results section (line 97) as follows: “These results show that cyp19a1b-deficient fish have reduced estrogen levels coupled with increased androgen levels in the brain, confirming the loss of cyp19a1b function.”

      Line 473: A previous study reported that female medaka lacking esr2a fail to release eggs due to oviduct atresia (Kayo et al., 2019, Sci Rep 9:8868). Similarly, in this study, some esr2a-deficient females exhibited spawning behavior but were unable to release eggs, although the sample size was limited (Δ8 line: 2/3; Δ4 line: 1/1). In contrast, this was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function. To incorporate this information into the manuscript, the following text has been added to the Materials and Methods: “A previous study reported that esr2a-deficient female medaka cannot release eggs due to oviduct atresia (59). Likewise, some esr2a-deficient females generated in this study, despite the limited sample size, exhibited spawning behavior but were unable to release eggs (Δ8 line: 2/3; Δ4 line: 1/1), while such failure was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function.”

      The following reference (#59), cited in the newly added text above, have been included in the reference list:

      D. Kayo, B. Zempo, S. Tomihara, Y. Oka, S. Kanda, Gene knockout analysis reveals essentiality of estrogen receptor β1 (Esr2a) for female reproduction in medaka. Sci. Rep. 9, 8868 (2019).

      (2) The stereotypic sequence of sexual behavior is poorly described, in particular, the part played by the two sexual partners, such that the conclusions are not easily understandable, notably with regards to the distinction between motivation and performance.

      Line 103: To provide a more detailed description of medaka mating behavior, we have revised the text from “The mating behavior of medaka follows a stereotypical pattern, wherein a series of followings, courtship displays, and wrappings by the male leads to spawning” to “The mating behavior of medaka follows a stereotypical sequence. It begins with the male approaching and closely following the female (following). The male then performs a courtship display, rapidly swimming in a circular pattern in front of the female. If the female is receptive, the male grasps her with his fins (wrapping), culminating in the simultaneous release of eggs and sperm (spawning).”

      (3) The behavior of females is only assessed from the perspective of the male, which raises questions about the interpretation of the reduced behavior of the males.

      In medaka, female mating behavior is largely passive, except for rejecting courtship attempts and releasing eggs. Therefore, its analysis relies on measuring the latency to receive following, courtship displays, or wrappings from the male and the frequency of courtship rejection or wrapping refusal. We understand the reviewer’s perspective that cyp19a1b-deficient females might not be less receptive but instead less attractive to males, potentially leading to reduced male mating efforts. However, since these females are approached and followed by males at levels comparable to wild-type females, this possibility appears unlikely. Moreover, cyp19a1b-deficient females tend to avoid males and exhibit a slightly female-oriented sexual preference. While these traits are closely associated with reduced sexual receptivity, they do not readily align with reduced sexual attractiveness. Therefore, it is more plausible to conclude that these females have decreased receptivity rather than being less attractive to males.

      (4) At no point do the authors seem to consider that a reduced behavior of one sex could result from a reduced sensory perception from this sex or a reduced attractivity or sensory communication from the other sex.

      Line 112: As noted above, the impaired mating behavior of cyp19a1b-deficient females is unlikely to be due to reduced attractiveness to males. Similarly, mating behavior tests using esr2b-deficient females as stimulus females suggest that the impaired mating behavior of cyp19a1b-deficient males cannot be attributed to reduced attractiveness to females. However, the possibility that their impaired mating behavior could be attributed to altered cognition or sexual preference cannot be ruled out. To reflect this in the manuscript, we have revised the text “, suggesting that they are less motivated to mate” to “. These results suggest that they are less motivated to mate, though an alternative interpretation that their cognition or sexual preference may be altered cannot be dismissed.”

      (5) Aspects of the methods are not detailed enough to allow proper evaluation of their quality or replication of the data.

      In response to this and other specific comments from this reviewer, we have revised the Materials and Methods section to include more detailed descriptions of the methods.

      Line 469: The following text has been added to describe the method for domain identification in medaka Esr2a: “The DNA- and ligand-binding domains of medaka Esr2a were identified by sequence alignment with yellow perch (Perca flavescens) Esr2a, for which these domain locations have been reported (58).”

      The following reference (#58), cited in the newly added text above, have been included in the reference list:

      S. G. Lynn, W. J. Birge, B. S. Shepherd, Molecular characterization and sex-specific tissue expression of estrogen receptor α (esr1), estrogen receptor βa (esr2a) and ovarian aromatase (cyp19a1a) in yellow perch (Perca flavescens). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 149, 126–147 (2008).

      Line 540: The text “, and the total area of signal in each brain nucleus was calculated using Olyvia software (Olympus)” has been revised to include additional details on the single ISH method as follows: “. The total area of signal across all relevant sections, including both hemispheres, was calculated for each brain nucleus using Olyvia software (Olympus). Images were converted to a 256-level intensity scale, and pixels with intensities from 161 to 256 were considered signals. All sections used for comparison were processed in the same batch, without corrections between samples.”

      Line 596: The following text has been added to include additional details on the double ISH method: “Cells were identified as coexpressing the two genes when Alexa Fluor 555 and fluorescein signals were clearly observed in the cytoplasm surrounding DAPI-stained nuclei, with intensities markedly stronger than the background noise.”

      (6) It seems very dangerous to use the response to a mutant abnormal behavior (ESR2-KO females) as a test, given that it is not clear what is the cause of the disrupted behavior.

      esr2b-deficient females have fully developed ovaries, a normal sex steroid milieu, and sexual attractiveness to males comparable to wild-type females, yet they are completely unreceptive to male courtship (Nishiike et al., 2021, Curr Biol, 1699–1710). Although, as the reviewer noted, the detailed mechanisms underlying this phenotype remain unclear, it is evident that the loss of estrogen/Esr2b signaling in the brain severely impairs sexual receptivity. Therefore, using esr2b-deficient females as stimulus females in the mating behavior test eliminates the influence of female sexual receptivity and male attractiveness to females, enabling the exclusive assessment of male mating motivation. This rationale is already presented in the Results section (lines 116–120), and we believe this experimental design offers a robust framework for assessing male mating motivation.

      Additionally, the mating behavior test with esr2b-deficient females complemented the test with wildtype females, and its results were not the sole basis for our discussion of the male mating behavior phenotype. The results of both tests were largely concordant, and we believe that the conclusions drawn from them are highly reliable.

      Meanwhile, in the test with esr2b-deficient females, cyp19a1b-deficient males were courted more frequently by these females than wild-type males. As the reviewer noted, this may suggest an anomaly in the test. Accordingly, we have confined our discussion to the possibility that “Perhaps cyp19a1b<sup>−/−</sup> males are misidentified as females by esr2b-deficient females because they are reluctant to court or they exhibit some female-like behavior” (line 131).

      (7) Most experiments are weakly powered (low sample size) and analyzed by multiple T-tests while 2 way ANOVA could have been used in several instances. No mention of T or F values, or degrees of freedom.

      Histological analysis was conducted with a relatively small sample size, as our previous experience suggested that interindividual variability in the results would not be substantial. As significant differences were detected in many analyses, further increasing the sample size is unnecessary.

      Although two-way ANOVA could be used instead of multiple T-tests for analyzing the data in Figures 4D, 4F, 6D, S4A, and S4B, we applied the Bonferroni–Dunn correction to control for multiple pairwise comparisons in multiple T-tests. As this comparison method is equivalent to the post hoc test following two-way ANOVA, the statistical results are identical regardless of whether T-tests or two-way ANOVA are used.

      For the data in Figures 4D, 4F, S4A, and S4B, the primary focus is on whether relative luciferase activity differs between E2-treated and untreated conditions for each mutant construct. Therefore, two-way ANOVA is not particularly relevant, as assessing the main effect of construct type or its interaction with E2 treatment does not provide meaningful insights. Similarly, in Figure 6D, the focus is solely on whether wild-type and mutant females differ in time spent at each distance. Given this, two-way ANOVA is unnecessary, as analyzing the main effect of distance is not meaningful.

      Accordingly, two-way ANOVA was not employed in this study, and therefore, its corresponding F values were not included. As the figure legends specify the sample sizes for all analyses, specifying degrees of freedom separately was deemed unnecessary.

      (8) The variability of the mRNA content for the same target gene between experiments (genotype comparison vs E2 treatment comparison) raises questions about the reproducibility of the data (apparent disappearance of genotype effect).

      As the reviewer pointed out, the overall area of ara expression is larger in Figure 2J than in Figure 2F. However, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, this difference is unlikely to affect the conclusions of this study.

      Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear less pronounced in Figures 2J and 2K than in Figures 2F and 2H. This is likely attributable to the smaller sample size used in the experiments for Figures 2J and 2K, resulting in less distinct differences. However, as the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

      (9) The discussion confuses the effects of estrogens on sexual differentiation (developmental programming = permanent) and activation (= reversible activation of brain circuits in adulthood) of the brain and behavior. Whether sex differences in the circuits underlying social behaviors exist is not clear.

      We recognize that the effects of adult steroids are sometimes not considered to be sexual differentiation, as they do not differentiate the neural substrate, but rather transiently activate the already masculinized or feminized substrate. Arnold (2017, J Neurosci Res 95:291–300) contends that all factors that cause sex differences, including the transient effects of adult steroids, should be incorporated into a theory of sexual differentiation, and indeed, these effects may be the most potent proximate factors that make males and females different. We concur with this perspective and have adopted it as a foundation for our manuscript.

      In teleosts, early developmental exposure to steroids has minimal impact, and sexual differentiation relies primarily on steroid action in adulthood (Okubo et al., 2022, Spectrum of Sex, pp. 111–133). This is evidenced by the effective reversal of sex-typical behaviors through experimental hormonal manipulation in adult teleosts and the absence of transient early-life steroid surges observed in mammals and birds. Accordingly, our discussion on brain sexual differentiation, including the statement in line 347, “This variation among species may represent the activation of neuroestrogen synthesis at life stages critical for sexual differentiation of behavior that are unique to each species”, remains well-supported. Additionally, given these considerations, while sex differences in neural circuit activation are evident in teleosts, substantial structural differences in these circuits are unlikely.

      (10) Overall, the claims regarding the activational role of neuro-estrogens on male sexual behavior are supported by converging evidence from multiple mutant lines. The role of neuroestrogens on gene expression in the brain is mostly solid too. The data for females are comparatively weaker. Conclusions regarding sexual differentiation should be considered carefully.

      We agree that the data for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when integrated with our prior findings, the data on females in this study provide significant insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      The authors set out to answer an intriguing question regarding the hormonal control of innate social behaviors in medaka. Specifically, they wanted to test the effects of cyp19a1b mutation on mating and aggression in males. They also test these effects in females. Their approach takes them down several distinct experimental pathways, including one investigating how cyp19a1a function is related to androgen receptor expression and how estrogens themselves may act on the androgen receptor to modulate its expression, as well as how different esr genes may be involved. The study and its results are valuable and a clear, general conclusion of a pathway from brain aromatase>estrogens>esr genes> androgen receptor can be made. This is important, novel, and impactful. However, there are issues with how the study logic is set up, the approach for assessing certain behaviors, the statistics used, the interpretation of findings, and placing the findings in the proper context based on previous work, which manifests as a general issue where previous work is not properly attributed to.

      Thank you for your thoughtful review. We have carefully addressed each specific comment, as detailed below.

      Major comments:

      (1) The background for the rationale of the current study is misleading and lacks proper context. The authors root the logic of their experiment in determining whether estrogens regulate male-typical behaviors because the current assumption is androgens are "solely responsible" for male-typical behaviors in teleosts. This is not the case. Previous studies have shown aromatase/estrogens are involved in male-typical aggression in teleosts. For example, to name a couple:

      Huffman, L. S., O'Connell, L. A., & Hofmann, H. A. (2013). Aromatase regulates aggression in the African cichlid fish Astatotilapia burtoni. Physiology & behavior, 112, 77-83.

      O'Connell, L. A., & Hofmann, H. A. (2012). Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology, 153(3), 1341-1351.

      And even a recent paper sheds light on a possible AR>aromatase.estradiol hypothesis of male typical behaviors:

      Lopez, M. S., & Alward, B. A. (2024). Androgen receptor deficiency is associated with reduced aromatase expression in the ventromedial hypothalamus of male cichlids. Annals of the New York Academy of Sciences.

      Interestingly, the authors cite Hufmann et al in the discussion, so I don't understand why they make the claims they do about estrogens and male-typical behavior.

      Related to this, is an issue of proper attribution to published work. Indeed, missing are key references from lab groups using AR mutant teleosts. Here are a couple:

      Yong, L., Thet, Z., & Zhu, Y. (2017). Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. Journal of Experimental Biology, 220(17), 3017-3021.

      Alward, B. A., Laud, V. A., Skalnik, C. J., York, R. A., Juntti, S. A., & Fernald, R. D. (2020). Modular genetic control of social status in a cichlid fish. Proceedings of the National Academy of Sciences, 117(45), 28167-28174.

      Ogino, Y., Ansai, S., Watanabe, E., Yasugi, M., Katayama, Y., Sakamoto, H., ... & Iguchi, T. (2023). Evolutionary differentiation of androgen receptor is responsible for sexual characteristic development in a teleost fish. Nature communications, 14(1), 1428.

      As noted in Response to reviewer #1’s comment 3 on weaknesses, we have revised the Introduction and Discussion sections as follows.

      Line 56: “solely responsible” in the Introduction has been modified to “largely responsible”.

      Line 57: The text “This is consistent with the recent finding in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (10)” has been revised to “This is consistent with recent observations in a few teleost species that genetic ablation of AR severely impairs male-typical behaviors (13–16) and with findings in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (12)” to include previous studies on the behavior of AR mutant fish (Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023; Nishiike and Okubo, 2024) in the Introduction.

      Line 65: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)” has been added to the Introduction, providing an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015).

      Line 367: “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31– 33)” has been edited to read “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      After the revisions described above, the following references (#13, 14, and 22) have been added to the reference list:

      L. Yong, Z. Thet, Y. Zhu, Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. J. Exp. Biol. 220, 3017–3021 (2017).

      B. A. Alward, V. A. Laud, C. J. Skalnik, R. A. York, S. A. Juntti, R. D. Fernald, Modular genetic control of social status in a cichlid fish. Proc. Natl. Acad. Sci. U.S.A. 117, 28167–28174 (2020).

      L. A. O’Connell, H. A. Hofmann, Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153, 1341–1351 (2012).

      While Lopez and Alward (2024) provide valuable insights into the regulation of cyp19a1b expression by androgens, our study focuses specifically on the functional aspects of cyp19a1b. Expanding the discussion to include expression regulation would divert from the primary focus of our manuscript. For this reason, we have opted not to cite this reference.

      (2) As it is now, the authors are only citing a book chapter/review from their own group. This is a serious issue as it does not provide the proper context for the work. The authors need to fix their issues of attribution to previously published work and the proper interpretation of the work that they are aware of as it pertains to ideas proposed on the roles of androgens and estrogens in the control of male-typical behaviors. This is also important to get the citations right because the common use of "contrary to expectations" when describing their results is actually not correct. Many of the observations are expected to a degree. However, this doesn't take away from a generally stellar experimental design and mostly clear results. The authors do not need to rely on enhancing the impact of their paper by making false claims of unexpected findings. The depth and clarity of your findings are where the impact of your work is.

      As detailed in Response to reviewer #1’s comment 3 on weaknesses, we have cited previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction.

      Additionally, as noted in Response to reviewer #1’s comment 4 on weaknesses, we have made the following revisions to avoid phrases such as “contrary to expectation” and “unexpected.”

      Line 76: “Contrary to our expectations” → “Remarkably.”

      Line 109: “Contrary to this expectation, however” → “Nevertheless.”

      Line 135: “Again, contrary to our expectation, cyp19a1b<sup>−/−</sup> males” → “cyp19a1b<sup>−/−</sup> males.”

      Line 333: “unexpected” → “noteworthy.”

      Line 337: “unexpected” → “notable.”

      (3) The experimental design for studying aggression in males has flaws. A standard test like a residentintruder test should be used. An assay in which only male mutants are housed together? I do not understand the logic there and the logic for the approach isn't even explained. Too many confounds that are not controlled for. It makes it seem like an aspect of the study that was thrown in as an aside.

      As noted in Response to reviewer #1’s comment 5 on weaknesses, medaka form shoals and lack strong territoriality. As a result, even slight differences in dominance between the resident and intruder can substantially impact the outcomes of the resident-intruder test. Therefore, we adopted an alternative approach in this study.

      (4) Hormonal differences in the mutants seem to vary based on sex, and the meaning of these differences, or how they affect interpreting the findings, wasn't discussed. There was no acknowledegment of the fact that female central E2 was still at 50%, meaning the "rescue" experiments using peripheral injections are not given the proper context. For example, this is different than giving a fish with only 16% of their normal central E2 an E2 injection. Missing as well is a clear hypothesis for why E2 injections did not rescue aggression deficits in cyp19a1b mutant males.

      Line 385: As the reviewer pointed out, the degree of brain estrogen reduction in cyp19a1b-deficient fish differs greatly between males and females. This is likely because females receive a large supply of estrogens from the ovaries. Given that estrogen levels in cyp19a1b-deficient females were 50% of those in wild-type females, it can be inferred that half of their brain estrogens are synthesized locally, while the other half originates from the ovaries. This is an important finding, and we have already noted in the Discussion that “females have higher brain levels of estrogens, half of which are synthesized locally in the brain (i.e., neuroestrogens)” However, as this explanation was not sufficiently clear, we have revised it to “females have higher brain levels of estrogens, with half being synthesized locally and the other half supplied by the ovaries.”

      The reviewer raised a concern that conducting the estrogen rescue experiment in females, where 50% of brain estrogens remain, might be inappropriate. However, as this experiment was conducted exclusively in males, this concern is not applicable.

      Line 377: As noted in the reviewer’s subsequent comment, the failure of aggression recovery in E2treated cyp19a1b-deficient males could be due to insufficient induction of ara/arb expression in aggression-relevant brain regions. To address this concern, we have inserted the following statement into the Discussion after “the development of male behaviors may require moderate neuroestrogen levels that are sufficient to induce the expression of ara and arb, but not esr2b, in the underlying neural circuitry”: “This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.”

      (5) In relation to that, the "null" results may have some of the most interesting implications, but they are barely discussed. For example, what does it mean that E2 didn't restore aggression in male cyp19 mutants? Is this a brain region factor? Could this relate to findings from Lopez et al NYAS, where male and female Ara mutants show different effects on brain-region-specific aromatase expression? And maybe this relates to the different impact of estrogens on ar expression. Were the different effects impacted in aggression areas? Maybe this is why E2 injection didn't retore aggression in males. You could make the argument that: (1) E2 doesn't restore ar expression in aggression regions and that's why there was no rescue. Or (2) that the circuits in adulthood that regulate aggression are NOT dependent on aggression but in early development they are. Another null finding not expanded on is why the two esr2a mutant lines showed differences. There is no reason to trust one line over the other, meaning we still don't know whether esr2a is required for latency to follow.

      As stated in our response to the previous comment, we have added the following text to the Discussion (line 377): “This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.” Meanwhile, as discussed in lines 341–342, it is highly unlikely that the neural circuits regulating aggression are primarily influenced by early-life estrogen exposure, because androgen administration in adulthood alone is sufficient to induce high levels of aggression in both sexes. This notion is further supported by previous observations that cyp19a1b expression in the brain is minimal during embryonic development (Okubo et al., 2011, J Neuroendocrinol, 23:412–423).

      The findings of Lopez and Alward (2024) pertain to the regulation of cyp19a1b expression by androgen receptors. While this represents an important aspect of neuroendocrine regulation, it does not appear to be directly relevant to our discussion on cyp19a1b-mediated regulation of androgen receptor expression.

      To ensure the reliability of behavioral analyses in mutant fish, we consider a phenotype valid only when it is consistently observed in two independent mutant lines. In the mating behavior test examining esr2adeficient males using esr2b-deficient females as stimulus females, Δ8 line males exhibited a shorter latency to initiate following than wild-type males, whereas Δ4 line males did not. This discrepancy led us to refrain from drawing conclusions about the role of esr2a in mating behavior, even though the mating behavior test using wild-type females as stimulus females yielded consistent results in the Δ8 and Δ4 lines. Therefore, we do not consider the reviewer’s concern to be a significant issue.

      (6) Not sure what's going on with the statistics, but it is not appropriate here to treat a "control" group as special. All groups are "experimental" groups. There is nothing special about the control group in this context. all should be Bonferroni post-hoc tests.

      Line 619: As detailed in Response to reviewer #1’s comment 7 on weaknesses, we consider Dunnett’s test the most appropriate choice for the experiments presented in Figures 4C and 4E. We acknowledge that the reviewer’s concern may stem from the phrase “comparisons between control and experimental groups” in the Materials and Methods section. To clarify this point, we have revised it to “comparisons between untreated and E2-treated groups in Fig. 4, C and D” for clarity.

      Minor comments:

      Line 47: then how can you say the aromatization hypothesis is "correct"? it only applies to a few species so far. Need to change the framing, not state so strongly such a vague thing as a hypothesis being "correct".

      Line 45: To address this concern, we have modified “widely accepted as correct” to “widely acknowledged”, ensuring a more precise characterization.

      Figure 1: looks like a dosage effect in males but not females. this should be discussed at some point, even if just to mention a dosage effect exists and put it in context.

      Line 91: We have revised the sentence “In males, brain E2 in heterozygotes (cyp19a1b+/−) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A)” by adding “, indicating a dosage effect of cyp19a1b mutation” to make this point explicit.

      Were male cyp19 KO aggressive towards females?

      We have not observed cyp19a1b-deficient males exhibiting aggressive behavior towards females in our experiments. Therefore, we do not consider them aggressive toward females.

      Please explain how infertility would lead to reduced mating.

      Line 142: As the reviewer has questioned, even if cyp19a1b-deficient males exhibit infertility due to efferent duct obstruction, it is difficult to imagine that this directly leads to reduced mating. However, the inability to release sperm could indirectly affect behavior. To address this, we have added “, possibly due to the perception of impaired sperm release” after “If this is also the case in medaka, the observed behavioral defects might be secondary to infertility.”

      Describe something about the timing of the treatment here. How can peripheral E2 injections restore it when peripheral levels are normal? Did these injections restore central levels? This needs to be shown experimentally.

      Line 517: As described in the Materials and Methods, E2 treatment was conducted by immersing fish in E2-containing water for 4 days. However, we had not explicitly stated that the water was changed daily to maintain the nominal concentration. To clarify this and address reviewer #2’s comment 9, we have revised “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan) or vehicle (ethanol) alone by immersion in water for 4 days” to “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan), which was first dissolved in 100% ethanol (vehicle), or with the vehicle alone by immersion in water for 4 days, with daily water changes to maintain the nominal concentration.”

      Line 522: The treatment effectively restored mating activity and ara/arb expression in the brain, suggesting a sufficient increase in brain E2 levels. However, we did not measure the actual increase, and its extent remains uncertain. To reflect this in the manuscript, we have now added the following sentence: “Although the exact increase in brain E2 levels following E2 treatment was not quantified, the observed positive effects on behavior and gene expression suggest that it was sufficient.”

      I know the nomenclature differs among those who study teleosts, but it's ARa and then gene is ar1 (as an example; arb would be ar2). You're recommended the following citation to remain consistent:

      Munley, K. M., Hoadley, A. P., & Alward, B. A. (2023). A phylogenetics-based nomenclature system for steroid receptors in teleost fishes. General and Comparative Endocrinology, 114436.

      Paralogous genes resulting from the third round of whole-genome duplication in teleosts are typically designated by adding the suffixes “a” and “b” to their gene symbols. This convention also applies to the two androgen receptor genes, commonly referred to as ara and arb. While the alternative names ar1 and ar2 may gain broader acceptance in the future, ara and arb remain more widely used at present. Therefore, we have chosen to retain ara and arb in this manuscript.

      Line 268: how is this "suggesting" less aggression? They literally showed fewer aggressive displays, so it doesn't suggest it - it literally shows it.

      Line 285: Following this thoughtful suggestion, we have changed “suggesting less aggression” to “showing less aggression.”

      Line 317: how can you still call it the primary driver?

      The stimulatory effects of aromatase/estrogens on male-typical behaviors are exerted through the potentiation of androgen/AR signaling. Thus, we still believe that androgens—specifically 11KT in teleosts—serve as the primary drivers of these behaviors.

      Line 318: not all deficits, like aggression, were rescued.

      Line 334: To address this comment, “These behavioral deficits were rescued by estrogen administration, indicating that reduced levels of neuroestrogens are the primary cause of the observed phenotypes: in other words, neuroestrogens are pivotal for male-typical behaviors in teleosts” has been modified and now reads “Deficits in mating were rescued by estrogen administration, indicating that reduced brain estrogen levels are the primary cause of the observed mating impairment; in other words, brain-derived estrogens are pivotal at least for male-typical mating behaviors in teleosts.”

      Line 324: what do you mean by "sufficient"? To show that, you'd have to castrate the male and only give estrogen back. the authors continue to overstate virtually every aspect of their study, seemingly in an unnecessary manner.

      Line 341: Our intention was to convey that brain-derived estrogens early in life are not essential for the expression of male-typical behaviors in teleosts. However, we recognize that the term “sufficient” could be misinterpreted as implying that estrogens alone are adequate, without contributions from other factors such as androgens. To clarify this, we have revised the text from “neuroestrogen activity in adulthood is sufficient for the execution of male-typical behaviors, while that in early in life is not requisite. Thus, while” to “brain-derived estrogens early in life is not essential for the execution of male-typical behaviors. While.”

      Line 329: so? in adult mice, amygdala aromatase neurons still regulate aggression. The amount in adulthood seems less important compared to site-specific functions.

      Line 346: We do not intend to suggest that brain aromatase activity in adulthood plays a negligible role in male behaviors in rodents, as we have already acknowledged its necessity in the Introduction (lines 42–43). To enhance clarity and prevent misinterpretation, we have added “, although it remains important for male behavior in adulthood” to the end of the sentence: “brain aromatase activity in rodents reaches its peak during the perinatal period and thereafter declines with age.”

      Line 351: This contradicts what you all have been saying.

      Line 65: As mentioned in Response to reviewer #1’s comment 3 on weaknesses, the following text has been added to the Introduction: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)”, providing an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015). With this revision, we believe the inconsistency has been addressed.

      Line 367: Additionally, we have revised the sentence from “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31–33)” to “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      Line 360: change to "...possibility that is not mutually exclusive,"

      Line 378: We have revised the phrase as suggested from “Another possibility, not mutually exclusive,” to “Another possibility that is not mutually exclusive.”

      Line 363: but it didn't rescue aggression

      Line 381: In response, we have revised the sentence from “This possibility is supported by the present observation that estrogen treatment facilitated mating behavior in cyp19a1b-deficient males but not in their wild-type siblings” to “This possibility is at least likely for mating behavior, as estrogen treatment facilitated mating behavior in cyp19a1b-deficient males but not in their wild-type siblings.”

      Line 367: on average

      To explain the sex differences in the role of aromatase, what about the downstream molecular or neural targets? In mammals, hodology is related to sex differences. there could be convergent sex differences in regulating the same type of behaviors as well.

      Our findings demonstrate that brain-derived estrogens promote the expression of ara, arb, and their downstream target genes vt and gal in males, while enhancing the expression of npba, a downstream target of Esr2b signaling, in females. The identity of additional target genes and their roles in specific neural circuits remain to be elucidated, and we aim to address these in future research.

      Lines 378-382: this doesn't logically follow. pgf2a could be the target of estrogens which in the intact animal do regulate female sexual receptivity. And how can you say this given that your lab has shown in esr2b mutants females don't mate?

      We agree that PGF2α signaling may be activated by estrogen signaling, as stated in lines 404–407: “the present finding provides a likely explanation for this apparent contradiction, namely, that neuroestrogens, rather than or in addition to ovarian-derived circulating estrogens, may function upstream of PGF2α signaling to mediate female receptivity.” The observation that esr2b-deficient females do not accept male courtship is also stated in lines 401–403: “we recently challenged it by showing that female medaka deficient for esr2b are completely unreceptive to males, and thus estrogens play a critical role in female receptivity.”

      Line 396-397: or the remaining estrogens are enough to activate esr2b-dependent female-typical mating behaviors.

      We agree that cyp19a1b deficiency did not completely preclude female mating behavior, most likely because residual estrogens in the brains of cyp19a1b-deficient females enable weak activation of Esr2b signaling. However, the relevant section in the Discussion is not focused on examining why mating behavior persisted, but rather on considering the implications of this finding for the neural circuits regulating mating behavior. Therefore, incorporating the suggested explanation here would shift the focus and would not be appropriate.

      Line 420-421: this is a lot of variation. Was age controlled for?

      The time required for medaka to reach sexual maturity varies with rearing density and food availability. Due to space constraints, we adjust these parameters as needed, which led to variation in the ages of the experimental fish. However, since all experiments were conducted using sibling fish of the same age that had just reached sexual maturity, we believe this does not affect our conclusions.

      Line 457: have these kits been validated in medaka?

      Although we have not directly validated its applicability in medaka, its extensive use in this species suggests that it us unlikely to pose any issues (e.g., Ussery et al., 2018, Aquat Toxicol, 205:58–65; Lee et al., 2019, Ecotoxicol Environ Saf, 173:174–181; Kayo et al., 2020, Gen Comp Endocrinol, 285:113272; Fischer et al., 2021, Aquat Toxicol, 236:105873; Royan et al., 2023, Endocrinology, 164:bqad030).

      Line 589, re fish that spawned: how many times did this happen? Please note it is based on genotype and experiment. This could be important.

      Line 627: In response to this comment, we have added the following details: “Specifically, 7/18 cyp19a1b<sup>+/+</sup>, 11/18 cyp19a1b<sup>+/−</sup>, and 6/18 cyp19a1b<sup>−/−</sup> males were excluded in Fig. 1D; 6/10 cyp19a1b<sup>+/+</sup>, 3/10 cyp19a1b<sup>+/−</sup>, and 6/10 cyp19a1b<sup>−/−</sup> females were excluded in Fig. 6B; 2/23 esr1+/+ and 5/24 esr1−/− males were excluded in Fig. S7; 2/24 esr2a+/+ and 3/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8A; 0/23 esr2a+/+ and 0/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8B.”

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      (A1) The framing of neuroestrogens being important for male-typical rodents, and not for other vertebrate lineages, does not account for other groups (birds) in which this is true (the authors can consult their cited work by Balthazart (Reference 6) for extensive accounting of this). This makes the novelty clause in the abstract "indicating that neuro-estrogens are pivotal for male-typical behaviors even in nonrodents" less surprising and should be acknowledged by the authors by amending or omitting this novelty clause. The findings regarding androgen receptor transcription (next sentence) are more important and pertinent.

      Line 27: We recognize that the aromatization hypothesis applies to some birds, including zebra finches, as stated in the Introduction (lines 48–49) and Discussion (lines 432–433). However, this was not reflected in the Abstract. Following the reviewer’s suggestion, we have changed “in non-rodents” to “in teleosts.”

      (A2) The medaka line that has been engineered to have aromatase absent in the brain is presented briefly in the abstract, but can be misinterpreted as naturally occurring. This should be amended, by including something like "engineered" or "directed mutant" before 'male medaka fish'.

      Line 24: We have added “mutagenesis-derived” before “male medaka fish” in response to this comment.

      Introduction:

      (I1) The paragraph on teleost brain aromatase should acknowledge that while the capacity for estrogen synthesis in the brain is 100-1000 fold higher in teleosts as compared to rodents and other vertebrates, the majority of this derives from glial and not neural sources. This can be confusing for readers since the term 'neuroestrogens' often refers to the neuronal origin and signalling. And this observation includes the exclusive radial glial expression of cyp19a1b in medaka (Diotel et al., 2010), and first discovered in midshipman (Forlano et al., 2001), each of which should also be cited here. In addition, the authors expend much text comparing teleosts and rodents, but it is worth expanding these kinds of comparisons, especially by pointing out that parts of the primate brain are found to densely express aromatase (see work by Ei Terasawa and others).

      In response to this comment and a similar comment from reviewer #1, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript.

      Line 63: We have also added the text “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (18– 20).” As a result of this addition, we have changed “This observation suggests” to “These observations suggest” in the subsequent sentence.

      Line 51: Additionally, to include information on aromatase in the primate brain, we have added the following text: “In primates, the hypothalamic aromatization of androgens to estrogens plays a central role in female gametogenesis (10) but is not essential for male behaviors (7, 8).”

      The following references (#10 and 18–20), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      E. Terasawa, Neuroestradiol in regulation of GnRH release. Horm. Behav. 104, 138–145 (2018).

      P. M. Forlano, D. L. Deitcher, D. A. Myers, A. H. Bass, Anatomical distribution and cellular basis for high levels of aromatase activity in the brain of teleost fish: aromatase enzyme and mRNA expression identify glia as source. J. Neurosci. 21, 8943–8955 (2001).

      N. Diotel, Y. Le Page, K. Mouriec, S. K. Tong, E. Pellegrini, C. Vaillant, I. Anglade, F. Brion, F. Pakdel, B. C. Chung, O. Kah, Aromatase in the brain of teleost fish: expression, regulation and putative functions. Front. Neuroendocrinol. 31, 172–192 (2010).

      A. Takeuchi, K. Okubo, Post-proliferative immature radial glial cells female-specifically express aromatase in the medaka optic tectum. PLoS One 8, e73663 (2013).

      (I2) It is difficult to resolve from the introduction and work cited how restricted cyp19a1b is to the medaka brain. Important for the results of this study, it is not clear whether it is more of a bias in the brain vs other tissues, or if the cyp19a1b deficiency is restricted to the brain, and gonadal/peripheral cyp19 expression persists. The authors need to improve their consideration of the alternatives, i.e., that this manipulation is not somehow affecting: 1) peripheral aromatase expression (either cyp19a1a or cyp19a1b) in the gonad or elsewhere, 2) compensatory processes, such as other steroidogenic genes (are androgen synthesizing enzymes increasing?).

      Our previous study demonstrated that cyp19a1b is expressed in the gonads, but at levels tens to hundreds of times lower than those in the brain (Okubo et al., 2011, J Neuroendocrinol 23:412–423). Additionally, a separate study in medaka reported that cyp19a1b expression in the ovary is considerably lower than that of cyp19a1a (Nakamoto et al., 2018, Mol Cell Endocrinol 460:104–122). Given these observations, any potential effect of cyp19a1b knockout on peripheral estrogen synthesis is likely negligible. Indeed, Figures S1C and S1D confirm that cyp19a1b knockout does not alter peripheral E2 levels.

      Line 72: To incorporate this information into the Introduction and address the following comment, we have added the following text: “In medaka, cyp19a1b is also expressed in the gonads, but only at a level tens to hundreds of times lower than in the brain and substantially lower than that of cyp19a1a (26, 27).”

      The following references (#26 and 27), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      K. Okubo, A. Takeuchi, R. Chaube, B. Paul-Prasanth, S. Kanda, Y. Oka, Y. Nagahama, Sex differences in aromatase gene expression in the medaka brain. J. Neuroendocrinol. 23, 412–423 (2011).

      M. Nakamoto, Y. Shibata, K. Ohno, T. Usami, Y. Kamei, Y. Taniguchi, T. Todo, T. Sakamoto, G. Young, P. Swanson, K. Naruse, Y. Nagahama, Ovarian aromatase loss-of-function mutant medaka undergo ovary degeneration and partial female-to-male sex reversal after puberty. Mol. Cell. Endocrinol. 460, 104–122 (2018).

      We have not assessed whether the expression of other steroidogenic enzymes is altered in cyp19a1bdeficient fish, and this may be investigated in future studies.

      (I3) Related, there are documented sex differences in the brain expression of cyp19a1b especially in adulthood (Okubo et al 2011) and this study should be cited here for context.

      Line 72: As stated in our previous response, we have cited Okubo et al. (2011) by adding the following sentence: “In medaka, cyp19a1b is also expressed in the gonads, but only at a level tens to hundreds of times lower than in the brain and substantially lower than that of cyp19a1a (26, 27).”

      Methods

      (M1) The rationale is unclear as presented for using mutagen screening for cype19a1b while using CRISPR for esr2a. Are there methodological/biochemical reasons why the authors chose to not use the same method for both?

      At the time we generated the cyp19a1b knockouts, genome editing was not yet available, and the TILLING-based screening was the only method for obtaining mutants in medaka. In contrast, by the time we generated the esr2a knockouts, CRISPR/Cas9 had become available, enabling a more efficient and convenient generation of knockout lines. This is why the two knockout lines were generated using different methods.

      (M2) Measurement of steroids in biological matrices is not straightforward, and it is good that the authors use multiple extraction steps (organic followed by C18 columns) before loading samples on the ELISA plates, which are notoriously sensitive. Even though these methods have been published before by this group of authors previously, the quality control and ELISA performance values (recovery, parallelism, etc.) should be presented for readers to evaluate.

      Thank you for appreciating our sample purification method. Unfortunately, we have not evaluated the recovery rate or parallelism, but we recognize this a subject for future studies.

      (M3) Mating behavior - E2 treated males were not co-housed with social partners for the full 24 hr before testing, but instead a few hours (?) prior to testing. The rationale for this should be spelled out explicitly.

      Line 494: In response to this comment, we have added “to ensure the efficacy of E2 treatment” to the end of the sentence “The set-up was modified for E2-treated males, which were kept on E2 treatment and not introduced to the test tanks until the day of testing.”

      (M4) The E2 treatment is listed as 1ng/ml vs. vehicle (ethanol). Is the E2 dissolved in 100% ethanol for administration to the tank water? Clarification is needed.

      Line 517: As the reviewer correctly assumed, E2 was first dissolved in 100% ethanol before being added to the tank water. To provide this information and address reviewer #1’s minor comment 5, we have revised “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan) or vehicle (ethanol) alone by immersion in water for 4 days” to “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan), which was first dissolved in 100% ethanol (vehicle), or with the vehicle alone by immersion in water for 4 days, with daily water changes to maintain the nominal concentration.”

      (M5) The authors exclude fish from the analysis of courtship display behavior for those individuals that spawned immediately at the start of the testing (and therefore it was impossible to register courtship display behaviors). How often did fish in the various treatment groups exhibit this "fast spawning" behavior? Was the occurrence rate different by treatment group? It is unlikely that these omissions from the data set drove large-scale patterns, but an indication of how often this occurred would be reassuring.

      Line 627: In response to this comment, we have included the following details: “Specifically, 7/18 cyp19a1b<sup>+/+</sup>, 11/18 cyp19a1b<sup+/−</sup>, and 6/18 cyp19a1b<sup>−/−</sup> males were excluded in Fig. 1D; 6/10 cyp19a1b+/+, 3/10 cyp19a1b+/−, and 6/10 cyp19a1b<sup>−/−</sup> females were excluded in Fig. 6B; 2/23 esr1+/+ and 5/24 esr1−/− males were excluded in Fig. S7; 2/24 esr2a+/+ and 3/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8A; 0/23 esr2a+/+ and 0/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8B.” These data indicate that the proportion of excluded males is nearly constant within each trial and is independent of the genotype of the focal fish.

      Results

      (R1) It is striking to see the genetic-'dose' dependent suppression of brain E2 content by heterozygous and homozygous cyp19a1b deficiency, indicating that, as the authors point out, the majority of E2 in the male medaka brain (and 1/2 in the female brain) have a brain-derived origin. It is important also for the interpretation that there are large compensatory increases in brain levels of androgens, when E2 levels drop in the cyp19a1b mutant homozygotes. This latter point should receive more attention.

      Also, there are large increases in peripheral androgen levels in the homozygote mutants for cyp19a1b in both males and females. This indicates a peripheral effect in addition to the clear brain knockdown of E2 synthesis. These nuances need to be addressed.

      In response to this comment, we have revised the Results section as follows:

      Line 91: “, indicating a dosage effect of cyp19a1b mutation” has been added to the end of the sentence “In males, brain E2 in heterozygotes (cyp19a1b<sup>+/−</sup>) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A).”

      Line 94: To draw more attention to the increase in brain androgen levels caused by cyp19a1b deficiency, “Brain levels of testosterone” has been modified to “Strikingly, brain levels of testosterone.”

      Line 100: “Their peripheral 11KT levels also increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D)” has been modified and now reads “In addition, peripheral 11KT levels in cyp19a1b<sup>−/−</sup> males and females increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D), indicating peripheral influence in addition to central effects.”

      (R2) The interpretation on page 4 that cyp19a1b deficient males are 'less motivated' to mate is premature, given the behavioral measures used in this study. There are several competing explanations for these findings (e.g., alterations in motivation, sensory discrimination, preference, etc.) that could be followed up in future work, but the current results are not able to distinguish among these possibilities.

      Line 112: We agree that the possibility of altered cognition or sexual preference cannot be dismissed. To incorporate this perspective, we have revised the text “, suggesting that they are less motivated to mate” to “These results suggest that they are less motivated to mate, though an alternative interpretation that their cognition or sexual preference may be altered cannot be dismissed.”

      (R3) On page 5, the authors present that peripheral E2 manipulation (delivery to the fish tank) restores courtship behavior in males, and then go on to erroneously conclude that this demonstrates "that reduced E2 in the brain was the primary cause of the mating defects, indicating a pivotal role of neuroestrogens in male mating behavior." Because this is a peripheral E2 treatment, there can be manifold effects on gonadal physiology or other endocrine events that can have indirect effects on the brain and behavior. Without manipulation of E2 directly to the brain to 'rescue' the cyp19a1b deficiency, the authors cannot conclude that these effects are directly on the central nervous system. Tellingly, the tank E2 treatment did not rescue aggressive behavior, suggestive of the potential for indirect effects.

      Line 155: As detailed in Response to reviewer #2’s specific comment 1, we have revised the text from “These results demonstrated that reduced E2 in the brain was the primary cause of the mating defects, indicating a pivotal role of neuroestrogens in male mating behavior. In contrast” to “These results suggest that reduced E2 in the brain is the primary cause of the mating defects, highlighting a pivotal role of brain-derived estrogens in male mating behavior. However, caution is warranted, as an indirect peripheral effect of bath-immersed E2 on behavior cannot be ruled out, although this is unlikely given the comparable peripheral E2 levels in cyp19a1b-deficient and wild-type males. In contrast to mating.”

      (R4) The downregulation of androgen-dependent gene expression (vasotocin in pNVT and galanin in pPMp) in the cyp19a1b deficient males (Figure 3) could be due to exceedingly high levels of brain androgens in the cyp19a1b deficient males. The best way to test the idea that estrogens can restore the expression to be more wild-type directly (like what is happening for ara and arb) is to look at these same markers (vasotocin and galanin) in these same brain areas in the brains of E2-treated males. The authors should have these brains from Figure 2. Unless I missed something, those experiments were not performed/reported here. It is clear that the ara and arb receptors have EREs and are 'rescued' by E2 treatment, but in principle, there could be indirect actions for reasons stated above for the behavior due to the peripheral E2 tank application.

      Thank you for your insightful comment. We agree that the current results cannot exclude the possibility that excessive androgen levels caused the downregulation of vt and gal. However, our previous studies showed that excessive 11KT administration to gonadectomized males and females increased the expression of these genes to levels comparable to wild-type males (Yamashita et al., 2020, eLife, 9:e59470; Kawabata-Sakata et al., 2024, Mol Cell Endocrinol 580:112101), making this scenario unlikely. That said, testing whether estrogen treatment restores vt and gal expression in cyp19a1bdeficient males would be informative, and we see this as an important direction for future research.

      Discussion

      (D1) The authors need to clarify whether EREs are found in other vertebrate AR introns, or is this unique to the teleost genome duplication?

      We have identified multiple ERE-like sequences within intron 1 of the mouse AR gene. However, sequence data alone do not provide sufficient evidence of their functionality, rendering this information of limited relevance. Therefore, we have chosen not to include this discussion in the current paper.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors are strongly encouraged to report information regarding the effect of Cyp19a1b deletion on the brain content of aromatase protein (ideally both isoforms investigated separately) as the two isoforms are mostly but not completely brain vs gonad specific. The analysis of other tissues would also strengthen the characterization of this model.

      We agree that measuring aromatase protein levels in the brain of our fish would be valuable for confirming the loss of cyp19a1b function. However, as no suitable method is currently available, this issue will need to be addressed in future studies. While this constitutes indirect evidence, the observed reduction in brain E2 levels, with no change in peripheral E2 levels, in cyp19a1b-deficient fish strongly suggests the loss of cyp19a1b function, as noted in Response to reviewer #3’s comment 1 on weaknesses.

      (2) As presented, this study reads as niche work. A better description of the behavior and reproductive significance of the different aspects of the behavioral sequence would allow a better understanding of the results and would thus allow the non-specialist to appreciate the significance of the observations.

      Line 103: In response to this comment and Reviewer #3’s comment 2 on weaknesses, we have revised the sentence from “The mating behavior of medaka follows a stereotypical pattern, wherein a series of followings, courtship displays, and wrappings by the male leads to spawning” to “The mating behavior of medaka follows a stereotypical sequence. It begins with the male approaching and closely following the female (following). The male then performs a courtship display, rapidly swimming in a circular pattern in front of the female. If the female is receptive, the male grasps her with his fins (wrapping), culminating in the simultaneous release of eggs and sperm (spawning)” in order to provide a more detailed description of medaka mating behavior.

      (3) The data regarding female behavior are limited and incomplete. It is suggested to keep this for another manuscript unless data on the behavior of the female herself is added. Indeed, analyzing female's behavior from the male's perspective complicates the interpretation of the results while a description of what the females do would provide valuable and interpretable information.

      We thank the reviewer for this thoughtful suggestion and agree that the data and discussion for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when combined with our prior findings, the female data in this study offer valuable insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      (4) In Figure 2, the validity to run multiple T-tests rather than a two-way ANOVA comparing TRT and genotype is questionable. Moreover, why are the absolute values in CTL higher than in the initial experiment comparing genotypes for ara in PPa, pPPp, and NVT as well as for arb in aPPp. More importantly, these graphs do not seem to reproduce the genotype effects for ara in pPPp and NVT and for arb in aPPp.

      The data in Figures 2J and 2K were analyzed with an exclusive focus on the difference between vehicletreated and E2-treated males, without considering genotype differences. Therefore, the use of T-tests for significance testing is appropriate.

      As the reviewer noted, the overall ara expression area is larger in Figure 2J than in Figure 2F. However, as detailed in Response to reviewer #3’s comment 8 on weaknesses, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, we consider this difference unlikely to affect the conclusions of this study.

      Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear smaller in Figures 2J and 2K compared to Figures 2F and 2H. This is likely due to the smaller sample size used in the experiments for Figures 2J and 2K, which makes the differences less distinct. However, since the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

      (5) More information is required regarding the analysis of single ISH - How was the positive signal selected from the background in the single ISH analyses? How was this measure standardized across animals? How many sections were imaged per region? Do the values represent unilateral or bilateral analysis?

      Line 540: Following this comment, we have provided additional details on the single ISH method in the manuscript. Specifically, “, and the total area of signal in each brain nucleus was calculated using Olyvia software (Olympus)” has been revised to “The total area of signal across all relevant sections, including both hemispheres, was calculated for each brain nucleus using Olyvia software (Olympus). Images were converted to a 256-level intensity scale, and pixels with intensities from 161 to 256 were considered signals. All sections used for comparison were processed in the same batch, without corrections between samples.”

      (6) More information should be provided in the methods regarding the image analysis of double ISH. In particular, what were the criteria to consider a cell as labeled are not clear. This is not clear either from the representative images.

      Line 596: To provide additional details on the single ISH method in the manuscript, we have added the following sentence: “Cells were identified as coexpressing the two genes when Alexa Fluor 555 and fluorescein signals were clearly observed in the cytoplasm surrounding DAPI-stained nuclei, with intensities markedly stronger than the background noise.”

      (7) There is no description of the in silico analyses run on ESR2a in the methods.

      The method for identifying estrogen-responsive element-like sequences in the esr2a locus is described in line 549: “Each nucleotide sequence of the 5′-flanking region of ara and arb was retrieved from the Ensembl medaka genome assembly and analyzed for potential canonical ERE-like sequences using Jaspar (version 5.0_alpha) and Match (public version 1.0) with default settings.”

      However, the method for domain identification in Esr2a was not described. Therefore, we have added the following text in line 469: “The DNA- and ligand-binding domains of medaka Esr2a were identified by sequence alignment with yellow perch (Perca flavescens) Esr2a, for which these domain locations have been reported (58).”

      The following reference (#58), cited in the newly added text above, have been included in the reference: S. G. Lynn, W. J. Birge, B. S. Shepherd, Molecular characterization and sex-specific tissue expression of estrogen receptor α (esr1), estrogen receptor βa (esr2a) and ovarian aromatase (cyp19a1a) in yellow perch (Perca flavescens). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 149, 126–147 (2008).

      (8) Information about the validation steps of the EIA that were carried out as well as the specificity of the antibody the steroids and the extraction efficacy should be provided.

      We have not directly validated the applicability of the EIA kit, but its extensive use in medaka suggests that it us unlikely to pose any issues (e.g., Ussery et al., 2018, Aquat Toxicol, 205:58–65; Lee et al., 2019, Ecotoxicol Environ Saf, 173:174–181; Kayo et al., 2020, Gen Comp Endocrinol, 285:113272; Fischer et al., 2021, Aquat Toxicol, 236:105873; Royan et al., 2023, Endocrinology, 164:bqad030).

      The specificity (cross-reactivity) of the antibodies is detailed as follows.

      (1) Estradiol ELISA kits: estradiol, 100%; estrone, 1.38%; estriol, 1.0%; 5α-dihydrotestosterone, 0.04%; androstenediol, 0.03%; testosterone, 0.03%; aldosterone, <0.01%; cortisol, <0.01%; progesterone, <0.01%.

      (2) Testosterone ELISA kits: testosterone, 100%; 5α-dihydrotestosterone, 27.4%; androstenedione, 3.7%; 11-ketotestosterone, 2.2%; androstenediol, 0.51%; progesterone, 0.14%; androsterone, 0.05%; estradiol, <0.01%.

      (3) 11-Keto Testosterone ELISA kits: 11-ketotestosterone, 100%; adrenosterone, 2.9%; testosterone, <0.01%.

      As this information is publicly available on the manufacturer’s website, we deemed it unnecessary to include it in the manuscript.

      Unfortunately, we have not evaluated the extraction efficacy of the samples, but we recognize this a subject for future studies.

      (9) I wonder whether the evaluation of the impact of the mutation by comparing the behavior of a group of wild-type males to a group of mutated males is the most appropriate. Justifying this approach against testing the behavior of one mutated male facing one or several wild-type males would be appreciated.

      We agree that the resident-intruder test, in which a single focal resident is confronted with one or more stimulus intruders, is the most commonly used method for assessing aggression. However, medaka form shoals and lack strong territoriality, and even slight dominance differences between the resident and the intruder can increase variability in the results, compromising data consistency. Therefore, in this study, we adopted an alternative approach: placing four unfamiliar males together in a tank and quantifying aggressive interactions in total. This method allows for the assessment of aggression regardless of territorial tendencies, making it more appropriate for our investigation.

      (10) Lines 329-331: this sentence should be rephrased as it contributes to the confusion between sexual differentiation and activation of circuits. The restoration of sexual behavior by adult estrogen treatment pleads in favor of an activational role of neuro-estrogens on behavior rather than an organizational role. Therefore, referring to sexual differentiation is misleading, even more so that the study never compares sexes.

      As detailed in Response to reviewer #3’s comment 9 on weaknesses, we consider that all factors that cause sex differences, including the transient effects of adult steroids, need to be incorporated into a theory of sexual differentiation. In teleosts, since steroids during early development have little effect and sexual differentiation primarily relies on steroid action in adulthood, our discussion on brain sexual differentiation remains valid, including the statement in line 347: “This variation among species may represent the activation of neuroestrogen synthesis at life stages critical for sexual differentiation of behavior that are unique to each species.”

      (11) Lines 384-386: I may have missed something but I do not see data supporting the notion that neuroestrogens may function upstream of PGF2a signaling to mediate female receptivity.

      Line 403: We acknowledge that our explanation was insufficient and apologize for any confusion. To clarify this point, “Given that estrogen/Esr2b signaling feminizes the neural substrates that mediate mating behavior, while PGF2α signaling triggers female sexual receptivity,” has been added before the sentence “The present finding provides a likely explanation for this apparent contradiction, namely, that neuroestrogens, rather than or in addition to ovarian-derived circulating estrogens, may function upstream of PGF2α signaling to mediate female receptivity.”

      Additional alteration

      Reference list (line 682): a preprint article has now been published in a peer-reviewed journal, and the information has been updated accordingly as follows: “bioRxiv doi: 10.1101/2024.01.10.574747 (2024)” to “Proc. Natl. Acad. Sci. U.S.A. 121, e2316459121 (2024).”

    1. Visual signs and images, even when they bear a close resemblance to the things to which theyrefer, are still signs: they carry meaning and thus have to be interpreted.

      Even if a picture shows a cat that looks exactly like a real cat—same fur, same face, same eyes—it’s still just a sign. It’s not the actual cat. It only makes sense to us because we already know what a cat is, and we’ve learned to connect that image to the concept of “cat” in our minds. Without that shared understanding, it would just be a random image. So even realistic photos or drawings aren’t just showing reality—they're part of a whole system of representation that helps us make sense of what we see.

    2. We have called this a ‘system of representation’. That is because it consists not of individualconcepts, but of different ways of organizing, clustering, arranging and classifying concepts, andof establishing complex relations between them.

      The ‘system of representation’ means the whole way we understand, think, speak, and share meaning about the world. It’s not just about seeing something; it’s about how we interpret it and give it meaning through a layered, complex process.

    3. Representation means using language to say something meaning-ful about, or to represent, the world meaningfully, to other people

      Representation isn't just about copying what we see. It's about creating meaning using language and symbols that society and culture agree on.

    1. Author response:

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

      Reviewer #1:

      In the future, could you please include the exact changes made to the manuscript in the relevant section of the rebuttal, so it's clear which changes addressed the comment? That would make it easier to see what you refer to exactly - currently I have to guess which manuscript changes implement e.g. "We have tried to make these points more evident".

      Yes, we apologize for the inconvenience.

      On possible navigation solutions:

      I'm not sure if I follow this argument. If the networks uses a shifted allocentric representation centred on its initial state, it couldn't consistently decode the position from different starting positions within the same environment (I don't think egocentric is the right term here - egocentric generally refers to representations relative to the animal's own direction like "to the left" rather than "to the west" but these would not work in the allocentric decoding scheme here). In other words: If I path integrate my location relative to my starting location s1 in environment 1 and learn how to decode that representation to an environment location, I cannot use the same representation when I start from s2 in environment 1, because everything will have shifted. I still believe using boundaries is the only solution to infer the absolute location for the agent here (because that's the only information that it gets), and that's the reason for finding boundary representations (and not grid cells). Imagine doing this task on a perfect torus where there are no boundaries: it would be impossible to ever find out at what 'absolute' location you are in the environment. I have therefore not updated this part of my review, but do let me know if I misunderstood.

      Thank you for addressing this point, which is a somewhat unusual feature of our network: We believe the point you raise applies if the decoding were fixed. However, in our case, the decoding is dynamic and depends on the firing pattern, as place unit centers are decoded on a per-trajectory basis. Thus, a new place-like basis may be formed for each trajectory (and in each environment). Hence, the model is not constrained to reuse its representation across trajectories or environments, as place centers are inferred based on unit firing. However, we do observe that the network learns to use a fixed place field placement in each geometry, which likely reflects some optimal solution to the decoding problem. This might also help to explain the hexagonal arrangement of learned field centers. Finally, we agree that egocentric may not be entirely accurate, but we found it to be the best word to distinguish from the allocentric-type navigation adopted by the network.

      Regarding noise injection:

      Beyond that noise level, the network might return to high correlations, but that must be due to the boundary interactions - very much like what happens at the very beginning of entering an environment: the network has learned to use the boundary to figure out where it is from an uninformative initial hidden state. But I don't think this is currently reflected well in the main text. That still reads "Thus, even though the network was trained without noise, it appears robust even to large perturbations. This suggests that the learned solutions form an approximate attractor." I think your new (very useful!) velocity ablations show that only small noise is compensated for by attractor dynamics, and larger noise injections are error corrected through boundary interactions. I've added this to the new review.

      Thank you for your kind feedback: We have changed the phrasing in the text to say “robust even to moderate perturbations. ” As we hold that, while numerically small, the amount of injected noise is rather large when compared to the magnitude of activities in the network (see Fig. A5d); the largest maximal rate is around 0.1, which is similar to the noise level at which output representations fail to re-converge. However, some moderation is appropriate, we agree.

      On contexts being attractive:

      In the new bit of text, I'm not sure why "each environment appears to correspond to distinct attractive states (as evidenced by the global-type remapping behavior)", i.e. why global-type remapping is evidence for attractive states. Again, to me global-type remapping is evidence that contexts occupy different parts of activity space, but not that they are attractive. I like the new analysis in Appendix F, as it demonstrates that the context signal determines which region of activity space is selected (as opposed to the boundary information!). If I'm not mistaken, we know three things: 1. Different contexts exist in different parts of representation space, 2. Representations are attractive for small amounts of noise, 3. The context signal determines which point in representation space is selected (thanks to the new analysis in Appendix F). That seems to be in line with what the paper claims (I think "contexts are attractive" has been removed?) so I've updated the review.

      It seems to us that we are in agreement on this point; our aim is simply to point out that a particular context signal appears to correspond to a particular (discrete) attractor state (i.e., occupying a distinct part of representation space, as you state), it just seems we use slightly different language, but to avoid confusion, we changed this to say that “representations are attractive”.

      Thanks again for engaging with us, this discussion has been very helpful in improving the paper.

      Reviewer #2:

      However, I still struggle to understand the entire picture of the boundary-to-place-to-grid model. After all, what is the role of grid cells in the proposed view? Are they just redundant representations of the space? I encourage the authors to clarify these points in the last two paragraphs on pages 17-18 of the discussion.

      Thank you for your feedback. While we have discussed the possible role of a grid code to some extent, we agree that this point requires clarification. We have therefore added to the discussion on the role of grid cells, which now reads “While the lack of grid cells in this model is interesting, it does not disqualify grid cells from serving as a neural substrate for path integration. Rather, it suggests that path integration may also be performed by other, non-grid spatial cells, and/or that grid cells may serve additional computational purposes. If grid cells are involved during path integration, our findings indicate that additional tasks and constraints are necessary for learning such representations. This possibility has been explored in recent normative models, in which several constraints have been proposed for learning grid-like solutions. Examples include constraints concerning population vector magnitude, conformal isometry \cite{xu_conformal_2022, schaeffer_self-supervised_2023, schoyen_hexagons_2024}, capacity, spatial separation and path invariance \cite{schaeffer_self-supervised_2023}. Another possibility is that grid cells are geared more towards other cognitive tasks, such as providing a neural metric for space \cite{ginosar_are_2023, pettersen_self-supervised_2024}, or supporting memory and inference-making \cite{whittington_tolman-eichenbaum_2020}. That our model performs path integration without grid cells, and that a myriad of independent constraints are sufficient for grid-like units to emerge in other models, presents strong computational evidence that grid cells are not solely defined by path integration, and that path integration is not only reserved for grid cells.”

      Thank you again for your time and input.

    1. “But, Mary, just listen to me. Your feelings are all quite right, dear, and interesting, and I love you for them; but, then, dear, we mustn’t suffer our feelings to run away with our judgment; you must consider it’s not a matter of private feeling,—there are great public interests involved,—there is such a state of public agitation rising, that we must put aside our private feelings.”

      Her husband was trying to talk her out of it.

    1. collectiveleadership

      I love this concept. I'm constantly wondering how I can develop my teachers into leaders and I'm always wondering where I can find them opportunities to lead. Even if it's just leading a session during a department meeting, it's still an opportunity for others to learn and grow from them, and it's a chance for that teacher to grow in another area.

    1. Foundations of Early Childhood Education is your entry point into that meaningful work. Whether you’re just beginning to explore this field or already have experience with children, this course will give you a strong understanding of how early learning environments support the physical, emotional, social, and cognitive development of young children. You’ll learn about the history of the profession, guiding principles of developmentally appropriate practice, and the vital role educators play in shaping young lives. Like Jamal, you’ll come to see that early childhood education isn’t just a job—it’s a foundation for everything that follows.

      Love this introductory paragraph!

    1. Note de synthèse : Le rôle du Replay et de la Connaissance Structurelle dans l'Apprentissage et la Cognition

      Cette note de synthèse explore les idées principales présentées par le Professeur Tim Behrens sur la manière dont le cerveau construit des modèles du monde, en mettant l'accent sur le rôle du "replay" (rejeu) et des représentations structurelles dans l'apprentissage et l'inférence.

      1. Le Débat Fondamental en Psychologie : Skinner vs. Tolman L'exposé de Behrens débute par une rétrospective historique du débat entre deux figures majeures de la psychologie du milieu du XXe siècle :

      • B.F. Skinner (Behaviorisme) : Skinner soutenait que tout comportement est contrôlé par la récompense et la punition, à travers un processus de "conditionnement".

      Il affirmait qu'un comportement sophistiqué pouvait être créé en "assemblant" des comportements prédisant une récompense, même à long terme.

      Sa célèbre citation, "The real problem is not where the machines think but whether men do," illustre sa vision externaliste du contrôle du comportement.

      Ses idées ont eu une influence notable sur le système éducatif américain des années 60 et 70.

      • Edward Tolman (Cognitivisme) : À l'opposé, Tolman, étudiant les rats dans des labyrinthes, a démontré que les animaux apprenaient la structure du labyrinthe même en l'absence de récompense.

      Les rats pouvaient trouver des raccourcis plus tard lorsque des récompenses étaient introduites, ce qui suggère l'existence d'une "carte cognitive" interne ou d'un "modèle interne du monde".

      Sa métaphore était que "the central office itself is far more like a map controll room than it is like an oldfashioned telephone exchange."

      • Behrens conclut que "obviously like all debates in the history of science both of them are right to some extent or the other both of these processes exist in the brain", mais que la "knowledge structure process is a very interesting one and certainly has an increasing reliance as you get higher up The evolutionary chain towards primates and humans." Le cœur de la présentation est de comprendre à quoi ressemble cette "carte" et comment elle est construite dans le cerveau.

      2. La Représentation des Relations et la Généralisation

      • La construction d'un modèle du monde repose sur la compréhension des relations entre les choses. Behrens propose deux manières pour le cerveau de représenter ces liens :

      • Liaisons synaptiques directes : Des neurones associés à deux concepts (ex: Starbucks et café) pourraient former de nouvelles synapses pour s'activer mutuellement. C'est le principe de l'apprentissage associatif.

      • Neurones représentant explicitement les relations : Une approche plus sophistiquée implique des populations distinctes de neurones qui encodent la structure des relations elles-mêmes. L'avantage crucial de cette méthode est la capacité d'inférence et de généralisation.

      Si la relation entre la fée Starbucks et le café est encodée explicitement, le même mécanisme neuronal peut être utilisé pour comprendre la relation entre le "whoosh" de Nike et une chaussure, permettant une inférence comme "I can buy the shoe wherever I see the Nike whoosh."

      3. Les Cellules de Grille et de Lieu : Les Fondements des Cartes Cognitives

      • La démonstration de la représentation structurelle débute avec les découvertes en neurosciences spatiales chez les rongeurs :

      • Cellules de Lieu (Hippocampe) : Ces neurones s'activent lorsque l'animal se trouve dans une position spécifique de l'environnement.

      • Cellules de Grille (Cortex Entorhinal) : Ces cellules sont "super cool cells" qui s'activent selon un motif hexagonal régulier et "understand the structure of the problem that you're talking about". Elles fournissent un système de coordonnées pour la navigation spatiale. Il n'y aurait "no reason to have such a cell if you were not optimized for running around a two-dimensional plane."

      • Cellules de Vecteur d'Objet : Ces neurones représentent un vecteur entre la position actuelle de l'animal et un objet intéressant (ex: "I'm this Vector away from something interesting like a piece of cheese").

      • L'existence de ces cellules permet des inférences spatiales complexes, comme trouver un raccourci vers la récompense, au lieu de simplement reproduire le chemin appris (comme le prédirait Skinner).

      4. La Généralisation des Cellules de Grille aux Domaines Non-Spatiaux

      • Une idée centrale de l'équipe de Behrens est que ces "types of solutions are general types of solutions for many of the problems". Des preuves s'accumulent que les cellules de grille et des systèmes de coordonnées similaires peuvent être utilisés pour des problèmes non-spatiaux :

      • Espace de Fréquence : Des cellules de grille ont été observées chez les rats naviguant dans un espace de fréquence tonale, se comportant "as if you're moving through real space".

      • Dimensions Sémantiques : Dans des études sur les humains (via des "smoke signals" ou des signaux indirects détectés par MEG), des activités similaires aux cellules de grille ont été observées pour des problèmes à deux dimensions non spatiales, comme la longueur des pattes et du cou des oiseaux.

      Plus récemment, des cellules de grille non spatiales ont été enregistrées directement chez le singe, ce qui est "very exciting for me because I started this this thing I guess 12 15 years ago and now we can really see them."

      • Hiérarchies de Séquences : Même pour des structures non bidimensionnelles, comme les hiérarchies temporelles (jours, semaines, mois, années), des preuves indirectes de systèmes de coordonnées similaires ont été trouvées chez l'homme.

      • Ces découvertes renforcent l'idée que ces représentations structurelles sont des "General structural representations of the relationships between objects in your brain."

      5. Le Phénomène de Replay et son Rôle dans l'Inférence

      • Le "replay" est un phénomène d'activité neuronale rapide qui récapitule des séquences d'événements, se produisant souvent pendant le repos ou le sommeil. Behrens soutient que le replay n'est pas seulement une consolidation passive de la mémoire, mais un processus actif d'inférence sur des futurs possibles :

      • Décodage du Replay chez l'Humain : En utilisant la magnétoencéphalographie (MEG), les chercheurs peuvent décoder l'activité cérébrale pendant le repos et voir quelles séquences de stimuli sont "rejouées".

      • Replay Inférentiel : Dans une expérience inspirée de Kill Bill de Tarantino (où l'ordre des chapitres est jumbé), les sujets humains apprenaient des séquences d'événements dans le désordre.

      Pendant les périodes de repos, le replay ne montrait pas la séquence désordonnée vécue, mais la séquence "désembrouillée" et logiquement correcte.

      "What's happening in this rest period after we show subjects the jumbled up sequences... their brain in this rest period is going ABCD ABCD ABCD ABCD with like 40 milliseconds between them and these little packets the little packets being played out doing an inference over what's what's happened."

      Ce processus se produit rapidement (environ 40-50 millisecondes par élément).

      • Lien avec la Structure : Le replay utilise la connaissance structurelle préexistante (par exemple, l'ordre causal des événements) pour réorganiser et inférer des relations non directement vécues.

      • Replay dans la Vie Quotidienne : Des études montrent que le replay se produit "whenever the movie pauses at a scene boundary doing all all sorts of reorganization."

      Ce qui suggère aux éducateurs de "pause regularly when you're trying to explain something to give the opportunity for lots of Replay packets to be to be reorganized in your in your other people's brains."

      6. Mécanismes Cellulaires du Replay Inférentiel

      • Behrens explique comment le replay pourrait permettre la construction de cartes et l'inférence au niveau neuronal :

      • Construction de Cartes à partir de la Récompense :

      L'argument est que l'animal ne planifie pas seulement au moment de l'action, mais que le fait de trouver une récompense déclenche des paquets de replay qui construisent une carte de directions vers cette récompense depuis d'autres endroits de l'environnement. Ces replays "build an entire map to get to that cheese."

      • Association Coordonnée-Vecteur : Le replay lierait la position actuelle (coordonnée) avec un "neurone vecteur" (ex: "cheese over there neuron") pour créer une nouvelle "place field" (champ de lieu) ou modifier une existante. Si le point de récompense se déplace, le replay désassocie l'ancien vecteur et l'associe au nouveau.

      "The replay is taking the neuron that says cheese over here and it's moving it to say cheese is over there now right and so that's what the replay is doing and it's doing this throughout the whole map it's just going around the whole map organizing your representations to point you towards the cheese doing this inference in rest not at the time when you're going to need it when you need it but doing it way before you need it so that it's much quicker when you need it."

      • Surreprésentation des Neurones Vecteurs : Les neurones de vecteur seraient particulièrement représentés dans ces replays, suggérant qu'ils sont la cible principale de cette réorganisation.

      7. Replay Rapide vs. Replay Lent et leur Impact sur la Performance

      • Dans des tâches cognitives plus complexes chez l'humain (comme un jeu de cartes avec des dimensions d'attaque/défense), deux types de replay sont observés :

      • Replay Rapide (Fast Replay, ~50 ms) : Associé à une meilleure performance et à l'utilisation du système de coordonnées interne.

      Plus on a de replay rapide, moins on a besoin de "réfléchir" consciemment.

      Il se situe dans les zones du lobe temporal médian (hippocampe, cortex entorhinal).

      • Replay Lent (Slow Replay, ~150 ms) : Associé à une performance plus faible et à la nécessité de la "pensée" consciente. Il semble provenir davantage du cortex frontal.

      • Ces résultats suggèrent que le replay rapide, qui construit des cartes en mode hors ligne, "is preventing you from having to do this rapid thinking online."

      8. L'Importance du Repos et du Sommeil pour la Construction de Connaissances Structurelles

      • Behrens insiste sur le fait que le cerveau continue de "remplir la carte" pendant les périodes de repos :

      • Construction Hors Ligne de la Carte : Même lorsque les informations sont apprises de manière unidimensionnelle, le cerveau utilise le replay pendant le repos pour construire la carte complète bidimensionnelle.

      "It replays in the two- dimensional in the full two two Dimensions during rest when there's no inferences going on in in their behavior they're just sitting there the brain in the background is is sending these little packets filling out the map building in the two-dimensional replay."

      • Impact sur la Performance Future : "If you do that offline then later online you have more grid cell activity and you're better at the task."

      • Conseil Pratique : "All the time when you're just sitting around having a cup of tea what you should be doing what your brain is doing is filling in all the experiences that you've had that day and making them all match up with each other so that you um so so that you when you get asked a question later you know the answer already you don't have to think about it there and then so get lots of rest and have lots of Cups of Tea."

      • Lien avec l'Éducation et le QI : Les signaux de replay et de structure se développent "in the school time in the age of of school from 8 to 18 and they predict measures real world measures like IQ much better than things like age do."

      Ceci soutient l'idée que les programmes éducatifs devraient se concentrer sur le "developing Rich structural knowledge" plutôt que sur le conditionnement par récompense/punition ("take that Skinner!").

      • Replay et Sommeil Lent : Ces paquets d'activité sont les plus forts pendant le sommeil à ondes lentes, soulignant l'importance du sommeil pour l'organisation des connaissances.

      9. Discussion : Homme vs. Rongeur et Spécialisation Corticale

      • Similarité des Principes Mécanistiques : Behrens reconnaît que le répertoire structurel est "much richer in humans than it is in other species" mais pense que "the principles by which they mechanistically operate are the same." Cela justifie l'étude des mécanismes de base chez les rongeurs.

      • Transfert de Connaissances Hippocampe-Cortex : Il y a "definitely such a transfer" d'informations de l'hippocampe vers le reste du cortex pendant le sommeil.

      Une "onde d'activité" se propage de l'hippocampe vers le cortex entorhinal, puis le cortex préfrontal médian et pariétal médian, avant d'atteindre les cortex associatifs et sensorimoteurs. Ces régions médianes pourraient "mediate the communication of hippocampus to the rest of Cortex."

      • Le Modèle en "Anneaux d'Oignon" (Onion Rings) : Behrens propose une métaphore où les structures les plus universelles et fréquemment utilisées (comme l'espace ou les hiérarchies) seraient représentées "closest to the hippocampus", tandis que les structures plus spécialisées (comme la grammaire du langage) seraient plus éloignées, dans les systèmes corticaux dédiés.

      L'objectif est de construire les souvenirs avec le moins de nouvelles synapses possible, en utilisant au maximum les connaissances antérieures.

    1. Author response:

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

      eLife Assessment

      This valuable study reports the development of a novel organoid system for studying the emergence of autorhythmic gut peristaltic contractions through the interaction between interstitial cells of Cajal and smooth muscle cells. While the utility of the organoids for studying hindgut development is well illustrated by showing, for example, a previously unappreciated potential role for smooth muscle cells in regulating the firing rate of interstitial cells of Cajal, some of the functional analyses are incomplete. There are some concerns about the specificity and penetrance of perturbations and the reproducibility of the phenotypes. With these concerns properly addressed, this paper will be of interest to those studying the development and physiology of the gut.

      We greatly appreciate constructive comments raised by the Editors and all the Reviewers. We have newly conducted pharmacological experiments using Nifedipine, a L-type Ca<sup>2+</sup> blocker known to operate in smooth muscles (new Fig 7). The treatment abrogated not only the oscillation of SMCs but also that in ICCs, further corroborating our model that not only ICC-to-SMC interactions but also the reverse direction, namely SMC-to-ICC feedback signals, are operating to achieve coordinated/stable rhythm of gut contractile organoids.

      Concerning the issues of the specificity and penetrance in pharmacological experiments with gap junction inhibitors, we have carefully re-examined effects by multiple blockers (CBX and 18b-GA) at different concentrations (new Fig 5D and Fig. S3B).We have newly found that: (1) the effects observed by CBX (100 µM) that the latency of Ca<sup>2+</sup> peaks between ICCs (preceding) and SMCs (following) was abolished are not seen by 18b-GA at any concentrations including 100 µM, implying that the latency of Ca<sup>2+</sup> peaks between these cells is governed by connexin(s) that are not inhibited by18bGA. Such difference in inhibiting effects by these two drugs were previously reported in multiple model systems including guts (Daniel et al., 2007; Parsons & Huizinga, 2015; Schultz et al., 2003).

      Regarding the penetrance of the drugs, we have carried out earlier administration (Day 3) of the gap junction inhibitor, either CBX (100 µM) or 18b-GA (100 µM), in the course of organoidal formation in culture when cells are still at 2D to exclude a possible penetrance problem (new Fig. S3C). There treatments render no or little effects to the patterns of organoidal contractions in a way similar to the drug administration at Day 7. As already shown in the first version, CBX (100 µM) eliminates the latency of Ca<sup>2+</sup> peaks, we believe that this drug successfully penetrates into the organoid and exerts its specific effects.

      Unfortunately, due to very unstable condition in climate including extreme heat and sporadically occurring bird flu epidemic since the last summer in Japan, the poultry farm must have faced problems. In the course of revision experiments, we got in a serious trouble at multiple times with unhealthy eggs/embryos lasting from last summer until present. These unfortunate incidents did not allow us to engage in the revision experiments as fully as we originally planned. Nevertheless, we did our very best within a limited time fame, and we believe that the revised version is suitable as a final version of an eLife article.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, the authors developed an organoid system that contains smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs; pacemaker) but few enteric neurons, and generates rhythmic contractions as seen in the developing gut. The stereotypical arrangements of SMCs and ICCs in the organoid allowed the authors to identify these cell types in the organoid without antibody staining. The authors took advantage of this and used calcium imaging and pharmacology to study how calcium transients develop in this system through the interaction between the two types of cells. The authors first show that calcium transients are synchronized between ICC-ICC, SMC-SMC, and SMC-ICC. They then used gap junction inhibitors to suggest that gap junctions are specifically involved in ICC-to-SMC signaling. Finally, the authors used an inhibitor of myosin II to suggest that feedback from SMC contraction is crucial for the generation of rhythmic activities in ICCs. The authors also show that two organoids become synchronized as they fuse and SMCs mediate this synchronization.

      Strengths:

      The organoid system offers a useful model in which one can study the specific roles of SMCs and ICCs in live samples.

      Thank you very much for the constructive comments.

      Weaknesses:

      Since only one blocker each for gap junction and myosin II was used, the specificities of the effects were unclear.

      We appreciate these comments. We have addressed those of “weaknesses” as described in “Responses to the eLife assessment” (please see above).

      Reviewer #2 (Public Review):

      Summary:

      In this study, Yagasaki et al. describe an organoid system to study the interactions between smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). While these interactions are essential for the control of rhythmic intestinal contractility (i.e., peristalsis), they are poorly understood, largely due to the complexity of and access to the in vivo environment and the inability to co-culture these cell types in vitro for long term under physiological conditions. The "gut contractile organoids" organoids described herein are reconstituted from stromal cells of the fetal chicken hindgut that rapidly reorganize into multilayered spheroids containing an outer layer of smooth muscle cells and an inner core of interstitial cells. The authors demonstrate that they contract cyclically and additionally use calcium imagining to show that these contractions occur concomitantly with calcium transients that initiate in the interstitial cell core and are synchronized within the organoid and between ICCs and SMCs. Furthermore, they use several pharmacological inhibitors to show that these contractions are dependent upon non-muscle myosin activity and, surprisingly, independent of gap junction activity. Finally, they develop a 3D hydrogel for the culturing of multiple organoids and found that they synchronize their contractile activities through interconnecting smooth muscle cells, suggesting that this model can be used to study the emergence of pacemaking activities. Overall, this study provides a relatively easy-to-establish organoid system that will be of use in studies examining the emergence of rhythmic peristaltic smooth muscle contractions and how these are regulated by interstitial cell interactions. However, further validation and quantification will be necessary to conclusively determine show the cellular composition of the organoids and how reproducible their behaviors are.

      Strengths:

      This work establishes a new self-organizing organoid system that can easily be generated from the muscle layers of the chick fetal hindgut to study the emergence of spontaneous smooth muscle cell contractility. A key strength of this approach is that the organoids seem to contain few cell types (though more validation is needed), namely smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). These organoids are amenable to live imaging of calcium dynamics as well as pharmacological perturbations for functional assays, and since they are derived from developing tissues, the emergence of the interactions between cell types can be functionally studied. Thus, the gut contractile organoids represent a reductionist system to study the interactions between SMCs and ICCs in comparison to the more complex in vivo environment, which has made studying these interactions challenging.

      Thank you very much for the constructive comments.

      Weaknesses:

      The study falls short in the sense that it does not provide a rigorous amount of evidence to validate that the gut organoids are made of bona fide smooth muscle cells and ICCs. For example, only two "marker" proteins are used to support the claims of cell identity of SMCs and ICCs. At the same time, certain aspects of the data are not quantified sufficiently to appreciate the variance of organoid rhythmic contractility. For example, most contractility plots show the trace for a single organoid. This leads to a concern for how reproducible certain aspects of the organoid system (e.g. wavelength between contractions/rhythm) might be, or how these evolve uniquely over time in culture. Furthermore, while this study might be able to capture the emergence of ICC-SMC interactions as they related to muscle contraction and pacemaking, it is unclear how these interactions relate to adult gastrointestinal physiology given that the organoids are derived from fetal cells that might not be fully differentiated or might have distinct functions from the adult. Finally, despite the strength of this system, discoveries made in it will need to be validated in vivo. Thank you very much for the comments, which are helpful to improve our MS. In the revised version, we have additionally used antibody against desmin, known to be a maker for mature SMCs (new Fig 3B). The signal is seen only in the peripheral cells overlapping with the αSMA staining (line 169-170).

      Concerning the reproducibility, while contractility changes were shown for a representative organoid in the original version, experiments had been carried out multiple times, and consistent data were reproduced as already mentioned in the text of the first version of MS. However, we agree with this reviewer that it must be more convincing if we assess quantitatively. We have therefore conducted quantitative assessments of organoidal contractions and Ca<sup>2+</sup> transients (new Fig. 2B, new Fig. 4D, new Fig 5D, E, new Fig. 6B, new Fig. 7B, new Fig. 8C, new Fig. S2, S3). Details such as repeats of experiments and size of specimens are carefully described in the revised version (Figure legends)

      In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, with your suggestion, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca<sup>2+</sup> transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

      As already mentioned in the “Our provisional responses” following the receipt of Reviewers’ comments, we agree that our organoids derived from embryonic hind gut (E15) might not necessarily recapitulate the full function of cells in adult. However, it has well been accepted in the field of developmental biology that studies with embryonic tissue/cells make a huge contribution to unveil complicated physiological cell functions. Nevertheless, we have carefully considered in the revised version so that the MS would not send misleading messages. We agree that in vivo validation of our gut contractile organoid must be wonderful, and this is a next step to go.

      Reviewer #3 (Public Review):

      Summary:

      The paper presents a novel contractile gut organoid system that allows for in vitro studying of rudimentary peristaltic motions in embryonic tissues by facilitating GCaMPlive imaging of Ca<sup>2+</sup> dynamics, while highlighting the importance and sufficiency of ICC and SMC interactions in generating consistent contractions reminiscent of peristalsis. It also argues that ENS at later embryonic stages might not be necessary for coordination of peristalsis.

      Strengths:

      The manuscript by Yagasaki, Takahashi, and colleagues represents an exciting new addition to the toolkit available for studying fundamental questions in the development and physiology of the hindgut. The authors carefully lay out the protocol for generating contractile gut organoids from chick embryonic hindgut, and perform a series of experiments that illustrate the broader utility of these organoids for studying the gut. This reviewer is highly supportive of the manuscript, with only minor requests to improve confidence in the findings and broader impact of the work. These are detailed below.

      Thank you very much for the constructive comments.

      Weaknesses:

      (1) Given that the literature is conflicting on the role GAP junctions in potentiating communication between intestinal cells of Cajal (ICCs) and smooth muscle cells (SMCs), the experiments involving CBX and 18Beta-GA are well-justified. However, because neither treatment altered contractile frequency or synchronization of Ca++ transients, it would be important to demonstrate that the treatments did indeed inhibit GAP junction function as administered. This would strengthen the conclusion that GAP junctions are not required, and eliminate the alternative explanation that the treatments themselves failed to block GAP junction activity.

      Thank you for these comments, and we agree. In the revised version, we have verified the drugs, CBX and 18b-GA, using dissociated embryonic heart cells in culture, a well-established model for the gap junction study (new Fig. S3D, line 237-239). Expectedly, both inhibitors abrogate the rhythmic beats of heart cells, and importantly, cells’ beats resume after wash-out of the drug.

      (2) Given that 5uM blebbistatin increases the frequency of contractions but 10uM completely abolishes contractions, confirming that cell viability is not compromised at the higher concentration would build confidence that the phenotype results from inhibition of myosin activity. One could either assay for cell death, or perform washout experiments to test for recovery of cyclic contractions upon removal of blebbistatin. The latter may provide access to other interesting questions as well. For example, do organoids retain memory of their prior setpoint or arrive at a new firing frequency after washout?

      We greatly appreciate these suggestions and also interesting ideas to explore! In the revised version, we have newly conducted washout experiments (new Fig. 6B) (10 µM drug is washed-out from culture medium), and found that contractions resume, showing that cell viability is not compromised at 10 µM concentration (line 257-259). Intriguingly, the resumed rhythm appears more regular than that before drug administration. Thus, the contraction rhythm of the organoid might be determined by cellcell interactions at any given time rather than by memory of their prior setpoint. This is an interesting issue we would like to further explore in the future. These issues, although potentially interesting, are not mentioned in the text of the revised version, since it is too early to interpret there observations.

      (3) Regulation of contractile activity was attributed to ICCs, with authors reasoning that Tuj1+ enteric neurons were only present in organoids in very small numbers (~1%).

      However, neuronal function is not strictly dependent on abundance, and some experimental support for the relative importance of ICCs over Tuj1+ cells would strengthen a central assumption of the work that ICCs the predominant cell type regulating organoid contraction. For example, one could envision forming organoids from embryos in which neural crest cells have been ablated via microdissection or targeted electroporation. Another approach would be ablation of Tuj1+ cells from the formed organoids via tetrodotoxin treatment. The ability of organoids to maintain rhythmic contractile activity in the total absence of Tuj1+ cells would add confidence that the ICCs are indeed the driver of contractility in these organoids.

      We agree. In the revised version, we have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

      (4) Given the implications of a time lag between Ca++ peaks in ICCs and SMCs, it would be important to quantify this, including standard deviations, rather than showing representative plots from a single sample.

      In the revised version, we have elaborated a series of quantitative assessments as mentioned above (please see our responses to the “eLife assessments” at the beginning of these correspondences). The latency between Ca<sup>2+</sup> peaks in ICCs and SMCs is shown in new Fig. 4D, in which measured value is 700 msec-terraced since the time-lapse imaging was performed with 700 msec intervals (as already described in the first version).

      117 peaks for 14 organoids have been assessed (line 218).

      (5) To validate the organoid as a faithful recreation of in vivo conditions, it would be helpful for authors to test some of the more exciting findings on explanted hindgut tissue. One could explant hindguts and test whether blebbistatin treatment silences peristaltic contractions as it does in organoids, or following RCAS-GCAMP infection at earlier stages, one could test the effects of GAP junction inhibitors on Ca++ transients in explanted hindguts. These would potentially serve as useful validation for the gut contractile organoid, and further emphasize the utility of studying these simplified systems for understanding more complex phenomena in vivo.

      Thank you very much for insightful comments. We would love to explore these issues in near future. Just a note is that it was previously reported that Nifedipine silences peristaltic contractions in ex-vivo cultured gut (Chevalier et al., 2024; Der et al., 2000).

      (6) Organoid fusion experiments are very interesting. It appears that immediately after fusion, the contraction frequency is markedly reduced. Authors should comment on this, and how it changes over time following fusion. Further, is there a relationship between aggregate size and contractile frequency? There are many interesting points that could be discussed here, even if experimental investigation of these points is left to future work.

      It would indeed be interesting to explore how cell communications affect/determine the contraction rhythm, and our novel organoids must serve as an excellent model to address these fundamental questions. We have observed multiple times that when two organoids fuse, they undergo “pause”, and resume coordinated contractions as a whole, and we have mentioned such notice briefly in the revised version (line 282). To know what is going on during this pause time should be tempting. In addition, we have an impression that the larger in size organoids grow, the slower rhythm they count. We would love to explore this in near future.

      (7) Minor: As seen in Movie 6 and Figure 6A, 5uM blebbistatin causes a remarkable increase in the frequency of contractions. Given the regular periodicity of these contractions, it is a surprising and potentially interesting finding, but authors do not comment on it. It would be helpful to note this disparity between 5 and 10 uM treatments, if not to speculate on what it means, even if it is beyond the scope of the present study to understand this further.

      We assume that the increase in the frequency of contractions at 5 µM might be due to a shorter refractory period caused by a decreasing magnitude (amplitude) of contraction. We have made a short description in the revised text (line 256-257).

      (8) Minor: While ENS cells are limited in the organoid, it would be helpful to quantify the number of SMCs for comparison in Supplemental Figure S2. In several images, the number of SMCs appears quite limited as well, and the comparison would lend context and a point of reference for the data presented in Figure S2B.

      In the revised version, the number of SMCs has been counted and added in Fig. S2B. Contrary to that SMCs are more abundant than ICCs in an intact gut, the proportion is reversed in our organoid (line 181-183). It might due to treatments during cell dissociation/plating.

      (9) Minor: additional details in the Figure 8 legend would improve interpretation of these results. For example, what is indicated in orange signal present in panels C, G and H? Is this GCAMP?

      We apologize for this confusion. In the revised version, we have added labeling directly in the photos of new Fig. 9 (old Fig. 8). For C, G and H, the left photo is mRuby3+GCaMP6s, and the right one is GCaMP6s only.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I have a few comments for the authors to consider:

      (1) Figure 4C: The authors propose that calcium signals propagate from ICC to SMC based on the results presented in this figure. While it is observed that the peak of the calcium signal in ICC precedes that in SMC, it's worth noting that the onset of the rise in calcium signals occurs simultaneously in ICC and SMC. Doesn't this suggest that they are activated simultaneously? The latency observed for the peaks of calcium signals could reflect different kinetics of the rise in calcium concentration in the two types of cells rather than the order of calcium signal propagation.

      We greatly appreciate these comments. We have re-examined kinetics of GCaMP signals in ICC and SMC, but we did not succeed in validating rise points precisely. We agree that the possibility that the rise in calcium signals could be occurring simultaneously. To clarify these issues, analyses with higher resolution is required, such as using GCaMP6f or GCaMP7/8. Nevertheless, the disappearance of the latency of Ca<sup>2+</sup> peak by CBX implies a role of gap junction in ICC to SMC signaling. In the revised version, we replaced the wording “rise” by “peak” when the latency is discussed.

      (2) Figure 5C: The specific elimination of the latency in the calcium signal peaks between ICC and SMC is interesting. However, I am curious about how gap junction inhibitors specifically eliminate the latency between ICC and SMC without affecting other aspects of calcium transients in these cells, such as amplitude and synchronization among ICCs and/or SMCs. Readers of the manuscript would expect some discussion on possible mechanisms underlying this specificity. Additionally, I wonder if the elimination of the latency was observed consistently across all samples examined. The authors should provide information on the frequency and number of samples examined, and whether the elimination occurs when 18-beta-GA is used.

      In the revised version, we have elaborated quantitative demonstration. For the effects by CBX on latency or Ca<sup>2+</sup> peaks, a new graph has been added to new Fig 5, in which 100 µM eliminated the latency. Intriguingly, the latency appears to be attributed to a gap junction that is not inhibited by18-beta-GA (please see new Fig. S3E). As already mentioned above, inhibiting activity of both CBX and 18-beta-GA has been verified using dissociated cells of embryonic heart, a popular model for gap junction studies.

      At present, we do not know how gap junction(s) contribute to the latency of Ca<sup>2+</sup> peaks without affecting synchronization among ICCs and/or SMCs (we have not addressed amplitude of the oscillation in this study). Actually, it was surprising to us to find that GJ’s contribution is very limited. We do not exclude the importance of GJs, and currently speculate that GJs might be important for the initiation of contraction/oscillation signals, whereas the requirement of GJs diminishes once the ICC-SMC interacting rhythm is established. What we observed in this study might be the synchronization signals AFTER these interactions are established (Day 7 of organoidal culture). Upon the establishment, it is possible that mechanical signaling elicited by smooth muscles’ contraction might become prominent as a mediator for the (stable) synchronization, as implicated by experiments with blebbistatin and Nifedipin, the latter being newly added to the revised version (new Fig. 7). We have added such speculation, although briefly in Discussion (line 374-377)

      (3) Figure 6: The significant effects of blebbistatin on calcium dynamics in both ICC and SMC are intriguing. However, since only one blocker is utilized, the specificity of the effects is unclear. If other blockers for muscle contraction are available, they should be employed. Considering that a rise in calcium concentration precedes contraction, calcium transients should persist even if muscle contraction is inhibited. One concern is whether blebbistatin inadvertently rendered the cells unhealthy. The authors should demonstrate at least that contraction and calcium transients recover after removal of the drug. The frequency and number of samples examined should be shown, as requested for Figure 5C above.

      Thank you for these critical comments. A possible harmfulness of the drugs was also raised by other reviewers, and we have therefore conducted wash-out experiments in the revised version (new Fig. 6B). Contractions resume after wash-out showing that cell viability is not compromised at 10 µM concentration. The number of samples examined has been described more explicitly in the revised version. Regarding the blocker of SMC, we have newly carried out pharmacological assays using nifedipine, a blocker of a L-type Ca<sup>2+</sup> channel known to operate in smooth muscle cells (new Fig 7) (Chevalier et al., 2024; Der et al., 2000). As already explained in the “Responses to eLife assessment”, the treatment abrogated ICCs’ rhythm and synchronous Ca<sup>2+</sup> transients between ICCs and SMCs, further corroborating our model that not only ICC-to-SMC interactions but also SMC-to-ICC feedback signals are operating to achieve coordinated/stable rhythm of gut contractile organoids of Day 7 culture (please also see our responses shown above for Comment (2)).

      Reviewer #2 (Recommendations For The Authors):

      Major:

      (1) The claim that organoids contain functional SMCs and ICCs is insufficient as it currently relies on only c-Kit and aSMA antibodies. This conclusion could be additionally supported by staining with other markers of contractile smooth muscle (e.g. TAGLN and MYH14) and an additional accepted marker of ICCs (e.g. ANO1/TMEM16). Moreover, it should be demonstrated whether these cells are PDGFRA+, as PDGFRA is a known marker of other mesenchymal fibroblast cell types. These experiments would additionally rule out whether these cells were simply less differentiated myofibroblasts. Given that there might not be available antibodies that react with chicken protein versions, the authors could support their conclusions using alternative approaches, such as fluorescent in situ hybridization. A more thorough approach, such as single-cell RNA sequencing to compare the cell composition of the in vitro organoids to the in vivo colon, would fully justify the use of these organoids as a system for studying in vivo cell physiology.

      With these suggestions provided, we have newly stained contractile organoids with anti-desmin antibody, known to be a marker for differentiated SMCs. As shown in new Fig. 3B, desmin-positive cells perfectly overlapped with aSMA-staining, indicating that the peripherally enclosing cells are SMCs. Regarding the interior cells, as this Reviewer concerned, there are no antibodies against ANO1/TMEM16 which are available for avian specimens. The anti- c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization. We have attempted several times to perform organoidal whole-mount in situ hybridization for expression of PDGFRα, but we have not succeeded so far. In addition, as explained to the Editor, the very unhealthy condition of purchased eggs these past 7 months did not allow us to continue any further. We are planning to interrogate cell types residing in the central area of the organoid, results of which will be reported in a separate paper in near future.

      (2) The key ICC-SMC relationship and physiological interaction seems to arise developmentally, but the mechanisms of this transition are not well defined (Chevalier 2020). To further support the claim that ICC-SMC interactions can be interrogated in this system, this study would benefit from establishing organoids at distinct developmental stages to (a) show that they have unique contractile profiles, and (b) demonstrate that they evolve over time in vitro toward an ICC-driven mechanism.

      We agree with these comments. We tried to prepare gut contractile organoids derived from different stages of development, and we had an impression that slightly younger hindguts are available for the organoid preparations. In addition, not only the hindgut, but also midgut and caecum also yield organoids. However, since formed organoids derived from these “non-E15 hindgut” vary substantially in shapes, contraction frequencies/amplitudes etc., we are currently not ready to report these preliminary observations. Instead, we decided to optimize and elaborate in vitro culture conditions by focusing on the E15 hindgut, which turned out to be most stable in our hands. Nevertheless, it is tempting to see how organoid evolves over time during gut development.

      (3) This manuscript would be greatly enhanced by a functional examination of the prospective organoid ICCs. For example, the authors could test whether the c-Kit inhibitor Imatinib, which has previously been used to impair ICC differentiation and function in the developing chick gut (Chevalier 2020), has an effect on contractility at different stages.

      Following the paper of (Chevalier 2020), we had already conducted similar experiments with Imatinib in the culture with our organoids, but we did not see detectable effects. In that paper, the midgut of younger embryos was used, whereas we used E15 hindgut to prepare organoids. It would be interesting to see if we add Imanitib earlier during organoidal formation, and this is a next step to go.

      (4) It is claimed that there is a 690s msec delay in SMC spike relative to ICC spike, however, it is unclear where this average is derived from and whether the organoid calcium trace shown in Figure 4C is representative of the data. The latency quantification should be shown across multiple organoids, and again in the case of carbenoxolone treatment, to better understand the variations in treatment.

      We apologize that the first version failed to clearly demonstrate quantitative assessments. In the revised version, we have elaborated quantitative assessments (117 peaks for 14 organoids) (line 216-218). In new Fig. 4D, measured value is 700 msecterraced since as already mentioned in the first version, the time-lapse imaging was performed with 700 msec intervals.

      (5) As above, a larger issue is that only single traces are shown for each organoid. This makes it challenging to understand the variance in contractile properties across multiple organoids. While contraction frequencies are shown several times, the manuscript would benefit from additional quantifications, such as rhythm (average wavelength between events) in control and perturbed conditions.

      We have substantially elaborated quantitative assessments (please also see our responses to the “Public Review”). In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca<sup>2+</sup> transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

      (6) The synchronicity observed between ICCs and SMCs within the organoid is interesting, and should be emphasized by making analyses more quantitative so as to understand how consistent and reproducible this phenomenon is across organoids. Moreover, one of the most exciting parts of the study is the synchronicity established between organoids in the hydrogel system, but it is insufficiently quantified. For example, how rapidly is pacemaking synchronization achieved?

      As we replied above to (5), and described in the responses to the “Public Review”, we have substantially elaborated quantitative assessments in the revised version. Concerning the synchronicity between ICCs and SMCs, our data explicitly show that as long as the organoid undergoes healthy contraction, they perfectly match their rhythm (Fig. 4) making it difficult to display quantitatively. Instead, to demonstrate such synchronicity more convincingly, we have carefully described the number of peaks and the number of independent organoids we analyzed in each of Figure legends. In the experiments with hydrogels, the time required for two organoids to start/resume synchronous contraction varies greatly. For example, for the experiment shown in new Fig 9F, it takes 1 day to 2 days for cells crawling out of organoids and cover the surface of the hydrogel. In the experiments shown in new Fig. 8, two organoids undergo “pause” before resuming contractions. In the revised version, we have briefly mentioned our notice and speculation that active cell communications take place during this pausing time, (line 282-283 in Result and line 437-439 in Discussion). We agree with this reviewer saying that the pausing time is potentially very interesting. However, it is currently difficult to quantify these phenomena. More elaborate experimental design might be needed.

      (7) Smooth muscle layers in vivo are well organized into circular and longitudinal layers. To establish physiological relevance, the authors should demonstrate if these organoids have multiple layers (though it looks like just a single outer layer) and if they show supracellular organization across the organoid.

      The immunostaining data suggest that peripherally lining cells are of a single layer, and we assume that they might be aligned in register with contracting direction. However, to clarify these issues, observation with higher resolution would be required.

      (8) To further examine whether the organoids contain true functional ICCs, the authors should test whether their calcium transients are impacted by inhibitors of L-type calcium channels, such as nifedipine and nicardipine. These channels have been demonstrated to be important for SMCs but not ICCs, so one might expect to see continued transients in the core ICCs but a loss of them in SMCs (Lee et al., 1999; PMID: 10444456)

      We appreciate these comments. We have accordingly conducted new experiments with Nifedipine. Contrary to the expectation, Nifedipine ceases not only organoidal contractions, but also ICC activities (and its resulting synchronization) (new Fig. 7). These findings actually corroborate our model already mentioned in the first version that ICCs receive mechanical feedback from SMC’s contraction to stably maintain their oscillatory rhythm. We believe that the additional findings with Nifedipine have improved the quality of our paper. Concerning the central cells in the organoid, we have additionally used anti-desmin antibody known to mark differentiated SMCs. Desmin signals perfectly overlap with those of aSMA in the peripheral single layer, supporting that the peripheral cells are SMCs and central cells are ICCs. The anti c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization.

      ANO1/TMEM16 are known to stain ICCs in mice. Antibodies against ANO1/TMEM16 available for avian specimens are awaited.

      (9) Despite Tuj1+ enteric neurons only making up a small fraction of the organoids, the authors should still functionally test whether they regulate any aspect of contractility by treating organoids with an inhibitor such as tetrodotoxin to rule out a role for them.

      Thank you for these advices, which are also raised by other reviewers. We have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

      (10) Finally, the manuscript is written to suggest that the focus of the study is to establish a system to interrogate ICC-SMC interactions in gut physiology and peristalsis. However, the organoids designed in this study are derived from the fetal precursors to the adult cell types. Thus, they might not accurately portray the adult cell physiology. I don't believe that this is a downfall, but rather a strength of the study that should be emphasized. That is, the focus could be shifted toward stressing the power of this new system as a reductionist, self-organizing model to examine the developmental emergence of contractile synchronization in the intestine - in particular that arising through ICC-SMC interactions.

      We appreciate these advices. In the revised MS, we are careful so that our findings do not necessarily portray the physiological functions in adult gut.

      Minor:

      More technical information could be used in the methods:

      (1) What concentration of Matrigel is used for coating, and what size were the wells that cells were deposited into?

      We have added, “14-mm diameter glass-bottom dishes (Matsunami, D11130H)” and “undiluted Matrigel (Corning, 354248) at 38.5°C for 20 min” (line 471473).

      (2) How were organoids transferred to the hydrogels? And were the hydrogels coated?

      We have added “Organoids were transferred to the hydrogel using a glass capillary” (line 560-561).

      (3) Tests for significance and p values should be added where appropriate (e.g. Figure S3B).

      We have added these in Figure legend of new Fig. S3.

      Reviewer #3 (Recommendations For The Authors):

      This is an exciting study, and while the majority of our comments are minor suggestions to improve the clarity and impact of findings, it would be important to verify the effective disruption of GAP junction function with CBX or 18Beta-GA treatments before concluding they are not required for coordination of contractility and initiation by ICCs. It is possible that sufficient contextual support exists in the literature for the nature of treatments used, but this may need to be conveyed within the manuscript to allay concerns that the results could be explained by ineffective inhibition of GAP junctions.

      Thank you very much for these advices. In the revised version, we have newly carried out experiments with dissociated embryonic heart cells cultured in vitro, a model widely used for gap junction studies (Fig. S3D). Both CBX or 18b-GA exert efficient inhibiting activity on contractions of heart cells. We have added the following sentence, “The inhibiting activity of the drugs used here was verified using embryonic heart culture (line 237-239)”.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

      The study demonstrates that RBMX2 plays a role in:

      (1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

      (2) Disrupting tight junctions and promoting EMT.

      (3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

      By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

      Strengths:

      This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

      Weaknesses:

      (1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

      We sincerely appreciate the reviewer's valuable feedback regarding the need to clarify RBMX2's role throughout the manuscript. We have carefully revised the text to ensure consistent messaging about RBMX2's function in promoting M. bovis infection. Below we detail the specific modifications made:

      (1) Introduction Revisions:

      Changed "The objective of this study was to elucidate the correlation between host genes and the susceptibility of M.bovis infection" to "The objective of this study was to identify host factors that promote susceptibility to M.bovis infection"

      Revised "RBMX2 polyclonal and monoclonal cell lines exhibited favorable phenotypes" to "RBMX2 knockout cell lines showed reduced bacterial survival"

      Replaced "The immune regulatory mechanism of RBMX2" with "The role of RBMX2 in facilitating M.bovis immune evasion"

      (2) Results Revisions:

      Modified "RBMX2 fails to affect cell morphology and the ability to proliferate and promotes M.bovis infection" to "RBMX2 does not alter cell viability but significantly enhances M.bovis infection"

      Strengthened conclusion in Figure 4: "RBMX2 actively disrupts tight junctions to facilitate bacterial invasion"

      (3) Discussion Revisions:

      Revised screening description: "We screened host factors affecting M.bovis susceptibility and identified RBMX2 as a key promoter of infection"

      Strengthened concluding statement: "In summary, RBMX2 drives TB pathogenesis by compromising epithelial barriers and inducing EMT"

      These targeted revisions ensure that:

      All sections consistently present RBMX2 as promoting infection; the language aligns with our experimental finding; potential protective interpretations have been eliminated. We believe these modifications have successfully addressed the reviewer's concern while maintaining the manuscript's original structure and scientific content. We appreciate the opportunity to improve our manuscript and thank the reviewer for this constructive suggestion.

      (2) >While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

      We sincerely appreciate the reviewer's insightful comment regarding the roles of MAPK/p38 and JNK in our study. Our experimental data clearly demonstrated that RBMX2 knockout significantly reduced phosphorylation levels of p65, p38, and JNK (Fig. 5A), indicating potential involvement of all three pathways in RBMX2-mediated regulation.

      Through systematic functional validation, we obtained several important findings:

      In pathway inhibition experiments, p65 activation (PMA treatment) showed the most dramatic effects on both tight junction disruption (ZO-1, OCLN reduction) and EMT marker regulation (E-cadherin downregulation, N-cadherin upregulation);

      p38 activation (ML141 treatment) exhibited moderate effects on these processes;

      JNK activation (Anisomycin treatment) displayed minimal impact.

      Most conclusively, siRNA-mediated silencing of p65 alone was sufficient to:

      Restore epithelial barrier function

      Reverse EMT marker expression

      Reduce bacterial adhesion and invasion

      These results establish a clear hierarchy in pathway importance: p65 serves as the primary mediator of RBMX2's effects, while p38 plays a secondary role and JNK appears non-essential under our experimental conditions. We have now clarified this relationship in the revised Discussion section to strengthen this conclusion.

      This refined understanding of pathway hierarchy provides important mechanistic insights while maintaining consistency with all our experimental data. We thank the reviewer for this valuable suggestion that helped improve our manuscript.

      (3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

      Thank you for this constructive suggestion. In this article, we detected the metabolome of RBMX2 knockout and wild-type cells after Mycobacterium bovis infection, which mainly served as supporting evidence for our EMT model. However, we did not conduct an in-depth discussion of these findings. We have now added a detailed discussion of this section to further support our EMT model.

      ADD:Meanwhile, metabolic pathways enriched after RBMX2 deletion, such as nucleotide metabolism, nucleotide sugar synthesis, and pentose interconversion, primarily support cell proliferation and migration during EMT by providing energy precursors, regulating glycosylation modifications, and maintaining redox balance; cofactor synthesis and amino sugar metabolism participate in EMT regulation through influencing metabolic remodeling and extracellular matrix interactions; chemokine and cGMP-PKG signaling pathways may further mediate inflammatory responses and cytoskeletal rearrangements, collectively promoting the EMT process.

      (4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

      Thank you for your comment. We have supplemented the experiments in this part and found that Mycobacterium bovis infection can significantly enhance the expression level of RBMX2 protein.

      (5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

      We sincerely appreciate the valuable comments from the reviewer. We fully agree with your suggestion to further explore the relationship between tuberculosis (TB) and lung cancer. In the revised manuscript, we will add a new paragraph in the Discussion section to systematically integrate the current literature on the epidemiological and mechanistic links between chronic tuberculosis infection and lung cancer development, including the potential bridging roles of chronic inflammation, tissue damage repair, immune microenvironment remodeling, and the epithelial-mesenchymal transition (EMT) pathway. This addition will help more comprehensively interpret the clinical implications of the observed EMT activation in the context of our study, thereby enhancing the biological plausibility and clinical translational value of our findings.

      ADD:There is growing epidemiological evidence suggesting that chronic TB infection represents a potential risk factor for the development of lung cancer. Studies have shown that individuals with a history of TB exhibit a significantly increased risk of lung cancer, particularly in areas of the lung with pre-existing fibrotic scars, indicating that chronic inflammation, tissue repair, and immune microenvironment remodeling may collectively contribute to malignant transformation 74. Moreover, EMT not only endows epithelial cells with mesenchymal features that enhance migratory and invasive capacity but is also associated with the acquisition of cancer stem cell-like properties and therapeutic resistance 75. Therefore, EMT may serve as a crucial molecular link connecting chronic TB infection with the malignant transformation of lung epithelial cells, warranting further investigation in the intersection of infection and tumorigenesis.

      Reviewer #2 (Public review):

      Summary:

      I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

      Strengths:

      (1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

      (2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

      Weaknesses:

      (1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

      Thank you for your valuable and constructive comments. In this study, we primarily utilized the CRISPR whole-genome screening approach to identify key factors involved in bovine tuberculosis infection. Through four rounds of screening using a whole-genome knockout cell line of bovine lung epithelial cells infected with Mycobacterium bovis, we identified RBMX2 as a critical factor.

      Although the transcriptional level change of RBMX2 was less than two-fold, following the suggestion of Reviewer 1, we examined its expression at the protein level, where the change was more pronounced, and we have added these results to the manuscript.

      Regarding the mechanism by which RBMX2 is activated upon M. bovis infection, we previously screened for interacting proteins using a Mycobacterium tuberculosis secreted and membrane protein library, but unfortunately, we did not identify any direct interacting proteins from M. tuberculosis (https://doi.org/10.1093/nar/gkx1173).

      (2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

      We appreciate the valuable comments from the reviewers. Although our study included multiple time points post-infection, in our experimental design we focused on different biological processes and phenotypes at distinct time points:

      During the early phase (e.g., 2 hours post-infection), we focused on barrier phenotypes; during the intermediate phase (e.g., 24 hours post-infection), we concentrated more on pathway activation and EMT phenotypes;

      And during the later phase (e.g., 48–72 hours post-infection), we focused more on cell death phenotypes, which were validated in another FII article (https://doi.org/10.3389/fimmu.2024.1431207).

      We also examined the impact of varying infection durations on RBMX2 knockout EBL cellular lines via GO analysis. At 0 hpi, genes were primarily related to the pathways of cell junctions, extracellular regions, and cell junction organization. At 24 hpi, genes were mainly associated with pathways of the basement membrane, cell adhesion, integrin binding and cell migration By 48 hpi, genes were annotated into epithelial cell differentiation and were negatively regulated during epithelial cell proliferation. This indicated that RBMX2 can regulate cellular connectivity throughout the stages of M. bovis infection.

      For KEGG analysis, genes linked to the MAPK signaling pathway, chemical carcinogen-DNA adducts, and chemical carcinogen-receptor activation were observed at 0 hpi. At 24 hpi, significant enrichment was found in the ECM-receptor interaction, PI3K-Akt signaling pathway, and focal adhesion. Upon enrichment analysis at 48 hpi, significant enrichment was noted in the TGF-beta signaling pathway, transcriptional misregulation in cancer, microRNAs in cancer, small cell lung cancer, and p53 signaling pathway.

      Reviewer #3 (Public review):

      Summary:

      This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

      Strengths:

      The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

      Weaknesses:

      Although it's a solid study, there are a few weaknesses noted below.

      (1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

      We appreciate the reviewer's thoughtful questions regarding our transcriptomic analysis. In this study, we employed a localized enrichment approach focusing specifically on gene expression profiles from our bovine lung epithelial cell system. This cell-type-specific analysis provides more biologically relevant results than global database searches alone.

      Regarding the highlighted pathways, these represent:

      (1) Temporally significant pathways showing strongest enrichment at each stage:

      • 0h: Cell junction organization (immediate barrier response)

      • 24h: ECM-receptor interaction (early EMT initiation)

      • 48h: TGF-β signaling (chronic remodeling)

      (2) Mechanistically linked to our core findings about RBMX2's role in:

      • Epithelial barrier disruption

      • Mesenchymal transition

      • Chronic infection outcomes

      We selected these particular pathways because they:

      (1) Showed the most statistically significant changes (FDR <0.001)

      (2) Formed a coherent biological narrative across infection stages

      (3) Were independently validated in our functional assays

      This targeted approach allows us to focus on the most infection-relevant pathways while maintaining statistical rigor.

      (2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

      We sincerely appreciate the reviewer's in-depth questions regarding the mechanisms by which RBMX2 activates p65 and its association with EMT. Although the molecular mechanism remains to be fully elucidated, our study has provided experimental evidence supporting a direct regulatory relationship between RBMX2 and the p65 subunit of the NF-κB pathway. Specifically, we investigated whether the transcription factor p65 could directly bind to the promoter region of RBMX2 using CHIP experiments. The results demonstrated that the transcription factor p65 can physically bind to the RBMX2 region.

      Furthermore, dual-luciferase reporter assays were conducted, showing that p65 significantly enhances the transcriptional activity of the RBMX2 promoter, indicating a direct regulatory effect of RBMX2 on p65 expression.

      These findings support our hypothesis that RBMX2 activates the NF-κB signaling pathway through direct interaction with the p65 protein, thereby participating in the regulation of EMT progression and barrier function.

      In our subsequent work papers, we will also employ experiments such as CLIP to further investigate the specific mechanisms through which RBMX2 exerts its regulatory functions.

      (3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

      Thank you for your valuable comments. When we initially designed this experiment, we were interested in whether the RBMX2 knockout cell line could confer effective resistance not only against Mycobacterium bovis but also against Gram-negative and Gram-positive bacteria. Surprisingly, we indeed observed resistance to the invasion of these pathogens, albeit weaker compared to that against Mycobacterium bovis.

      Nevertheless, we believe these findings merit publication in eLife. Moreover, RBMX2 knockout does not affect the phenotype of epithelial barrier disruption under normal conditions; its significant regulatory effect on barrier function is only evident upon infection with Mycobacterium bovis.

      Importantly, during our genome-wide knockout library screening, RBMX2 was not identified in the screening models for Salmonella or Escherichia coli, but was consistently detected across multiple rounds of screening in the Mycobacterium bovis model.

      (4) The quality of the figures is very poor. High-resolution images should be provided.

      Thank you for your feedback; we provided higher-resolution images.

      (5) The methods are not very descriptive, particularly the omics section.

      Thank you for your comments; we have revised the description of the sequencing section.

      (6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

      Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

    2. Reviewer #3 (Public review):

      Summary:

      This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

      Strengths:

      The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

      Weaknesses:

      Although it's a solid study, there are a few weaknesses noted below.

      (1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

      (2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

      (3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

      (4) The quality of the figures is very poor. High-resolution images should be provided.

      (5) The methods are not very descriptive, particularly the omics section.

      (6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

    1. “verbal ability’ in the ocean stacy has become equated with “school achievement”

      It’s not fair to say that “verbal ability” = “school achievement.” Just because a student doesn’t speak a certain way or doesn’t use academic language yet, that doesn’t mean they’re not smart or learning. We need to recognize that students show understanding in different ways especially those learning English or using more than one language.

    1. Author response:

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

      Public Reviews:

      Reviewer #1 (Public Review):

      Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1.

      Comments on revisions:

      I am satisfied with the revisions made by the authors, which helped clarify some points that were confusing in the initial submission.

      Thank you.

      Reviewer #2 (Public Review):

      This revised manuscript mostly addresses previous concerns by doubling down on the model without providing additional direct evidence of interactions between Srs2 and PCNA, and that "precise sites of Srs2 actions in the genome remain to be determined." One additional Srs2 allele has been examined, showing some effect in combination with rfa1-zm2. Many of the conclusions are based on reasonable assumptions about the consequences of various mutations, but direct evidence of changes in Srs2 association with PNCA or other interactors is still missing. There is an assumption that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects, which may not be the case. How SLX4 might interact with Srs2 is unclear to me, again assuming that the SLX4 defect is "surgical" - removing only one of its many interactions.

      Previous studies have already provided direct evidence for the interaction between Srs2 and PCNA through the Srs2’s PIM region (Armstrong et al, 2012; Papouli et al, 2005); we have added these citations in the text. Similarly. Srs2 associations with SUMO and Rad51 have also been demonstrated (Colavito et al, 2009; Kolesar et al, 2016; Kolesar et al., 2012), and these studies were cited in the text.

      We did not state that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects. We only assessed whether these previously characterized mutant alleles could mimic srs2∆ in rescuing rfa1-zm2 defects.

      We assessed the genetic interaction between slx4-RIM and srs2-∆PIM mutants, and not the physical interaction between the two proteins. As we described in the text, our rationale for this genetic test is based on that the reports that both slx4 and srs2 mutants impair recovery from the Mec1 induced checkpoint, thus they may affect parallel pathways of checkpoint dampening.

      One point of concern is the use of t-tests without some sort of correction for multiple comparisons - in several figures. I'm quite sceptical about some of the p < 0.05 calls surviving a Bonferroni correction. Also in 4B, which comparison is **? Also, admittedly by eye, the changes in "active" Rad53 seem much greater than 5x. (also in Fig. 3, normalizing to a non-WT sample seems odd).

      Claims made in this work were based only on pairwise comparison not multi-comparison. We have now made this point clearer in the graphs and in Method. As the values were compared between a wild-type strain and a specific mutant strain, or between two mutants, we believe that t-test is suitable for statistical analysis.

      Figure 4B, ** indicates that the WT value is significantly different from that of the slx4-RIM srs2-∆PIM double mutant and from that of srs2-∆PIM single mutant. We have modified the graph to indicate the pair-wide comparison. The 5-fold change of active Rad53 levels was derived by comparing the values between the srs2∆ PIM slx4<sup>RIM</sup>-TAP double mutant and wild-type Slx4-TAP. In Figure 3, normalization to the lowest value affords better visualization. This is rather a stylish issue; we would like to maintain it as the other reviewers had no issues.

      What is the WT doubling time for this strain? From the FACS it seems as if in 2 h the cells have completed more than 1 complete cell cycle. Also in 5D. Seems fast...

      Wild-type W303 strain has less than 90 min doubling time as shown by many labs, and our data are consistent with this. The FACS profiles for wild-type cells shown in Figures 3C, 4C, and 5C are consistent with each other, showing that after G1 cells entered the cell cycle, they were in G2 phase at the 1-hour time points, and then a percentage of the cells exited the first cell cycle by two hours.

      I have one over-arching confusion. Srs2 was shown initially to remove Rad51 from ssDNA and the suppression of some of srs2's defects by deleting rad51 made a nice, compact story, though exactly how srs2's "suppression of rad6" fit in isn't so clear (since Rad6 ties into Rad18 and into PCNA ubiquitylation and into PCNA SUMOylation). Now Srs2 is invoked to remove RPA. It seems to me that any model needs to explain how Srs2 can be doing both. I assume that if RPA and Rad51 are both removed from the same ssDNA, the ssDNA will be "trashed" as suggested by Symington's RPA depletion experiments. So building a model that accounts for selective Srs2 action at only some ssDNA regions might be enhanced by also explaining how Rad51 fits into this scheme.

      While the anti-recombinase function of Srs2 was better studied, its “anti-RPA” role in checkpoint dampening was recently described by us (Dhingra et al, 2021) following the initial report by the Haber group some time ago (Vaze et al, 2002). A better understanding of this new role is required before we can generate a comprehensive picture of how Srs2 integrates the two functions (and possibly other functions). Our current work addresses this issue by providing a more detailed understanding of this new role of Srs2.

      Single molecular data showed that Srs2 strips both RPA and Rad51 from ssDNA, but this effect is highly dynamic (i.e. RPA and Rad51 can rebind ssDNA after being displaced) (De Tullio et al, 2017). As such, generation of “deserted” ssDNA regions lacking RPA and Rad51 in cells can be an unlikely event. Rather, Srs2 can foster RPA and Rad51 dynamics on ssDNA. Additional studies will be needed to generate a model that integrates the anti-recombinase and the anti-RPA roles of Srs2.

      As a previous reviewer has pointed out, CPT creates multiple forms of damage. Foiani showed that 4NQO would activate the Mec1/Rad53 checkpoint in G1- arrested cells, presumably because there would be singlestrand gaps but no DSBs. Whether this would be a way to look specifically at one type of damage is worth considering; but UV might be a simpler way to look. As also noted, the effects on the checkpoint and on viability are quite modest. Because it isn't clear (at least to me) why rfa1 mutants are so sensitive to CPT, it's hard for me to understand how srs2-zm2 has a modest suppressive effect: is it by changing the checkpoint response or facilitating repair or both? Or how srs2-3KR or srs2-dPIM differ from rfa1-zm2 in this respect. The authors seem to lump all these small suppressions under the rubric of "proper levels of RPA-ssDNA" but there are no assays that directly get at this. This is the biggest limitation.

      CPT treatment is an ideal condition to examine how cells dampen the DNA damage checkpoint, because while most genotoxic conditions (e.g. 4NQO, MMS) induce both the DNA replication checkpoint and the DNA damage checkpoint, CPT was shown to only induced the latter (Menin et al, 2018; Minca & Kowalski, 2011; Redon et al, 2003; Tercero et al, 2003). Future studies examining 4NQO and UV conditions can further expand our understanding of checkpoint dampening in different conditions.

      We have previously provided evidence to support the conclusion that srs2 suppression of rfa1-zm is partly mediated by changing checkpoint levels (Dhingra et al., 2021). We cannot exclude the possibility that the suppression may also be related to changes of DNA repair; we have now added this note in the text.

      Regarding direct testing RPA levels on DNA, we have previously shown that srs2∆ increased the levels of chromatin associated Rfa1 and this is suppressed by rfa1-zm2 (Dhingra et al., 2021). We have now included chromatin fractionation data to show that srs2-∆PIM also led to an increase of Rfa1 on chromatin, and this was suppressed by rfa1-zm2 (new Fig. S2).

      Srs2 has also been implicated as a helicase in dissolving "toxic joint molecules" (Elango et al. 2017). Whether this activity is changed by any of the mutants (or by mutations in Rfa1) is unclear. In their paper, Elango writes: "Rare survivors in the absence of Srs2 rely on structure-specific endonucleases, Mus81 and Yen1, that resolve toxic joint-molecules" Given the involvement of SLX4, perhaps the authors should examine the roles of structure-specific nucleases in CPT survival?

      Srs2 has several roles, and its role in RPA antagonism can be genetically separated from its role in Rad51 regulation as we have shown in our previous work (Dhingra et al., 2021) and this notion is further supported by evidence presented in the current work. Srs2’s role in dissolving "toxic joint molecules” was mainly observed during BIR (Elango et al, 2017). Whether it is related to checkpoint dampening will be interesting to address in the future but is beyond of the scope of the current work that seeks to answer the question how Srs2 regulates RPA during checkpoint dampening. Similarly, determining the roles of Mus81 and Yen1 and other structural nucleases in CPT survival is a worthwhile task but it is a research topic well separated from the focus of this work.

      Experiments that might clarify some of these ambiguities are proposed to be done in the future. For now, we have a number of very interesting interactions that may be understood in terms of a model that supposes discriminating among gaps and ssDNA extensions by the presence of PCNA, perhaps modified by SUMO. As noted above, it would be useful to think about the relation to Rad6.

      Several studies have shown that Srs2’s functional interaction with Rad6 is based on Srs2-mediated recombination regulation (reviewed by (Niu & Klein, 2017). Given that recombinational regulation by Srs2 is genetically separable from the Srs2 and RPA antagonism (Dhingra et al., 2021), we do not see a strong rationale to examine Rad6 in this work, which addresses how Srs2 regulates RPA. With this said, this study has provided basis for future studies of possible cross-talks among different Srs2-mediated pathways.

      Reviewer #3 (Public Review):

      The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2.

      Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. Double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of this mutants in a RFA1 wild type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. This data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction, did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step.

      Strengths:

      (1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights.

      (2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data.

      Weaknesses:

      (1) Additional mutants of interest could have been tested, such as the recently reported Pin mutant, srs2-Y775A (PMID: 38065943), and the Rad51 interaction point mutant, srs2-F891A (PMID: 31142613).

      (2) The use of deletion mutants for PCNA and RAD51 interaction is inferior to using specific point mutants, as done for the SUMO interaction and the sites for post-translational modifications.

      (3) Figure 4D and Figure 5A report data with standard deviations, which is unusual for n=2. Maybe the individual data points could be plotted with a color for each independent experiment to allow the reader to evaluate the reproducibility of the results.

      Comments on revisions:

      In this revision, the authors adequately addressed my concerns. The only issue I see remaining is the site of Srs2 action. The authors argue in favor of gaps and against R-loops and ssDNA resulting from excessive supercoiling. The authors do not discuss ssDNA resulting from processing of onesided DSBs, which are expected to result from replication run-off after CPT damage but are not expected to provide the 3'-junction for preferred PCNA loading. Can the authors exclude PCNA at the 5'-junction at a resected DSB?

      We have now added a sentence stating that we cannot exclude the possibility that PCNA may be positioned at a 5’-junction, as this can be observed in vitro, albert that PCNA loading was seen exclusively at a 3’-junction in the presence of RPA (Ellison & Stillman, 2003; Majka et al, 2006).

      Recommendations For the authors:

      Reviewer #2 (Recommendations For the authors):

      A Bonferroni correction should be made for the multiple comparisons in several figures.

      Specific comments:

      l. 41. This is a too long and confusing sentence.

      Sentence shortened: “These data suggest that Srs2 recruitment to PCNA proximal ssDNA-RPA filaments followed by its sumoylation can promote checkpoint recovery, whereas Srs2 action is minimized at regions with no proximal PCNA to permit RPA-mediated ssDNA protection”.

      l. 60. Identify Ddc2 and Mec1 as ATRIP and ATR.

      Done.

      l. 125 "fails to downregulate RPA levels on chromatin and Mec1-mediated DDC..." fails to downregulate RPA and fails to reduce Mec1-mediated DDC?

      Sentence modified: “fails to downregulate both the RPA levels on chromatin and the Mec1-mediated DDC”

      l. 204 "consistent with the notion that Srs2 has roles beyond RPA regulation"... What other roles? It's stripping of Rad51? Removing toxic joint molecules? Something else?

      Sentence modified: “consistent with the notion that Srs2 has roles beyond RPA regulation, such as in Rad51 regulation and removing DNA joint molecules”.

      l. 249 "Significantly, srs2-ΔPIM and -3KR increased the percentage of rfa1-zm2 cells transitioning into the G1 phase" No. Just back to normal. As stated in l. 258: "258 We found that srs2-ΔPIM and srs2-3KR mutants on their own behaved normally in the two DDC assays described above." All of these effects are quite small.

      Sentence modified: “Compared with rfa1-zm2 cells, srs2-∆PIM rfa1-zm2 and srs2-3KR rfa1-zm2 cells showed increased percentages of cells transitioning into the G1 phase”.

      l. 468 "Our previous work has provided several lines of evidence to support that Rad51 removal by Srs2 is separable from the Srs2-RPA antagonism (Dhingra et al., 2021). What evidence? See my comment above about not having both proteins removed at the same time.

      We have addressed this point in our initial rebuttal and some key points are summarized below. In our previous report (Dhingra et al., 2021), we provided several lines of evidence to support the conclusion that Rad51 is not relevant to the Srs2-RPA antagonism. For example, while rad51∆ rescues the hyper-recombination phenotype of srs2∆ cells, rad51∆ did not affect the hyper-checkpoint phenotype of srs2∆. In contrast, rfa1-zm1/zm2 have the opposite effects, that is, rfa1zm1/zm2 suppressed the hyper-checkpoint, but not the hyper-recombination, phenotype of srs2∆ cells. The differential effects of rad51∆ and rfa1-zm1/zm2 were also seen for the ATPase dead allele of Srs2 (srs2K41A). For example, rfa1-zm2 rescued hyper-checkpoint and CPT sensitivity of srs2-K41A cells, while rad51∆ had neither effect. These and other data described by Dhingra et al (2021) suggest that Srs2’s effects on checkpoint vs. recombination can be separated genetically. Consistent with our conclusion summarized above, deleting the Rad51 binding domain in Srs2 (srs2-∆Rad51BD) has no effect on rfa1-zm2 phenotype in CPT (Fig. 2D). This data provides yet another evidence that Srs2 regulation of Rad51 is separable from the Srs2RPA antagonism.

      l. 525 "possibility, we tested the separation pin of Srs2 (Y775), which was shown to enables its in vitro helicase activity during the revision of our work..." ?? there was helicase activity during the revision of your work? Please fix the sentence.

      Sentence modified: “we tested the separation pin of Srs2 (Y775). This residue was shown to be key for the Srs2’s helicase activity in vitro in a report that was published during the revision of our work (Meir et al, 2023).”

      Fig. 3. "srs2-ΔPIM and -3KR allow better G1 entry of rfa1-zm2 cells." is it better entry or less arrest at G2/M? One implies better turning off of a checkpoint, the other suggests less activation of the checkpoint.

      This is a correct statement. For all strains examined in Figure 3, cells were seen in G2/M phase after 1-hour CPT treatment, suggesting proper arrest.

      References:

      Armstrong AA, Mohideen F, Lima CD (2012) Recognition of SUMO-modified PCNA requires tandem receptor motifs in Srs2. Nature 483: 59-63

      Colavito S, Macris-Kiss M, Seong C, Gleeson O, Greene EC, Klein HL, Krejci L, Sung P (2009) Functional significance of the Rad51-Srs2 complex in Rad51 presynaptic filament disruption. Nucleic Acids Res 37: 6754-6764.

      De Tullio L, Kaniecki K, Kwon Y, Crickard JB, Sung P, Greene EC (2017) Yeast Srs2 helicase promotes redistribution of single-stranded DNA-bound RPA and Rad52 in homologous recombination regulation. Cell Rep 21: 570-577

      Dhingra N, Kuppa S, Wei L, Pokhrel N, Baburyan S, Meng X, Antony E, Zhao X (2021) The Srs2 helicase dampens DNA damage checkpoint by recycling RPA from chromatin. Proc Natl Acad Sci U S A 118: e2020185118

      Elango R, Sheng Z, Jackson J, DeCata J, Ibrahim Y, Pham NT, Liang DH, Sakofsky CJ, Vindigni A, Lobachev KS et al (2017) Break-induced replication promotes formation of lethal joint molecules dissolved by Srs2. Nat Commun 8: 1790

      Ellison V, Stillman B (2003) Biochemical characterization of DNA damage checkpoint complexes: clamp loader and clamp complexes with specificity for 5' recessed DNA. PLoS Biol 1: E33

      Kolesar P, Altmannova V, Silva S, Lisby M, Krejci L (2016) Pro-recombination Role of Srs2 Protein Requires SUMO (Small Ubiquitin-like Modifier) but Is Independent of PCNA (Proliferating Cell Nuclear Antigen) Interaction. J Biol Chem 291: 7594-7607.

      Kolesar P, Sarangi P, Altmannova V, Zhao X, Krejci L (2012) Dual roles of the SUMO-interacting motif in the regulation of Srs2 sumoylation. Nucleic Acids Res 40: 7831-7843.

      Majka J, Binz SK, Wold MS, Burgers PM (2006) Replication protein A directs loading of the DNA damage checkpoint clamp to 5'-DNA junctions. J Biol Chem 281: 27855-27861

      Meir A, Raina VB, Rivera CE, Marie L, Symington LS, Greene EC (2023) The separation pin distinguishes the pro- and anti-recombinogenic functions of Saccharomyces cerevisiae Srs2. Nat Commun 14: 8144

      Menin L, Ursich S, Trovesi C, Zellweger R, Lopes M, Longhese MP, Clerici M (2018) Tel1/ATM prevents degradation of replication forks that reverse after Topoisomerase poisoning. EMBO Rep 19: e45535

      Minca EC, Kowalski D (2011) Replication fork stalling by bulky DNA damage: localization at active origins and checkpoint modulation. Nucleic Acids Res 39: 2610-2623

      Niu H, Klein HL (2017) Multifunctional roles of Saccharomyces cerevisiae Srs2 protein in replication, recombination and repair. FEMS Yeast Res 17: fow111

      Papouli E, Chen S, Davies AA, Huttner D, Krejci L, Sung P, Ulrich HD (2005) Crosstalk between SUMO and ubiquitin on PCNA is mediated by recruitment of the helicase Srs2p. Mol Cell 19: 123-133

      Redon C, Pilch DR, Rogakou EP, Orr AH, Lowndes NF, Bonner WM (2003) Yeast histone 2A serine 129 is essential for the efficient repair of checkpoint-blind DNA damage. EMBO Rep 4: 678-684

      Tercero JA, Longhese MP, Diffley JFX (2003) A central role for DNA replication forks in checkpoint activation and response. Mol Cell 11: 1323-1336

      Vaze MB, Pellicioli A, Lee SE, Ira G, Liberi G, Arbel-Eden A, Foiani M, Haber JE (2002) Recovery from checkpointmediated arrest after repair of a double-strand break requires Srs2 helicase. Mol Cell 10: 373-385

    1. Author response:

      eLife Assessment

      This useful study presents Altair-LSFM, a solid and well-documented implementation of a light-sheet fluorescence microscope (LSFM) designed for accessibility and cost reduction. While the approach offers strengths such as the use of custom-machined baseplates and detailed assembly instructions, its overall impact is limited by the lack of live-cell imaging capabilities and the absence of a clear, quantitative comparison to existing LSFM platforms. As such, although technically competent, the broader utility and uptake of this system by the community may be limited.

      We thank the reviewers and editors for their thoughtful evaluation of our work and for recognizing the technical strengths of the Altair-LSFM platform, including the custom-machined baseplates and detailed documentation provided to support accessibility and reproducibility. We respectfully disagree, however, with the assessment that the system lacks live-cell imaging capabilities. We are fully confident in the system’s suitability for live-cell applications and will demonstrate this by including representative live-cell imaging data in the revised manuscript, along with detailed instructions for implementing environment control. Moreover, we will expand our discussion to include a broader, more quantitative comparison to existing LSFM platforms—highlighting trade-offs in cost, performance, and accessibility—to better contextualize Altair’s utility and adaptability across diverse research settings.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The article presents the details of the high-resolution light-sheet microscopy system developed by the group. In addition to presenting the technical details of the system, its resolution has been characterized and its functionality demonstrated by visualizing subcellular structures in a biological sample.

      Strengths:

      (1) The article includes extensive supplementary material that complements the information in the main article.

      (2) However, in some sections, the information provided is somewhat superficial.

      Our goal was to make the supplemental content as comprehensive and useful as possible. In addition to the materials provided with the manuscript, our intention is for the online documentation (available at thedeanlab.github.io/altair) to serve as a living resource that evolves in response to user feedback. For this reason, we are especially interested in identifying and expanding any sections that are perceived as superficial, and we would greatly appreciate the reviewer’s guidance on which areas would benefit from further elaboration.

      Weaknesses:

      (1) Although a comparison is made with other light-sheet microscopy systems, the presented system does not represent a significant advance over existing systems. It uses high numerical aperture objectives and Gaussian beams, achieving resolution close to theoretical after deconvolution. The main advantage of the presented system is its ease of construction, thanks to the design of a perforated base plate.

      We appreciate the reviewer’s assessment and the opportunity to clarify our intent. Our primary goal was not to introduce new optical functionality beyond that of existing high-performance light-sheet systems, but rather to reduce the barrier to entry for non-specialist labs.

      (2) Using similar objectives (Nikon 25x and Thorlabs 20x), the results obtained are similar to those of the LLSM system (using a Gaussian beam without laser modulation). However, the article does not mention the difficulties of mounting the sample in the implemented configuration.

      We agree that there are practical challenges associated with handling 5 mm diameter coverslips. However, the Nikon 25x can readily be replaced by a Zeiss W Plan-Apochromat 20x/1.0 objective, which eliminates the need for the 5 mm coverslip[1]. In the revised manuscript, we will more explicitly detail the practical challenges in handling a 5 mm coverslip and mention the alternative detection objective.

      (3) The authors present a low-cost, open-source system. Although they provide open source code for the software (navigate), the use of proprietary electronics (ASI, NI, etc.) makes the system relatively expensive. Its low cost is not justified.

      We understand the reviewer’s concern regarding the use of proprietary control hardware such as the ASI Tiger Controller and NI data acquisition cards. While lower-cost alternatives for analog and digital control (e.g., microcontroller-based systems) do exist, our choice was intentional. By relying on a unified and professionally supported platform, we minimize the complexity of sourcing, configuring, and integrating components from disparate vendors—each of which would otherwise demand specialized technical expertise. Moreover, in future releases, we aim to further streamline the system by eliminating the need for the NI card, consolidating all optoelectronic control through the ASI Tiger Controller. This approach allows users to purchase a fully assembled and pre-configured system that can be operational with minimal effort.

      It is worth noting that the ASI components are not the primary cost driver. The full set—including XYZ and focusing stages, a filter wheel, a tube lens, the Tiger Controller, and basic optomechanical adapters—costs approximately $27,000, or ~18% of the total system cost. Additional cost reductions are possible. For example, replacing the motorized sample positioning and focusing stages with manual alternatives could reduce the cost by ~$12,000. However, this would eliminate key functionality such as autofocusing, 3D tiling, and multi-position acquisition. Open-source mechanical platforms such as OpenFlexure could in principle be adapted, but they would require custom assembly and would need to be integrated into our control software. Similarly, the filter wheel could be omitted in favor of a multi-band emission filter, reducing the cost by ~$5,000. However, this comes at the expense of increased spectral crosstalk, often necessitating spectral unmixing. An industrial CMOS camera—such as the Ximea MU196CR-ON, recently demonstrated in a Direct View Oblique Plane Microscopy configuration[2]—could substitute for the sCMOS cameras typically used in high-end imaging. However, these industrial sensors often exhibit higher noise floors and lower dynamic range, limiting sensitivity for low-signal imaging applications.

      While a $150,000 system represents a significant investment, we consider it relatively cost-effective in the context of advanced light-sheet microscopy. For comparison, commercially available systems with similar optical performance—such as LLSM systems from 3i or Zeiss—are several-fold more expensive.

      (4) The fibroblast images provided are of exceptional quality. However, these are fixed samples. The system lacks the necessary elements for monitoring cells in vivo, such as temperature or pH control.

      We thank the reviewer for their positive comment regarding the quality of our fibroblast images. As noted, the current manuscript focuses on the optical design and performance characterization of the system, using fixed specimens to validate resolution and imaging stability. We acknowledge the importance of environmental control for live-cell imaging. Temperature regulation is routinely implemented in our lab using flexible adhesive heating elements paired with a power supply and PID controller. For pH stabilization in systems that lack a 5% CO<sub>2</sub> atmosphere, we typically supplement the imaging medium with 10–25 mM HEPES buffer. In the revised manuscript, we will introduce a modified sample chamber capable of maintaining user-specified temperatures, along with detailed assembly instructions. We will also include representative live-cell imaging data to demonstrate the feasibility of in vitro imaging using this system.

      Reviewer #2 (Public review):

      Summary:

      The authors present Altair-LSFM (Light Sheet Fluorescence Microscope), a high-resolution, open-source microscope, that is relatively easy to align and construct and achieves sub-cellular resolution. The authors developed this microscope to fill a perceived need that current open-source systems are primarily designed for large specimens and lack sub-cellular resolution or are difficult to construct and align, and are not stable. While commercial alternatives exist that offer sub-cellular resolution, they are expensive. The authors' manuscript centers around comparisons to the highly successful lattice light-sheet microscope, including the choice of detection and excitation objectives. The authors thus claim that there remains a critical need for high-resolution, economical, and easy-to-implement LSFM systems.

      Strengths:

      The authors succeed in their goals of implementing a relatively low-cost (~ USD 150K) open-source microscope that is easy to align. The ease of alignment rests on using custom-designed baseplates with dowel pins for precise positioning of optics based on computer analysis of opto-mechanical tolerances, as well as the optical path design. They simplify the excitation optics over Lattice light-sheet microscopes by using a Gaussian beam for illumination while maintaining lateral and axial resolutions of 235 and 350 nm across a 260-um field of view after deconvolution. In doing so they rest on foundational principles of optical microscopy that what matters for lateral resolution is the numerical aperture of the detection objective and proper sampling of the image field on to the detection, and the axial resolution depends on the thickness of the light-sheet when it is thinner than the depth of field of the detection objective. This concept has unfortunately not been completely clear to users of high-resolution light-sheet microscopes and is thus a valuable demonstration. The microscope is controlled by an open-source software, Navigate, developed by the authors, and it is thus foreseeable that different versions of this system could be implemented depending on experimental needs while maintaining easy alignment and low cost. They demonstrate system performance successfully by characterizing their sheet, point-spread function, and visualization of sub-cellular structures in mammalian cells, including microtubules, actin filaments, nuclei, and the Golgi apparatus.

      We thank the reviewer for their thoughtful summary of our work. We are pleased that the foundational optical principles, design rationale, and emphasis on accessibility came through clearly. We agree that the approach used to construct the microscope is highly modular, and we anticipate that these design principles will serve as the basis for additional system variants tailored to specific biological samples and experimental contexts. To support this, we provide all Zemax simulations and CAD files openly on our GitHub repository, enabling advanced users to build upon our design and create new functional variants of the Altair system.

      Weaknesses:

      There is a fixation on comparison to the first-generation lattice light-sheet microscope, which has evolved significantly since then:

      (1) The authors claim that commercial lattice light-sheet microscopes (LLSM) are "complex, expensive, and alignment intensive", I believe this sentence applies to the open-source version of LLSM, which was made available for wide dissemination. Since then, a commercial solution has been provided by 3i, which is now being used in multiple cores and labs but does require routine alignments. However, Zeiss has also released a commercial turn-key system, which, while expensive, is stable, and the complexity does not interfere with the experience of the user. Though in general, statements on ease of use and stability might be considered anecdotal and may not belong in a scientific article, unreferenced or without data.

      The referee is correct that our comparisons reference the original LLSM design, which was simultaneously disseminated as an open-source platform and commercialized by 3i. While we acknowledge that newer variants of LLSM have been developed—including systems incorporating adaptive optics[3] and the MOSAIC platform (which remains unpublished)—the original implementation remains the most widely described and cited in the literature. It is therefore the most appropriate point of comparison for contextualizing Altair’s performance, complexity, and accessibility. Importantly, this version of LLSM is far from obsolete; it continues to be one of the most commonly used imaging systems at Janelia Research Campus’s Advanced Imaging Center.

      We acknowledge that more recent commercial implementation by Zeiss has addressed several of the practical limitations associated with the original design. In particular, we agree that the Zeiss Lattice Lightsheet 7 system, which integrates a meniscus lens to facilitate oblique imaging through a coverslip, offers a user-friendly experience—albeit with a modest tradeoff in resolution (reported deskewed resolution: 330 nm × 330 nm × 500–1000 nm).

      While we recognize that statements on usability and stability can be subjective, one objective proxy for system complexity is the number of optical elements that require precise alignment during assembly. The original LLSM setup includes approximately 29 optical components that must each be carefully positioned laterally, angularly, and coaxially along the optical path. In contrast, the first-generation Altair system contains only 9 such elements. By this metric, Altair is considerably simpler to assemble and align, supporting our overarching goal of making high-resolution light-sheet imaging more accessible to non-specialist laboratories. In the revised manuscript, we will clarify the scope of our comparison and provide more precise language about what we mean by complexity (e.g., number of optical elements needed to align).

      (2) One of the major limitations of the first generation LLSM was the use of a 5 mm coverslip, which was a hinderance for many users. However, the Zeiss system elegantly solves this problem, and so does Oblique Plane Microscopy (OPM), while the Altair-LSFM retains this feature, which may dissuade widespread adoption. This limitation and how it may be overcome in future iterations is not discussed.

      We agree that the use of 5 mm diameter coverslips, while enabling high-NA imaging in the current Altair-LSFM configuration, may serve as an inconvenience for many users. We will discuss this more explicitly in the revised manuscript. Specifically, we note that changing the detection objective is sufficient to eliminate the need for a 5 mm coverslip. For example, as demonstrated in Moore et al., Lab Chip 2021, pairing the Zeiss W Plan-Apochromat 20x/1.0 objective with the Thorlabs TL20X-MPL allows imaging beyond the physical surfaces of both objectives, removing the constraint imposed by small-format coverslips[1]. In the revised manuscript, we will propose this modification as a straightforward path for increasing compatibility with more conventional sample mounting formats.

      (3) Further, on the point of sample flexibility, all generations of the LLSM, and by the nature of its design, the OPM, can accommodate live-cell imaging with temperature, gas, and humidity control. It is unclear how this would be implemented with the current sample chamber. This limitation would severely limit use cases for cell biologists, for which this microscope is designed. There is no discussion on this limitation or how it may be overcome in future iterations.

      We appreciate the reviewer’s emphasis on the importance of environmental control for live-cell imaging applications. It is worth noting that the original LLSM design, including the system commercialized by 3i, provided temperature control only, without integrated gas or humidity regulation. Despite this, it has been successfully used by a wide range of scientists to generate important biological insights.

      We agree that both OPM and the Zeiss implementation of LLSM offer clear advantages in terms of environmental control, as we previously discussed in detail in Sapoznik et al., eLife, 2020[4]. However, assembly of high numerical aperture OPM systems is highly technical, and no open-source variant of OPM delivers sub-cellular scale resolution yet.

      (4) The authors' comparison to LLSM is constrained to the "square" lattice, which, as they point out, is the most used optical lattice (though this also might be considered anecdotal). The LLSM original design, however, goes far beyond the square lattice, including hexagonal lattices, the ability to do structured illumination, and greater flexibility in general in terms of light-sheet tuning for different experimental needs, as well as not being limited to just sample scanning. Thus, the Alstair-LSFM cannot compare to the original LLSM in terms of versatility, even if comparisons to the resolution provided by the square lattice are fair.

      We thank the reviewer for this comment. It is true that our discussion focused primarily on the square lattice implementation of LLSM. While this could be viewed as a subset of the system’s broader capabilities, we chose this focus intentionally, as the square lattice remains by far the most commonly used variant in practice. Even in the original LLSM publication, 16 out of 20 figure subpanels utilized the square lattice, with only one panel each representing the hexagonal lattice in SIM mode, a standard Bessel beam in incoherent SIM mode, a hex lattice in dithered mode, and a single Bessel in dithered mode. This usage pattern largely reflects the operational simplicity of the square lattice: it minimizes sidelobe growth and enables more straightforward alignment and data processing compared to hexagonal or structured illumination modes.

      In 2019, we performed an exhaustive accounting of published illumination modes in LLSM and found that the SIM mode had only been used in two additional peer-reviewed publications at that time. We will consider updating this table in the revised manuscript and will expand our discussion to acknowledge the broader flexibility of the LLSM platform—including its capacity for structured illumination and alternative light-sheet geometries. However, we will also emphasize that, despite these advanced capabilities, the square lattice remains the dominant mode used by the community and therefore serves as a fair and practical benchmark for comparison.

      (5) There is no demonstration of the system's live-imaging capabilities or temporal resolution, which is the main advantage of existing light-sheet systems.

      In the revised manuscript, we will include a demonstration of live-cell imaging to directly validate the system’s suitability for dynamic biological applications. We will also characterize the temporal resolution of the system. As a sample-scanning microscope, the imaging speed is primarily limited by the performance of the Z-piezo stage. For simplicity and reduced optoelectronic complexity, we currently power the piezo through the ASI Tiger Controller. We will expand the supplementary material to describe the design criteria behind this choice, including potential trade-offs, and provide data quantifying the achievable volume rates under typical operating conditions.

      While the microscope is well designed and completely open source, it will require experience with optics, electronics, and microscopy to implement and align properly. Experience with custom machining or soliciting a machine shop is also necessary. Thus, in my opinion, it is unlikely to be implemented by a lab that has zero prior experience with custom optics or can hire someone who does. Altair-LSFM may not be as easily adaptable or implementable as the authors describe or perceive in any lab that is interested, even if they can afford it. The authors indicate they will offer "workshops," but this does not necessarily remove the barrier to entry or lower it, perhaps as significantly as the authors describe.

      We appreciate the reviewer’s perspective and agree that building any high-performance custom microscope—Altair-LSFM included—requires a baseline familiarity with optics and instrumentation. Our goal is not to eliminate this requirement entirely, but to significantly reduce the technical and logistical barriers that typically accompany custom light-sheet microscope construction.

      Importantly, no machining experience or in-house fabrication capabilities are required—users can simply submit provided design files and specifications directly to the vendor. We will make this process as straightforward as possible by supplying detailed instructions, recommended materials, and vendor-ready files. Additionally, we draw encouragement from the success of related efforts such as mesoSPIM, which has seen over 30 successful implementations worldwide using a similar model of exhaustive online documentation, open-source control software, and community support through user meetings and workshops.

      We recognize that documentation alone is not always sufficient, and we are committed to further lowering barriers to adoption. To this end, we are actively working with commercial vendors to streamline procurement and reduce the logistical burden on end users. Additionally, Altair-LSFM is supported by a Biomedical Technology Development and Dissemination (BTDD) grant, which provides dedicated resources for hosting workshops, offering real-time community support, and generating supplementary materials such as narrated video tutorials. We will expand our discussion in the revised manuscript to better acknowledge these implementation challenges and outline our ongoing strategies for supporting a broad and diverse user base.

      There is a claim that this design is easily adaptable. However, the requirement of custom-machined baseplates and in silico optimization of the optical path basically means that each new instrument is a new design, even if the Navigate software can be used. It is unclear how Altair-LSFM demonstrates a modular design that reduces times from conception to optimization compared to previous implementations.

      We appreciate the reviewer’s comment and agree that our language regarding adaptability may have been too strong. It was not our intention to suggest that the system can be easily modified without prior experience. Meaningful adaptations of the optical or mechanical design would require users to have expertise in optical layout, optomechanical design, and alignment.

      That said, for labs with sufficient expertise, we aim to facilitate such modifications by providing comprehensive resources—including detailed Zemax simulations, CAD models, and alignment documentation. These materials are intended to reduce the development burden for those seeking to customize the platform for specific experimental needs.

      In the revised manuscript, we will clarify this point and explicitly state in the discussion what technical expertise is required to modify the system. We will also revise our language around adaptability to better reflect the intended audience and realistic scope of customization.

      Reviewer #3 (Public review):

      Summary:

      This manuscript introduces a high-resolution, open-source light-sheet fluorescence microscope optimized for sub-cellular imaging.

      The system is designed for ease of assembly and use, incorporating a custom-machined baseplate and in silico optimized optical paths to ensure robust alignment and performance. The authors demonstrate lateral and axial resolutions of ~235 nm and ~350 nm after deconvolution, enabling imaging of sub-diffraction structures in mammalian cells.

      The important feature of the microscope is the clever and elegant adaptation of simple gaussian beams, smart beam shaping, galvo pivoting and high NA objectives to ensure a uniform thin light-sheet of around 400 nm in thickness, over a 266 micron wide Field of view, pushing the axial resolution of the system beyond the regular diffraction limited-based tradeoffs of light-sheet fluorescence microscopy.

      Compelling validation using fluorescent beads and multicolor cellular imaging highlights the system's performance and accessibility. Moreover, a very extensive and comprehensive manual of operation is provided in the form of supplementary materials. This provides a DIY blueprint for researchers who want to implement such a system.

      Strengths:

      (1) Strong and accessible technical innovation: With an elegant combination of beam shaping and optical modelling, the authors provide a high-resolution light-sheet system that overcomes the classical light-sheet tradeoff limit of a thin light-sheet and a small field of view. In addition, the integration of in silico modelling with a custom-machined baseplate is very practical and allows for ease of alignment procedures. Combining these features with the solid and super-extensive guide provided in the supplementary information, this provides a protocol for replicating the microscope in any other lab.

      (2) Impeccable optical performance and ease of mounting of samples: The system takes advantage of the same sample-holding method seen already in other implementations, but reduces the optical complexity. At the same time, the authors claim to achieve similar lateral and axial resolution to Lattice-light-sheet microscopy (although without a direct comparison (see below in the "weaknesses" section). The optical characterization of the system is comprehensive and well-detailed. Additionally, the authors validate the system imaging sub-cellular structures in mammalian cells.

      (3) Transparency and comprehensiveness of documentation and resources: A very detailed protocol provides detailed documentation about the setup, the optical modeling, and the total cost.

      Weaknesses:

      (1) Limited quantitative comparisons: Although some qualitative comparison with previously published systems (diSPIM, lattice light-sheet) is provided throughout the manuscript, some side-by-side comparison would be of great benefit for the manuscript, even in the form of a theoretical simulation. While having a direct imaging comparison would be ideal, it's understandable that this goes beyond the interest of the paper; however, a table referencing image quality parameters (taken from the literature), such as signal-to-noise ratio, light-sheet thickness, and resolutions, would really enhance the features of the setup presented. Moreover, based also on the necessity for optical simplification, an additional comment on the importance/difference of dual objective/single objective light-sheet systems could really benefit the discussion.

      In the revised manuscript, we will expand our discussion to include a broader range of light-sheet microscope designs and imaging modes, including both single- and dual-objective configurations. We agree that highlighting the trade-offs between these approaches—such as working distance, sample geometry constraints, and alignment complexity—will enhance the overall context and utility of the manuscript.

      To further aid comparison, we will include a summary table referencing key image quality parameters such as lateral and axial resolution, and illumination beam NA for Altair-LSFM. Where available, we will reference values from published work—such as the axial resolution reported in Valm et al. (Nature, 2017)—to provide a clearer benchmark. Because such comparisons can be technically nuanced, especially when comparing across systems with different geometries and sample mounting constraints, we will also include a supplementary note outlining the assumptions and limitations of these comparisons.

      (2) Limitation to a fixed sample: In the manuscript, there is no mention of incubation temperature, CO₂ regulation, Humidity control, or possible integration of commercial environmental control systems. This is a major limitation for an imaging technique that owes its popularity to fast, volumetric, live-cell imaging of biological samples.

      We thank the reviewer for highlighting this important consideration. In the revised manuscript, we will provide a detailed description of how temperature control can be implemented using flexible adhesive heating elements, a power supply, and a PID controller. Step-by-step assembly instructions and recommended components will be included to facilitate adoption by users interested in live-cell imaging. We also note that most light-sheet microscopy systems capable of sub-cellular resolution—including the original LLSM design, diSPIM, and ASLM—typically do not incorporate integrated CO<sub>2</sub> or humidity control. These systems often rely on HEPES-buffered media to maintain pH stability, which is generally sufficient for short- to intermediate-term imaging. While full environmental control may be necessary for extended time-lapse studies, it is not a prerequisite for high-resolution volumetric imaging in many applications. Nonetheless, we will include a discussion of the challenges associated with adding CO<sub>2</sub> and humidity control to open or semi-enclosed architectures like Altair-LSFM, and outline potential future paths for integration with commercial incubation systems.

      (3) System cost and data storage cost: While the system presented has the advantage of being open-source, it remains relatively expensive (considering the 150k without laser source and optical table, for example). The manuscript could benefit from a more direct comparison of the performance/cost ratio of existing systems, considering academic settings with budgets that most of the time would not allow for expensive architectures. Moreover, it would also be beneficial to discuss the adaptability of the system, in case a 30k objective could not be feasible. Will this system work with different optics (with the obvious limitations coming with the lower NA objective)? This could be an interesting point of discussion. Adaptability of the system in case of lower budgets or more cost-effective choices, depending on the needs.

      We thank the reviewer for raising this important point. First, we would like to clarify that the quoted $150k cost estimate includes the optical table and laser source. We apologize for any confusion and will communicate this more effectively in the revised manuscript.

      We agree that adaptability is a key concern, especially in academic settings with limited budgets. The detection path can be readily altered depending on experimental needs and cost constraints. For example, in our discussion of alternatives to the 5 mm coverslip geometry, we will describe how switching to a Zeiss W Plan-Apochromat 20x/1.0 in combination with a compatible excitation objective allows high-resolution imaging while accommodating more conventional sample formats. We will expand this to include cost-effective alternatives as well.

      We will also expand our discussion on cost-reduction strategies and the associated trade-offs. These include replacing motorized stages with manual ones, omitting the filter wheel in favor of a multi-band emission filter, or using industrial-grade cameras in place of scientific CMOS detectors. While each change entails some loss in functionality or sensitivity, such modifications allow users to tailor the system to their specific budget and application.

      Finally, we recognize the challenge in communicating exact costs of commercial systems due to variability in configuration and pricing. Nonetheless, we will include approximate figures where possible and note that comparable commercial systems—such as LLSM platforms from 3i and Zeiss—are several-fold more expensive than the system presented here.

      Last, not much is said about the need for data storage. Light-sheet microscopy's bottleneck is the creation of increasingly large datasets, and it could be beneficial to discuss more about the storage needs and the quantity of data generated.

      Data storage is indeed a critical consideration in light-sheet microscopy. In the revised manuscript, we will provide a note outlining typical volume dimensions for live-cell imaging experiments along with the associated data overhead. This will include estimates for voxel counts, bit depth, time-lapse acquisitions, and multi-channel datasets to help users anticipate storage needs. We will also briefly discuss strategies for managing large datasets, file types and compression formats.

      Conclusion:

      Altair-LSFM represents a well-engineered and accessible light-sheet system that addresses a longstanding need for high-resolution, reproducible, and affordable sub-cellular light-sheet imaging. While some aspects-comparative benchmarking and validation, limitation for fixed samples-would benefit from further development, the manuscript makes a compelling case for Altair-LSFM as a valuable contribution to the open microscopy scientific community.

      References

      (1) Moore, R. P. et al. A multi-functional microfluidic device compatible with widefield and light sheet microscopy. Lab Chip 22, 136-147 (2021). https://doi.org/10.1039/d1lc00600b

      (2) Lamb, J. R., Mestre, M. C., Lancaster, M. & Manton, J. D. Direct-view oblique plane microscopy. Optica 12, 469-472 (2025). https://doi.org/10.1364/OPTICA.558420

      (3) Liu, T. L. et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms. Science 360 (2018). https://doi.org/10.1126/science.aaq1392

      (4) Sapoznik, E. et al. A versatile oblique plane microscope for large-scale and high-resolution imaging of subcellular dynamics. eLife 9 (2020). https://doi.org/10.7554/eLife.57681

      (5) Huisken, J. & Stainier, D. Y. Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Opt Lett 32, 2608-2610 (2007). https://doi.org/10.1364/ol.32.002608

      (6) Ricci, P. et al. Removing striping artifacts in light-sheet fluorescence microscopy: a review. Prog Biophys Mol Biol 168, 52-65 (2022). https://doi.org/10.1016/j.pbiomolbio.2021.07.003

    1. Money provides not just access to material goods, but also access to a lot of power.

      I agree with the idea that money gives people access to power, not just material things. This is a fact in our society. Wealth often comes with privileges that others don’t have, like better healthcare, education, and even legal advantages. Some people may not want to admit this, but it’s the reality in many places.

    1. Author response:

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

      Public reviews:

      Concerning the grounding in experimental phenomenology, it would be beneficial to identify specific experiments to strengthen the model. In particular, what evidence supports reversible beta cell inactivation? This could potentially be tested in mice, for instance, by using an inducible beta cell reporter, treating the animals with high glucose levels, and then measuring the phenotype of the marked cells. Such experiments, if they exist, would make the motivation for the model more compelling.

      There is some direct evidence of reversible beta cell inactivation in rodent / in vitro models. We had already mentioned this in the discussion, but we have added some text emphasizing / clarifying the role of this evidence (lines 359–362).

      Others have also argued that some analyses of insulin treatment in conventional T2D, which has a stronger effect in patients with higher glucose before treatment, provides indirect evidence of reversal of glucotoxicity. We have also mentioned this in the revised paper (lines 284–285).

      For quantitative experiments, the authors should be more specific about the features of beta cell dysfunction in KPD. Does the dysfunction manifest in fasting glucose, glycemic responses, or both? Is there a ”pre-KPD” condition? What is known about the disease’s timescale?

      The answers to some of these questions are not entirely clear—patients present with very high glucose, and thus must be treated immediately. Due to a lack of antecedent data it is not entirely clear what the pre-KPD condition is, but there is some evidence that KPD is at least not preceded by diabetes symptoms. This point is already noted in the introduction of the paper and Table 1. However, we have added a small note clarifying that this does not rule out mild hyperglycemia, as in prediabetes (and indeed, as our model might predict) (lines 76–77). Similarly, due to the necessity of immediate insulin treatment, it is not clear from existing data whether the disorder manifests more strongly in fasting glucose or glucose response, although it is likely in both. (We might infer this since continuous insulin treatment does not produce fasting hypoglycemia, and the complete lack of insulin response to glucose shortly after presentation should produce a strong effect in glycemic response.) We believe our existing description of KPD lists all of the relevant timescales, however we have also slightly clarified this description in response to the first referee’s comments (lines 66–73, 83)

      The authors should also consider whether their model could apply to other conditions besides KPD. For example, the phenomenology seems similar to the ”honeymoon” phase of T1D. Making a strong case for the model in this scenario would be fascinating.

      This is an excellent idea, which had not occurred to us. We have briefly discussed this possibility in the remission (lines 281–291), but plan to analyze it in more detail in a future manuscript.

      Reviewer #1 (Recommendations for the author):

      Whenever simulation results are presented, parameter values should be specified right there in the figure captions.

      We have added the values of glucotoxicity parameters to the caption of Figure 2. In other figures, we have explicitly mentioned which panel of Figure 2 the parameters are taken from. Description of the non-glucotoxicity parameters is a bit cumbersome (there are a lot of them, but our model of fast dynamics is slightly different from Topp et al. so it does not suffice to simply say we took their parameters) so we have referred the reader to the Materials and Methods for those.

      I was confused by the language in Figure 4. Could the authors clarify whether they argue that: (1) the observed KPD behaviour is the result of the system switching from one stable state to another when perturbed with high glucose intake? (2) the observed KPD behaviour is the result of one of the steady states disappearing with high glucose intake?

      What we mean to say is that during a period of high sugar intake or exogeneous insulin treatment, one of the fixed points is temporarily removed—it is still a fixed point of the “normal” dynamics, but not a fixed point of the dynamics with the external condition added. Since when glucose (insulin) intake is high enough, only the low (high)-β fixed point is present, under one of these conditions the dynamics flow toward that fixed point. When the external influx of glucose/insulin is turned off, both fixed points are present again—but if the dynamics have moved sufficiently far during the external forcing, the fixed point they end up in will have switched from one fixed point to the other. We have edited the text to make this clearer (lines 153–185). Do note, however, that in response to both referee’s comments (see below), Figures 3 and 4 have been replaced with more illuminating ones. This specific point is now addressed by the new Figure 3.

      The adaptation of the prefactor ’c’ was confusing to me. I think I understood it in the end, but it sounded like, ”here’s a complication, but we don’t explain it because it doesn’t really matter”. I think the authors can explain this better (or potentially leave out the complication with ’c’ altogether?).

      Indeed, the existence of an adaptation mechanism is important for our overall picture of diabetes pathogenesis, but not for many of our analyses, which assume prediabetes. Nonetheless, we agree that the current explanation of it’s role is confusing because of its vagueness. We have elaborated the explanation of the type of dynamics we assume for c, adding an equation for its dynamics to the “Model” section of the Materials and methods, explained in lines 456–465. We have also amended Figure 1 to note this compensation.

      I expect the main impact of this work will be to get clinical practitioners and biomedical researchers interested in the intermediate timescale dynamics of β-cells and take seriously the possibility that reversible inactive states might exist. But this impact will only be achieved when the results are clearly and easily understandable by an audience that is not familiar with mathematical modelling. I personally found it difficult to understand what I was supposed to see in the figures at first glance. Yes, the subtle points are indeed explained in the figure captions, but it might be advantageous to make the points visually so clear that a caption is barely needed. For example, when claiming that a change in parameters leads to bistability, why not plot the steady state values as a function of that parameter instead of showing curves from which one has to infer a steady state?

      I would advise the authors to reconsider their visual presentation by, e.g., presenting the figures to clinical practitioners or biomedical researchers with just a caption title to test whether such an audience can decipher the point of the figure! This is of course merely a personal suggestion that the authors may decide to ignore. I am making this suggestion only because I believe in the quality of this work and that improving the clarity of the figures and the ease with which one can understand the main points would potentially lead to a much larger impact on the presented results.

      This is a very good point. We have made several changes. Firstly, we have added smaller panels showing the dynamics of β to Figure 2; previously, the reader had to infer what was happening to β from G(t). Secondly, we have completely replaced the two figures showing dβ/dt, and requiring the reader to infer the fixed points of β, with bifurcation diagrams that simply show the fixed points of G and β. The new figures show through bifurcation diagrams how there are multiple fixed points in KPD, how glucose or insulin treatment force the switching of fixed points, and how the presence of bistability depends on the rate of glucotoxicity. (These new figures are Fig. 3–5 in the revised manuscript.)

      Could the authors explicitly point out what could be learned from their work for the clinic? At the moment treatment consists of giving insulin to patients. If I understand correctly, nothing about the current treatment would change if the model is correct. Is there maybe something more subtle that could be relevant to devising an optimal treatment for KPD patients?

      This is another very good point. We have added a new figure (Fig. 7) in our results section showing how this model, or one like it, can be analyzed to suggest an insulin treatment schedule (once parameters for an individual patient can be measured), and added some discussion of this point (lines 224–240) as well as lifestyle changes our model might suggest for KPD patients to the discussion (lines 413–425).

      Similarly, could the authors explicitly point out how their model could be experimentally tested? For example, are the functions f(G) and g(G) experimentally accessible? Related to that, presumably the shape of those functions matters to reproduce the observed behaviour. Could the authors comment on that / analyze how reproducing the observed behaviour puts constraints on the shape of the used functions and chosen parameter values?

      g(G) has not been carefully measured in cellular data, however it could be in more quantative versions of existing experiments. Further, our model indeed requires some general features for the forms of f(G) and g(G) to produce KPD-like phenomena. We have added some comment on this to the discussion section of the revised manuscript (lines 367–372).

      Could the authors explicitly spell out which parameters they think differ between individual KPD patients, and which parameters differ between KPD patients and ’regular’ type 2 diabetics?

      In general we expect all parameters should vary both among KPD patients and between KPD / “conventional” T2D. The primary parameter determining whether KPD and conventional T2D, is seen, however, is the ratio kIN/kRE. We have elaborated on both these points in the revised mansuscript. (Lines 186–192, 250–257.)

      I was confused about the timescale of remission. At one point the authors write “KPD patients can often achieve partial remission: after a few weeks or months of treatment with insulin” but later the authors state that “the duration of the remission varies from 6 months to 10 years”.

      The former timescale is the typical timescale achieve remission. After remission is reached, however, it may or may not last—patients may experience a relapse, where their condition worsens and they again require insulin. We have edited the text to clarify this distinction (lines 66–73).

      When the authors talk about intermediate timescales in the main text could they specify an actual unit of time, such as days, weeks, or months as it would relate to the rate constants in their model for those transitions?

      We have done so (lines 86–87, figure 1 caption, figure 2 caption). Getting KPD-like behavior requires (at high glucose) the deactivation process to be somewhat faster than the reactivation process, so the relevant scales are between weeks (reactivation) and days (deactivation at high G).

      The authors state ”Our simple model of β-cell adaptation also neglects the known hyperglycemiainduced leftward shift in the insulin secretion curve f(G) in Eq. (2)) ”. This seems an important consideration. Could the authors comment on why they did not model this shift, and/or explicitly discuss how including it is expected to change the model dynamics?

      We agree that this process seems potentially relevant, as it seems to happen on a relatively fast timescale compared to glucose-induced β-cell death. It is, however, not so well characterized quantitatively that including it is a simple matter of putting in known values—we would be making assumptions that would complicate the interpretation of our results.

      It is clear that this effect will need to be considered when quanitatively modelling real patient data. However, it is also straightforward to argue that this effect by itself cannot produce KPD-like symptoms, and will only tend to reduce the rate of glucotoxocity necessary to produce bibstability. We have added a discussion of this in the revisions (lines 307–315). We have also, in general, expanded the discussion of the effects that each neglected detail we have mentioned is expected to have (lines 292–315).

      The authors end with a statement that their results may “contribute to explanation of other observations that involve rapid onset or remission of diabetes-like phenomena, such as during pregnancy or for patients on very low calorie diets.” Could the authors spell out exactly how their model potentially relates to these phenomena?

      Our thinking is that, even when another direct cause, such as loss of insulin resistance, is implicated in reversal of diabetes, some portion of the effect may be explained by reversal of glucotoxicity. This is indeed at this point just a hypothesis, but we have expanded on it briefly in the revision. (Lines 281–291.)

      Minor typos:

      In Figure 2.D the last zero of 200 on the axis was cut off.

      Line 359 - there is a missing word ”in the analysis”.

      We have fixed these typos, thanks.

      Reviewer #2 (Recommendations for the author):

      The manuscript could be significantly improved in two key areas: the presentation of the analysis, and the relation with experimental phenomenology.

      Regarding the analysis presentation, the figures could be substantially enhanced with minimal effort from the authors. At present, they are sparse, lack legends, and offer only basic analysis. The authors should consider presenting, for example, a bifurcation diagram for beta cell mass and fasting glucose levels as a function of kIN, and how insulin sensitivity and average meal intake modulate this relationship. The goal should be to present clear, testable predictions in an intuitive manner. Currently, the specific testable predictions of the model are unclear.

      The response to this question is copied from the reponses to related questions from the first referee.

      This is a very good point. We have made several changes. Firstly, we have added smaller panels showing the dynamics of β to Figure 2; previously, the reader thad to infer what was happening to β from G(t). Secondly, we have completely replaced the two figures showing dβ/dt, and requiring the reader to infer the fixed points of β, with bifurcation diagrams that simply show the fixed points of G and β. The new figures show through bifurcation diagrams how there are multiple fixed points in KPD, how glucose or insulin treatment force the switching of fixed points, and how the presence of bistability depends on the rate of glucotoxicity. We have also supplemented our phase diagram that shows the effects of SI and the total beta cell population with bifurcation diagrams showing β as SI and βTOT are varied. (These new figures are Fig. 3–5 in the present manuscript.) Finally, we have added another figure analyzing the model’s predictions for the optimal insulin treatment and the resulting time needed to achieve remission (Fig. 7)

    1. Reviewer #1 (Public review):

      I congratulate the authors on this beautiful work.

      This manuscript introduces a biologically informed RNN (bioRNN) that predicts the effects of optogenetic perturbations in both synthetic and in vivo datasets. By comparing standard sigmoid RNNs (σRNNs) and bioRNNs, the authors make a compelling case that biologically grounded inductive biases improve generalization to perturbed conditions. This work is innovative, technically strong, and grounded in relevant neuroscience, particularly the pressing need for data-constrained models that generalize causally.

      I have some suggestions for improvement, which I present in the order of re-reading the paper.

      Major

      (1) In line 76, the authors make a very powerful statement: 'σRNN simulation achieves higher similarity with unseen recorded trials before perturbation, but lower than the bioRNN on perturbed trials.' I couldn't find a figure showing this. This might be buried somewhere and, in my opinion, deserves some spotlight - maybe a figure or even inclusion in the abstract.

      (2) It's mentioned in the introduction (line 84) and elsewhere (e.g., line 259) that spiking has some advantage, but I don't see any figure supporting this claim. In fact, spiking seems not to matter (Figure 2C, E). Please clarify how spiking improves performance, and if it does not, acknowledge that. Relatedly, in line 246, the authors state that 'spiking is a better metric but not significant' when discussing simulations. Either remove this statement and assume spiking is not relevant, or increase the number of simulations.

      (3) The authors prefer the metric of predicting hits over MSE, especially when looking at real data (Figure 3). I would bring the supplementary results into the main figures, as both metrics are very nicely complementary. Relatedly, why not add Pearson correlation or R2, and not just focus on MSE Loss?

      (4) I really like the 'forward-looking' experiment in closed loop! But I felt that the relevance of micro perturbations is very unclear in the intro and results. This could be better motivated: why should an experimentalist care about this forward-looking experiment? Why exactly do we care about micro perturbation (e.g., in contrast to non-micro perturbation)? Relatedly, I would try to explain this in the intro without resorting to technical jargon like 'gradients'.

      Minor

      (1) In the intro, the authors refer to 'the field' twice. Personally, I find this term odd. I would opt for something like 'in neuroscience'.

      (2) Line 45: When referring to previous work using data-constrained RNN models, Valente et al. is missing (though it is well cited later when discussing regularization through low-rank constraints).

      (3) Line 11: Method should be methods (missing an 's').

      (4) In line 250, starting with 'So far', is a strange choice of presentation order. After interpreting the results for other biological ingredients, the authors introduce a new one. I would first introduce all ingredients and then interpret. It's telling that the authors jump back to 2B after discussing 2C.

      (5) The black dots in Figure 3E are not explained, or at least I couldn't find an explanation.

    1. Reviewer #2 (Public review):

      Summary:

      The manuscript presents valuable new datasets from two ancient maize seeds that contribute to our growing understanding of the maize evolution and biodiversity landscape in pre-colonial South America. Some of the analyses are robust, but the selection elements are not supported.

      Strengths:

      The data collection is robust, and the data appear to beof sufficiently high quality to carry out some interesting analytical procedures. The central finding that aBM maize is closely related to maize from the core Inca region is well supported, although the directionality of dispersal is not supported.

      Weaknesses:

      The selection results are not justified, see examples in the detailed comments below.

      (1) The manuscript mentions cultural and natural selection (line 76), but then only gives a couple of examples of selecting for culinary/use traits. There are many examples of selection to tolerate diverse environments that could be relevant for this discussion, if desired.

      (2) I would be extremely cautious about interpreting the observations of a Spanish colonizer (lines 95-99) without very significant caveats. Indigenous agriculture and foodways would have been far more nuanced than what could be captured in this context, and the genocidal activities of the Europeans would have impacted food production activities to a degree, and any contemporaneous accounts need to be understood through that lens.

      (3) The f3 stats presented in Figure 2 are not set up to test any specific admixture scenarios, so it is unsupported to conclude that the aBM maize is not admixed on this basis (lines 201-202). The original f3 publication (Patterson et al, 2012) describes some scenarios where f3 characteristics associate with admixture, but in general, there are many caveats to this approach, and it's not the ideal tool for admixture testing, compared with e.g., f4 and D (abba-baba) statistics.

      (4) I'm a little bit skeptical that the Locator method adds value here, given the small training sample size and the wide geographic spread and genetic diversity of the ancient samples that include Central America. The paper describing that method (Battey et al 2020 eLife) uses much larger datasets, and while the authors do not specifically advise on sample sizes, they caution about small sample size issues. We have already seen that the ancient Peruvian maize has the most shared drift with aBM maize on the basis of the f3 stats, and the Locator analysis seems to just be reiterating that. I would advise against putting any additional weight on the Locator results as far as geographic origins, and personally I would skip this analysis in this case.

      (5) The overlap in PCA should not be used to confirm that aBM is authentically ancient, because with proper data handling, PCA placement should be agnostic to modern/ancient status (see lines 224-226). It is somewhat unexpected that the ancient Tehuacan maize (with a major teosinte genomic component) falls near the ancient South American maize, but this could be an artifact of sampling throughout the PCA and the lack of teosinte samples that might attract that individual.

      (6) What has been established (lines 250-251) is genetic similarity to the Inca core area, not necessarily the directionality. Might aBM have been part of a cultural region supplying maize to the Inca core region, for example? Without a specific test of dispersal directionality, which I don't think is possible with the data at hand, this is somewhat speculative.

      (7) Singleton SNPs are not a typical criterion for identifying selection; this method needs some citations supporting the exact approach and validation against neutral expectations (line 278). Without Datasets S2 and S3, which are not included with this submission, it is difficult to assess this result further. However, it is very unexpected that ~18,000 out of ~49,000 SNPs would be unique to the aBM lineage. This most likely reflects some data artifact (unaccounted damage, paralogs not treated for high coverage, which are extremely prevalent in maize, etc). I'm confused about unique SNPs in this context. How can they be unique to the aBM lineage if the SNPs used overlap the Grzybowski set? The GO results do not include any details of the exact method used or a statistical assessment of the results. It is not clear if the GO terms noted are statistically enriched.

      (8) The use of XP-EHH with pseudohaplotype variant calls is not viable (line 293). It is not clear what exact implementation of XP-EHH was used, but this method generally relies on phased or sometimes unphased diploid genotype calls to observe shared haplotypes, and some minimum population size to derive statistical power. No implementation of XP-EHH to my knowledge is appropriate for application to this kind of dataset.

    1. $600 for refurbishing a machine that only cost $250 is not a great investment. I think your prices are a bit unrealistic. As far as the posts go: there are always two sides to every story🤷‍♀️

      reply to u/SisterGoldenHair70 at https://reddit.com/r/typewriters/comments/1lbjr5f/sorry_to_say/mxz1ag2/

      I'm honestly curious about your definition of a "machine that only cost $250" is? Make/Model? Where was it bought? What is the actual condition (both cosmetic exterior and mechanical interior as well as cleanliness)? Also, what is your definition of investment with respect to how you're going to use it? Is it a display machine? Is it for display and the occasional addressed envelope or grocery store list? Or is it something you're planning on giving moderate to heavy use on a daily or weekly basis as a professional writer?

      Most typewriter repair shops won't spend more than $20 on purchasing a machine and typically they're getting machines donated for free (or for the cost of coming to pick them up); then they spend hours to clean, oil, adjust, repair and restore them for sale in the $400-600 range unless they've got rarer colors or typefaces. (At this level, you're probably not getting any new rubber at all.) The level of restoration is definitely a choice as there are restorers out there who can easlily charge in the $1,500+ range depending on how much work you want done and to what level.

      As an example, if someone chooses to spend $120-200 pus shipping on something like an old/dirty Olympia SM3 with old gaskets from an outlet like ShopGoodwill.com (the current going rate for one in unknown condition and only using photos) that is going to need several hundred dollars of repair work, that's their choice. If they don't have the expertise, it's far better to buy one outright in the $500 range from a shop. Usually people are just keeping that $120 machine and limping along with what it offers instead of cleaning it up or they attempt to do the clean up work themselves. This work may seem "free", but it's time, effort, and expense of one sort or another. Economists call this opportunity cost.

      There is a massive information imbalance in the typewriter market between buyers and sellers, because incredibly few people really have any idea of what they've got and what sort of condition it is in. This imbalance means that most professional shops are an absolute steal if you're looking for something in excellent condition that's going to be a work-a-day typewriter or be in good shape for another 50-75 years.

      As a small example, an indiviual could call JJ Short and get a platen recovered for around $100 plus two way shipping, but you take it to a shop and you're probably looking at $180 because they're going to charge you for the time, effort, and cost of knowing what to do, where to ship it, taking it off, putting it back on, and doing the appropriate ring & cylinder, on-feet, and motion adjustments on

    2. reply to u/TypewriterJustice at https://old.reddit.com/r/typewriters/comments/1lbjr5f/sorry_to_say/mxunsb6/

      I think the real crime here was the quote of $200 for all this work. $200 should just barely cover the recovered platen, rollers, and new feet with any margin. The full clean, oil, and adjust is a few more hours at $40-75/hour and that's not even getting to the parts or labor on the tougher troubleshooting and repair portions. With this rough diagnosis and potential issues, I (and many others I'm sure) would be quoting closer to $500-600 for a refurbish job at this level.

      Living in the LA area, I'm blessed to have 7 shops within a reasonable drive, but if I put a machine into the queue at most of them it'd be a two or three month's wait time at the very best. Most of them have been at the game for decades much less in the midst of also recently setting up a brick and mortar shop.

      As a point of comparison, Lucas Dul publishes his wait list on his website (currently 84 people) where he states "Average repair cost is $300-350 for general cleaning, service, and minor repairs. Average turnaround time is 2-3 weeks." Perhaps Charlie might benefit from creating a wait list and not taking machines into the shop until his time and attention can turn directly to them?

      It's not often addressed here in this fora how much one should expect to either pay or wait for repair services which aren't evenly distributed across the United States and likely even less so in many other countries. In the broader scheme of things, I think that you get a far better deal at professional shops than you're going to find within the broader public of so-called typewriter sellers (antique shops, thrift stores, etc.)

      As a point of reference, I'm an advanced hobbyist with my own garage-based shop for my personal collection and even I get one or two queries a week about repairing or restoring the machines of others, so I'm at least reasonably aware of what some of the wait times can look like. I wish I had the time or stock of parts machines to do more than a handful of friends and family repairs on top of my own personal repair work.

      Sadly, at the end of the day, it sounds like both sides were potentially not good at communicating expectations about how long repairs would take. If nothing else we should all be sharing more details about these issues to help level set how this all works for the broader typewriter community.

    1. It appears that we have few specific environments (factory facilities) forthe economical production of programs. I contend that the productioncosts are affected far more adversely by the absence of such anenvironment than by the absence of any tools in the environment… Afactory supplies power, work space, shipping and receiving, labordistribution, and financial controls, etc. Thus a software factory should bea programming environment residing upon and controlled by a computer.Program construction, checkout and usage should be done entirely withinthis environment. Ideally it should be impossible to produce programsexterior to this environment…Economical products of high quality […]are not possible (in most instances) when one instructs the programmer ingood practice and merely hopes that he will make his invisible productaccording to those rules and standards. This just does not happen underhuman supervision. A factory, however, has more than humansupervision. It has measures and controls for productivity and quality.18

      Hsu again cites only Mahoney for this, and the passage here is presented as one quote, but it's actually a quote within a quote: first Bemer and then Mahoney. The original Bemer quote ends with the second sentence ("I contend that the production costs are affected far more adversely by the absence of such an environment than by the absence of any tools in the environment…" which ends prematurely here but ends with a parenthetical "e.g. writing a program in PL/1 is using a tool"), and the remainder is Mahoney's commentary.

      The Bemer source is:

      R.W. Bemer, "Position Paper for Panel Discussion [on] the Economics of Program Production", Information Processing 68, North-Holland Publishing Company, 1969, vol. II, p. 1626.

    Tags

    Annotators

    1. research is about the long game

      Indeed—which should really cast the norms around scientific computing in stark light as being clearly the wrong way to go about doing things. (Which itself isn't to say that there's anything special about scientific computing here—there are plenty of programmers (working on open source or otherwise) that get things just as wrong. Most of them, even. It's virtually everyone.)

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In the manuscript submission by Zhao et al. entitled, "Cardiac neurons expressing a glucagon-like receptor mediate cardiac arrhythmia induced by high-fat diet in Drosophila" the authors assert that cardiac arrhythmias in Drosophila on a high-fat diet are due in part to adipokinetic hormone (Akh) signaling activation. High-fat diet induces Akh secretion from activated endocrine neurons, which activate AkhR in posterior cardiac neurons. Silencing or deletion of Akh or AkhR blocks arrhythmia in Drosophila on a high-fat diet. Elimination of one of two AkhR-expressing cardiac neurons results in arrhythmia similar to a high-fat diet.

      Strengths:

      The authors propose a novel mechanism for high-fat diet-induced arrhythmia utilizing the Akh signaling pathway that signals to cardiac neurons.

      Weaknesses:

      Major comments:

      (1) The authors state, "Arrhythmic pathology is rooted in the cardiac conduction system." This assertion is incorrect as a blanket statement on arrhythmias. There are certain arrhythmias that have been attributable to the conduction system, such as bradycardic rhythms, heart block, sinus node reentry, inappropriate sinus tachycardia, AV nodal reentrant tachycardia, bundle branch reentry, fascicular ventricular tachycardia, or idiopathic ventricular fibrillation to name a few. However the etiological mechanism of many atrial and ventricular arrhythmias, such as atrial fibrillation or substrate-based ventricular tachycardia, are not rooted in the conduction system. The introduction should be revised to reflect a clear focus (away from?) on atrial fibrillation (AF). In addition, AF susceptibility is known to be modulated by autonomic tone, which is topically relevant (irrelevant?) to this manuscript.

      Thank you for the helpful comment. We rephrased the sentence as “Arrhythmic pathology is often rooted in the cardiac conduction system”.

      (2) The authors state that "HFD led to increased heartbeat and an irregular rhythm." In representative examples shown, HFD resulted in pauses, slower heart rate, and increased irregularity in rhythm but not consistently increased heart rate (Figures 1B, 3A, and 4C). Based on the cited work by Ocorr et al (https://doi.org/10.1073/pnas.0609278104), Drosophila heart rate is highly variable with periods of fast and slow rates, which the authors attributed to neuronal and hormonal inputs. Ocorr et al then describe the use of "semi-intact" flies to remove autonomic input to normalize heart rate. Were semi-intact flies used? If not, how was heart rate variability controlled? And how was heart rate "increase" quantified in high-fat diet compared to normal-fat diet? Lastly, how does one measure "arrhythmia" when there is so much heart rate variability in normal intact flies?

      We also observed that fly heart rate is highly variable with periods of fast and slow rates. To control heart rate variability, Ocorr et al. used semi-intact flies to record the heartbeat  (https://doi.org/10.1073/pnas.0609278104). We consider it a rigorous method to get highly consistent results with high quality videos/images. Since our work has a focus on the neuronal inputs to the heart, we did not use the semi-intact method. Our concern is that it is likely to disrupt the neuronal processes during the dissection. Using OCT, we recorded the heartbeat of intact flies in an 8 s time window, when the heartbeat was relatively stable. The different groups of flies, which were fed on a high-fat diet or a normal-fat diet, were recorded using the same method. Thus, we could compare the differences in heart rate.

      (3) The authors state, "to test whether the HFD-induced increase in Akh in the APC affects APC neuron activity, we used CaLexA (https://doi.org/10.3109/01677063.2011.642910)." According to the reference, CaLexA is a tool to map active neurons and would not indicate, as the authors state, whether Akh affects APC neuron activity specifically. It is equally possible that APC neurons may be activated by HFD and produce more Akh. Please clarify this language.

      Thank you for clarifying the calcium reporter, CaLexA. We rephrased this sentence to “to test whether HFD affects APC neuron activity, we used CaLexA”.

      (4) Are the AkhR+ neurons parasympathetic or sympathetic? Please provide additional experimentation that characterizes these neurons. The AkhR+ neurons appear to be anti-arrhythmic. Please expand the discussion to include a working hypothesis of the overall findings on Akh, AkhR, and AkhR+ neurons.

      Noyes et al. showed that Akh treatment increases heartbeat (Noyes, B. E., F. N. Katz, and M. H. Schaffer. 1995. “Identification and Expression of the Drosophila Adipokinetic Hormone Gene.” Molecular and Cellular Endocrinology 109 (2): 133–41.), suggesting that AkhR+ neurons are sympathetic. We showed that high-fat diet induced Akh expression and secretion, which led to stimulation of AkhR+ neuron and increased heart rate, supporting the sympathetic role of the AkhR+ neurons. Additional explanation on the sympathetic & anti-arrhythmic role of the Akh, AkhR, and AkhR+ neurons were added to the discussion.

      (5) The authors state, "Heart function is dependent on glucose as an energy source." However, the heart's main energy source is fatty acids with minimal use of glucose (doi: 10.1016/j.cbpa.2006.09.014). Glucose becomes more utilized by cardiomyocytes under heart failure conditions. Please amend/revise this statement.

      Thank you for pointing this out and providing the reference. We rephrased this sentence “Heart function is dependent on continuous ATP production. Cardiac ATP in Drosophila might come from fatty acids, glucose, and lactate (Kodde et al., 2007), as well as trehalose.”

      Reviewer #2 (Public Review):

      This manuscript explores mechanisms underlying heart contractility problems in metabolic disease using Drosophila as a model. They confirm, as others have demonstrated, that a high-fat diet (HFD) induces cardiac problems in flies. They showed that a high-fat diet increased Akh mRNA levels and calcium levels in the Akh-producing cells (APC), suggesting there is increased production and release of this hormone in a HFD context. When they knock down Akh production in the APCs using RNAi they see that cardiac contractility problems are abolished. They similarly show that levels of the Akh receptor (Akhr) are increased on a HFD and that loss of Akhr also rescues contractility problems on a HFD.

      One highlight of the paper was the identification of a pair of neurons that express a receptor for the metabolic hormone Akh, and showing initial data that these neurons innervate the cardiac muscle. They then overexpress cell death gene reaper (rpr) in all Akhr-positive cells with Akhr-GAL4 and see that cardiac contractility becomes abnormal.

      However, this paper contains several findings that have been reported elsewhere and it contains key flaws in both experimental design and data interpretation. There is some rationale for doing the experiments, and the data and images are of good quality. However, others have shown that HFD induces cardiac contractility problems (Birse 2010), that Akh mRNA levels are changed with HFD (Liao 2021) that Akh modulates cardiac rhythms (Noyes 1995), so Figures 1-4 are largely a confirmation of what is already known. This limits the overall magnitude of the advances presented in these figures. Overall, the stated concerns limit the impact of the manuscript in advancing our understanding of heart contractility.

      We thank the reviewer for the positive comments and appreciate the reviewer for the instructive suggestions. Birse 2010 (PMID: 21035763) was cited in our manuscript. Liao 2021 showed that Akh mRNA levels are changed with HFD. We added the reference to the revised manuscript and modified the text as: “In consistent with a previous work (Liao et al., 2020), we showed that the expression of Akh was significantly up-regulated in the flies fed a HFD, compared to NFD-fed flies (Figure 2B)”. Our qPCR verified Liao’s results. On top of this, we investigated the calcium levels in the Akh producing cells (APCs) and showed elevated calcium levels in the APC in HFD fed flies. In the revised version, we added more data to show that Akh protein levels were increased with HFD (Figure 2E-F). In line with Noyes' discovery, which showed that Akh injection caused cardioaccelation in prepupae, we showed that genetic manipulation of Akh expression affected heartbeat in the adults.   

      Reviewer #3 (Public Review):

      Zhao et al. provide new insights into the mechanism by which a high-fat diet (HFD) induces cardiac arrhythmia employing Drosophila as a model. HFD induces cardiac arrhythmia in both mammals and Drosophila. Both glucagon and its functional equivalent in Drosophila Akh are known to induce arrhythmia. The study demonstrates that Akh mRNA levels are increased by HFD and both Akh and its receptor are necessary for high-fat diet-induced cardiac arrhythmia, elucidating a novel link. Notably, Zhao et al. identify a pair of AKH receptor-expressing neurons located at the posterior of the heart tube. Interestingly, these neurons innervate the heart muscle and form synaptic connections, implying their roles in controlling the heart muscle. The study presented by Zhao et al. is intriguing, and the rigorous characterization of the AKH receptor-expressing neurons would significantly enhance our understanding of the molecular mechanism underlying HFD-induced cardiac arrhythmia.

      Many experiments presented in the manuscript are appropriate for supporting the conclusions while additional controls and precise quantifications should help strengthen the authors' augments. The key results obtained by loss of Akh (or AkhR) and genetic elimination of the identified AkhR-expressing cardiac neurons do not reconcile, complicating the overall interpretation.

      It is intriguing to see an increase in Akh mRNA levels in HFD-fed animals. This is a key result for linking HFD-induced arrhythmia to Akh. Thus, demonstrating that HFD also increases the Akh protein levels and Akh is secreted more should significantly strengthen the manuscript.

      Thank you for the positive comments and the instructive suggestions. We performed immunostaining to show that Akh protein levels increased, which is consistent with elevated Akh mRNA expression in HFD-fed flies. The data was added to Figure 2, panels E and F. Akh secretion from the APCs is regulated by APC activity (https://doi.org/10.1038/s41586-019-1675-4). We used a calcium reporter CaLexA (https://doi.org/10.3109/01677063.2011.642910) to monitor APC activity and showed that HFD increased APC activity (Figure 2, C-D).

      The experiments employing an AkhR null allele nicely demonstrate its requirement for HFD-induced cardiac arrhythmia. Depletion of Akh in Akh-expressing cells recapitulates the consequence of AkhR knockout, supporting that both Akh and its receptor are required for HFD-induced cardiac arrhythmia. Given that RNAi is associated with off-target effects and some RNAi reagents do not work, testing multiple independent RNAi lines is the standard procedure. It is also important to show the on-target effect of the RNAi reagents used in the study.

      Indeed, RNAi approaches can suffer from off-target effects. For Akh experiments, we used an RNAi line BL_34960, which was generated using artificial microRNAs shRNA (DOI: 10.1038/nmeth.1592). In comparison to long-hairpin constructs, shRNA constructs are expected to be advantageous, e.g., more efficient and minimized off-target. We performed immunostaining to determine Akh-Gal4>UAS-Akh-RNAi efficiency. We showed that anti-Akh fluorescence diminished in Akh-Gal4>UAS-Akh-RNAi APCs. The data was added to Figure 3-figure supplement 1.

      The most exciting result is the identification of AkhR-expressing neurons located at the posterior part of the heart tube (ACNs). The authors attempted to determine the function of ACNs by expressing rpr with AkhR-GAL4, which would induce cell death in all AkhR-expressing cells, including ACNs. The experiments presented in Figure 6 are not straightforward to interpret. Moreover, the conclusion contradicts the main hypothesis that elevated Akh is the basis of HFD-induced arrhythmia. The results suggest the importance of AkhR-expressing cells for normal heartbeat. However, elimination of Akh or AkhR restores normal rhythm in HFD-fed animals, suggesting that Akh and AkhR are not important for maintaining normal rhythms. If Akh signaling in ACNs is key for HFD-induced arrhythmia, genetic elimination of ACNs should unalter rhythm and rescue the HFD-induced arrhythmia. An important caveat is that the experiments do not test the specific role of ACNs. ACNs should be just a small part of the cells expressing AkhR. The experiments presented in Figure 6 cannot justify the authors' conclusion. Specific manipulation of ACNs will significantly improve the study. Moreover, the main hypothesis suggests that HFD may alter the activity of ACNs in a manner dependent on Akh and AkhR. Testing how HFD changes calcium, possibly by CaLexA (Figure 2) and/or GCaMP, in wild-type and AkhR mutants could be a way to connect ACNs to HFD-induced arrhythmia. Moreover, optogenetic manipulation of ACNs will allow for specific manipulation of ACNs, which is crucial for studying the specific role of ACNs in controlling cardiac rhythms.

      Thank you for the insightful comments. We have been trying to find a way to only target the AkhR neurons using split-Gal4. Up to now, it’s not successful. Akh/AkhR signaling shall play a key role in the ACNs, however, we cannot rule out the possibility that ACNs also receive signals other than Akh in the modulation of heartbeat.

      Interestingly, expressing rpr with AkhR-GAL4 was insufficient to eliminate both ACNs. It is not clear why it didn't eliminate both ACNs. Given the incomplete penetrance, appropriate quantifications should be helpful. Additionally, the impact on other AhkR-expressing cells should be assessed. Adding more copies of UAS-rpr, AkhR-GAL4, or both may eliminate all ACNs and other AkhR-expressing cells. The authors could also try UAS-hid instead of UAS-rpr.

      We added more data to show that AkhR+ neurons are positive in anti-Akh staining, indicating the AkhR+ neurons indeed receive Akh.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Typo in line 765: "increased Akh section into the circulation." Section should be secretion.

      Thank you for finding the typo. We changed section to secretion.

      Reviewer #2 (Recommendations For The Authors):

      One interesting extension to our knowledge in Figures 3 & 4 is that loss of Akhr and loss of Akh both block the cardiac contractility defects that accompany a HFD. The main concern I have with the Akh finding is that the authors use only a GAL4 control and no UAS alone control. Metabolic phenotypes often show strain-specific effects, so to make conclusions it is essential that the authors include a UAS alone control alongside the other genotypes to be sure it does not rescue the cardiac contractility defects that accompany a HFD by itself.

      I am interested in the authors' identification of a pair of Akhr-positive neurons that innervate the cardiac muscle. I am not aware of any other studies identifying these neurons, or revealing their function. The contents of Figure 5 therefore represent the largest advance in the study. However, the characterization of these neurons is very superficial, and a lot more work to understand their regulation and function in a HFD context is needed to make conclusions about their role in any HFD-induced cardiac contractility problems. Or to determine how Akh influences the function of these specific neurons in an HFD context.

      The reason I say this is that the authors ablate all Akhr-positive cells in Figure 6 and show that this disturbs normal cardiac contractility. While studies on the one pair of Akhr-positive neurons would be really interesting, ablating all Akhr-positive cells, which includes the fat and many other cell types in the fly, is not a scientifically rigorous approach to answering this question. As a result, the authors are only able to make the claim that ablating many cell types throughout the animal disrupts cardiac contractility, which does not advance our understanding of mechanisms underlying heart contractility problems. In addition, because the experiments they designed did not test whether it was Akh binding to Akhr on those neurons that regulate cardiac contractility problems in a HFD context, their experiments do not support their model in Figure 7.

      The authors also make conclusions that are fairly speculative around Line 231 when describing their model in Figure 7. These claims are simply not supported by the data they present and must be removed. For example, the authors have not identified an endocrine-heart axis, they simply showed that changes in Akh can influence the heart, but this is not necessarily a direct effect on a specific cell type. They do not show data that Akh binds the newly identified Akhr-positive neuron pair to mediate the effects of HFD-induced contractility defects - they just ablate all Akhr-positive cells (fat, neurons, and other types) and show cardiac defects. If those neurons did mediate the abnormal cardiac rhythm promoted by Akh, then ablating those neurons (and not a large number of additional tissues) should rescue HFD-induced heart defects just like reducing Akhr or Akh did (but this is the opposite of what they see). Overall, concerns with experimental design, data interpretation, and relatively few findings that aren't reported elsewhere reduce the impact of this paper.

      We appreciate the positive comments and helpful suggestions. Indeed, it is important to get clean genetic access to the cardiac neurons. We intended to use split Gal4 system to target the AkhR cardiac neurons. We have tried to build a split Gal4 driver AkhR-p65.AD. Two rounds of injection were carried out. However, we did not recover a transgenic line.

      In the revised version, we performed immunostaining using Akh antibodies to show that anti-Akh fluorescence was observed in AkhR neurons (Figure 5-figure supplement 1), indicating an endocrine-heart axis.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Azlan et al. identified a novel maternal factor called Sakura that is required for proper oogenesis in Drosophila. They showed that Sakura is specifically expressed in the female germline cells. Consistent with its expression pattern, Sakura functioned autonomously in germline cells to ensure proper oogenesis. In Sakura KO flies, germline cells were lost during early oogenesis and often became tumorous before degenerating by apoptosis. In these tumorous germ cells, piRNA production was defective and many transposons were derepressed. Interestingly, Smad signaling, a critical signaling pathway for GSC maintenance, was abolished in sakura KO germline stem cells, resulting in ectopic expression of Bam in whole germline cells in the tumorous germline. A recent study reported that Bam acts together with the deubiquitinase Otu to stabilize Cyc A. In the absence of sakura, Cyc A was upregulated in tumorous germline cells in the germarium. Furthermore, the authors showed that Sakura co-immunoprecipitated Otu in ovarian extracts. A series of in vitro assays suggested that the Otu (1-339 aa) and Sakura (1-49 aa) are sufficient for their direct interaction. Finally, the authors demonstrated that the loss of otu phenocopies the loss of sakura, supporting their idea that Sakura plays a role in germ cell maintenance and differentiation through interaction with Otu during oogenesis.

      Strengths:

      To my knowledge, this is the first characterization of the role of CG14545 genes. Each experiment seems to be well-designed and adequately controlled.

      Weaknesses:

      However, the conclusions from each experiment are somewhat separate, and the functional relationships between Sakura's functions are not well established. In other words, although the loss of Sakura in the germline causes pleiotropic effects, the cause-and-effect relationships between the individual defects remain unclear.

      Reviewer #2 (Public review):

      In this study, the authors identified CG14545 (and named it Sakura), as a key gene essential for Drosophila oogenesis. Genetic analyses revealed that Sakura is vital for both oogenesis progression and ultimate female fertility, playing a central role in the renewal and differentiation of germ stem cells (GSC).

      The absence of Sakura disrupts the Dpp/BMP signaling pathway, resulting in abnormal bam gene expression, which impairs GSC differentiation and leads to GSC loss. Additionally, Sakura is critical for maintaining normal levels of piRNAs. Also, the authors convincingly demonstrate that Sakura physically interacts with Otu, identifying the specific domains necessary for this interaction, suggesting a cooperative role in germline regulation. Importantly, the loss of otu produces similar defects to those observed in Sakura mutants, highlighting their functional collaboration.

      The authors provide compelling evidence that Sakura is a critical regulator of germ cell fate, maintenance, and differentiation in Drosophila. This regulatory role is mediated through the modulation of pMad and Bam expression. However, the phenotypes observed in the germarium appear to stem from reduced pMad levels, which subsequently trigger premature and ectopic expression of Bam. This aberrant Bam expression could lead to increased CycA levels and altered transcriptional regulation, impacting piRNA expression. Given Sakura's role in pMad expression, it would be insightful to investigate whether overexpression of Mad or pMad could mitigate these phenotypic defects (UAS-Mad line is available at Bloomington Drosophila Stock Center).

      As suggested reviewer 1, we tested whether overexpression of Mad could rescue or mitigate the loss of sakura phenotypic defects, by using nos-Gal4-VP16 > UASp-Mad-GFP in the background of sakura<sup>null</sup>. As shown in Fig S11, we did not observe any mitigation of defects.

      Then, we also tested whether expressing a constitutive active form of Tkv, by using UAS-Dcr2, NGT-Gal4 > UASp-tkv.Q235D in the background of sakura<sup>RNAi</sup>. As shown in Fig S12, we did not observe any mitigation of defects by this approach either.

      A major concern is the overstated role of Sakura in regulating Orb. The data does not reveal mislocalized Orb; rather, a mislocalized oocyte and cytoskeletal breakdown, which may be secondary consequences of defects in oocyte polarity and structure rather than direct misregulation of Orb. The conclusion that Sakura is necessary for Orb localization is not supported by the data. Orb still localizes to the oocyte until about stage 6. In the later stage, it looks like the cytoskeleton is broken down and the oocyte is not positioned properly, however, there is still Orb localization in the ~8-stage egg chamber in the oocyte. This phenotype points towards a defect in the transport of Orb and possibly all other factors that need to localize to the oocyte due to cytoskeletal breakdown, not Orb regulation directly. While this result is very interesting it needs further evaluation on the underlying mechanism. For example, the decrease in E-cadherin levels leads to a similar phenotype and Bam is known to regulate E-cadherin expression. Is Bam expressed in these later knockdowns?

      We examined Bam and DE-Cadherin expression in later RNAi knockdowns driven by ToskGal4. As shown in Fig S9, Bam was not expressed in these later knockdowns compared with controls. DE-Cadherin staining suggested a disorganized structure in late-stage egg chambers.

      We agree that we overstated a role of Sakura in regulating Orb in the initial manuscript. We changed the text to avoid overstating.

      The manuscript would benefit from a more balanced interpretation of the data concerning Sakura's role in Orb regulation. Furthermore, a more expanded discussion on Sakura's potential role in pMad regulation is needed. For example, since Otu and Bam are involved in translational regulation, do the authors think that Mad is not translated and therefore it is the reason for less pMad? Currently the discussion presents just a summary of the results and not an extension of possible interpretation discussed in context of present literature.

      We changed the text to avoid overstating a role of Sakura in regulating Orb localization.

      Based on our newly added results showing that transgenic overexpression of Mad could not rescue or mitigate the phenotypic defects of sakura<sup>null</sup> mutant (Fig S11), we do not think the reason for less pMad is less translation of Mad.

      Reviewer #3 (Public review):

      In this very thorough study, the authors characterize the function of a novel Drosophila gene, which they name Sakura. They start with the observation that sakura expression is predicted to be highly enriched in the ovary and they generate an anti-sakura antibody, a line with a GFP-tagged sakura transgene, and a sakura null allele to investigate sakura localization and function directly. They confirm the prediction that it is primarily expressed in the ovary and, specifically, that it is expressed in germ cells, and find that about 2/3 of the mutants lack germ cells completely and the remaining have tumorous ovaries. Further investigation reveals that Sakura is required for piRNA-mediated repression of transposons in germ cells. They also find evidence that sakura is important for germ cell specification during development and germline stem cell maintenance during adulthood. However, despite the role of sakura in maintaining germline stem cells, they find that sakura mutant germ cells also fail to differentiate properly such that mutant germline stem cell clones have an increased number of "GSC-like" cells. They attribute this phenotype to a failure in the repression of Bam by dpp signaling. Lastly, they demonstrate that sakura physically interacts with otu and that sakura and otu mutants have similar germ cell phenotypes. Overall, this study helps to advance the field by providing a characterization of a novel gene that is required for oogenesis. The data are generally high-quality and the new lines and reagents they generated will be useful for the field. However, there are some weaknesses and I would recommend that they address the comments in the Recommendations for the authors section below.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      General Comments:

      (1) The gene nomenclature: As mentioned in the text, Sakura means cherry blossom and is one of the national flowers of Japan. I am not sure whether the phenotype of the CG14545 mutant is related to Sakura or not. I would like to suggest the authors reconsider the naming.

      The striking phenotype of sakura mutant­ is tumorous and germless ovarioles. The tumorous phenotype, exhibiting lots of round fusome in germarium visualized by anti-Hts staining, looks like cherry blossom blooming to us. Also, the germless phenotype reminds us falling of the cherry blossom, especially considering that the ratio of tumorous phenotype decreases and that of germless decreases over fly age. Furthermore, “Sakura” symbolizes birth and renewal in Japanese culture (the last author of this manuscript is Japanese). Our findings indicated that the gene sakura is involved in regulation of renewal and differentiation of GSCs (which leads to birth). These are the reasons for the naming, which we would like to keep.

      (2) In many of the microscopic photographs in the figures, especially for the merged confocal images, the resolution looks low, and the images appear blurred, making it difficult to judge the authors' claims. Also, the Alpha Fold structure in Figure 10A requires higher contrast images. The magnification of the images is often inadequate (e.g. Figures 3A, 3B, 5E, 7A, etc). The authors should take high-magnification images separately for the germarium and several different stages of the egg chambers and lay out the figures.

      We are very sorry for the low-resolution images. This was caused when the original PDF file with high-resolution images was compressed in order to meet the small file size limit in the eLife submission portal. In the revised submission, we used high-resolution images.

      Specific Comments

      (1) How Sakura can cooperate with Otu remains unanswered. Sakura does not regulate deubiquitinase activity in vitro. Both sakura and otu appear to be involved in the Dpp-Smad signaling pathway and in the spatial control of Bam expression in the germarium, whereas Otu has been reported to act in concert with Bam to deubiquitinate and stabilize Cyc A for proper cystoblast differentiation. Therefore, it is plausible that the stabilization of Cyc A in the Sakura mutant is an indirect consequence of Bam misexpression and independent of the Sakura-Otu interaction. The authors may need to provide much deeper insight into the mechanism by which Sakura plays roles in these seemingly separable steps to orchestrate germ cell maintenance and differentiation during early oogenesis.

      Yes, it is possible that the stabilization of CycA in the sakura mutant is an indirect consequence of Bam misexpression and independent of the Sakura-Otu interaction. To test the significance and role of the Sakura-Otu interaction, we have attempted to identify Sakura point mutants that lose interaction with Otu. If such point mutants were successfully obtained, we were planning to test if their transgene expression could rescue the phenotypes of sakura mutant as the wild-type transgene did. However, after designing and testing the interaction of over 30 point mutants with Otu, we could not obtain such mutant version of Sakura yet. We will continue making efforts, but it is beyond the scope of the current study. We hope to address this important point in future studies.

      (2) Figure 3A and Figure 4: The authors show that piRNA production is abolished in Sakura KO ovaries. It is known that piRNA amplification (the ping-pong cycle) occurs in the Vasa-positive perinuclear nuage in nurse cells. Is the nuage normally formed in the absence of Sakura? The authors provide high-magnification images in the germarium expressing Vas-GFP. How does Sakura, and possibly Out, contribute to piRNA production? Are the defects a direct or indirect consequence of the loss of Sakura?

      We provided higher magnification images of germarium expressing Vasa-EGFP in sakura mutant background (Fig 3A and 3B). The nuage formation does not seem to be dysregulated in sakura mutant. Currently, we do not know if the piRNA defects are direct or indirect consequence of the loss of Sakura. This question cannot be answered easily. We hope to address this in future studies.

      (3) Figure 7 and Figure 12: The authors showed that Dpp-Smad signaling was abolished in Sakura KO germline cells. The same defects were also observed in otu mutant ovaries (Figure 12B). How does the Sakura-Otu axis contribute to the Dpp-Smad pathway in the germline?

      As we mentioned in the response to comment (1), we attempted to test the significance and role of the Sakura-Otu interaction, including in the Dpp-Smad pathway in the germline, but we have not yet been able to obtain loss-of-interaction mutant(s) of Sakura. We hope to address this in future studies.

      (4) Figure 9 and Fig 10: The authors raised antibodies against both Sakura and Otu, but their specificities were not provided. For Western blot data, the authors should provide whole gel images as source data files. Also, the authors argue that the Otu band they observed corresponds to the 98-kDa isoform (lines 302-304). The molecular weight on the Western blot alone would be insufficient to support this argument.

      When we submitted the initial manuscript, we also submitted original, uncropped, and unmodified whole Western blot images for all gel images to the eLife journal, as requested. We did the same for this revised submission. I believe eLife makes all those files available for downloading to readers.

      In the newly added Fig S13B, we used very young 2-5 hours ovaries and 3-7 days ovaries. 2-5 days ovaries contain only mostly pre-differentiated germ cells. Older ovaries (3-7 days in our case here) contain all 14 stages of oogenesis and later stages predominate in whole ovary lysates.

      As reported in previous literature (Sass et al. 1995), we detected a higher abundance of the 104 kDa Otu isoform than the 98 kDa isoform in from 2-5 hours ovaries and predominantly the 98 kDa isoform in 3-7 days ovaries (Fig S13B). These results confirmed that the major Otu isoform we detected in Western blot, all of which uses old ovaries except for the 2-5 hours ovaries in Fig S13B, is the 98 kDa isoform.

      (5) Otu has been reported to regulate ovo and Sxl in the female germline. Is Sakura involved in their regulation?

      We examined sxl alternative splicing pattern in sakura mutant ovaries. As shown in Fig S6, we detected the male-specific isoform of sxl RNA and a reduced level of the female-specific sxl isoform in sakura mutant ovaries. Thus Sakura seems to be involved in sxl splicing in the female germline, while further studies will be needed to understand whether Sakura has a direct or indirect role here.

      (6) Lines 443-447: The GSC loss phenotype in piwi mutant ovaries is thought to occur in a somatic cell-autonomous manner: both piwi-mutant germline clones and germline-specific piwi knockdown do not show the GSC-loss phenotype. In contrast, the authors provide compelling evidence that Sakura functions in the germline. Therefore, the Piwi-mediated GSC maintenance pathway is likely to be independent of the Sakura-Otu axis.

      We changed the text accordingly.

      Reviewer #2 (Recommendations for the authors):

      Overall, this is a cleanly written manuscript, with some sentences/sections that are confusing the way they are constructed (i.e. Line 37-38, 334, section on Flp/FRT experiments).

      We rewrote those sections to avoid confusion.

      Comment for all merged image data: the quality of the merged images is very poor - the individual channels are better but should also be reprocessed for more resolved image data sets. Also, it would be helpful to have boundaries drawn in an individual panel to identify the regions of the germarium, as cartooned in Figure S1A (which should be brought into Figure 1) F-actin or Vsg staining would have helped throughout the manuscript to enhance the visualization of described phenotypes.

      We are very sorry for the low-resolution images. This was caused when the original PDF file with high-resolution images was compressed in order to meet the small file size limit in the eLife submission portal. In the revised submission, we used high-resolution images.

      We outlined the germarium in Fig 1E.

      We brought the former FigS1 into Fig 1A.

      We provided Phalloidin (F-Actin) staining images in Fig S7.

      All p-values seem off. I recommend running the data through the student t-test again.

      We used the student t-test to calculate p-values and confirmed that they are correct. We don’t understand why the reviewer thinks all p-values seem off.

      In the original manuscript, as we mentioned in each figure legends, we used asterisk (*) to indicate p-value <0.05, without distinguishing whether it’s <0.001, <0.01< or <0.05.

      Probably reviewer 2 is suggesting us to use ***, **, and *, to indicate p-value of <0.001, <0.01, and <0.05, respectively? If so, we now followed reviewer2’s suggestions.

      Figure 1

      (1) Within the text, C is mentioned before A.

      We updated the text and now we mentioned Fig 1A before Fig 1C.

      (2) B should be the supplemental figure.

      We moved the former Fig 1B to Supplemental Figure 1.

      (3) C - How were the different egg chamber stages selected in the WB? Naming them 'oocytes' is deceiving. Recommend labeling them as 'egg chambers', since an oocyte is claimed to be just the one-cell of that cyst.

      We changed the labeling to egg chambers.

      (4) Is the antibody not detecting Sakura in IF? There is no mention of this anywhere in the manuscript.

      While our Sakura antibody detects Sakura in IF, it seems to detect some other proteins as well. Since we have Sakura-EGFP fly strain (which fully rescues sakura<sup>null</sup> phenotypes) to examine Sakura expression and localization without such non-specific signal issues, we relied on Sakura-EGFP rather than anti-Sakura antibodies for IF.

      (5) Expand on the reliance of the sakura-EGFP fly line. Does this overexpression cause any phenotypes?

      sakura-EGFP does not cause any phenotypes in the background of sakura[+/+] and sakura[+/-].

      (6) Line 95 "as shown below" is not clear that it's referencing panel D.

      We now referenced Fig 1D.

      (7) Re: Figures 1 E and F. There is no mention of Hts or Vasa proteins in the text.<br /> "Sakura-EGFP was not expressed in somatic cells such as terminal filament, cap cells, escort cells, or follicle cells (Figure 1E). In the egg chamber, Sakura-EGFP was detected in the cytoplasm of nurse cells and was enriched in developing oocytes (Figure 1F)". Outline these areas or label these structures/sites in the images. The color of Merge labels is confusing as the blue is not easily seen.

      We mentioned Hts and Vasa in the text. We labeled the structures/sites in the images and updated the color labeling.

      Figure 2

      (1) Entire figure is not essential to be a main figure, but rather supplemental.

      We don’t agree with the reviewer. We think that the female fertility assay data, where sakura null mutant exhibits strikingly strong phenotype, which was completely rescued by our Sakura-EGFP transgene, is very important data and we would like to present them in a main figure.

      (2) 2A- one star (*) significance does not seem correct for the presented values between 0 and 100+.

      In the original manuscript, as we mentioned in each figure legends, we used asterisk (*) to indicate p-value <0.05, without distinguishing whether it’s <0.001, <0.01< or <0.05.

      Probably reviewer 2 is suggesting us to use ***, **, and *, to indicate p-value of <0.001, <0.01, and <0.05, respectively? If so, we now followed reviewer2’s suggestions.

      (3) 2C images are extremely low quality. Should be presented as bigger panels.

      We are very sorry for the low-resolution images. This was caused when the original PDF file with high-resolution images was compressed in order to meet the small file size limit in the eLife submission portal. In the revised submission, we used high-resolution images. We also presented as bigger panels.

      Figure 3

      (1) "We observed that some sakura<sup>null</sup> /null ovarioles were devoid of germ cells ("germless"), while others retained germ cells (Fig 3A)" What is described is, that it is hard to see. Must have a zoomed-in panel.

      We provided zoomed-in panels in Fig 3B

      (2) C - The control doesn't seem to match. Must zoom in.

      We provided matched control and also zoomed in.

      (3) For clarity, separate the tumorous and germless images.

      In the new image, only one tumorous and one germless ovarioles are shown with clear labeling and outline, for clarity.

      (4) Use arrows to help clearly indicate the changes that occur. As they are presented, they are difficult to see.

      We updated all the panels to enhance clarity.

      (5) Line 158 seems like a strong statement since it could be indirect.

      We softened the statement.

      Figure 4

      (1) Line 188-189 - Conclusion is an overstatement.

      We softened the statement.

      (2) Is the piRNA reduction due to a change in transcription? Or a direct effect by Sakura?

      We do not know the answers to these questions. We hope to address these in future studies.

      Figure 5

      (1) D - It might make more sense if this graph showed % instead of the numbers.

      We did not understand the reviewer’s point. We think using numbers, not %, makes more sense.

      (2) Line 213 - explain why RNAi 2 was chosen when RNAi 1 looks stronger.

      Fly stock of RNAi line 2 is much healthier than RNAi line 1 (without being driven Gal4) for some reasons. We had a concern that the RNAi line 1 might contain an unwanted genetic background. We chose to use the RNAi 2 line to avoid such an issue.

      (3) In Line 218 there's an extra parenthesis after the PGC acronym.

      We corrected the error.

      (4) TOsk-Gal4 fly is not in the Methods section.

      We mentioned TOsk-Gal4 in the Methods.

      Figure 6:

      (1) The FLP-FRT section must be rewritten.

      We rewrote the FLP-FRT section.

      (2) A - include statistics.

      We included statistics using the chi-square test.

      (3) B - is not recalled in the Results text.

      We referred Fig 6B in the text.

      (4) Line 232 references Figure 3, but not a specific panel.

      We referred Fig 3A, 3C, 3D, and 3E, in the text.

      Figure 7/8 - can go to Supplemental.

      We moved Fig 8 to supplemental. However, we think Fig 7 data is important and therefore we would like to present them as a main figure.

      (1) There should be CycA expression in the control during the first 4 divisions.

      Yes, there is CycA expression observed in the control during the first 4 divisions, while it’s much weaker than in sakura<sup>null</sup> clone.

      (2) Helpful to add the dotted lines to delineate (A) as well.

      We added a dotted outline for germarium in Fig 7A.

      (3) Line 263 CycA is miswritten as CyA.

      We corrected the typo.

      Figure 9

      (1) Otu antibody control?

      We validated Otu antibody in newly added Fig 10C and Fig S13A.

      (2) Which Sakura-EGFP line was used? sakura het. or null background? This isn't mentioned in the text, nor legend.

      We used Sakura-EGFP in the background of sakura[+/+]. We added this information in the methods and figure legend.

      (3) C - Why the switch to S2 cells? Not able to use the Otu antibody in the IP of ovaries?

      We can use the Otu antibody in the IP of ovaries. However, in anti-Sakura Western after anti-Otu IP, antibody light chain bands of the Otu antibodies overlap with the Sakura band. Therefore, we switched to S2 cells to avoid this issue by using an epitope tag.

      Figure 10

      (1) A- The resolution of images of the ribbon protein structure is poor.

      We are very sorry for the low-resolution images. This was caused when the original PDF file with high-resolution images was compressed in order to meet the small file size limit in the eLife submission portal. In the revised submission, we used high-resolution images.

      (2) A table summarizing the interactions between domains would help bring clarity to the data presented.

      We added a table summarizing the fragment interaction results.

      (3) Some images would be nice here to show that the truncations no longer colocalize.

      We did not understand the reviewer’s points. In our study, even for the full-length proteins.

      We have not shown any colocalization of Sakura and Otu in S2 cells or in ovaries, except that they both are enriched in developing oocytes in egg chambers.

      Figure 12

      (1) A - control and RNAi lines do not match.

      We provided matched images.

      (2) In general, since for Sakura, only its binding to Otu was identified and since they phenocopy each other, doesn't most of the characterization of Sakura just look at Otu phenotypes? Does Sakura knockdown affect Otu localization or expression level (and vice versa)?

      We tested this by Western (Fig S15) and IF (Fig 12). Sakura knockdown did not decrease Otu protein level, and Otu knockdown did not decrease Sakura protein level (Fig S15). In sakura<sup>null</sup> clone, Otu level was not notably affected (Fig 12). In sakura<sup>null</sup> clone, Otu lost its localization to the posterior position within egg chambers.

      Figure S6

      (1) It is Luciferase, not Lucifarase.

      We corrected the typo.

      Reviewer #3 (Recommendations for the authors):

      (1) It is interesting that germless and tumorous phenotypes coexist in the same population of flies. Additional consideration of these essentially opposite phenotypes would significantly strengthen the study. For example, do they co-exist within the same fly and are the tumorous ovarioles present in newly eclosed flies or do they develop with age? The data in Figure 8 show that bam knockdown partially suppresses the germless phenotype. What effect does it have on the tumorous phenotype? Is transposon expression involved in either phenotype? Do Sakura mutant germline stem cell clones overgrow relative to wild-type cells in the same ovariole? Does sakura RNAi driven by NGT-Gal4 only cause germless ovaries or does it also cause tumorous phenotypes? What happens if the knockdown of Sakura is restricted to adulthood with a Gal80ts? It may not be necessary to answer all of these questions, but more insight into how these two phenotypes can be caused by loss of sakura would be helpful.

      We performed new experiments to answer these questions.

      do they co-exist within the same fly and are the tumorous ovarioles present in newly eclosed flies or do they develop with age?

      Tumorous and germless ovarioles coexist in the same fly (in the same ovary). Tumorous ovarioles are present in very young (0-1 day old) flies, including newly eclosed (Fig S5). The ratio of germless ovarioles increases and that of tumorous ovarioles decreases with age (Fig S5).

      The data in Figure 8 show that bam knockdown partially suppresses the germless phenotype. What effect does it have on the tumorous phenotype?

      bam knockdown effect on tumorous phenotype is shown in Fig S10. bam knockdown increased the ratio of tumorous ovarioles and the number of GSC-like cells.

      Is transposon expression involved in either phenotype?

      Since our transposon-piRNA reporter uses germline-specific nos promoter, it is expressed only in germ line cells, so we cannot examine in germless ovarioles.

      Do Sakura mutant germline stem cell clones overgrow relative to wild-type cells in the same ovariole?

      Yes, Sakura mutant GSC clones overgrow. Please compare Fig 6C and Fig S8.

      Does sakura RNAi driven by NGT-Gal4 only cause germless ovaries or does it also cause tumorous phenotypes?

      Fig S10 and Fig S12 show the ovariole phenotypes of sakura RNAi driven by NGT-Gal4. It causes both germless and tumorous phenotypes.

      What happens if the knockdown of Sakura is restricted to adulthood with a Gal80ts?

      Our mosaic clone was induced at the adult stage, so we already have data of adulthood-specific loss of function. Gal80ts does not work well with nos-Gal4.

      (2) The idea that the excessive bam expression in tumorous ovaries is due to a failure of bam repression by dpp signaling is not well-supported by the data. Dpp signaling is activated in a very narrow region immediately adjacent to the niche but the images in Figure 7A show bam expression in cells that are very far away from the niche. Thus, it seems more likely to be due to a failure to turn bam expression off at the 16-cell stage than to a failure to keep it off in the niche region. To determine whether bam repression in the niche region is impaired, it would be important to examine cells adjacent to the niche directly at a higher magnification than is shown in Figure 7A.

      We provided higher magnification images of cells adjacent to the niche in new Fig 7A.

      We found that cells adjacent to the niche also express Bam-GFP.

      That said, we agree with the reviewer. A failure to turn bam expression off at the 16-cell stage may be an additional or even a main cause of bam misexpression in sakura mutant. We added this in the Discussion.

      (3) In addition, several minor comments should be addressed:

      a. Does anti-Sakura work for immunofluorescence?

      While our Sakura antibody detects Sakura in IF, it seems to detect some other proteins as well. Since we have Sakura-EGFP fly strain to examine Sakura expression and localization without such non-specific signal issues, we relied on Sakura-EGFP rather than anti-Sakura antibodies.

      b. Please provide insets to show the phenotypes indicated by the different color stars in Figure 3C more clearly.

      We provided new, higher-magnification images to show the phenotypes more clearly.

      c. Please indicate the frequency of the expression patterns shown in Figure 4D (do all ovarioles in each genotype show those patterns or is there variable penetrance?).

      We indicated the frequency.

      d. An image showing TOskGal4 driving a fluorophore should be provided so that readers can see which cells express Gal4 with this driver combination.

      It has been already done in the paper ElMaghraby et al, GENETICS, 2022, 220(1), iyab179, so we did not repeat the same experiment.

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