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.

eLife Assessment

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides compelling evidence demonstrating reliable longitudinal identification of neurons across the second postnatal week in mice. The study should be of interest to development neuroscientists engaged in population-level recordings using two-photon imaging.

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling and innovative approach that combines Track2p neuronal tracking with advanced analytical methods to investigate early postnatal brain development. The work provides a powerful framework for exploring complex developmental processes such as the emergence of sensory representations, cognitive functions, and activity-dependent circuit formation. By enabling the tracking of the same neurons over extended developmental periods, this methodology sets the stage for mechanistic insights that were previously inaccessible.

Strengths:

(1) Innovative Methodology:<br /> The integration of Track2p with longitudinal calcium imaging offers a unique capability to follow individual neurons across critical developmental windows.

(2) High Conceptual Impact:<br /> The manuscript outlines a clear path for using this approach to study foundational developmental questions, such as how early neuronal activity shapes later functional properties and network assembly.

(3) Future Experimental Potential:<br /> The authors convincingly argue for the feasibility of extending this tracking into adulthood and combining it with targeted manipulations, which could significantly advance our understanding of causality in developmental processes.

(4) Broad Applicability:<br /> The proposed framework can be adapted to a wide range of experimental designs and questions, making it a valuable resource for the field.

Weaknesses:

No major weaknesses were identified by this reviewer. The manuscript is conceptually strong and methodologically sound. Future studies will need to address potential technical limitations of long-term tracking, but this does not detract from the current work's significance and clarity of vision.

Reviewer #2 (Public review):

Summary:

The manuscript by Majnik and colleagues introduces "Track2p", a new tool designed to track neurons across imaging sessions of two-photon calcium imaging in developing mice. The method addresses the challenge of tracking cells in the growing brain of developing mice. The authors showed that "Track2p" successfully tracks hundreds of neurons in the barrel cortex across multiple days during the second postnatal week. This enabled the identification of the emergence of behavioral state modulation and desynchronization of spontaneous network activity around postnatal day 11.

Strengths:

The manuscript is well written, and the analysis pipeline is clearly described. Moreover, the dataset used for validation is of high quality, considering the technical challenges associated with longitudinal two-photon recordings in mouse pups. The authors provide a convincing comparison of both manual annotation and "CellReg" to demonstrate the tracking performance of "Track2p". Applying this tracking algorithm, Majnik and colleagues characterized hallmark developmental changes in spontaneous network activity, highlighting the impact of longitudinal imaging approaches in developmental neuroscience. Additionally, the code is available on GitHub, along with helpful documentation, which will facilitate accessibility and usability by other researchers.

Weaknesses:

(1) The main critique of the "Track2p" package is that, in its current implementation, it is dependent on the outputs of "Suite2p". This limits adoption by researchers who use alternative pipelines or custom code. One potential solution would be to generalize the accepted inputs beyond the fixed format of "Suite2p", for instance, by accepting NumPy arrays (e.g., ROIs, deltaF/F traces, images, etc.) from files generated by other software. Otherwise, the tool may remain more of a useful add-on to "Suite2p" (see https://github.com/MouseLand/suite2p/issues/933) rather than a fully standalone tool.

(2) Further benchmarking would strengthen the validation of "Track2p", particularly against "CaIMaN" (Giovannucci et al., eLife, 2019), which is widely used in the field and implements a distinct registration approach.

(3) The authors might also consider evaluating performance using non-consecutive recordings (e.g., alternate days or only three time points across the week) to demonstrate utility in other experimental designs.

Reviewer #3 (Public review):

Summary:

In this manuscript, Majnik et al. developed a computational algorithm to track individual developing interneurons in the rodent cortex at postnatal stages. Considerable development in cortical networks takes place during the first postnatal weeks; however, tools to study them longitudinally at a single-cell level are scarce. This paper provides a valuable approach to study both single-cell dynamics across days and state-driven network changes. The authors used Gad67Cre mice together with virally introduced TdTom to track interneurons based on their anatomical location in the FOV and AAVSynGCaMP8m to follow their activity across the second postnatal week, a period during which the cortex is known to undergo marked decorrelation in spontaneous activity. Using Track2P, the authors show the feasibility of tracking populations of neurons in the same mice, capturing with their analysis previously described developmental decorrelation and uncovering stable representations of neuronal activity, coincident with the onset of spontaneous active movement. The quality of the imaging data is compelling, and the computational analysis is thorough, providing a widely applicable tool for the analysis of emerging neuronal activity in the cortex. Below are some points for the authors to consider.

Major points:

(1) The authors used 20 neurons to generate a ground truth dataset. The rationale for this sample size is unclear. Figure 1 indicates the capability to track ~728 neurons. A larger ground truth data set will increase the robustness of the conclusions.

(2) It is unclear how movement was scored in the analysis shown in Figure 5A. Was the time that the mouse spent moving scored after visual inspection of the videos? Were whisker and muscle twitches scored as movement, or was movement quantified as the amount of time during which the treadmill was displaced?

(3) The rationale for binning the data analysis in early P11 is unclear. As the authors acknowledged, it is likely that the decoder captured active states from P11 onwards. Because active whisking begins around P14, it is unlikely to drive this change in network dynamics at P11. Does pupil dilation in the pups change during locomotor and resting states? Does the arousal state of the pups abruptly change at P11?

eLife Assessment

This is an important study providing molecular insight into how cross-talk between histone modifications regulates the histone H3K36 methyltransferase SETD2. The manuscript contains excellent quality data, and the conclusions are convincing and justified. This work will be of interest to many biochemists working in the field of chromatin biology and epigenetics.

Reviewer #1 (Public review):

Summary:

In this manuscript, Mack and colleagues investigate the role of posttranslational modifications, including lysine acetylation and ubiquitination, in methyltransferase activity of SETD2 and show that this enzyme functions as a tumor suppressor in a KRASG12C-driven lung adenocarcinoma. In contrast to H3K36me2-specific oncogenic methyltransferases, the deletion of SETD2, which is capable of H3K36 trimethylation, increases lethality in a KRASG12C-driven lung adenocarcinoma mouse tumor model. In vitro, the authors demonstrate that polyacetylation of histone H3, particularly of H3K27, H3K14, and H3K23, promotes the catalytic activity of SETD2, whereas ubiquitination of H2A and H2B has no effect.

Strengths:

Overall, this is a well-designed study that addresses an important biological question regarding the functioning of the essential chromatin component. The manuscript contains excellent quality data, and the conclusions are convincing and justified. This work will be of interest to many biochemists working in the field of chromatin biology and epigenetics.

Weaknesses:

A minor comment: labels should be added in the Figures and should be uniform across all Figures (some are distorted).

Reviewer #2 (Public review):

Summary:

Human histone H3K36 methyltransferase Setd2 has been previously shown to be a tumor suppressor in lung and pancreatic cancer. In this manuscript by Mack et al., the authors first use a mouse KRASG12D-driven lung cancer model to confirm in vivo that Setd2 depletion exacerbates tumorigenesis. They then investigate the enzymatic regulation of the Setd2 SET domain in vitro, demonstrating that H2A, H3, or H4 acetylation stimulates Setd2-SET activity, with specific enhancement by mono-acetylation at H3K14ac or H3K27ac. In contrast, histone ubiquitination has no effect. The authors propose that H3K27ac may regulate Setd2-SET activity by facilitating its binding to nucleosomes. This work provides insight into how cross-talk between histone modifications regulates Setd2 function. However, the manuscript lacks a clear discussion on how Setd2's in vivo tumor suppressor role and the in vitro mechanistic regulation findings are connected. Additionally, some experiments require more controls and better data quality for proper interpretation.

Specific comments:

(1) As for Figure 2F, Setd2-SET activity on WT rNuc (H3) appears to be significantly lower compared to what is extensively reported in the literature. This is particularly puzzling given that Figure 2B suggests that using 3H-SAM, H3-nuc are much better substrates than K36me1, whereas in Figure 3F, rH3 is weaker than K36me1. It is recommended for the authors to perform additional experimental repeats and include a quantitative analysis to ensure the consistency and reliability of these findings.

(2) The additional bands observed in Figure 4B, which appear to be H4, should be accompanied by quantification of the intensity of the H3 bands to better assess K36me3 activity. Additionally, the quantification presented in Figure 4C for SAH does not seem accurate as it potentially includes non-specific methylation activity, likely from H4. This needs to be addressed for clarity and accuracy.

(3) In Figure 4E, the differences between bound and unbound substrates are not sufficiently pronounced. Given the modest differences observed, authors might want to consider repeating the assay with sufficient replicates to ensure the results are statistically robust.

(4) Regarding labeling, there are multiple issues that need correction: In the depiction of Epicypher's dNuc, it is crucial to clearly mark H2B as the upper band, rather than ambiguously labeling H2A/H2B together when two distinct bands are evident. In Figure 3B and D, the histones appear to be mislabeled, and the band corresponding to H4 has been cut off. It would be beneficial to refer to Figure 3E for correct labeling to maintain consistency and accuracy across figures.

(5) There are issues with the image quality in some blots; for instance, Figure 2EF and Figure 2D exhibit excessive contrast and pixelation, respectively. These issues could potentially obscure or misrepresent the data, and thus, adjustments in image processing are recommended to provide clearer, more accurate representations.

(6) The authors are recommended to provide detailed descriptions of the materials used, including catalog numbers and specific products, to allow for reproducibility and verification of experimental conditions.

(7) The identification of Setd2 as a tumor suppressor in KrasG12C-driven LUAD is a significant finding. However, the discussion on how this discovery could inspire future therapeutic approaches needs to be more balanced. The current discussion (Page 10) around the potential use of inhibitors is somewhat confusing and could benefit from a clearer explanation of how Setd2's role could be targeted therapeutically. It would be beneficial for the authors to explore both current and potential future strategies in a more structured manner, perhaps by delineating between direct inhibitors, pathway modulators, and other therapeutic modalities.

Author response:

We thank the Reviewers for their thoughtful and helpful critiques. Below we provide a point-bypoint response to the comment raised.

Reviewer #1:

(1) Labels should be added in the Figures and should be uniform across all Figures (some are distorted).

We thank the Reviewer for pointing out this issue. As requested, labels have been edited to ensure they are legible and are consistent in font, size, and style.  

Reviewer #2:

(1) As for Figure 2F, Setd2-SET activity on WT rNuc (H3) appears to be significantly lower compared to what is extensively reported in the literature. This is particularly puzzling given that Figure 2B suggests that using 3H-SAM, H3-nuc are much better substrates than K36me1, whereas in Figure 3F, rH3 is weaker than K36me1. It is recommended for the authors to perform additional experimental repeats and include a quantitative analysis to ensure the consistency and reliability of these findings.  

We appreciate the Reviewer’s points. We respectfully suggest that these comments may reflect potential confusion around interpreting how different assays detect in vitro methylation, what data can and cannot be compared, and the nature of the different substrates used. 

With respect to point 1 (Western signal significantly lower compared to extensive literature): To the best of our knowledge, it would be extremely challenging to make a quantitative argument comparing the strength of the Western signal in Figure 2F with results reported in the literature. Specifically, comparing our results with previous studies would require (1) all the studies to have used the exact same antibodies as antibody signal intensities vary depending on the specific activity and selectively of a particular antibody and even its lot number, (2) similar in vitro methylation reaction condition, (3) the same type of recombinant nucleosomes used, and so on. Further, given that these are Western blots, we do not understand how one could interpret an absolute activity level. In the figure, all we can conclude is that in in vitro methylation reactions, our recombinant SETD2 protein methylates rNucs to generate mono-, di-, and tri-methylation at K36 (using vetted antibodies (see Fig. 2e)). If there is a specific paper within the extensive literature that the Reviewer highlights, we could look more into the details of why the signals are different (our guess is that any difference would largely be due to the use of different antibodies). We add that it might be challenging to find a similar experiment performed in the literature; we are not aware of a similar experiment. 

With respect to comparing Figure 2B and 2F: We do not understand how one can meaningfully compare incorporation of radiolabeled SAM to antibody-based detection on film using an antibody against specific methyl states. In particular, regarding the question regarding comparing rH3 vs H3K36me1 nucleosomes, we point out that in using recombinant nucleosomes installed with native modifications (e.g. H3K36me1), in which the entire population of the starting material is mono-methylated, then naturally the Western signal with an anti-H3K36me1 antibody will be strong. In Fig. 2b, the assay is incorporation of radiolabeled methyl, which is added to the preexiting mono-methylated substrate. In other words, the results are entirely consistent if one understands how the methylation reactions were performed, how methylation was detected, and the nature of the reagents.

(2) The additional bands observed in Figure 4B, which appear to be H4, should be accompanied by quantification of the intensity of the H3 bands to better assess K36me3 activity. Additionally, the quantification presented in Figure 4C for SAH does not seem accurate as it potentially includes non-specific methylation activity, likely from H4. This needs to be addressed for clarity and accuracy. 

We thank the reviewer for this comment. The additional bands observed in Figure 4B represent degradation products of histone H3, not H4 methylation. This is commonly seen in in vitro reactions using recombinant nucleosomes, where partial proteolysis of H3 can occur under the assay conditions.  

(3) In Figure 4E, the differences between bound and unbound substrates are not sufficiently pronounced. Given the modest differences observed, authors might want to consider repeating the assay with sufficient replicates to ensure the results are statistically robust.

In Figure 4E, we observe a clear difference between the bound and unbound substrate. To aid interpretation, we have clarified in the figure where the bound complex migrates on the gel, while the unbound nucleosomes migrate at the bottom of the gel. The differences are indeed subtle, which we highlight in the text.  

(4) Regarding labeling, there are multiple issues that need correction: In the depiction of Epicypher's dNuc, it is crucial to clearly mark H2B as the upper band, rather than ambiguously labeling H2A/H2B together when two distinct bands are evident. In Figure 3B and D, the histones appear to be mislabeled, and the band corresponding to H4 has been cut off. It would be beneficial to refer to Figure 3E for correct labeling to maintain consistency and accuracy across figures. 

Thank you for pointing this out. To avoid any confusion, we have delineated the H2B and H2A markers and indicate the band corresponding to H4.

(5) There are issues with the image quality in some blots; for instance, Figure 2EF and Figure 2D exhibit excessive contrast and pixelation, respectively. These issues could potentially obscure or misrepresent the data, and thus, adjustments in image processing are recommended to provide clearer, more accurate representations. 

Contrast adjustments were applied uniformly across each entire image and were not used to modify any specific region of the blot. We have corrected the issue of increased pixelation in Figure 2D. 

(6) The authors are recommended to provide detailed descriptions of the materials used, including catalog numbers and specific products, to allow for reproducibility and verification of experimental conditions. 

We have added the missing product specifications and catalog numbers to ensure clarity and reproducibility of the experiments.

(7) The identification of Setd2 as a tumor suppressor in KrasG12C-driven LUAD is a significant finding. However, the discussion on how this discovery could inspire future therapeutic approaches needs to be more balanced. The current discussion (Page 10) around the potential use of inhibitors is somewhat confusing and could benefit from a clearer explanation of how Setd2's role could be targeted therapeutically. It would be beneficial for the authors to explore both current and potential future strategies in a more structured manner, perhaps by delineating between direct inhibitors, pathway modulators, and other therapeutic modalities. 

SETD2 is a tumor suppressor in lung cancer (as we show here and many others have clearly established in the literature) and thus we would recommend avoiding a SETD2 inhibitor to treat solid tumors, as it could have a very much unwanted affect.  Our discussion addresses a different point regarding the relative importance of the enzymatic activity versus other, nonenzymatic functions of SETD2. We believe that a detailed exploration of the therapeutic potential of inhibiting SETD2 would be better suited in a review or a more therapy-focused manuscript.

eLife Assessment

In this important manuscript, Cassell and colleagues set out on a mechanistic and pharmacological exploration of an engineered chimeric small conductance calcium-activated potassium channel 2 (SK2). They show convincing evidence that the SK2 channel possesses a unique extracellular structure that modulates the conductivity of the selectivity filter, and that this structure is the target for the SK2 inhibitor apamin. While the interpretations are sound and the writing is clear, the manuscript would be strengthened by providing more detailed information for the electrophysiological experiments and the structural analyses attempted, in addition to relating dilation of the filter to mechanisms of inactivation in other potassium channels. This high-quality study will be of interest to membrane protein structural biologists, ion channel biophysicists, and chemical biologists, and will inform future drug development targeting SK channels.

Reviewer #1 (Public review):

The small conductance calcium-activated potassium channel 2 (SK2) is an important drug target for treating neurological and cardiovascular diseases. However, structural information on this subtype of SK channels has been lacking, and it has been difficult to draw conclusions about activator and inhibitor binding and action in the absence of structural information.

Here the authors set out to (1) determine the structure of the transmembrane regions of a mammalian SK2 channel, (2) determine the binding site of apamin, a historically important SK2 inhibitor whose mode of action is unclear, and (3) use the structural information to generate a novel set of activators/inhibitors that selectively target SK2.

The authors largely achieved all the proposed goals, and they present their data clearly.

Unable to solve the structure of the human SK2 due to excessive heterogeneity in its cytoplasmic regions, the authors create a chimeric construct using SK4, whose structure was previously solved, and use it for structural studies. The data reveal a unique extracellular structure formed by the S2-S3 loop, which appears to directly interact with the selectivity filter and modulate its conductivity. Structures of SK2 in the absence and presence of the activating Ca2+ ions both possess non-K+-selective/conductive selectivity filters, where only sites 3 and 4 are preserved. The S6 gates are captured in closed and open states, respectively. Apamine binds to the S2-S3 loop, and unexpectedly, induces a K+ selective/conductive conformation of the selectivity filter while closing the S6 gate.

Through high-throughput screening of small compound libraries and compound optimization, the group identified a reasonably selective inhibitor and a related compound that acts as an activator. The characterization shows that these compounds bind in a novel binding site. Interestingly, the inhibitor, despite binding in a site different from that of apamine, also induces a K+ selective/conductive conformation of the selectivity filter while the activator induces a non-K+ selective/conductive conformation and an open S6 gate.

The data suggest that the selectivity filter and the S6 gate are rarely open at the same time, and the authors hypothesize that this might be the underlying reason for the small conductance of SK2. The data will be valuable for understanding the mechanism of SK2 channel (and other SK subtypes).

Overall, the data is of good quality and supports the claims made by the authors. However, a deeper analysis of the cryo-EM data sets might yield some important insights, i.e., about the relationship between the conformation of the selectivity filter and the opening of the S6 gate.

Some insight and discussion about the allosteric networks between the SF and the S6 gate would also be a valuable addition.

Reviewer #2 (Public review):

Summary:

The authors have used single-particle cryoEM imaging to determine how small-molecule regulators of the SK channel interact with it and modulate their function.

Strengths:

The reconstructions are of high quality, and the structural details are well described.

Weaknesses:

The electrophysiological data are poorly described. Several details of the structural observations require a mechanistic context, perhaps better relating them to what is known about SK channels or other K channel gating dynamics.

The most pressing point I have to make, which could help improve the manuscript, relates to the selectivity filter (SF) conformation. Whether the two ion-bound state of SK2-4 (Figure 4A) represents a non-selective, conductive SF occluded by F243 or represents a C-type inactivated SF, further occluded by F243, is unclear. It would be important to discuss this. Reconstructions of Kv1.3 channels also feature a similar configuration, which has been correlated to its accelerated C-type inactivation.

Furthermore, binding of a toxin derivative to Kv1.3 restores the SF into a conductive form, though occluded by the toxin. It appears that apamin binding to SK2-4 might be doing something similar. Although I am not sure whether SK channels undergo C-type inactivation like gating, classical MTS accessibility studies have suggested that dynamics of the SF might play a role in the gating of SK channels. It would be really useful (if not essential) to discuss the SF dynamics observed in the study and relate them better to aspects of gating reported in the literature.

The SF of K channels, in conductive states, are usually stabilized by an H-bond network involving water molecules bridged to residues behind the SF (D363 in the down-flipped conformation and Y361). Considering the high quality of the reconstructions, I would suspect that the authors might observe speckles of density (possibly in their sharpened map) at these sites, which overlap with water molecules identified in high-resolution X-ray structures of KcsA, MthK, NaK, NaK2K, etc. It could be useful to inspect this region of the density map.

Reviewer #3 (Public review):

This is a fundamentally important study presenting cryo-EM structures of a human small conductance calcium-activated potassium (SK2) channel in the absence and presence of calcium, or with interesting pharmacological probes bound, including the bee toxin apamin, a small molecule inhibitor, and a small molecule activator. As efforts to solve structures of the wild-type hSK2 channel were unsuccessful, the authors engineered a chimera containing the intracellular domain of the SK4 channel, the subtype of SK channel that was successfully solved in a previous study (reference 13). The authors present many new and exciting findings, including opening of an internal gate (similar to SK4), for the first time resolving the S3-S4 linker sitting atop the outer vestibule of the pore and unanticipated plasticity of the ion selectivity filter, and the binding sites for apamin, one new small molecule inhibitor and another small molecule activator. Appropriate functional data are provided to frame interpretations arising from the structures of the chimeric protein; the data are compelling, the interpretations are sound, and the writing is clear. This high-quality study will be of interest to membrane protein structural biologists, ion channel biophysicists, and chemical biologists, and will be valuable for future drug development targeting SK channels.

The following are suggestions for strengthening an already very strong and solid manuscript:

(1) It would be good to include some information in the text of the results section about the method and configuration used to obtain electrophysiological data and the limitations. It is not until later in the text that the Qube instrument is mentioned in the results section, and it is not until the methods section that the reader learns it was used to obtain all the electrophysiological data. Even there, it is not explicitly mentioned that a series of different internal solutions were used in each cell where the free calcium concentration was varied to obtain the data in Figure1C. Also, please state the concentration of free calcium for the data in Figure 1B.

(2) The authors do a nice job of discussing the conformations of the selectivity filter they observed here in SK as they relate to previous work on NaK and HCN, but from my perspective the authors are missing an opportunity to point out even more striking relationships with slow C-type inactivation of the selectivity filter in Shaker and Kv1 channels. C-type inactivation of the filter in Shaker was seen in 150 mM K using the W434F mutant (PMC8932672) or in 4 mM K for the WT channel (PMC8932672), and similar results have been reported for Kv1.2 (PMC9032944; PMC11825129) and for Kv1.3 (PMC9253088; PMC8812516) channels. For Kv1.3, C-type inactivation occurs even in 150 mM K (PMC9253088; PMC8812516). Not unlike what is seen here with apamin, binding of the sea anemone toxin (ShK) with a Fab attached (or the related dalazatide) inserts a Lys into the selectivity filter and stabilizes the conducting conformation of Kv1.3 even though the Lys depletes occupancy of S1 by potassium (PMC9253088; PMC8812516). What is known about how the functional properties of SK2 channels (where the filter changes conformation) differ from SK4, where the filter remains conducting (reference 13)? Is there any evidence that SK2 channels inactivate? Or might the conformation of the filter be controlled by regulatory processes in SK2 channels? I think connecting the dots here would enhance the impact of this study, even if it remains relatively speculative.

Author response:

The following is the authors’ response to the original reviews

We thank the reviewers and editors for their careful consideration of our work and pointing out areas where the current version lacked clarity or necessary experiments. Based on the reviews we have made the following significant changes to the revised version:

(1) Revised the text to focus on the distinct pathogen responses to indole in isolation versus fecal material.

We believe the key takeaway from this work is that the native context of a given effector, in this case indole, can elicit markedly different bacterial responses compared to the pure compound in isolation. This is because natural environments contain multiple, often conflicting, stimuli that complicate predictions of overall chemotactic behavior. For example, while indole has been proposed to mediate chemorepulsion and contribute to colonization resistance against enteric pathogens, our findings challenge this model. We provide evidence that feces, the intestinal source of indole, actually induces attraction, and that indole taxis may in fact benefit the pathogen through prioritizing niches with low microbial competition. Put another way, the biological reservoir of indole, fecal material, generates an attraction response but indole regulated the degree of attraction.

Most current understanding of chemotaxis is based on responses to individual, purified effectors. Our study highlights the need to investigate chemotactic responses in the presence of native mixtures, which better reflect the complexity of natural environments and may reveal new functional insights relevant for disease.

Reviewer comments indicated that these core points above were not clearly conveyed in the previous version, and that the manuscript's logical flow needed improvement. In this revised version, we have substantially rewritten the text and removed extraneous content to sharpen the focus on these central findings. We have also aligned our discussion more closely with the experimental data. While we appreciated the reviewers’ thoughtful suggestions, we chose not to expand on topics that fall outside the scope of our current experiments.

(2) Provide new chemotaxis data with mixtures of fecal effectors (Fig. 5).

Related to the above, the reviewers and editors brought up concerns that our discovery of pathogen fecal attraction was underexplored. Although we showed Tsr to be important for mediating fecal attraction, even the tsr mutant showed attraction to a lesser degree, and the reviewers noted that we did not identify what other fecal attractants could be involved.

Fecal material is a complex biological material (as noted by Reviewer 3) and contains effectors already characterized as chemoattractants and chemorepellents. It would be ideal to be able to perform some experiment where individual effectors are removed from fecal material and then quantify chemotaxis. We considered methods to do this but ultimately found this approach unfeasible. Instead, we employed a reductionist approach and developed a synthetic approximate of fecal material containing a mixture of known chemoeffectors at fecal-relevant concentrations (Fig. 5). We used this defined system as a way to test the specific roles of the Tsr effectors L-Ser (attractant) and indole (repellent) in relation to glucose, galactose, and ribose (sensed through the chemoreceptor Trg), and L-Asp (sensed through the chemoreceptor Tar). We chose these effectors as they have reasonable structure-function relationships established in prior work, and had information available about their concentrations in fecal material. We present these data as a new Figure 5, and also provide videos clearly showing the responses to each treatment (Movies 7-10).

This defined system provided several new insights that help understand and model indole taxis amidst other fecal effectors. First, the complete effector mixture, like fecal treatment, elicits attraction. Second, L-Ser is able to negate indole chemorepulsion in cotreatments of the two effectors, and also other chemoattractants in the absence of L-Ser also negate this repulsion, albeit to a lesser degree, helping to explain why the tsr mutant still shows attraction to fecal material. Lastly, we also show that the degree of attraction in this system is controlled by indole, with mixtures containing greater indole showing less attraction. We feel this is an important addition to the study because it provides a new view on how indole-taxis functions in pathogen colonization; rather than causing the pathogen to swim away (like pure indole does) indole helps the pathogen rank and prioritize its attraction to fecal effector mixtures, biasing navigation toward lower indolecontaining niches.

We also acknowledge that this defined system does not capture all possible interactions. Indeed, there are even a few chemoreceptors in Salmonella for which the sensing functions remain poorly understood. Nonetheless, we believe the data offer mechanistic context for understanding fecal attraction and suggest that factors beyond Tsr, L-Ser, and indole also contribute to the observed behaviors, aligning with other data we present.

(3) Provide new data that show that E. coli MG1655, and disease-causing clinical isolate strains of the Enterobacteriaceae Tsr-possessing species E. coli, Citrobacter koseri, and Enterobacter cloacae exhibit fecal attraction (Fig. 4).

An important new finding from this study is our direct test of whether indole-rich fecal material elicits repulsion. Contrary to expectations, given that for E. coli indole is a wellcharacterized strong chemorepellent, we show that fecal material instead elicits attraction in non-typhoidal Salmonella.

Reviewers raised the question of whether our observations regarding indole taxis and attraction to indole-rich feces in Salmonella are similar or relevant to E. coli. While a full dissection of indole taxis in E. coli is beyond the scope of this study and has been the focus of extensive prior research, we sought to address this point by examining whether other enteric pathogens respond similarly to the native indole reservoir, fecal material. To this end, we present new data demonstrating that, like S. Typhimurium, E. coli and other representative enteric pathogens and pathobionts possessing Tsr are also attracted to indole-rich feces (Fig. 4, Movies 4–6, Fig. S4).

Notably, these new results represent some of the first characterizations of chemotactic behavior in the clinical isolates we examined, including E. coli NTC 9001 (a urinary tract infection isolate), Citrobacter koseri, and Enterobacter cloacae, adding another element of novelty to this work.

(4) Repeated all of the explant Salmonella Typhimurium infection studies and added a new experimental control competition between WT and an invasion-deficient mutant (invA).

Although our new colonic explant system was noted as a novelty and strength of this work, it was also seen as a weakness in that some of the results were surprising and difficult to link to chemotactic behavior. Reviewer 3 also brought up the need to be clear about our usage of the term ‘invasion’ in reference to S. Typhimurium entering nonphagocytic host cells, and requested we test an invasion-inhibited mutant (which we do in new experiments, now Fig. S1). We also note that some of the interpretations of these data were made challenging by result variability.

To help address these issues we performed additional replicates for all of our explant experiments (contained within Figure 1, Fig. S1-S2, and Data S1), to provide greater power for our analyses. These new data provide a clearer view of this system that revise our interpretations from the prior version of this study. While treatment with indole alone does suppress the WT advantage over chemotactic mutants for both total colonization and cellular invasion, essentially all other treatments have a similar result with a timedependent increase in both colonization and invasion, dependent on chemotaxis and Tsr. A remaining unique feature of fecal treatment is an increase in the cellular invaded population of the cells at 3 h post-infection. As requested by Reviewer 3, we provide new experimental data showing that in competitions between WT and an invasion-deficient mutant (invA), with fecal material pretreatment, we see the WT has an advantage only for the gentamicin-treated qualifications, providing some support that our model selects for the invaded sub-population. Although we note that the invA still can invade through alternative mechanisms (as discussed in earlier work such as here: https://doi.org/10.1111/1574-6968.12614), so the relative amount of presumed cellular invasion is less than WT, and not zero, in our experiments (Fig. S1).

One point of confusion in the previous version of the text was the assay design for the explant experiments, which is important to understand in order to interpret the results. During the explant infection bacteria are not immersed in the effector treatment solution, rather the tissue is soaked in the effector solution beforehand and then exposed to a 300 µl buffer solution containing the bacteria. This means that the bacteria experience only the residue of that treatment at concentrations far lower. We have added clarity about this through revising Fig. 1 to include a conceptual diagram of the assay (Fig. 1C), and added a new supplementary Fig. S5 that summarizes the explant data in this same conceptual model. We provide detail on the method in the text in lines 115-137. In describing the results, and synthesizing them in the discussion, we now state:

Line 112: “This establishes a chemical gradient which we can use to quantify the degree to which different effector treatments are permissive of pathogen association with, and cellular invasion of, the intestinal mucosa (Fig. 1C).”

And, a new section in the discussion devoted to describing the explant infections:

Line: 366: “Our explant experiments can be thought of as testing whether a layer of effector solution is permissive to pathogen entry to the intestinal mucosa, and whether chemotaxis provides an advantage in transiting this chemical gradient to associate with, and invade, the tissue (Fig. 1C, Fig. S5).”

As mentioned above, we have honed the text to focus on the disparity between the effects of indole alone versus treatments with indole-rich feces to help clarify how these data advance our understanding of the indole taxis in directing pathogenesis. While our explant studies still confirm the role of factors other than L-Ser, indole, and Tsr in directing Salmonella infection and cellular invasion, we now include further analyses of other fecal effectors (described above) that provide some insights into how fecal effectors have some redundancy in their impact.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study shows, perhaps surprisingly, that human fecal homogenates enhance the invasiveness of Salmonella typhimurium into cells of a swine colonic explant. This effect is only seen with chemotactic cells that express the chemoreceptor Tsr. However, two molecules sensed by Tsr that are present at significant concentrations in the fecal homogenates, the repellent indole and the attractant serine, do not, either by themselves or together at the concentrations in which they are present in the fecal homogenates, show this same effect. The authors then go on to study the conflicting repellent response to indole and attractant response to serine in a number of different in vitro assays.

Strengths:

The demonstration that homogenates of human feces enhance the invasiveness of chemotactic Salmonella Typhimurium in a colonic explant is unexpected and interesting. The authors then go on to document the conflicting responses to the repellent indole and the attractant serine, both sensed by the Tsr chemoreceptor, as a function of their relative concentration and the spatial distribution of gradients.

Thank you for your summary and acknowledgement of the strengths of this work. We hope the revised text and additional data we provide further improve your view of the study.

Weaknesses:

The authors do not identify what is the critical compound or combination of compounds in the fecal homogenate that gives the reported response of increased invasiveness. They show it is not indole alone, serine alone, or both in combination that have this effect, although both are sensed by Tsr and both are present in the fecal homogenates. Some of the responses to conflicting stimuli by indole and serine in the in vitro experiments yield interesting results, but they do little to explain the initial interesting observation that fecal homogenates enhance invasiveness.

Thank you for noting these weaknesses. We have provided new data using a defined mixture of fecal effectors to further investigate the roles of L-Ser, indole, and other effectors present in feces that we did not initially study. We have refined our discussion of these results to hopefully improve the clarity of our conclusions. We show now both in explant studies (Fig. 1I) and chemotaxis responses to a defined fecal effector system (Fig. 5) that L-Ser is able to abolish both the suppression of indole-mediated WT advantage and also indole chemorepulsion, respectively. We also show the latter can be accomplished by other fecal chemoattractants (Fig. 5). This is in line with our earlier finding that Tsr, the sensor of indole and L-Ser, is an important mediator of fecal attraction but not the sole mediator.

As this reviewer points out, there are indeed other factors mediating invasion that we do not elucidate here, but we do note these possibilities in the text (lines: 125-127):

“This benefit may arise from a combination of factors, including sensing of host-emitted effectors, redox or energy taxis, and/or swimming behaviors that enhance infection [5,30,31,35].”

Reviewer #2 (Public review):

Summary:

The manuscript presents experiments using an ex vivo colonic tissue assay, clearly showing that fecal material promotes Salmonella cell invasion into the tissue. It also shows that serine and indole can modulate the invasion, although their effects are much smaller. In addition, the authors characterized the direct chemotactic responses of these cells to serine and indole using a capillary assay, demonstrating repellent and attractant responses elicited by indole and serine, respectively, and that serine can dominate when both are present. These behaviors are generally consistent with those observed in E. coli, as well as with the observed effects on cell invasion.

Strengths:

The most compelling finding reported here is the strong influence of fecal material on cell invasion. Also, the local and time-resolved capillary assay provides a new perspective on the cell's responses.

Thank you for acknowledging these aspects of the study.

Weaknesses:

The weakness is that indole and serine chemotaxis does not seem to control the fecal-mediated cell invasion and thus the underlying cause of this effect remains unclear.

In addition, the fact that serine alone, which clearly acts as a strong attractant, did not affect cell invasion (compared to buffer) is somewhat puzzling. Additionally, wild-type cells showed nearly a tenfold advantage even without any ligand (in buffer), suggesting that factors other than chemotaxis might control cell invasion in this assay, particularly in the serine and indole conditions. These observations should probably be discussed.

Addressed above.

Final comment. As shown in reference 12, Tar mediates attractant responses to indole, which appear to be absent here (Figure 3J). Is it clear why? Could it be related to receptor expression?

Thank you for noting this. We now mention this in the discussion. In the course of this work, we encountered a number of apparent inconsistencies, or differences, between what we were observing with S. Typhimurium and what had been reported previously in studies of Tsr function in E. coli. We indeed noted that some studies had investigated a role of Tar for indole taxis (in E. coli), hence why we determined whether, and confirmed, that Tsr is required for indole taxis for S. Typhimurium (Fig. 6).

We do not know the reason for this apparent difference between the two bacteria, but we have previously shown with our same strain of S. Typhimurium IR715, under the same growth assay, and preparation protocol, that L-Asp is a strong chemoattractant for both WT and the tsr mutant (see Glenn et al. 2024, eLife, Fig. 5G: https://iiif.elifesciences.org/lax:93178%2Felife-93178-fig5-v1.tif/full/1500,/0/default.jpg).

This supports that this strain of Salmonella indeed has a functional Tar present and is expressed at a level sufficient for sensing L-Asp. So, if Tar generally mediates indole sensing we do not know why we would not see that in Salmonella. Hence, we do not see any role for Tar in indole chemorepulsion in our strain of study, which is different than reported for E. coli, but we cannot confirm the reason.

Reviewer #3 (Public review):

Summary:

In this manuscript, Franco and colleagues describe careful analyses of Salmonella chemotactic behavior in the presence of conflicting environmental stimuli. By doing so, the authors describe that this human pathogen integrates signals from a chemoattractant and a chemorepellent into an intermediate "chemohalation" phenotype.

Strengths:

The study was clearly well-designed and well-executed. The methods used are appropriate and powerful. The manuscript is very well written and the analyses are sound. This is an interesting area of research and this work is a positive contribution to the field.

Thank you for your comments.

Weaknesses:

Although the authors do a great job in discussing their data and the observed bacterial behavior through the lens of chemoattraction and chemorepulsion to serine and indole specifically, the manuscript lacks, to some extent, a deeper discussion on how other effectors may play a role in this phenomenon. Specifically, many other compounds in the mammalian gut are known to exhibit bioactivity against Salmonella. This includes compounds with antibacterial activity, chemoattractants, chemorepellers, and chemical cues that control the expression of invasion genes. Therefore, authors should be careful when making conclusions regarding the effect of these 2 compounds on invasive behavior.

Thank you for this comment, and we agree with your point. We hope we have revised the text and provided new data to address your concern. We have also chosen for clarity to keep our text close to our experimental data and so have refrained from speculating about some topics, even though you are absolutely correct about the immense complexity of these systems.

It is important that the word invasion is used in the manuscript only in its strictest sense, the ability displayed by Salmonella to enter non-phagocytic host cells. With that in mind, authors should discuss how other signals that feed into the control of Salmonella invasion can be at play here.

Thank you for your recommendation. We have revised the text to hopefully be clearer on our meaning of invasion in regard to Salmonella entering non-phagocytic host cells, essentially changing our usage to ‘cellular invasion’ throughout.

It is also a commonly-used phrase in reference to enteric infections and the colonization resistance conferred by the microbiome to refer to ‘invading pathogens’ (i.e. invasion in the sense of a new microbe colonizing the intestines), For instance, this recent review on Salmonella makes use of the term invading pathogen (https://www.nature.com/articles/s41579-021-00561-4). We acknowledge the confusion by this dual use of the term. We have mostly removed our statements using invasion in this context. We hope our language is clearer in this revised version.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

It was difficult to understand the true intent or importance of the study described in this manuscript. The first figure in the paper showed that a Salmonella Typhimurium strain lacking either CheY, and thus incapable of any chemotaxis, or the Tsr chemoreceptor, and thus incapable of sensing serine or indole, was modestly inferior to the wild-type version of that strain in invading the cells of a swine colonic explant. It then showed that, in the presence of a human fecal homogenate, the wild-type strain had a much greater advantage in invading the colonic cells. Thus, the presence of the fecal homogenate significantly increased invasiveness in a way that depends on chemotaxis and the Tsr chemoreceptor.

As human feces were determined to contain 882 micromolar indole and 338 micromolar serine, the effects of those concentrations of either indole or serine alone or in combination were tested. The somewhat surprising finding was that neither indole nor serine alone nor in combination changed the result from the experiment done with just buffer in the colonic explant.

The clear conclusion of this initial study is that both chemotaxis in general and chemotaxis mediated by Tsr improve the invasiveness of S. Typhimurium. They provide a much bigger advantage in the presence of human feces. However, two molecules present in the feces that are sensed by Tsr, serine, and indole, seem to have no effect on invasiveness either alone or in combination.

At this point, the parsimonious interpretation is that there is something else in human feces that is responsible for the increased invasiveness, and the authors acknowledge this possibility. However, they do not take what appears to be the obvious approach: to look for additional factors in human feces that might be responsible, either by themselves or in combination with indole and/or serine, for the increased invasiveness. Instead, they carry out a detailed examination of the counteracting effects of indole as a repellent and of serine as an attractant as a function of their relative concentrations and their spatial distributions.

Thank you for your comments. In our revised version, we have undertaken some additional studies of other fecal effectors that help better understand the relationship between L-Ser and indole, but also the roles of other chemoattractants (glucose, galactose, ribose, L-Asp) in mediating fecal attraction (Fig. 5). We agree with the reviewer and conclude that fecal attraction and the cell invasion phenotype mediated by fecal treatment are influenced by factors other than only Tsr, indole, and L-Ser. Our new data do show that L-Ser is sufficient to block both the invasion suppression effects of indole (negating the WT advantage) and also indole chemorepulsion, therefore making our detailed examination of the counteracting effects more relevant for understanding this system.

What they find is what other studies have shown, primarily with S. Typhimurium's relative, the gamma-proteobacterium Escherichia coli.

At high indole and low serine concentrations, the repulsion by indole wins out. At low indole and high serine concentrations, attraction by serine wins out. What is perhaps novel is what happens at an intermediate ratio of concentrations. Repulsion by indole dominates at short distances from the source, so there is a zone of clearing. At longer distances, attraction by serine dominates, so there is an accumulation of cells in a "halo" around the zone of clearing. Thus, assuming that serine and indole diffuse equally, the repulsive effect of indole dominates until its concentration falls below some critical level at which the concentration of serine is still high enough to exert an attractive effect.

They go on to show, using ITC, that serine binds to the periplasmic ligand-binding domain (LBD) of Tsr, something that has been studied extensively with very similar E. coli Tsr.

They also show that indole does not bind to the Tsr LBD, which also is known for E. coli Tsr.

This would be newsworthy only if the results were different for S. Typhimurium than for E. coli. As it is, it is merely confirmatory of something that was already known about Tsr of enteric bacteria.

An idea that the authors introduce, if I understand it correctly, is that a repellent response to something in feces, perhaps indole, drives S. Typhimurium chemotactically competent cells out of the colonic lumen and promotes invasion of the bacteria into the cells of the colonic lining. If the feces contain both an attractant and a repellent, bacteria might be attracted by the feces to the lining of the intestine and then enter the colonic cells to escape a repellent, perhaps indole. That is an interesting proposition.

In summary, I think that the initial experimental approach is fine. I do not understand the failure to follow up on the effect of the fecal homogenates in promoting invasion by chemotactic bacteria possessing Tsr. It seems there must be something else in the homogenates that is sensed by Tsr. Other amino acids and related compounds are also sensed by Tsr. Perhaps it is energy or oxygen taxis, which is partially mediated by Tsr, as the authors acknowledge.

Much of the work reported here is quasi-repetitive with work done with E. coli Tsr. Minimally, previous work on E. coli Tsr should be explained more thoroughly rather than dealt with only as a citation.

Thank you for your comments.

We would like to confirm our agreement that E. coli and S. enterica indeed possess similarities. They are Gammaproteobacteria and inhabit/infect the gut. But also we note they diverged evolutionarily during the Jurassic period (ca. 140 million years ago, see: PMC94677). In the context of colonizing humans, the former is a pathobiont, indoleproducer, and a native member of the microbiome, whereas the latter is a frank pathogen and does not produce indole. Hence, there are many reasons to believe one is not an approximate of the other, especially when it comes to causing disease.

We agree that much of what is known about indole taxis has come from excellent studies in well-behaved laboratory strains of E. coli, a powerful model. We believe that expanding this work to include clinically relevant pathogens is important for understanding its role in human disease. In this study, we contribute to that broader understanding by providing new mechanistic insights into Tsr-mediated indole taxis in S. Typhimurium, along with data demonstrating fecal attraction in other enteric pathogens and pathobionts. These findings help define a more general role for Tsr in enteric colonization and disease. While some of our results indeed confirm and extend prior findings, we respectfully believe that such confirmation in relevant pathogenic strains adds value to the field.

Regarding our ITC studies, to our knowledge no other study has investigated, using ITC whether indole does or does not bind the LBD (which we show it does not), nor investigated whether it interferes with L-Ser sensing (which we show it does not). Hence, these are not duplicate findings, although we do acknowledge this leaves the mechanism of indolesensing undiscovered. If we are incorrect in this regard, please provide us a citation and we will be happy to include it and revise our comments.

We now clarify in the text on lines 378-381: “While these leave the molecular mechanism of indole-sensing unresolved, it does eliminate two possibilities that have not, to our knowledge, been tested previously. Overall, our data add support to the hypothesis that a non-canonical sensing mechanism is employed by Tsr to respond to indole [8,18,69].”

Lastly, as noted by the reviewer, and which we mention in the text, essentially all prior studies on indole taxis were conducted in E. coli, and this is not what is new and novel about the work we present, which is focused on S. Typhimurium and testing the prediction that fecal indole protects against pathogen invasion. We have added in a few additional points of comparisons between our results and prior studies. While we appreciate that much understanding has come from E. coli as a model for indole taxis, we feel discussing prior work in extensive detail would be more suitable for a review and would occlude our new findings about Salmonella, and other enterics.

In an earlier version of the manuscript, we included more background on E. coli indole taxis. However, we found that the historical literature in this area was somewhat inconsistent, with different assays using varying time points and indole concentrations, often leading to results that were difficult to reconcile. Providing sufficient context to explain these discrepancies required considerable space and, ultimately, detracted from the focus of our current study. Hence, we have only brought in comparisons with E. coli where most relevant to the present work. Also, we provide new data that E. coli also exhibits fecal attraction, and so there is reason to believe the mechanisms we study here are also relevant to that system.

Some minor points

(1) Hyphens are not needed with constructs like "naturally occurring" or "commonly used".

Thank you. Revisions made throughout.

(2) The word "frank" as in "frank pathogen" seems odd. It seems "potent" would be better.

Thank you for this comment. Per your recommendation, we have removed this term.

The term ‘frank pathogen’ is standard usage in the field of bacterial pathogenesis in reference to a microbe that always causes disease in its host (in this case humans) and causes disease in otherwise healthy hosts (example: https://www.sciencedirect.com/science/article/pii/S1369527420300345). We actually used this specific term to distinguish an aspect of novelty of our study because E. coli can, sometimes, be a pathogen (i.e. a pathobiont) and of course E. coli indole taxis has been previously studied. Ours is the first study of indole taxis in a frank pathogen.

(3) It is unnecessary to coin a new word, chemohalation, to describe a phenomenon that is a simple consequence of repulsion by higher concentrations of a repellent and attraction by lower concentrations of attractant to generate a halo pattern of cell distribution.

Thank you for your opinion on this. We have softened our statements on this point, and in the newly revised version of the text less space is devoted to this idea. We now state in line 304-307:

“There exists no consensus descriptor for taxis of this nature, and so we suggest expanding the lexicon with the term “chemohalation,” in reference to the halo formed by the cell population, and which is congruent with the commonly-used terms chemoattraction and chemorepulsion.”

We appreciate the reviewer’s perspective and agree that the behavior we describe can be viewed as the result of competing attractant and repellent cues. However, we find that the traditional framework of “chemoattraction” and “chemorepulsion” is often insufficient to describe the spatial positioning behaviors we observe in our system. In our experience presenting and discussing this work, especially with audiences outside the chemotaxis field, it has been challenging to convey these dynamics clearly using only those two terms.

For this reason, we introduced the term chemohalation to describe this more nuanced behavior, which appears to reflect a balance of signals rather than a simple unidirectional response. More bacteria enter the field of view, but they are clearly positioned differently than regular ‘chemoattraction.’ We also note that Reviewers 2 and 3 did not raise concerns about the term, and after careful consideration, we have opted to retain it in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

Lines 143-156 seem somewhat overcomplicated and may be confusing. For example: in line 143: "However, when colonic tissue was treated with purified indole at the same concentration, the competitive advantage of WT over the chemotactic mutants was abolished compared to fecaltreated tissue...". But indole was tested alone, so it did not abolish the response; rather the absence of fecal material did.

We appreciate your point. We have made revisions throughout to help improve the clarity of how we discuss the explant infection data and provide new visuals to help explain the experiment and data (Fig. 1C, Fig. S5).

Reviewer #3 (Recommendations for the authors):

(1) Line 46 - Are references 9-11 really about topography?

Thank you. You are correct. Revised and eliminated this statement.

(2) Lines 87-89 - It seems to me that a bit more information on this would be helpful to the reader.

In our revision of the text, to make it more centered on our primary findings of the differences between indole taxis when indole is the sole effector versus amidst other effectors, we have removed this section.

(3) Line 112 - When mentioning the infection of the cecum and colon, authors should specify that this is in mice.

Thank you for this comment. In our revised version we provide references both for animal model infections and work in human patients (ex: https://www.sciencedirect.com/science/article/abs/pii/S0140673676921000)

We have revised our statement to be (Line 99-100: “Salmonella Typhimurium preferentially invades tissue of the distal ileum but also infects the cecum and colon in humans and animal models [42–46].”

(4) Lines 122-123 - Authors state that "This experimental setup simulates a biological gradient in which the effector concentration is initially highest near the tissue and diffuses outward into the buffer solution.". Was this experimentally demonstrated? If not, authors should tone this down.

We have removed this comment and instead present a conceptual diagram illustrating this idea (Fig. 1C). Also, addressed by above.

(5) When looking at the results in Figure 1, I wonder what the results of this experiment would be if the authors tested an invasion mutant of Salmonella. In a strain that is able to perform chemotaxis (attraction and repulsion) but unable to actively invade, would there be a phenotype here? Is it possible that the fecal material affects cellular uptake of Salmonella, independently of active invasion? I don't think the authors necessarily need to perform this experiment, but I think it could be informative and this possibility should at least be discussed.

Thank you for your comments and suggestions. We have included new data of an explant co-infection experiment with WT and an invasion-deficient mutant invA (Fig. S1). Under these conditions, WT exhibits an advantage in the gentamicin-treated homogenate, but not the untreated homogenate, suggestive of an advantage in cellular invasion.

However, we did not repeat all experiments with this genetic background. We felt that would be outside the scope of this work, and would probably require dual chemotaxis/invA deletions to assess the impact of each, which also could be difficult to interpret. The hypothesis mentioned by the Reviewer is possible, but we were not able to devise a way to test this idea, as it seems we would need to deactivate all other mechanisms of Salmonella invasion.

(6) Lines 137-140 - Because this is a competition experiment and results are plotted as CI, the reader can't readily assess the impact of human feces on invasion by WT Salmonella.

Thank you for pointing this out. We want to mention that the data are plotted as CI in the main text, but the supplemental contains the disaggregated CFU data (Fig. S1-2) and the numerical values (Data S1).

Please include the magnitude of induction in this sentence, compared to the buffer control.

The text of this section has been changed to account for new data.

Additionally, although unlikely, the presence of the chemotaxis mutants in the same infection may be a confounding factor. In order to irrefutably ascertain that feces induces invasion, I suggest authors perform this experiment with the wildtype strain (and mutant) alone in different conditions.

Thank you for this suggestion, although after careful consideration we have decided not to repeat these explant studies with monoinfections. Coinfections are a common tool in Salmonella pathogenesis studies, including prior chemotaxis studies which our work builds upon (ex: https://pmc.ncbi.nlm.nih.gov/articles/PMC3630101/). The explant experiments, even controlling as many aspects as we did, still show lots of variability and one way to mitigate this is through competition experiments so that each strain experiences the same environment.

We agree that a cost of this approach is that one strain may affect the other, or may alter the environment in a way that impacts the other. Thus, the resulting data must also be understood through this lens. We have revised the text to stay closer to the competitive advantage phenotype.

(7) Line 150 - Authors state that bacterial loads are similar. However, authors should perform and report statistical analyses of these comparisons, at least in the supplementary data.

We have removed this statement as requested. We do note, however, that the mean CFU values across treatments at identical time points appear qualitatively similar, which is an observation that does not require statistical testing.

(8) Lines 154-154 - This seems incorrect, as the effect observed with the mixture of indole and serine is very similar to the addition of serine alone. Therefore, there was no "neutralization" of their individual effects.

We have revised this statement.

(9) Line 159-161 - I strongly suggest authors reword this sentence. I don't think this is the best way to describe these results. The stronger phenotype observed was with the fecal material. Therefore, it is the indole (alone) condition that does not "elicit a response". Focusing on indole too much here ignores everything else that is present in feces and also the fact that there was a drastic phenotype when feces were used.

Thank you for your opinion on this. We believe this is one of the ways in which our earlier draft was unclear. It was actually a primary motivation of this work to test whether there were differences in pathogen infection, mediated by chemotaxis, in the presence of indole as a singular effector or in its near-native context in fecal material, and our revised text centers our study around this question. We believe this distinction is important for the reasons mentioned earlier.

Relative to buffer treatment, indole changes the behavior of the system, eliminating the WT advantage, and this is the effect we refer to. We have made many revisions to the text of these sections and hope it better conveys this idea. We expect we may still have differences regarding the interpretation of these results, but regardless, thank you for your suggestions and we have tried to implement them to improve the clarity of the text.

(10) Line 162 - Again, I disagree with this. Indole does not have an effect to be cancelled out by serine.

Addressed above, and this text has been changed. Also, we provide new chemotaxis data that at fecal-relevant concentrations of indole and L-Ser, indole chemorepulsion is overridden (Fig. 5).

(11) Lines 166-168 - Again, this is a skewed analysis. Indole and serine could not possibly provide an "additive effect" since they do not provide an effect alone. There is nothing to be added.

This text has been deleted.

(12) Lines 168-170 - Most of the citations provided to this sentence are inadequate. Our group has previously shown that the mammalian gut harbors thousands of small molecules (Antunes LC et al. Antimicrob Agents Chemother 2011). You obviously do not have to cite our work, but there is significant literature out there about the complexity of the gut metabolome.

Thank you for this comment. We have revised this particular text, but do make mention of potential other effectors driving these effects, which was also requested by the other reviewers.

Your work and others indeed support there being thousands of molecules in the gut, but our work centers on chemotaxis, and bacteria have a small number of chemoreceptors and only sense a very tiny fraction of these molecules as effectors. Since the impacts of infection of the explants depends on chemotaxis, we keep our comments restricted to those, but agree that there are likely many interactions involved, such as those impacting gene expression.

Please note our more detailed description of the explant infection assay (and shown in Fig. 1C) that may change your view on the significance of non-chemotaxis effects. The bacteria only experience the effectors at low concentration, not the high concentration that is used to soak and prepare the tissue prior to infection.

(13) Figure 2 - The letter 'B' from panel B is missing.

Thank you very much for bringing this oversite to our attention. We have fixed this.

(14) Legend of Figure 3 - Panel J is missing a proper description. Figure legends need improvement in general, to increase clarity.

Thank you for noting this. This is now Fig. 6E. We have provided an additional description of what this panel shows. We have edited the legend text to read: “E. Shows a quantification of the relative number of cells in the field of view over time following treatment with 5 mM indole for a competition experiment with WT and tsr (representative image shown in F).”

We also have made other edits to figure legends to improve their clarity and add additional experimental details and context. By breaking up larger figures into smaller figures, we also hope to have improved the clarity of our data presentation.

(15) Lines 264-265 - Maybe I am missing something, but I do not see the ITC data for serine alone.

We have clarified in the text that this was measured in our previous study https://elifesciences.org/articles/93178). The present study is a ‘Research Advance’ article format, and so builds on our prior observation.

We have revised the text to read: “To address these possibilities, we performed ITC of 50 μM Tsr LBD with L-Ser in the presence of 500 μM indole and observed a robust exothermic binding curve and KD of 5 µM, identical to the binding of L-Ser alone, which we reported previously (Fig. 6H) [36].”

(16) Lines 296-297 - What is the effect of these combinations of treatments on bacterial cells? I commend the authors for performing the careful growth assays, but I wonder if bacterial lysis could be a factor here. I am not doubting the effect of chemotaxis, but I am wondering if toxic effects could be a confounding factor. For instance, could it be that the "avoidance" close to the compound source and subsequent formation of a halo suggest bacterial death and lysis? I suggest the authors perform a very simple experiment, where bacteria are exposed to the compounds at various concentrations and combinations, and cells are observed over time to ensure that no bacterial lysis occurs.

Thank you for mentioning this possibility. If we understand correctly, the Reviewer is asking if the chemohalation effect we report could be from the bacteria lysing near the source. Our data actually argue against this possibility through a few lines of evidence.

First, if this were the case in experiments with the cheY mutant, we would also see an effect near the source. But actually, in experiments with either the cheY mutant or the tsr mutant, neither of which can sense indole, the bacteria just ignore the stimulus and show an even distribution (see current Fig. 6F).

Second, our calculations suggest that in the chemotaxis assay (CIRA), the bacteria only experience rather low local concentration of indole, mostly I the nM concentration range, because as soon as the effector treatment is injected into the greater volume, it is immediately diluted. This means the local concentration is far below what we see inhibits growth of the cells in the long run and may not be toxic (Fig. 7, Fig. S3).

Lastly, in the representative video presented we can observe individual cells approach and exit the treatment (Movie 11). Due to the above we have not performed additional experiments to test for lysis.

(17) Lines 310-311 - Isn't this the opposite of the model you propose in Figure 5? The higher the concentration of indole in the lumen the more likely Salmonella is to swim away from it and towards the epithelium, favoring invasion, no?

We appreciate the opportunity to clarify this point and apologize for any confusion caused. In response, we have revised the text to place less emphasis on chemohalation, and the specific statement and model in question have now been removed. Instead, we provide a summary of our explant data in light of the other analyses in the study (Fig. S5).

What we meant here was in relation to the microscopic level, not whether or not a host/intestine is colonized. To put it another way, we think our data supports that the pathogen colonizes and infects the host regardless of indole presence, but it uses indole as a means to prioritize which tissues are optimal for colonization at the microscopic level. The prediction made by others was that bacteria swim away from indole source and therefor this could prevent or inhibit pathogen colonization of the intestines, which our data does not support.

(18) Lines 325-326 - Maybe, but feces also contain several compounds with antibacterial activity, as well as other compounds that could elicit chemorepulsion. This should be stated and discussed.

We have removed this statement since we did not explicitly test the growth of the bacteria with fecal treatments. We have refrained from speculating further in the text since we do not have direct knowledge of how that relationship with differing effectors could play out.

We agree with the reviewer that the growth assays are reductionist and give insight only into the two effectors studied. We provide evidence from several different types of enterics that they all exhibit fecal attraction, and it seems unlikely the bacteria would be attracted to something deleterious, but we have not confirmed.

(19) Lines 371-374 - How preserved (or not) is the mucus layer in this model? The presence of an inhibitory molecule in the lumen does not necessarily mean that it will protect against invasion. It is possible that by sensing indole in the lumen Salmonella preferentially swims towards the epithelium, thus resulting in enhanced evasion.

The text in question has been removed. However, we acknowledge the reviewer’s point, and that these explant tissues do not fully model an in vivo intestinal environment. Other than a gentle washing with PBS to remove debris prior to the experiment the tissue is not otherwise manipulated, and feasibly the mucus layer is similar to its in vivo state.

In mentioning this hypothesis about indole, which our data do not support, we were echoing a prediction from the field, proposed in the studies we cite. We agree with the reviewer that there were other potential outcomes of indole impacting chemotaxis and invasion, and indeed our data supports that.

(20) Lines 394-395 - The authors need to remember that the ability to invade the intestinal epithelium is not only a product of chemoattraction and repulsion forces. Several compounds in the gut are used by Salmonella as cues to alter invasion gene expression. See PMID: 25073640, 28754707, 31847278, and many others.

Thank for you for this point, and we now include these citations. We have revised the text in question, stating:

“In addition to the factors we have investigated, it is already well-established in the literature that the vast metabolome in the gut contains a complex repertoire of chemicals that modulate Salmonella cellular invasion, virulence, growth, and pathogenicity [79–81].”

Our intent is not to diminish the role of other intestinal chemicals but rather to put our new findings into the context of bacterial pathogenesis. We do provide evidence that specific chemoeffectors present in fecal material alter where bacteria localize through chemotaxis, which is one method of control over colonization.

(21) Line 408 - I think it could be hard to observe this using your experimental approach.

Because you need to observe individual cells, the number of cells you observe is relatively small. If, in a bet-hedging strategy, the proportion of cells that were chemoattracted to indole was relatively low you likely would not be able to distinguish it from an occasional distribution close to the repellent source. You may or may not want to discuss this.

Thank you for this observation. It is indeed challenging to both observe large scale population behaviors and also the behaviors of individual cells in the same experiment. Our ability to make this distinction is similar to the approach used in the study we cite, so that is our comparison.

But, if there was a subpopulation that was attracted we would predict a ‘bull’s-eye’ population structure, with some cells attracted and other avoiding the source, which we do not see - we see the halo. So, we find no evidence of the bet-hedging response seen in a different study using E. coli and using different time scales than we have.

(22) Lines 410-411 - What could the other attractants be? Would it be possible/desirable to speculate on this?

We have changed the text here, but we present new data that examines some of these other attractants (Fig. 5).

(23) Line 431 - What exactly do you mean by "running phenotype"? Please, provide a brief explanation.

We have removed this text, but a running phenotype means the swimming bacteria rarely make direction changes (i.e. tumbles), which has been associated with promoting contact with the epithelium, described in the references we cite. Hence, this type of swimming behavior could contribute to the effects we observe in the explant studies, potentially explaining some of the Tsr-mediated advantage that was not dependent on L-Ser/indole.

(24) Line 441 - Other work has shown that feces contain inhibitors of invasion gene expression. The authors should integrate this knowledge into their model. In fact, indole has been shown to repress host cell invasion by Salmonella, so it is important that authors understand and discuss the fact that the impact of indole is multifaceted and not only a reflection of its action as a chemorepellent. PMID: 29342189, 22632036.

We agree with the reviewer about this point, and mention this in the text (lines 55-57): “Indole is amphipathic and can transit bacterial membranes to regulate biofilm formation and motility, suppress virulence programs, and exert bacteriostatic and bactericidal effects at high concentrations [16–18,20–22].”

We have added in the references suggested.

What we test here is the specific hypothesis made by others in the field about indole chemorepulsion serving to dissuade pathogens from colonizing.

For instance, the statement from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190613

“Since indole is also a chemorepellent for EHEC [23], it is intriguing to speculate that in addition to attenuating Salmonella virulence, indole also attenuates the recruitment and directed migration of Salmonella to its infection niche in the GI tract.”

And from: https://doi.org/10.1073/pnas.1916974117

“We propose that indole spatially segregates cells based on their state of adaptation to repel invaders while recruiting beneficial resident bacteria to growing microbial communities within the GI tract.”

And

“Thus, foreign ingested bacteria, including invading pathogens such as E. coli O157:H7 and S. enterica, are likely to be prevented by indole from gaining a foothold in the mucosa.”

As shown by others, indole certainly does have many roles in controlling pathogenesis, and there are other chemicals we do not investigate that control invasion and bacterial growth, but we keep our statements here restricted to chemotaxis since that is what are experiments and data show.

(25) Line 472 - "until fully motile". How long did this take, how variable was it, and how was it determined?

Thank you for asking for this clarification. We have added that the time was between 1-2 h, and confirmed visually. Our methods are similar to those described in earlier chemotaxis studies (ex: 10.1128/jb.182.15.4337-4342.2000).

(26) Line 487 - I worry that the fact fecal samples were obtained commercially means that compound stability/degradation may be a factor to consider here. How long had the sample been in storage? Is this information available?

Thank you for this question. We agree that the fecal sample we used serves as a model system and we cannot rule out that handling by the supplier could potentially alter its contents in some way that would impact bacterial chemosensing. However, we note that the measurements of L-Ser and indole we obtained are in the appropriate range for what other studies have shown.

The fecal sample used for all work in the study were from a single healthy human donor, obtained from Lee Biosolutions (https://www.leebio.com/product/395/fecal-stool-samplehuman-donor-991-18). The supplier did not state the explicit date of collection, nor indicated any specific handline or storage methods that would obviously degrade its native metabolites, but we cannot rule that out. In our hands, the fecal sample was collected and kept frozen at -20 C. For research purposes, portions were extracted and thawed as needed, maintaining the frozen state of the original sample to limit degradation from freeze-thaws.

eLife Assessment

In this manuscript, Franco and colleagues present compelling evidence that fecal extracts containing high concentrations of indole, a known repellent, enhance rather than protect against invasion of colonic tissue by Salmonella. The authors describe important findings that lead to the conclusion that the competing effects of attractants present in fecal matter, including L-serine, also sensed by the Tsr chemoreceptor that senses indole, override the repulsive effect of indole.

Reviewer #1 (Public review):

Summary:

The study shows, perhaps surprisingly, that human fecal homogenates enhance the invasiveness of Salmonella typhimurium into cells of a swine colonic explant. This effect is only seen with chemotactic cells that express the chemoreceptor Tsr. However, two molecules sensed by Tsr that are present at significant concentrations in the fecal homogenates, the repellent indole and the attractant serine, do not, either by themselves or together at the concentrations in which they are present in the fecal homogenates, show this same effect. The authors then go on to study the conflicting repellent response to indole and attractant response to serine in a number of different in vitro assays.

Strengths:

The demonstration that homogenates of human feces enhance the invasiveness of chemotactic Salmonella Typhimurium in a colonic explant is unexpected and interesting. The authors then go on to document the conflicting responses to the repellent indole and the attractant serine, both sensed by the Tsr chemoreceptor, as a function of their relative concentration and the spatial distribution of gradients.

Weaknesses:

The authors do not identify what is the critical compound or combination of compounds in the fecal homogenate that gives the reported response of increased invasiveness. They show it is not indole alone, serine alone, or both in combination that have this effect, although both are sensed by Tsr and both are present in the fecal homogenates. Some of the responses to conflicting stimuli by indole and serine in the in vitro experiments yield interesting results, but they do little to explain the initial interesting observation that fecal homogenates enhance invasiveness.

Reviewer #3 (Public review):

Summary:

In this manuscript, Franco and colleagues describe careful analyses of Salmonella chemotactic behavior in the presence of conflicting environmental stimuli. By doing so, the authors describe that this human pathogen integrates signals from a chemoattractant and a chemorepellent into an intermediate "chemohalation" phenotype.

Strengths:

The study was clearly well-designed and well-executed. The methods used are appropriate and powerful. The manuscript is very well written, and the analyses are sound. This is an interesting area of research, and this work is a positive contribution to the field.

Weaknesses:

No significant weaknesses noted.

eLife Assessment

This is a potentially useful study that provides solid, yet confirmatory structural findings about the complex (FtsEX) that controls peptidoglycan remodeling during bacterial cell division. The authors capitalize on the fact that ATP binding stabilizes the FtsEX complex allowing structural characterization for this system. A model is then developed to explain ATP regulation but there is a gap between the model presented here and in vivo data reported previously.

Reviewer #1 (Public Review):

Summary:

In this paper, Li and colleagues overcome solubility problems to determine the structure of FtsEX bound to EnvC from E. coli.

Strengths:

The structural work is well done, and the work is consistent with previous work on the structure of this complex from P. aerugionsa.

Weaknesses:

The model does not take into account all the information that the authors obtained, as well as known in vivo data.

The work lacks a clear comparison to the Pseudomonas structure highlighting new information that was obtained so that it is readily available to the reader.

The authors set out to obtain the structure of FtsEX-EnvC complex from E. coli. Previously, they were unable to do so but were able to determine the structure of the complex from P. aeruginosa. Here they persisted in attacking the E. coli complex since more is known about its involvement in cell division and there is a wealth of mutants in E. coli. The structural work is well done and recapitulates the results this lab obtained with this complex from P. aeruginosa. It would be helpful to compare more directly the results obtained here with the E. coli complex with the previously reported P. aeruginosa complex - are they largely the same or has some insight been obtained from the work that was not present in the previous complex from P. aeruginosa. This is particularly the case in discussing the symmetrical FtsX dimer binding to the asymmetrical EnvC, since this is emphasized in the paper. However, Figures 3C & D of this paper appear similar to Figures 2D & E of the P. aeruginosa structure. Presumably, the additional information obtained and presented in Figure 4 is due to the higher resolution, but this needs to be highlighted and discussed to make it clear to a general audience.

The main issue is the model (Figure 6). In the model ATP is shown to bind to FtsEX before EnvC, however, in Figure 1c, it is shown that ADP is sufficient to promote binding of FtsEX to EnvC.

The work here is all done in vitro, however, information from in vivo needs to be considered. In vivo results reveal that the ATP-binding mutant FtsE(D162N)X promotes the recruitment of EnvC (Proc Natl Acad Sci U S A 2011 108:E1052-60). Thus, even FtsEX in vivo can bind EnvC without ATP (not sure if this mutant can bind ADP).

Perhaps the FtsE protein from E. coli has to have bound nucleotides to maintain its 3D structure.

Comments after revision:

The most interesting aspect of this complex is that it has yet to be determined the order of events in the ATPase cycle as the authors acknowledge. Although the authors have responded quite well to the comments, I am still worried about the significance of the in vitro results compared to the in vivo results reported by others. In vivo ATP binding does not appear required for complex formation (of course it is possible that ADP is responsible in vivo). Have the authors tried to solve the complex with ADP since they suggested that it is sufficient to hold the complex together). If possible, it would confirm the role of ATP binding by comparing the structures. Also, it is not clear if ADP binds to any of the mutants made by the Bernhardt lab (D162N, K41M). If they do not bind ADP then FtsEX without nucleotide is able to bind EnvC as the authors indicate is the case in Pseudomonas. It is also unclear the significance of the ATPase activity of FtsEX in vitro with or without EnvC. Could the activity be some basal activity that is not relevant to the in vivo situation. If EnvC caused FtsEX to hydrolyze ATP it would be a futile cycle as FtsEX and EnvC are localized to the septum long before they are involved septal hydrolysis.

Reviewer #2 (Public Review):

Summary:

Peptidoglycan remodeling, particularly that carried out by enzymes known as amidases, is essential for the later stages of cell division including cell separation. In E. coli, amidases are generally activated by the periplasmic proteins EnvC (AmiA and AmiB) and NlpD (AmiC). The ABC family member, FtsEX, in turn, has been implicated as a modulator of amidase activity through interactions with EnvC. Specifically, how FtsEX regulates EnvC activity in the context of cell division remains unclear.

Strengths:

Li et al. make two primary contributions to the study of FtsEX. The first, the finding that ATP binding stabilizes FtsEX in vitro, enables the second, structural resolution of full-length FtsEX both alone (Figure 2) and in combination with EnvC (Figure 3). Leveraging these findings, the authors demonstrate that EnvC binding stimulates FtsEX-mediated ATP hydrolysis approximately two-fold. The authors present structural data suggesting EnvC binding leads to a conformational change in the complex. Biochemical reconstitution experiments (Figure 5) provide compelling support for this idea.

Weaknesses:

The potential impact of the study is curtailed by the lack of experiments testing the biochemical or physiological relevance of the model which is derived almost entirely from structural data.

Altogether the data support a model in which interaction with EnvC, results in a conformational change stimulating ATP hydrolysis by FtsEX and EnvC-mediated activation of the amidases, AmiA and AmiB. However, the study is limited in both approach and scope. The importance of interactions revealed in the structures to the function of FtsEX and its role in EnvC activation are not tested. Adding biochemical and/or in vivo experiments to fill in this gap would allow the authors to test the veracity of the model and increase the appeal of the study beyond the small number of researchers specifically interested in FtsEX.

Comments after revision:

Although I appreciate the authors' desire to save future biochemical experiments for a separate publication, the lack of in vitro data verifying their model makes it challenging to reconcile with published studies from other groups. The other reviewer's point about EnvC activating FtsEX ATPase activity resulting in a futile cycle since both are recruited to the septum well before constriction, is a good example of the disconnect between the model presented here and in vivo data.

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this paper, Li and colleagues overcome solubility problems to determine the structure of FtsEX bound to EnvC from E. coli.

Strengths:

The structural work is well done and the work is consistent with previous work on the structure of this complex from P. aerugionsa.

Weaknesses:

The model does not take into account all information that the authors obtained as well as known in vivo data.

The work lacks a clear comparison to the Pseudomonas structure highlighting new information that was obtained so that it is readily available to the reader.

The authors set out to obtain the structure of FtsEX-EnvC complex from E. coli. Previously, they were unable to do so but were able to determine the structure of the complex from P. aeruginosa. Here they persisted in attacking the E. coli complex since more is known about its involvement in cell division and there is a wealth of mutants in E. coli. The structural work is well done and recapitulates the results this lab obtained with this complex from P. aeruginosa. It would be helpful to compare more directly the results obtained here with the E. coli complex with the previously reported P. aeruginosa complex - are they largely the same or has some insight been obtained from the work that was not present in the previous complex from P. aeruginosa. This is particularly the case in discussing the symmetrical FtsX dimer binding to the asymmetrical EnvC, since this is emphasized in the paper. However, Figures 3C & D of this paper appear similar to Figures 2D & E of the P. aeruginosa structure. Presumably, the additional information obtained and presented in

Figure 4 is due to the higher resolution, but this needs to be highlighted and discussed to make it clear to a general audience.

The main issue is the model (Figure 6). In the model ATP is shown to bind to FtsEX before EnvC, however, in Figure 1c it is shown that ADP is sufficient to promote binding of FtsEX to EnvC.

The work here is all done in vitro, however, information from in vivo needs to be considered. In vivo results reveal that the ATP-binding mutant FtsE(D162N)X promotes the recruitment of EnvC (Proc Natl Acad Sci U S A 2011 108:E1052-60). Thus, even FtsEX in vivo can bind EnvC without ATP (not sure if this mutant can bind ADP).

Perhaps the FtsE protein from E. coli has to have bound nucleotides to maintain its 3D structure.

Thank you for your thoughtful feedback and valuable suggestions. We have carefully revised the manuscript to address these concerns, incorporating additional analysis and discussion to enhance clarity and improve the accuracy of our interpretation.

Regarding the relationship between EnvC binding and nucleotide binding to FtsEX, our previous study on P. aeruginosa FtsEX demonstrated that FtsEX can bind EnvC even in the absence of nucleotide (PMID: 37186861, Fig. 3C). However, for E. coli FtsEX (Fig. S1 in this study), ATP is required to stabilize the complex in vitro, preventing us from directly testing whether EnvC binding is ATP-dependent. The reviewer raised an important point about the FtsED162N mutant study, from which previous studies suggests that this mutant may still retain ATP binding, as observed in its homolog MacB (PMID: 29109272, PMID: 32636250). Additionally, previous work (PMID: 22006325) has shown that the PLD domain of FtsX can bind EnvC directly, even in the absence of the NBD domain, a finding further supported by Crow’s lab (PMID: 33097670). Taken together, these studies indicate that EnvC binding to FtsEX is likely nucleotideindependent, while ATP binding primarily stabilizes FtsE dimerization, reinforcing FtsEX complex formation.

In line with these findings, our results suggest a stabilizing role of ATP in FtsEX assembly, whereas EnvC binding does not appear to be nucleotide-dependent. However, we acknowledge that the precise sequence of ATP binding and EnvC recruitment within the cell remains unresolved. To reflect this, we have revised the manuscript to incorporate these insights (L190-201, L445-451), clearly stated the limitations (L450-451, L887-890), and updated our model (Fig. 6) to avoid assigning a definitive sequence to EnvC and ATP binding.

Additionally, we have strengthened the structural comparison between E. coli and P. aeruginosa FtsEX, as the reviewer suggested. We have now included a detailed comparative analysis (L282-306, Fig. S9), which reveals that the transmembrane and nucleotide-binding domains are highly superimposable. The primary structural distinction lies in a slight tilting difference in the bound EnvC, which appears to stem from the conformation of the X-lobes within the PLD domains. Highlighting these differences helps clarify how our new structural data provide additional insights beyond what was previously observed in P. aeruginosa.

Reviewer #2 (Public Review):

Summary:

Peptidoglycan remodeling, particularly that carried out by enzymes known as amidases, is essential for the later stages of cell division including cell separation. In E. coli, amidases are generally activated by the periplasmic proteins EnvC (AmiA and AmiB) and NlpD (AmiC). The ABC family member, FtsEX, in turn, has been implicated as a modulator of amidase activity through interactions with EnvC. Specifically how FtsEX regulates EnvC activity in the context of cell division remains unclear.

Strengths:

Li et al. make two primary contributions to the study of FtsEX. The first, the finding that ATP binding stabilizes FtsEX in vitro, enables the second, structural resolution of fulllength FtsEX both alone (Figure 2) and in combination with EnvC (Figure 3). Leveraging these findings, the authors demonstrate that EnvC binding stimulates FtsEX-mediated ATP hydrolysis approximately two-fold. The authors present structural data suggesting EnvC binding leads to a conformational change in the complex. Biochemical reconstitution experiments (Figure 5) provide compelling support for this idea.

Weaknesses:

The potential impact of the study is curtailed by the lack of experiments testing the biochemical or physiological relevance of the model which is derived almost entirely from structural data.

Altogether the data support a model in which interaction with EnvC, results in a conformational change stimulating ATP hydrolysis by FtsEX and EnvC-mediated activation of the amidases, AmiA and AmiB. However, the study is limited in both approach and scope. The importance of interactions revealed in the structures to the function of FtsEX and its role in EnvC activation are not tested. Adding biochemical and/or in vivo experiments to fill in this gap would allow the authors to test the veracity of the model and increase the appeal of the study beyond the small number of researchers specifically interested in FtsEX.

Thank you for your thoughtful review and constructive feedback. We appreciate your recognition of our study’s contributions, particularly the structural resolution of fulllength E coli FtsEX, its interaction with EnvC, and our biochemical characterization of EnvC-stimulated ATP hydrolysis.

We understand the importance of further biochemical and in vivo validation to support our model. While our study primarily provides a structural framework for understanding FtsEX function, many key residues identified in our E. coli structures have already been tested in prior cell physiological studies. For example, residues critical for the FtsEXEnvC interaction were examined in our collaborator David Roper’s lab in collaboration with Crow’s lab (PMID: 33097670, L319-321).

With the structural blueprint provided by our full-length E. coli FtsEX-EnvC complex, we now have a foundation to explore several key functional aspects of this system. Future mutagenesis studies will help dissect the roles of specific residues in ATP binding/hydrolysis, coupling between the TMD and NBD domains, interactions between the PLD and TMD domains of FtsX, and signal transduction from the NBD, through the TMD and PLD to EnvC. Additionally, we aim to investigate how the symmetrical PLD domain recruits asymmetrical EnvC and how the dynamics of PLD of FtsX and CCD domains of EnvC contribute to the complex’s function.

As these experiments require specialized expertise in cell physiology and PG degradation assays, we are actively collaborating with experts in these areas to pursue them. We are committed to furthering this work and providing deeper biochemical and in vivo insights into the function of the FtsEX complex in cell division.

Reviewer #1 (Recommendations For The Authors):

(1) As mentioned, two things could strengthen the paper. One is to take into account that ADP or possibly nucleotide-free FtsEX can bind EnvC. The second is to highlight any differences between the structures from E. coli and P. aeruginosa.

Thank you for these insightful suggestions. In our revision, we have (1) carefully considered the possibility of EnvC binding independently of nucleotide and (2) have incorporated a detailed comparison between the newly obtained E. coli FtsEX/EnvC structure and that of P. aeruginosa.

Regarding the relationship between EnvC binding and ATP binding to FtsEX, our previous study on P. aeruginosa FtsEX demonstrated that FtsEX can bind EnvC in the absence of nucleotide (PMID: 37186861, Fig 3C). However, for E. coli FtsEX systems (Fig S1 in this study), ATP is necessary for FtsEX stabilization in vitro, which limited us from further directly testing whether EnvC binding is ATP-dependent or not.

We appreciate the reviewer’s reference to the FtsE(D162N) mutant study. Previous studies suggest that D162N mutant may still retain ATP binding, similar to its homolog MacB (PMID: 29109272; PMID: 32636250). Additionally, findings from Winkler’s lab (PMID: 22006325) indicate that the PLD domain of FtsX can bind EnvC directly, even in the absence of the NBD domain, a result further supported by study from Crow’s lab (PMID: 33097670). Collectively, these studies suggest that EnvC binding to FtsEX is nucleotide-independent, while ATP binding likely stabilizes FtsE dimerization, thereby reinforcing FtsEX complex formation, as the reviewer suggested.

Thus, consistent with previous studies, our results so far support a stabilizing role of ATP in FtsEX assembly, while EnvC binding itself does not appear to be nucleotidedependent. However, the available evidence remains inconclusive, and the precise sequence of ATP binding and EnvC recruitment within the cell is still unclear. In our revision, we have now incorporated these analyses in L190-201 and L445-451, stated the limitations (L450-451 and L887-890) and updated our model (Fig. 6) to avoid assigning a definitive sequence to EnvC and ATP binding.

For the structural comparison between E. coli and P. aeruginosa FtsEX, we have added a detailed analysis in L282-306 and Supplementary figure 9. In summary, we found that the transmembrane domain and nucleotide-binding domain are highly superimposable, with only minor differences observed. The primary distinction lies in a slight tilting difference in the bound EnvC, which appears to come from the conformation of the X-lobes within the PLD domains.

(2) Line 129. Concerning the role of ATP in stabilizing the complex. It is clear that ADP can do it as well (Figure 1c). This is mentioned in line 131 but not considered in the model.

Thank you for pointing this out. We have now revised the relevant sections in the manuscript (L190-201 and L445-451) and updated the model (Fig 6) accordingly. In the revised manuscript, we acknowledge the reviewer’s point that ATP may primarily serve to stabilize the FtsEX complex. Additionally, we have explicitly clarified that EnvC binding appears to be nucleotide-independent. Regarding the model, we state that the current study does not provide sufficient evidence to determine the precise sequence of EnvC and ATP binding to FtsEX in the cell. We believe these revisions, incorporating the reviewer’s suggestions, improve the accuracy of our interpretation.

Reviewer #2 (Recommendations For The Authors):

(1) The introduction is written for an audience with significant expertise in bacterial PG synthesis and is thus difficult for those outside the field to follow.

Thank you for your feedback. We have revised the introduction, particularly the first passage (L51–63), to improve readability and make it more accessible to a broader audience.

(1) Figure 1: Please express ATP hydrolysis data in ATP/FtsEX/minute. (It is currently nmol/mg/min).

Changed accordingly, thank you!

(2) Figure 4: Please clarify in the legend and in the figure itself which structures correspond to full-length data from cryoEM data or truncated (FtsEX-PLD domain) protein data from previous crystallographic studies.

Both the FtsEX and FtsEX/EnvC complex structures shown in Figure 4 were obtained from our cryo-EM data using full-length proteins. To avoid any confusion, we have now further clarified this in the figure legend (L857).

eLife Assessment

This valuable work used molecular biology, cell biology, and genetic approaches to unravel individual genes and potential pathways that contribute to paternal mitochondrial inheritance using C. elegans as the model organism. Their microscopy method is cutting edge, with sufficient biological replicates, proper control, and appropriate statistics. These findings are convincing and are of general interest for understanding mitochondrial inheritance in C. elegans, which could have implications for understanding similar biological processes in other organisms.

Reviewer #1 (Public review):

Summary:

Melin et al. developed a quantitative assay to measure the fate of paternal mitochondria after fertilization. They combine this assay with C. elegans genetics to show that multiple genes contribute to paternal mitochondrial elimination. However, despite their claims, they unconvincingly place these genes into distinct pathways and fail to determine whether additional unknown genes are involved in the process.

Strengths:

Melin et al. develop a new assay to quantify the fate of paternal mitochondria during embryonic development in C. elegans. They use complex C. elegans genetics to disrupt 5 different genes and nicely measure their contributions to paternal mitochondrial elimination. In an attempt to place these genes into pathways, the authors interrupt genes in various combinations and measure paternal mitochondrial persistence. The authors discovered that disrupting 4 of the genes known to contribute to paternal mitochondrial elimination still resulted in paternal mitochondrial elimination, suggesting that more genes also contribute to this process. Finally, the authors discovered that pink-1, which had previously been discounted, indeed contributes to paternal mitochondrial elimination when the major pathway involving allo-1 is also disrupted.

Weaknesses:

In the introduction, the authors describe the importance of studying the maternal inheritance of mitochondrial DNA. However, the authors mostly study the inheritance of paternally-derived mitochondrial proteins (HSP6::GFP). While the authors do use a PCR approach to measure paternal mitochondrial DNA, their results are not as quantitative and thorough (applied to multiple mutant combinations) as their microscopy assay. Using their microscopy assay, the authors did not combine mutants for all 5 genes. Therefore, they cannot support or discount the possibility that undiscovered paternal mitochondrial elimination mechanisms exist. The author's genetic epistasis experiments are incomplete and occasionally improperly interpreted (as described below). Finally, the authors were unable to achieve paternal mitochondrial transmission to the F2 generation (which admittedly has not been achieved in any experimental system).

Reviewer #2 (Public review):

Summary:

Mitochondrial DNA (mtDNA) is exclusively maternally transmitted in almost all species. Paternal mitochondria, with their mtDNA, must be rapidly degraded after fertilisation to prevent their transmission to progeny, which could lead to subsequent detrimental mito-nuclear incompatibilities. Multiple layers of mechanisms contribute to blocking the transmission of paternal mitochondria and their mtDNA to progeny. Endonuclease activity and mitophagy form a part of these strategies. However, other key regulatory mechanisms remain to be elucidated, as inactivating endonuclease and mitophagy activity only delays the clearance of paternal mitochondria. In this study, the authors mainly focused on genes involved in endonuclease function (csp-6) and autophagy (allo-1) in C. elegans, demonstrating a synergic genetic interaction that potentialize their activity. They also revealed a contribution by pink-1/pink1, in the absence of allo-1.

Strengths:

The majority of data relies on confocal microscopy images and corresponding image analysis and quantification. Images are clear, and quantifications are supported by several biological replicates of >10 n and standard statistical tests. Mutants used were obtained from the Caenorhabditis Genetics Center (CGC) and were previously validated and confirmed by the C. elegans community. The scientific approach is solid and rigorous and in line with state-of-the-art C. elegans methods. Proper controls have been performed to rule out the effect of animal viability on observed results or to confirm the staining validity of TUBES on subcellular structures surrounding paternal mitochondria. Controls validating uaDf5 PCR specificity were conducted.

Weaknesses:

However, the embryonic expression of paternally contributing genes in feminised animals cannot be completely ruled out, as RNAi was used to alter gene expression levels. An issue inherent to RNAi approaches. Also, the impact of pink-1/pink1 is significant, but there is a lack of evidence demonstrating its mitophagic function.

Goal achievements and data supportive of conclusions:

In the first part of the study, the authors strongly and clearly demonstrate the synergistic interaction between the csp-6 and allo-1 in delaying paternal mitochondria degradation and associated mtDNA in the fertilised egg. In wild-type animals, paternal mitochondria are visible (using a mitochondrial HSP-::GFP marker) until the 4-cell stage embryo. In the csp-6; allo-1 double mutant genetic background, paternal mitochondria very significantly perdures until the 2-fold embryonic stage. The uaDf5 mitochondrial deletion, detectable by PCR, that was introduced by crossing with a male, followed the same trend. In addition, loss of fncd-1/fndc1 and phb-2 did not extend the perdurance of paternal mitochondria. In the second part of the study, the authors demonstrate a contribution of the loss of pink-1/pink1, in the absence of allo-1, in delaying paternal mitochondria degradation until the 100-cell stage. Overall, the conclusions are in accordance with the data shown.

Impact on the field:

Endonuclease activity and mitophagy aren't sufficient to prevent the transmission of paternal mitochondria and associated mtDNA to progeny, but they still contribute significantly to regulating the perdurance of paternal mitochondria in early embryos. Understanding how these two functions work in concert to potentialize their activity is important, as they could potentially be manipulated/enhanced to improve paternal mitochondrial degradation in the future. Here, the authors demonstrate a detailed synergistic genetic interaction between these functions. Also, they pointed out a new potential contribution of pink-1/pink1, which may underlie a potentially more complex mitophagic protective function.

Reviewer #3 (Public review):

Summary:

The present study examines the cooperation among four allophagy/mitophagy factors, ALLO-1, CPS-6, FNDC-1, and PHB-2, implicated in the elimination of the sperm-derived mitochondria in C. elegans embryos. The key finding of the cumulative effect of ALLO-1 and CPS-6 inactivation causing delayed sperm mitophagy is significant for the understanding of mitochondrial inheritance in the nematode model and in general. Below are some specific suggestions on how the impact of the article could be elevated:

Abstract:

The authors should shorten the description of previously identified mitophagy factors and provide more detail on the present study results. An impact statement should be added at the end, with significance for understanding mitochondrial inheritance across taxa, all the way to mammals/humans.

Introduction:

The authors should provide more details on ALLO-1 and its interaction with LC-3. Also, it should be specified which of those previously identified allophagy factors are unique to worms and which ones are conserved. See also my comment below about including a diagram and a table of pathways and determinants involved in allophagy/paternal mitophagy.

Results:

If I understand the mtDNA data correctly, paternal mtDNA is maintained throughout the lifespan of the F1 generation but absent from the F2 generation. This is reminiscent of past studies of interspecific Mus musculus/Mus spretus mouse crosses by Kaneda/Shitara in which the paternal mtDNA was maintained F1 generation, resulting in heteroplasmy, but was lost from the F2 generation after back-crossing. Are CPS-6 and ALLO-1 effectors, but not determinants of maternal mtDNA inheritance in the nematode?

The finding that PINK-1 inactivation stabilizes sperm-derived mitochondria in the embryos is interesting. Are the substrates of PINK1 known in C. elegans? This could provide a clue concerning the aforementioned mitophagy determinants acting independently of ALLO-1.

Discussion:

A summary-diagram compiling the intersecting allophagy pathways would be helpful to accompany discussion, in addition to or expanding on the simple diagram presented as Figure 5; also, a table listing all the factors implicated in nematode allophagy next to those implicated in human/mammalian sperm mitophagy, which would highlight the divergences and overlaps between vertebrates and invertebrates.

Is it known how CPC-6 enters/gets imported into the sperm mitochondria inside the embryo? This pathway could potentially be targeted to decipher the allophagy mechanism.

PINK/PARKIN/PACRG and FUNDC1/2 pathways have been implicated in mammalian neurodegeneration as well as in mitophagy, including but not limited to sperm mitophagy after fertilization. These pathways in mammals should be briefly reviewed as they may provide further clues to how the allophagy pathways intersect in C. elegans.

eLife Assessment

This valuable study addresses the structural basis of voltage-activation of BK channels using atomistic simulations of several microseconds, to assess conformational changes that underlie both voltage-sensing and gating of the pore. The findings, including movement of specific charged residues, combined with the degree to which these movements are coupled to pore movements, provide a solid basis for understanding voltage-gating mechanisms in this class of channels. This paper will likely be of interest to ion channel biologists and biophysicists focused on voltage-dependent channel gating mechanisms.

Reviewer #1 (Public review):

Summary:

This study provides new insight into the non-canonicial voltage-gating mechanism of BK channels through prolonged (10 us) MD simulations of the Slo1 transmembrane domain conformation and K+ conduction in response to high imposed voltages (300, 750 mV). The results support previous conclusions based on functional and structural data and MD simulations that the voltage-sensor domain (VSD) of Slo1 undergoes limited conformational changes compared to Kv channels, and predicts gating charge movement comparable in magnitude to experimental results. The gating charge calculations further indicate that R213 and R210 in S4 are the main contributors owing to their large side chain movements and the presence of a locally focused electric field, consistent with recent experimental and MD simulation results by Carrasquel-Ursulaez et al.,2022. Most interestingly, changes in pore conformation and K+ conduction driven by VSD activation are resolved, providing information regarding changes in VSD/pore interaction through S4/S5/S6 segments proposed to underly electromechanical coupling.

Strengths:

Include that the prolonged timescale and high voltage of the simulation allow apparent equilibration in the voltage-sensor domain (VSD) conformational changes and at least partial opening of the pore. The study extends the results of previous MD simulations of VSD activation by providing quantitative estimates of gating charge movement, showing how the electric field distribution across the VSD is altered in resting and activated states, and testing the hypothesis that R213 and R210 are the primary gating charges by steered MD simulations. The ability to estimate gating charge contributions of individual residues in the WT channel is useful as a comparison to experimental studies based on mutagenesis which have yielded conflicting results that could reflect perturbations in structure. Use of dynamic community analysis to identify coupling pathways and information flow for VSD-pore (electromechanical) coupling as well as analysis of state-dependent S4/S5/S6 interactions that could mediate coupling provide useful predictions extending beyond what has been experimentally tested.

Weaknesses:

Weaknesses include that a truncated channel (lacking the C-terminal gating ring) was used for simulations, which is known to have reduced single channel conductance and electromechanical coupling compared to the full-length channel. In addition, as VSD activation in BK channels is much faster than opening, the timescale of simulations was likely insufficient to achieve a fully open state as supported by differences in the degree of pore expansion in replicate simulations, which are also smaller than observed in Ca-bound open structures of the full-length channel. Taken together, these limitations suggest that inferences regarding coupling pathways and interactions in the fully open voltage-activated channel may be only partially supported and therefore incomplete. That said, adequate discussion regarding these limitations are provided together with dynamic community analysis based on the Ca-bound open structure. The latter supports the main conclusions based on simulations, while providing an indication of potential interaction differences between simulated and fully open conformations. Another limitation is that while the simulations convincingly demonstrate voltage-dependent channel opening as evidenced by pore expansion and conduction of K+ and water through the pore, single channel conductance is underestimated by at least an order of magnitude, as in previous studies of other K+ channels. These quantitative discrepancies suggest that MD simulations may not yet be sufficiently advanced to provide insight into mechanisms underlying the extraordinarily large conductance of BK channels.

Comments on revisions:

My previous questions and concerns have been adequately addressed.

Reviewer #2 (Public review):

Summary:

The manuscript by Jia and Chen addresses the structural basis of voltage-activation of BK channels using computational approaches. Although a number of experimental studies using gating current and patch-clamp recording have analyzed voltage-activation in terms of observed charge movements and the apparent energetic coupling between voltage-sensor movement and channel opening, the structural changes that underlie this phenomenon have been unclear. The present studies use a reduced molecular system comprising the transmembrane portion of the BK channel (i.e. the cytosolic domain was deleted), embedded in a POPC membrane, with either 0 or 750 mV applied across the membrane. This system enabled acquisition of long simulations of 10 microseconds, to permit tracking of conformational changes of the channel. The authors principal findings were that the side chains of R210 and R213 rapidly moved toward the extracellular side of the membrane (by 8 - 10 Å), with greater displacements than any of the other charged transmembrane residues. These movements appeared tightly coupled to movement of the pore-lining helix, pore hydration, and ion permeation. The authors estimate that R210 and R213 contribute 0.25 and 0.19 elementary charges per residue to the gating current, which is roughly consistent with estimates based on electrophysiological measurements that used the full-length channel.

Strengths:

The methodologies used in this work are sound, and these studies certainly contribute to our understanding of voltage-gating of BK channels. An intriguing observation is the strongly coupled movement of the S4, S5, and S6 helices that appear to underlie voltage-dependent opening. Based on Fig 2a-d, the substantial movements of the R210 and R213 side chains occur nearly simultaneously to the S6 movement (between 4 - 5 usec of simulation time). This seems to provide support for a "helix-packing" mechanism of voltage gating in the so-called "non-domain-swapped" voltage-gated K channels.

Weaknesses:

The main limitation is that these studies used a truncated version of the BK channel, and there are likely to be differences in VSD-pore coupling in the context of the full-length channels that will not be resolved in the present work. Nonetheless, the authors provide a strong rationale for their use of the truncated channel, and the results presented will provide a good starting point for future computational studies of this channel.

Author response:

The following is the authors’ response to the previous reviews

Recommendations for the authors:

Reviewing Editor Comments:

The resubmitted version of the manuscript adequately addressed several initial comments made by reviewing editors, including a more detailed analysis of the results (such as those of bilayer thickness). This version was seen by 2 reviewers. Both reviewers recognize this work as being an important contribution to the field of BK and voltage-dependent ion channels in general. The long trajectories and the rigorous/novel analyses have revealed important insights into the mechanisms of voltage-sensing and electromechanical coupling in the context of a truncated variant of the BK channel. Many of these observations are consistent with structural and functional measurements of the channel, available thus far. The authors also identify a novel partially expanded state of the channel pore that is accessed after gating-charge displacement, which informs the sequence of structural events accompanying voltage-dependent opening of BK.

However, there are key concerns regarding the use of the truncated channel in the simulations. While many gating features of BK are preserved in the truncated variant, studies have suggested that opening of the channel pore to voltage-sensing domain rearrangement is impaired upon gating-ring deletion. So the inferences made here might only represent a partial view of the mechanism of electromechanical coupling.

It is also not entirely clear whether the partially expanded pore represents a functionally open, sub-conductance, or another closed state. Although the authors provide evidence that the inner pore is hydrated in this partially open state, in the absence of additional structural/functional restraints, a confident assignment of a functional state to this structure state is difficult. Functional measurements of the truncated channel seem to suggest that not only is their single channel conductance lower than full-length channels, but they also appear to have a voltage-independent step that causes the gates to open. It is unclear whether it is this voltage-independent step that remains to be captured in these MD trajectories. A clean cut resolution of this conundrum might not be feasible at this time, but it could help present the various possibilities to the readers.

We appreciate the positive comments and agree that there will likely be important differences between the mechanistic details of voltage activation between the Core-MT and full-length constructs of BK channels. We also agree that the dilated pore observed in the simulation may not be the fully open state of Core-MT.

Nonetheless, the notion that the simulation may not have captured the full pore opening transition or the contribution of the CTD should not render the current work “incomplete”, because a complete understanding of BK activation would be an unrealistic goal beyond the scope of this work. We respectfully emphasize that the main insights of the current simulations are the mechanisms of voltage sensing (e.g., the nature of VSD movements, contributions of various charged residues, how small charge movements allow voltage sensing, etc.) as well as the role of the S4-S5-S6 interface in VSD-pore coupling. As noted by the Editor and reviewers, these insights represent important steps towards establishing a more complete understanding of BK activation.

Below are the specific comments of the two experts who have assessed the work and made specific suggestions to improve the manuscript.

Reviewer #1 (Recommendations for the authors):

(1) Although the successful simulation of V-dependent K+ conduction through the BK channel pore and analysis of associated state dependent VSD/pore interactions and coupling analysis is significant, there are two related questions that are relevant to the conclusions and of interest to the BK channel community which I think should be addressed or discussed.

One key feature of BK channels is their extraordinarily large conductance compared to other K+ selective channels. Do the simulations of K+ conductance provide any insight into this difference? Is the predicted conductance of BK larger than that of other K+ channels studied by similar methods? Is there any difference in the conductance mechanism (e.g., the hard and soft knock-on effects mentioned for BK)?

The molecular basis of the large conductance of BK channels is indeed an interesting and fundamental question. Unfortunately, this is beyond the scope of this work and the current simulation does not appear to provide any insight into the basis of large conductance. It is interesting to note, though, the conductance is apparently related to the level of pore dilation and the pore hydration level, as increasing hydration level from ~30 to ~40 waters in the pore increases the simulated conductance from ~1.5 to 6 pS (page 8). This is consistent with previous atomistic simulations (Gu and de Groot, Nature Communications 2023; ref. 33) showing that the pore hydration level is strongly correlated with observed conductance. As noted in the manuscript, the conductance mechanism through the filter appears highly similar to previous simulations of other K+ channels (Page 8). Given the limit conductance events observed in the current simulations, we will refrain from discussing possible basis of the large conductance in BK channels except commenting on the role of pore hydration (page 8; also see below in response to #5).

The pore in the MD simulations does not open as wide as the Ca-bound open structure, which (as the authors note) may mean that full opening requires longer than 10 us. I think that is highly likely given that the two 750 mV simulations yielded different degrees of opening and that in BK channels opening is generally much slower than charge movement. Therefore, a question is - do any of the conclusions illustrated in Figures 6, S5, S6 differ if the Ca-bound structure is used as the open state? For example, I expect the interactions between S5 and S6 might at least change to some extent as S6 moves to its final position. In this case, would conclusions about which residues interact, and get stronger or weaker, be the same as in Figures S6 b,c? Providing a comparison may help indicate to what extent the conclusions are dependent on achieving a fully open conformation.

We appreciate the reviewer’s suggestion and have further analyzed the information flow and coupling pathways using the simulation trajectory initiated from the Ca²⁺-bound cryo-EM structure (sim 7, Table S1). The new results are shown in two new SI Figures S7 and S8, and new discussion has been added to pages 14-15. Comparing Figures 5 and S7, we find that dynamic community, coupling pathways, and information flow are highly similar between simulation of the open and closed states, even though there are significant differences in S5 contacts in the simulated open state vs Ca²⁺-bound open state (Figure S8). Interestingly, there are significant differences in S4-S5 packing in the simulated and Ca²⁺-bound open states (Figure S8 top panel), which likely reflect important difference in VSD/pore interactions during voltage vs Ca²⁺ activation.

(2) P4 Significance -"first, successful direct simulation of voltage-activation"

This statement may need rewording. As noted above Carrasquel-Ursulaez et al.,2022 (reference 39) simulated voltage sensor activation under comparable conditions to the current manuscript (3.9 us simulation at +400 mV), and made some similar conclusions regarding R210, R213 movement, and electric field focusing within the VSD. However, they did not report what happens to the pore or simulate K+ movement. So do the authors here mean something like "first, successful direct simulation of voltage-dependent channel opening"?

We agree with the reviewer and have revised the statement to “ … the first successful direct simulation of voltage-dependent activation of the big potassium (BK) channel, ..”

(3) P5 "We compare the membrane thickness at 300 and 750 mV and the results reveal no significant difference in the membrane thickness (Figure S2)"

The figure also shows membrane thickness at 0 mV and indicates it is 1.4 Angstroms less than that at 300 or 750 mV. Whether or not this difference is significant should be stated, as the question being addressed is whether the structure is perturbed owing to the use of non-physiological voltages (which would include both 300 and 750 mV).

We have revised the Figure S2 caption to clarify that one-way ANOVA suggest the difference is not significant.

(4) P7 "It should be noted that the full-length BK channel in the Ca2+ bound state has an even larger intracellular opening (Figure 2f, green trace), suggesting that additional dilation of the pore may

occur at longer timescales."

As noted above, I agree it is likely that additional pore dilation may occur at longer timescales. However, for completeness, I suppose an alternative hypothesis should be noted, e.g. "...suggesting that additional dilation of the pore may occur at longer timescales, or in response to Ca-binding to the full length channel."

This is a great suggestion. Revised as suggested.

(5) Since the authors raise the possibility that they are simulating a subconductance state, some more discussion on this point would be helpful, especially in relation to the hydrophobic gate concept. Although the Magleby group concluded that the cytoplasmic mouth of the (fully open) pore has little impact on single channel conductance, that doesn't rule out that it becomes limiting in a partially open conformation. The simulation in Figure 3A shows an initial hydration of the pore with ~15 waters with little conductance events, suggesting that hydration per se may not suffice to define a fully open state. Indeed, the authors indicate that the simulated open state (w/ ~30-40 waters) has 1/4th the simulated conductance of the open structure (w/ ~60 waters). So is it the degree of hydration that limits conductance? Or is there a threshold of hydration that permits conductance and then other factors that limit conductance until the pore widens further? Addressing these issues might also be relevant to understanding the extraordinarily large conductance of fully open BK compared to other K channels.

We agree with the reviewer’s proposal that pore hydration seems to be a major factor that can affect conductance. This is also well in-line with the previous computational study by Gu and de Groot (2023). We have now added a brief discussion on page 8, stating “Besides the limitation of the current fixed charge force fields in quantitively predicting channel conductance, we note that the molecular basis for the large conductance of BK channels is actually poorly understood (78). It is noteworthy that the pore hydration level appears to be an important factor in determining the apparent conductance in the simulation, which has also been proposed in a previous atomistic simulation study of the Aplysia BK channel (33).”

Minor points

(1) P5 "the fully relaxed pore profile (red trace in Figure S1d, top row) shows substantial differences compared to that of the Ca2+-free Cryo-EM structure of the full-length channel."

For clarity, I suggest indicating which is the Ca-free profile - "... Ca2+-free Cryo-EM structure of the full-length channel (black trace)."

We greatly appreciate the thoughtful suggestion. Revised as suggested.

(2) P8 "Consistent with previous simulations (78-80), the conductance follows a multi-ion mechanism, where there are at least two K+ ions inside the filter"

For clarity, I suggest indicating these are not previous simulations of BK channels (e.g., "previous simulations of other K+ channels ...").

Author response: Revised as suggested. Thank you.

(3) Figure 2, S1 - grey traces representing individual subunits are very difficult to see (especially if printed). I wonder if they should be made slightly darker. Similar traces in Figure 3 are easier to see.

The traces in Figure S1 are actually the same thickness in Figure 3 and they appear lighter due to the size of the figure. Figure 2 panels a-c have been updated to improve the resolution.

(4) Figure 2 - suggest labeling S6 as "S6 313-324" (similar to S4 notation) to indicate it is not the entire segment.

Figure 2 panel d) has been updated as suggested.

(5) Figure 2 legend - "Voltage activation of Core-MT BK channels. a-d)..."

It would be easier to find details corresponding to individual panels if they were referenced individually. For example:

"a-d) results from a 10-μs simulation under 750 mV (sim2b in Table S1). Each data point represents the average of four subunits for a given snapshot (thin grey lines), and the colored thick lines plot the running average. a) z-displacement of key side chain charged groups from initial positions. The locations of charged groups were taken as those of guanidinium CZ atoms (for Arg) and sidechain carboxyl carbons (for Asp/Glu) b) z-displacement of centers-of-mass of VSD helices from initial positions, c) backbone RMSD of the pore-lining S6 (F307-L325) to the open state, and d) tilt angles of all TM helices. Only residues 313-324 of S6 were included inthe tilt angle calculation, and the values in the open and closed Cryo-EM structures are marked using purple dashed lines. "

We appreciate the thoughtful suggestion and have revised the caption as suggested.

(6) Figure S1 - column labels a,b,c, and d should be referenced in the legend.

The references to column labels have been added to Figure S1 caption.

(7) References need to be double-checked for duplicates and formatting.

a) I noticed several duplicate references, but did not do a complete search: Budelli et al 2013 (#68, 100), Horrigan Aldrich 2002 (#22,97), Sun Horrigan 2022 (#40, 86), Jensen et al 2012 (#56,81).

b) Reference #38 is incorrectly cited with the first name spelled out and the last name abbreviated.

We appreciate the careful proofreading of the reviewer. The duplicated references were introduced by mistake due to the use of multiple reference libraries. We have gone through the manuscript and removed a total of 5 duplicated references.

Response to additional reviewer comments

My only new comment is that the numbering of residues in Fig. S8 does not match the standard convention for hSlo and needs to be doublechecked. For the residues I checked, the numbers appear to be shifted 3 compared hSlo (e.g. Y315, P317, E318, G324 should be Y318, P320, E321, G327).

We greatly appreciate the reviewer for catching the errors in residue labels. Figure S8 has now been updated to include correct residue labels. Thanks!

Reviewer #2 (Recommendations for the authors):

This manuscript has been through a previous level of review. The authors have provided their responses to the previous reviewers, which appear to be satisfactory, and I have no additional comments, beyond the caveats concerning interpretations based on the truncated channel, which are noted above.

We greatly appreciate the constructive comments and insightful advice. Please see above response to the Reviewing Editor’s comments for response and changes regarding the caveats concerning interpretations of the current simulations.

eLife Assessment

The open-source software Chromas tracks and analyses cephalopod chromatophore dynamics. The software features a user-friendly interface alongside detailed instructions for its application, with compelling exemplary applications to two widely divergent cephalopod species, a squid and a cuttlefish, over time periods large enough to encompass new chromatophore development among existing ones. It demonstrates accurate tracking of the position and identity of each chromatophore. The software and methods outlined therein will become an important tool for scientists tracking dynamic signaling in animals.

Reviewer #1 (Public review):

Summary:

This study provides comprehensive instructions for using the chromatophore tracking software, Chromas, to track and analyse the dynamics of large numbers of cephalopod chromatophores across various spatiotemporal scales. This software addresses a long-standing challenge faced by many researchers who study these soft-bodied creatures, known for their remarkable ability to change colour rapidly. The updated software features a user-friendly interface that can be applied to a wide range of applications, making it an essential tool for biologists focused on animal dynamic signalling. It will also be of interest to professionals in the fields of computer vision and image analysis.

Strengths:

This work provides detailed instructions for this tool kit along with examples for potential users to try. The Gitlab inventory hosts the software package, installation documentation, and tutorials, further helping potential users with a less steep learning curve.

Weaknesses:

The evidence supporting the authors' claims is solid, particularly demonstrated through the use of cuttlefish and squid. However, it may not be applicable to all coleoid cephalopods yet, such as octopuses, which have an incredibly versatile ability to change their body forms.

Comments on revisions:

I am pleased to see the more detailed version of this useful tool along with tutorials designed for diverse users who are interested in animal dynamic colouration. This study provides detailed instructions for using the chromatophore tracking software Chromas to track and analyse the dynamics of large numbers of cephalopod chromatophores across various spatiotemporal scales. The software features a user-friendly interface that is highly compelling and can be applied to a wide range of applications.

Reviewer #2 (Public review):

Summary:

The authors developed a computational pipeline named CHROMAS to track and analyze chromatophore dynamics, which provides a wide range of biological analysis tools without requiring the user to write code.

Strengths:

(1) CHROMAS is an integrated toolbox that provides tools for different biological tasks such as: segment, classify, track and measure individual chromatophores, cluster small groups of chromatophores, analyze full-body patterns, etc.

(2) It could be used to investigate different species. The authors have already applied it to analyze the skin of the bobtail squid Euprymna berryi and the European cuttlefish Sepia officinalis.

(3) The tool is open-source and easy to install. The paper describes in detail the experiment requirements, command to complete each task and provides relevant sample figures, which are easy to follow.

Weaknesses:

(1) There are some known limitations for the current version. The users should read the "Discussion" chapter carefully before preparing their experiments and using CHROMAS.

Author response:

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

Reviewer #1 (Public review):

Summary:

This study provides comprehensive instructions for using the chromatophore tracking software, Chromas, to track and analyse the dynamics of large numbers of cephalopod chromatophores across various spatiotemporal scales. This software addresses a long-standing challenge faced by many researchers who study these soft-bodied creatures, known for their remarkable ability to change colour rapidly. The updated software features a user-friendly interface that can be applied to a wide range of applications, making it an essential tool for biologists focused on animal dynamic signalling. It will also be of interest to professionals in the fields of computer vision and image analysis.

Strengths:

This work provides detailed instructions for this toolkit along with examples for potential users to try. The Gitlab inventory hosts the software package, installation documentation, and tutorials, further helping potential users with a less steep learning curve.

Weaknesses:

The evidence supporting the authors' claims is solid, particularly demonstrated through the use of cuttlefish and squid. However, it may not be applicable to all coleoid cephalopods yet, such as octopuses, which have an incredibly versatile ability to change their body forms.

The reviewer is right to highlight this limitation. We clarified, in the revised manuscript, that CHROMAS relies on the assumption that chromatophore activity occurs primarily in a plane — a condition that is valid most of the time in squid and cuttlefish, where the majority of skin deformations are in-plane (with small occasional papillae). In cephalopods such as octopuses, however, in which the skin may undergo large 3-dimensional deformations through the action of papillary musculature, this assumption may not always hold. Although octopods’ bodies are more spherical (less flat) than those of squid and cuttlefish, CHROMAS should still be usable and useful if applied to smaller skin areas, especially because chromatophore density is often even higher in octopoda than in sepiidae.

We added the following paragraph in the discussion:

Another known limitation concerns the biological assumptions underlying the current version of CHROMAS. The pipeline is designed for surfaces that remain reasonably planar and undergo deformations primarily in two dimensions. In cephalopods such as octopuses, in which the skin can undergo substantial three-dimensional morphological changes, analysing chromatophore dynamics may require complementary three-dimensional tracking of the skin surface to correct for out-of-plane deformations and maintain accurate measurement of chromatophore activity.

Reviewer #2 (Public review):

Summary:

The authors developed a computational pipeline named CHROMAS to track and analyse chromatophore dynamics, which provides a wide range of biological analysis tools without requiring the user to write code.

Strengths:

(1) CHROMAS is an integrated toolbox that provides tools for different biological tasks such as: segment, classify, track and measure individual chromatophores, cluster small groups of chromatophores, analyse full-body patterns, etc.

(2) It could be used to investigate different species. The authors have already applied it to analyse the skin of the bobtail squid Euprymna berryi and the European cuttlefish Sepia officinalis.

(3) The tool is open-source and easy to install. The paper describes in detail the command format to complete each task and provides relevant sample figures.

Weaknesses:

(1) The generality and robustness of the proposed pipeline need to be verified through more experimental evaluations. For example, the implementation algorithm depends on relatively specific or obvious image features, clean backgrounds, and objects that do not move too fast.

(2) The pipeline lacks some kind of self-correction mechanism. If at one moment there is a conflicting match with the previous frames, how does the system automatically handle it to ensure that the tracking results are accurate over a long period of time?

We thank the reviewer for raising this important point. CHROMAS does rely on relatively clean imaging conditions for optimal performance. However, the computational features of the pipeline — segmentation, tracking, and downstream analysis — have been designed to perform reliably as long as the segmentation models are trained on frames that reflect the diversity of the dataset (e.g., variations in lighting or minor background noise). It is correct, however, that acquiring the necessary quality of input data is both important and non-trivial. The pipeline is designed to work best with high-resolution footage of chromatophores under clear imaging conditions — specifically, with minimal water surface distortion, minimal particulate matter in the water column, and stable focus.

To mitigate issues arising from motion blur or focus loss, CHROMAS includes an automatic frame quality control step that detects and discards frames that are out of focus, including those where the animal moves too fast for reliable tracking.

To assist future users, we have now added a section under Discussion detailing the recommended recording conditions and video characteristics for effective analysis with CHROMAS. It reads:

Recommended Video Parameters for Optimal Use of CHROMAS

The performance of CHROMAS depends on the quality of the input videos. Although the pipeline analyses each frame independently and has no frame rate requirement, we recommend recording at 20 frames per second at least, to capture chromatophore dynamics accurately. Sharp, in-focus frames are critical, particularly for moving subjects, where higher shutter speeds help minimize motion blur. For reliable segmentation, each chromatophore should cover at least 10 pixels across its fully expanded diameter. Higher spatial resolution, with chromatophores covering around 50 pixels in diameter, are recommended if sub-chromatophore dynamics are of interest. Recording conditions should minimize background noise, and the water column should be as clear as possible, free of particles or debris. The water surface should be kept as calm and planar as possible to avoid optical artifacts. If wide-angle lenses or other optics that may introduce distortion are used, lens correction algorithms should be applied during preprocessing to compensate for the optical distortions. For long-term tracking applications (e.g., developmental studies), frequent imaging sessions are recommended. Newly differentiated chromatophores are initially light colored (e.g., yellow) and thus visually distinct from mature chromatophores (which are dark); over days to weeks, however, the light chromatophores darken and become increasingly difficult to differentiate from older ones. Recording at appropriate and regular intervals thus helps track individual chromatophores across developmental stages and improves the reliability of long-term analyses. Following these recommendations will help segmentation, tracking, and analysis with CHROMAS.

CHROMAS does not implement an active self-correction mechanism in the sense of real-time error recovery. Yet, several steps are in place to ensure the reliability of registration and tracking over time. During registration, a set of points is tracked across frames using optical flow. If the displacement of a point between two frames exceeds a biologically plausible threshold, that point is automatically discarded from the registration calculation to prevent error propagation. If too many points are discarded, the registration step fails, preventing the acceptance of a poor alignment.

In addition, masterframes (the averages of all aligned frames in a chunk) are generated at the end of the registration process to enable the visual verification of the quality of the mapping.

During stitching, CHROMAS calculates reprojection errors between chunks, providing a quantitative measure of stitching validity and allowing users to detect and correct potential mismatches.

We have revised the Results section to explicitly highlight the error-checking mechanisms implemented during registration and stitching to maintain tracking accuracy over time.

Reviewer #1 (Recommendations for the authors):

(1) Figures 2, 3, 5, 6, 8 showed the bobtail squid, however, all command lines for these figures were referred to "sepia_example.dataset".

We thank the reviewer for noticing this inconsistency. We have corrected the labeling of the dataset name in the command line examples from "sepia_example.dataset" to the neutral term "example.dataset" to avoid any confusion regarding the species used in the figures.

(2) It's excellent that Chromas includes a manual pre-alignment function. However, it's unclear how the authors determined the registration of selected chromatophores across different ages in the long-term tracking session. Given the rapid growth of cephalopods and presumably skin expansion with increased chromatophores, it would be helpful to provide more details or examples on this process.

The manual pre-alignment function provides an interactive interface allowing the user to select a set of matching chromatophores across frames from different developmental stages. The accuracy of this process depends on the user's ability to recognize individual chromatophores reliably over time. Critically, it is not necessary to identify all those chromatophores; a representative subset is sufficient to interpolate the spatial mapping and align the surrounding chromatophores.

To limit the potential challenges associated with chromatophore development, frequent imaging sessions (every few days) are recommended initially. Excessive intervals between recordings can result in relative displacements among existing chromatophores and the sudden appearance of newly matured chromatophores, both of which complicate manual matching.

It should be noted that these challenges are not limitations of the CHROMAS pipeline itself, but rather relate to experimental design choices that affect the quality and traceability of the dataset. The exact parameters (e.g., size/duration of the datasets, spatial resolution, frame rate and intervals between recording sessions) to be used must be adapted to each experimental animal, each age, and ultimately, each question.

Recommended video acquisition parameters, including guidance on recording frequency for long-term chromatophore tracking, have been added to the Discussion section.

Reviewer #2 (Recommendations for the authors):

(1) More detailed information should be given, such as operating system requirements, camera frame rate requirements, target size and speed limitations, when chunking videos into usable segments, the minimum length of each segment, etc.

CHROMAS is platform-independent and requires only a functioning Python 3.9+ environment, regardless of the operating system or OS version, as described in “Methods – Implementation details”.

Although CHROMAS does not require specific frame rates and because it analyses each frame independently, the quality of each image—and thus of imaging parameters—is critical to enable reliable chromatophore segmentation. If an animal remains relatively calm during recording, low shutter speeds will be adequate for image sharpness. Conversely, if the animal moves frequently or rapidly, it will be preferable to use a higher frame rate and a higher shutter speed to minimize motion blur. Recording parameters should therefore be adjusted accordingly, primarily to optimize image clarity and maintain frames in sharp focus.

The frame rate should be sufficiently high also to capture the fast dynamics of chromatophore expansions and contractions. Although the pipeline has no specific frame rate requirement, we recommend image rates of at least 20 frames per second to sample the temporal patterns of chromatophore activity adequately, based on biological considerations.

Each chromatophore should be represented by a sufficiently large number of pixels in each recorded image to enable the reliable estimation of its size, shape, and dynamics. If the spatial resolution is too low, individual chromatophores may appear as small pixel clusters, reducing the accuracy of area and shape measurements and introducing quantization artifacts. Based on our experience, we recommend recording conditions that result in each chromatophore covering at least 10 pixels across its diameter when fully expanded to ensure accurate segmentation and quantitative whole-chromatophore analysis. For sub-chromatophore motion analysis, we recommend a minimum of 50 pixels across the fully expanded diameter.

These considerations relate to optimizing biological sampling and image quality for analysis, and are not technical requirements imposed by CHROMAS itself.

We added a Discussion section outlining the recommended recording conditions and video parameters to facilitate effective use of CHROMAS.

(2) This pipeline does not include functionality to correct for lens distortion, which may affect the results when accurate measurement of single chromatophore morphology is required.

We thank the reviewer for this observation. We agree that lens distortion can affect the accurate measurement of chromatophore morphology if present. However, the current datasets analysed with CHROMAS were recorded using a long macro lens with minimal distortion, and visual inspections as well as quantitative assessments of chromatophore geometry did not indicate measurable optical deformation. We acknowledge that for other imaging setups —particularly those relying on the use of wide-angle lenses— lens distortion could introduce artifacts. In such cases, we recommend applying standard lens distortion correction during preprocessing, prior to analysis with CHROMAS.

We have also addressed this point in the newly added section under the Discussion.

(3) How to perform expansion for single chromatophores shown in Figure 6, and how to keep the expansion area consistent?

The graph in Figure 6 illustrates the expansion of a single chromatophore over time and was generated entirely using the "areas" command and visualization tools available within CHROMAS.

Spatial consistency is maintained because CHROMAS, through its registration and area extraction steps, tracks the identity of each chromatophore across the video, allowing the same individual to be followed reliably over time.

(4) Tables 1 and 2: it's better to add the units of the values in each column.<br />

We thank the reviewer for the suggestion. We have added the appropriate units to each column in Tables 1 and 2 to improve clarity.

eLife Assessment

This study presents a rather valuable finding that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple negative cancer cells. The evidence supporting the claims of the authors is solid, although some comments and elaborations in the main text would have enhanced the comprehension and clarity of the data. The work will be of interest to scientists working in the field of breast cancer.

Reviewer #2 (Public review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple negative cancer cell lines in vitro and in vivo using preclinical mouse models.

Strengths and weaknesses

The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors aimed to enhance the effectiveness of PARP inhibitors (PARPi) in treating high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) by inhibiting PRMT1/5 enzymes. They conducted a drug screen combining PARPi with 74 epigenetic modulators to identify promising combinations.

Zhang et al. reported that protein arginine methyltransferase (PRMT) 1/5 inhibition acts synergistically to enhance the sensitivity of Poly (ADP-ribose) polymerase inhibitors (PARPi) in high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) cells. The authors are the first to perform a drug screen by combining PARPi with 74 well-characterized epigenetic modulators that target five major classes of epigenetic enzymes. Their drug screen identified both PRMT1/5 inhibitors with high combination and clinical priority scores in PARPi treatment. Notably, PRMT1/5 inhibitors significantly enhance PARPi treatment-induced DNA damage in HR-proficient HGSOC and TNBC cells through enhanced maintenance of gene expression associated with DNA damage repair, BRCAness, and intrinsic innate immune pathways in cancer cells. Additionally, bioinformatic analysis of large-scale genomic and functional profiles from TCGA and DepMap further supports that PRMT1/5 are potential therapeutic targets in oncology, including HGSOC and TNBC. These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.

Strengths:

(1) Innovative Approach: First to screen PARPi with a large panel of epigenetic modulators.

(2) Significant Results: Found that PRMT1/5 inhibitors significantly boost PARPi effectiveness in HR-proficient HGSOC and TNBC cells.

(3) Mechanistic Insights: Showed how PRMT1/5 inhibitors enhance DNA damage repair and immune pathways.

(4) Robust Data: Supported by extensive bioinformatic analysis from large genomic databases.

Weaknesses:

(1) Novelty Clarification: Needs clearer comparison to existing studies showing similar effects.

(2) Unclear Mechanisms: More investigation is needed on how MYC targets correlate with PRMT1/5.

(3) Inconsistent Data: ERCC1 expression results varied across cell lines.

(4) Limited Immune Study: Using immunodeficient mice does not fully explore immune responses.

(5) Statistical Methods: Should use one-way ANOVA instead of a two-tailed Student's t-test for multiple comparisons.

We sincerely thank Reviewer #1 for the insightful and constructive feedback, as well as for the kind acknowledgment of the significance of our work: “These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.” We greatly appreciate the reviewer #1’s thoughtful comments, which have significantly improved the quality of our manuscript. In response, we conducted additional experiments and analyses, and made comprehensive revisions to the text, figures, and supplementary materials. In the “Recommendations for the authors” sections, we have provided point-by-point responses to each of the reviewer’s comments, which were immensely helpful in guiding our revisions. We believe these updates have substantially strengthened the manuscript and have fully addressed all reviewer concerns.

Reviewer #2 (Public Review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple-negative cancer cell lines in vitro and in vivo using preclinical mouse models.

PARP inhibitors have been the common targeted-therapy options to treat high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC). PRMTs are oncological therapeutic targets and specific inhibitors have been developed. However, due to the insufficiency of PRMTi or PARPi single treatment for HGSOC and TNBC, designing novel combinations of existing inhibitors is necessary. In previous studies, the authors and others developed an "induced PARPi sensitivity by epigenetic modulation" strategy to target resistant tumors. In this study, the authors presented a triple combination of PRMT1i, PRMT5i and PARPi that synergistically kills TNBC cells. A drug screen and RNA-seq analysis were performed to indicate cancer cell growth dependency of PRMT1 and PRMT5, and their CRISPR/Cas9 knockout sensitizes cancer cells to PARPi treatment. It was shown that the cells accumulate DNA damage and have increased caspase 3/7 activity. RNA-seq analysis identified BRCAness genes, and the authors closely studied a top hit ERCC1 as a downregulated DNA damage protein in PRMT inhibitor treatments. ERCC1 is known to be synthetic lethal with PARP inhibitors. Thus, the authors add back ERCC1 and reduce the effects of PRMT inhibitors suggesting PRMT inhibitors mediate, in part, their effect via ERCC1 downregulation. The combination therapy (PRMT/PARP) is validated in 2D cultures of cell lines (OVCAR3, 8 and MDA-MB-231) and has shown to be effective in nude mice with MDA-MB-231 xenograph models.

Strengths and weaknesses:

Overall, the data is well-presented. The experiments are well-performed, convincing, and have the appropriate controls (using inhibitors and genetic deletions) and statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

We sincerely thank Reviewer #2 for the insightful and constructive feedback, as well as for the kind acknowledgment of the significance of our work: “Overall, the data are well-presented. The experiments are well-performed, convincing, and supported by appropriate controls (using inhibitors and genetic deletions) and statistics.” We greatly appreciate the reviewer #2’s thoughtful comments, which have significantly improved the quality of our manuscript. In response, we conducted additional experiments and analyses, and made comprehensive revisions to the text, figures, and supplementary materials. In the “Recommendations for the authors” sections, we have provided point-by-point responses to each of the reviewer’s comments, which were immensely helpful in guiding our revisions. We believe these updates have substantially strengthened the manuscript and have fully addressed all reviewer concerns.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Recent studies have revealed promising synergistic effects between PRMT inhibitors and chemotherapy, as well as DDR-targeting drugs (ref. 89-92). In the discussion, the authors should highlight what is novel in this study compared to the reported studies.

We thank the reviewer for this important comment and fully agree that prior studies have demonstrated the potential of PRMT inhibitors to enhance the efficacy of DNA damage-targeting agents and certain chemotherapies[1-4]. In response to the reviewer’s constructive suggestion, we have now revised the discussion to highlight the novel aspects of our study compared to previously reported findings. Specifically, our work presents several key advances that go beyond prior studies. Below, we would like to emphasize the novelty of our current study as follows:

In the clinic, a strategy termed “induced PARP inhibitor (PARPi) sensitivity by epigenetic modulation” is being evaluated to sensitize homologous recombination (HR)-proficient tumors to PARPi treatments. Together with other groups, we reported that repression of BET activity significantly reduces the expression levels of essential HR genes by inhibiting their super-enhancers[5]. This preclinical discovery is now being assessed in a Phase 1b/2 clinical trial combining the BET inhibitor ZEN-3694 with the PARPi talazoparib for the treatment of patients with metastatic triple-negative breast cancer (TNBC) who do not carry germline BRCA1/2 mutations. Promising anti-tumor activity has been observed in this ongoing trial[6]. Importantly, gene expression profiles from paired tumor biopsies demonstrated robust target engagement, evidenced by repression of BRCA1 and RAD51 mRNA expression, consistent with our preclinical findings in xenograft models. Based on these encouraging results, the trial is being expanded to a Phase 2b stage to enroll additional TNBC patients. Moreover, other combination strategies[7-13] based on this “induced PARPi sensitivity by epigenetic modulation” approach have also shown promising clinical responses in both intrinsic and acquired HR-proficient settings. Notably, these clinical studies indicate that the strategy is well-tolerated, likely due to cancer cells being particularly sensitive to epigenetic repression of DNA damage response (DDR) genes, compared with normal cells.

However, two key clinical challenges remain for broader application of this strategy in oncology: 1) which clinically actionable epigenetic drugs can produce the strongest synergistic effects with PARPi? and 2) can a BRCA-independent approach be developed? To address these questions, we performed a drug screen combining the FDA-approved PARPi olaparib with a panel of clinically relevant epigenetic drugs. This panel includes 74 well-characterized epigenetic modulators targeting five major classes of epigenetic enzymes, comprising 7 FDA-approved drugs, 14 agents in clinical trials, and 54 in preclinical development. Notably, both type I PRMT inhibitors (PRMTi) and PRMT5 inhibitors (PRMT5i) achieved high combination and clinical prioritization scores in the screen. Functional assays demonstrated that PRMT inhibition markedly enhances PARPi-induced DNA damage in HR-proficient cancer cell lines. In line with a strong positive correlation between PRMT and DDR gene expression across primary tumors, we observed that PRMT activity supports the transcription of DDR genes and maintains a BRCAness-like phenotype in cancer cells. These findings provide strong rationale for clinical development of PRMT/PARPi combinations in patients with HR-proficient ovarian or breast cancers. Mechanistic characterization from our study further supports PRMTi clinical development by elucidating mechanisms of action, identifying rational combinations, defining predictive biomarkers, and guiding dosing strategies.

We believe our studies will be of significant interest to the cancer research community for several reasons. First, they address major clinical challenges in women’s cancers, specifically, high-grade serous ovarian cancer (HGSOC) and TNBC, both of which are aggressive malignancies with limited therapeutic options. Second, they offer a novel solution to overcome PARPi resistance. Our earlier discovery of “induced PARPi sensitivity by epigenetic modulation” has already shown promising clinical results and represents a new path to overcome both primary and acquired resistance to PARPi and platinum therapies. Third, they focus on a clinically translatable drug class. Selective and potent PRMT inhibitors have been developed by leading pharmaceutical companies, with more than ten currently in advanced clinical trials. Fourth, they support mechanism-driven combination strategies. Preclinical evaluation of PRMTi-based combinations with other therapeutic agents is urgently needed for future clinical success. Finally, our work highlights understudied but therapeutically relevant mechanisms in cancer biology. In-depth mechanistic analysis of the PRMT regulome is essential, and our studies provide important new insights into how PRMTs regulate transcription, RNA splicing, DNA damage repair, and anti-tumor immune responses in the context of HGSOC and TNBC.

In summary, our study identifies PRMT1 and PRMT5 as key epigenetic regulators of DNA damage repair and shows that their inhibition sensitizes HR-proficient tumors to PARP inhibitors by repressing transcription and altering splicing of BRCAness genes. Distinct from prior strategies, dual inhibition of type I PRMT and PRMT5 exhibits strong synergy, allowing for lower-dose combination treatments that may reduce toxicity. Our findings also nominate ERCC1 as a potential predictive biomarker and suggest that MYC-driven tumors may be particularly responsive to this approach. Collectively, these results offer a mechanistic rationale and translational framework to broaden the clinical application of PARP inhibitors.

(2) In Figures 3H-J, MYC targets were likely to correlate with the expression levels of PRMT1/PRMT5 in various public datasets, supporting previous reports that the Myc-PRMT loop plays critical roles during tumorigenesis (ref. 45). "Myc-targets" signatures were also the most significant signatures correlated with the expression of PRMT1 and PRMT5. The authors suggest that under MYC-hyperactivated conditions, tumors may be extremely sensitive to PRMT inhibitors or PRMTi/PARPi combination. However, the underlying mechanism remains unclear.

We sincerely thank the reviewer for the critical and insightful comments. We fully agree that more direct evidence is needed to establish the regulatory relationship between MYC and PRMT1/5. To investigate the effect of c-Myc on PRMT1 and PRMT5 expression, we analyzed RNA-seq data from P493-6 Burkitt lymphoma cells, which harbor a tetracycline (Tet)-repressible MYC transgene. In this system, MYC expression can be suppressed to very low levels and then reactivated, enabling a gradual increase in c-Myc protein levels[14]. Upon Tet removal to induce MYC expression, we observed a robust upregulation of both PRMT1 (4.3-fold) and PRMT5 (3.6-fold) RNA levels within 24 hours, as measured by RNA-seq. These findings indicate that MYC activation can transcriptionally upregulate PRMT1 and PRMT5. To determine whether this regulation is directly driven by MYC, we further analyzed MYC ChIP-seq profiles from the same cell line following 24 hours of MYC induction. Consistently, we observed remarkably increased MYC binding at the promoter regions of both PRMT1 and PRMT5 genes. Interestingly, MYC’s regulatory influence was not limited to PRMT1 and PRMT5, we also observed transcriptional upregulation of other PRMT family members, including PRMT3, PRMT4, and PRMT6, in response to MYC activation. Together with the data presented in Figure 3H, these new results strongly suggest that MYC directly upregulates the expression of PRMT family genes by binding to their promoter regions. Consequently, increased PRMT expression may facilitate MYC’s regulation of target gene expression and splicing in cancer cells. In cancers with MYC hyperactivation, this feed-forward loop may be amplified, creating a potential therapeutic vulnerability. In response to the reviewer’s insightful suggestion, we have further explored how MYC regulates PRMT1/5 and whether this regulation modulates the efficacy of PRMT inhibitors in oncology. These unpublished observations are currently being prepared for a separate manuscript, and we have now incorporated a discussion of these unpublished findings into the revised version of this manuscript. We thank the reviewer again for the thoughtful and constructive comments regarding the MYC–PRMT regulatory axis.

(3) In Figure 5F, ERCC1 expression was unlikely to be reduced in cells treated with GSK025, especially in OVCAR8 cells, although other cells, including TNBC cells, are dramatically changed after treatment.

We sincerely thank the reviewer for the critical and insightful comments. We agree with the reviewer that in Figure 5F, although GSK025 treatment reduced ERCC1 expression, the loading control Tubulin also showed a notable decrease in the OVCAR8 cell line. This may be because Tubulin expression is not specifically affected by the chemical inhibitor GSK025 in this particular cell line, or it may be secondarily reduced as a consequence of PRMT inhibitor-induced cell death. As the reviewer pointed out, this phenomenon was not observed in other cell lines, suggesting that the effect on Tubulin is not specific to PRMT inhibition. To further investigate, we employed CRISPR/Cas9-mediated knockout of PRMT1 or PRMT5 in OVCAR8 cells, a more specific genetic approach to inhibit PRMT activity. In both cases, ERCC1 expression was significantly reduced, whereas Tubulin levels remained stable (Figure 5G). These results support the conclusion that PRMT1 and PRMT5 specifically regulate ERCC1 expression in OVCAR8 cells. The inconsistent effect on Tubulin is likely due to nonspecific cellular responses to chemical inhibition, which are generally more variable and less precise than those induced by genetic perturbation.

(4) In Figure 7H-L, MDA-MB-231 cells were implanted subcutaneously in nude immunodeficient mice to confirm the synergistic therapeutic action of the PRMTi/PARPi combination in vivo. Although PRMT inhibition activates intrinsic innate immune pathways in cancer cells, suggesting that PRMTi treatments may enhance intrinsic immune reactions in tumor cells, the use of nude immune deficient mice means that changes in the tumor immune microenvironment remain unknown.

We sincerely thank the reviewer for the critical and insightful comments. We fully agree with the reviewer that our in vivo experiments using the human cancer cell line MDA-MB-231 in immunodeficient nude mice limit our ability to assess changes in the tumor immune microenvironment. We thank the reviewer for highlighting this important limitation. While the primary goal of the current study was to investigate the therapeutic synergy between PRMT inhibition and PARP inhibition in cancer cells, we would like to take this opportunity to share additional unpublished data that further support and extend the reviewer’s point regarding the immunomodulatory effects of PRMT inhibitors. In syngeneic mouse tumor models, we have observed that the combination of PRMT inhibition and PARP inhibition leads to a more robust anti-tumor immune response compared to either treatment alone. Specifically, we found increased infiltration of CD8⁺ cytotoxic T cells within the tumor microenvironment, suggesting enhanced immune activation and tumor immunogenicity. Furthermore, we have also obtained preliminary evidence that PRMT inhibition can potentiate immune checkpoint blockade therapy. Mechanistically, this may be mediated through the activation of the STING1 pathway and the upregulation of splicing-derived neoantigens, both of which have been implicated in promoting tumor immune visibility. These findings indicate that beyond enhancing DNA damage response, PRMT inhibition may have a broader impact on tumor-immune interactions and could serve as a promising strategy to sensitize tumors to immunotherapy. A separate manuscript detailing these results is currently in preparation and will be submitted for publication as an independent research article. In light of the reviewer’s thoughtful suggestions and in consideration of feedback from Reviewer #2, who recommended removing Figure 6 from the manuscript, we have carefully reevaluated the overall organization of the manuscript. Given the scope and focus of the current work, as well as the desire to maintain a concise and coherent narrative, we decided to move the content originally presented in Figure 6 to the supplementary materials. This figure is now included as Supplementary Figure S5 in the revised version of the manuscript. We believe this change helps streamline the main text while still making the additional data available for interested readers.

(5) In Figures 6-7, a two-tailed Student's t-test was used to determine the statistical differences among multiple comparisons, which should be performed by one-way ANOVA followed by a post hoc test.

We thank the reviewer for this thoughtful and important comment regarding the choice of statistical method. We fully agree with the reviewer that one-way ANOVA followed by a post hoc test is one of the standard approaches for multiple group comparisons. In response to the suggestion, we have performed one-way ANOVA on our data and found that the statistical conclusions are consistent with those obtained from the two-tailed Student’s t-tests. For example, in the first panel of Figure 6A (OVCAR8 treated with GSK715), one-way ANOVA (p = 1.1 × 10^-6), followed by Tukey’s HSD test, confirmed significant differences between control and Olaparib (p = 0.000165), control and GSK715 (p = 0.000145), control and combination (p = 6.067 × 10^-7), Olaparib and combination (p = 0.0003523), and GSK715 and combination (p = 0.0004015), consistent with the conclusions from the two-tailed t-test shown in Figure 6H. Additionally, we would like to explain why two-tailed Student’s t-tests were used in our current study. When comparisons are predefined and conducted pairwise (i.e., two groups at a time), a two-tailed Student’s t-test is statistically equivalent to one-way ANOVA for those comparisons. In our study, each comparison involved only two groups, and we therefore chose t-tests for hypothesis-driven, specific comparisons rather than exploratory multiple testing. This approach aligns with valid statistical principles. All statistical analyses presented in Figures 6-7 were designed to evaluate specific, biologically meaningful comparisons (e.g., treatment vs. control or treatment A vs treatment B). The study was hypothesis-driven, not exploratory, and did not involve simultaneous comparisons across multiple groups. In such cases, the t-test provides a more direct and interpretable result for targeted comparisons. The use of Student’s t-tests reflects the focused nature of the analysis, where each test directly addresses a specific biological question rather than a global group comparison. We sincerely appreciate the reviewer’s thoughtful comments on the statistical methods.

Reviewer #2 (Recommendations for the authors):

(1) If the authors kept the tumors of various sizes in Figure 7I, it would be important to assess the protein and/or mRNA level of ERCC1 to further support their mechanism.

We sincerely thank the reviewer for the insightful comments. We fully agree that evaluating ERCC1 expression in drug-treated tumor samples is critical to support the proposed mechanism. Due to the limited volume of tumor specimens and extensive necrosis observed after three weeks of treatment in the condition used for Figure 7I, we were unable to obtain sufficient material for expression analysis in the original cohort. To address this, we conducted an additional experiment using xenograft-bearing mice (MDA-MB-231 model), initiating treatment when tumors reached approximately 200 mm³ to ensure adequate tissue collection. We also shortened the treatment duration to 7 days to assess early molecular responses to therapy, rather than downstream effects. Consistent with our in vitro results, both GSK715 and GSK025 significantly reduced ERCC1 RNA expression (0.79 ± 0.17, p = 0.03; 0.82 ± 0.11, p = 0.02, respectively), and the combination treatment further decreased ERCC1 expression (0.49 ± 0.20, p = 0.0003), as determined by qRT-PCR. A two-tailed Student’s t-test was used for statistical analysis. In this experiment, we used the same dosing regimen as in the three-week treatment shown in Figure 7I. Importantly, the shorter treatment period and moderate tumor size at treatment initiation minimized necrosis and did not significantly affect tumor growth, allowing for reliable molecular evaluation. We sincerely thank the reviewer for highlighting this important point.

(2) Figure 2G: please explain why two bands remain for sgPRMT1.

We greatly appreciate the reviewer for raising this insightful and important question. As the reviewer pointed out, an additional band appeared after PRMT1 knockdown in OVCAR8 cells using two sequence-independent gRNAs. Notably, this band was not observed in MDA-MB-231 cells. The antibody used to detect PRMT1 (clone A33, #2449, Cell Signaling Technology) is widely adopted in PRMT1 research, with over 65 citations supporting its specificity. Interestingly, previous studies[15] have identified seven PRMT1 isoforms (v1–v7), generated through alternative splicing and exhibiting tissue-specific expression patterns. Of these, three isoforms are detectable using the A33 antibody. We believe the additional band observed upon sgRNA treatment likely represents a PRMT1 isoform that is normally expressed at low levels in OVCAR8 cells. Upon knockdown of the major isoforms by CRISPR/Cas9, expression of this minor isoform may have increased as part of a compensatory feedback mechanism, rendering it detectable by immunoblotting. Because PRMT1 isoform expression is largely tissue-type specific, it is not surprising that the same band was absent in MDA-MB-231 cells, which are derived from a different lineage than OVCAR8 cells. The reviewer raised an important question regarding the role of PRMT1 isoforms in regulating DNA damage response in cancer. We agree this is an intriguing direction and will investigate it further in future studies.

(3) Figure 4D: Please correct the figure legend so the description matches the color in the figure. Red and blue are absent.

We sincerely thank the reviewer for the critical and insightful comments. The figure legend for Figure 4D has been corrected in the revised version of the manuscript to accurately match the colors shown in the figure. We thank the reviewer for pointing out this issue.

(4) Figure 7A and B: please indicate the cell lines used.

We sincerely thank the reviewer for the critical and insightful comments. In Figure 7A and 7B, human embryonic kidney 293T (HEK293T) cells were used due to their high transfection efficiency and widespread application in reporter assays. This information has been incorporated into the figure legend for Figures 7A and 7B.

(5) What is the link with ERCC1 splicing because reduced overall ERCC1 expression is clear?

We sincerely thank the reviewer for the critical and insightful comments. As the reviewer pointed out, although the direct impact of ERCC1 alternative splicing on its protein expression remains to be fully elucidated, it is likely that PRMT inhibition induces aberrant splicing events that result in the production of alternative ERCC1 isoforms with impaired or altered function. These splicing changes may compromise ERCC1’s role in DNA repair pathways. Furthermore, as shown in Figure 4G, we observed a reduction in the total ERCC1 mRNA reads following PRMTi treatment. This decrease may be attributed, at least in part, to the instability of the alternatively spliced ERCC1 transcripts, which could be more prone to degradation. In combination with the transcriptional downregulation of ERCC1 induced by PRMT inhibition, these alternative splicing events may lead to a further reduction in functional ERCC1 protein levels. This dual impact on ERCC1 expression, through both decreased transcription and the generation of unstable or non-functional isoforms, likely contributes to the enhanced cellular sensitivity to PARP inhibitors observed in our study. We believe this represents an important mechanistic insight into how PRMT inhibition modulates the DNA damage response in cancer cells, and further studies are warranted to investigate the precise role of ERCC1 splicing regulation in this context. We thank the reviewer for pointing out this interesting future research direction.

(6) Figure 7J: From the graph, it seems like Olaparib+G715 and G715+G025 have a similar effect on tumor volume (two curves overlap). Please discuss.

We sincerely thank the reviewer for the critical and insightful comments. In the current study, the doses used for single-agent treatments were selected based on prior publications. For example, the dose of GSK715 was guided by a recent study from the GSK group[16]. Our in vitro and in vivo findings, together with previously published data, consistently demonstrate that GSK715 is more potent than both GSK025 and Olaparib. Notably, treatment with GSK715 alone led to significantly greater inhibition of tumor growth compared to either GSK025 or Olaparib administered individually. This higher potency of GSK715 also explains the comparable levels of tumor suppression observed in the combination groups, including GSK715 plus Olaparib and GSK715 plus GSK025. These results suggest that GSK715 is likely the primary driver of efficacy in the two drug combination settings. Importantly, this observation provides a valuable opportunity to further refine and optimize the dosing strategy for GSK715. Specifically, because GSK715 is highly potent, its dose may be reduced when used in combination regimens without compromising therapeutic efficacy. This approach could significantly improve the safety profile of GSK715 by minimizing potential dose-related toxicities, thereby enhancing its suitability for future clinical development in combination therapy contexts.

(7) Discussion: "PRMT5i increased global sDMA levels"-> "... aDMA levels.".

We sincerely thank the reviewer for the critical and insightful comments. In response, we have corrected the sentence in the discussion from “PRMT5i increased global sDMA levels, which suggested that type I PRMT and PRMT5 share a substrate (i.e., MMA) and/or their functions are compensatory” to “PRMT1i increased global sDMA levels, which suggested that type I PRMT and PRMT5 share a substrate (i.e., MMA) and/or their functions are compensatory.” We apologize for the misstatement and have corrected this error in the revised version of the manuscript.

(8) In addition to the methods, add that nude mice were used in the body of the results and the figure legend for Figure 7J.

We sincerely thank the reviewer for the critical and insightful comments. In the revised version of the manuscript, we have added that immunodeficient nude mice were used in both the body of the Results section and the figure legend for Figure 7J, in addition to the Methods section. We thank the reviewer for this helpful suggestion.

(9) Figure 6 can be deleted to focus the manuscript. It does not add to the PARP inhibition story, but only suggests a link to immunotherapy where this has been reported previously PMID: 35578032 and 32641491.

We sincerely thank the reviewer for the critical and insightful comments. Reviewer #1 also raised a related concern regarding the relevance of this section to the main focus of the manuscript. In consideration of both reviewers’ comments, we have decided to move the data previously shown in Figure 6 to the supplementary section as Supplementary Figure S5. This revision allows us to streamline the main text and maintain a clear focus on the core findings related to PARP inhibition. At the same time, we believe the immunotherapy-related observation may still be of interest to some readers. By presenting these results in the supplementary materials, we ensure that this potentially relevant link remains accessible without distracting from the primary narrative of the manuscript. We greatly appreciate the reviewers’ guidance in helping us improve the clarity and focus of our work. We thank the reviewer for the thoughtful suggestion.

References

(1) Dominici, C., et al. Synergistic effects of type I PRMT and PARP inhibitors against non-small cell lung cancer cells. Clin Epigenetics 13, 54 (2021).

(2) O'Brien, S., et al. Inhibiting PRMT5 induces DNA damage and increases anti-proliferative activity of Niraparib, a PARP inhibitor, in models of breast and ovarian cancer. BMC Cancer 23, 775 (2023).

(3) Carter, J., et al. PRMT5 Inhibitors Regulate DNA Damage Repair Pathways in Cancer Cells and Improve Response to PARP Inhibition and Chemotherapies. Cancer Res Commun 3, 2233-2243 (2023).

(4) Li, Y., et al. PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition. Cell Rep Med 4, 101326 (2023).

(5) Yang, L., et al. Repression of BET activity sensitizes homologous recombination-proficient cancers to PARP inhibition. Sci Transl Med 9(2017).

(6) Aftimos, P.G., et al. A phase 1b/2 study of the BET inhibitor ZEN-3694 in combination with talazoparib for treatment of patients with TNBC without gBRCA1/2 mutations. Journal of Clinical Oncology 40, 1023-1023 (2022).

(7) Karakashev, S., et al. BET Bromodomain Inhibition Synergizes with PARP Inhibitor in Epithelial Ovarian Cancer. Cell Rep 21, 3398-3405 (2017).

(8) Sun, C., et al. BRD4 Inhibition Is Synthetic Lethal with PARP Inhibitors through the Induction of Homologous Recombination Deficiency. Cancer Cell 33, 401-416 e408 (2018).

(9) Johnson, S.F., et al. CDK12 Inhibition Reverses De Novo and Acquired PARP Inhibitor Resistance in BRCA Wild-Type and Mutated Models of Triple-Negative Breast Cancer. Cell Rep 17, 2367-2381 (2016).

(10) Iniguez, A.B., et al. EWS/FLI Confers Tumor Cell Synthetic Lethality to CDK12 Inhibition in Ewing Sarcoma. Cancer Cell 33, 202-216 e206 (2018).

(11) Shan, W., et al. Systematic Characterization of Recurrent Genomic Alterations in Cyclin-Dependent Kinases Reveals Potential Therapeutic Strategies for Cancer Treatment. Cell Rep 32, 107884 (2020).

(12) Muvarak, N.E., et al. Enhancing the Cytotoxic Effects of PARP Inhibitors with DNA Demethylating Agents - A Potential Therapy for Cancer. Cancer Cell 30, 637-650 (2016).

(13) Abbotts, R., et al. DNA methyltransferase inhibitors induce a BRCAness phenotype that sensitizes NSCLC to PARP inhibitor and ionizing radiation. Proc Natl Acad Sci U S A 116, 22609-22618 (2019).

(14) Lin, C.Y., et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56-67 (2012).

(15) Goulet, I., Gauvin, G., Boisvenue, S. & Cote, J. Alternative splicing yields protein arginine methyltransferase 1 isoforms with distinct activity, substrate specificity, and subcellular localization. J Biol Chem 282, 33009-33021 (2007).

(16) Fedoriw, A., et al. Anti-tumor Activity of the Type I PRMT Inhibitor, GSK3368715, Synergizes with PRMT5 Inhibition through MTAP Loss. Cancer Cell 36, 100-114 e125 (2019).

eLife Assessment

This study presents a valuable finding that the biomechanical force of heart contractility is required for robust endocardial id2b expression, which in return promotes valve development and myocardial function through upregulation of Neuregulin 1. The data were collected and analyzed using solid methodology and can be used as a starting point for deeper mechanistic insights into the genetic programs regulating endocardial-myocardial crosstalk during heart development.

Joint Public Review:

Summary:

How mechanical forces transmitted by blood flow contribute to cardiac development remains incompletely understood. Using the unique advantages of the zebrafish model, Chen et al make the fundamental discovery that endocardial expression of the transcriptional repressor, Id2b, is maintained in endocardial cells by blood flow. Id1b zebrafish mutants fail to form the valve in the atrioventricular canal (AVC) and show reduced myocardial contractility that they suggest is due to impaired calcium transients. Id2b mutants are largely viable during the first 6 months of life until ~20% display cardiomyopathy characterized by visible edema, structural abnormalities, retrograde blood flow, and reduced systolic function and calcium handling. Mechanistically, the authors suggest that flow-mediated expression of Id2b leads to neuregulin 1 (nrg1) upregulation by physically interacting with and sequestering the Tcf3b transcriptional repressor from conserved tcf3b binding sites upstream of nrg1. Overall, this study advances our understanding of flow-mediated endocardial-myocardial crosstalk during heart development.

Strengths:

The strengths of the study are the significance of the biological question being addressed, use of the zebrafish model, data quality, and use of genetic tools. The text is generally well-written and easy to understand.

Weaknesses:

The main weakness that remains is the lack of rigor surrounding the molecular mechanism where the authors suggest that blood flow induces endocardial expression of Id2b, which binds to Tcf3b and sequesters it from binding the Nrg1 promoter to repress transcription. Although good faith efforts were made to bolster their model, the physical interaction between Id2b and Tcf3b is limited to overexpression of tagged proteins in HEK293 cells. Moreover, no mutagenesis was performed on the tcf3b binding sites identified in the nrg1 promoter to learn their importance in vivo.

eLife Assessment

This important study combines agent-based modelling and in vivo experiments in medaka embryos to provide new insights into the role of the thymic niche in T cell development. The modelling yields some interesting and solid findings regarding the importance of thymic epithelial cells. This study would be of interest to oncologists, immunologists, and mathematical modelers.

Reviewer #1 (Public review):

Summary:

This study uses a cell-based computational model to simulate and study T cell development in the thymus. They initially applied this model to assess the effect of the thymic epithelial cells (TECs) network on thymocyte proliferation and demonstrated that increasing TEC size, density, or protrusions increased the number of thymocytes. They postulated and confirmed that this was due to changes in IL7 signalling and then expanded this work to encompass various environmental and cell-based parameters, including Notch signalling, cell cycle duration, and cell motility. Critical outcomes from the computational model were tested in vivo using medaka fish, such as the role of IL-7 signalling and minimal effect of Notch signalling.

Strengths:

The strength of the paper is the use of computational modelling to obtain unique insights into the niche parameters that control T cell development, such as the role of TEC architecture, while anchoring those findings with in vivo experiments. I can't comment on the model itself, as I am not an expert in modelling, however, the conclusions of the paper seem to be well-supported by the model.

Reviewer #2 (Public review):

Summary:

The authors have worked up a ``virtual thymus' using EPISIM, which has already been published. Attractive features of the computational model are stochasticity, cell-to-cell variability, and spatial heterogeneiety. They seek to explore the role of TECs, that release IL-7 which is important in the process of thymocyte division.

In the model, ordinary clones have IL7R levels chosen from a distribution, while `lesioned' clones have an IL7R value set to the maximum. The observation is that the lesioned clones are larger families, but the difference is not dramatic. This might be called a cell-intrinsic mechanism. One promising cell-extrinsic mechanism is mentioned: if a lesioned clone happens to be near a source of IL-7 and begins to proliferate, the progeny can crowd out cells of other clones and monopolise the IL-7 source. The effect will be more noticeable if sources are rare, so is seen when the TEC network is sparse.

Strengths:

Thymic disfunctions are of interest, not least because of T-ALL. New cells are added, one at a time, to simulate the conveyor belt of thymocytes on a background of stationary cells. They are thus able to follow cell lineages, which is interesting because one progenitor can give rise to many progeny.

There are some experimental results in Figures 4,5 and 6. For example, il7 crispant embryos have fewer thymocytes and smaller thymii; but increasing IL-7 availability produces large thymii.

Reviewer #3 (Public review):

Summary:

Tsingos et al. seek to advance beyond the current paradigm that proliferation of malignant cells in T-cell acute lymphoblastic leukemia occurs in a cell-autonomous fashion. Using a computational agent-based model and experimental validation, they show instead that cell proliferation also depends on interaction with thymic epithelial cells (TEC) in the thymic niche. One key finding is that a dense TEC network inhibits the proliferation of malignant cells and favors the proliferation of normal cells, whereas a sparse TEC network leads to rapid expansion of malignant thymocytes.

Strengths:

A key strength of this study is that it combines computational modeling using an agent-based model with experimental work. The original modeling and novel experimental work strengthen each other well. In the agent-based model, the authors also tested the effects of varying a few key parameters of cell proliferation.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study uses a cell-based computational model to simulate and study T cell development in the thymus. They initially applied this model to assess the effect of the thymic epithelial cells (TECs) network on thymocyte proliferation and demonstrated that increasing TEC size, density, or protrusions increased the number of thymocytes. They postulated and confirmed that this was due to changes in IL7 signalling and then expanded this work to encompass various environmental and cell-based parameters, including Notch signalling, cell cycle duration, and cell motility. Critical outcomes from the computational model were tested in vivo using medaka fish, such as the role of IL-7 signalling and minimal effect of Notch signalling.

Strengths:

The strength of the paper is the use of computational modelling to obtain unique insights into the niche parameters that control T cell development, such as the role of TEC architecture, while anchoring those findings with in vivo experiments. I can't comment on the model itself, as I am not an expert in modelling, however, the conclusions of the paper seem to be wellsupported by the model.

Weaknesses:

One potential issue is that many of the conclusions are drawn from the number of thymocytes, or related parameters such as the thymic size or proliferation of the thymocytes. The study only touches briefly on the influence of the thymic niche on other aspects of thymocyte behaviour, such as their differentiation and death.

We thank the reviewer for this constructive feedback. Indeed, the strength of our approach lies in the close cooperation between modellers and experimentalists. One advantage of the model is its ability to manipulate challenging or even impossible variables, such as TEC dimensions, which cannot be varied experimentally with current tools. 

The reviewer rightly pointed out that our validation focuses on comparing cell numbers or organ size as a proxy for cell numbers.

In our previous study (Aghaallaei et al., Science Advances, 2021), we focused more on differentiation and used the computational model to predict how proportions of T-cell sublineages would vary according to different parameter values, including the IL-7 availability. One of the initial inspirations for the focus on proliferation in this manuscript was the observation in this previous work that overexpression of IL-7 in the niche resulted in overproliferation. We also focused on proliferation and organ size because these are more easily measured in experimental conditions with the tools that we have available in medaka, allowing better comparisons to the computational results.

Regarding cell death, our experimental observations do not suggest that it plays a role before the final stages of T cell maturation. Hence, the model also does not include apoptosis before this stage either. 

However, we do agree that taking a closer look at the regulation of differentiation and cell death would be an exciting avenue for future study!

Please see our response to author recommendations below for more information on these points. Moreover, to make the model more accessible to non-experts, we have created new schematic figures, which we can be found in the Appendix of the revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors have worked up a ``virtual thymus' using EPISIM, which has already been published. Attractive features of the computational model are stochasticity, cell-to-cell variability, and spatial heterogeneity. They seek to explore the role of TECs, that release IL-7 which is important in the process of thymocyte division.

In the model, ordinary clones have IL7R levels chosen from a distribution, while `lesioned' clones have an IL7R value set to the maximum. The observation is that the lesioned clones are larger families, but the difference is not dramatic. This might be called a cell-intrinsic mechanism. One promising cell-extrinsic mechanism is mentioned: if a lesioned clone happens to be near a source of IL-7 and begins to proliferate, the progeny can crowd out cells of other clones and monopolise the IL-7 source. The effect will be more noticeable if sources are rare, so is seen when the TEC network is sparse.

Strengths:

Thymic disfunctions are of interest, not least because of T-ALL. New cells are added, one at a time, to simulate the conveyor belt of thymocytes on a background of stationary cells. They are thus able to follow cell lineages, which is interesting because one progenitor can give rise to many progeny.

There are some experimental results in Figures 4,5 and 6. For example, il7 crispant embryos have fewer thymocytes and smaller thymii; but increasing IL-7 availability produces large thymii.

Weaknesses:

On the negative side, like most agent-based models, there are dozens of parameters and assumptions whose values and validity are hard to ascertain.

The stated aim is to mimic a 2.5-to-11 day-old medaka thymus, but the constructed model is a geometrical subset that holds about 100 cells at a time in a steady state. The manuscript contains very many figures and lengthy descriptions of simulations run with different parameters values and assumptions. The abstract and conclusion did not help me understand what exactly has been done and learned. No attempt to synthesise observations in any mathematical formula is made.

The reviewer raises several important points to consider when working with mathematical or computational models.

As in many other agent-based models, we agree that our model makes use of many parameters. Many of these parameters summarize multiple steps and are treated as phenomenological, i.e. they do not represent a microscopic event such as the rate of an individual chemical reaction, but more high-level processes such as "rate of differentiation". Realistically, this process should consist of cascades of pathway components that regulate transcription factors.

In the supplementary material of our previous work (Aghaallaei et al., Science Advances, 2021) we provided an in-depth explanation of the mathematical formulation and rationale behind our choices in relation to the available biological data to select assumptions and restrict parameter value ranges. Four parameters that could not be characterized with pre-existing data, but which were crucial to the model's predictions, were studied in detail in that publication. Hence, the submitted manuscript starts with a well-calibrated model that has been tailored for the medaka thymus. The submitted manuscript explores the robustness of the system to lesions,  which we conceptualize as alterations in parameter values. We were surprised by how well the model recapitulated the time scales of overproliferation in the thymus of medaka embryos, which further supports the notion that our previous model calibration was successful.

Another important point raised by the reviewer is that the "validity [of parameters and assumptions is] hard to ascertain". We agree, which is precisely the reason why we aim to test the model's predictions through experimentation. Importantly, a model does not need to be perfect to be useful. For example, in the submitted manuscript we observed a discrepancy between model predictions and experimental results that led us to hypothesize negative feedback regulation from the proliferative state to differentiation. 

Thus, a major strength of modelling approaches is that they allow to identify erroneous or missing assumptions about the structure of the regulatory interaction network and its parametrization which can advance our scientific understanding of the underlying biology. Using models as an investigative tool is fundamental to the philosophy of systems biology (Kitano, Science, 2002), and is what we strive for.

The reviewer rightfully points out that we only represent a geometric subset of the organ. In our preliminary work, we considered representing the full three-dimensional thymus; however, we later simplified our approach, as the organ is a symmetric ellipsoid at this developmental stage. This decision vastly reduced our computational costs, enabling us to explore parameter space more effectively.

Nevertheless, we apologize if the submitted manuscript did not sufficiently emphasize the main insights of the paper, model limitations, and model construction. In the revised manuscript, we have improved the abstract and discussion sections to explicitly highlight the main results and limitations. We have also provided further details of the model's structure and underlying logic in the appendix.

Reviewer #3 (Public review):

Summary:

Tsingos et al. seek to advance beyond the current paradigm that proliferation of malignant cells in T-cell acute lymphoblastic leukemia occurs in a cell-autonomous fashion. Using a computational agent-based model and experimental validation, they show instead that cell proliferation also depends on interaction with thymic epithelial cells (TEC) in the thymic niche. One key finding is that a dense TEC network inhibits the proliferation of malignant cells and favors the proliferation of normal cells, whereas a sparse TEC network leads to rapid expansion of malignant thymocytes.

Strengths:

A key strength of this study is that it combines computational modeling using an agent-based model with experimental work. The original modeling and novel experimental work strengthen each other well. In the agent-based model, the authors also tested the effects of varying a few key parameters of cell proliferation.

Weaknesses:

A minor weakness is that the authors did not conduct a global sensitivity analysis of all parameters in their agent-based model to show that the model is robust to variation, which would demonstrate that their results would still hold under a reasonable level of variation in the model and model parameters. This is a minor point, and such a supporting study would end in an appendix or supplement.

The reviewer highlights the lack of a global sensitivity analysis as a minor weakness. 

In our previous work (Aghaallaei et al., Science Advances, 2021), we studied parameters sensitivity for some parameters, while in the submitted manuscript, we extended this exploration to parameters that we expected to be the most meaningful for cell proliferation.

In the revised version of the manuscript, we have included an additional supplementary figure alongside Figure 4 to show the effect of changing parameters in "control" simulations lacking a lesioned clone. These data are also provided in the source data to Figure 4. While this does not constitute an exhaustive exploration of all parameter space, it provides a useful overview of the effect of the studied parameters on thymocyte population size in the absence of lesioned clones.

Response to reviewer recommendations

In the revision, we have improved the manuscript to address the reviewers’ points. The following is an overview of the changes to the manuscript:

• We wrote an extensive Appendix to better explain the model implementation.

• The Abstract was rewritten to improve clarity on what was done and to highlight the main findings.

• Subheadings to paragraphs were rewritten to better emphasize the main findings.

• Font sizes in Figure 2J and Figure 4E were increased to improve readability.

• The spacing of graphical elements in the legend of Figure 4E was improved.

• An error in Figure 5B was corrected (the legend labels had been accidentally swapped).

• A new supplementary figure to Figure 4 shows the sensitivity of clone size in control simulations for a subset of the tested parameter combinations.

• The Conclusion section was rewritten to better highlight limitations of the study and Improve the summary of the main findings. 

• Minor wording improvements were done throughout the text to improve readability.

In the following we respond to the reviewers’ individual recommendations.

Reviewer #1 (Recommendations for the authors):

I am not an expert in modelling, so I apologise if I missed these points in the manuscript. I am slightly confused about how differentiation and death are included in the model. At the beginning of the results you mention that you model a 5 um slice, is it known which stages of development occur in that section of the thymus? 

We thank the reviewer for this question and appreciate the opportunity to clarify. Our virtual thymus is based on the medaka embryonic thymus, which we have extensively characterized using functional analyses and noninvasive in toto imaging (Bajoghli et al., Cell, 2009; Bajoghli et al., J Immunology, 2015; Aghaallaei et al., Science Advances, 2021; Aghaallaei, Eur J Immunology, 2022). These studies allowed us to map thymocyte developmental stages and migratory trajectories within the spatial context of a fully functional medaka thymus (see Figure 7 in Bajoghli et al., J Immunology, 2015).

To simplify the biological system without compromising model fidelity, we chose to simulate a representative 5 µm slice from the ventral half of the thymus. Importantly, the medaka thymus is a symmetric organ (Bajoghli et al., J Immunology 2015), hence this slice captures all key events of T-cell development, including thymus homing, differentiation, proliferation, selection, and egress akin to our in vivo observations (see Figure 7 in Bajoghli et al., 2015 and Figure 7a in Aghaallaei et al., Science Advances, 2021).

Furthermore, our model incorporates the spatial organization of the thymic cortex and medulla by including two types of thymic epithelial cells (TECs): cortical TECs positioned on the outer side, and medullary TECs on the inner side (see Figure Supplement 7 in Aghaallaei et al., Science Advances, 2021). Differentiation and cell death are modeled as discrete steps along the developmental trajectory, informed by our in vivo observations.

We apologize to the reviewer if the workings of the model were not sufficiently clear in the original manuscript. To address this, and as also requested by reviewer 2, we provided an extensive Appendix in the revised version of the manuscript that also includes visual summaries of the model logic in the form of intuitive flowcharts.

And is it known, or do you factor in, whether there are changes in the responsiveness of the thymocytes to signals, such as notch and IL7, depending on their state of differentiation?

We have previously examined the roles of IL-7 (Aghaallaei et al., Science Advances, 2021) and Notch1 (Aghaallaei et al., Europ J Immunology, 2022) signaling in the medaka thymus. These studies demonstrated that T cell progenitors are responsive to both IL7 and Notch signaling, whereas more differentiated, non-proliferative thymocytes are unresponsive to IL-7. Our in vivo observations further suggest that mature thymocytes require Notch signaling during the thymic selection process. This appears to be a species-specific phenomenon (Aghaallaei et al., Europ J Immunology, 2022). 

In the computational model, we include this state-specific responsiveness by incorporating a dependence on IL-7 and Notch signaling in the cellular decision to commit to the cell cycle (see Appendix Figure 6, and Appendix section X.) and in the decision of differentiating into αβ⁺ or γδ⁺ T cell subtypes (see Appendix Figure 5, and Appendix section IX.). Although the model still calculates pathway signaling activity for thymocytes in the differentiated stage belonging to the αβ⁺ or γδ⁺ subtype, this signaling activity has no downstream consequences for the cells’ behavior in the model.

Note that in the computational model we do not incorporate feedback loops that regulate pathway activity (for example, it could be that thymocytes upregulate the IL7R receptor at some point in their differentiation trajectory – in the absence of speciesspecific knowledge of such regulatory feedbacks, we have chosen not to include any in our model).

And you mention the stages of development are incorporated into the model but the main output that you discuss is thymocyte number or proliferation. It would be interesting to use the model to explore how parameters related to differentiation are changed by, for example, the level of IL7 signalling.

We agree that examining how factors like IL-7 signaling influence thymocyte differentiation is a promising direction for future work. Based on our previous modelling work (Aghaallaei et al., Science Advances, 2021), we expect that increased IL7 availability or sensitivity should result in an increase of cells differentiating into the γδ⁺ T cell subtype. As molecular tools for medaka continue to advance, we anticipate being able to refine and expand the model accordingly.

Moreover, we see strong potential for adapting the current computational framework to model thymopoiesis in other species, such as mouse or human, where stage-specific markers are well characterized. We have now explicitly mentioned this opportunity for future development in the conclusion section of the revised manuscript (see page #26).

It is also mentioned in the description of the model that the cells can die at the end of the development process. However, is death incorporated into the earlier stages of development? For instance, it is possible that when signals, such as a notch, are at low levels the thymocytes at certain stages of development will die.

We thank the reviewer for this comment. In a previous study, we mapped the spatial distribution of apoptotic cells within the medaka thymus and did not observe cell death in the region where ETPs enter the cortical thymus (Bajoghli et al., J Immunology, 2015) and where Notch1 signaling becomes activated (Aghaallaei et al., Europ J Immunology, 2021). Notch mutants exhibit a markedly reduced number of thymocytes, this reduction could be attributed either to impaired thymus homing or increased cell death within the thymus. However, our unpublished data shows that the total number of apoptotic cells in Notch1b-deficient thymus is comparable to their wild-type siblings. In fact, our in vivo observations revealed that the frequency of thymus colonization by progenitors is significantly reduced in the notch1b mutant (Aghaallaei et al., J E Immunol., 2021). Based on these in vivo observations, our computational model incorporates cell death only at the end of the thymocyte developmental trajectory. The current model does not consider cell death at earlier stages. 

Overall, the manuscript was well-written and the figures were clear and well-presented. A minor point would be that the writing in some of the figures was too small and difficult to read, such as in Figure 4. I also sometimes struggled to find the definition of the acronyms in the figures, for example in Figure 3 it would be helpful if the definitions for D, SD, and SA were given in the figure legend as well as in the figure itself.

We thank the reviewer for the kind words. We have reworked the figures to have larger more readable font sizes and improved figure legends as suggested.

Reviewer #2 (Recommendations for the authors):

Suppose the computational results did throw up an important new phenomenon. How might researchers seek to replicate it? If no mathematical relations can be given, can at least the code be made publicly available?

We apologize to the reviewer if the workings of the model were not sufficiently clear in the submitted manuscript. However, we believe there may have been a misunderstanding, and we would like to clarify that both the mathematical formulations and the code used in this study were publicly available in the scientific record at the time of submission.

Specifically, the full source code for the virtual thymus model is hosted in a permanent Zenodo repository (accessible here: https://zenodo.org/records/11656320), which includes:

- Model files and links to source codes for the simulation environment;

- Pre-compiled binary versions of the simulation environment (EPISIM) for both Windows and Linux platforms;

- Detailed documentation, including step-by-step instructions on how to install and use the provided files.

The repository link is cited in the manuscript (see page 38) and in the section “Data and materials availability”.  

In addition, the mathematical framework that underpins the computational model has already been published and described in detail in our previous work (Aghaallaei, et al. Science Advances, 2021). In the supplementary material of this publication, we provide extensive documentation of the model, including:

- A 13-page textual explanation of the design rationale;

- 44 equations describing model implementation;

- Parameter choices, partial sensitivity analysis, additional simulations, and supporting data presented in two figures and four tables.

Nonetheless, to improve transparency, we have added an extensive Appendix in the revised version of the manuscript that also includes visual summaries of the model logic in the form of intuitive flowcharts. We hope this clarification and the new provided appendix assures the reviewer that both reproducibility and transparency have been central to our approach. 

What about the growth of the animal and its thymus over weeks 2-11?

We thank the reviewer for this insightful question. Indeed, our current computational model does not incorporate thymus growth over time. We decided not to model the dynamic increase in TEC numbers or organ size over time because we wanted to maintain simplicity and computational tractability. Therefore, we assumed a steadystate thymic environment. The model is therefore limited to representing thymopoiesis under homeostatic conditions, as it appears to stabilize by day 11. This is a recognized limitation of the current model. Looking ahead, we plan to develop a more advanced computational framework that incorporates thymic growth and dynamic changes in cellular composition over time. We have now included a brief note on this limitation in the conclusion of the revised manuscript (see page #26).

eLife Assessment

The authors investigate arrestin2-mediated CCR5 endocytosis in the context of clathrin and AP2 contributions. Using an extensive set of NMR experiments, and supported by microscopy and other biophysical assays, the authors provide convincing data on the roles of AP2 and clathrin in CCR5 endocytosis. This important work will appeal to an audience beyond those studying chemokine receptors, including those studying GPCR regulation and trafficking. The distinct role of AP2 and not clathrin will be of particular interest to those studying GPCR internalization mechanisms.

Reviewer #1 (Public review):

Petrovic et al. investigate CCR5 endocytosis via arrestin2, with a particular focus on clathrin and AP2 contributions. The study is thorough and methodologically diverse. The NMR titration data are particularly compelling, clearly demonstrating chemical shift changes at the canonical clathrin-binding site (LIELD), present in both the 2S and 2L arrestin splice variants.

To assess the effect of arrestin activation on clathrin binding, the authors compare: truncated arrestin (1-393), full-length arrestin, and 1-393 incubated with CCR5 phosphopeptides. All three bind clathrin comparably, whereas controls show no binding. These findings are consistent with prior crystal structures showing peptide-like binding of the LIELD motif, with disordered flanking regions. The manuscript also evaluates a non-canonical clathrin binding site specific to the 2L splice variant. Though this region has been shown to enhance beta2-adrenergic receptor binding, it appears not to affect CCR5 internalization.

Similar analyses applied to AP2 show a different result. AP2 binding is activation-dependent and influenced by the presence and level of phosphorylation of CCR5-derived phosphopeptides. These findings are reinforced by cellular internalization assays.

In sum, the results highlight splice-variant-dependent effects and phosphorylation-sensitive arrestin-partner interactions. The data argue against a (rapidly disappearing) one-size-fits-all model for GPCR-arrestin signaling and instead support a nuanced, receptor-specific view, with one example summarized effectively in the mechanistic figure.

Reviewer #2 (Public review):

Summary:

Based on extensive live cell assays, SEC, and NMR studies of reconstituted complexes, these authors explore the roles of clathrin and the AP2 protein in facilitating clathrin-mediated endocytosis via activated arrestin-2. NMR, SEC, proteolysis, and live cell tracking confirm a strong interaction between AP2 and activated arrestin using a phosphorylated C-terminus of CCR5. At the same time, a weak interaction between clathrin and arrestin-2 is observed, irrespective of activation.

These results contrast with previous observations of class A GPCRs and the more direct participation by clathrin. The results are discussed in terms of the importance of short and long phosphorylated bar codes in class A and class B endocytosis.

Strengths:

The 15N,1H, and 13C, methyl TROSY NMR and assignments represent a monumental amount of work on arrestin-2, clathrin, and AP2. Weak NMR interactions between arrestin-2 and clathrin are observed irrespective of the activation of arrestin. A second interface, proposed by crystallography, was suggested to be a possible crystal artifact. NMR establishes realistic information on the clathrin and AP2 affinities to activated arrestin, with both kD and description of the interfaces.

Weaknesses:

This reviewer has identified only minor weaknesses with the study.

(1) Arrestin-2 1-418 resonances all but disappear with CCR5pp6 addition. Are they recovered with Ap2Beta2 addition, and is this what is shown in Supplementary Figure 2D?

(2) I don't understand how methyl TROSY spectra of arrestin2 with phosphopeptide could look so broadened unless there are sample stability problems.

(3) At one point, the authors added an excess fully phosphorylated CCR5 phosphopeptide (CCR5pp6). Does the phosphopeptide rescue resolution of arrestin2 (NH or methyl) to the point where interaction dynamics with clathrin (CLTC NTD) are now more evident on the arrestin2 surface?

(4) Once phosphopeptide activates arrestin-2 and AP2 binds, can phosphopeptide be exchanged off? In this case, would it be possible for the activated arrestin-2 AP2 complex to re-engage a new (phosphorylated) receptor?

(5) Did the authors ever try SEC measurements of arrestin-2 + AP2beta2+CCR5pp6 with and without PIP2, and with and without clathrin (CLTC NTD? The question becomes what the active complex is and how PIP2 modulates this cascade of complexation events in class B receptors.

Reviewer #3 (Public review):

Summary:

Overall, this is a well-done study, and the conclusions are largely supported by the data, which will be of interest to the field.

Strengths:

(1) The strengths of this study include experiments with solution NMR that can resolve high-resolution interactions of the highly flexible C-terminal tail of arr2 with clathrin and AP2. Although mainly confirmatory in defining the arr2 CBL 376LIELD380 as the clathrin binding site, the use of the NMR is of high interest (Figure 1). The 15N-labeled CLTC-NTD experiment with arr2 titrations reveals a span from 39-108 that mediates an arr2 interaction, which corroborates previous crystal data, but does not reveal a second area in CLTC-NTD that in previous crystal structures was observed to interact with arr2.

(2) SEC and NMR data suggest that full-length arr2 (1-418) binding with the 2-adaptin subunit of AP2 is enhanced in the presence of CCR5 phospho-peptides (Figure 3). The pp6 peptide shows the highest degree of arr2 activation and 2-adaptin binding, compared to less phosphorylated peptides or not phosphorylated at all. It is interesting that the arr2 interaction with CLTC NTD and pp6 cannot be detected using the SEC approach, further suggesting that clathrin binding is not dependent on arrestin activation. Overall, the data suggest that receptor activation promotes arrestin binding to AP2, not clathrin, suggesting the AP2 interaction is necessary for CCR5 endocytosis.

(3) To validate the solid biophysical data, the authors pursue validation experiments in a HeLa cell model by confocal microscopy. This requires transient transfection of tagged receptor (CCR5-Flag) and arr2 (arr2-YFP). CCR5 displays a "class B"-like behavior in that arr2 is rapidly recruited to the receptor at the plasma membrane upon agonist activation, which forms a stable complex that internalizes into endosomes (Figure 4). The data suggest that complex internalization is dependent on AP2 binding, not clathrin (Figure 5).

Weaknesses:

The interaction of truncated arr2 (1-393) was not impacted by CCR5 phospho-peptide pp6, suggesting the interaction with clathrin is not dependent on arrestin activation (Figure 2). This raises some questions.

Overall, the data are solid, but for added rigor, can these experiments be repeated without tagged receptor and/or arr2? My concern stems from the fact that the stability of the interaction between arr2 and receptor may be related to the position of the tags.

eLife Assessment

This study introduces ambisim, a rigorously validated and well-documented simulation framework that enables the generation of synthetic, genotype-aware single-cell RNA and ATAC sequencing datasets under realistic conditions. The authors provide solid evidence of its utility by benchmarking multiple demultiplexing methods and proposing a new variant consistency metric. While the tool is valuable for guiding method selection, the interpretation of the new metric requires further clarification.

Reviewer #1 (Public review):

Summary:

The authors developed a tool for simulating multiplexed single-cell RNA-seq and ATAC-seq data with various adjustable settings like ambient RNA/DNA rate and sequencing depth. They used the simulated data with different settings to evaluate the performance of many demultiplexing methods. They also proposed a new metric at single-cell level that correlates with the RNA/DNA contamination level.

Strengths:

The simulation tool has a straightforward design and provides adjustability in multiple parameters that have practical relevance, such as sequencing depth and ambient contamination rate. With the growing use of multiplexing in single-cell RNAseq and ATACseq experiments, the tools and results in this paper can guide the experimental design and tool selection for many researchers. The simulation tool also provides a platform for benchmarking newly developed demultiplexing tools.

Weaknesses:

The usefulness of the proposed new metric of "variant consistency" and how it can guide users in selecting demultiplexing methods seems a little unclear. It correlates with the level of ambient RNA/DNA contamination, which makes it look like a metric on data quality. However, it does depend on the exact demultiplexing method, yet it's not clear how it directly connects to the "accuracy" of each demultiplexing method, which is the most important property that users of these methods care about. Since the simulated data has ground truth of donor identities available, I would suggest using the simulated data to show whether "variant consistency" directly indicates the accuracy of each method, especially the accuracy within those "C2" reads.

I also think the tool and analyses presented in this paper need some further clarification and documentation on the details, such as how the cell-type gene and peak probabilities are determined in the simulation, and how doublets from different cell types are handled in the simulation and analysis. A few analyses and figures also need a more detailed description of the exact methods used.

Reviewer #2 (Public review):

Li et al. describe ambisim, a tool with the goal of creating realistic synthetic single-nucleus RNA/ATAC sequencing datasets. It has become standard to pool multiple genetically distinct donors when using single-cell sequencing followed by genotype-based demultiplexing (i.e., using donor single-nucleotide variants to identify specific donor origin). A plethora of tools exist to accomplish this demultiplexing, but advanced tools to create synthetic datasets, and therefore provide definitive benchmarking, are lacking. Ambisim is a well-thought-out simulator that improves upon previous tools available by allowing for modeling of variable ambient contamination proportions and doing so in a genotype-aware fashion. This provides more realistic synthetic datasets that provide challenging scenarios for future demultiplexing tools. The authors use ambisim to benchmark a large number of available and commonly used genotype-free and -dependent demultiplexing tools. They identify the strengths and weaknesses of these tools. They also go on to define a new metric, variant consistency, to further assess demultiplexing performance across tools. Overall, this manuscript provides a useful framework to more thoroughly evaluate future demultiplexing tools, as well as provides rationale for tool selection depending on a user's experimental conditions.

The authors provide measured conclusions that are supported by their findings. There are some aspects that are unclear.

(1) Throughout the manuscript, the figure legends are difficult to understand, and this makes it difficult to interpret the graphs.

(2) Since this is both a new tool and a benchmark, it would be worthwhile in the Discussion to comment on which demultiplexing tools one may want to choose for their dataset, especially given the warning against ensemble methods. From this extensive benchmarking, one may want to choose a tool based on the number of donors one has pooled, the modalities present, and perhaps even the ambient RNA (if it has been estimated previously).

(3) What are the minimal computational requirements for running ambisim? What is the time cost?

Author response:

Reviewer #1 (Public review):

The usefulness of the proposed new metric of "variant consistency" and how it can guide users in selecting demultiplexing methods seems a little unclear. It correlates with the level of ambient RNA/DNA contamination, which makes it look like a metric on data quality. However, it does depend on the exact demultiplexing method, yet it's not clear how it directly connects to the "accuracy" of each demultiplexing method, which is the most important property that users of these methods care about. Since the simulated data has ground truth of donor identities available, I would suggest using the simulated data to show whether "variant consistency" directly indicates the accuracy of each method, especially the accuracy within those "C2" reads.

I also think the tool and analyses presented in this paper need some further clarification and documentation on the details, such as how the cell-type gene and peak probabilities are determined in the simulation, and how doublets from different cell types are handled in the simulation and analysis. A few analyses and figures also need a more detailed description of the exact methods used. 

We thank the reviewer for their suggestions. We plan on revising the manuscript to reflect their suggestions, which will include clarification of the variant consistency metric and its relationship with demultiplexing accuracy based on the simulations and additional detail regarding ambisim’s generation of multiplexed snRNA/snATAC.

Reviewer #2 (Public review):

(1) Throughout the manuscript, the figure legends are difficult to understand, and this makes it difficult to interpret the graphs.

(2) Since this is both a new tool and a benchmark, it would be worthwhile in the Discussion to comment on which demultiplexing tools one may want to choose for their dataset, especially given the warning against ensemble methods. From this extensive benchmarking, one may want to choose a tool based on the number of donors one has pooled, the modalities present, and perhaps even the ambient RNA (if it has been estimated previously).

(3) What are the minimal computational requirements for running ambisim? What is the time cost? 

We thank the reviewer for their suggestions. We plan on updating the manuscript to better clarify figure legends. We will also outline a set of concrete recommendations in our discussion section based on different multiplexed experimental designs. Finally, we will also include extra computational benchmarks for ambisim.

eLife Assessment

This study dissects the function of 3 outputs of a specific population of modulatory neurons, dorsal raphe dopamine neurons, in social and affective behavior. It provides valuable information that both confirms prior results and provides new insights. The strength of the evidence is convincing, based on cutting-edge approaches and analysis. This study will be of interest to behavioral and systems neuroscientists, especially those interested in social and emotional behavior.

Reviewer #1 (Public review):

Summary:

The authors had previously found that a brief social isolation could increase the activity of these neurons, and that manipulation of these neurons could alter social behavior in a social rank dependent fashion. This manuscript explored which of the outputs were responsible for this, identifying the central nucleus of the amygdala as the key output region. The authors identified some discrete behavior changes associated with these outputs, and found that during photostimulation of these outputs, neuronal activity appeared altered in 'social response' neurons. In the revised manuscript, the authors address the comments in a rigorous fashion.

Strengths:

Rigorous analysis of the anatomy. Careful examination of the hetergenous effects on cell activity due to stimulation, linking the physiology with the behavior via photostimulation during recording in vivo.

Weaknesses:

The authors have responded to all of my comments.

Reviewer #2 (Public review):

Summary:

The authors perform a series of studies to follow up on their previous work, which established a role for dorsal raphe dopamine neurons (DRN) in the regulation of social-isolation-induced rebound in mice. In the present study, Lee et. al, use a combination of modern circuit tools to investigate putatively distinct roles of DRN dopamine transporting containing (DAT) projections to the bed nucleus of the stria terminalis (BNST), central amygdala (CeA), and posterior basolateral amygdala (BLP). Notably, they reveal that optogenetic stimulation of distinct pathways confers specific behavioral states, with DRNDAT-BLP driving aversion, DRNDAT-BNST regulating non-social exploratory behavior, and DRNDAT-CeA promoting socialability. A combination of electrophysiological studies and in situ hybridization studies reveal heterogenous dopamine and neuropeptide expression and different firing properties, providing further evidence of pathway-specific neural properties. Lastly, the authors combine optogenetics and calcium imaging to resolve social encoding properties in the DRNDAT-CeA pathway, which correlates observed social behavior to socially engaged neural ensembles.

Collectively, these studies provide an interesting way of dissecting out separable features of a complex multifaceted social-emotional state that accompanies social isolation and the perception of 'loneliness.' The main conclusions of the paper provide an important and interesting set of findings that increase our understanding of these distinct DRN projections and their role in a range of social (e.g., prosocial, dominance), non-social, and emotional behaviors. However, as noted below, the examination of these circuits within a homeostatic framework is limited given that a number of the datasets did not include an isolated condition. The DRNDAT-CeA pathway was investigated with respect to social homeostatic states in the present study for some of the datasets.

Strengths:

(1) The authors perform a comprehensive and elegant dissection of the anatomical, behavioral, molecular, and physiological properties of distinct DRN projections relevant to social, non-social, and emotional behavior, to address multifaceted and complex features of social state.

(2) This work builds on prior findings of isolation-induced changes in DRN neurons and provides a working framework for broader circuit elements that can be addressed across social homeostatic state.

(3) This work characterizes a broader circuit implicated in social isolation and provides a number of downstream targets to explore, setting a nice foundation for future investigation.

(4) The studies account for social rank and anxiety-like behavior in several of the datasets, which are important consideration to the interpretation of social motivation states, especially in male mice with respect to dominance behavior.

Weaknesses:

(1) The conceptual framework of the study is based on the premise of social isolation and perceived 'loneliness' under the framework of social homeostasis, analogous to hunger. In this framework, social isolation should provoke an aversive state and compensatory social contact behavior. In the authors' prior work, they demonstrate synaptic changes in DRN neurons and social rebound following acute social isolation. Thus, the prediction would be that downstream projections also would show state dependent changes as a function of social isolation state (e.g., grouped/socially engaged vs. isolated). In the current paper, a social isolation condition was included for some but not all experiments, which should be considered in the interpretation of the data, specifically within the context of dynamic isolation states.

(2) Figure 1 confirms co-laterals in the BNST and CeA via anatomical tracing studies. The goal of the optogenetic studies is to dissociate functional/behavioral roles of distinct projections. One limitation of optogenetic projection targeting is the possibility of back-propagating action potentials (stimulation of terminals in one region may back-propagate to activate cell bodies, and then afferent projections to other regions), and/or stimulation of fibers of passage. However, this is addressed in the discussion and the present data are convincing, which minimizes the concern.

(3) Sex as a biological variable should be considered in the present data, as included in the discussion.

Reviewer #3 (Public review):

Summary:

The authors investigated the role of dopaminergic neurons (dopamine transporter expressing, DAT) in the dorsal raphe nucleus (DRN) in regulating social and affective behavior through projections to the central nucleus of the amygdala (CeA), bed nucleus of the stria terminalis (BNST), and the posterior subdivision of the basolateral amygdala. The largest effect observed was in the DRN-DAT projections to the CeA. Augmenting previously published results from this group (Matthews et al., 2016), the comprehensive behavioral analysis relative to social dominance, gene expression analysis, electrophysiological profiling, and in vivo imaging provides novel insights into how DRN-DAT projections to the CeA influence the engagement of social behavior in the contexts of group housed and socially isolated mice.

Strengths:

Correlational analysis with social dominance is a nice addition to the study. The overall computational analyses performed are well-designed and rigorous.

Weaknesses:

(1) Analysis of dopamine receptor expression did not include Drd3, Drd4, or Drd5 which may provide more insights into how dopamine modulates downstream targets. This is particularly relevant to the BNST projection in which the densest innervation did not robustly co-localize with the expression of either Drd1 or Drd2. It is also possible that dopamine release from DRN-DAT neurons in any or all of these structures in modulating neurotransmitter release from inputs to these regions that contain D2 receptors on their terminals.

(2) Although not the focus of this study, without pharmacological blockade of dopamine receptors, it is not possible to assess what the contribution of dopamine is to the behavioral outcomes. Given the co-release of glutamate and GABA from these neurons it is possible that dopamine plays only a marginal role in the functional connectivity of DRN-DAT neurons.

(3) Photostimulation parameters used during the behavioral studies (8 pulses of light delivered at 30 Hz for several minutes) could lead to confounding results limiting data interpretation. As shown in Figure 6J, 8 pulses of light delivered at 30 Hz results in a significant attenuation of the EPSC amplitude in the BLP and CeA projection. Thus, prolonged stimulation could lead to significant synaptic rundown resulting in an overall suppression of connectivity in the later stages of the behavioral analyses.

Comments on revisions:

No further issues have been identified.

eLife Assessment

This paper reports on an important study that aims to move beyond current experimental approaches in speech production by (1) investigating speech in the context of a fully interactive task and (2) employing advanced methodology to record intracranial brain activity. Together these allow for examination of the unfolding temporal dynamics of brain-behaviour relationships during interactive speech. This approach and the analyses presented in support of the authors' claims pose convincing evidence.

Reviewer #1 (Public review):

Summary:

This paper reports an intracranial SEEG study of speech coordination, where participants synchronize their speech output with a virtual partner that is designed to vary its synchronization behavior. This allows the authors to identify electrodes throughout the left hemisphere of the brain that have activity (both power and phase) that correlates with the degree of synchronization behavior. They find that high-frequency activity in secondary auditory cortex (superior temporal gyrus) is correlated to synchronization, in contrast to primary auditory regions. Furthermore, activity in inferior frontal gyrus shows a significant phase-amplitude coupling relationship that is interpreted as compensation for deviation from synchronized behavior with the virtual partner.

Strengths:

(1) The development of a virtual partner model trained for each individual participant, which can dynamically vary its synchronization to the participant's behavior in real time, is novel and exciting.

(2) Understanding real-time temporal coordination for behaviors like speech is a critical and understudied area.

(3) The use of SEEG provides the spatial and temporal resolution necessary to address the complex dynamics associated with the behavior.

(4) The paper provides some results that suggest a role for regions like IFG and STG in the dynamic temporal coordination of behavior both within an individual speaker and across speakers performing a coordination task.

Reviewer #2 (Public review):

Summary:

This paper investigates the neural underpinnings of an interactive speech task requiring verbal coordination with another speaker. To achieve this, the authors recorded intracranial brain activity from the left (and to a lesser extent, the right) hemisphere in a group of drug-resistant epilepsy patients while they synchronised their speech with a 'virtual partner'. Crucially, the authors were able to manipulate the degree of success of this synchronisation by programming the virtual partner to either actively synchronise or desynchronise their speech with the participant, or else to not vary its speech in response to the participant (making the synchronisation task purely one-way). Using such a paradigm, the authors identified different brain regions that were either more sensitive to the speech of the virtual partner (primary auditory cortex), or more sensitive to the degree of verbal coordination (i.e. synchronisation success) with the virtual partner (left secondary auditory cortex and bilateral IFG). Such sensitivity was measured by (1) calculating the correlation between the index of verbal coordination and mean power within a range of frequency bands across trials, and (2) calculating the phase-amplitude coupling between the behavioural and brain signals within single trials (using the power of high-frequency neural activity only). Overall, the findings help to elucidate some of the brain areas involved in interactive speaking behaviours, particularly highlighting high-frequency activity of the bilateral IFG as a potential candidate supporting verbal coordination.

Strengths:

This study provides the field with a convincing demonstration of how to investigate speaking behaviours in more complex situations that share many features with real-world speaking contexts e.g. simultaneous engagement of speech perception and production processes, the presence of an interlocutor and the need for inter-speaker coordination. The findings thus go beyond previous work that has typically studied solo speech production in isolation, and represent a significant advance in our understanding of speech as a social and communicative behaviour. It is further an impressive feat to develop a paradigm in which the degree of cooperativity of the synchronisation partner can be so tightly controlled; in this way, this study combines the benefits of using pre-recorded stimuli (namely, the high degree of experimental control) with the benefits of using a live synchronisation partner (allowing the task to be truly two-way interactive, an important criticism of other work using pre-recorded stimuli). A further key strength of the study lies in its employment of stereotactic EEG to measure brain responses with both high temporal and spatial resolution, an ideal method for studying the unfolding relationship between neural processing and this dynamic coordination behaviour.

Weaknesses:

One limitation of the current study is the relatively sparse coverage of the right hemisphere by the implanted electrodes (91 electrodes in the right compared to 145 in the left). Of course, electrode location is solely clinically motivated, and so the authors did not have control over this. In a previous version of this article, the authors therefore chose not to include data from the right hemisphere in reported analyses. However, after highlighting previous literature suggesting that the right hemisphere likely has high relevance to verbal coordination behaviours such as those under investigation here, the authors have now added analyses of the right hemisphere data to the results. These confirm an involvement of the right hemisphere in this task, largely replicating left hemisphere results. Some hemispheric differences were found in responses within the STG; however, interpretation should be tempered by an awareness of the relatively sparse coverage of the right hemisphere meaning that some regions have very few electrodes, resulting in reduced statistical power.

Author response:

The following is the authors’ response to the previous reviews

Recommendations for the authors: 

Reviewer #1 (Recommendations for the authors): 

(1) The use of the term "language network" throughout is unclear. Does this refer to work by Ev Fedorenko (i.e., does it distinguish language from other cognitive and sensorimotor domains)? There does not seem to be much in the behavior presented here that aligns with an interpretation about language per se. 

We understand the reviewer’s point according to the work by Evelina Fedorenko considering this distinction. It is important to precise that in our present study we did not refer to her work when using the term “language network”.

(2) Fig 4A: the "B" is missing on the figure panel to denote which Broadmann areas are shown. 

We updated the figure panel by adding the “B” for more clarity.

Reviewer #2 (Recommendations for the authors): 

I think it would be worth mentioning the relatively sparse coverage of the right hemisphere in your abstract. 

We agree with this suggestion, we updated the abstract as follows :  

“Our use of language, which is profoundly social in nature, essentially takes place in interactive contexts and is shaped by precise coordination dynamics that interlocutors must observe. Thus, language interaction is highly demanding on fast adjustment of speech production. Here, we developed a real-time coupled-oscillators virtual partner that allows - by changing the coupling strength parameters - to modulate the ability to synchronise speech with a virtual speaker. Then, we recorded the intracranial brain activity of 16 patients with drug-resistant epilepsy while they performed a verbal coordination task with the virtual partner (VP). More precisely, patients had to repeat short sentences synchronously with the VP. This synchronous speech task is efficient to highlight both the dorsal and ventral language pathways. Importantly, combining time-resolved verbal coordination and neural activity shows more spatially differentiated patterns and different types of neural sensitivity along the dorsal pathway. More precisely, high-frequency activity in left secondary auditory regions is highly sensitive to verbal coordinative dynamics, while primary regions are not. Finally, while bilateral engagement was observed in the high-frequency activity of the IFG BA44— which seems to index online coordinative adjustments that are continuously required to compensate deviation from synchronisation—interpretation of right hemisphere involvement should be approached cautiously due to relatively sparse electrode coverage. These findings illustrate the possibility and value of using a fully dynamic, adaptive and interactive language task to gather deeper understanding of the subtending neural dynamics involved in speech perception, production as well as their interaction.”

There are a few places in your results section which haven't been updated to reflect the fact that some sections refer only to the left hemisphere e.g. 

Page 11 line 347: "Overall, neural responses are present in all six canonical frequency bands" I think this should be "In the left hemisphere, neural responses are present...". 

Page 12 line 355: "As expected, the whole language network is strongly involved..." I think this should be "As expected, the whole left hemisphere language network is strongly involved".  Page 17 (third paragraph of the discussion): "The observed negative correlation between verbal coordination and high-frequency activity (HFa) in STG BA22" I think this should be "in left STG BA22". 

We thank the reviewer for highlighting these important points. The updated lines are as follows:

Page 11 line 348: ”In the left hemisphere, neural responses are present in all six canonical frequency bands…”  

Page 12 line 356: ”As expected, the whole left hemisphere language network is strongly involved..." Page 17 lines 502-503 : “The observed negative correlation between verbal coordination and highfrequency activity (HFa) in left STG BA22 suggests a suppression of neural responses as the degree of behavioural synchrony increases.”

Reviewer #3 (Public review):

Summary:

In microbiology, accurately characterizing microbial populations and communities is essential. One widely used approach is to measure the absolute or relative abundance of microbial species. Recent research in microbial ecology, for instance, has shown that even genetically identical hosts exposed to the same microbial pool can develop very different gut microbiota, largely due to random colonization events. This study builds on that idea but adds a valuable layer: it suggests that some of the observed variability might actually result from experimental noise, specifically the randomness introduced by dilution and plate counting techniques. To address this, the authors introduce REPOP, a new tool designed to improve the quantification of microbial populations by explicitly accounting for the inherent stochasticity in these methods. They test REPOP using both simulated and experimental datasets, showing how it can help recover meaningful trends.

Strengths:

Overall, this paper is a good contribution to the field. The motivation is clear: improving our ability to quantify microbial populations is crucial for many research areas. The authors make a strong case that ignoring experimental noise is no longer acceptable, and they offer a well-argued solution. The manuscript is well-written and easy to follow, and the logic behind REPOP is convincingly laid out. The use of simulated data is especially valuable, as it allows the authors to test whether the method can recover known inputs, an important validation step. Even with experimental data, where true values are unknown, the method seems to behave in a reasonable and expected way, which is reassuring. All in all, this is an important step forward in how we quantify microbial populations.

Weaknesses:

While the study is promising, there are a few areas where the paper could be strengthened to increase its impact and usability. First, the extent to which dilution and plating introduce noise is not fully explored. Could this noise significantly affect experimental conclusions? And under what conditions does it matter most? Does it depend on experimental design or specific parameter values? Clarifying this would help readers appreciate when and why REPOP should be used. Second, more practical details about the tool itself would be very helpful. Simply stating that it is available on GitHub may not be enough. Readers will want to know what programming language it uses, what the input data should look like, and ideally, see a step-by-step diagram of the workflow. Packaging the tool as an easy-to-use resource, perhaps even submitting it to CRAN or including example scripts, would go a long way, especially since microbiologists tend to favor user-friendly, recipe-like solutions. Third, it would be great to see the method tested on existing datasets, such as those from Nic Vega and Jeff Gore (2017), which explore how colonization frequency impacts abundance fluctuation distributions. Even if the general conclusions remain unchanged, showing that REPOP can better match observed patterns would strengthen the paper's real-world relevance. Lastly, it would be helpful for the authors to briefly discuss the limitations of their method, as no approach is without its constraints. Acknowledging these would provide a more balanced and transparent perspective.

Reviewer #2 (Public review):

Summary:

Microbial population abundances are regularly estimated by multiplying plate counts by dilution factors, with inferences made about sample heterogeneity without taking into account heterogeneity generated through dilution and plating methods. The authors have developed REPOP, a method for disentangling methodological stochasticity from ecological heterogeneity using a Bayesian framework. They present three models: a unimodal distribution, a multimodal distribution, and a multimodal distribution that incorporates a colony count cutoff. They use a combination of simulated and experimental data to show the effectiveness of the REPOP method in resolving true microbial population distributions.

Strengths:

Overall, this paper addresses a significant issue in microbial ecology and reliably demonstrates that the REPOP method improves upon current methods of estimating microbial population heterogeneity, particularly with simulation data. The three models presented build upon each other and are discussed in a way that is fairly accessible to a broad audience. The authors also show that leveraging the information provided by non-countable plates is important. Additionally, the authors address the potential for extending this method to other sources of methodological stochasticity that may occur in microbial plating. However, it does seem that they could extend this further by discussing ways that this method could be applied to non-microbial systems, allowing this work to appeal to a broader audience.

Weaknesses:

A more thorough discussion of when and by how much estimated microbial population abundance distributions differ from the ground truth would be helpful in determining the best practices for applying this method. Not only would this allow researchers to understand the sampling effort necessary to achieve the results presented here, but it would also contextualize the experimental results presented in the paper. Particularly, there is a disconnect between the discussion of the large sample sizes necessary to achieve accurate multimodal distribution estimates and the small sample sizes used in both experiments.

Reviewer #1 (Public review):

Summary:

The authors developed a novel theoretical/computational procedure to count bacterial populations without introducing artificial randomness effects due to dilution. Surprisingly, this very important aspect of studies of bacterial systems has been overlooked. The proposed method provides a simple and transparent approach to eliminate the randomness of bacterial accounting procedures, allowing now to fully concentrate on the intrinsic effects of the studied systems.

Strengths:

A very simple and clear procedure is introduced and explained in full detail. This elegant approach finds an excellent compromise between mathematical rigor and computational efficiency, which is important for practical applications. The provided examples are convincing beyond a doubt, clearly indicating the potential strong impact of the proposed framework. Various complications and possible issues are also discussed and analyzed. This seems to be a very powerful novel method that should significantly advance the analysis of complex biological systems.

Weaknesses:

The only minor weakness that I found is the assumption of independence of bacterial species, which is expressed as the well-stirred approximation. One could imagine that bacterial species might cooperate, leading to non-uniform distributions that are real. How to distinguish such situations?

I believe that this method can be extended to determine if this is the case or not before the application. For example, if the bacteria species are independent of each other and one can use the binomial distributions, then the Fano factor would be proportional to the overall relative fraction of bacterial species. Maybe a simple test can be added to test it before the application of REPOP. However, I believe that this is a minor issue.

eLife Assessment

This important study introduces a Bayesian method to determine bacterial counts that accounts for the experimental noise inherent to dilution and plating methods, and distinguishes it from biological uncertainty. The evidence supporting the conclusions is convincing, combining simulated data and experimental data. The method will be of interest to microbial ecologists, and potentially to the broader community interested in inference from biological data, even more so if the domain of application and the limitations are further clarified.

eLife Assessment

This valuable study addresses a gap in our understanding of how the size of the attentional field is represented within the visual cortex. The evidence supporting the role of visual cortical activity is convincing, based on a novel modeling analysis of fMRI data. The results will be of interest to psychologists and cognitive neuroscientists.

Reviewer #1 (Public review):

The authors conducted an fMRI study to investigate the neural effects of sustaining attention to areas of different sizes. Participants were instructed to attend to alphanumeric characters arranged in a circular array. The size of attention field was manipulated in four levels, ranging from small (18 deg) to large (162 deg). They used a model-based method to visualize attentional modulation in early visual cortex V1 to V3, and found spatially congruent modulations of the BOLD response, i.e., as the attended area increased in size, the neural modulation also increased in size in the visual cortex. They suggest that this result is a neural manifestation of the zoom-lens model of attention and that the model-based method can effectively reconstruct the neural modulation in the cortical space.

The study is well-designed with sophisticated and comprehensive data analysis. The results are robust and show strong support for a well-known model of spatial attention, the zoom-lens model. Overall, I find the results interesting and useful for the field of visual attention research.

Comments on revisions:

The authors have addressed my previous comments satisfactorily. I would encourage the authors to make data and code publicly available, which appears to be the custom in this era.

Reviewer #2 (Public review):

Summary:

The study in question utilizes functional magnetic resonance imaging (fMRI) to dynamically estimate the locus and extent of covert spatial attention from visuocortical activity. The authors aim to address an important gap in our understanding of how the size of the attentional field is represented within the visual cortex. They present a novel paradigm that allows for the estimation of the spatial tuning of the attentional field and demonstrate the ability to reliably recover both the location and width of the attentional field based on BOLD responses.

Strengths:

(1) Innovative Paradigm: The development of a new approach to estimate the spatial tuning of the attentional field is a significant strength of this study. It provides a fresh perspective on how spatial attention modulates visual perception.

(2) Refined fMRI Analysis: The use of fMRI to track the spatial tuning of the attentional field across different visual regions is methodologically rigorous and provides valuable insights into the neural mechanisms underlying attentional modulation.

(3) Clear Presentation: The manuscript is well-organized, and the results are presented clearly, which aids in the reader's comprehension of the complex data and analyses involved.

Weaknesses:

(1) Lack of Neutral Cue Condition: The study does not include a neutral cue condition where the cue width spans 360{degree sign}, which could serve as a valuable baseline for assessing the BOLD response enhancements and diminishments in both attended and non-attended areas.

(2) Clarity on Task Difficulty Ratios: The explicit reasoning for the chosen letter-to-number ratios for various cue widths is not detailed. Ensuring clarity on these ratios is crucial, as it affects the task difficulty and the comparability of behavioral performance across different cue widths. It is essential that observed differences in behavior and BOLD signals are attributable solely to changes in cue width and not confounded by variations in task difficulty.

Comments on revisions:

(1) Please standardize the naming of error metrics across Figures 4-6 to improve clarity (e.g., "angular error" (Figure 4), "|angular error|" (Figure 5), and "absolute error" (Figure 6) appear to refer to the same measure). This inconsistency is also present in the main text.

(2) Consider briefly mentioning the baseline offset in Lines 179-186. It is included in Figures 4-7 and serves as a reference for interpreting attentional modulation alongside gain. Introducing it with other model parameters would improve clarity.

(3) It may be valuable to examine BOLD responses in unattended visual regions. As shown in Figure 2a, suppression patterns (e.g., the most negative responses) appear to vary in extent and distribution with attentional cue width. Analyzing these unattended regions may offer a more complete view of how attention shapes the spatial profile of cortical activity.

eLife Assessment

This important work offers a fresh perspective central to merozoite surface biology and potential implications on vaccine design, challenging the dogma that MSPs are indispensable invasion engines. Although the authors only deleted bp 132-819, the data based on Western blot, IFA, and RNA‐seq provide compelling evidence that while MSP2 is dispensable for growth, it serves as an immune modulator for AMA1. This work will be of particular interest to scientists working on different aspects of Plasmodium biology and vaccinology.

Reviewer #1 (Public review):

Henshall et al. delete the highly abundant merozoite surface protein PfMSP2 from two Plasmodium falciparum laboratory lines (3D7 and Dd2) using CRISPR-Cas9. Parasites lacking MSP2 replicate and invade red cells normally, opposing the experimental history that suggests MSP2 is essential. Unexpectedly, the knock-outs become more susceptible to several inhibitory antibodies - most strikingly those that target the apical antigen AMA1-while antibodies to other surface or secreted proteins are largely unaffected. Recombinant MSP2 added in vitro can dampen AMA1-antibody binding, supporting a "conformational masking" model. The reported data suggest that MSP2 helps shield key invasion ligands from host antibodies and may itself be a double-edged vaccine target.

Reviewer #2 (Public review):

Summary:

The authors were trying to establish the role of Plasmodium falciparum surface protein 2 in merozoite biology, specifically the process of erythrocyte invasion.

Strengths:

The major strengths of the manuscript are in the Plasmodium falciparum genetic and phenotyping approaches. PfMSP2 knockouts are made in two different strains, which is important as it is known that invasion pathways can vary between strains, but is a level of comprehensiveness that is not always delivered in P. falciparum genetic studies. The knockout strains are characterised very thoroughly using multiple different assays, and the authors should be commended for publishing a good deal of negative data, where no phenotype was detected. This is not always done, but is very helpful for the field and reduces the potential for experimental redundancy, i.e., others repeating work that has already been performed but never published. The quality of the writing, referencing, and figures is also generally strong, although a few minor typos and technical comments on presentation have been communicated to the authors.

Weaknesses:

There are, however, some areas that are weaker.

(1) The section describing Laverania and avian Plasmodium MSP2 comparison is a lengthy section and could be told much more concisely for clarity in delivering the key message, i.e., that conservation in distantly related Plasmodium species could indicate an important function. The identification of MSP2-like genes in avian Plasmodium species was highlighted previously in the referenced Escalante paper, so it is not entirely novel, although this paper goes into more detailed characterisation of the extent of conservation. Overall, this section takes up much more space in the manuscript than is merited by the novelty and significance of the findings.

(2) Characterisation of the knockout strains is generally thorough, though relatively few interactions were followed by live microscopy (Figures 3E-H). A minimum of 30 merozoites were followed in each assay (although the precise number is not specified in the figure or legend), but there are intriguing trends in the data that could potentially have become significant if n was increased.

(3) The comparative RNAseq data is interesting, but is not followed up to any significant degree. Multiple transcripts are up-regulated in the absence of PfMSP2, but they are largely dismissed because they are genes of unknown function, not previously linked to invasion, or lack an obvious membrane anchor. Having gone to the lengths of exploring potentially compensatory changes in gene expression, it is disappointing not to validate or explore the hits that result.

(4) Given the abundance of PfMSP2 on the merozoite surface, it would have been interesting to see whether the knockout lines have any noticeable difference in surface composition, as viewed by electron microscopy, although, of course, this experiment relies on access to the appropriate facilities.

(5) One of the key findings is that deletion of PfMSP2 increases inhibition by some antibodies/nanobodies (some anti-CSS2, some anti-AMA1) but not others (anti-EBA/RH, anti-EBA175, anti-Rh5, anti-TRAMP, some anti-CSS2, some anti-AMA1). The data supporting these changes in inhibition are solid, but the selectivity of the effect (only a few antibodies, and generally those targeting later stages in invasion) is not really discussed in any detail. Do the authors have a hypothesis for this selectivity? The authors make attempts to explore the mechanisms for this antibody-masking (Figure 7), but the data is less solid. Surface Plasmon Resonance was non-conclusive, while an ELISA approach co-incubating MSP2 and anti-AMA1 antibodies to wells coated with AMA1 lacks appropriate controls (eg, including other merozoite proteins in similar experiments).

Overall, the claim that PfMSP2 is non-essential for in vitro growth is well justified and is an important contribution to the field. The impact of PfMSP2 deletion on antibody inhibition (which is highlighted in the title of the manuscript) and the mechanism behind it is much less definitive, but does open up an interesting area for further investigation, with more work to be done.

eLife Assessment

This valuable work explores how synaptic activity encodes information during memory tasks. All reviewers agree that the work is of very high quality and that the methodological approach is praiseworthy. Although the experimental data support the possibility that phospholipase diacylglycerol signaling and synaptotagmin 7 (Syt7) dynamically regulate the vesicle pool required for presynaptic release, concerns remain that the central finding of paired-pulse depression at very short intervals may be more likely due to Ca²⁺ channel inactivation rather than vesicle pool depletion. Overall, this is a solid study although the results warrant consideration of alternative interpretations.

Reviewer #3 (Public review):

The central issue for evaluating the overfilling hypothesis is the identity of the mechanism that causes the very potent (>80% when inter pulse is 20 ms), but very quickly reverting (< 50 ms) paired pulse depression (Fig 1G, I). To summarize: the logic for overfilling at local cortical L2/3 synapses depends critically on the premise that probability of release (pv) for docked and fully primed vesicles is already close to 100%. If so, the reasoning goes, the only way to account for the potent short-term enhancement seen when stimulation is extended beyond 2 pulses would be by concluding that the readily releasable pool overfills. However, the conclusion that pv is close to 100% depends on the premise that the quickly reverting depression is caused by exocytosis dependent depletion of release sites, and the evidence for this is not strong in my opinion. Caution is especially reasonable given that similarly quickly reverting depression at Schaffer collateral synapses, which are morphologically similar, was previously shown to NOT depend on exocytosis (Dobrunz and Stevens 1997). Note that the authors of the 1997 study speculated that Ca2+-channel inactivation might be the cause, but did not rule out a wide variety of other types of mechanisms that have been discovered since, including the transient vesicle undocking/re-docking (and subsequent re-priming) reported by Kusick et al (2020), which seems to have the correct timing.

In an earlier round of review, I suggested raising extracellular Ca2+, to see if this would increase synaptic strength. This is a strong test of the authors' model because there is essentially no room for an increase in synaptic strength. The authors have now done experiments along these lines, but the result is not clear cut. On one hand, the new results suggest an increase in synaptic strength that is not compatible with the authors' model; technically the increase does not reach statistical significance, but, likely, this is only because the data set is small and the variation between experiments is large. Moreover, a more granular analysis of the individual experiments seems to raise more serious problems, even supporting the depletion-independent counter hypothesis to some extent. On the other hand, the increase in synaptic strength that is seen in the newly added experiments does seem to be less at local L2/3 cortical synapses compared to other types of synapses, measured by other groups, which goes in the general direction of supporting the critical premise that pv is unusually high at L2/3 cortical synapses. Overall, I am left wishing that the new data set were larger, and that reversal experiments had been included as explained in the specific points below.

Specific Points:

(1) One of the standard methods for distinguishing between depletion-dependent and depletion-independent depression mechanisms is by analyzing failures during paired pulses of minimal stimulation. The current study includes experiments along these lines showing that pv would have to be extremely close to 1 when Ca2+ is 1.25 mM to preserve the authors' model (Section "High double failure rate ..."). Lower values for pv are not compatible with their model because the k1 parameter already had to be pushed a bit beyond boundaries established by other types of experiments. The authors now report a mean increase in synaptic strength of 23% after raising Ca to 2.5 mM. The mean increase is not quite statistically significant, but this is likely because of the small sample size. I extracted a 95% confidence interval of [-4%, +60%] from their numbers, with a 92% probability that the mean value of the increase in the full population is > 5%. I used the 5% value as the greatest increase that the model could bear because 5% implies pv < 0.9 using the equation from Dodge and Rahamimoff referenced in the rebuttal. My conclusion from this is that the mean result, rather than supporting the model, actually undermines it to some extent. It would have likely taken 1 or 2 more experiments to get above the 95% confidence threshold for statistical significance, but this is ultimately an arbitrary cut off.

(2) The variation between experiments seems to be even more problematic, at least as currently reported. The plot in Figure 3-figure supplement 3 (left) suggests that the variation reflects true variation between synapses, not measurement error. And yet, synaptic strength increased almost 2-fold in 2 of the 8 experiments, which back extrapolates to pv < 0.2. If all of the depression is caused by depletion as assumed, these individuals would exhibit paired pulse facilitation, not depression. And yet, from what I can tell, the individuals depressed, possibly as much as the synapses with low sensitivity to Ca2+, arguing against the critical premise that depression equals depletion, and even arguing - to some extent - for the counter hypothesis that a component of the depression is caused by a mechanism that is independent of depletion. I would strongly recommend adding an additional plot that documents the relationship between the amount of increase in synaptic strength after increasing extracellular Ca2+ and the paired pulse ratio as this seems central.

(3) Decrease in PPR. The authors recognize that the decrease in the paired-pulse ratio after increasing Ca2+ seems problematic for the overfilling hypothesis by stating: "Although a reduction in PPR is often interpreted as an increase in pv, under conditions where pv is already high, it more likely reflects a slight increase in pocc or in the number of TS vesicles, consistent with the previous estimates (Lin et al., 2025)." I looked quickly, but did not immediately find an explanation in Lin et al 2025 involving an increase in pocc or number of TS vesicles, much less a reason to prefer this over the standard explanation that reduced PPR indicates an increase in pv. The authors should explain why the most straightforward interpretation is not the correct one in this particular case to avoid the appearance of cherry picking explanations to fit the hypothesis.

(4) The authors concede in the rebuttal that mean pv must be < 0.7, but I couldn't find any mention of this within the manuscript itself, nor any explanation for how the new estimate could be compatible with the value of > 0.99 in the section about failures.

(5) Although not the main point, comparisons to synapses in other brain regions reported in other studies might not be accurate without directly matching experiments. As it is, 2 of 8 synapses got weaker instead of stronger, hinting at possible rundown, but this cannot be assessed because reversibility was not evaluated. In addition, comparing axons with and without channel rhodopsins might be problematic because the channel rhodopsins might widen action potentials.

(6) Perhaps authors could double check with Schotten et al about whether PDBu does/does not decrease the latency between osmotic shock and transmitter release. This might be an interesting discrepancy, but my understanding is that Schotten et al didn't acquire information about latency because of how the experiments were designed.

(7) The authors state: "These data are difficult to reconcile with a model in which facilitation is mediated by Ca2+-dependent increases in pv." However, I believe that discarding the premise that depression is always caused by depletion would open up wide range of viable possibilities.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Shin et al. conduct extensive electrophysiological and behavioral experiments to study the mechanisms of short-term synaptic plasticity at excitatory synapses in layer 2/3 of the rat medial prefrontal cortex. The authors interestingly find that short-term facilitation is driven by progressive overfilling of the readily releasable pool, and that this process is mediated by phospholipase C/diacylglycerol signaling and synaptotagmin-7 (Syt7). Specifically, knockdown of Syt7 not only abolishes the refilling rate of vesicles with high fusion probability, but it also impairs the acquisition of trace fear memory.

Overall, the authors offer novel insight to the field of synaptic plasticity through well-designed experiments that incorporate a range of techniques.

Comments on revisions:

The authors have adequately addressed my earlier comments and questions.

Reviewer #2 (Public review):

All the comments from Reviewer #2 are the same as her/his comments to our original manuscript. Therefore, we have already responded to all the following comments in the first revision. Here we described our additional responses to the same comments.

Summary:

Shin et al aim to identify in a very extensive piece of work a mechanism that contributes to dynamic regulation of synaptic output in the rat cortex at the second time scale. This mechanism is related to a new powerful model and is well versed to test if the pool of SV ready for fusion is dynamically scaled to adjust supply demand aspects. The methods applied are state-of-the-art and both address quantitative aspects with high signal to noise. In addition, the authors examine both excitatory output onto glutamatergic and GABAergic neurons, which provides important information on how general the observed signals are in neural networks. The results are compellingly clear and show that pool regulation may be predominantly responsible. Their results suggests that a regulation of release probability, the alternative contender for regulation, is unlikely to be involved in the observed short term plasticity behavior (but see below). Besides providing a clear analysis of the underlying physiology, they test two molecular contenders for the observed mechanism by showing that loss of Synaptotagmin7 function and the role of the Ca dependent phospholipase activity seems critical for the short term plasticity behavior. The authors go on to test the in vivo role of the mechanism by modulating Syt7 function and examining working memory tasks as well as overall changes in network activity using immediate early gene activity. Finally, they model their data, providing strong support for their interpretation of TS pool occupancy regulation.

Strengths:

This is a very thorough study, addressing the research question from many different angles and the experimental execution is superb. The impact of the work is high, as it applies recent models of short term plasticity behavior to in vivo circuits further providing insights how synapses provide dynamic control to enable working memory related behavior through non-permanent changes in synaptic output.

Weaknesses:

While this work is carefully examined and the results are presented and discussed in a detailed manner, the reviewer is still not fully convinced that regulation of release probability is not a putative contributor to the observed behavior. No additional work is needed, but in the moment, I am not convinced that changes in release probability are not in play. One solution may be to extend the discussion of changes in rules probability as an alternative.

As the Reviewer #3 suggested, we examined the dependence of EPSC amplitude on extracellular [Ca²⁺] ([Ca²⁺]_o) in order to test our assertion that vesicular release probability (p_v) is already saturated in resting conditions at L2/3 recurrent synapses. A three-fold increase is expected according to Dodge and Rahamimoff (1967), if resting p_v has enough room to increase, when [Ca²⁺]_o is elevated from 1.3 to 2.5 mM. We found an increase in the baseline EPSC amplitude only by 23%, and this change was not statistically significant, supporting our assertion.

Fig 3. I am confused about the interpretation of the Mean Variance analysis outcome. Since the data points follow the curve during induction of short term plasticity, doesn't these suggests that release probability and not the pool size increases?

We separated the conventional release probability into a multiplication of p_v and p_occ, in which p_v = probability of TS vesicles and p_occ = occupancy of release sites by TS vesicles. In this regard, the abscissa of V-M plot represents the conventional release probability. Because p_v is close to unity, we interpreted a change along the abscissa as a change of p_occ.

Related, to measure the absolute release probability and failure rate using the optogenetic stimulation technique is not trivial as the experimental paradigm bias the experiment to a given output strength, and therefore a change in release probability cannot be excluded.

We agree to this concern. Because EPSC data were obtained by optogenetic stimulation, it cannot be ruled out a possibility that optogenetic stimulation biased the release probability. Although we found that STP obtained by dual patch experiment was not different from that by optogenetic stimulation, it needs to confirm our conclusion using dual patch or other methods.

Fig. 4B interprets the phorbol ester stimulation to be the result of pool overfilling, however, phorbol ester stimulation has also been shown to increase release probability without changing the size of the readily releasable pool. The high frequency of stimulation may occlude an increased paired pulse depression in presence of OAG, that others have interpreted in mammalian synapses as an increase in release probability.

Provided that pv of TS vesicles is very high, the OAG-induced increase in EPSC1 and low STF and PTA are consistent with higher baseline p_occ in PDBu conditions, while the number of docking sites is limited. It should be noted that previous PDBu-induced invariance of the RRP size is based on measuring the RRP size using hypertonic solution (Basu et al., 2007). Given that this sucrose method releases not only TS but also LS vesicles, the sucrose-based RRP size may not be affected by PDBu or OAG at L2/3 synapses too. Therefore, PDBu or OAG-induced increase in p_occ (proportion of TS vesicles over LS+TS vesicles) would result in an increase in release probability without a change in the RRP size.

The literature on Syt7 function is still quite controversial. An observation in the literature that loss of Syt7 function in the fly synapse leads to an increase of release probability. Thus the observed changes in short term plasticity characteristics in the Syt7 KD experiments may contain a release probability component. Can the authors really exclude this possibility? Figure 5 shows for the Syt7 KD group a very prominent depression of the EPSC/IPSC with the second stimulus, particularly for the short interpulse intervals, usually a strong sign of increased release probability, as lack of pool refilling can unlikely explain the strong drop in synaptic output.

Comments on revisions:

I am satisfied with the reply of the authors and I do not have any further points of concern.

Reviewer #3 (Public review):

The results are consistent with the main claim that facilitation is caused by overfilling a readily releasable pool, but alternative interpretations continue to seem more likely, especially when the current results are taken together with previous studies. Key doubts could be resolved with a single straightforward experiment (see below).

The central issue is the interpretation of paired pulse depression that occurs when the interval between action potentials is 25 ms, but not when 50. To summarize: a similar phenomenon was observed at Schaffer collateral synapses (Dobrunz and Stevens, 1997), but was interpreted as evidence for a decrease in pv. Ca2+-channel inactivation was proposed as the mechanism, but this was not proven. The key point for evaluating the current study is that Dobrunz and Stevens specifically ruled out the kind of decrease in pocc that is the keystone premise of the current study because the depression occurred independently of whether or not the first action potential elicited exocytosis. Of course, the mechanism might be different at layer 2/3 cortical synapses. But, it seems reasonable to hope that the older hypothesis would be ruled out for the cortical synapses before concluding that the new hypothesis must be correct.

The old and new hypotheses could be distinguished from each other cleanly with a straightforward experiment. Most/maybe all central synapses strengthen a great amount when extracellular Ca2+ is increased from 1.3 to 2 mM, even when intracellular Ca2+ is buffered with EGTA. According to the authors' model, this is only possible when pv is low, and so could not occur at synapses between layer 2/3 neurons. Because of this, confirmation that increasing extracellular Ca2+ does not change synaptic strength would support the hypothesis that baseline pv is high, as the authors claim, and the support would be impressive because large changes have been seen at every other type of synapse where this has been studied (to my knowledge at least). In contrast, the Ca2+ imaging experiment that has been added to the new version of the manuscript does not address the central issue because a wide range of mechanisms could, in principle, decrease release without involving prior exocytosis or altering bulk Ca2+ signals, including: a small decrease in nano-domain Ca2+, which wouldn't be detected because nano-domains contribute a minuscule amount to the bulk signal during Ca2+-imaging; or even very fast activity-dependent undocking of synaptic vesicles, which was reported in the same Kusick et al, 2020 study that is central to the LS/TS terminology adopted by the authors.

Additional points:

(1) A new section in the Discussion (lines 458-475) suggests that previous techniques employed to show that augmentation and facilitation are caused by increases in pv did not have the resolution to distinguish between pv and pocc, but this is misleading. The confusion might be because the terminology has changed, but this is all the more reason to clarify this section. The previous evidence for increases in pv - and against increases in pocc - is as follows: The residual Ca2+ that drives augmentation decreases the latency between the onset of hypertonic solution and onset of the postsynaptic response by about 150 ms, which is large compared to the rise time of the response. The decrease indicates that the residual Ca2+ drives a decrease in the energy barrier that must be overcome before readily releasable vesicles can undergo exocytosis, which is precisely the type of mechanism that would enhance pv. In contrast, an increase in pocc could change the rise time, but not the latency. There is a small change in the rise time, but this could be caused by changes in either pv or pocc, and one of the studies (Garcia-Perez and Wesseling, 2008) showed that augmentation occluded facilitation, even at times when pocc was reduced by a factor of 3, which would seem to argue against parallel increases in both pv and pocc.

We greatly appreciate for pointing out our mis-understanding. We acknowledge that the post-tetanic acceleration of the latency in the hypertonicity-induced vesicle release may reflect a decrease in the activation energy barrier (ΔEa) for vesicle fusion resulting in an increase in fusion probability of TS vesicles (Stevens and Wesseling, 1999; Garcia-Perez and Wesseling, 2008). We agree that such latency changes are not easily explained by increases in p_occ alone. Indeed, Taschenberger et al (2016) concluded that PTP is similar to the PDBu-induced increase in baseline EPSCs. Subsequently, Lin et al (2025) estimated PDBu-induced changes of TS vesicle pool size and p_fusion of TS vesicles (these correspond to p_occ and p_v in this study, respectively), and found that PDBu increases majorly the former (2 folds) and minorly the latter (1.3 folds). Although it has not been directly tested, it is possible that PTP increases p_v. Accordingly, we corrected the first statement of the paragraph, and mentioned the possibility for a post-tetanic increase in p_v of TS vesicles.

It should be noted, however, it is still puzzling what is represented by the acceleration of the latency in the hypertonicity-induced vesicle release. Schotten et al (2015) simulated how vesicle release rate is affected by reducing ΔEa for vesicle fusion. They found that a reduction of ΔEa resulted in increases in the peak amplitude and shorter time-to-peak of vesicle fusion, but did not accelerate the latency. Therefore, it remains to be clarified whether shorter latency can be regarded as lower activation barrier.  Moreover, the sucrose-induced release rate is comparable with the vesicle recruitment rate (1-2/s; Neher, Neuron, 2008). This slowness of sucrose-induced vesicle release rate makes it difficult to distinguish the vesicle fusion rate from their priming rate.

(2) Similar evidence from hypertonic stimulation indicates that Phorbol esters increase pv, but I am not aware of evidence ruling out a parallel increase in pocc.

As noted above, none of known mechanisms can clearly explain the PDBu-induced shorter latency to hypertonicity-induced vesicle fusion (Schotten et al, 2015). Even if shorter latency reflects higher p_v, it does not rule out a concurrent change in p_occ. Supporting this notion, Lin et al. (2025) showed in the framework of the two state vesicle fusion model that PDBu application leads to a substantial increase in the number of TS vesicles (vesicles having high fusion propensity), with a moderate change in fusion probability (p_fusion). In light of previous observation that high tonicity (500 or 1000 mOsm) did not alter the RRP size (Basu et al., 2007), the results of Lin et al. (2025) can be interpreted as an increase of ‘p_occ’ in terms of the present study.

Reference:

Schotten et al. (2015). Additive effects on the energy barrier for synaptic vesicle fusion cause supralinear effects on the vesicle fusion rate. eLife 4:e05531.

Lin, K.-H., Ranjan, M., Lipstein, N., Brose, N., Neher, E., & Taschenberger, H. (2025). Number and relative abundance of synaptic vesicles in functionally distinct priming states determine synaptic strength and short-term plasticity. J. Physiology.

Comments on revisions:

There are at least two straightforward ways to address the main concern.

The first would be experiments analogous to those in Dobrunz and Stevens that show that - unlike at Schaffer collateral synapses - paired pulse depression at L2/3 synapses requires neurotransmitter release. I proposed this in the first round, but realized since that a simpler and more powerful strategy would be to test directly that pv is/is-not near 1.0 in 1.2 mM Ca2+ simply by increasing to 2 mM Ca2+ (and showing that synaptic strength does-not/does change). This would be powerful because the increase in Ca2+ greatly increases synaptic strength at Schaffer collaterals by about 2.5-fold. Concerns about a confounding elevation in the basal intracellular Ca2+ concentration could be easily neutralized by pre-treating with EGTA-AM, which the authors have already done for other experiments.

We thank to Reviewer #3 for suggesting an experiment for testing our assertion that the vesicular release probability (p_v) is very high at layer 2/3 recurrent excitatory synapses. As the Reviewer recommended, we assessed EPSC changes induced by an increase in extracellular calcium concentration ([Ca²⁺]_o). The results are added as Figure 3—figure supplement 3 to the revised manuscript.

Dodge and Rahamimoff (1967) discovered a fourth-power relationship between end-plate potential (EPP) and [Ca²⁺]_o at a neuromuscular junction. More specifically they found

EPP amplitude µ  ([Ca²⁺]_o / (1 + [Ca²⁺]_o /1.1 mM + [Ma²⁺]_o /2.97 mM))⁴.

This equation nicely predicts the effects of high external calcium on EPSC amplitudes observed at the calyx synapses: a 2.6-fold increase of EPSC by changing [Ca²⁺]_o from 1.25 to 2 mM  (Thanawala and Regehr, 2013; predicted as 2.57);  a 2.36-fold increase by changing [Ca²⁺] from 1.5 to 2 mM (Lin and Taschenberger, 2025; predicted as 2.16). In the framework of two-step priming model, Lin et al. (2015) estimated a 1.9-fold increase (from 0.22 to 0.42) in p_v of TS vesicles and a 1.23-fold increase in the number of TS vesicles. It is clear that the increase in p_v would be possible only if p_v is not saturated, while the increase in the number of TS vesicles is still possible regardless of baseline p_v of TS vesicles.

The Dodge and Rahamimoff’s equation predicts a 3.24-fold increase in baseline EPSC amplitude by elevating [Ca Ca²⁺]_o from 1.3 mM to 2.5 mM at L2/3 synapses. Contrary to this prediction, our recordings revealed a 1.23 fold increase in baseline EPSC amplitude, and this change was not statistically significant.

Given the steep dependence of vesicle release on [Ca²⁺]_o, this minimal increase strongly suggests that p_v at L2/3 recurrent synapses is already near maximal at rest, limiting the dynamic range for further enhancement through increased calcium influx. Accordingly, we observed a small but statistically significant decrease in the paired-pulse ratio (PPR) at higher [Ca²⁺]_o. Although this reduction in PPR might be indicative of increased p_v, it is more consistent with a slight increase in p_occ rather than a substantive increase in p_v under the context of very high p_v. Accordingly, Lin et al. (2025) recently estimated an increase in the TS vesicle subpool size as 1.23-fold by elevating [Ca²⁺]_o under the framework of the two-step vesicle priming mode. Taken together, these findings suggest that an increase in the number of TS vesicles or p_occ may contribute to both an increase in baseline EPSC amplitudes and a decrease in PPR.

Overall, our central claim that baseline p_v is near maximal at L2/3 recurrent synapses is supported by 1) high baseline PPR; 2) insensitivity to EGTA-AM; 3) high double failure rate; 4) insensitivity to elevating [Ca²⁺]_o. These data are difficult to reconcile with a model in which facilitation is mediated by Ca²⁺-dependent increases in p_v. Instead, our results support a mechanism in which facilitation arises from changes in release site occupancy.

References

Dodge, F.A., & Rahamimoff, R. (1967). Co-operative action of calcium ions in transmitter release at the neuromuscular junction. J Physiol, 193(2), 419–432. 

Thanawala, M.S., & Regehr, W.G. (2013). Presynaptic calcium influx controls neurotransmitter release in part by regulating the effective size of the readily releasable pool. J Neurosci, 33(11), 4625–4633.

Lin, K.-H., Ranjan, M., Lipstein, N., Brose, N., Neher, E., & Taschenberger, H. (2025). Number and relative abundance of synaptic vesicles in functionally distinct priming states determine synaptic strength and short-term plasticity. J. Physiology.

Neher E, Sakaba T (2008) Multiple Roles of Calcium Ions in the Regulation of Neurotransmitter Release. Neuron 59:861-872.

eLife Assessment

This important study presents an evaluation of several tools used for detecting Identity-By-Descent (IBD) segments in highly recombining genomes, using simulated data to replicate the high recombination and low marker density of Plasmodium falciparum, the parasite responsible for malaria. The evidence presented by the authors is convincing demonstrating that users should be cautious calling IBD when SNP density is low and recombination rate is high. This study will be of interest to scientists working in the field of genome evolution and infectious diseases

Reviewer #1 (Public review):

Summary:

Authors benchmarked five IBD detection methods (hmmIBD, isoRelate, hap-IBD, phasedIBD, and Refined IBD) in Plasmodium falciparum using simulated and empirical data. Plasmodium falciparum has a mutation rate similar to that of humans but a much higher recombination rate and lower SNP density. Thus, the authors evaluated how recombination rate and marker density affect IBD segment detection. Next, they performed parameter optimization for Plasmodium falciparum and benchmarked the robustness of downstream analyses (selection detection and Ne inference) using IBD segments detected by each method. They also tracked the computational efficiency of these methods. The authors' work is valuable for the tested species, and the analyses presented support their claim that users should be cautious when calling IBD in contexts of low SNP density and high recombination rate.

Strengths:

The study design is convincing and well-structured. The authors chose to use P. falciparum, which presents an interesting case due to its high recombination rate and a mutation rate similar to that of humans. The authors note that despite the widespread use of IBD for genomic surveillance, comprehensive evaluation of these methods in high-recombination, low-marker-density contexts has been lacking. Furthermore, they also examined the performance of IBD detection methods developed specifically for P. falciparum, and evaluated it with phased data which broadened the applicability of the work.

Weaknesses:

The authors thoughtfully addressed our prior concerns by 1) expanding the simulations; 2) updating figures and methods for clarity; and 3) more clearly framing the broader utility of their benchmarking effort. These updates strengthen the manuscript and make the relevance of their findings beyond Plasmodium falciparum more apparent.

More specifically:

The authors added three full replicates per simulation scenario and updated figures to reflect uncertainty at relevant levels, which addresses earlier concerns about reproducibility. The limited number of replicates is due to computational intensity. In the future, broader generalizability and deeper exploration of variance in benchmarking accuracy across parameter space would further strengthen the conclusions/generalizability. The author's also emphasized that, while the study is centered on Plasmodium falciparum, the benchmarking framework, not the parameters, are broadly applicable to other sexually recombining species. Lastly, they extensively updated multiple figures to include simulation models, results from simulation replicates, and improved the figures from the previous version of the manuscript.

Reviewer #2 (Public review):

Summary:

Guo et al. benchmarked and optimized methods for detecting Identity-By-Descent (IBD) segments in Plasmodium falciparum (Pf) genomes, which are characterized by high recombination rates and low marker density. Their goal was to address the limitations of existing IBD detection tools, which were primarily developed for human genomes and do not perform well in the genomic context of highly recombinant genomes. They first analysed various existing IBD callers, such as hmmIBD, isoRelate, hap-IBD, phased-IBD, and refinedIBD. They focused on the impact of recombination on the accuracy, which was calculated based on two metrics, the false negative rate and the false positive rate. The results suggest that high recombination rates significantly reduce marker density, leading to higher false negative rates for short IBD segments. This effect compromises the reliability of IBD-based downstream analyses, such as effective population size (Ne) estimation.<br /> They showed that the best tool for IBD detection in Pf is hmmIBD, because it has relatively low FN/FP error rates and is less biased for relatedness estimates. However, this method is less computationally efficient.<br /> Their suggestion is to optimize human-oriented IBD methods and use hmmIBD only for the estimation of Ne.

Strengths:

Although I am not an expert on Plasmodium falciparum genetics, I believe the authors have developed a valuable benchmarking framework tailored to the unique genomic characteristics of this species. Their framework enables a thorough evaluation of various IBD detection tools for non-human data, such as high recombination rates and low marker density, addressing a key gap in the field.

This study provides a comparison of multiple IBD detection methods, including probabilistic approaches (hmmIBD, isoRelate) and IBS-based methods (hap-IBD, Refined IBD, phased IBD). This comprehensive analysis offers researchers valuable guidance on the strengths and limitations of each tool, allowing them to make informed choices based on specific use cases. I think this is important beyond the study of Pf.

The authors highlight how optimized IBD detection can help identify signals of positive selection, infer effective population size (Ne), and uncover population structure.

They demonstrate the critical importance of tailoring analytical tools to suit the unique characteristics of a species. Moreover, the authors provide practical recommendations, such as employing hmmIBD for quality-sensitive analyses and fine-tuning parameters for tools originally designed for non-P. falciparum datasets before applying them to malaria research.

Overall, this study represents a meaningful contribution to both computational biology and malaria genomics, with its findings and recommendations likely to have an impact on the field.

Weaknesses:

One weakness of the study is the lack of emphasis on the broader importance of studying Plasmodium falciparum as a critical malaria-causing organism. Malaria remains a significant global health challenge, causing hundreds of thousands of deaths annually.

While the study provides a thorough technical evaluation of IBD detection methods and their application to Pf, it does not adequately connect these findings to the broader implications for malaria research and control efforts. Additionally, the discussion on malaria and its global impact could have framed the study in a more accessible and compelling way, making the importance of these technical advances clearer to a broader audience, including researchers and policymakers in the fight against malaria. In the revised version, the authors have placed greater emphasis on this aspect, while still maintaining the methodological focus of the paper.

eLife Assessment

This important study investigates the role of Drp1 in early embryo development. The authors have addressed most of the original comments and the work now presents convincing evidence on how this protein influences mitochondrial localization and partitioning during the first embryonic divisions. The research employs the Trim-Away technique to eliminate Drp1 in zygotes, revealing critical insights into mitochondrial clustering, spindle formation, and embryonic development.

Reviewer #1 (Public review):

Summary:

Gekko, Nomura et al., show that Drp1 elimination in zygotes using the Trim-Away technique leads to mitochondrial clustering and uneven mitochondrial partitioning during the first embryonic cleavage, resulting in embryonic arrest. They monitor organellar localization and partitioning using specific targeted fluorophores. They also describe the effects of mitochondrial clustering in spindle formation and the detrimental effect of uneven mitochondrial partitioning to daughter cells.

Strengths:

The authors have gathered solid evidence for the uneven segregation of mitochondria upon Drp1 depletion through different means: mitochondrial labelling, ATP labelling and mtDNA copy number assessment in each daughter cell. Authors have also characterised the defects in cleavage mitotic spindles upon Drp1 loss

Weaknesses:

This study convincingly describes the phenotype seen upon Drp1 loss. Further studies should be conducted to elucidate the mechanism by which Drp1 ensures even mitochondrial partitioning during the first embryonic cleavage.

Reviewer #2 (Public review):

Gekko et al investigate the impact of perturbing mitochondrial during early embryo development, through modulation of the mitochondrial fission protein Drp1 using Trim-Away technology. They aimed to validate a role for mitochondrial dynamics in modulating chromosomal segregation, mitochondrial inheritance and embryo development and achieve this through the examination of mitochondrial and endoplasmic reticulum distribution, as well as actin filament involvement, using targeted plasmids, molecular probes and TEM in pronuclear stage embryos through the first cleavages divisions. Drp1 deletion perturbed mitochondrial distribution, leading to asymmetric partitioning of mitochondria to the 2-cell stage embryo, prevented appropriate chromosomal segregation and culminated in embryo arrest. Resultant 2-cell embryos displayed altered ATP, mtDNA and calcium levels. Microinjection of Drp1 mRNA partially rescued embryo development. A role for actin filaments in mitochondrial inheritance is described, however the actin-based motor Myo19 does not appear to contribute.

Overall, this study builds upon their previous work and provides further support for a role of mitochondrial dynamics in mediating chromosomal segregation and mitochondrial inheritance. In particular, Drp1 is required for the redistribution of mitochondria to support symmetric partitioning and ongoing development.

Strengths:

The study is well designed, the methods are appropriate and the results are clearly presented. The findings are nicely summarised in a schematic.

The addition of further quantification, including mitochondrial cluster size, elongation/aspect ratio and ROS, as requested by the reviewers, has provided further evidence for the impact of Drp1 depletion on mitochondrial morphology and function.

Understanding the role of mitochondria in binucleation and mitochondrial inheritance is of clinical relevance for patients undergoing infertility treatment, particularly those undergoing mitochondrial replacement therapy.

Weaknesses:

The only remaining weakness is that the authors have not undertaken additional experiments to clarify any role for mitochondrial transport following Drp1 depletion.

Reviewer #3 (Public review):

Why mitochondria are finely maintained in the female germ cell (oocyte), zygotes, and preimplantation embryos? Mitochondrial fusion seems beneficial in somatic cells to compensate for unhealthy mitochondria, for example, mitochondria with mutated mtDNA that potentially defuel the respiratory activity if accumulated above a certain threshold. However, in the germ cells, it may rather increase the risk of transmitting mutated mtDNA to the next generation. Also, finely maintained mitochondria would also be beneficial for efficient removal when damaged, as the authors briefly discussed. Due in part to the limited suitable model, physiological role of mitochondrial fission in embryos were obscure. In this study, authors demonstrated that mitochondrial fission prevents multiple adverse outcomes, especially including the aberrant demixing of parental genome (a clinical phenotype of human embryos) in zygotic stage. Thus, this study would be also of clinical importance that could contribute by proposing a novel mechanism.

The authors have adequately indicated the limitations at each of the specific points. The revisions the authors made have consolidated their conclusion, thus still, making this study an excellent one.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Recommendations for the authors):

The authors have taken into consideration and addressed all my previous comments.

This referee has one major concern remaining: although the authors have refined their analysis of mitochondrial morphology, my concern regarding the characterization of mitochondria in Drp1-depleted zygotes as "elongated" persists.

Taking into account this reviewers' comment, the following description has been changed. Line 256-257: “Quantification of the aspect ratio (major axis/minor axis) suggests that mitochondria are significantly elongated in Drp1-depleted embryos" to “The mean aspect ratio (major axis/minor axis) increased slightly from 1.36 in control to 1.66 in Drp1-depleted embryos ."

(1) The morphological analysis of mitochondria reveals that both axes increase in length. Yet, the aspect ratio it is virtually unchanged, at least in biologically relevant terms, if not statistically.

- Please calculate and represent mitochondrial aspect ratio as major axis/minor axis in fig 2M.

- Could the authors also display individual data points in the graphs of Figure 2 K, L and M?

We have revised the graph display format in accordance with the reviewer's suggestions.

(2) The authors provide PMID: 25264261 as an example, yet mitochondria in PMID: 35704569 are apparently elongated. Judging by the authors discussion about the differences between these two studies, it would be enriching to comment, in the discussion of the manuscript, on the differences in morphology and to the reason why these might arise

This referee believes that the unconventional mitochondrial morphology upon fission inhibition, reported here, enhances the relevance of the study and raises questions that could promote novel research lines, if thoroughly discussed in the manuscript.

Thank you for your insightful suggestion. However, since the latter paper (PMID: 35704569) lacks EM images, it would be difficult to accurately assess the elongation. Thus, we would like to reconsider the mitochondrial morphological changes in zygotes caused by Drp1 deletion levels based on the results of future research.

Minor

(1) Labels for the staining used are missing in figure 1-figure supplement 1

(2) Line 218. Could the intended sentence be:

"Live imaging of mitochondria (mt-GFP) and chromosomes (H2B-mCherry) in Myo19 depleted zygotes shows symmetric distribution and partitioning of mitochondria during the first embryonic cleavage (Figure 1-figure supplement 2A, 2B; Figure 1-Video 2)."

(3) Figure 2M: Please calculate and represent mitochondrial aspect ratio as major axis/minor axis.

(4) Include a label with the experimental condition in figure 1 fig supp 2.

(5) Line 592: missing reference.

Thank you for your careful correction. We have corrected all the points the reviewer pointed out in the revised version.

Reviewer #2 (Recommendations for the authors):

The authors have sufficiently revised the manuscript to accommodate the majority of suggestions provided by myself and the other reviewers. While it would have been useful to see further clarity around mitochondrial transport, the data presented provide valuable insight into the role of a mitochondrial dynamics regulator in mediating the first mitosis event in embryo development.

We thank again reviewer 2 for the helpful comment. We would like to address the issue of (aggregated) mitochondrial transport, including analysis methods, as a future challenge.

Reviewer #3 (Recommendations for the authors):

After reading through the comments of other reviewers, what authors could potentially improve their manuscript had been largely summarized in three following points.

(1) Authors would better clarify whether a loss of Drp1 contributes to the chromosome segregation defects directly (e.g. checking SAC-like activity) or indirectly (aggregated mitochondria became physically obstacle; maybe in part getting the cytoskeleton involved).

(2) Although the level of Myo19 may not be so high (given the low level of TRAK2 in oocytes: Lee et al. PNAS 2024, PMID 38917013), authors would better further clarify the effect of Myo19-Trim with timelapse (e.g. EB3-GFP/Mt-DsRed) and EM analysis (detailed mitochondrial architecture).

(3) Authors would better clarify phenotypic heterogeneity/variety regarding the degree of alteration in mitochondrial morphology/ architecture dependent on the levels of Drp1 loss with detailed quantification of EM images to address why aggregation of mitochondria in Drp1-/- parthenote (possibly, more likely Drp1 protein-free) looks different/weaker than Trim-awayed one. Employment of the parthenotes of Trim-awayed MII oocytes might also complement the further discussion.

The revised preprinted have addressed all the points described above. Authors have also adequately indicated the limitations at each of the specific points. Revisions authors made have consolidated their conclusion, thus still, making this study an excellent one.The only remaining weakness is that the authors have not undertaken additional experiments to clarify any role for mitochondrial transport following Drp1 depletion.

We thank again reviewer 3 for the insightful comments. We would like to address the comments you have raised (points that were unclear in this study) as issues for future study.

eLife Assessment

This valuable manuscript describes cryo-EM structures of archaeal proteasomes that reveal insights into how occupancy of binding pockets on the 20S proteasome regulates proteasome gating. The evidence supporting these claims is convincing, although the extrapolation of these findings to the more complex eukaryotic proteasome may prove challenging. This work will be of high interest to researchers interested in proteasome structure and regulation.

Reviewer #1 (Public review):

Summary:

In this manuscript, Chua, Daugherty, and Smith analyze a new set of archaeal 20S proteasomes obtained by cryo-EM that illustrate how the occupancy of the HbYX binding pocket induces gate opening. They do so primarily through a V24Y mutation in the α-subunit. These results are supported by a limited set of mutations in K66 in the α subunit, bringing new emphasis to this unit.

Strengths:

The new structure's analysis is comprehensive, occupying the entire manuscript. As such, the scope of this manuscript is very narrow, but the strength of the data are solid, and they offer an interesting and important new piece to the gate-opening literature.

Weaknesses:

Extrapolating from the core HbYX activating motif shared by Archae and Eukaryotes to the specific operations of gate opening, which is more elaborate in eukaryotes, may prove challenging.

Reviewer #2 (Public review):

Summary:

The manuscript by Chuah et al. reports the experimental results that suggest the occupancy of the HbYX pockets suffices for proteasome gate opening. The authors conducted cryo-EM reconstructions of two mutant archaeal proteasomes. The work is technically sound and may be of special interest in the field of structural biology of the proteasomes.

Strengths:

Overall, the work incrementally deepens our understanding of the proteasome activation and expands the structural foundation for therapeutic intervention of proteasome function. The evidence presented appears to be well aligned with the existing literature, which adds confidence in the presentation.

Comments on revisions:

The authors have addressed all my questions.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Chua, Daugherty, and Smith analyze a new set of archaeal 20S proteasomes obtained by cryo-EM that illustrate how the occupancy of the HbYX binding pocket induces gate opening. They do so primarily through a V24Y mutation in the αsubunit. These results are supported by a limited set of mutations in K66 in the α subunit, bringing new emphasis to this unit.

Strengths:

The new structure's analysis is comprehensive, occupying the entire manuscript. As such, the scope of this manuscript is very narrow, but the strength of the data is solid, and they offer an interesting and important new piece to the gate-opening literature.

Weaknesses:

Major Concerns

(1) This manuscript rests on one new cryo-EM structure, leading to a single (albeit convincing) experiment demonstrating the importance of occupying the pocket and moving K66. Could a corresponding bulky mutation at K66 not activate the 20S proteasome?

Thank you for this insightful question. We believe such a mutation would likely not activate the proteasome, and would likely  be detrimental to gate opening. Our previous work (Smith et al., Molecular Cell, 2007), and data presented in this manuscript, demonstrate that a K66A mutation, which removes the side chain, blocks 20S gate opening. Furthermore, our new αV24Y T20S structure reveals that Lys66 forms specific hydrogen bonds with surrounding residues that are crucial for stabilizing the open gate conformation (Fig. 5). An aromatic or bulky hydrophobic mutation at this position would be unable to form these essential hydrogen bonds and would likely disrupt the necessary stabilizing interactions.  

(2) To emphasize the importance of this work, the authors highlight the importance of gateopening to human 20S proteasomes. However, the key distinctions between these proteasomes are not given sufficient weight.

(a) As the authors note, the six distinct Rpt C-termini can occupy seven different pickets. However, how these differences would impact activation is not thoroughly discussed.

We appreciate the reviewer's point regarding the complexities of eukaryotic 26S proteasome activation. While our manuscript discusses some aspects of this, we agree that a detailed mechanistic extrapolation from our archaeal T20S model to the diverse interactions within the human 26S proteasome is challenging. As we elaborate in our response to Reviewer #2 (Recommendation #3), the significant differences in α-ring composition (homoheptameric vs. heteroheptameric) and the multifactorial nature of Rpt C-termini binding make direct, wide-reaching speculations about specific pocket contributions in the eukaryotic system difficult at this stage. Our aim was to focus on the conserved fundamental role of the HbYX hydrophobic pocket itself. 

(b) With those other sites, the relative importance of various pockets, such as the one controlling the α3 N-terminus, should be discussed more thoroughly as a potential critical difference.

The reviewer raises an excellent point about the regulation of specific α-subunits, like the α3 N-terminus, which acts as a lynchpin in gating. Understanding its precise regulation in the eukaryotic 26S proteasome is indeed a key goal in the field. However, determining which specific HbYX binding events (e.g., in the α2-α3 pocket, the α3-α4 pocket, or cooperative binding across multiple pockets) control the α3 subunit's conformation is beyond the scope of what our current T20S structural data can definitively inform. The cooperative nature of HbYX binding and its precise allosteric consequences across the heteroheptameric α-ring are complex questions that remain to be fully elucidated in the eukaryotic system. Our study focuses on demonstrating the sufficiency of hydrophobic pocket occupancy for activation in a conserved manner, which we propose is a fundamental aspect of HbYX action. Identifying which of the seven distinct eukaryotic hydrophobic pockets must be engaged for full activation remains an important area for future research.

(c) These differences can lead to eukaryote 20S gates shifting between closed and open and having a partially opened state. This becomes relevant if the goal is to lead to an activated 20S. It would have been interesting to have archaea 20S with a mix of WT and V24Y α-subunits. However, one might imagine the subclassification problem would be challenging and require an extraordinary number of particles.

We agree with the reviewer that exploring mixed subunit populations is an interesting idea, particularly given the dynamic and potentially partially open states of eukaryotic proteasomes. We have previously considered co-expressing WT and V24Y α-subunits. However, the interpretation of such experiments would be challenging. With 14 potential sites for mutant incorporation across the two homoheptameric α-rings, a heterogeneous population of proteasomes with varying numbers and arrangements of V24Y subunits would be generated. Correlating any observed changes in activity or structure (e.g. via cryoEM subclassification, would be exceedingly difficult) to specific stoichiometries or arrangements of mutant subunits would be highly complex and likely inconclusive for deriving clear mechanistic insights.

(d) Furthermore, the conservation of the amino acids around the binding pocket was not addressed. This seems particularly important in the relative contribution of a residue analogous to K66 or V24.

We apologize for the mislabeled figure title in the previous submission, which may have made this information less accessible. We have now corrected the title for Supplemental Figure S10 (previously S9). This figure presents the sequence alignment showing the conservation of residues in and around the HbYX hydrophobic pocket, including those analogous to T20S αV24, αL21, and αA154. As discussed in the manuscript, key residues that form this pocket, such as those corresponding to and surrounding T20S L21 and A154, are indeed well conserved in human α-subunits. This conservation supports the relevance of our findings to eukaryotic proteasomes.

Reviewer #2 (Public review):

Summary:

The manuscript by Chuah et al. reports the experimental results that suggest the occupancy of the HbYX pockets suffices for proteasome gate opening. The authors conducted cryo-EM reconstructions of two mutant archaeal proteasomes. The work is technically sound and may be of special interest in the field of structural biology of the proteasomes.

Strengths:

Overall, the work incrementally deepens our understanding of the proteasome activation and expands the structural foundation for therapeutic intervention of proteasome function. The evidence presented appears to be well aligned with the existing literature, which adds confidence in the presentation.

Weaknesses:

The paper may benefit from some minor revision by making improvements on the figures and necessary quantitative comparative studies.

We appreciate the reviewers thoughtful critique of our manuscript and have made the requested changes and provided further perspectives mentioned below.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Line 467: Mammalian should be replaced with eukaryotic.

Done.  

(2) Figure 1 Caption: The descriptions of the blue and green boxes should be described in panel A's caption rather than waiting until panel C.

Done.

(3) Figure 2 A: For greater clarity, the asterisks should be replaced with the numbers H4, H5, and H6.

Done.

(4) Figure 7 caption: The panels are misannotated. What is listed as E should become D, and what is listed as F should become E.

Done.

(5) The title for Figure S9, "αV24Y T20S validation," is inappropriate. A better title should discuss the sequence conservation of those amino acids. Why is the arrow drawing attention to L21 when the paper is about V24? There should be a corresponding alignment that includes K66.

Thank you for pointing out the title issue for Figure S10 (previously S9); this has now been corrected to reflect its focus on sequence conservation. The arrow highlighting L21 (and its eukaryotic analogues) is intended to draw attention to a key residue that, along with A154, forms part of the hydrophobic pocket occupied by V24Y. As detailed in the main text and shown in Figures 3C, 3D, and 4G, measurements involving L21 were used to demonstrate the widening of this pocket upon V24Y mutation or ZYA binding.

Reviewer #2 (Recommendations for the authors):

The authors might consider improving the manuscript by addressing the following minor issues:

(1) Figure 1: it might be easier for readers to understand what the authors meant to show by superimposing the atomic model of the mutated sidechain with the density map. In this case, the density map could be rendered half-transparent, or it could be represented by mesh.

We appreciate this suggestion for enhancing Figure 1. While we agree that showing the model fit within the density is valuable, we found that incorporating this directly into the comparative overlay panels of Figure 1 (which already depict multiple aligned density maps) made the figure overly complex and visually detracted from its primary message of comparing overall conformational states. However, we do provide a clear illustration of the model-to-map fit for the αV24Y T20S structure in Supplemental Figure S3, where the atomic model is shown within the transparent map surface. Furthermore, all our maps and models are publicly available, and we encourage interested readers to perform detailed comparisons. We believe this approach balances clarity in the main figure with the provision of detailed validation data.  

(2) What is the solvent-inaccessible surface area of the mutated side-chain buried by its hydrophobic interaction with the HbYX pockets? How is this buried surface area compared to the solvent-accessible surface area of the HbYX pocket without the mutation?

We appreciate the idea of another visual to answer the question and provide the reader with a better perception of this pocket in the WT versus V24Y T20S. To address this we added a new Supplemental Figure 7 with surfaces showing this comparison including each separate pocket and an overlay with solid and mesh surfaces. We also added this line to the text: “Moreover, molecular surface representations of the hydrophobic pocket clearly show occupancy by the mutant tyrosine’s side chain (Fig. S7)”.

(3) Based on the data of the buried surface area of the mutated side-chain (requested above), can the authors make some quantitative comparison with the activated eukaryotic proteasome (either human or yeast 26S) with the alpha-pocket occupied with HbYX motifs from Rpt subunits? How similar are they?

This is a thoughtful suggestion, and we understand the interest in directly comparing pocket occupancy across systems. While we draw general parallels regarding HbYXdependent activation in the discussion, we believe a direct quantitative extrapolation of specific surface area occupancies from our T20S V24Y mutant to the eukaryotic system would be overly speculative and unlikely to yield further definitive insights into the eukaryotic gate-opening mechanism at this time. The primary reason for this is the significant disparity in complexity between the archaeal T20S and eukaryotic 26S proteasomes. The eukaryotic α-ring is a heteroheptamer, composed of seven distinct αsubunits, which creates seven non-identical inter-subunit pockets. In contrast, our study utilizes the homoheptameric archaeal T20S. Furthermore, eukaryotic 26S proteasome activation involves the intricate binding of multiple C-terminal tails from the six different Rpt ATPase subunits of the 19S regulatory particle. These C-termini include various HbYX motifs as well as non-HbYX tails, and they interact with the diverse α-subunit pockets in a highly complex, multifactorial manner that drives what appears to be an allosteric mechanism for gate regulation.

Crucially, the precise number of C-termini required for 20S gate-opening in the eukaryotic system, the specific combination of these Rpt C-termini, and even the exact inter-subunit pockets that must be occupied to induce robust gate opening are still areas of active investigation and are not resolved (as discussed in our manuscript). Therefore, attempting to extrapolate nuances, such as the precise degree of hydrophobic pocket occupancy from our single, engineered αV24Y side-chain (which models one specific type of Hb-pocket interaction in a simplified system) to each of the potentially five or more different Rpt Ctermini interactions within the various 20S inter-subunit pockets in the eukaryotic 26S proteasome, would involve too many assumptions and would not provide reliable predictive power to understand mechanism.

However, regarding the fundamental question of how a hydrophobic group occupies the HbYX pocket in our archaeal model system, we believe Figure 4D provides relevant insight that may address the reviewer's underlying curiosity. This figure carefully illustrates the spatial overlap, showing that the engineered αV24Y side-chain and the hydrophobic 'Z' group of the ZYA HbYX-mimetic occupy the same region within the T20S inter-subunit hydrophobic pocket. This provides a clear visual comparison of this key 'Hb' interaction in our defined and structurally characterized system.

(4) It may be helpful that at the end of the discussion, the authors make some comments on how the current results might offer insights into the eukaryotic proteasome activation, and on what the limitations of the current study are.

We thank the reviewer for this suggestion. We agree that discussing the implications for eukaryotic proteasome activation and the study's limitations is important.

Insights into Eukaryotic Proteasome Activation:

We have indeed discussed how our current findings with the αV24Y T20S mutant offer insights into eukaryotic proteasome activation in the Discussion section. To briefly summarize:

(1) Conservation of the Target Site: Our study highlights that the key residues forming the hydrophobic pocket targeted by the αV24Y mutation (αL21 and αA154 in T20S) are well-conserved in the human 20S α-subunits (as shown in Fig. S9). This suggests that the mechanism of inducing gate opening through occupancy of this specific hydrophobic 'Hb' pocket by an aromatic residue is a plausible strategy for activating eukaryotic proteasomes.

(2) Relevance of the IT Switch: The αV24Y mutation, by occupying the Hb-pocket, allosterically affects the conserved IT switch, promoting an open-gate conformation. As detailed in our previous work (Chuah et al., Commun. Biol. 2023; Ref. 31 in the current manuscript), this IT switch mechanism is also functionally conserved in most human α-subunits. The current study reinforces that direct manipulation of the Hb-pocket is sufficient to trigger this conserved downstream gating machinery.

(3) Therapeutic Implications: These findings further pinpoint the HbYX hydrophobic pocket as a specific and promising target for the design of small molecule proteasome activators aimed at human proteasomes.

While these parallels are informative, we reiterate our caution (as also mentioned in response to comment #3 and in the manuscript regarding direct quantitative extrapolation due to the increased complexity of the heteroheptameric eukaryotic α-ring and the multifactorial nature of Rpt C-termini interactions.

We also agree that we should add a statement regarding key limitation raised by the reviewer, to our manuscript. Below is the key limitations paragraph that has been added to the penultimate paragraph of the discussion: 

“While this study provides significant insights, it is important to acknowledge certain limitations. A key limitation stems from using the homoheptameric archaeal T20S as our model. Although this simpler system allows for more reliable dissection of fundamental mechanisms, and core elements like HbYX-induced gate opening are conserved at the intersubunit pocket level, the overall T20S and eukaryotic 20S/26S proteasomes differ significantly in their complexity. Specifically, our engineered αV24Y mutation results in a tyrosine constitutively occupying all seven identical hydrophobic pockets. This contrasts with the eukaryotic proteasome, which possesses seven distinct α-subunit pockets that interact with various Rpt C-termini through dynamic binding. Moreover, the specific Rpt Ctermini interactions—whether acting individually or cooperatively—that are essential to drive gate opening in the eukaryotic system remain incompletely understood. Therefore, while insights from our archaeal system are valuable for understanding general principles, direct comparisons and extrapolations to the intricate allostery and interaction complexities of the eukaryotic 26S proteasome must be made with caution.”

eLife Assessment

This useful study describes expression profiling by scRNA-seq of thousands of cells of recombinant yeast genotypes from a system that models natural genetic variation. The rigorous new method presented here shows promise for improving the efficiency of genotype-to-phenotype mapping in yeast, providing convincing evidence for its efficacy. This manuscript focuses on overcoming technical challenges with this approach and identifies several new biological insights that build upon the field of genotype-to-phenotype mapping, a central question of interest to geneticists and evolutionary biologists.

Reviewer #1 (Public review):

N'Guessan et al have improved the report of their study of expression QTL (eQTL) mapping in yeast using single cells. The authors make use of advances in single cell RNAseq (scRNAseq) in yeast to increase the efficiency with which this type of analysis can be undertaken. Building on prior research led by the senior author that entailed genotyping and fitness profiling of almost 100,000 cells derived from a cross between two yeast strains (BY and RM) they performed scRNAseq on a subset of ~5% (n = 4,489) individual cells. To address the sparsity of genotype data in the expression profiling they used a Hidden Markov Model (HMM) to infer genotypes and then identify the most likely known lineage genotype from the original dataset. To address the relationship between variance in fitness and gene expression the authors partition the variance to investigate the sources of variation. They then perform eQTL mapping and study the relationship between eQTL and fitness QTL identified in the earlier study.

This paper seeks to address the question of how quantitative trait variation and expression variation are related. scRNAseq represents an appealing approach to eQTL mapping as it is possible to simultaneously genotype individual cells and measure expression in the same cell. As eQTL mapping requires large sample sizes to identify statistical relationships, the use of scRNAseq is likely to dramatically increase the statistical power of such studies. However, there are several technical challenges associated with scRNAseq and the authors' study is focused on addressing those challenges. The authors have successfully demonstrated their stated goal of developing, and illustrating the benefit of, a one-pot scRNA-seq experiment and analysis for eQTL mapping.

Reviewer #2 (Public review):

This work describes the single-cell expression profiling of thousands of cells of recombinant genotypes from a model natural-variation system, a cross between two divergent yeast strains.

I appreciate the addition of lines 282-291, which now makes the authors' point about one advantage of the single-cell technique for eQTL mapping clearly: the authors don't need to normalize for culture-to-culture variation the way standard bulk methods do (e.g. in Albert et al., 2018 for the current yeast cross), and without this normalization, they can integrate analyses of expression with those of estimates of growth behaviors from the abundance of a genotype in the pool. The main question the manuscript addresses with the latter, in Figure 3, is how much variation in growth appears to have nothing to do with expression, for which the answer the authors given is 30%. I agree that this represents a novel finding. The caveats are (1) the particular point will perhaps only be interesting to a small slice of the eQTL research community; (2) the authors provide no statistical controls/error estimate or independent validation of the variance partitioning analysis in Figure 3, and (3) the authors don't seem to use the single-cell growth/fitness estimates for anything else, as Figure 4 uses loci mapped to growth from a previously published, standard culture-by-culture approach.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2:

Minor reviews:

The caveats are (1) the particular point will perhaps only be interesting to a small slice of the eQTL research community; (2) the authors provide no statistical controls/error estimate or independent validation of the variance partitioning analysis in Figure 3, and (3) the authors don't seem to use the single-cell growth/fitness estimates for anything else, as Figure 4 uses loci mapped to growth from a previously published, standard culture-by-culture approach. It would be appropriate for the manuscript to mention these caveats.

We have added two small mention of these caveats – mainly that the study may not generalize, and that the study does not attempt to try the variance partitioning on other traits or other system where the values of the partitions are better established.

I also think it is not appropriate for the manuscript to avoid a comparison between the current work and Boocock et al., which reports single-cell eQTL mapping in the same yeast system. I recommend a citation and statement of the similarities and differences between the papers.

We have added this reference and a clear statement of similarities between the two studies. It was not our intention to avoid this; we had simply not seen that study in the initial submission.

eLife Assessment

This important study provides convincing evidence that the Kinesin protein family member KIF7 regulates the development of the cerebral cortex and its connectivity and the specificity of Sonic Hedgehog signaling by controlling the details of Gli repressor vs activator functions. This study provides new insights into general aspects of cortical development.

Reviewer #1 (Public review):

Summary:

This is an interesting follow-up to a paper published in Human Molecular Genetics reporting novel roles in corticogenesis of the Kif7 motor protein that can regulate the activator as well as the repressor functions of the Gli transcription factors in Shh signalling. This new work investigates how a null mutation in the Kif7 gene affects the formation of corticofugal and thalamocortical axon tracts and the migration of cortical interneurons. It demonstrates that Kif7 null mutant embryos present with ventriculomegaly and heterotopias as observed in patients carrying KIF7 mutations. The Kif7 mutation also disrupts the connectivity between cortex and thalamus and leads to an abnormal projection of thalamocortical axons. Moreover, cortical interneurons show migratory defects that are mirrored in cortical slices treated with the Shh inhibitor cyclopamine suggesting that the Kif7 mutation results in a down-regulation of Shh signalling. Interestingly, these defects are much less severe at later stages of corticogenesis.

Strengths/weaknesses:

The findings of this manuscript are clearly presented and are based on detailed analyses. Using a compelling set of experiments, especially the live imaging to monitor interneuron migration, the authors convincingly investigate Kif7's roles and their results support their major claims. The migratory defects in interneurons and the potential role of Shh signalling present novel findings and provide some mechanistic insights but rescue experiments would further support Kif7's role in interneuron migration. Similarly, the mechanism underlying the misprojection which has previously been reported in other cilia mutants remains unexplored. Taken together, this manuscript makes novel contributions to our understanding of the role of primary cilia in forebrain development and to the aetiology of the neural symptons in ciliopathy patients.

Comments on revisions:

The authors addressed most of the points I raised in my original review. However, I am not convinced by the figures the authors present on Shh protein expression. The "bright tiny dots" of Shh protein in the cortex are not visible on the images in Figure 7. I wonder whether the authors could present higher magnification and/or black and white images with increased contrast.

Reviewer #2 (Public review):

Summary:

This study investigates the role of KIF7, a ciliary kinesin involved in the Sonic Hedgehog (SHH) signaling pathway, in cortical development using Kif7 knockout mice. The researchers examined embryonic cortex development (mainly at E14.5), focusing on structural changes and neuronal migration abnormalities.

Strengths:

(1) The phenotype observed is interesting, and the findings provide neurodevelopmental insight into some of the symptoms and malformations seen in patients with KIF7 mutations.

(2) The authors assess several features of cortical development, including structural changes in layers of the developing cortex, connectivity of the cortex with thalamus, as well as migration of cINs from CGE and MGE to cortex.

Weaknesses:

(1) The Kif7 null does have phenotype differences from individual mutations seen in patients. It would be interesting to add more thoughts about how the null differs from these mutants in ciliary structure and SHH signaling via the cilium.

(2) The description of altered cortex development at E14.5 is perhaps rather descriptive. It would be useful to assess more closely the changes occurring in different cell types and stages. For this it seems very important to have a time course of cortical development and how the structural organization changes over time. This would be easy to assess with the addition of serial sections from the same mice. It might also be interesting to see how SHH signaling is altered in different cortical cell types over time with a SHH signaling reporter mouse.

(3) Abnormal neurodevelopmental phenotypes have been widely reported in the absence of other key genes affecting primary cilia function (Willaredt et al., J Neurosci 2008; Guo et al., Nat Commun 2015). It would be interesting to have more discussion of how the Kif7 null phenotype compares to some of these other mutants.

(4) The authors see alterations in cIN migration to the cortex and observe distinct differences in the pattern of expression of Cxcl12 as well as suggest cell intrinsic differences within cIN in their ability to migrate. The slice culture experiments though make it a little difficult to interpret the cell intrinsic effects on cIN of loss of Kif7, as the differences in Cxcl12 patterns still exist presumably in the slice cultures. It would be useful to assess their motility in an assay where they were isolated, as well as assess transcriptional changes in cINs in vivo lacking KIF7 for expression patterns that may affect motility or other aspects of migration.

Comments on revisions:

The authors have made significant and thoughtful responses as well as experimental additions to the authors comments. Their efforts are appreciated and the manuscript is much improved.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

This is an interesting follow-up to a paper published in Human Molecular Genetics reporting novel roles in corticogenesis of the Kif7 motor protein that can regulate the activator as well as the repressor functions of the Gli transcription factors in Shh signalling. This new work investigates how a null mutation in the Kif7 gene affects the formation of corticofugal and thalamocortical axon tracts and the migration of cortical interneurons. It demonstrates that the Kif7 null mutant embryos present with ventriculomegaly and heterotopias as observed in patients carrying KIF7 mutations. The Kif7 mutation also disrupts the connectivity between the cortex and thalamus and leads to an abnormal projection of thalamocortical axons. Moreover, cortical interneurons show migratory defects that are mirrored in cortical slices treated with the Shh inhibitor cyclopamine suggesting that the Kif7 mutation results in a down-regulation of Shh signalling. Interestingly, these defects are much less severe at later stages of corticogenesis.

Strengths/weaknesses:

The findings of this manuscript are clearly presented and are based on detailed analyses. Using a compelling set of experiments, especially the live imaging to monitor interneuron migration, the authors convincingly investigate Kif7's roles and their results support their major claims. The migratory defects in interneurons and the potential role of Shh signalling present novel findings and provide some mechanistic insights but rescue experiments would further support Kif7's role in interneuron migration. Similarly, the mechanism underlying the misprojection which has previously been reported in other cilia mutants remains unexplored. Taken together, this manuscript makes novel contributions to our understanding of the role of primary cilia in forebrain development and to the aetiology of neural symptoms in ciliopathy patients.

We again thank Reviewer 1 for her/his positive assessment of our article. We have addressed several weaknesses identified by the reviewer, supplementing the initial results with new data, and correcting or clarifying the text where necessary. Our detailed responses to the reviewer’s recommendations appear at the end of each comment.

Reviewer #1 (Recommendations for the authors):

(1) The authors report remarkable phenotypic changes in E14.5 embryos in the projection patterns of corticofugal/thalamocortical axons and in interneuron migration, but some of those phenotypes appear much less severe at E16.5. This might be indicative of a delay in development. Does the migration of interneurons to more dorsal regions correspond to an extended Cxcl12 expression? Do interneuorons still show migratory defects at E16.5? To address a potential delay, the authors could, if feasible, repeat Tbr2/Tomato and L1 or neurofilament stainings in E18.5 embryos?

The question of a possible developmental delay in Kif7 -/- embryos is important. To document this topic, we have extended our study initially focused on embryonic stage E14.5 to earlier (E12.5) and later (E16.5, E18.5/P0) developmental stages. We added new data on E12.5 (Fig. 1, Fig. 3, Fig. S4) and E18.5 (Fig. 3, Fig. 4) embryos in the main figures, and considerably extended the data on E16.5 embryos (Fig. 1, Fig. 3). The legends of figures and the text of the result section (p5-p6) have been modified accordingly. We now describe developmental defects in Kif7 -/- embryos, which are not simple developmental delays. The sequences of thalamic axon development and cIN migration are representative of this complexity.

Thalamic axons: the pioneer projection is misrouted to the amygdala at E14.5 (Fig. 4B) whereas most Kif7 -/- thalamic axons extend to the cortex at E16.5, with a slight delay compared to WT axons (Fig. 4D). At E18.5, the Kif7 -/- thalamo-cortical projection appears rather normal in the rostral forebrain but is drastically reduced in the median and caudal forebrain (Fig. 4E). This strong decrease is confirmed by neurofilament staining performed at E18.5 which identifies a major loss of corticofugal and thalamo-cortical projections in Kif7 -/- brains (Fig. 4F). 

Migrating cIN: During normal development, CXCL12 maintains cIN in their tangential pathways as they start to colonize the cortical wall (E13.5/E14.5). Then CXCL12 drops in the SVZ (Tiveron et al., 2006; Caronia-Brown and Grove, 2011) allowing wild type cIN to invade the cortical plate (Stumm et al., 2003; Li et al., 2008; Atkins et al., 2023). In Kif7 -/- embryos, CXCL12 is never expressed in the SVZ of the dorsal cortex. Therefore Kif7 -/- cIN migrate radially in the dorsal cortex instead of tangentially. We have improved our text in the result section to clarify this transient defect (p8-9).

(2) Figure 1D: The overview of the Gsh2 and Tbr2 stainings does not allow us to see details of the PSPB. The lines indicating the position of the PSPB are not helpful either. Higher magnifications are required to see whether there are subtle differences at these boundaries as observed for other cilia mutants.

We thank the reviewer for her/his question that allowed us to identify a mild default of patterning at the PSB, illustrated by high magnification pictures in the Fig. 1D and described in the result section (p5). This subtle defect of PSB patterning is consistent with previous observations in Kif7 -/- embryos (Putoux et al, 2019) and appears milder than the PSB defect in hypomorphic Gli3 Pdn mutants (PSB shifted dorsally and less well defined as illustrated in Kuschel et al, 2003 and Magnani et al., 2010).

(3) Figure 3: The authors report an interesting mis-projection of thalamocortical axons towards the amygdala. A very similar pattern has been described in Gli3 hypomorphic Pdn mutants (Magnani et al., 2010), in Rfx3, and in Inpp5e null mutant embryos (Magnani et al., 2015). These papers lend further support that this Kif7 phenotype is Gli3 dependent and should be cited in the manuscript. Moreover, the mechanism(s) underlying this mis-projection remain unexplored. Is this phenotype rescued in the previously reported Kif7/ Gli3D699 double mutants? Is there an abnormal expression of axon guidance molecules?

We deeply thank the reviewer for drawing our attention to the abnormal projection of thalamic axons to the amygdala described in the Gli3 Pdn mutant and in two ciliary mutants, Rfx3 -/- and Lnpp5e -/-. We cite these two papers (Magnani et al., 2010, 2015) in the revised manuscript (p7). In the Gli3 Pdn mutant, transplantation experiments show that a patterning defect of the ventral telencephalon (VT) underlies the mis-projection of the thalamus to the amygdala (Magnani et al, 2010). In the Rfx3 ciliary mutant, two possible mechanisms are proposed: pre-thalamus patterning defect and ectopic Netrin and Slit1 expression in the VT (Magnani et al, 2015). We do agree that understanding the mechanism of the thalamic misprojection in the Kif7 mutant would be of great interest. However, given the complexity of the putative mechanisms described in the Gli3 Pdn and Rfx3 mutants, we believe that this question deserves further investigation in a future study. Finally, the possibility that the thalamic projection defect observed in Kif7 -/- embryos could be rescued in Kif7/Gli3699 (double mutants in which Gli3R is overexpressed in the dorsal and ventral forebrain) is very unlikely. Our two main arguments are:

(1) Magnani et al (2015) did not rescue the TCA pathfinding defect in the Rfx3 -/- ciliary mutant when they overexpressed GLI3-R (see TCA description in the Rfx3/ Gli3699 double mutant, last paragraph of the result section). The authors concluded “This finding could be explained by a requirement for Gli activator and not Gli repressor function in VT {ventral telencephalon} patterning and indeed, Gli3 western blots showed that the levels of Gli3R are not altered in the VT of Rfx3 -/- embryos”.

(2) The GLI3-R/Gli3-FL ratio is decreased in the cortex of the Kif7 -/- embryos (dorsal telencephalon) as expected, whereas it is very low in the MGE of WT embryos (ventral telencephalon) and remains unaltered in the Kif7 -/- embryos (Fig. 2B).  

Similarly, the analysis of Kif7 -/- cIN migratory defects leads us to conclude that Kif7 ablation impairs Gli activation function rather than Gli repressor function in the VT where cIN are generated.

(4) Figure 4: The authors should discuss the difference between Tbr2 and Cxcl12 expression which does not extend into the dorsal-most cortical SVZ.

We observed that the transient CXCL12 expression is lacking in the SVZ of the dorsal cortex of Kif7 -/- embryos at E14.5, in a region where TBR2 cells abnormally reach the cortical surface and intermingle with post-mitotic cells. A sentence in our previous version (lines 233-234) could suggest a link between the abnormal location of TBR2 expressing cells and the lack of CXCL12 expression. Having found no data in the literature to explain the absence of CXCL12 expression in the brain by an abnormal cellular environment or by a defect in transcription factor expression, we do not want to further elaborate on differences and similarities between TBR2 and CXCL12 expression patterns in the Kif7 -/- brain. We have modified our text accordingly in the result section of the revised manuscript (p8-9). 

(5) Figure 5: The authors convincingly describe migratory defects of interneurons. The treatment with Shh agonist and antagonist provides some mechanistic insights but genetic or pharmacological rescue experiments would lend further support. For example, they could treat Kif7 mutant sections with Shh agonists or analyse Kif7/Gli3D699 double mutants.

We thank the reviewer for her/his positive assessment of our analysis of the cIN migration. Unfortunately, the rescue experiments proposed by the reviewer should not help to further support our conclusions. First, Kif7 ablation in cIN prevents the processing of any SHH signal in the transcriptional pathway. Second, increasing GLI3R by crossing Kif7 -/- animals with Gli3D699 mice could possibly rescue the alterations of layering in the dorsal cortex where the GLI3R/GLI-FL ratio is strongly decreased and the SHH pathway activated. Such a rescue had been previously described for corpus callosum defects (Putoux et al., 2019). However, because cIN are generated in the ventral forebrain where SHH signaling predominantly activates the formation of GLI-A and where Kif7 ablation does not alter the GLI3 ratio, GLI3R re-introduction in the basal forebrain should rather increase the migratory defects of Kif7 -/- cIN instead of producing a rescue. To further support our conclusion, we analyzed the migratory behavior of Kif7 -/- cIN in a WT cortical environment. The results illustrated in the Fig. 6A and described in page 9 of the result section confirm that the migration defects of Kif7 -/-  cIN are reminiscent of an inhibition but not an activation of the  transcriptional SHH pathway (same phenotype as in Kif3a ciliary mutants described in Baudoin et al, 2012).

(6) Figure 6: The authors describe the Shh mRNA and protein expression with relevance to interneuron migration. In contrast to the in situ hybridisation, the immunofluorescence analysis is not very convincing and requires further controls. The authors should at least show a no primary antibody control and, if available, could include a staining on Shh mutants. These additional controls are important as Shh protein expression in the developing cortex is highly controversial and a recent paper describes a different pattern (Manuel et al., 2022: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001563#). Moreover, it remains unclear whether the Shh protein expression is uniform within the cortex or follows lateral to medial or ventricular to pial gradients. A more thorough description and corresponding figures would be helpful. 

Manuel et al. (2022) used the SHH KO (generated by Chiang et al., 1996) that develops a long proboscis to validate the rabbit anti-SHH antibodies (from Genetech) used in their study. They show a lack of SHH signal in the SHH KO. However, it is difficult to identify the cortex in this mouse line and the authors did not specify which part of the SHH protein was used to generate antibodies. We wished to use the SHH KO generated by Chiang and backcrossed on a C57B/6 line (Rash and Grove, 2007) that develops a layered neocortex at E17.5. However,

(1) the SHH KO was obtained by replacing exon2 with a PGK-neo cassette and could express a 101 aa truncated protein comprising the N-ter part of the protein, and

(2) the antibody we used, is a polyclonal N-ter antibody that targets the active SHH protein (Cys25-Gly198 part of SHH protein used as immunogen to produce the antibody). We thus thought that this labeling experiment will not give information on the specificity of the antibody, some epitopes being able to recognize the truncated protein produced in the SHH KO.

To overcome the lack of a good mutant mice to validate the SHH N-ter antibodies, we analyzed the SHH immunostaining pattern at E12.5 and compared the expression profile with previously published SHH mRNA expression patterns. The border of the third ventricle and the ZLI were strongly immunostained by SHH-Nter antibodies and these regions were shown to express SHH mRNA at E12.5-E13.5 (Kicker et al. 2004, Loulier et al., 2005, Sahara et al., 2007 and Fig. 7B1). In brain sections at E14.5, only the choroid plexus was strongly labeled and some structures showed diffused labeling. We analyzed the distribution of SHH mRNAs in the cortex using a highly sensitive technique (RNAscop) at E14.5 and showed that very few cortical cells expressed SHH mRNA and at very low level. Anti-SHH-Nter antibodies immunostained numerous bright dots throughout the cortical neuropile, which is not surprising for a diffusible factor like SHH. However, the labeling was not homogeneous and showed a ventricle to pial gradient at E12.5 and aligned distributions in the different cortical layers at E14.5. We have described the expression pattern in more detail and modified the Fig. S4 by adding an image of immunostaining performed without SHH N-ter antibody.  

(7) Figure S1: The Gli3 Western blot needs to be quantified. As the authors only show one control and one mutant sample, it remains unclear how representative this blot is. In addition to Gli3R and Gli3FL, the authors should also determine the ratio of both isoforms. Are there also differences in the MGE?

We now produce results of Gli3 western blots in the cortex and MGE of several E14.5 Kif7 KO (n=4) and WT (n=4) embryos. The GLI3R/GLI3FL ratio has been determined in the cortex and in the MGE of WT and mutant embryos. Results are illustrated in the Fig. 2. 

Minor points:

The authors should carefully amend the literature on Gli genes and forebrain development. For example:

(1) Line 85: Add Hasenpusch-Theil et al., 2018.

We added this reference.

(2) Line 141: Remove Magnani et al., 2010 (they characterized hypomorphic Gli3 Pdn mutants) and replace with Kuschel et al., 2003.

Since our revised figure 2 illustrates GLI3 western blots and compare GLI3R/GLI3FL ratios in the cortex and MGE of WT and Kif7-/- embryos, we no longer cite these papers in the result section.

(3) Line 380: Replace reference with Theil, 2005.

We have replaced Magnani et al, 2014 by Theil 2005 in the sentence.

(4) Line 414: Rallu et al is not an appropriate reference for this as this manuscript does not investigate the expression of a single cortical marker in Shh/Gli3 double mutants.

We removed the reference Rallu et al. in the sentence.

(5) Reference in line 355: do not use Vancouver style.

We apologize for the mistake that was corrected.

(6) Spelling: Line 447 it should read "choroid plexus"

We again apologize for the mistake that has been corrected.

Reviewer #2 (Public review):

Summary:

This study investigates the role of KIF7, a ciliary kinesin involved in the Sonic Hedgehog (SHH) signaling pathway, in cortical development using Kif7 knockout mice. The researchers examined embryonic cortex development (mainly at E14.5), focusing on structural changes and neuronal migration abnormalities.

Strengths:

(1) The phenotype observed is interesting, and the findings provide neurodevelopmental insight into some of the symptoms and malformations seen in patients with KIF7 mutations.<br /> (2) The authors assess several features of cortical development, including structural changes in layers of the developing cortex, connectivity of the cortex with the thalamus, as well as migration of cINs from CGE and MGE to the cortex.

We greatly thank Reviewer 2 for her/his positive assessment of our work that characterize the neurodevelopmental defects induced by KIF7 ablation. We have deeply reorganized and implemented data in the figures to show changes occurring in different cortical cell types and at different stages. We have moreover corrected and clarified the text where necessary. Our detailed responses to the reviewer’s recommendations appear at the end of each comment.

Weaknesses:

(1) The Kif7 null does have phenotype differences from individual mutations seen in patients. It would be interesting to add more thoughts about how the null differs from these mutants in ciliary structure and SHH signaling via the cilium.

We are grateful to the Reviewer for recalling that Kif7 ablation alters SHH signaling within primary cilium and has a strong effect on ciliary structure. In the revised version of the manuscript, we discuss data from the literature that describe these alterations in human (Putoux et al, 2011) and in murine KIF7 depleted cells (He et al, 2015; Cheung et al., 2009; Lai et al., 2021) (discussion p13).

(2) The description of altered cortex development at E14.5 is perhaps rather descriptive. It would be useful to assess more closely the changes occurring in different cell types and stages. For this it seems very important to have a time course of cortical development and how the structural organization changes over time. This would be easy to assess with the addition of serial sections from the same. It might also be interesting to see how SHH signaling is altered in different cortical cell types over time with a SHH signaling reporter mouse.

We thank the Reviewer for her/his request that helped us to improve our description of developmental defaults in the Kif7 -/- cortex.  In the revised manuscript, we have expanded our study initially focused on embryonic stage E14.5 to earlier (E12.5) and later (E16.5, E18.5 /P0) developmental stages. Instead of focusing on median forebrain sections, we have expanded our observations to rostral and caudal sections. Altogether, these new observations allow us to describe more precisely the complex developmental defects in the Kif7 -/- cortex over time, in specific cortical regions (dorsal versus lateral cortex, and rostral versus caudal levels). Figures 1, 3, 4, and S4 have been deeply edited to present new data on E12.5 (Fig. 1, Fig. 3, Fig. S4), E16.5 (Fig. 1, Fig. 3) and E18.5 (Fig. 3, Fig. 4) embryos. We have modified the legends and text in the result section (p5-6) accordingly. We agree with the Reviewer that deciphering how SHH signaling is altered in the different cortical cells over time should be highly interesting and relevant. Nevertheless, we anticipate complex analyses and consider that they should be retained for future studies.

(3) Abnormal neurodevelopmental phenotypes have been widely reported in the absence of other key genes affecting primary cilia function (Willaredt et al., J Neurosci 2008; Guo et al., Nat Commun 2015). It would be interesting to have more discussion of how the Kif7 null phenotype compares to some of these other mutants.

We agree with this Reviewer concern. In the revised manuscript, we discuss our results with regard to previous observations in other ciliary mutants. The murine cobblestone mutant described in Willaredt et al. (2008) indeed shows defects similar to those we describe in the Kif7 -/- mouse. We thank again the Reviewer for her/his helpful comment that allowed us to strengthen and better interpret our results. Guo et al (2015) did not conduct a study of ciliary mutants. Nevertheless, their characterization of cortical developmental defects following invalidation of genes involved in human ciliopathies identified cell autonomous defects in cortical progenitors and in differentiating cortical neurons, which corroborate our observations (p.15)

(4) The authors see alterations in cIN migration to the cortex and observe distinct differences in the pattern of expression of Cxcl12 as well as suggest cell-intrinsic differences within cIN in their ability to migrate. The slice culture experiments though make it a little difficult to interpret the cell intrinsic effects on cIN of loss of Kif7, as the differences in Cxcl12 patterns still exist presumably in the slice cultures. It would be useful to assess their motility in an assay where they were isolated, as well as assess transcriptional changes in cINs in vivo lacking KIF7 for expression patterns that may affect motility or other aspects of migration.

To circumvent the difference in the expression profile of CXCL12 in the dorsal cortex of WT and Kif7 -/- embryos on the migratory behavior of cIN, we compared the trajectories and dynamics of WT and Kif7 -/- cIN imaged in the lateral cortex where CXCL12 expression appears similar in WT and Kif7 -/- brains.

We moreover followed the reviewer recommendation and analyzed the migratory behavior of Kif7 -/- cIN that migrate as isolated cells on a dissociated substrate of WT cortical cells. We sincerely thank the reviewer for her/his suggestion as the results revealed an interesting and relevant ciliary phenotype in migrating Kif7 -/- cIN. This additional experiment confirms that Kif7 -/- cIN exhibit the same migratory defects as those initially characterized in the Kif3a -/-  ciliary mutant.  The new results are illustrated in the Fig. 6A and described in the result section (p9). We agree with the reviewer that the analysis of transcriptional changes that could affect Kif7 -/- cIN motility and migration would be very interesting to study, but this study is beyond the scope of the present article.

eLife Assessment

This manuscript by Li, Lu et al., presents important findings on the role of cDC1 in atherosclerosis and their influence on the adaptive immune system. Using Xcr1Cre-Gfp Rosa26LSL-DTA ApoE-/- mouse models, these data convincingly reveal an unexpected, non-redundant role of the XCL1-XCR1 axis in mediating cDC1 contributions to atherosclerosis.

Reviewer #1 (Public review):

Summary:

In this study by Li et al., the authors re-investigated the role of cDC1 for atherosclerosis progression using the ApoE model. First, the authors confirmed the accumulation of cDC1 in atherosclerotic lesions in mice and humans. Then, in order to examine the functional relevance of this cell type, the authors developed a new mouse model to selectively target cDC1. Specifically, they inserted the Cre recombinase directly after the start codon of the endogenous XCR1 gene, thereby avoiding off-target activity. Following validation of this model, the authors crossed it with ApoE-deficient mice and found a striking reduction of aortic lesions (numbers and size) following a high-fat diet. The authors further characterized the impact of cDC1 depletion on lesional T cells and their activation state. Also, they provide in-depth transcriptomic analyses of lesional in comparison to splenic and nodal cDC1. These results imply cellular interactions between lesion T cells and cDC1. Finally, the authors show that the chemokine XCL1, which is produced by activated CD8 T cells (and NK cells), plays a key role in the interaction with XCR1-expressing cDC1 and particularly in the atherosclerotic disease progression.

Strengths:

The surprising results on XCL1 represent a very important gain in knowledge. The role of cDC1 is clarified with a new genetic mouse model.

Weaknesses:

My criticism is limited to the analysis of the scRNAseq data of the cDC1. I think it would be important to match these data with published data sets on cDC1. In particular, the data set by Sophie Janssen's group on splenic cDC1 might be helpful here (PMID: 37172103; https://www.single-cell.be/spleen_cDC_homeostatic_maturation/datasets/cdc1). It would be good to assign a cluster based on the categories used there (early/late, immature/mature, at least for splenic DC).

Reviewer #2 (Public review):

This study investigates the role of cDC1 in atherosclerosis progression using Xcr1Cre-Gfp Rosa26LSL-DTA ApoE-/- mice. The authors demonstrate that selective depletion of cDC1 reduces atherosclerotic lesions in hyperlipidemic mice. While cDC1 depletion did not alter macrophage populations, it suppressed T cell activation (both CD4+ and CD8+ subsets) within aortic plaques. Further, targeting the chemokine Xcl1 (ligand of Xcr1) effectively inhibits atherosclerosis. The manuscript is well-written, and the data are clearly presented. However, several points require clarification:

(1) In Figure 1C (upper plot), it is not clear what the Xcr1 single-positive region in the aortic root represents, or whether this is caused by unspecific staining. So I wonder whether Xcr1 single-positive staining can reliably represent cDC1. For accurate cDC1 gating in Figure 1E, Xcr1+CD11c+ co-staining should be used instead.

(2) Figure 4D suggests that cDC1 depletion does not affect CD4+/CD8+ T cells. However, only the proportion of these subsets within total T cells is shown. To fully interpret effects, the authors should provide:<br /> a) Absolute numbers of total T cells in aortas.<br /> b) Absolute counts of CD4+ and CD8+ T cells.

(3) How does T cell activation mechanistically influence atherosclerosis progression? Why was CD69 selected as the sole activation marker? Were other markers (e.g., KLRG1, ICOS, CD44) examined to confirm activation status?

(4) Figure 7B: Beyond cDC1/2 proportions within cDCs, please report absolute counts of: Total cDCs,cDC1, and cDC2 subsets. Figure 7D: In addition to CD4+/CD8+ T cell proportions, the following should be included:<br /> a) Total T cell numbers in aortas<br /> b) Absolute counts of CD4+ and CD8+ T cells.

(5) cDC1 depletion reduced CD69+CD4+ and CD69+CD8+ T cells, whereas Xcl1 depletion decreased Xcr1+ cDC1 cells without altering activated T cells. How do the authors explain these different results? This discrepancy needs explanation.

eLife Assessment

This valuable study presents an innovative noninvasive immunotherapeutic strategy for hepatocellular carcinoma by combining ultrasound stimulation with calcium-loaded nanodroplets to activate splenic immune responses. The authors provide solid preclinical data, including single-cell transcriptomic analyses and evidence of tumor growth suppression, supported by a creative and well-executed methodology. Further validation of the calcium signaling mechanisms and assessment of long-term safety will strengthen the translational potential of this approach. The work will be of broad interest to researchers in oncology, immunotherapy, and biomedical engineering.

Reviewer #1 (Public review):

Summary:

This study presents a compelling strategy for ultrasound-mediated immunomodulation in HCC, supported by robust scRNA-Seq data. While the mechanistic depth and translational validation require further refinement, the work significantly advances the field of noninvasive cancer immunotherapy. Addressing the major concerns, particularly regarding calcium signaling specificity and STNDs@Ca²⁺ safety, will strengthen the manuscript's impact.

Strengths:

(1) Innovative Approach:<br /> The integration of noninvasive ultrasound with calcium-targeted nanotechnology (STNDs@Ca²⁺) represents a significant advancement in cancer immunotherapy. The dual mechanism - direct immunomodulation via FUS and calcium delivery via nanoparticles - is both novel and promising.

(2) Comprehensive Mechanistic Insights:<br /> The use of scRNA-seq and flow cytometry provides a detailed map of immune cell dynamics, highlighting key pathways (TNF, NFκB, MAPK) and cellular transitions (e.g., MDSC suppression, CD8⁺/NK cell activation).

(3) Robust Preclinical Validation:<br /> The study validates findings in two distinct HCC models (H22 and Hepal-6), demonstrating consistent tumor suppression (>70-90%) and prolonged survival, which strengthens translational relevance.

Weaknesses:

Major Issues:

(1) Mechanistic Specificity of Calcium Influx:<br /> While the study attributes immunomodulation to ultrasound-induced calcium influx, the exact mechanism (e.g., involvement of mechanosensitive channels like Piezo1 or TRP families) remains underdiscussed. The qRT-PCR data shows no changes in TRP channels, but the upregulation of Piezo1 warrants deeper exploration.

Suggestion: The authors should include experiments to inhibit Piezo1 or other calcium channels to confirm their role in FUS-mediated effects.

(2) STNDs@Ca²⁺ Biodistribution and Safety:<br /> Although biodistribution data show splenic accumulation, potential off-target effects (e.g., liver/lung uptake) and long-term toxicity are not fully addressed. The serum biochemical analysis (Table 2) lacks critical markers like inflammatory cytokines or immune cell counts.

Suggestion: The authors should provide longitudinal toxicity data (e.g., histopathology beyond 3 hours) and assess systemic immune activation/inflammation.

(3) Statistical and Technical Clarifications:<br /> The statistical methods for multi-group comparisons (e.g., ANOVA vs. t-test) are inconsistently described. For instance, Figure 1 labels significance without specifying correction for multiple comparisons.

Suggestion: the authors should clarify statistical methods in figure legends and the Methods section; apply Bonferroni or FDR correction where appropriate.

(4) Interpretation of scRNA-seq Data:<br /> The clustering of MDSCs using surface markers (Itgam/Ly6c2/Ly6g) overlaps with conventional myeloid populations (Supplementary Figure 16), raising questions about subset specificity.

Suggestion: The authors should validate MDSC identity using functional assays (e.g., T cell suppression) or additional markers (e.g., Arg1, iNOS).

Reviewer #2 (Public review):

Summary:

Wang et al. studied the therapeutic potential of focused ultrasound noninvasively stimulating the spleen (FUS sti. spleen) to modulate the splenic immunity, with an aim to exert anti-tumor effect. They found that the treatment enhanced antitumor capability in CD8 T/ NK cells and reduced the immunosuppression, facilitating the inhibition of HCC growth in vivo.

Strengths:

They have utilized bulk RNA sequencing, single cell RNA sequencing, and flow cytometry to investigate the immune and tumor cell profiling in the mouse models upon FUS sti. spleen. Moreover, they highlighted the importance of combining FUS with spleen-targeted nanodroplets encapsulating bioavailable calcium ions (STND@Ca2+), which facilitated the calcium influx into the murine spleen and further enhanced the therapeutic efficacy of FUS.

Weaknesses:

While the study is interesting and potentially clinically impactful, the mechanism of action of the therapy is not fully elucidated. It would benefit from more rigorous approaches. With the theoretical part strengthened, this paper would be of interest to cancer cell biologists and clinician scientists working on the oncology field.

eLife Assessment

This study presents a valuable finding on the neural representation of time from two distinct egocentric and allocentric reference frames. The presentation of evidence in the version of the original submission is incomplete, as further conceptual clarifications, methodological details, and addressing potential confounds would strengthen the study. The work will be of interest to cognitive neuroscientists working on the perception and memory of time.

Reviewer #1 (Public review):

Summary:

In this fMRI study, the authors wished to assess neural mechanisms supporting flexible "temporal construals". For this, human participants learned a story consisting of fifteen events. During fMRI, events were shown to them and they were instructed to consider the event from "an internal" or from "an external" perspective. The authors found opposite patterns of brain activity in the posterior parietal cortex and the anterior hippocampus for the internal and the external viewpoint. They conclude that allocentric sequences are stored in the hippocampus, whereas egocentric sequences are used in the parietal cortex. The claims align with previous fMRI work addressing this question.

Strengths:

The research topic is fascinating, and very few labs in the world are asking the question of how time is represented in the human brain. Working hypotheses have been recently formulated, and this work seems to want to tackle some of them.

Weaknesses:

The current writing is fuzzy both conceptually and experimentally. I cannot provide a sufficiently well-informed assessment of the quality of the experimental work because there is a paucity of details provided in the report. Any future revisions will likely improve transparency.

(1) Improving writing and presentation:

The abstract and the introduction make use of loaded terms such as "construals", "mental timeline", "panoramic views" in very metaphoric and unexplained ways. The authors do not provide a comprehensive and scholarly overview of these terms, which results in verbiage and keywords/name-dropping without a clear general framework being presented. Some of these terms are not metaphors. They do refer to computational concepts that the authors should didactically explain to their readership. This is all the more important that some statements in the Introduction are misattributed or factually incorrect; some statements lack attributions (uncited published work).

Once the theory, the question, and the working hypothesis are clarified, the authors should carefully explain the task.

(2) The experimental approach lacks sufficient details to be comprehensible to a general audience. In my opinion, the results are thus currently uninterpretable. I highlight only a couple of specific points (out of many). I recommend revision and clarification.

a) No explanation of the narrative is being provided. The authors report a distribution of durations with no clear description of the actual sequence of events. The authors should provide the text that was used, how they controlled for low-level and high-level linguistic confounds.

b) The authors state, "we randomly assigned 15 phrases to the events twice". It is impossible to comprehend what this means. Were these considered stimuli? Controls? IT is also not clear which event or stimulus is part of the "learning set" and whether these were indicated to be such to participants.

c) The left/right counterbalancing is not being clearly explained. The authors state that there is counterbalancing, but do not sufficiently explain what it means concretely in the experiment. If a weak correlation exists between sequential position and distance, it also means that the position and the distance have not been equated within. How do the authors control for these?

d) The authors used two tasks. In the "external perspective" one, the authors asked participants to report whether events were part of the same or a different part of the day. In the "internal perspective one", the authors asked participants to project themselves to the reference event and to determine whether the target event occurred before or after the projected viewpoint. The first task is a same/different recognition task. The second task is a temporal order task (e.g., Arzy et al. 2009). These two asks are radically different and do not require the same operationalization. The authors should minimally provide a comprehensive comparison of task requirements, their operationalization, and, more importantly, assess the behavioral biases inherent to each of these tasks that may confound brain activity observed with fMRI.

e) The authors systematically report interpreted results, not factual data. For instance, while not showing the results on behavioral outcomes, the authors directly interpret them as symbolic distance effects.

Crucially, the authors do not comment on the obvious differences in task difficulty in these two tasks, which demonstrates a substantial lack of control in the experimental design. The same/different task (task 1 called "external perspective") comes with known biases in psychophysics that are not present in the temporal order task (task 2 called " internal perspective"). The authors also did not discuss or try to match the performance level in these two tasks. Accordingly, the authors claim that participants had greater accuracy in the external (same/different) task than in the internal task, although no data are shown and provided to support this report. Further, the behavioral effect is trivialized by the report of a performance accuracy trade-off that further illustrates that there is a difference in the task requirements, preventing accurate comparison of the two tasks.

All fMRI contrasts are also confounded by this experimental shortcoming, seeing as they are all reported at the interaction level across a task. For instance, in Figure 4, the authors report a significant beta difference between internal and external tasks. It is impossible to disentangle whether this effect is simply due to task difference or to an actual processing of the duration that differs across tasks, or to the nature of the representation (the most difficult to tackle, and the one chosen by the authors).

Conclusion:

In conclusion, the current experimental work is confounded and lacks controls. Any behavioral or fMRI contrasts between the two proposed tasks can be parsimoniously accounted for by difficulty or attentional differences, not the claim of representational differences being argued for here.

Reviewer #2 (Public review):

Summary:

Xu et al. used fMRI to examine the neural correlates associated with retrieving temporal information from an external compared to internal perspective ('mental time watching' vs. 'mental time travel'). Participants first learned a fictional religious ritual composed of 15 sequential events of varying durations. They were then scanned while they either (1) judged whether a target event happened in the same part of the day as a reference event (external condition); or (2) imagined themselves carrying out the reference event and judged whether the target event occurred in the past or will occur in the future (internal condition). Behavioural data suggested that the perspective manipulation was successful: RT was positively correlated with sequential distance in the external perspective task, while a negative correlation was observed between RT and sequential distance for the internal perspective task. Neurally, the two tasks activated different regions, with the external task associated with greater activity in the supplementary motor area and supramarginal gyrus, and the internal condition with greater activity in default mode network regions. Of particular interest, only a cluster in the posterior parietal cortex demonstrated a significant interaction between perspective and sequential distance, with increased activity in this region for longer sequential distances in the external task, but increased activity for shorter sequential distances in the internal task. Only a main effect of sequential distance was observed in the hippocampus head, with activity being positively correlated with sequential distance in both tasks. No regions exhibited a significant interaction between perspective and duration, although there was a main effect of duration in the hippocampus body with greater activity for longer durations, which appeared to be driven by the internal perspective condition. On the basis of these findings, the authors suggest that the hippocampus may represent event sequences allocentrically, whereas the posterior parietal cortex may process event sequences egocentrically.

Strengths:

The topic of egocentric vs. allocentric processing has been relatively under-investigated with respect to time, having traditionally been studied in the domain of space. As such, the current study is timely and has the potential to be important for our understanding of how time is represented in the brain in the service of memory. The study is well thought out, and the behavioural paradigm is, in my opinion, a creative approach to tackling the authors' research question. A particular strength is the implementation of an imagination phase for the participants while learning the fictional religious ritual. This moves the paradigm beyond semantic/schema learning and is probably the best approach besides asking the participants to arduously enact and learn the different events with their exact timings in person. Importantly, the behavioural data point towards successful manipulation of internal vs. external perspective in participants, which is critical for the interpretation of the fMRI data. The use of syllable length as a sanity check for RT analyses, as well as neuroimaging analyses, is also much appreciated.

Weaknesses/Suggestions:

Although the design and analysis choices are generally solid, there are a few finer details/nuances that merit further clarification or consideration in order to strengthen the readers' confidence in the authors' interpretation of their data.

(1) Given the known behavioural and neural effects of boundaries in sequence memory, I was wondering whether the number of traversed context boundaries (i.e., between morning-afternoon, and afternoon-evening) was controlled for across sequential length in the internal perspective condition? Or, was it the case that reference-target event pairs with higher sequential numbers were more likely to span across two parts of the day compared to lower sequential numbers? Similarly, did the authors examine any potential differences, whether behaviourally or neurally, for day part same vs. day part different external task trials?

(2) I would appreciate further insight into the authors' decision to model their task trials as stick functions with duration 0 in their GLMs, as opposed to boxcar functions with varying durations, given the potential benefits of the latter (e.g., Grinband et al., 2008). I concur that in certain paradigms, RT is considered a potential confound and is taken into account as a nuisance covariate (as the authors have done here). However, given that RTs appear to be critical to the authors' interpretation of participant behavioural performance, it would imply that variations in RT actually reflect variations in cognitive processes of interest, and hence, it may be worth modelling trials as boxcar functions with varying durations.

(3) The activity pattern across tasks and sequential distance in the posterior parietal cortex appears to parallel the RT data. Have the authors examined potential relationships between the two (e.g., individual participant slopes for RT across sequential distance vs. activity betas in the posterior parietal cortex)?

(4) There were a few places in the manuscript where the writing/discussion of the wider literature could perhaps be tightened or expanded. For instance:

i) On page 16, the authors state 'The negative correlation between the activation level in the right PPC and sequential distance has already been observed in a previous fMRI study (Gauthier & van Wassenhove, 2016b). The authors found a similar region (the reported MNI coordinate of the peak voxel was 42, -70, 40, and the MNI coordinate of the peak voxel in the present study was 39, -70, 35), of which the activation level went up when the target event got closer to the self-positioned event. This finding aligns with the evidence suggesting that the posterior parietal cortex implements egocentric representations.' Without providing a little more detail here about the Gauthier & van Wassenhove study and what participants were required to do (i.e., mentally position themselves at a temporal location and make 'occurred before' vs. 'occurred after' judgements of a target event), it could be a little tricky for readers to follow why this convergence in finding supports a role for the posterior parietal cortex in egocentric representations.

ii) Although the authors discuss the Lee et al. (2020) review and related studies with respect to retrospective memory, it is critical to note that this work has also often used prospective paradigms, pointing towards sequential processing being the critical determinant of hippocampal involvement, rather than the distinction between retrospective vs. prospective processing.

iii) The authors make an interesting suggestion with respect to hippocampal longitudinal differences in the representation of event sequences, and may wish to relate this to Montagrin et al. (2024), who make an argument for the representation of distant goals in the anterior hippocampus and immediate goals in the posterior hippocampus.

eLife Assessment

This important study describes a computational model of the rat spinal locomotor circuits and how they could be plastically reconfigured after lateral hemisection or contusion injuries to replicate gaits observed experimentally in vivo. Overall, the simulation results convincingly mirror the gait parameters observed experimentally. The model suggests the emergence of detour circuits after lateral hemisection, whereas after a midline contusion, the model suggests plasticity of left-right and sensory inputs below the injury.

Reviewer #1 (Public review):

Summary:

This is a rigorous data-driven modeling study, extending the authors' previous model of spinal locomotor central pattern generator (CPG) circuits developed for the mouse spinal cord and adapted here to the rat to explore potential circuit-level changes underlying altered speed-dependent gaits, due to asymmetric (lateral) thoracic spinal hemisection and symmetric midline contusion. The model reproduces key features of the rat speed-dependent gait-related experimental data before injury and after recovery from these two different thoracic spinal cord injuries and suggests injury-specific mechanisms of circuit reorganization underlying functional recovery. There is much interest in the mechanisms of locomotor behavior recovery after spinal cord injury, and data-driven behaviorally relevant circuit modeling is an important approach. This study represents an important advance in the authors' previous experimental and modeling work on locomotor circuitry and in the motor control field.

Strengths:

(1) The authors use an advanced computational model of spinal locomotor circuitry to investigate potential reorganization of neural connectivity underlying locomotor control following recovery from symmetrical midline thoracic contusion and asymmetrical (lateral) hemisection injuries, based on an extensive dataset for the rat model of spinal cord injury.

(2) The rat dataset used is from an in vivo experimental paradigm involving challenging animals to perform overground locomotion across the full range of speeds before and after the two distinct spinal cord injury models, enabling the authors to more completely reveal injury-specific deficits in speed-dependent interlimb coordination and locomotor gaits.

(3) The model reproduces the rat gait-related experimental data before injury and after recovery from these two different thoracic spinal cord injuries, which exhibit roughly comparable functional recovery, and suggests injury-specific, compensatory mechanisms of circuit reorganization underlying recovery.

(4) The model simulations suggest that recovery after lateral hemisection mechanistically involves partial functional restoration of descending drive and long propriospinal pathways. In contrast, recovery following midline contusion relies on reorganization of sublesional lumbar circuitry combined with altered descending control of cervical networks.

(5) These observations suggest that symmetrical (contusion) and asymmetrical (lateral hemisection) injuries induce distinct types of plasticity in different spinal cord regions, suggesting that injury symmetry partly dictates the location and type of neural plasticity supporting recovery.

(6) The authors suggest that therapeutic strategies may be more effective by targeting specific circuits according to injury symmetry.

Weaknesses:

The recovery mechanisms implemented in the model involve circuit connectivity/connection weights adjustment based on assumptions about the structures involved and compensatory responses to the injury. As the authors acknowledge, other factors affecting locomotor patterns and compensation, such as somatosensory afferent feedback, neurochemical modulator influences, and limb/body biomechanics, are not considered in the model.

Reviewer #2 (Public review):

Summary:

In this paper, the authors present a detailed computational model and experimental data concerning overground locomotion in rats before and after recovery from spinal cord injury. They are able to manually tune the parameters of this physiologically based, detailed model to reproduce many aspects of the observed animals' locomotion in the naive case and in two distinct injury cases.

Strengths:

The strengths are that the model is driven to closely match clean experimental data, and the model itself has detailed correspondence to proposed anatomical reality. As such, this makes the model more readily applicable to future experimental work. It can make useful suggestions. The model reproduces a large number of conditions across frequencies, and with the model structure changed by injury and recovery. The model is extensive and is driven by known structures, with links to genetic identities, and has been extensively validated across multiple experiments and manipulations over the years. It models a system of critical importance to the field, and the tight coupling to experimental data is a real strength.

Weaknesses:

A downside is that, scientifically, here, the only question tackled is one of sufficiency. By manually tuning parameters in a manner that aligns with the field's understanding from experimental work, the detailed model can accurately reproduce the experimental findings. One of the benefits of computational models is that the counterfactual can be tested to provide evidence against alternative hypotheses. That isn't really done here. I'm fairly certain that there are competing theories regarding what happens during recovery from a hemi-section injury and a contusion injury. The model could be used to make predictions for some alternative hypotheses, supporting or rejecting theories of recovery. This may be part of future plans. Here, the focus is on showing that the model is capable of reproducing the experimental results at all, for any set of parameters, however tuned.

Reviewer #3 (Public review):

Summary:

This study describes a computational model of the rat spinal locomotor circuit and how it could be reconfigured after lateral hemisection or contusion injuries to replicate gaits observed experimentally.

The model suggests the emergence of detour circuits after lateral hemisection, whereas after a midline contusion, the model suggests plasticity of left-right and sensory inputs below the injury.

Strengths:

The model accurately models many known connections within and between forelimb and hindlimb spinal locomotor circuits.

The simulation results mirror closely gait parameters observed experimentally. Many gait parameters were studied, as well as variability in these parameters in intact versus injured conditions.

Weaknesses:

The study could provide some sense of the relative importance of the various modified connectivities after injury in setting the changes in gait seen after the two types of injuries.

Overall, the authors achieved their aims, and the model provides solid support for the changes in connectivity after the two types of injuries were modelled. This work emphasizes specific changes in connectivity after lateral hemisection or after contusion that could be investigated experimentally. The model is available for public use and could serve as a tool to analyze the relative importance of various highlighted or previously undiscovered changes in connectivity that may underlie the recovery of locomotor function in spinalized rats.

eLife Assessment

This useful manuscript addresses some key molecular mechanisms on the neuroprotective roles of soluble TREM2 in neurodegenerative diseases. The study will advance our understanding of TREM2 mutations, particularly on the damaging effect of known TREM2 mutations, and also provides solid evidence why soluble TREM2 can antagonize Aβ aggregation.

Reviewer #1 (Public review):

In this manuscript, Saeb et al reported the mechanistic roles of the flexible stalk domain in sTREM2 function using molecular dynamics simulations. They have reported some interesting molecular bases explaining why sTREM2 shows protective effects during AD, such as partial extracellular stalk domain promoting binding preference and stabilities of sTREM2 with its ligand even in the presence of known AD-risk mutation, R47H. Furthermore, they found that the stalk domain itself acts as the site for ligand binding by providing an "expanded surface", known as 'Expanded Surface 2' together with the Ig-like domain. Also, they observed no difference in the binding free energy of phosphatidyl-serine with wild TREM2-Ig and mutant TREM2-Ig, which is a bit inconsistent with the previous report with experiment studies by Journal of Biological Chemistry 293, (2018), Alzheimer's and Dementia 17, 475-488 (2021), Cell 160, 1061-1071 (2015).

Reviewer #2 (Public review):

Significance:

TREM2 is an immunomodulatory receptor expressed on myeloid cells and microglia in the brain. TREM2 consists of a single immunoglobular (Ig) domain that leads into a flexible stalk, transmembrane helix, and short cytoplasmic tail. Extracellular proteases can cleave TREM2 in its stalk and produce a soluble TREM2 (sTREM2). TREM2 is genetically linked to Alzheimer's disease (AD), with the strongest association coming from an R47H variant in the Ig domain. Despite intense interest, the full TREM2 ligand repertoire remains elusive, and it is unclear what function sTREM2 may play in the brain. The central goal of this paper is to assess the ligand-binding role of the flexible stalk that is generated during the shedding of TREM2. To do this, the authors simulate the behavior of constructs with and without stalk. However, it is not clear why the authors chose to use the isolated Ig domain as a surrogate for full-length TREM2. Additionally, experimental binding evidence that is misrepresented by the authors contradicts the proposed role of the stalk.

Summary and strengths:

The authors carry out MD simulations of WT and R47H TREM2 with and without the flexible stalk. Simulations are carried out for apo TREM2 and for TREM2 in complex with various lipids. They compare results using just the Ig domain to results including the flexible stalk that is retained following cleavage to generate sTREM2. The computational methods are well-described and should be reproducible. The long simulations are a strength, as exemplified in Figure 2A where a CDR2 transition happens at ~400-600 ns. The stalk has not been resolved in structural studies, but the simulations suggest the intriguing and readily testable hypothesis that the stalk interacts with the Ig domain and thereby contributes to the stability of the Ig domain and to ligand binding. I suspect biochemists interested in TREM2 will make testing this hypothesis a high priority.

Comments on latest version:

The authors have addressed my critiques and carried out additional simulations, as requested. I would upgrade my assessment of the evidence to "solid."

Author response:

The following is the authors’ response to the original reviews

Public Review:

Review #1 (Public review):

Also, they observed no difference in the binding free energy of phosphatidyl-serine with wild TREM2-Ig and mutant TREM2-Ig, which is a bit inconsistent with the previous report with experiment studies by Journal of Biological Chemistry 293, (2018), Alzheimer's and Dementia 17, 475-488 (2021), Cell 160, 1061-1071 (2015).

We agree with the reviewer that our results do not fully recapitulate experimental findings and directly note this in the body of our work, particularly given the known limitations of free energy calculations in MD simulations, as outlined in the Limitations section. Our claim is that the loss-of-function effects of the R47H variant extend beyond decreased binding affinities which are likely due to variable binding patterns. We have also re-analyzed and highlighted statistically significant differences in interaction entropies. Ultimately, our claim is that mutational effects extend beyond experimentally confirmed differences in binding affinities.

Perhaps the authors made significant efforts to run a number of simulations for multiple models, which is nearly 17 microseconds in total; none of the simulations has been repeated independently at least a couple of times, which makes me uncomfortable to consider this finding technically true. Most of the important conclusions that authors claimed, including the opposite results from previous research, have been made on the single run, which raises the question of whether this observation can be reproduced if the simulation has been repeated independently. Although the authors stated the sampling number and length of MD simulations in the current manuscript as a limitation of this study, it must be carefully considered before concluding rather than based on a single run.

To address this comment, we have added numerous replicates to our simulations of WT and R47H (s)TREM2 without lipids and substantially increased the total simulation time. Each pure protein system now has six total microsecond-long technical replicates. The addition of replicates strengthens the validity of the work and allows us to make stronger novel conclusions than with one simulation alone, particularly for claims regarding the CDR2 loop and sTREM2 stalk.  In our models with phospholipids, running multiple independent biological replicates of the same system offers a more rigorous methodology than simply repeating simulations of the same docked model. This strategy allows us to sample several distinct starting configurations, thereby minimizing biases introduced by docking algorithms and single-model reliance.

sTREM2 shows a neuroprotective effect in AD, even with the mutations with R47H, as evidenced by authors based on their simulation. sTREM2 is known to bind Aβ within the AD and reduce Aβ aggregation, whereas R47H mutant increases Aβ aggregation. I wonder why the authors did not consider Aβ as a ligand for their simulation studies. As a reader in this field, I would prefer to know the protective mechanism of sTREM2 in Aβ aggregation influenced by the stalk domain.

Our initial approach for this study used Aβ as a ligand rather than phospholipids. However, we noted the difficulties in simulating Aβ, particularly in choosing relevant Aβ structures and oligomeric states (n-mers). We believe that phospholipids represent an equally pertinent ligand for TREM2, given its critical role in lipid sensing and metabolism. Furthermore, there is growing recognition in the AD research community of the need to move beyond Aβ and focus on other understudied pathological mechanisms.

In a similar manner, why only one mutation is considered "R47H" for the study? There are more server mutations reported to disrupt tethering between these CDRs, such as T66M. Although this "T66M" is not associated with AD, I guess the stalk domain protective mechanism would not be biased among different diseases. Therefore, it would be interesting to see whether the findings are true for this T66M.

In most previous studies, the mechanism for CDR destabilization by mutant was explored, like the change of secondary structures and residue-wise interloop interaction pattern. While this is not considered in this manuscript, neither detailed residue-wise interaction that changed by mutant or important for 'ligand binding" or "stalk domain".

These are both excellent points that deserve extensive investigation, although we note that our paper does include significant protein-protein and protein-ligand interaction mapping that encompasses both the CDR2 loop and stalk, analyses which were not performed in any previous papers. In a separate paper, we explored more detailed residue-wise interactions for the CDR2 loop (Lietzke et al., Alzheimer’s and Dementia, 2025). While R47H is the most common and prolific mutation in literature, an extensive catalog of other mutations is important to explore. To this end, we are currently preparing a separate publication that will explore a larger mutational library and include more detailed sTREM2 analyses. 

The comparison between the wild and mutant and other different complex structures must be determined by particular statistical calculations to state the observed difference between different structures is significant. Since autocorrelation is one of the major concerns for MD simulation data for predicting statistical differences, authors can consider bootstrap calculations for predicting statistical significance.

The addition of numerous replicates across systems negates potential effects from autocorrelation and allows us to include standard deviations to critically assess the validity of our claims.

Review #2 (Public review):

The authors state that reported differences in ligand binding between the TREM2 and sTREM2 remain unexplained, and the authors cite two lines of evidence. The first line of evidence, which is true, is that there are differences between lipid binding assays and lipid signaling assays. However, signaling assays do not directly measure binding. Secondly, the authors cite Kober et al 2021 as evidence that sTREM2 and TREM2 showed different affinities for Abeta1-42 in a direct binding assay. Unfortunately, when Kober et al measured the binding of sTREM2 and Ig-TREM2 to Abeta they reported statistically identical affinities (Kd = 3.8 {plus minus} 2.9 µM vs 5.1 {plus minus} 3.7 µM) and concluded that the stalk did not contribute measurably to Abeta binding.

We appreciate the reviewer’s insight and acknowledge the need to clarify our interpretation of Kober et al. (2021). We have adjusted how we cite Kober et al. and reframed the first paragraph in the second results section.

In line with these findings, our energy calculations reveal that sTREM2 exhibits weaker—but still not statistically significant—binding affinities for phospholipids compared to TREM2. These results suggest that while overall binding affinity might be similar, differences in binding patterns or specific lipid interactions could still contribute to functional differences observed between TREM2 and sTREM2.

The authors appear to take simulations of the Ig domain (without any stalk) as a surrogate for the full-length, membrane-bound TREM2. They compare the Ig domain to a sTREM2 model that includes the stalk. While it is fully plausible that the stalk could interact with and stabilize the Ig domain, the authors need to demonstrate why the full-length TREM2 could not interact with its own stalk and why the isolated Ig domain is a suitable surrogate for this state.

We believe that this is a major limitation of all computational work of TREM2 to-date, and of experimental work which only presents the Ig-like domain. This is extensively discussed in the limitations section of our paper and treated carefully throughout the text. We are currently working toward a separate manuscript that will represent the first biologically relevant model of full-length TREM2 in a membrane and will rigorously assess the current paradigm of using the Ig-like domain as an experimental surrogate for TREM2.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Perhaps the authors made significant efforts to run a number of simulations for multiple models, which is nearly 17 microseconds in total; none of the simulations has been repeated independently at least a couple of times, which makes me uncomfortable to consider this finding technically true. Most of the important conclusions that authors claimed, including the opposite results from previous research, have been made on the single run, which raises the question of whether this observation can be reproduced if the simulation has been repeated independently. Although the authors stated the sampling number and length of MD simulations in the current manuscript as a limitation of this study, it must be carefully considered before concluding rather than based on a single run.

To address this comment, we have added numerous replicates to our simulations of WT and R47H (s)TREM2 without lipids and substantially increased the total simulation time. Each pure protein system now has six total microsecond-long technical replicates. The addition of replicates strengthens the validity of the work and allows us to make stronger novel conclusions than with one simulation alone, particularly for claims regarding the CDR2 loop and sTREM2 stalk.  In our models with phospholipids, running multiple independent biological replicates of the same system offers a more rigorous methodology than simply repeating simulations of the same docked model. This strategy allows us to sample several distinct starting configurations, thereby minimizing biases introduced by docking algorithms and single-model reliance. 

(2) sTREM2 shows a neuroprotective effect in AD, even with the mutations with R47H, as evidenced by authors based on their simulation. sTREM2 is known to bind Aβ within the AD and reduce Aβ aggregation, whereas R47H mutant increases Aβ aggregation. I wonder why the authors did not consider Aβ as a ligand for their simulation studies. As a reader in this field, I would prefer to know the protective mechanism of sTREM2 in Aβ aggregation influenced by the stalk domain.

Our initial approach for this study used Aβ as a ligand rather than phospholipids. However, we noted the difficulties in simulating Aβ, particularly in choosing relevant Aβ structures and oligomeric states (n-mers). We believe that phospholipids represent an equally pertinent ligand for TREM2, given its critical role in lipid sensing and metabolism. Furthermore, there is growing recognition in the AD research community of the need to move beyond Aβ and focus on other understudied pathological mechanisms.

(3) In a similar manner, why only one mutation is considered "R47H" for the study? There are more server mutations reported to disrupt tethering between these CDRs, such as T66M. Although this "T66M" is not associated with AD, I guess the stalk domain protective mechanism would not be biased among different diseases. Therefore, it would be interesting to see whether the findings are true for this T66M.

(4) In most previous studies, the mechanism for CDR destabilization by mutant was explored, like the change of secondary structures and residue-wise interloop interaction pattern. While this is not considered in this manuscript, neither detailed residue-wise interaction that changed by mutant or important for 'ligand binding" or "stalk domain".

These are both excellent points that deserve extensive investigation, although we note that our paper does include significant protein-protein and protein-ligand interaction mapping that encompasses both the CDR2 loop and stalk, analyses which were not performed in any previous papers. In a separate paper, we explored more detailed residue-wise interactions for the CDR2 loop (Lietzke et al., Alzheimer’s and Dementia, 2025). While R47H is the most common and prolific mutation in literature, an extensive catalog of other mutations is important to explore. To this end, we are currently preparing a separate publication that will explore a larger mutational library and include more detailed sTREM2 analyses.

(5) The comparison between the wild and mutant and other different complex structures must be determined by particular statistical calculations to state the observed difference between different structures is significant. Since autocorrelation is one of the major concerns for MD simulation data for predicting statistical differences, authors can consider bootstrap calculations for predicting statistical significance.

The addition of numerous replicates across systems negates potential effects from autocorrelation and allows us to include standard deviations to critically assess the validity of our claims.

Reviewer #2 (Recommendations for the authors):

Major points:

(1) I encourage the authors to review Figure 5D and the text of section 2.7 from Kober et al 2021, which argued that "(t)he identical (within error) binding affinities indicated that the TREM2 Ig domain composes the majority (if not entirety) of the mAβ42 binding surface."

We appreciate the reviewer’s insight and acknowledge the need to clarify our interpretation of Kober et al. (2021). We have adjusted how we cite Kober et al and reframed the first paragraph in the second results section.

(2) The abstract and text need extensive revision to address the major concerns, which jeopardize the biological premise and significance of the work.

We have made changes to the abstract and text to reflect concerns and revisions.

(3) The title and abstract should change to reflect the contents of the paper. The authors do not directly measure lipid binding, nor are any of the computations done in a membrane environment. The authors do not measure anything in the brain.

We have modified the title to better reflect the content of the paper. The paper measures lipid binding in the form of free energy calculations and interaction maps.

Minor points:

(1) How does the conservation of the TREM2 stalk compare to the Ig domain as they relate to the TREM2 family?

While this study may inspire further exploration of other TREM receptors, we do not believe that our results extend to other TREM family members because of relatively low homology.

(2) Please show the locations of the glycosylation sites on a model in Figure 1 and discuss their potential contribution to the ligand binding surfaces.

N-linked glycosylation points are now noted on the sequence map of Figure 1 and updated in the text.

(3) There is an isoform of TREM2 that produces a secreted product that is similar to the sTREM2 produced by proteolysis. The authors should comment as to whether their findings would apply to secreted TREM2.

We have addressed this with a new line in the ‘Ideas and Speculation’ section.

(4) This sentence on p. 2, line 73 references a review, not a study:

This has been corrected.

(5) "Yet, one study suggested effective TREM2 stimulation by PLs may require co-presentation with other molecules, potentially reflecting the nature of lipoprotein endocytosis30"

This has been corrected.

(6) Is "inclusive" on line 88 a typo for inconclusive?

This has been corrected.

(7) "Further, there is a strong correlation between the levels of sTREM2 in the cerebrospinal fluid and that of Tau, however correlation with Aβ is inclusive"

This has been corrected.

eLife Assessment

This study convincingly demonstrates that odors evoke a feeding response in Drosophila, mediated by gustatory receptors and observed as a proboscis extension. The evidence is comprehensive, encompassing behavior, functional imaging and electrophysiology. This important results on the molecular and cellular basis of multimodal integration across olfaction and gustation will be of interest for the study of chemosensation, sensory biology, and animal behavior.

Reviewer #1 (Public review):

Summary:

Odor- and taste-sensing are mediated by two different systems, the olfactory and gustatory systems, and have different behavioral roles. In this study, Wei et al. challenge this dichotomy by showing that odors can activate gustatory receptor neurons (GRNs) in Drosophila to promote feeding responses, including the proboscis extension response (PER) that was previously thought to be driven only by taste. While previous studies suggested that odors can promote PER to appetitive tastants, Wei et al. go further to show that odors alone cause PER, this effect is mediated through sweet-sensing GRNs, and sugar receptors are required. The study also shows that odor detection by bitter-sensing GRNs suppresses PER. The authors' conclusions are supported by behavioral assays, calcium imaging, electrophysiological recordings, and genetic manipulations. The observation that both attractive and aversive odors promote PER leaves an open question as to why this effect is adaptive. Overall, the study sheds new light on chemosensation and multimodal integration by showing that odor and taste detection converge at the level of sensory neurons, a finding that is interesting and surprising while also being supported by another recent study (Dweck & Carlson, Sci Advances 2023).

Strengths:

(1) The main finding that odors alone can promote PER by activating sweet-sensing GRNs is interesting and novel.

(2) The study uses video tracking of the proboscis to quantify PER rather than manual scoring, which is typically used in the field. The tracking method is less subjective and provides a higher-resolution readout of the behavior.

(3) The study uses calcium imaging and electrophysiology to show that odors activate GRNs. These represent complementary techniques that measure activity at different parts of the GRN (axons versus dendrites, respectively) and strengthen the evidence for this conclusion.

(4) Genetic manipulations show that odor-evoked PER is primarily driven by sugar GRNs and sugar receptors rather than olfactory neurons. This is a major finding that distinguishes this work from previous studies of odor effects on PER and feeding (e.g., Reisenman & Scott, 2019; Shiraiwa, 2008) that assumed or demonstrated that odors were acting through olfactory neurons.

Weaknesses/Limitations:

(1) Many of the odor effects on behavior or neuronal responses were only observed at very high concentrations. Most effects seemed to require concentrations of at least 10^-2 (0.01 v/v), which is at the high end of the concentration range used in olfactory studies (e.g., Hallem et al., 2004), and most experiments in the paper used a far higher concentration of 0.5 v/v. It is unclear whether these are concentrations that would be naturally encountered by flies. In addition, it is difficult to compare the concentrations used for electrophysiology and behavior given that they are presented in solution versus volatile form.

(2) The timecourse of GRN activation by odors seems quite prolonged (and possibly delayed, depending on the exact timing of odor onset to the fly), and this timecourse is not directly compared with activation by tastes to determine whether it is a property of the calcium sensor or a real difference.

(3) While the overall effect of different conditions is tested using appropriate statistical methods, post-hoc tests are not always used to determine which specific groups are different from each other (e.g., which odors and concentrations elicit significant PER compared to air or mineral oil controls in Fig. 1; which odors show impaired responses without olfactory organs in Fig. 2A).

Discrepancies with previous studies:

These discrepancies are important to note but should not necessarily be considered "weaknesses" of the present study.

(1) It is not entirely clear why PER to odors alone has not been previously reported, especially as this study shows that it is a broad effect evoked by many different odors. Previous studies (Oh et al., 2021; Reisenman & Scott, 2019; Shiraiwa, 2008) tested the effect of odors on PER and only observed enhancement of PER to sugar rather than odor-evoked PER; some of these studies explicitly show no effect of odor alone or odor with low sugar concentration. In the Response to Reviewers, the authors propose that genetic background may explain discrepancies, but this is not discussed much in the paper itself. Differences in behavioral quantification (automated vs. manual scoring, quantification of PER duration versus probability) may also contribute.

(2) The calcium imaging data showing that sugar GRNs respond to a broad set of odors contrasts with results from Dweck & Carlson (Sci Adv, 2023) who recorded sugar neurons with electrophysiology and observed responses to organic acids, but not other odors. This discrepancy is mentioned in the Discussion but the underlying reason is not clear.

Reviewer #3 (Public review):

Summary:

Using flies, Kazama et al. combined behavioral analysis, electrophysiological recordings, and calcium imaging experiments to elucidate how odors activate gustatory receptor neurons (GRNs) and elicit a proboscis extension response, which is interpreted as a feeding response.

The authors used DeepLabCut v2.0 to estimate the extension of the proboscis, which represents an unbiased and more precise method for describing this behavior compared to manual scoring.

They demonstrated that the probability of eliciting a proboscis extension increases with higher odor concentrations. The most robust response occurs at a 0.5 v/v concentration, which, despite being diluted in the air stream, remains a relatively high concentration. Although the probability of response is not particularly high it is higher than control stimuli. Notably, flies respond with a proboscis extension to both odors that are considered positive and those regarded as negative.

The authors used various transgenic lines to show that the response is mediated by GRNs. Specifically, inhibiting Gr5a reduces the response, while inhibiting Gr66a increases it in fed flies. Additionally, they find that odors induce a strong positive response in both types of GRNs, which is abolished when the labella of the proboscis are covered. This response was also confirmed through electrophysiological tip recordings.

Finally, the authors demonstrated that the response increases when two stimuli of different modalities, such as sucrose and odors, are presented together, suggesting clear multimodal integration

Strengths:

The integration of various techniques, which collectively supports the robustness of the results.<br /> The assessment of electrophysiological recordings in intact animals, preserving natural physiological conditions.

Weaknesses:

Only highly concentrated odours are capable of evoking positive responses and, even then, the proportion remains relatively low.

The authors have incorporated my suggestions.

eLife Assessment

This study reports important new insights into the roles of a long noncoding RNA, lnc-FANCI-2, in the progression of cervical cancer induced by a type of human papillomavirus. Through a blend of cell biological, biochemical, and genetic analyses of RNA and protein expression, protein-protein interaction, cell signaling, and cell morphology, the authors provide convincing evidence that lnc-FANCI-2 affects cervical cancer outcome by regulating the RAS signaling pathway. These findings will be of interest to scientists in the fields of cervical cancer, long noncoding RNA, and cell signaling.

Reviewer #1 (Public review):

Summary:

The authors attempted to dissect the function of a long non-coding RNA, lnc-FANCI-2, in cervical cancer. They profiled lnc-FANCI-2 in different cell lines and tissues, generated knockout cell lines, and characterized the gene using multiple assays.

Strengths:

A large body of experimental data has been presented and can serve as a useful resource for the scientific community, including transcriptomics and proteomics datasets. The reported results also span different parts of the regulatory network and open up multiple avenues for future research.

Weaknesses:

The write-up is somewhat unfocused and lacks deep mechanistic insights in some places.

Comments on revisions:

The manuscript is much improved. I am satisfied with the authors' responses.

Reviewer #3 (Public review):

Summary:

A long noncoding RNA, lnc-FANCI-2, was reported to be regulated by HPV E7 oncoprotein and a cell transcription factor, YY1 by this group. The current study focuses on the function of lnc-FANCI-2 in HPV-16 positive cervical cancer is to intrinsically regulate RAS signaling, thereby facilitating our further understanding additional cellular alterations during HPV oncogenesis. Authors used the advanced technical approaches such as KO, transcriptome and (IRPCRP) and LC- MS/MS analyses in the current study and concluded that KO Inc-FANCI-2 significantly increase RAS signaling, especially phosphorylation of Akt and Erk1/2.

Strengths:

(1) HPV E6E7 are required for full immortalization and maintenance of malignant phenotype of cervical cancer, but they are NOT sufficient for full transformation and tumorigenesis. This study helps further the understanding of other cellular alterations in HPV oncogenesis.<br /> (2) lnc-FANCI-2 is upregulated in cervical lesion progression from CIN1, CIN2-3 to cervical cancer, cancer cell lines and HPV transduced cell lines.<br /> (3) Viral E7 of high-risk HPVs and host transcription factor YY1 are two major factors promoting lnc-FANCI-2 expression.<br /> (4) Proteomic profiling of cytosolic and secreted proteins showed inhibition of MCAM, PODXL2 and ECM1 and increased levels of ADAM8 and TIMP2 in KO cells.<br /> (5) RNA-seq analyses revealed that KO cells exhibited significantly increased RAS signaling but decreased IFN pathways.<br /> (6) Increased phosphorylated Akt and Erk1/2, IGFBP3, MCAM, VIM, and CCND2 (cyclin D2) and decreased RAC3 were observed in KO cells.

Comments on revisions:

The revised manuscript has been significantly improved. The authors addressed all my concerns.

eLife Assessment

The paper addresses the question of gene epistasis and asks what is the correct null model for which we should declare no epistasis. By reanalyzing synthetic gene array datasets regarding single and double-knockout yeast mutants, and considering two theoretical models of cell growth, the authors reach the valuable conclusion that the product function is a good null model. While the justification of some assumptions is incomplete, the results have the potential to be of value to the field of gene epistasis.

Reviewer #1 (Public review):

Summary

Detecting unexpected epistatic interactions between multiple mutations requires a robust null expectation-or neutral function-that predicts the combined effects of multiple mutations on phenotype based on the individual effects of single mutations. This study evaluated the relevance of the product neutrality function, where double-mutant fitness is represented as a multiplicative combination of single-mutant fitness in the absence of epistatic interactions. The authors used a recent large dataset on fitness, specifically yeast colony size, to analyze epistatic interactions.

The study confirmed that the product function outperformed other neutral functions in predicting double-mutant fitness, showing no bias between negative and positive epistatic interactions. Additionally, in the theoretical portion of the study, the authors employed a previously established theoretical model of bacterial cell growth to simulate growth rates of both single- and double-mutants under multiple parameters. The simulations similarly demonstrated that the product function was superior to other functions in predicting the fitness of hypothetical double-mutants. Based on these findings, the authors concluded that the product function is a robust tool for analyzing epistatic interactions in growth fitness and effectively reflects how growth rates depend on the combination of multiple biochemical pathways.

Strength

By leveraging a previously published large dataset of yeast colony sizes for single- and double-knockout mutants, this study validated the relevance of the product function, which has frequently been used in genetics to analyze epistatic interactions. The confirmation that the product function provides a more reliable prediction of double-mutant fitness compared to other neutral functions is valuable for researchers analyzing epistatic interactions, particularly those working with the same dataset.<br /> Notably, this dataset has been previously used in studies exploring epistatic interactions with the product neutrality function. This study's findings affirm the validity of using the product function, which could enhance confidence in the conclusions drawn by those earlier studies. Consequently, both researchers utilizing this dataset and readers of prior research will benefit from the confirmation provided by this study.

Weakness

This study contains several serious problems, primarily stemming from the following issues: ignoring the substantial differences in the mechanisms regulating cell growth between prokaryotes and eukaryotes and adopting an overly specific and unrealistic set of assumptions in the mutation model. Below, the details are discussed.

(1) Misapplication of prokaryotic growth models

The mechanistic origin of the multiplicative model observed in yeast colony fitness is explained using a bacterial cell growth model. However, there is no valid justification for linking these two systems. The bacterial growth model, the Scott-Hwa model, heavily rely on specific molecular mechanisms, such as ppGpp-mediated regulation, which adjusts ribosome expression and activity during translation. In particular, this mechanism is critical to ensure growth-dependency of the fraction of ribosome in proteome in the Scott-Hwa model [https://doi.org/10.1111/j.1462-2920.2010.02357.x; https://doi.org/10.1073/pnas.2201585119]. Yeast cells lack this regulatory mechanism, making it inappropriate to directly apply bacterial growth models to yeast.<br /> The Weiße model is based on a larger set of underlying equations and involves more parameters than the Scott-Hwa model. In the original paper by Weiße et al. (PNAS, 2015), however, the model parameters were fitted solely to experimental data from E. coli, and the model's applicability to yeast was never assessed. In summary, for neither the Scott-Hwa model nor the Weiße model has it been demonstrated that the entire model quantitatively fits experimental data from yeast. A positive correlation between growth rate and RNA/protein ratio, often observed in yeast, supports only a limited portion of either model, and does not constitute validation of the models as a whole.

(2) Overly specific assumptions in the theoretical model

The theoretical model assumes that two mutations affect only independent parameters of specific biochemical processes. However, this overly restrictive assumption weakens the model's validity in explaining the general occurrence of the multiplicative model in mutations. Furthermore, experimental evidence suggests limitations of this approach. For example, in most viable yeast deletion mutants with reduced growth rates, the expression of ribosomal proteins remained largely unchanged, contrary to the predictions of the Scott-Hwa model [https://doi.org/10.7554/eLife.28034]. This discrepancy highlights that the Scott-Hwa model and its derivatives cannot reliably explain mutants' growth rates based on current experimental evidence.

(3) Limited reliability of the mechanistic origin of the multiplicative model

The authors seem to regard growth-optimizing feedback as the mechanistic origin of the multiplicative model. However, the importance of growth-optimizing feedback in explaining product neutrality heavily depends on the very specific framework of the Scott-Hwa model. As I pointed out above, the Scott-Hwa model is a bacterial growth model that considers only a narrowly defined set of biochemical reactions. Using such a narrow model to explore the mechanistic origin of product neutrality observed on a genome-wide scale appears to be inappropriate. Arguments based on either the Scott-Hwa model or the Weiße model fail to account for the generality of product neutrality across diverse genetic perturbations. These models, in their current form, do not explain the broader patterns of product neutrality observed experimentally.

Reviewer #2 (Public review):

The paper deals with the important question of gene epistasis, focusing on asking what is the correct null model for which we should declare no epistasis.

In the first part, they use the Synthetic Genetic Array dataset to claim that the effects of a double mutation on growth rate is well predicted by the product of the individual effects (much more than e.g. the additive model). The second (main) part shows this is also the prediction of two simple, coarse-grained models for cell growth.

I find the topic interesting, the paper well written, and the approach innovative.

Comments on revisions:

The authors have adequately addressed the comments raised in the review below, and I find that the paper has improved.

eLife Assessment

This paper highlights an important physiological function of PGAM in the differentiation and suppressive activity of Treg cells by regulating serine synthesis. This role is proposed to intersect with glycolysis and one-carbon metabolism. The study's conclusion is supported by solid evidence from in-vitro cellular and in-vivo mouse models.

Reviewer #1 (Public review):

Summary:

This work provides a new potential tool to manipulate Tregs function for therapeutic use. It focuses on the role of PGAM in Tregs differentiation and function. The authors, interrogating publicly available transcriptomic and proteomic data of human regulatory T cells and CD4 T cells, state that Tregs express higher levels of PGAM (at both message and protein levels) compared to CD4 T cells. They then inhibit PGAM by using a known inhibitor ECGC and show that this inhibition affects Tregs differentiation. This result was also observed when they used antisense oligonucleotides (ASOs) to knockdown PGAM1.

PGAM1 catalyzes the conversion of 3PG to 2PG in the glycolysis cascade. However, the authors focused their attention on the additional role of 3PG: acting as starting material for the de novo synthesis of serine.

They hypothesized that PGAM1 regulates Tregs differentiation by regulating the levels of 3PG that are available for de novo synthesis of serine, which has a negative impact on Tregs differentiation. Indeed, they tested whether the effect on Tregs differentiation observed by reducing PGAM1 levels was reverted by inhibiting the enzyme that catalyzes the synthesis of serine from 3PG.

The authors continued by testing whether both synthesized and exogenous serine affect Tregs differentiation and continued with in vivo experiments to examine the effects of dietary serine restriction on Tregs function.

In order to understand the mechanism by which serine impacts Tregs function, the authors assessed whether this depends on the contribution of serine to one-carbon metabolism and to DNA methylation.

The authors therefore propose that extracellular serine and serine whose synthesis is regulated by PGAM1 induce methylation of genes Tregs associated, downregulating their expression and overall impacting Tregs differentiation and suppressive functions.

Strengths:

The strength of this paper is the number of approaches taken by the authors to verify their hypothesis. Indeed, by using both pharmacological and genetic tools in in vitro and in vivo systems they identified a potential new metabolic regulation of Tregs differentiation and function.

Reviewer #2 (Public review):

Summary:

The authors have tried to determine the regulatory role of Phosphoglycerate mutate (PGAM), an enzyme involved in converting 3-phosphoglycerate to 2-phosphoglycerate in glycolysis, in differentiation and suppressive function of regulatory CD4 T cells through de novo serine synthesis. This is done by contributing one carbon metabolism and eventually epigenetic regulation of Treg differentiation.

Strengths:

The authors have rigorously used inhibitors and antisense RNA to verify the contribution of these pathways in Treg differentiation in-vitro. This has also been verified in an in-vivo murine model of autoimmune colitis. This has further clinical implications in autoimmune disorders and cancer.

[Editors' note: The authors addressed important comments by the reviewers.]

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

This work provides a new potential tool to manipulate Tregs function for therapeutic use. It focuses on the role of PGAM in Tregs differentiation and function. The authors, interrogating publicly available transcriptomic and proteomic data of human regulatory T cells and CD4 T cells, state that Tregs express higher levels of PGAM (at both message and protein levels) compared to CD4 T cells. They then inhibit PGAM by using a known inhibitor ECGC and show that this inhibition affects Tregs differentiation. This result was also observed when they used antisense oligonucleotides (ASOs) to knockdown PGAM1.

PGAM1 catalyzes the conversion of 3PG to 2PG in the glycolysis cascade. However, the authors focused their attention on the additional role of 3PG: acting as starting material for the de novo synthesis of serine.

They hypothesized that PGAM1 regulates Tregs differentiation by regulating the levels of 3PG that are available for de novo synthesis of serine, which has a negative impact on Tregs differentiation. Indeed, they tested whether the effect on Tregs differentiation observed by reducing PGAM1 levels was reverted by inhibiting the enzyme that catalyzes the synthesis of serine from 3PG.

The authors continued by testing whether both synthesized and exogenous serine affect Tregs differentiation and continued with in vivo experiments to examine the effects of dietary serine restriction on Tregs function.

In order to understand the mechanism by which serine impacts Tregs function, the authors assessed whether this depends on the contribution of serine to one-carbon metabolism and to DNA methylation.

The authors therefore propose that extracellular serine and serine whose synthesis is regulated by PGAM1 induce methylation of genes Tregs associated, downregulating their expression and overall impacting Tregs differentiation and suppressive functions.

Strengths:

The strength of this paper is the number of approaches taken by the authors to verify their hypothesis. Indeed, by using both pharmacological and genetic tools in in vitro and in vivo systems they identified a potential new metabolic regulation of Tregs differentiation and function.

We are grateful to the reviewer for their thoughtful and constructive consideration of our work. We appreciate their comment that the number of approaches taken to test our hypothesis represents a strength that increases confidence in the conclusions.

Weaknesses:

Using publicly available transcriptomic and proteomic data of human T cells, the authors claim that both ex vivo and in vitro polarized Tregs express higher levels of PGAM1 protein compared to CD4 T cells (naïve or cultured under Th0 polarizing conditions). The experiments shown in this paper have all been carried out in murine Tregs. Publicly available resources for murine data (ImmGen -RNAseq and ImmPRes - Proteomics) however show that Tregs do not express higher PGAM1 (mRNA and protein) compared to CD4 T cells. It would be good to verify this in the system/condition used in the paper.

This is a fair comment. Although our pharmacologic and genetic studies demonstrated the importance of PGAM in Treg differentiation and suppressive function in murine cells, thereby corroborating the hypothesis formed based on human CD4 cell expression data, we agree that investigating PGAM expression in murine Tregs is important in the context of our work. In reviewing the ImmPres proteomics database, the reviewer is correct that PGAM1 expression was not higher in iTregs compared to other subsets, including Th17 cells. However, when compared to other glycolytic enzymes, expression of PGAM1 increases out of proportion in iTregs. In particular, the ratio of PGAM1 to GAPDH expression is much greater in iTregs compared to Th17 cells. This data is now shown in the revised Figure S5. The disproportionate increase in PGAM1 expression is consistent with the regulatory role of PGAM in the Treg-Th17 axis via modulation of 3PG concentrations, a metabolite that lies between GAPDH and PGAM in the glycolytic pathway. The divergent expression changes between GAPDH and PGAM furthermore support the conclusion that GAPDH and PGAM play opposite roles in Treg differentiation.

It would also be good to assess the levels of both PGAM1 mRNA and protein in Tregs PGAM1 knockdown compared to scramble using different methods e.g. qPCR and western blot. However, due to the high levels of cell death and differentiation variability, that would require cells to be sorted.

We appreciate this comment. As noted by the reviewer, assessing PGAM1 expression via qPCR and Western blot would require cell sorting, which we do not currently have the resources to pursue. However, we measured the effect of ASOs on PGAM1 protein expression using anti-PGAM1 antibody via flow cytometry, which allowed gating on viable cells. As shown in Figure S3A, PGAM-targeted ASOs led to an approximately 40% decrease in PGAM1 expression, as measured by mean fluorescence intensity (MFI). Furthermore, we now show in revised Figure S2 that ASO uptake was near-complete in our cultured CD4 cells.

It is not specified anywhere in the paper whether cells were sorted for bulk experiments. Based on the variability of cell differentiation, it would be good if this was mentioned in the paper as it could help to interpret the data with a different perspective.

Cells were not sorted for bulk experiments. In the revised manuscript, this point is made clear in the text, figure legends, and Methods. It is worth noting that all bulk experiments were conducted on samples with greater than 70% cell viability (greater than 90% for stable isotope tracing studies).

Reviewer #2 (Public review):

Summary:

The authors have tried to determine the regulatory role of Phosphoglycerate mutate (PGAM), an enzyme involved in converting 3-phosphoglycerate to 2-phosphoglycerate in glycolysis, in differentiation and suppressive function of regulatory CD4 T cells through de novo serine synthesis. This is done by contributing one carbon metabolism and eventually epigenetic regulation of Treg differentiation.

Strengths:

The authors have rigorously used inhibitors and antisense RNA to verify the contribution of these pathways in Treg differentiation in-vitro. This has also been verified in an in-vivo murine model of autoimmune colitis. This has further clinical implications in autoimmune disorders and cancer.

We very much appreciate these comments about the rigor of the work and its implications.

Weaknesses:

The authors have used inhibitors to study pathways involved in Treg differentiation. However, they have not studied the context of overexpression of PGAM, which was the actual reason to pursue this study.

We appreciate this comment and agree that overexpression of PGAM would be an excellent way to complement and further corroborate our findings. Unfortunately, despite attempting several methods, we were unable to consistently induce overexpression of PGAM1 in our primary T cell cultures.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I would suggest increasing the font size for flow cytometry gates. Percentages are the focus of the analysis, and it is very hard to read any.

We have increased the font size on all flow cytometry gates, as suggested.

Moreover, most of the flow data show Tregs polarization based on CD25 and FOXP3 expression. However, Figure 3 A, Figure 4D and Figure S3 show Tregs polarization based on FSC and Foxp3. Is there any reason for this?

Antibody staining against CD25 was poor in the experiments noted, which is why Foxp3 alone was used to identify Treg cells in these experiments.

Especially for Figure 3A, other cells could also express Foxp3 making interpretation difficult.

This is a fair comment. With respect to Figures 4D and S3 (now revised Figure S4), these experiments were conducted in isolated CD4 cells, in which the population of CD25-Foxp3+ cells is minimal following Treg polarization (as evident in our other figures). Regarding Figure 3A, previous work has found minimal expression of Foxp3 in circulating non-T cells (Devaud et al., 2014, PMID 25063364), such that we have confidence the identified Foxp3 expressing cells are, in fact, Treg cells. Notably, Figure 3A was already gated on CD4+ T cells, and in the periphery of wild-type mice, these would be reasonably referred to as Tregs, although this does not apply to diseased states or specific cases such as the tumor microenvironment.

The level of murine Tregs differentiation varies a lot among experiments. The % of CD4+CD25+FOXP3+ is ranging from 14% to 77% (controls). It would be good to understand and verify why such differentiation variability.

For most of our Treg polarization experiments, % differentiation in the control group falls within the 35 – 55% range. We found that treatment with ASOs (even scrambled control ASOs) tended to decrease Treg polarization overall, leading to lower numbers of Foxp3 expression in these experiments. Differentiation was similarly low in a few experiments that did not involve the use of ASOs, which we believe was caused by batch variability in the recombinant TGF-b that was used for polarization. Despite this variability, experiments were conducted with sufficient independent experiments and biological replicates to observe consistent trends and to have confidence in the results, as corroborated by statistical testing and the wide variety of experimental approaches used to verify our conclusions. Notably controls were run in every experiment, allowing accurate comparisons to be made in each individual experiment.

Similar comments apply to the level of cell death observed in the cultures of polarizing Tregs.

Although there was some variability in cell viability between experiments, flow cytometry experiments were always gated on live cells, and we believe concerns about reproducibility are substantially mitigated by the number of independent experiments, biological replicates, and distinct experimental approaches used for verification of the experimental findings. For all bulk experiments, cell viability was greater than 70% and equal across samples. For the flux studies, viability was greater than 90% and equal across samples.

Figure 2 B and D: EGCG has been used at two different concentrations. Is it lower in Figure 2D because of one condition being a combination of inhibitors or is it a typo?

The doses stated in the original legend are correct. Yes, drug doses were optimized for combination-treatment experiments. This point is now clarified in the figure legend.

Figure 2G: The description in the results does not match figure legend - Text - serine/glycine-free media or control (serine/glycine-containing) media; figure legend - serine/glycine-free media or media containing 4 mM serine.

We thank the reviewer for pointing out this discrepancy, which was an error in the text. The two conditions used were 1) serine/glycine-free media, and 2) serine/glycine-free media supplemented with 4 mM serine. The text and figure legend have both been updated to clarify this point.

Figure 3 F and G: the graphs do not show the individual points.

Individual points were not shown in these graphs because they are derived from scRNA-seq data, with SCFEA calculated from individual cells. As such, there are far too many data points to display all individual values.

CD4+ T-cell isolation and culture: cells were cultured in 50%RPMI and 50% AIM-V.

I thought that AIM-V medium was intended to be for human cultures. Could some of the conditions explain the low level of differentiation observed in some experiments? If there is such variability it might be because the conditions used are not optimal and therefore not reproducible.

We appreciate this critique. Although AIM-V media is often used for ex vivo human T cell cultures, it can similarly be used for mouse T cell culture with the addition of b-mercaptoethanol, as suggested by ThermoFisher and as used in prior publications, such as PMID 36947105. As outlined in the responses above, the differentiation we observed was consistent in most experiments, with some variability based on experimental conditions (such as lower differentiation in the setting of ASO treatment). Furthermore, we believe the number of independent experiments, biological replicates, and independent experimental approaches used in the study supports the reproducibility of our findings.

Figures S1 A, S2 B, and S4: the flow data are shown using both heights (FSC) and area (zombie NIR dye). It would be better to use areas for both parameters.

In the revised manuscript, areas are now used on both the x- and y-axes for these figures.

Figure S1 B and S2 C: The bar graphs are both showing proliferation index, however, the graphs are labelled differently in the two figures and in the legend (proliferation index -Fig S1 B; division index -Fig S2 C and replication index in the legend of Fig S2 C). The explanation of how the index has been calculated should probably go in the legend of the first figure that shows it.

We thank the reviewer for this comment. In the revised manuscript, we have ensured consistency in the terminology (“proliferation index” is now used consistently), and the explanation of the proliferation index calculation is now included in the legend to Figure S1, where the proliferation index first appears.

Were Tregs PGAM1 KD used for RNAseq sorted or not? Based on the plots shown in Figure S2 B there is ~ 50% death which needs to be taken into consideration for the analysis if not depleted.

Similar question for all bulk experiments. It is not specified in the methods or figure legends.

The cells used for RNAseq and other bulk experiments were not sorted. This point is now made clear in the text, figure legends, and Methods. However, cultures were only used for bulk analyses if the viability in those particular experiments was greater than 70%. Given the sensitivity of stable isotope tracing analyses, cultures were only analyzed for those studies if viability was greater than 90%. In these experiments, viability was similar across samples.

It was mentioned in Figure 1 that the PGAM KD led to transcriptional changes that impacted MYC targets and mTORC1 signalling. It would be good to validate these findings maybe with more targeted experiments.

We appreciate this suggestion and agree that validation and further investigation of these critical targets would be worthwhile. However, because of limitations to resources and the fact that these findings are not critical to the main conclusions of the study, we consider these experiments as future directions beyond the scope of the current work.

Reviewer #2 (Recommendations for the authors):

Here are a few suggestions and recommendations to improve the research study.

(1) The authors have used the word 'vehicle' in most of the figures, however, this word is not explained well in the figure legend. The authors may want to clarify to readers whether vehicle is a plasmid or a solvent for control purposes. For example, in Figure 1D, if vehicle is a plasmid, then another sample for vehicle +/-EGCG should be considered for the rigor in results.

Thank you for identifying this point of confusion. For all drug treatment experiments, vehicle controls consisted of solvent alone without drug. For ASO experiments, the control condition consisted of scrambled ASO. This point is now made clear in the Methods (“Drug and ASO Treatments” section) as well as in the main text. Furthermore, the figure legends and axes have been edited such that “vehicle” is only used to refer to drug experiments (in which solvent vehicle alone was used as control), and “control” is used to refer to ASO experiments (in which scrambled ASO served as control).

(2) Figure 1H represents the RNAseq data for knockdown of PGAM1. It might be interesting to see similar data for the overexpression of PGAM1.

We appreciate this comment and agree that overexpression of PGAM1 would be an excellent way to complement and further corroborate our findings using PGAM1 knockdown and pharmacologic inhibition. Unfortunately, despite attempting several methods, we were unable to consistently induce overexpression of PGAM1 in our primary T cell cultures.

(3) The font in most of the data from flow cytometry experiments (for example 1I) is not legible. Please increase the font size to make it legible.

Font sizes have been increased.

(4) Figure S2, PGAM expression was measured by Flow cytometry experiments. A similar experiment using western Blot, the direct measurement of protein expression, will strengthen the evidence.

We appreciate this comment. As noted in the public reviews, Western blot would require sorting of viable cells, and unfortunately we do not currently have the resources to conduct additional experiments with FACS. However, we respectfully note that assessing protein expression via flow cytometry quantifies protein levels based on antibody binding, similar to Western blot (or in-cell Western blot), while also allowing gating on viable cells. We also note that nearly 100% of cultured CD4 cells took up ASO, as shown in revised Figure S2.

(5) Figure 1J, it is mentioned in the text that 10 datasets were studied. a normalized parameter such as overexpression or suppression could be studied with the variance. It will be good to understand the variability in response among different datasets.

We thank the reviewer for the opportunity to clarify this data. This data was taken from a single published dataset (Dykema et al., 2023, PMID 37713507) in which 10 distinct subsets of tumor-infiltrating Tregs (TIL-Tregs) were identified, rather than from 10 distinct datasets. After identifying the Activated (1)/OX40hiGITRhi cluster of TIL-Tregs as a highly suppressive subset that correlates with resistance to immune checkpoint blockade, Dykema et al. compared gene expression in this subset to the bulked collection of the other 9 subsets, and the data shown in Figure 1J is derived from this analysis. As such, the data in Figure 1J is, indeed, a normalized parameter of overexpression, showing overexpression of PGAM1 in this highly suppressive subset versus other subsets, out of proportion to proximal rate-limiting glycolytic enzymes. The main text and figure/figure legend have been edited to clarify this point.

(6) It will be good to rephrase that the roles of PGAM and GAPDH are opposite, this paragraph is confusing since words such as "supporting Treg differentiation" and "augments Treg differentiation" have been used, although the data in S3 and 1D are opposite. Any possible explanation for the opposing roles of PGAM and GAPDH, despite their involvement in the same pathway of glycolysis, can be added to build up the interest of readers. What is the comparison of the expression of GAPDH and PGAM in Figure 1J?

We thank the reviewer for this comment, as we appreciate that the language used in our initial manuscript was confusing. We have edited the main text, in both the Results and Discussion section, in order to clarify this point and provide explanation as suggested. Indeed, our experimental data indicate that GAPDH and PGAM play opposing roles in Treg differentiation; whereas inhibiting GAPDH activity leads to greater Treg differentiation (shown in revised Figure S4 and our previously published work), similarly inhibiting PGAM leads to diminished Treg differentiation. We view this point (that enzymes within the same glycolytic pathway can have divergent roles in T cells) as a primary implication of these findings, with the explanation that individual enzymes within the same pathway can differentially regulate the concentrations of key immunoactive metabolites. In our study, we identified 3PG as a key immunoactive metabolite whose concentration would be differentially impacted by GAPDH activity versus PGAM activity, since it lies downstream of GAPDH but upstream of PGAM.

To provide further evidence for the opposing roles of GAPDH and PGAM, we analyzed existing datasets. In the revised Figure S5, we show that the PGAM1/GAPDH expression ratio increases in both human and mouse Tregs compared to other CD4 subsets.

(7) Figure 2C, what is M+1, M+2 etc. Does it represent the number of hrs? If so, why are the results for 6 hrs are not shown since the study was for 6 hrs? And what is happening with M+2?

We appreciate the opportunity to clarify this point and apologize for prior confusion. The terminology “M+n” refers to mass-shift produced by incorporation of 13-carbon. When a metabolite incorporates a single 13-carbon atom, it has a mass-shift of one (M+1), whereas incorporation of three 13-carbon atoms produces a mass-shift of three (M+3). Because we used uniformly 13-carbon labeled glucose, 3PG derived from the labeled glucose will have all three carbons labeled (M+3), as will serine that is newly synthesized from 3PG. Because serine can enter the downstream one-carbon cycle and be recycled, we also see the appearance of recycled serine with a single 13-carbon (M+1). The critical point in Figure 2C is that labeled serine is higher in Th17 versus Treg cells, demonstrating that de novo serine synthesis from glycolysis is greater. The main text has been edited to clarify this important point.

(8) Including the quantification of inhibition and rescuing effect of EDCG and NCT will be helpful to readers.

The inhibition and rescuing effects of these drugs are quantified in Figures 2D and 2E as they relate to Treg differentiation. The reviewer may be referring to quantification of relative effects on 3PG levels and serine synthesis. If so, we unfortunately do not have the resources to complete these studies, which would require large-scale quantitative mass spectrometry studies or enzyme activity assays.

(9) Figure 2D and 2E: The authors could also experiment with a dose dependence curve on EGCG and NCT on this phenotype for Treg differentiation. That can help understand the balance between serine pathways and glycolysis pathways. Similarly, the dose dependence of 3PG for Figure 2E and comparing it to the kinetic constants of these enzymes involved and cellular concentrations, these details will be helpful to understand the metabolic dynamics, because this phenotype could be an interplay of both 3PG and serine concentrations.

We appreciate this suggestion and agree that establishing detailed dose-dependence curves and relating these findings to enzyme kinetics would yield additional insights into the biochemical regulation provided by PGAM and PHGDH. Unfortunately we do not have the resources to pursue these additional studies, which therefore lie beyond the scope of our current work.

(10) Figure 4: Explanation for no effect of methionine supplementation?

Thank you for raising this point. We speculate that methionine supplementation had minimal effect because physiologic levels of serine were sufficient to provide basal substrates for the one-carbon cycle. On the other hand, eliminating methionine produced enough of a decrease in one-carbon metabolism to potentiate the effects of excess serine. This point is now briefly addressed in the text.

(11) For direct connection between PGAM and methylation, methylation experiments could be worked out with NCT1 and SHIN1 (as in Figure 4H).

We very much appreciate this suggestion, which we agree would provide a strong complementary approach. Unfortunately we do not have the resources to pursue these studies currently. However, we believe the increased methylation observed following PGAM knockdown (Figure 4G) as strong evidence that PGAM activity directly modulates methylation.

eLife Assessment

This important study fills an gap in our knowledge of the evolution of GPCRs in holozoans, as well as the phylogeny of associated signaling pathway components such as G proteins, GRKs, and RIC8 proteins. The evidence supporting the conclusions is compelling, with the analysis of extensive new genomic data from choanoflagellates and other non-animal holozoans. Overall, the study is thorough and well-executed. It will be a resource for researchers interested in both the comparative genomics of multicellularity and GPCR biology more broadly, especially given the importance of GPCRs as highly druggable targets.

Reviewer #1 (Public review):

Summary:

The authors strived for an inventory of GPCRs and GPCR pathway component genes within the genomes of 23 choanoflagellates and other close relatives of metazoans.

Strengths:

The authors generated a solid phylogenetic overview of the GPCR superfamily in these species. Intriguingly, they discover novel GPCR families, novel assortments of domain combinations, and novel insights into the evolution of those groups within the Opisthokonta clade. A particular focus is laid on adhesion GPCRs, for which the authors discover many hitherto unknown subfamilies based on Hidden Markov Models of the 7TM domain sequences, which were also reflected by combinations of extracellular domains of the homologs. In addition, the authors provide bioinformatic evidence that aGPCRs of choanoflagellates also contain a GAIN domain, which is self-cleavable, thereby reflecting the most remarkable biochemical feat of aGPCRs.

Weaknesses:

The chosen classification scheme for aGPCRs may require reassessment and amendment by the authors in order to prevent confusion with previously issued classification attempts of this family.

Reviewer #2 (Public review):

Summary:

The authors set out to characterise the GPCR family in choanoflagellates (and other unicellular holozoans). GPCRs are the most abundant gene family in many animal genomes, playing crucial roles in a wide range of physiological processes. Although they are known to evolve rapidly, GPCRs are an ancient feature of eukaryotic biology. Identifying conserved elements across the animal-protist boundary is therefore a valuable goal, and the increasing availability of genomes from non-animal holozoans provides new opportunities to explore evolutionary patterns that were previously obscured by limited taxon sampling. This study presents a comprehensive re-examination of GPCRs in choanoflagellates, uncovering examples of differential gene retention and revealing the dynamic nature of the GPCR repertoire in this group. As GPCRs are typically involved in environmental sensing, understanding how these systems evolved may shed light on how our unicellular ancestors adapted their signalling networks in the transition to complex multicellularity.

Strengths:

The paper combines a broad taxonomic scope with the use of both established and recently developed tools (e.g., Foldseek, AlphaFold), enabling a deep and systematic exploration of GPCR diversity. Each family is carefully described, and the manuscript also functions as an up-to-date review of GPCR classification and evolution. Although similar attempts to understand GPCR evolution were made over the last decade, the authors build on this foundation by identifying new families and applying improved computational methods to better predict structure and function. Notably, the presence of Rhodopsin-like GPCRs in some choanoflagellates and ichthyosporeans is intriguing, even though they do not fall within known animal subfamilies. The computational framework presented here is broadly applicable, offering a blueprint for surveying GPCR diversity in other non-model eukaryotes (and even in animal lineages), potentially revealing novel families relevant to drug discovery or helping revise our understanding of GPCR evolution beyond model systems.

Weaknesses:

While the study contributes several interesting observations, it does not radically revise the evolutionary history of the GPCR family. However, in an era increasingly concerned with the reproducibility of scientific findings, this is arguably a strength rather than a weakness. It is encouraging to see that previously established patterns largely hold, and that with expanded sampling and improved methods, new insights can be gained, especially at the level of specific GPCR subfamilies. Then, no functional follow-ups are provided in the model system Salpingoeca rosetta, but I am sure functional work on GPCRs in choanoflagellates is set to reveal very interesting molecular adaptations in the future.

eLife Assessment

This important theoretical study examines the possibility of encoding genomic information in a collective of short overlapping strands (e.g., the Virtual Circular Genome (VCG) model). The study presents convincing theoretical arguments, simulations and comparisons to experimental data to point at potential features and limitations of such distributed collective encoding of information. The work should be of relevance to colleagues interested in molecular information processing and to those interested in pre-Central Dogma or prebiotic models of self-replication.

Reviewer #1 (Public review):

Summary:

This is an interesting theoretical study examining the viability of Virtual Circular Genome (VCG) model, a recently proposed scenario of prebiotic replication in which a relatively long sequence is stored as a collection of its shorter subsequences (and their compliments). It was previously pointed out that VCG model is prone to so-called sequence scrambling which limits the overall length of such a genome. In the present paper, additional limitations are identified. Specifically, it is shown that VCG is well replicated when the oligomers are elongated by sufficiently short chains from "feedstock" pool. However, ligation of oligomers from VCG itself results in a high error rate. I believe the research is of high quality and well written. However, the presentation could be improved and the key messages could be clarified.

(1) It is not clear from the paper whether the observed error has the same nature as sequence scrambling

(2) The authors introduce two important lengths LS1 and LS2 only in the conclusions and do not explain enough which each of them is important. It would make sense to discuss this early in the manuscript.

(3) It is not entirely clear why specific length distribution for VCG oligomers has to be assumed rather than emerged from simulations.

(4) Furthermore, the problem has another important length, L0 that is never introduced or discussed: a minimal hybridization length with a lifetime longer than the ligation time. From the parameters given, it appears that L0 is sufficiently long (~10 bases). In other words, it appears that the study is done is a somewhat suboptimal regime: most hybridization events do not lead to a ligation. Am I right in this assessment? If that is the case, the authors might want to explore another regime, L0<br /> Strengths:

High-quality theoretical modeling of an important problem is implemented.

Reviewer #2 (Public review):

Summary:

This important theoretical and computational study by Burger and Gerland attempts to set environmental, compositional, kinetic, and thermodynamic constraints on the proposed virtual circular genome (VCG) model for the early non-enzymatic replication of RNA. The authors create a solid kinetic model using published kinetic and thermodynamic parameters for non-enzymatic RNA ligation and (de)hybridization, which allows them to test a variety of hypotheses about the VCG. Prominently, the authors find that the length (longer is better) and concentration (intermediate is better) of the VCG oligos have an outsized impact on the fidelity and yield of VCG production with important implications for future VCG design. They also identify that activation of only RNA monomers, which can be achieved using environmental separation of the activation and replication, can relax the constraints on the concentration of long VCG component oligos by avoiding the error-prone oligo-oligo ligation. Finally, in a complex scenario with multiple VCG oligo lengths, the authors demonstrate a clear bias for the extension of shorter oligos compared to the longer ones. This effect has been observed experimentally (Ding et al., JACS 2023) but was unexplained rigorously until now. Overall, this manuscript will be of interest to scientists studying the origin of life and the behavior of complex nucleic acid systems.

Strengths:

- The kinetic model is carefully and realistically created, enabling the authors to probe the VCG thoroughly.<br /> - Fig. 6 outlines important constraints for scientists studying the origin of life. It supports the claim that the separation of activation and replication chemistry is required for efficient non-enzymatic replication. One could easily imagine a scenario where activation of molecules occurs, followed by their diffusion into another environment containing protocells that encapsulate a VCG. The selective diffusion of activated monomers across protocell membranes would then result in only activated monomers being available to the VCG, which is the constraint outlined in this work. The proposed exclusive replication by monomers also mirrors the modern biological systems, which nearly exclusively replicate by monomer extension.<br /> - Another strength of the work is that it explains why shorter oligos extend better compared to the long ones in complex VCG mixtures. This point is independent of the activation chemistry used (it simply depends on the kinetics and thermodynamics of RNA base-pairing) so it should be very generalizable.

eLife Assessment

This is a valuable study that uses single-cell RNA sequencing to define tumor-intrinsic transcriptional programs that characterize distinct types of small intestine neuroendocrine tumors. The evidence supporting the claims of the authors is solid, but would benefit from a larger sample size. The work will be of interest to cancer biologists studying neuroendocrine tumors, as well as those studying tumor heterogeneity more broadly.

Reviewer #1 (Public review):

Summary:

The authors have assembled a cohort of 10 SiNET, 1 SiAdeno, and 1 lung MiNEN samples to explore the biology of neuroendocrine neoplasms. They employ single-cell RNA sequencing to profile 5 samples (siAdeno, SiNETs 1-3, MiNEN) and single-nuclei RNA sequencing to profile seven frozen samples (SiNET 4-10).

They identify two subtypes of siNETs, characterized by either epithelial or neuronal NE cells, through a series of DE analyses. They also report findings of higher proliferation in non-malignant cell types across both subtypes. Additionally, they identify a potential progenitor cell population in a single lung MiNEN samples.

In the revised study, they have addressed my points and I have no further comments.

Reviewer #2 (Public review):

Summary:

The research identifies two main SiNET subtypes (epithelial-like and neuronal-like) and reveals heterogeneity in non-neuroendocrine cells within the tumor microenvironment. The study validates findings using external datasets and explores unexpected proliferation patterns. While it contributes to understanding SiNET oncogenic processes, the limited sample size and depth of analysis present challenges to the robustness of the conclusions.

Strengths:

The studies effectively identified two subtypes of SiNET based on epithelial and neuronal markers. Key findings include the low proliferation rates of neuroendocrine (NE) cells and the role of the tumor microenvironment (TME), such as the impact of Macrophage Migration Inhibitory Factor (MIF).

Weaknesses:

However, the analysis faces challenges such as a small sample size and lack of clear biological interpretation in some analyses.

Reviewer #3 (Public review):

This study profiles small intestine NETs and one mixed lung NET at single cell resolution and identifies two subtypes of neuroendocrine cells, as well as explores the proliferation patterns in malignant and nonmalignant cell types, identifying MIF as a potential factor that promotes proliferation of B and plasma cells in siNETs. Furthermore, they explore the single-cell landscape of a mixed LCNEC and squamous cell carcinoma, from which they identify a putative stem cell population with expression of features from both lineages.

Strengths:

This work showcases single-cell profiling of a rare tumor type, which is very informative for the field of NETs. The authors highlight very interesting observations, including the identification of the epithelial and neuronal subtype of siNETs, which they validated with an independent bulk RNA sequencing cohort. Furthermore, the observation of low cycling in malignant cells and high cycling in nonmalignant cells is an interesting one which may be applicable to other NETs.

Weaknesses:

• The authors do not connect their findings to clinical outcome. For example, is the epithelial or neuronal subtype enriched in tumors with worse or better prognosis or high grade vs. low grade siNETs or in patients who metastasize vs. who don't? As the authors show they can identify epithelial vs. neuronal subtypes in bulk RNA seq, perhaps they can take advantage of these other studies with larger sample sizes to investigate this. Additionally, the authors identify that the phenomenon of higher B/plasma cell proliferation is particular to epithelial siNETs and write that "The implications of high B/plasma cell turnover, and of other downstream effects of high MIF expression, are unclear, but raise the possibility that MIF-CD74 interaction may constitute a relevant target for the epithelial-like SiNET subtype." However, if this interaction contributes to survival in these patients, targeting this interaction may not be beneficial. Thus, it is important for the authors to try to connect their finding to clinical outcomes to enhance the translational relevance of this paper.

• The generalizability of this study would be enhanced if the authors analyzed other available single cell studies of NETs and found a similar phenomenon of high proliferating nonmalignant cell types. Although these studies are also very limited in sample size, seeing concordance in findings across independent cohorts and different experimental techniques would help to strengthen the findings. While the authors rationalize that these other studies are too distinct from their own due to enrichment for immune cells, this limitation should be noted but does not prevent such an analysis from being attempted.

• On page 3, the authors claim that "Technical effects (e.g. single cell analysis of fresh samples vs. single nuclei analysis of frozen samples) could also impact the capture of distinct cell types, although we did not observe a clear pattern of such bias." Can the authors show that cell type frequencies are not significantly different between the samples profiled with these two methods?

• Why did siNET3 and siNET9 have much lower recovery of neuroendocrine cells compared to other samples? It would be interesting to see how similar or different the transcriptional profiles are of the samples that were obtained from the same patient, considering that multifocal siNETs are found to derive from distinct clones, although this analysis is understandably not possible in this case due to the lack of neuroendocrine cells in one of two samples from the same patient.

• It should be more clearly stated in the text that these samples were previously treated with somatostatin analogues, as this impacts the interpretation of the findings.

• The identification of a potential progenitor subtype in the miNEN is very intriguing, albeit a case study and represents a distinct cancer from the lowly proliferating siNETs. While the authors mention this in the text, the case study feels rather tangential to the other parts of the paper.

• How the authors compared the DE genes to known signatures for the fibroblast and endothelial cells should be clarified and discussed in the Methods section.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors have assembled a cohort of 10 SiNET, 1 SiAdeno, and 1 lung MiNEN samples to explore the biology of neuroendocrine neoplasms. They employ single-cell RNA sequencing to profile 5 samples (siAdeno, SiNETs 1-3, MiNEN) and single-nuclei RNA sequencing to profile seven frozen samples (SiNET 4-10).

They identify two subtypes of siNETs, characterized by either epithelial or neuronal NE cells, through a series of DE analyses. They also report findings of higher proliferation in non-malignant cell types across both subtypes. Additionally, they identify a potential progenitor cell population in a single-lung MiNEN sample.

Strengths:

Overall, this study adds interesting insights into this set of rare cancers that could be very informative for the cancer research community. The team probes an understudied cancer type and provides thoughtful investigations and observations that may have translational relevance.

Weaknesses:

The study could be improved by clarifying some of the technical approaches and aspects as currently presented, toward enhancing the support of the conclusions:

(1) Methods: As currently presented, it is possible that the separation of samples by program may be impacted by tissue source (fresh vs. frozen) and/or the associated sequencing modality (single cell vs. single nuclei). For instance, two (SiNET1 and SiNET2) of the three fresh tissues are categorized into the same subtype, while the third (SiNET9) has very few neuroendocrine cells. Additionally, samples from patient 1 (SiNET1 and SiNET6) are separated into different subtypes based on fresh and frozen tissue. The current text alludes to investigations (i.e.: "Technical effects (e.g., fresh vs. frozen samples) could also impact the capture of distinct cell types, although we did not observe a clear pattern of such bias."), but the study would be strengthened with more detail.

We thank the reviewer for the thoughtful and constructive review. Due to the difficulty in obtaining enough SiNET samples, we used two platforms to generate data - single cell analysis of fresh samples, and single nuclei analysis of frozen samples. We opted to combine both sample types in our analysis while being fully aware of the potential for batch effects. We therefore agree that this is a limitation of our work, and that differences between samples should be interpreted with caution.

Nevertheless, we argue that the two SiNET subtypes that we have identified are very unlikely to be due to such batch effect. First, the epithelial SiNET subtype was not only detected in two fresh samples but also in one frozen sample (albeit with relatively few cells, as the reviewer correctly noted). Second, and more importantly, the epithelial SiNET subtype was also identified in analysis of an external and much larger cohort of bulk RNA-seq SiNET samples that does not share the issue of two platforms (as seen in Fig. 2f). Moreover, the proportion of samples assigned to the two subtypes is similar between our data and the external data. We therefore argue that the identification of two SiNET subtypes cannot be explained by the use of two data platforms. However, we agree that the results should be further investigated and validated by future studies.

The reviewer also commented that two samples from the same patient which were profiled by different platforms (SiNET1 and SiNET6) were separated into different subtypes. We would like to clarify that this is not the case, since SiNET6 was not included in the subtype analysis due to too few detected Neuroendocrine cells, and was not assigned to any subtype, as noted in the text and as can be seen by its exclusion from Figure 2 where subtypes are defined. We apologize that our manuscript may have given the wrong impression about SiNET6 classification (it was labeled in Fig. 4a in a misleading manner). In the revised manuscript, we corrected the labeling in Fig. 4a and clarified that SiNET6 is not assigned to any subtype. We also further acknowledge the limitation of the two platforms and the arguments in favor of the existence of two SiNET subtypes.     

(Additional specific recommendations for the authors are provided below)

(2) Results:

Heterogeneity in the SiNET tumor microenvironment: It is unclear if the current analysis of intratumor heterogeneity distinguishes the subtypes. It may be informative if patterns of tumor microenvironment (TME) heterogeneity were identified between samples of the same subtype. The team could also evaluate this in an extension cohort of published SiNET tumors (i.e. revisiting additional analyses using the SiNET bulk RNAseq from Alvarez et al 2018, a subset of single-cell data from Hoffman et al 2023, or additional bulk RNAseq validation cohorts for this cancer type if they exist [if they do not, then this could be mentioned as a need in Discussion])

We agree that analysis of an independent cohort will assist in defining the association between TME and the SiNET subtype. However, the sample size required for that is significantly larger than the data available. In the revised manuscript we note that as a direction for future studies.

(3) Proliferation of NE and immune cells in SiNETs: The observed proliferation of NE and immune cells in SiNETs may also be influenced by technical factors (including those noted above). For instance, prior studies have shown that scRNA-seq tends to capture a higher proportion of immune cells compared to snRNA-seq, which should be considered in the interpretation of these results. Could the team clarify this element?

We agree that different platforms could affect the observed proportions of immune cells, and more generally the proportions of specific cell types. However, the low proliferation of Neuroendocrine cells and the higher proliferation of immune cells (especially B cells, but also T cells and macrophages) is consistently observed in both platforms, as shown in Fig. 4a, and therefore appears to be reliable despite the limitations of our work. We clarify this consistency in the revised manuscript. 

(4) Putative progenitors in mixed tumors: As written, the identification of putative progenitors in a single lung MiNEN sample feels somewhat disconnected from the rest of the study. These findings are interesting - are similar progenitor cell populations identified in SiNET samples? Recognizing that ideally additional validation is needed to confidently label and characterize these cells beyond gene expression data in this rare tumor, this limitation could be addressed in a revised Discussion.

We do not find evidence for similar progenitors in the SiNET samples, but they also do not contain two co-existing lineages of cancer cells within the same tumor, so this is harder to define. We agree about the need for additional validation for this specific finding and have noted that in the revised Discussion.

Reviewer #2 (Public review):

Summary:

The research identifies two main SiNET subtypes (epithelial-like and neuronal-like) and reveals heterogeneity in non-neuroendocrine cells within the tumor microenvironment. The study validates findings using external datasets and explores unexpected proliferation patterns. While it contributes to understanding SiNET oncogenic processes, the limited sample size and depth of analysis present challenges to the robustness of the conclusions.

Strengths:

The studies effectively identified two subtypes of SiNET based on epithelial and neuronal markers. Key findings include the low proliferation rates of neuroendocrine (NE) cells and the role of the tumor microenvironment (TME), such as the impact of Macrophage Migration Inhibitory Factor (MIF).

Weaknesses:

However, the analysis faces challenges such as a small sample size, lack of clear biological interpretation in some analyses, and concerns about batch effects and statistical significance.

Reviewer #3 (Public review):

Summary:

In this study, the authors set out to profile small intestine neuroendocrine tumors (siNETs) using single-cell/nucleus RNA sequencing, an established method to characterize the diversity of cell types and states in a tumor. Leveraging this dataset, they identified distinct malignant subtypes (epithelial-like versus neuronal-like) and characterized the proliferative index of malignant neuroendocrine cells versus non-malignant microenvironment cells. They found that malignant neuroendocrine cells were far less proliferative than some of their non-malignant counterparts (e.g., B cells, plasma cells, epithelial cells) and there was a strong subtype association such that epithelial-like siNETs were linked to high B/plasma cell proliferation, potentially mediated by MIF signaling, whereas neuronal-like siNETs were correlated with low B/plasma cell proliferation. The authors also examined a single case of a mixed lung tumor (neuroendocrine and squamous) and found evidence of intermediate/mixed and stem-like progenitor states that suggest the two differentiated tumor types may arise from the same progenitor.

Strengths:

The strengths of the paper include the unique dataset, which is the largest to date for siNETs, and the potentially clinically relevant hypotheses generated by their analysis of the data.

Weaknesses:

The weaknesses of the paper include the relatively small number of independent patients (n = 8 for siNETs), lack of direct comparison to other published single-cell NET datasets, mixing of two distinct methods (single-cell and single-nucleus RNA-seq), lack of direct cell-cell interaction analyses and spatially-resolved data, and lack of in vitro or in vivo functional validation of their findings.

The analytical methods applied in this study appear to be appropriate, but the methods used are fairly standard to the field of single-cell omics without significant methodological innovation. As the authors bring forth in the Discussion, the results of the study do raise several compelling questions related to the possibility of distinct biology underlying the epithelial-like and neuronal-like subtypes, the origin of mixed tumors, drivers of proliferation, and microenvironmental heterogeneity. However, this study was not able to further explore these questions through spatially-resolved data or functional experiments.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Methods:

a) Could the team clarify the discrepancy in subtype assignment between two samples from the same patient? i.e. are these samples from the same tumor? If so, what does the team think is the explanation for the difference in subtype assignment?

As noted above in response to the public review of reviewer #1, SiNET6 was in fact not assigned to any subtype (due to insufficient NE cells) and hence there was no discrepancy. We apologize for the misleading labeling of SiNET6 in the previous version and have corrected this In the revised version of Figure 4.

b) What is the rationale for scoring tumor-derived programs on samples with no tumor cells? For instance, SiNET3 does not contain NE cells, and SiNET9 has a very low fraction of NE cells. Please clarify how the scoring was performed on these samples, as the program assignments may be driven by other cell types in samples with little to no NE cells.

Scoring for tumor-derived programs was done only for the NE cells. Accordingly, SiNET3 was not scored or assigned to any of the programs. SINET9 was included in this analysis - although it had a relatively small fraction of NE cells, the absolute number of profiled cells was particularly high in this sample and therefore the number of NE cells was 130, higher than our cutoff of 100 cells.

c) Given the heterogeneity of cell types within each sample, would there be a way to provide a refined sense of confidence for certain cell type annotations? This would be helpful given the heterogeneity in marker gene expression and the absence of gold-standard markers for fibroblasts and endothelial cells in this cancer type. Additionally, there seems to be an unusually large proportion of NK and T cells - was there selection for this (given that these tumors are largely not immune infiltrated)?

Author Response: Except for the Neuroendocrine cells, there are six TME cell types that we consistently find in multiple SiNET samples: macrophages, T cells, B/plasma cells, fibroblasts, endothelial and epithelial cells. Each of these cell types are identified as discrete clusters in analysis of the respective tumors (as shown in Fig. 1a,b and Fig. S1), and these are exactly the six most common non-malignant cell types that we and others found in single cell analysis across various other tumor types (e.g. see Gavish et al. 2023, ref. #15). The signatures used to annotate these cell types are shown in Table S2, and they primarily consist of classical markers that are traditionally used to define those cell types. We therefore believe that the annotation of these typical tumor-associated cell types is robust and does not include major uncertainties. In addition to these five common cell types, there are three cell types that we find only in 1-2 of the samples – epithelial cells, plasma cells and NK cells. Again, we believe that their annotation is robust, and these cell types are primarily not used for further analysis.

There was no selection for any specific cell types in this study. Nevertheless, single cell (or single nuclei) analysis may lead to biases towards specific cell types, that we cannot evaluate directly from the data. NK cells were detected only in one tumor. T cells were detected in eight of the ten samples; but in four of those samples the frequency of T cells was lower than 5% and only in one sample the frequency was above 20%. Therefore, while we cannot exclude a technical bias towards high frequency of T/NK cells, we do not consider these frequencies as high enough to suggest this specific type of bias. In the revised manuscript, we clarify that the commonly observed cell types in SiNETs are the same as those commonly observed in other tumors and we acknowledge the possibility of a technical bias in cell type capture.  

d) Evaluating the expression of one gene at a time may not effectively demonstrate subtype-specific patterns, particularly when comparing NE cells from one tumor to non-NE cells from another, which may not be an appropriate approach for identifying differentially expressed genes. DE analysis coupled with concordance analysis, for example, could strengthen the results.

We apologize, but we do not fully understand this comment. We note that the initial normalization by non-NE cells was done in order to decrease batch effects when combining the data of the two platforms. We also note that the two subtypes were identified by two distinct approaches, as shown in Fig. 2c and in Fig. 2f.

(2) Results:

See the above public review.

(3) Minor Comments:

a) Results: Single cell and single nuclei RNA-seq profiling of SiNETs

The results say ten primary tumor samples from eight patients. Later in the paragraph it says, "After initial quality controls, we retained 29,198 cells from the ten patients." Please clarify to either ten samples or eight patients.

Indeed these are ten samples rather than ten patients. We corrected that in the revised version and thank the reviewer for noticing our error.

b) Methods:

- Please specify which computational tools were used to perform quality control, signature scoring, etc.

The approaches for quality control, scoring etc. are described in the methods. We implemented these approaches with R code and did not use other computational tools.

- Minor point but be consistent with naming convention (ie, siAdeno vs SiAdeno) throughout the paper. For example, under "Sample Normalization, Filtering and annotations" change "siAdeno" to "SiAdeno."

Thank you for noting this, we corrected that.

- Add processing and analysis of MiNEN sample to the methods section. It is not mentioned in the methods at all.

As noted in the revised manuscript, the MiNEN sample was analyzed in the same way as the SiNET fresh samples.

c) Supplementary Figures:

Figure S1: Change (A-H) to (A-I) to account for all panels in the figure.

Figure S4: Add (C) after "the siAdeno sample" in the legend.

Thank you for noting this, we corrected that.

(4) Font size is quite small in the main figures.

We enlarged the font in selected figure panels.

Reviewer #2 (Recommendations for the authors):

(1) The small number of samples used in some analyses affects the robustness of the findings. Increasing the sample size or including more validation data could improve the statistical reliability and make the results more convincing. The authors should consider expanding the cohort size or integrating additional external datasets to increase statistical power.

We agree with the reviewer that adding more samples would improve the reliability of the results. However, the external data that we found was not comparable enough to enable integration with our data, and we are unable to profile additional SiNET samples in our lab. We hope that future studies would support our results and extend them further.

(2) The biological significance of differentially expressed genes needs more depth, limiting the insights into SiNET biology. The authors should perform a comprehensive pathway enrichment analysis and integrate findings with existing literature. Tools like Gene Set Enrichment Analysis (GSEA) or Overrepresentation Analysis (ORA) could provide a more holistic view of altered biological processes.

We thank the reviewer for this suggestion. We did examine the functional enrichment of differentially expressed genes and did not find additional enrichments that we felt were important to highlight beyond what we described. We report the genes in supplementary tables, enabling other researchers to examine these lists further. 

(3) The unexpected finding of higher proliferation in non-malignant cells requires further investigation and plausible biological explanation. The authors should perform additional analyses to explore potential mechanisms, such as investigating cell cycle regulators or performing in vitro validation experiments. The authors should consider single-cell trajectory analysis to explore these highly proliferative non-malignant cells' potential differentiation or activation states.

We agree that our results are descriptive and that we do not fully explain the mechanism for the high level of non-malignant cell proliferation. We did attempt to perform follow up computational analysis. These analyses raised the hypothesis that high levels of MIF are causing the proliferation of immune cells. Additional analyses that we performed were not sufficient to conclusively identify a mechanism, and we felt that they were not informative enough to be included in the manuscript. Further in vitro (or in vivo) studies are beyond the scope of the current work.

(3) More details are required on methods used for p-value adjustment, and criteria for statistical significance should be clearly defined. Additionally, integrating scRNA-seq and snRNA-seq data needs a more thorough explanation, including batch effect mitigation and more explicit cell clustering representation. The authors should clearly describe p-value adjustments (e.g., FDR) and batch correction methods (e.g., Harmony, FastMNN integration) and include additional figures showing corrected UMAP plots or heatmaps post-batch correction to enhance the confidence in results.

We now clarify in the Methods our use of FDR for p-value adjustments. As for batch correction, we have avoided the use of integration methods as we believe that they tend to distort the data and decrease tumor-specific signals. Instead, we primarily analyzed one tumor at a time and never directly compared cell profiles across distinct tumors but only compared the differences between subpopulations; specifically, we normalized the expression of NE cells by subtracting the expression of reference non-NE cells from the same tumor as a method to decrease batch effects. We now clarify this point in the Methods section.

(4) The lack of analysis of interactions between different cell types limits understanding of tumor microenvironment dynamics. The authors should employ cell-cell interaction analysis tools (e.g., CellPhoneDB, NicheNet) to explore potential communication networks within the tumor microenvironment. This could provide valuable insights into how different cell types influence tumor progression and maintenance.

We thank the reviewer for this suggestion. We have tried to use such methods but found the results difficult to interpret since these approaches generated very long lists of potential cell-cell interactions that are largely not unique to the SiNET context and their relevance remains unclear without follow up experiments, which are beyond the scope of this work. We therefore focused only on ligand/receptors that came up robustly through specific analyses such as the differences between SiNET subtypes. In particular, MIF is highly expressed in the epithelial subtype, and remarkably, MIF upregulation is shared across multiple cell types. Thus, the cell-cell interactions that are suggested by the SiNET data as somewhat unique to this context are those involving MIF and its receptor (CD74 on immune cell types), while other interactions detected by the proposed methods primarily reflect the generic ligand/receptors expressed by corresponding TME cell types.   

Reviewer #3 (Recommendations for the authors):

(1) For a relatively small dataset, the mixing of single-cell versus single-nucleus RNA-seq should be discussed more. It would be nice to have 1-2 tumors that are analyzed by both methods to compare and increase our understanding of how these different approaches may affect the results. This could be accomplished by splitting a fresh tumor into two parts, processing it fresh for single-cell RNA-seq, and freezing the other part for single-nucleus RNA-seq.

We agree with the reviewer that the different techniques may bias our results and we refer to this limitation in the Results and Discussion sections. However, it is important to note that we do not directly integrate the primary data across these modalities, but rather analyze each tumor separately and only combine the results across tumors. For example, we first compare the NE cells from each tumor to control non-NE cells from the same tumor and then only compare the sets of NE-specific genes across tumors. Moreover, the subtypes that we detect cannot be explained by these modalities, as the first subtype contains samples from both methods and these subtypes are further demonstrated in external bulk data. Similarly, the results regarding low proliferation of NE cells and high proliferation of B/plasma cells are observed across both modalities. We therefore argue that while the combination of methods is a limitation of this work it does not account for the main results.  

(2) The authors state that they defined the siNET transcriptomic signature by comparing their siNET single-cell/nucleus data to other NETs profiled by bulk RNA-seq. Some of the genes in the signature, such as CHGA, are widely used as markers for NETs (and not specific for siNET). The authors should address this in more detail.

To define the SiNET transcriptomic signature we first analyzed each tumor separately and compared the expression of Neuroendocrine (NE) cells to that of non-NE cells to detect NE-specific genes. Next, we compared the lists of NE-specific genes across the 8 SiNET patients and found a subset of 26 genes which were shared across most of the analyzed SiNET samples (Fig. 2a). Thus, the signature was defined only from analysis of SiNETs and not based on comparison to other types of NETs and hence it is expected that the signature could contain both SiNET-specific genes and more generic NET genes such as CHGA.

Only after defining this signature, we went on to compare it between SiNETs and other types of NETs (pancreatic and rectal) based on external bulk RNA-seq data. In this comparison, we observed that the signature was clearly higher in SiNETs than in the other NETs (Fig. 2b). This result supports the accuracy of the signature and further suggests that it contains a fraction of SiNET-specific genes and not only generic NET genes such as CHGA. Thus, we would expect this signature to perform well also for distinguishing between SiNET and types of NETs, but it does contain a subset of genes that would be high in the other NETs. Finally, we note that even though CHGA is a generic NET marker, the bulk RNA-seq data would suggest that, at least at the mRNA level, this gene is still higher expressed in SiNETs than in other NETs. To avoid confusion regarding the definition and specificity of the SiNET transcriptomic signature we have extended the description of this section in the revised manuscript.

(3) The authors only compare their data to bulk transcriptomic data on NETs. While in some instances this makes sense given the bulk dataset has >80 tumors, they should at least cite and do some comparison to other published single-cell RNA-seq datasets of NETs (e.g., PMID: 37756410, 34671197). The former study listed has 3 siNETs, 4 pNETs, and 1 gNET. Do the epithelial-like and neuronal-like signatures show up in this dataset too?

We examined these studies but concluded that their data was inadequate to identify the two SiNET subtypes. The latter study was of pNETs, while the former study had 3 SiNET samples but only from 2 patients, and furthermore it was enriching for immune cells with only very low amounts of NE cells. Therefore, we now cite this work in the discussion but cannot use it to extend the results from our work.

(4) How did the authors statistically handle patients with more than one tumor sample (true for n = 2)? These tumor samples would not be truly independent.

In both cases where we had two distinct samples of the same patient, only one sample had sufficient NE cells to be included in NE-related analysis and therefore the other samples (SiNET3 and SiNET6) were excluded from all analysis of NE differential expression and subtypes. These samples were only included in the initial analysis (Fig. 1) and in TME-related analysis (Fig. 3-4) in which there was no statistical analysis of differences between patients and hence no problem with the inclusion of 2 samples for the same patient. We clarified this issue in the revised version.

(5) The association between siNET subtype and B/plasma cell proliferation is very interesting, as is the hypothesis regarding MIF signaling. It would be illuminating for the authors to perform cell-cell interaction analyses with methods such as CellChat in this context rather than just relying on DE. Spatial mapping would be helpful too and while this may be outside the scope of this study, it should at least be expounded upon in the Discussion section.

Indeed, spatial transcriptomic analysis would add interesting insight to our data and to SiNET biology. Unfortunately, this is not within the scope of the current project but we note this interesting possibility in the Discussion. Regarding additional methods for cell-cell interactions, we have performed such analysis but found it not informative as it highlighted a large number of interactions that are not unique SiNETs and are difficult to interpret, and therefore we do not include this in the revised version. 

(6) The authors note that in the mixed lung tumor, the NE component was more proliferative than that observed with siNETs. How does the proliferation compare to pNETs, gNETs, in other published studies? How about assessing the clonality of the SCC and LNET malignant cells with various genomic or combined genomic/transcriptomic methods?

The percentage of proliferating NE cells in the mixed lung tumor was higher than 60%. This is extremely high, approximately four-fold higher than the average that we found in a pan-cancer analysis and higher than the average of any of the >20 cancer types that we analyzed (Gavish et al. 2023, ref. #15). This remarkably high proliferation serves as a control for the low proliferation that we found in SiNET NE cells.

(7) In the Discussion on page 13, the authors write "Second, proliferation of NE cells may be inhibited by prior treatments with somatostatin analogues." How many patients were treated in this manner? This information should be made more explicit in the manuscript.

Details on pretreatment with somatostatin analogues are provided in Table S1. All patients were pre-pretreated with somatostatin analogues, with the possible exception of one patient (P8, SiNET10) for which we could not confidently obtain this information.

(8) On page 5, "bone-fide" is misspelled.

(9) On page 8, "exact identify" is misspelled.

We thank the reviewer and have corrected the typos.

eLife Assessment

This study offers a valuable assessment of the impact of antibiotics on the human gut microbiota across diverse observational cohorts. The findings presented are convincing, despite the observational design and residual confounding that may still contribute to discrepancies between the cross-sectional and longitudinal data. The work is relevant for researchers and clinicians interested in antimicrobial resistance and the impact of antibiotics on the host.

Reviewer #2 (Public review):

Summary:

In this manuscript by Peto et al., the authors describe the impact of different antimicrobials on gut microbiota in a prospective observational study of 225 participants (healthy volunteers, inpatients and outpatients). Both cross-sectional data (all participants) and longitudinal data (subset of 79 haematopoietic cell transplant patients) were used. Using metagenomic sequencing, they estimated the impact of antibiotic exposure on gut microbiota composition and resistance genes. In their models, the authors aim to correct for potential confounders (e.g. demographics, non-antimicrobial exposures and physiological abnormalities), and for differences in the recency and total duration of antibiotic exposure. I consider these comprehensive models an important strength of this observational study. Yet, the underlying assumptions of such models may have impacted the study findings and residual confounding is likely. Other strengths include the presence of both cross-sectional and longitudinal exposure data and presence of both healthy volunteers and patients. Together, these observational findings expand on previous studies (both observational and RCTs) describing the impact of antimicrobials on gut microbiota.

Weaknesses:

(1) The main weaknesses result from the observational design. This hampers causal interpretation and makes correction for potential confounding necessary. While the authors have used comprehensive models to correct for potential confounders and for differences between participants in duration of antibiotic exposure and time between exposure and sample collection, I believe residual confounding is likely (which is mentioned as a limitation in the discussion).<br /> For their models, the authors found a disruption half-life of 6 days to be the best fit based on Shannon diversity. Yet, the disruption caused by antimicrobials may be longer than represented in this model - as highlighted in the discussion.

(2) Another consequence of the observational design of this study is the relatively small number of participants available for some comparisons (e.g. oral clindamycin was only used by 6 participants). Care should be taken when drawing any conclusions from such small numbers.

Comments on revisions:

The authors have adequately addressed all of my comments.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors provide a study among healthy individuals, general medical patients and patients receiving haematopoietic cell transplants (HCT) to study the gut microbiome through shotgun metagenomic sequencing of stool samples. The first two groups were sampled once, while the patients receiving HCT were sampled longitudinally. A range of metadata (including current and previous (up to 1 year before sampling) antibiotic use) was recorded for all sampled individuals. The authors then performed shotgun metagenomic sequencing (using the Illumina platform) and performed bioinformatic analyses on these data to determine the composition and diversity of the gut microbiota and the antibiotic resistance genes therein. The authors conclude, on the basis of these analyses, that some antibiotics had a large impact on gut microbiota diversity, and could select opportunistic pathogens and/or antibiotic resistance genes in the gut microbiota.

Strengths:

The major strength of this study is the considerable achievement of performing this observational study in a large cohort of individuals. Studies into the impact of antibiotic therapy on the gut microbiota are difficult to organise, perform and interpret, and this work follows state-of-the-art methodologies to achieve its goals. The authors have achieved their objectives and the conclusion they draw on the impact of different antibiotics and their impact on the gut microbiota and its antibiotic resistance genes (the 'resistome', in short), are supported by the data presented in this work.

Weaknesses:

The weaknesses are the lack of information on the different resistance genes that have been identified and which could have been supplied as Supplementary Data.

We have now supplied a list of individual resistance genes as supplementary data.

In addition, no attempt is made to assess whether the identified resistance genes are associated with mobile genetic elements and/or (opportunistic) pathogens in the gut. While this is challenging with short-read data, alternative approaches like long-read metagenomics, Hi-C and/or culture-based profiling of bacterial communities could have been employed to further strengthen this work.

We agree this is a limitation, and we now refer to this in the discussion. Unfortunately we did not have funding to perform additional profiling of the samples that would have provided more information about the genetic context of the AMR genes identified.

Unfortunately, the authors have not attempted to perform corrections for multiple testing because many antibiotic exposures were correlated.

The reviewer is correct that we did not perform formal correction for multiple testing. This was because correlation between antimicrobial exposures meant we could not determine what correction would be appropriate and not overly conservative. We now describe this more clearly in the statistical analysis section.

Impact:

The work may impact policies on the use of antibiotics, as those drugs that have major impacts on the diversity of the gut microbiota and select for antibiotic resistance genes in the gut are better avoided. However, the primary rationale for antibiotic therapy will remain the clinical effectiveness of antimicrobial drugs, and the impact on the gut microbiota and resistome will be secondary to these considerations.

We agree that the primary consideration guiding antimicrobial therapy will usually be clinical effectiveness. However antimicrobial stewardship to minimise microbiome disruption and AMR selection is an increasingly important consideration, particularly as choices can often be made between different antibiotics that are likely to be equally clinically effective.

Reviewer #2 (Public Review):

Summary:

In this manuscript by Peto et al., the authors describe the impact of different antimicrobials on gut microbiota in a prospective observational study of 225 participants (healthy volunteers, inpatients and outpatients). Both cross-sectional data (all participants) and longitudinal data (a subset of 79 haematopoietic cell transplant patients) were used. Using metagenomic sequencing, they estimated the impact of antibiotic exposure on gut microbiota composition and resistance genes. In their models, the authors aim to correct for potential confounders (e.g. demographics, non-antimicrobial exposures and physiological abnormalities), and for differences in the recency and total duration of antibiotic exposure. I consider these comprehensive models an important strength of this observational study. Yet, the underlying assumptions of such models may have impacted the study findings (detailed below). Other strengths include the presence of both cross-sectional and longitudinal exposure data and the presence of both healthy volunteers and patients. Together, these observational findings expand on previous studies (both observational and RCTs) describing the impact of antimicrobials on gut microbiota.

Weaknesses:

(1) The main weaknesses result from the observational design. This hampers causal interpretation and corrects for potential confounding necessary. The authors have used comprehensive models to correct for potential confounders and for differences between participants in duration of antibiotic exposure and time between exposure and sample collection. I wonder if some of the choices made by the authors did affect these findings. For example, the authors did not include travel in the final model, but travel (most importantly, south Asia) may result in the acquisition of AMR genes [Worby et al., Lancet Microbe 2023; PMID 37716364). Moreover, non-antimicrobial drugs (such as proton pump inhibitors) were not included but these have a well-known impact on gut microbiota and might be linked with exposure to antimicrobial drugs. Residual confounding may underlie some of the unexplained discrepancies between the cross-sectional and longitudinal data (e.g. for vancomycin).

We agree that the observational design means there is the potential for confounding, which, as the reviewer notes, we attempt to account for as far as possible in the multivariable models presented. We cannot exclude the possibility of residual confounding, and we highlight this as a limitation in the  discussion. We have expanded on this limitation, and mention it as a possible explanation for inconsistencies between longitudinal and cross sectional models. Conducting randomised trials to assess the impacts of multiple antimicrobials in sick, hospitalised patients would be exceptionally difficult, and so it is hard to avoid reliance on observational data in these settings.

We did record participants’ foreign travel and diet, but these exposures were not included in our models as they were not independently associated with an impact on the microbiome and their inclusion did not materially affect other estimates. However, because most participants were recruited from a healthcare setting, few had recent foreign travel and so this study was not well powered to assess the effects of travel on AMR carriage. We have added this as a limitation.

In addition, the authors found a disruption half-life of 6 days to be the best fit based on Shannon diversity. If I'm understanding correctly, this results in a near-zero modelled exposure of a 14-day-course after 70 days (purple line; Supplementary Figure 2). However, it has been described that microbiota composition and resistome (not Shannon diversity!) remain altered for longer periods of time after (certain) antibiotic exposures (e.g. Anthony et al., Cell Reports 2022; PMID 35417701). The authors did not assess whether extending the disruption half-life would alter their conclusions.

The reviewer is correct that the best fit disruption half-life of 6 days means the model assumes near-zero exposure by 70 days. We appreciate that antimicrobials can cause longer-term disruption than is represented in our model, and we refer to this in the discussion (we had cited two papers supporting this, and we are grateful for the additional reference above, which we have added). We agree that it is useful to clarify that the longer term effects may be seen in individual components of the microbiome or AMR genes, but not in overall measures of diversity, so have added this to the discussion.

(2) Another consequence of the observational design of this study is the relatively small number of participants available for some comparisons (e.g. oral clindamycin was only used by 6 participants). Care should be taken when drawing any conclusions from such small numbers.

We agree. Although our participants received a large number of different antimicrobial exposures, these were dependent on routine clinical practice at our centre and we lack data on many potentially important exposures. We had mentioned this in relation to antimicrobials not used at our centre, and have now clarified in the discussion that this also limits reliability of estimates for antimicrobials that were rarely used in study participants.

(3) The authors assessed log-transformed relative abundances of specific bacteria after subsampling to 3.5 million reads. While I agree that some kind of data transformation is probably preferable, these methods do not address the compositional data of microbiome data and using a pseudocount (10-6) is necessary for absent (i.e. undetected) taxa [Gloor et al., Front Microbiol 2017; PMID 29187837]. Given the centrality of these relative abundances to their conclusions, a sensitivity analysis using compositionally-aware methods (such as a centred log-ratio (clr) transformation) would have added robustness to their findings.

We agree that using a pseudocount is necessary for undetected taxa, which we have done assuming undetected taxa had an abundance of 10^-6 (based on the lower limit of detection at the depth we sequenced). We refer to this as truncation in the methods section, but for clarity we have now also described this as a pseudocount.  Because our analysis focusses on major taxa that are almost ubiquitous in the human gut microbiome, a pseudocount was only used for 3 samples that had no detectable Enterobacteriaciae.

We are aware that compositionally-aware methods are often used with microbiome data, and for some analyses these are necessary to avoid introducing spurious correlations. However the flaws in non-compositional analyses outlined in Gloor et al do not affect the analyses in this paper:

(1) The problems related to differing sequence depths or inadequate normalisation do not apply to our dataset, as we took a random subset of 3.5 million reads from all samples (Gloor et al correctly point out that this method has the drawback of losing some information, but it avoids problems related to variable sequencing depth)

(2) The remainder Gloor et al critiques multivariate analyses that assess correlations between multiple microbiome measurements made on the same sample, starting with a dissimilarity matrix. With compositional data these can lead to spurious correlations, as measurements on an individual sample are not independent of other measurements made on the same sample. In contrast, our analyses do not use a dissimilarity matrix, but evaluate the association of multiple non-microbiome covariates (e.g. antibiotic exposures, age) with single microbiome measures. We use a separate model for each of 11 specified microbiome components, and display these results side-by side. This does not lead to the same problem of spurious correlation as analyses of dissimilarity matrices. However, it does mean that estimates of effects on each taxa outcome have to be interpreted in the context of estimates on the other taxa. Specifically, in our models, the associations of antimicrobial exposure with different taxa/AMR genes are not necessarily independent of each other (e.g. if an antimicrobial eradicated only one taxon then it would be associated with an increase in others). This is not a spurious correlation, and makes intuitive sense when using relative abundance as outcome. However, we agree this should be made more explicit.

For these reasons, at this stage we would prefer not to increase the complexity of the manuscript by adding a sensitivity analysis.

(4) An overall description of gut microbiota composition and resistome of the included participants is missing. This makes it difficult to compare the current study population to other studies. In addition, for correct interpretation of the findings, it would have been helpful if the reasons for hospital visits of the general medical patients were provided.

We have added a summary of microbiome and resistome composition in the results section and new supplementary table 2), and we also now include microbiome and resistome profiles of all samples in the supplementary data. We also provide some more detail about the types of general medical patients included. We are not able to provide a breakdown of the initial reason for admission as this was not collected.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Provide a supplementary table with information on the abundance of individual genes in the samples.

This supplementary data is now included.

(2) Engage with an expert in statistics to discuss how statistical analyses can be improved.

A experienced biostatistician has been involved in this study since its conception, and was involved in planning the analysis and in the responses to these comments.

(3) Typos and other minor corrections:

Methods: it is my understanding that litre should be abbreviated with a lowercase l.

Different journals have different house styles: we are happy to follow Editorial guidance.

p. 9: abuindance should be corrected to abundance.

Corrected

p. 9: relative species should be relevant species?  

Yes, corrected. Thank you.

p. 9 - 10: can the apparent lack of effect of beta-lactams on beta-lactamase gene abundance be explained by the focus on a small number of beta-lactamase resistance genes that are found in Enterobacteriaceae and which are not particularly prevalent, while other classes of resistance genes (e.g. Bacteroidal beta-lactamases) were excluded?

It is possible that including other beta-lactamases would have led to different results, but as a small number of beta-lactamases in Enterobacteriaceae are of major clinical importance we decided to focus on these (already justified in the Methods). A full list of AMR genes identified is now provided in the supplementary data.

p. 10: beta-lactamse should be beta-lactamase

Corrected

Figure 3A: could the data shown for tetracycline resistance genes be skewed by tetQ, which is probably one of the most abundant resistance genes in the human gut and acts through ribosome protection?

TetQ was included, but only accounted for 23% of reads assigned to tetracycline resistance genes so is unlikely to have skewed the overall result. We limited the analysis to a few major categories of AMR genes and, other than VanA, have avoided presenting results for single genes to limit the degree of multiple testing. We now include the resistome profile for each sample in the supplementary data so that readers can explore the data if desired.

Reviewer #2 (Recommendations For The Authors):

(1) Given the importance of obligate anaerobic gut microbiota for human health, it might be interesting to divide antibiotics into categories based on their anti-anaerobic activity and assess whether these antibiotics differ in their effects on gut microbiota.

The large majority of antibiotics used in clinical practice have activity against aerobic bacteria and anaerobic bacteria, so it is not possible to easily categorise them this way. There are two main exceptions (metronidazole and aminoglycosides) but there was insufficient use of these drugs to clearly detect or rule out a difference between them, even when categorising antimicrobials by class, so we prefer not to frame the results in these terms. Also see our comments on this categorisation below.

(2) For estimating the abundance of anaerobic bacteria, three major groups were assessed: Bacteroidetes, Actinobacteria and Clostridia. To me, this seems a bit aspecific. For example, the phylum Bacteroidetes contains some aerobic bacteria (e.g. Flavobacteriia). Would it be possible to provide a more accurate estimation of anaerobic bacteria?

We think that an emphasis on a binary aerobic/anaerobic classification is less biologically meaningful that the more granular genetic classification we use, and its use largely reflects the previous reliance on culture-based methods for bacterial identification. Although some important opportunistic human pathogens are aerobic, it is not clear that the benefit or harm of most gut commensals relates to their oxygen tolerance, and all luminal bacteria exist in an anaerobic environment. As such we prefer not to perform an additional analysis using this category. We are also not sure that this could be done reliably, as many of the taxa are characterised poorly, or not at all.

We appreciate that Bacteroidetes, Actinobacteria and Clostridia are diverse taxa that include many different species, so may seem non-specific, but these were chosen because:

i) they are non-overlapping with Enterobacteriaceae and Enterococcus, the major opportunistic pathogens of clinical relevance, so could be used in parallel, and

ii) they make up the large majority of the gut microbiome in most people and most species are of low pathogenicity, so it is plausible that their disruption might drive colonisation with more pathogenic organisms (or those carrying important AMR genes).

We have more clearly stated this rationale.

(3) A statement on the availability of data and code for analysis is missing. I would highly recommend public sharing of raw sequence data and R code for analysis. If possible, it would be very valuable if processed microbiome data and patient metadata could be shared.

We agree, and these have been submitted as supplementary data. We have added the following statement “The data and code used to produce this manuscript are available in the supplementary material, including processed microbiome data, and pseudonymised patient metadata. The sequence data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB86785.”

eLife Assessment

This study provides important insights into the evolution of pesticide resistance, demonstrating that resistance can arise rapidly and repeatedly, which complements prior work on parallel evolution across species. The combination of extensive temporal sampling in the field, experimental evolution, and genomics makes for compelling findings. The authors are to be commended for acknowledging the main limitations of their study in the Discussion. Framing the work in a broader context of resistance beyond arthropod pests would further increase the appeal of the study, which is of relevance for both agronomic practitioners and evolutionary biologists.

Reviewer #1 (Public review):

Summary:

The study by Cao et al. provides a compelling investigation into the role of mutational input in the rapid evolution of pesticide resistance, focusing on the two-spotted spider mite's response to the recent introduction of the acaricide cyetpyrafen. This well-documented introduction of the pesticide-and thus a clearly defined history of selection-offers a powerful framework for studying the temporal dynamics of rapid adaptation. The authors combine resistance phenotyping across multiple populations, extensive resequencing to track the frequency of resistance alleles, and genomic analyses of selection in both contemporary and historical samples. These approaches are further complemented by laboratory-based experimental evolution, which serves as a baseline for understanding the genetic architecture of resistance across mite populations in China. Their analyses identify two key resistance-associated genes, sdhB and sdhD, within which they detect 15 mutations in wild-collected samples. Protein modeling reveals that these mutations cluster around the pesticide's binding site, suggesting a direct functional role in resistance. The authors further examine signatures of selective sweeps and their distribution across populations to infer the mechanisms-such as de novo mutation or gene flow-driving the spread of resistance, a crucial consideration for predicting evolutionary responses to extreme selection pressure. Overall, this is a well-rounded, thoughtfully designed and well-written manuscript. It shows significant novelty, as it is relatively rare to integrate broad-scale evolutionary inference from natural populations with experimentally informed bioassays, however, follow up work will be needed to fully resolve haplotype structure and the functional effects of resistance mutations in the system.

Strengths:

One of the most compelling aspects of this study is its integration of genomic time-series data in natural populations with controlled experimental evolution. By coupling genome sequencing of resistant field populations with laboratory selection experiments, the authors tease apart the individual effects of resistance alleles along with regions of the genome where selection is expected to occur, and compare that to the observed frequency in the wild populations over space and time. Their temporal data clearly demonstrates the pace at which evolution can occur in response to extreme selection. This type of approach is a powerful roadmap for the rest of the field of rapid adaptation.

The study effectively links specific genetic changes to resistance phenotypes. The identification of sdhB and sdhD mutations as major drivers of cyetpyrafen resistance is well supported by allele frequency shifts in both field and experimental populations. The scope of their sampling clearly facilitated the remarkable number of observed mutations within these target genes, and the authors provide a careful discussion of the likelihood of these mutations from de novo or standing variation. Furthermore, the discovered cross-resistance that these mutations confer to other mitochondrial complex II inhibitors highlights the potential for broader resistance management and evolution.

Weaknesses:

(1) Pleiotropy without pesticide modes of action (cyflumetofen and cyetpyrafen) may also play a role in the rapid response to the focal pesticide in this study<br /> (2) Other aspects of the environment that might influence selection were not considered in the structure of resistance alleles (i.e. climate, elevation)<br /> (3) Very little data were used for haplotype reconstruction, only 8 SNPs, and this excluded all heterozygous alleles, which could dramatically influence the complexity of these inferred haplotype networks.<br /> (4) Single Mutations and Their Effects:<br /> - Allelic effects were not estimated in isogenic lines, so the effects presented also include heterogeneity from allelic interactions with the genomic background<br /> - The authors see populations that segregate for resistance mutations but that have no survival to pesticides. This suggests either not all of the resistance mutations studied here actually have functional effects or that dominance is playing a role in masking their effects in the heterozygous state.

Reviewer #2 (Public review):

Summary:

This paper investigates the evolution of pesticide resistance in the two-spotted spider mite following the introduction of an SDHI acaricide, cyatpyrafen, in China. The authors make use of cyatpyrafen-naive populations collected before that pesticide was first used, as well as more recent populations (both sensitive and resistant) to conduct comparative population genomics. They report 15 different mutations in the insecticide target site from resistant populations, many reported here for the first time, and look at the mutation and selection processes underlying the evolution of resistance, through GWAS, haplotype mapping, and testing for loss of diversity indicating selective sweeps. None of the target site mutations found in resistant populations was found in pre-exposure populations, suggesting that the mutations may have arisen de novo rather than being present as standing variation, unless initially present at very low frequencies; a de novo origin is also supported by evidence of selective sweeps in some resistant populations. Furthermore, there is no significant evidence of migration of resistant genotypes between the sampled field populations indicating multiple origins of common mutations. Overall, this indicates a very high mutation rate and a wide range of mutational pathways to resistance for this target site in this pest species. The series of population genomic analyses carried out here, in addition to the evolutionary processes that appear to underly resistance development in this case, could have implications for the study of resistance evolution more widely.

Strengths:

This paper combines phenotypic characterisation with extensive comparative population genomics, made possible by the availability of multiple population samples (each with hundreds of individuals) collected before as well as after then introduction of the pesticide cyatpyrafen, as well as lab-evolved lines. This resuts in findings of mutation and selection processes that can be related back to the pesticide resistance trait of concern. Large numbers of mites were tested phenotypically to show the levels of resistance present, and the authors also made near-isogenic lines to confirm the phenotypic effects of key mutations. The population genomic analyses consider a range of alternative hypotheses, including mutations arising by de novo mutation or selection from standing genetic variation; and mutations in different populations arising independently or arriving by migration. The claim that mutations most likley arose by multiple repeated de novo mutations is therefore supported by multiple lines of evidence: the direct evidence of none of the mutations being found in over 2000 individuals from naive populations, and the indirect evidence from population genomics showing evidence of selective sweeps but not of significant migration between the sampled populations.

Weaknesses:

As acknowledged within the discussion, whilst evidence supports a de novo origin of the resistance associated mutations, this cannot be proven definitively as mutations may have been present at a very low frequency and therefore not found within the tested pesticide-naive population samples.

Near-isofemale lines were made to confirm the resistance levels associated with five of the 15 mutations, but otherwise the genotype-phenotype associations are correlative as confirmation by functional genetics was beyond the scope of this study.

Comments on revisions:

My recommendations have all been addressed in the revised version.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Cao et al. provides a compelling investigation into the role of mutational input in the rapid evolution of pesticide resistance, focusing on the two-spotted spider mite's response to the recent introduction of the acaricide cyetpyrafen. This well-documented introduction of the pesticide - and thus a clearly defined history of selection - offers a powerful framework for studying the temporal dynamics of rapid adaptation. The authors combine resistance phenotyping across multiple populations, extensive resequencing to track the frequency of resistance alleles, and genomic analyses of selection in both contemporary and historical samples. These approaches are further complemented by laboratory-based experimental evolution, which serves as a baseline for understanding the genetic architecture of resistance across mite populations in China. Their analyses identify two key resistance-associated genes, sdhB and sdhD, within which they detect 15 mutations in wild-collected samples. Protein modeling reveals that these mutations cluster around the pesticide's binding site, suggesting a direct functional role in resistance. The authors further examine signatures of selective sweeps and their distribution across populations to infer the mechanisms - such as de novo mutation or gene flow-driving the spread of resistance, a crucial consideration for predicting evolutionary responses to extreme selection pressure. Overall, this is a well-rounded, thoughtfully designed, and well-written manuscript. It shows significant novelty, as it is relatively rare to integrate broad-scale evolutionary inference from natural populations with experimentally informed bioassays, however, some aspects of the methods and discussion have an opportunity to be clarified and strengthened.

Strengths:

One of the most compelling aspects of this study is its integration of genomic time-series data in natural populations with controlled experimental evolution. By coupling genome sequencing of resistant field populations with laboratory selection experiments, the authors tease apart the individual effects of resistance alleles along with regions of the genome where selection is expected to occur, and compare that to the observed frequency in the wild populations over space and time. Their temporal data clearly demonstrates the pace at which evolution can occur in response to extreme selection. This type of approach is a powerful roadmap for the rest of the field of rapid adaptation.

The study effectively links specific genetic changes to resistance phenotypes. The identification of sdhB and sdhD mutations as major drivers of cyetpyrafen resistance is well-supported by allele frequency shifts in both field and experimental populations. The scope of their sampling clearly facilitated the remarkable number of observed mutations within these target genes, and the authors provide a careful discussion of the likelihood of these mutations from de novo or standing variation. Furthermore, the discovered cross-resistance that these mutations confer to other mitochondrial complex II inhibitors highlights the potential for broader resistance management and evolution.

Weaknesses:

(1) Experimental Evolution:

- Additional information about the lab experimental evolution would be useful in the main text. Specifically, the dose of cyetpyrafen used should be clarified, especially with respect to the LD50 values. How does it compare to recommended field doses? This is expected to influence the architecture of resistance evolution. What was the sample size? This will help readers contextualize how the experimental design could influence the role of standing variation.

The experimental design involved sampling approximately 6,000 individuals from the wild population ZJSX1, which were subsequently divided into two parallel cohorts under controlled laboratory conditions. The selection group (LabR) was subjected to continuous selection pressure using cyetpyrafen, while the control group (LabS) was maintained under identical laboratory conditions without exposure to acyetpyrafen. A dynamic selection regime was implemented wherein the acaricide dosage was systematically adjusted every two generations to maintain a consistent selection intensity, achieving a mortality rate of 60% ± 10% in the LabR population. This adaptive dosage strategy ensured sustained evolutionary pressure while preventing population collapse. The LC₅₀ values were tested at F1, F32, F54, F60, F62, and F66 generations using standardized bioassay protocols to quantify resistance development trajectories and optimize dosage for subsequent selection cycles. We provided the additional information in subsection 4.1 of the materials and methods section.

- The finding that lab-evolved strains show cross-resistance is interesting, but potentially complicates the story. It would help to know more about the other mitochondrial complex II inhibitors used across China and their impact on adaptive dynamics at these loci, particularly regarding pre-existing resistance alleles. For example, a comparison of usage data from 2013, 2017, and 2019 could help explain whether cyetpyrafen was the main driver of resistance or if previous pesticides played a role. What happened in 2020 that caused such rapid evolution 3 years after launch?

Although the introduction of the other two SDHI acaricides complicates the story, we would like to provide a complete background on the usage of acaricides with this mode of action in China. Although cyflumetofen was released in 2013 before cyetpyrafen, and cyenopyrafen was released in 2019 after cyetpyrafen, their market share is minor (about 3.2%) compared to cyetpyrafen (about 96.8%, personal communication). Since cross-resistance is reported among SDHIs, we could not exclude the contribution of cyflumetofen to the initial accumulation of resistance alleles, but the effect should be minor, both because of their minimal market share and because of the independent evolution of resistance in the field as found in our study. Although the contribution of cyflumetofen and cyenopyrafen cannot be entirely excluded, the rapid evolution of resistance seems likely to be mainly explained by the intensive application of cyetpyrafen. To clarify this issue, we added relevant information in the first paragraph of the discussion section.

(2) Evolutionary history of resistance alleles:

- It would be beneficial to examine the population structure of the sampled populations, especially regarding the role of migration. Though resistance evolution appears to have had minimal impact on genome-wide diversity (as shown in Supplementary Figure 2), could admixture be influencing the results? An explicit multivariate regression framework could help to understand factors influencing diversity across populations, as right now much is left to the readers' visual acuity.

The genetic structure of the populations was examined by Treemix analysis. We detected only one migration event from JXNC to SHPD (no resistance data available for these two populations), suggesting a limited role for migration to resistance evolution. The multiple regression analysis revealed that overall genetic diversity and Tajima’s D across the genome were not significantly associated with resistance levels, genetic structure or geographic coordinates (P > 0.05), which all support a limited role of migration in resistance development.

- It is unclear why lab populations were included in the migration/treemix analysis. We might suggest redoing the analysis without including the laboratory populations to reveal biologically plausible patterns of resistance evolution.

Thank you for the constructive suggestion. The Treemix analysis was redone by removing laboratory populations and is now reported.

- Can the authors explore isolation by distance (IBD) in the frequency of resistance alleles?

Thank you for the constructive suggestion. No significant isolation-by-distance pattern was detected for resistance allele frequencies across all surveyed years (2020: P=0.73; 2021: P=0.52; 2023: P=0.16; Mantel test). We added these results to the text.

- Given the claim regarding the novelty of the number of pesticide resistance mutations, it is important to acknowledge the evolution of resistance to all pesticides (antibiotics, herbicides, etc.). ALS-inhibiting herbicides have driven remarkable repeatability across species based on numerous SNPs within the target gene.

We appreciate this comment, which highlights the need to place our findings within the broader evolutionary context of pesticide resistance. We have investigated references relevant to the evolution of resistance to diverse pesticides. As far as we can tell, the 15 target mutations in eight amino acid residues are among the highest number of pesticide resistance mutations detected, especially within the context of animal studies. We have added relevant text to the second paragraph of the discussion.

- Figure 5 A-B. Why not run a multivariate regression with status at each resistance mutation encoded as a separate predictor? It is interesting that focusing on the predominant mutation gives the strongest r2, but it is somewhat unintuitive and masks some interesting variation among populations.

We conducted a multiple regression analysis to explore the influence of multiple mutations on resistance levels of field populations. However of 15 putative resistant mutations, only five were detected in more than three populations where bioassay data are available, i.e. I260T, I260V, D116G, R119C, R119L. The frequency of three of these mutations, I260T (P = 0.00128), I260V (P = 0.00423) and D116G (P = 0.00058), are significantly correlated with the resistance level of field populations. This has been added.

(3) Haplotype Reconstruction (Line 271-):

- We are a bit sceptical of the methods taken to reconstruct these haplotypes. It seems as though the authors did so with Sanger sequencing (this should be mentioned in the text), focusing only on homozygous SNPs. How many such SNPs were used to reconstruct haplotypes, along what length of sequence? For how many individuals were haplotypes reconstructed? Nonetheless, I appreciated that the authors looked into the extent to which the reconstructed haplotypes could be driven by recombination. Can the authors elaborate on the calculations in line 296? Is that the census population size estimate or effective?

Because haplotypes could not be determined when more than two loci were heterozygous, we detected haplotypes from sequencing data with at most one heterozygous locus. In total 844 individuals and 696 individuals were used to detect haplotypes of sdhB and sdhD. We detected 11 haplotypes (with 8 SNPs) and 24 haplotypes (with 11 SNPs) along 216 bp of the sdhB and 155 bp of the sdhD genes, respectively. Please see the fifth paragraph of subsection 2.4. We used ρ = 4 × Ne × d (genetic distance) (Li and Stephens, 2003) to calculate the number of effective individuals for one recombination event.

(4) Single Mutations and Their Effect (line 312-):

- It's not entirely clear how the breeding scheme resulted in near-isogenic lines. Could the authors provide a clearer explanation of the process and its biological implications?

To investigate the effect of single mutations or their combination on resistance levels, we isolated the females and males with the same homozygous/ hemizygous genotypes for creating homozygous lines. Females from these lines were not near-isogenic, but homozygous for the critical mutations. We revised the description in the methods section to clearly define these lines.

- If they are indeed isogenic, it's interesting that individual resistance mutations have effects on resistance that vary considerably among lines. Could the authors run a multivariate analysis including all potential resistance SNPs to account for interactions between them? Given the variable effects of the D116G substitution (ranging from 4-25%), could polygenic or epistatic factors be influencing the evolution of resistance?

We couldn’t conduct multivariate analysis because most lines have only one resistant SNP. The four lines homozygous for 116G were from the same population. The variable mortality may reflect other unknown mechanisms but these are beyond the scope of this study.

- Why are there some populations that segregate for resistance mutations but have no survival to pesticides (i.e., the green points in Figure 5)? Some discussion of this heterogeneity seems required in the absence of validation of the effects of these particular mutations. Could it be dominance playing a role, or do the authors have some other explanation?

We didn’t investigate the degree of dominance of each mutation. The mutation I260V shows incompletely dominant inheritance (Sun, et al. 2022). To investigate survival rate of different populations, the two-spotted spider mite T. urticae was exposed to 1000 mg/L of cyetpyrafen, higher than the recommended field dose of 100 mg/L. Such a high concentration may lead to death of an individual heterozygous for certain mutations, such as I260V.

- The authors mention that all resistance mutations co-localized to the Q-site. Is this where the pesticide binds? This seems like an important point to follow their argument for these being resistance-related.

Yes. We revised Fig. 3c to show the Q-site.

(5) Statistical Considerations for Allele Frequency Changes (Figure 3):

- It might be helpful to use a logistic regression model to assess the rate of allele frequency changes and determine the strength of selection acting on these alleles (e.g., Kreiner et al. 2022; Patel et al. 2024). This approach could refine the interpretation of selection dynamics over time.

Thank you for this suggestion. A logistic regression model was used to track allele frequencies trajectories. The selection coefficient of each allele and their joint effects were estimated.

Reviewer #2 (Public review):

Summary:

This paper investigates the evolution of pesticide resistance in the two-spotted spider mite following the introduction of an SDHI acaricide, cyatpyrafen, in China. The authors make use of cyatpyrafen-naive populations collected before that pesticide was first used, as well as more recent populations (both sensitive and resistant) to conduct comparative population genomics. They report 15 different mutations in the insecticide target site from resistant populations, many reported here for the first time, and look at the mutation and selection processes underlying the evolution of resistance, through GWAS, haplotype mapping, and testing for loss of diversity indicating selective sweeps. None of the target site mutations found in resistant populations was found in pre-exposure populations, suggesting that the mutations may have arisen de novo rather than being present as standing variation, unless initially present at very low frequencies; a de novo origin is also supported by evidence of selective sweeps in some resistant populations. Furthermore, there is no significant evidence of migration of resistant genotypes between the sampled field populations, indicating multiple origins of common mutations. Overall, this indicates a very high mutation rate and a wide range of mutational pathways to resistance for this target site in this pest species. The series of population genomic analyses carried out here, in addition to the evolutionary processes that appear to underlie resistance development in this case, could have implications for the study of resistance evolution more widely.

Strengths:

This paper combines phenotypic characterisation with extensive comparative population genomics, made possible by the availability of multiple population samples (each with hundreds of individuals) collected before as well as after the introduction of the pesticide cyatpyrafen, as well as lab-evolved lines. This results in findings of mutation and selection processes that can be related back to the pesticide resistance trait of concern. Large numbers of mites were tested phenotypically to show the levels of resistance present, and the authors also made near-isogenic lines to confirm the phenotypic effects of key mutations. The population genomic analyses consider a range of alternative hypotheses, including mutations arising by de novo mutation or selection from standing genetic variation, and mutations in different populations arising independently or arriving by migration. The claim that mutations most likley arose by multiple repeated de novo mutations is therefore supported by multiple lines of evidence: the direct evidence of none of the mutations being found in over 2000 individuals from naive populations, and the indirect evidence from population genomics showing evidence of selective sweeps but not of significant migration between the sampled populations.

Weaknesses:

As acknowledged within the discussion, whilst evidence supports a de novo origin of the resistance-associated mutations, this cannot be proven definitively as mutations may have been present at a very low frequency and therefore not found within the tested pesticide-naive population samples.

We agree that we could not definitively exclude the presence of a very low incidence of favoured mutations before the introduction of this novel acaricide.

Near-isofemale lines were made to confirm the resistance levels associated with five of the 15 mutations, but otherwise, the genotype-phenotype associations are correlative, as confirmation by functional genetics was beyond the scope of this study.

We hope that future functional studies will validate the effects of these mutations on resistance in both the two-spotted spider mite T. urticae and other spider mite species. This could be done by creating near-isogenic female lines or using CRISPR-Cas9 technology, as gene knockouts have recently been established for T. urticae.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Could the authors elaborate on the environmental context (e.g., climate, geography) of the sampled populations to give more nuance to the analysis of genetic differentiation and resistance evolution?

We have explored the influence of geographic isolation on the frequency of resistance alleles by Mantel tests (isolation by distance). We didn’t investigate the influence of climate, because most of the samples were from greenhouses, where the climate to which the pest is exposed is unclear.

(2) Line 161: is this supposed to be one R and one S?

Yes, we added this information (LabR and LabS).

(3) Line 207: variation is not saturated at the first two sites because the different combinations are not seen. This is a bit misleading.

What we wanted to indicate was that the two codon positions are saturated, rather than their combinations. We revised this sentence by adding “of each codon position”.

(4) Line 376: continuous selection did not "result in a new mutation arising". Rather, the mutation arose and was subsequently selected on.

We revised the expression of this de novo mutation and selection process.

(5) Line 402: can the authors explore what Ne would be necessary to drive the number of mutational origins they observe, as in (Karasov et al. 2010)?

It is challenged to estimate Ne, especially when mutation rate data from the two-spotted spider mite T. urticae is unavailable. We observed 2.7 resistant mutations per population in samples collected in 2024, seven years after the release of cyetpyrafen. The estimated mutation rate (Θ) is  0.0193, given 20 generations per year for T. urticae. An effective population size (Ne) of 2.29*10⁶ would be necessary to reach the number of de novo mutations observed in this study, given Θ  =  3Neμ (haplodiploid sex determination of T. urticae) and a mutation rate of μ  =  2.8*10^-9 per base pair per generation as estimated for Drosophila melanogaster (Keightley et al., 2014). The high reproductive capacity of T. urticae (> 100 eggs per female) and short generation time makes it easier to reach such a population size in the field as we now note.

(6) Line 482: how did the authors precisely kill 60% of samples with their selection? What was the applied rate? In general, listing the rates of insecticide used in dose response would be useful to decipher if LD50s are projected outside of the doses used (seems like they are). In this case, authors should limit their estimates to those > the highest rate used in the dose response.

It is difficult to control mortality precisely. We applied cyetpyrafen every two generations but did not determine the LC₅₀ every two generations. When mortality was lower than 60%, another round of spraying was applied by increasing the dosage of the pesticide. The LC₅₀ values were tested at F₁, F₃₂, F₅₄, F₆₀, F₆₂, and F₆₆ generations to establish the trajectories around resistance.

(7) The light pink genomic region in Figure 2 was distracting. Why is it included if there is no discussion of genomic regions outside the sdh genes? Generally, there was a lot going on in this figure, and some guiding categories (i.e., lab selected vs wild population) on the figure itself could help orient the reader.

We included chromosome 2 colored in light pink/ red to show the selection signal across a wider genomic region. In the figure legend, we added a description of the lab selected, field resistant and field susceptible populations. Very little common selection signal was detected among resistant populations on chromosome 2, indicating this region was less likely to be involved in resistance evolution of T. urticae to cyetpyrafen. We also described the result briefly in the figure legend.

Reviewer #2 (Recommendations for the authors):

(1) The most significant aspect of this study is the use of multiple pest population samples taken before as well as after the introduction of a class of pesticides, allowing a thorough comparative population genomics study in a species where a range of resistance mutations have appeared within a few years. I would prefer to see a title conveying this significance, rather than the current study, which focuses on the total number of mutations and claimed notoriety of the (at that point unnamed) study species. Similarly, I would prefer an abstract that relies less on superlative claims and includes more details: the scientific name of the study species; the number of years in which resistance evolved; the number of historical specimens; how the resistance levels for single mutations were shown.

(1) The title was changed by adding “the two-spotted spider mite Tetranychus urticae” and removing the “unprecedented number” to emphasize that “recurrent mutations drive rapid evolution”, i.e., “Recurrent Mutations Drive the Rapid Evolution of Pesticide Resistance in the Two-spotted Spider Mite Tetranychus urticae.”

(2) The scientific name of the study species was added.

(3) The number of years in which resistance evolved was added.

(4) The number of historical specimens was added (2666).

(5) Because we used homozygous lines but not iso-genic lines or gene-edited lines, our bioassay data could not provide direct evidence on the level of resistance conferred by each mutation. We revised our description of the results and removed this content from the abstract.

Line 29: if you want to claim the number is unprecedented, please specify the context: unprecedented for a pesticide target in an arthropod pest? (more resistance mutations may have been found in bacteria/fungi...).

We revised the sentence by adding “in an arthropod pest”.

Line 30: rather than a claim of notoriety, it may be better to specify what damage this pest causes.

Revised by describing it as an arthropod pest.

Line 34: please clarify, was this all in different haplotypes, or were some mutations found in combination?

Done: We identified 15 target mutations, including six mutations on five amino acid residues of subunit sdhB, and nine mutations on three amino acid residues of subunit sdhD, with as many as five substitutions on one residue.

(2) The introduction begins by framing the context as resistance evolution in invertebrate pests. However, the evolutionary processes examined in the study are applicable to resistance in other systems, and potentially to other cases of rapid contemporary evolution. The authors could show wider significance for their work beyond the subfield of invertebrate pests by including more of this wider context in their introduction and discussion: even if this means they can no longer claim novelty based on the number of mutations alone, the study is a strong example of the use of population genomics combined with functional and phenotypic characterisation to investigate the evolutionary processes underlying the emergence of resistance, so could have wider importance than within its current framing.

The background was revised as mentioned above to take this into account.

For example, in lines 48-50, please clarify what is meant by pesticides here (insects/arthropods? weeds and pathogens too?) In lines 69-73, the opposite is sometimes seen in fungal pathogens, with large numbers of mutations generated in lab-evolved strains.

We extended pesticides to those targeting arthropods, weeds and pathogens. We still emphasize the situation mainly with respect to arthropod pests.

(3) Lines 91-93: how many modes of action? How recently were SDHI acaricides introduced?

Added: at least 11 groups of acaricides based on their modes of action. SDHI was launched in 2007.

(4) Line 98-102: Use in China is a useful background for the study populations, but the global context should be included too.

Yes, four SDHI acaricides developed around the globe were introduced.

(5) Line 113: They show diverse mutations, but all within the mechanism of target-site point mutations.

We agree to your suggestion. This sentence has been removed as it repeats information stated above it.

(6) Line 115-116: Yes, agreed; I think this is the main strength of the current study and should be emphasised sooner.

Thanks.

(7) Line 158: Selective sweep signals were clear in half of the resistant populations but not in the others. The suggestion that the others had undergine soft sweeps, with multiple mutations increasing in frequency simultaneously but no one reaching fixation, seems reasonable; but the authors could compare the populations that did show a sweep with those that did not (for example, was there greater diversity or evenness of genotypes in those that did not?).

Five resistant populations with selection signals identified by PBE analysis (Figure 2b) showed corresponding decreases in π and Tajima’s D near the two SDH genes but not across the genome (Figure S1).

(8) Line 313: please clarify "in combination with other mutations" within a mixed population or combined in one individual/haplotype? Also, the phrase "characterised the function" may be a little misleading, as this is a correlative analysis, not functional confirmation.

None of the combinations of different resistant mutations was observed in a single haplotype. Here, we examine resistance levels associated with a single mutation or two mutations on sdhB and sdhD in one individual, i.e. sdhB_I260V and sdhD_R119C. We revised the sentences to avoid any implication of functional confirmation.

(9) Line 358: again, please clarify the context: among arthropod pests?

Done.

(10) Line 360-363: please give some background on when and where these related compounds were introduced.

Added.

(11) Line 410: yes fitness costs may be a factor, but you could also give an example of a cost expressed in the absence of any pesticides, as well as the given example of negative cross-resistance.

We added the example of the H258Y mutation which causes both fitness costs and negative cross-resistance.

(12) Lines 419-438: this is one aspect where the situation for insecticides is in contrast with some other resistance areas.

Yes, we restricted these statements to arthropod pests.

(13) Line 466: some more detail could be given here: for example, SNP-specific monitoring would be less effective, but amplicon sequencing would be more suitable.

Yes, revised.

(14) Lines 472-475: Please list the numbers of field/lab, pre/post exposure, and sensitive/resistant populations within the main text.

Done. The number of sensitive/resistant populations was reported in the result section.

(15) Line 483: randomly selected individuals?

Yes, added randomly selected individuals.

(16) Line 556: Sanger sequencing to characterise populations? Or a number of individuals from each population?

Revised.

(17) References: there are some duplicate entries, please check this.

Checked.

(18) Figure 1e: consider a log(10) scale to better show large fold changes and avoid multiple axis breaks.

Thanks for your suggestions. However we didn’t scale the LC₅₀ value, because we wanted to show the specific impact of 1,000 mg/L. The breaks in the Y axis around 30 mg/L -1,000 mg/L reveal that the LC50s of the resistant populations were all greater than 1000 mg/L, while those of the susceptible populations were all below 30 mg/L. This justified the use 1000 mg/L as a discriminating dose to investigate resistance status and level in subsequent work.

eLife Assessment

This valuable study reports the first characterization of the CG14545 gene in Drosophila melanogaster, which the authors name "Sakura." Acting during germline stem cell fate and differentiation, Sakura is required for both oogenesis and female fertility, although some mechanistic details require further investigation. This solid study presents a wide-ranging and well-controlled characterization of Sakura, and accordingly the findings and associated reagents described will be of use to scientists interested in oogenesis and early development.

Reviewer #1 (Public review):

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 the 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.

Latest comments:

The reviewer acknowledges the importance of sharing the observed defects in Sakura mutant ovaries and the possible physiological significance of the Sakura-Out interaction with the research community, as this information could lay the groundwork for future functional analysis research.

Reviewer #2 (Public review):

In this study, the authors identified CG14545 (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 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. In this revised manuscript, the authors further investigated whether Sakura affects the function of Orb, a binding partner they identified, in deubiquitinase activity when Orb interacts with Bam.

This elaborate study will be embraced by both germline-focused scientists and the developmental biology community.

Latest comments:

The authors answered all my persistent concerns and made changes according to the recommendations I incorporated for the revised version of the manuscript.

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.

Latest comments:

As with my previous assessment, I remain supportive of publication of this manuscript. Though I agree with the other reviewers that additional experimentation would increase the value of this study even further, I feel it will also be a useful contribution to the field as is.

Author response:

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

Comments on latest version:

The authors have attempted to address my initial concerns with additional experiments and refutations. Unfortunately, my concerns, especially my specific comments 1-3, remain unaddressed. The present manuscript is descriptive and fails to describe the molecular mechanism by which Sakura exerts its function in the germline. Nevertheless, this reviewer acknowledges that the observed defects in sakura mutant ovaries and the possible physiological significance of the Sakura-Out interaction are worth sharing with the research community, as they may lay the groundwork for future research in functional analysis.

We thank the reviewer for valuable comments. We would like to investigate the molecular mechanism by which Sakura exerts its function in the germline in near future studies. 

Reviewer #2 (Public review):

In this study, the authors identified CG14545 (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 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. In this revised manuscript, the authors further investigated whether Sakura affects the function of Orb, a binding partner they identified, in deubiquitinase activity when Orb interacts with Bam.

We appreciate the authors' efforts to address all our comments. While these revisions have greatly improved the clarity of certain sections, some of the concerns remain unclear, while details mentioned in the responses about these studies should be incorporated in the manuscript. Specifically, the manuscript still lacks the demonstration that Sakura co-localizes with Orb/Bam despite having the means for staining and visualization. This would bring insight into the selective binding of Orb with Bam vs. Sakura perhaps at different stages of oogenesis. Such analyses would allow for more specific conclusions, further alluding to the underlying mechanism, rather than the general observations currently presented.

This elaborate study will be embraced by both germline-focused scientists and the developmental biology community.

We thank the reviewer for valuable comments. We believe that the author meant Otu, not Orb, for the binding partner of Sakura that we identified. We would like to investigate the colocalization of Sakura with other proteins including Otu and the molecular mechanism by which Sakura exerts its function in the germline in near future studies. 

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.

Comments on latest version:

With these revisions, the authors have addressed my main concerns.

We thank the reviewer for valuable comments.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The manuscript is much improved based on the changes made upon recommendations from the reviewers.

Though most of our comments have been addressed, we have a few more we wish to recommend. For previous points we made, we replied with further clarification for the authors.

Figure 1

(1) B should be the supplemental figure.

We moved the former Fig 1B to Supplemental Figure 1.

• Previous Fig1B (sakura mRNA expression level) is now Fig S2, not S1. Please make this data as Fig S1.

We moved Fig S1 to main Fig7A and renumbered Fig S2-S16 to Fig S1-S15.

(2) 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.

• The labels on lanes for Stages 12-13 and Stage 14, still only say "chambers", not "egg chambers". Also there is no Stage 1-3 egg chamber. More accurately, the label should be "Germarium - Stage 11 egg chambers".

We updated the lables on lanes as suggested by the reviewer.

(3) 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 sakuranull 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.

• Please put this info into the Methods section.

We added this info into the Methods section.

(4) 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[+/-].

• Please add this detail into the manuscript.

We added this info into the Methods section.

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.

• Having a different 'n' number for each experiment does not allow one to compare anything except numbers of the egg chambers. This must be normalized.

We still don’t agree with the reviewer. In Fig 5D, we are showing the numbers of stage 14 oocytes per fly (= per a pair of ovaries). ‘n’ is the number of flies (= number of a pair of ovaries) examined. We now clarified this in the figure legend. Different ‘n’ number does not prevent us from comparing the numbers of stage 14 oocytes per fly. Therefore, we would like to show as it is now.

(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.

• Please add this information to the manuscript.

We added this info into the Methods section.

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.

• Current Fig S1 should go to Fig 7, to better understand the relationship between pMad and Bam expression.

We moved Fig S1 to main Fig7A and renumbered Fig S2-S16 to Fig S1-S15.

Figure 9C - 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.

• Please add this info to the Methods section.

We added this info into the Methods section.

Figure 10- 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.

• Based on your binding studies, we would expect them to colocalize in the egg chamber, and since there are antibodies and a GFP-line available, it would be important to demonstrate that via visualization.

As we wrote in the response and now in the manuscript, our antibodies are not best for immunostaining. We will try to optimize the experimental conditions in the future studies.

eLife Assessment

This valuable study identifies a population of CD81-positive fibroblasts showing senescence signatures that can activate neutrophils through the C3/C3aR1 axis, hence contributing to the inflammatory response in periodontitis. Solid evidence, combining in vitro and in vivo analyses and mouse and human data, supports these findings. The revised manuscript has addressed many concerns significantly. The work would be of interest to researchers working in the senescence and oral medicine fields.

Reviewer #2 (Public review):

Summary:

The authors report the discovery of a population of gingival fibroblasts displaying the expression of cellular senescence markers P21 and P16 in human periodontitis samples and a murine ligature-induced periodontitis (LIP) model. They support this finding in the murine model through bulk RNA-sequencing and show that differentially expressed genes are significantly enriched in the SenMayo cellular senescence in aging dataset. They then show that Ligature-Induced Periodontitis (LIP) mice treated with the senomorphic drug metformin display overall diminished bone damage, reduced histomorphic alterations, and a reduction in P21 and P16 immunostaining signal. To explore the cell types expressing cellular senescence markers in periodontitis, the authors make use of a combination of bioinformatic analyses on publicly available scRNA-seq data, immunostainings on patient samples and their LIP model; as well as in vitro culture of healthy human gingival fibroblasts treated with LPS. They found that fibroblasts are a cell population expressing P16 in periodontitis which are also enriched for SenMayo genes, suggesting they have a senescent phenotype. They then point to a subgroup of fibroblasts expressing CD81+ with the highest enrichment for a SASP geneset in periodontitis. They also show that treatment of LIP mice and human LPS-treated gingival fibroblasts with metformin leads to a reduction of P21 and P16-positive cells, as well as the senescence-associated beta-galactosidase (SA-beta-gal) marker. Finally, they show evidence suggesting that CD81+ senescent fibroblasts are the source of C3 complement protein, which they stipulate signals through the C3AR1 receptor present in neutrophils in periodontitis. The authors observed that both CD81+ fibroblast and C3AR1+ neutrophil populations are expanded in periodontitis, that both populations appear to be in close contact, and that treatment with metformin reduced both C3 and the neutrophil marker MPO in their mouse LIP model.

After a round of revision, the authors have made significant improvements to their manuscript, such as improving the quality of the data/evidence and also included new data from experiments using a well-known senolytic and the senomorphic metformin, which all together provide a solid support to their main claims.

Strengths:

The study implements several different techniques and tools on human samples, mouse models, fibroblast cultures, and publicly available data to support their conclusions. In summary, they provide solid evidence showing that in the context of periodontitis, there is an expansion of cells expressing senescence markers P21, and P16, as well as members of the SASP, and that this includes CD81+ fibroblasts.

Weaknesses:

The fact that in this study the periodontitis samples belonged to patients with a significantly higher median age (all older than 50 years of age) and the healthy samples belonged to young adults (all younger than 35 years of age), raises the need for caution in interpretation due to a possible effect of aging in the accumulation of CD81+ senescent fibroblasts. However, the recruitment of similar age groups in this case is of course difficult due to the higher prevalence of periodontitis in older adults. In this regard it is important to note that the authors still support their findings using a mouse ligature model. Similar studies comparing healthy and periodontitic patients from similar age groups will be of great importance in the future.

Author response:

The following is the authors’ response to the original reviews

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

There are four main areas that need further clarification:

(1) Further and more complete assessment of senescence and the fibroblasts must be done to support the claims. 

We sincerely appreciate the Reviewing Editor's valuable suggestion regarding the addition of cellular senescence detection markers. In the revised manuscript, we have incorporated additional detection markers for cellular senescence, such as H3K9me3 and SA-β-gal staining, in healthy and periodontitis gingival samples to further validate our findings (Figure 1A, B in revised manuscripts).

(2) Confusion between ageing and senescence throughout the manuscript.

We fully understand the concerns raised by the Reviewing Editor and reviewers regarding the confusion between the concepts of ageing and senescence in the manuscript. Cellular senescence is a manifestation of ageing at the cellular level. In the revised manuscript, we have given priority to the term ‘senescence’ to describe the cell condition instead of ‘aging’.

(3) The lipid metabolism mechanistic claims are very speculative and largely unsupported by experimental data. 

We greatly appreciate the Reviewing Editor and reviewers for pointing out the incorrect statements regarding the role of lipid metabolism in regulating cellular senescence. Since the mechanism by which cellular metabolism regulates cellular senescence is not the core focus of this manuscript, we have moved the results of the metabolic analysis from the sc-RNA sequencing data to the figure supplement (Figure 4-figure supplement 1) and revised the related statements in the revised manuscript (Page 7-8, Line 186-194).

(4) Concerns about the use of Metformin as a senotherapy vs other pleiotropic effects in periodontitis and the suggestion of using an alternative Senolytic drug (Bcl2 inhibitors, etc.). 

We fully understand the concerns of the Reviewing Editor and reviewers regarding metformin as an anti-aging therapy. In the revised manuscript, we have included additional experiments using other senolytic drugs ABT-263, a Bcl2 inhibitor, in the ligature-induced periodontitis mouse model. The corresponding results could be found in the Figure 6. and Page 9-10, Line 248-264 in the revised manuscripts.

Reviewer #1 (Recommendations For The Authors):

While most of the experiments are elegantly designed and the procedures well conducted there are several critical weaknesses that temper my enthusiasm for this solid and timely work. Considering my main points, I would recommend the following:

(1) Potentiate the senescent assessment in vitro and, most importantly, in vivo. E.g. SABgal with fresh tissue, other senescent biomarkers like SAHFs (HP1g or H3K9me3), etc.

We sincerely appreciate the reviewers' suggestion to potentiate the assessment of cellular senescence. In the revised manuscript, we performed SA-β-gal staining on fresh frozen samples, revealing a significantly higher number of SA-β-gal positive cells in the gingival tissue of periodontitis, particularly in the lamina propria, while few SA-β-gal positive cells were observed in healthy gingival tissue (Figure. 1A). Additionally, we assessed the protein level changes of H3K9me3, a marker of senescence-associated heterochromatin foci (SAHF), in gingival tissues from healthy individuals and periodontitis patients. The results showed a notable increase in the number of H3K9me3 positive cells in periodontitis tissues, approximately double that found in healthy gingiva (Figure. 1B). This trend aligns with our previous findings of elevated p16 and p21 levels. Collectively, these results further confirm that periodontitis gingival tissues contain a greater number of senescent cells compared to healthy gingiva.  

(2) Claims on disturbances in lipid metabolism as a driver of CD81+ fibroblast senescence require appropriate functional/mechanistic validations and experiments of metabolism rewiring.

We sincerely appreciate the reviewers' suggestion for more experimental evidence regarding the role of lipid metabolism in driving CD81+ fibroblast senescence. The influence and mechanisms of lipid metabolism on cellular senescence is a complex and important scientific issue, and it is not the central focus of this manuscript. Therefore, to avoid causing confusion for the reviewers and readers, we have removed the metabolism analysis in the Figure 4-figure supplement 1 and revised the presentation of the relevant results in the revised manuscript to ensure a more rigorous interpretation of our findings (Page 7-8, Line 186-194). 

(3) Do LPS-stimulated HGFS implementing the senescent programme secrete C3? Detection of complement C3 at the protein level (e.g. by ELISA) would reinforce the proposed mechanism.

This is indeed a very interesting question. In response to the reviewers' suggestion, we measured the levels of C3 protein secreted by human gingival fibroblasts induced by Pg-LPS, which is one of the markers of the senescence-associated secretory phenotype (SASP). The results indicated that, compared to untreated fibroblasts, those induced by Pg-LPS exhibited significantly higher levels of C3 secretion, approximately 1.5 times that of the control group (Figure. 5G). Additionally, we also found that primary gingival fibroblasts derived from periodontitis tissues secreted more complement C3 compared to those derived from healthy tissues (Figure. 5F). These findings suggest that the increased secretion of complement C3 by gingival fibroblasts in periodontitis tissues may be related to Pg-LPS-induced cellular senescence.

(4) The mechanism of Metformin to impair senescence and/or the SASP is not fully validated and Metformin can produce other pleiotropic effects. A key experiment (including therapeutic implications) is using a senolytic drug (e.g. Navitoclax) to causally connect the eradication of senescent CD81+ fibroblasts with the recruitment of neutrophils. If the hypothesis of the authors is correct this approach should result in reduced levels of gingival CD81 and C3 positivity, prevention of neutrophils infiltration (reduced MPO positivity), and ameliorate bone damage in ligationinduced periodontitis murine models.

We fully understand the reviewers' concerns regarding the role of metformin in alleviating cellular senescence and the possibility of it acting through non-senescent pathways. To clarify the role of cellular senescence in the recruitment of neutrophils by CD81+ fibroblasts through C3 in periodontitis, we treated a ligature-induced periodontitis mouse model with ABT-263, also known as Navitoclax. The results showed that after ABT-263 treatment, the number of p16-positive or H3K9me3-positive senescent cells in the periodontitis mice significantly decreased. Additionally, we observed reductions in the quantities of CD81+ fibroblasts, C3 protein levels, neutrophil infiltration, and osteoclasts to varying degrees in the LIP model after ABT263 treatment (Figure. 6). These results further support our hypothesis that the eradication of senescent CD81+ fibroblasts could reduce neutrophil infiltration and alveolar bone resorption. 

(5) Have the authors considered using any of the available C3/C3aR inhibitors to validate the involvement of neutrophils and the inflammatory response in periodontitis? A C3/C3aR inhibitor would be an elegant treatment group in parallel with the senolytic approach.

Thank you very much for the reviewers' suggestion to investigate neutrophil infiltration and inflammatory responses after treating periodontitis with C3/C3aR inhibitors. In a clinical study by Hasturk et al. in 2021 (Reference 1), it was found that using the C3 inhibitor AMY-101 effectively alleviated gingival inflammation levels in periodontitis patients. This was reflected in significant decreases in clinical indicators such as the modified gingival index and bleeding on probing, as well as a marked reduction in inflammatory tissue destruction markers, including MMP-8 and MMP-9. In addition, Tomoki Maekawa et al. (Reference 2) demonstrated that a peptide inhibitor of complement C3 effectively reduced inflammation levels and the extent of bone resorption in periodontitis. Moreover, research by Guglietta et al. (Reference 3) clarified that the C3 complement promotes neutrophil recruitment and the formation of neutrophil extracellular traps (NETs) via C3aR. And neutrophil extracellular traps are considered key pathological factors in causing sustained chronic inflammation in periodontitis (References 4 and 5). In summary, existing studies have clearly indicated that C3/C3aR inhibitors likely reduce neutrophil recruitment and inflammation in periodontitis. 

Reference

(1) Hasturk, H., Hajishengallis, G., Forsyth Institute Center for Clinical and Translational Research staff, Lambris, J. D., Mastellos, D. C., & Yancopoulou, D. (2021). Phase IIa clinical trial of complement C3 inhibitor AMY-101 in adults with periodontal inflammation. The Journal of clinical investigation, 131(23), e152973.

(2) Maekawa, T., Briones, R. A., Resuello, R. R., Tuplano, J. V., Hajishengallis, E., Kajikawa, T., Koutsogiannaki, S., Garcia, C. A., Ricklin, D., Lambris, J. D., & Hajishengallis, G. (2016). Inhibition of pre-existing natural periodontitis in non-human primates by a locally administered peptide inhibitor of complement C3. Journal of clinical periodontology, 43(3), 238–249.

(3) Guglietta, S., Chiavelli, A., Zagato, E., Krieg, C., Gandini, S., Ravenda, P. S., Bazolli, B., Lu, B., Penna, G., & Rescigno, M. (2016). Coagulation induced by C3aR-dependent NETosis drives protumorigenic neutrophils during small intestinal tumorigenesis. Nature communications, 7, 11037.

(4) Kim, T. S., Silva, L. M., Theofilou, V. I., Greenwell-Wild, T., Li, L., Williams, D. W., Ikeuchi, T., Brenchley, L., NIDCD/NIDCR Genomics and Computational Biology Core, Bugge, T. H., Diaz, P. I., Kaplan, M. J., Carmona-Rivera, C., & Moutsopoulos, N. M. (2023). Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. The Journal of experimental medicine, 220(9), e20221751.

(5) Silva, L. M., Doyle, A. D., Greenwell-Wild, T., Dutzan, N., Tran, C. L., Abusleme, L., Juang, L. J., Leung, J., Chun, E. M., Lum, A. G., Agler, C. S., Zuazo, C. E., Sibree, M., Jani, P., Kram, V., 6 Martin, D., Moss, K., Lionakis, M. S., Castellino, F. J., Kastrup, C. J., … Moutsopoulos, N. M. (2021). Fibrin is a critical regulator of neutrophil effector function at the oral mucosal barrier. Science (New York, N.Y.), 374(6575), eabl5450.

Other comments

(1) Figure 1. The authors report upregulation of the aging pathway in bulk RNAseq analyses. What about the upregulation of senescence-related pathways and differential expression of SASP-related genes in this experiment?

Thanks for this interesting question. Through further analysis of the bulk RNA sequencing results of gingival tissues from LIP mice model, we found significant alterations in multiple senescence-associated secretory phenotype (SASP) genes and several cellular senescencerelated pathways. SASP genes, such as Icam1, Mmp3, Nos3, Igfbp7, Igfbp4, Mmp14, Timp1, Ngf, Il6, Areg, and Vegfa, were markedly upregulated in the periodontitis samples of ligature-induced mice (Figure 1-figure supplement 2A). Moreover, we observed a significant reduction in oxidative phosphorylation levels and the tricarboxylic acid (TCA) cycle in the periodontitis group, suggesting that the occurrence of cellular senescence may be related to mitochondrial dysfunction (Figure 1figure supplement 2B and C.).

Additionally, we noted the activation of the PI3K-AKT and MAPK pathways in LIP model (Figure 1-figure supplement 2D and E), both of which can induce cellular senescence by activating the tumor suppressor pathway TP53/CDKN1A, leading to cell cycle arrest (References 1, 2). Furthermore, the NF-κB signaling pathway was also significantly enriched in LIP model (Figure 1-figure supplement 2F), which is closely associated with the secretion of SASP factors (Reference 3).

In summary, our bulk RNA sequencing results suggest enrichment of cellular senescencerelated pathways in the periodontitis group, including mitochondrial metabolic dysregulation, senescence-related pathways, and alterations in the SASP. Related results were added into Page 56 of the revised manuscripts.

Reference

(1) Tang Q, Markby GR, MacNair AJ, Tang K, Tkacz M, Parys M, Phadwal K, MacRae VE, Corcoran BM. TGF-β-induced PI3K/AKT/mTOR pathway controls myofibroblast differentiation and secretory phenotype of valvular interstitial cells through the modulation of cellular senescence in a naturally occurring in vitro canine model of myxomatous mitral valve disease. Cell Prolif. 2023 Jun;56(6):e13435. doi: 10.1111/cpr.13435.

(2) Sayegh S, Fantecelle CH, Laphanuwat P, Subramanian P, Rustin MHA, Gomes DCO, Akbar AN, Chambers ES. Vitamin D3 inhibits p38 MAPK and senescence-associated inflammatory mediator secretion by senescent fibroblasts that impacts immune responses during ageing. Aging Cell. 2024 Apr;23(4):e14093.

(3) Raynard C, Ma X, Huna A, Tessier N, Massemin A, Zhu K, Flaman JM, Moulin F, Goehrig D, Medard JJ, Vindrieux D, Treilleux I, Hernandez-Vargas H, Ducreux S, Martin N, Bernard D. NF-κB-dependent secretome of senescent cells can trigger neuroendocrine transdifferentiation of breast cancer cells. Aging Cell. 2022 Jul;21(7):e13632.

(2) I wonder whether the authors could clarify how the semi quantifications for p21, p16, Masson's trichrome, C3, or MPO were done in Figures 1, 2, and 6.

Thank you very much for the reviewer's suggestion. We have added the semi-quantitative methods for p21, p16, Masson's trichrome, C3, and MPO in the Methods section. Specifically, for semi-quantification of protein expressions, the mean optical density (MOD) of positive stains for p21, p16, and C3 was measured using the ImageJ2 software (version 2.14.0, National Institutes of Health, Bethesda, MD). The number of MPO-positive cells and collagen volume fractions (stained blue) for individual sections were also measured using the ImageJ2 software. (Page 19, Line 537-541 in the revised manuscripts).  

(3) Figure 2. It is unclear whether N=6 refers to 6 mice, maxilla, or fields per group.

Thank you very much for the reviewer's question. To avoid any misunderstandings for the reviewer and readers, we have added a definition of the sample size in the description of the micro-CT analysis method. Specifically, in the micro-CT quantitative analysis, the sample size n for each group consists of 6 mice, with the average value of the BV/TV of the bilateral maxillary alveolar bone taken as one sample for statistical analysis (Page 17-18, Line 488-490 in the revised manuscripts).  

(4)  igure 4K. Please provide separated staining for p16, VIM, and CD81, and not only the Merge. It is difficult to identify the triple-positive cells. Also, the arrows are difficult to observe.

Thank you very much for the reviewer's suggestion. In the revised manuscript, we have included separated staining for p16, VIM, and CD81, and the triple-positive cells are indicated with white arrows (Figure 5-figure supplement 1). 

(5) Overall, improve the magnifications in the IF experiments and show where the magnified areas come from.

Thank you very much for the reviewer's suggestion. We have enlarged the fluorescence result images.

(6) Refer to the original datasets of the scRNAseq results in figure legends.

Thank you very much for the reviewer's suggestion. We have indicated the source of the raw single-cell sequencing data in the figure legend.

(7) Check English grammar and writing.

Thank you for the reviewer's suggestion. We checked the grammar and writing in the revised manuscript assisted by a native English speaker and AI tools like Chat-GPT.

Reviewer #2 (Recommendations For The Authors):

(1) When the authors refer to accelerated aging and/or senescence, they are doing so in comparison to what?

Thank you for the reviewer's question, which allows me to further clarify the concepts of accelerated aging and/or senescence. In sections 2.1 and Figure 1 of this manuscript, we referred to accelerated aging and/or senescence. This indicates that the gingival tissues of periodontitis patients exhibit a higher number of senescent cells and elevated levels of senescence-related markers compared to healthy gingival tissues. In the title of this manuscript, we describe CD81+ fibroblasts as a unique subpopulation with accelerated cellular senescence. This means that CD81+ fibroblasts display higher expression levels of senescence-related genes, cell cycle inhibitor p16, and SASP factors compared to other fibroblast subpopulations. To avoid any misunderstanding, we have deleted the text ‘accelerated senescence’ in the revised manuscripts. 

(2) In general, the main text does not describe the results using exact and reproducible terminology. Phrases like "X was most active", "a significant increase was observed", "the highest proportion was", and "the level of aging increased" should be supported by adding quantification details and by detailing what these comparisons are made to, to improve the reproducibility of the results.

Thank you for the reviewer's suggestion. To improve the reproducibility of the results, we have added quantification details in the results section and clarified what comparisons are being made through the whole manuscript.

(3) In some sections of the main text and figure legends, it is not entirely clear which sequencing experiments were conducted by the authors, which analyses were conducted by the authors on publicly available sequencing data, and which analyses were conducted on their mouse sequencing data.

Thank you for the valuable feedback from the reviewer. To further clarify the source of the sequencing data, we have clearly indicated the data source in both the results section and the figure legends. 

(4) In Figure 3H, the images showing SA-beta-gal staining on LPS-treated fibroblasts do not show convincingly the difference between treatments that are represented in the graph.

Thank you for the reviewer's suggestion. To further clearly show the differences between treatments, we have enlarged the partial image of SA-β-gal staining shown in Figure 2-figure supplement 2 of the revised manuscripts. 

(5) The choice of colors for Figure 4K is far from ideal as it is very difficult to tell apart red from purple channels and thus to visualize triple positive cells. A different LUT should be chosen, and separate individual channels should be shown to clearly identify triple-positive cells from others. Arrows also do not currently point at triple-positive cells.

Thank you for the reviewer's suggestion. In the revised manuscript, we have included separated staining for p16, VIM, and CD81, and the triple-positive cells are marked with white arrows shown in Figure 5-figure supplement 1 of the revised manuscripts.  

(6) The authors state that treatment with metformin "alleviated.... inflammatory cell infiltration (Figure 2C), and collagen degradation (Figure 2D) as observed through H&E and Masson staining." However, I cannot find a description of how the "relative fraction of collagen" in Figure 2Gc was calculated and how the H&E image they provide shows evidence of a reduction in inflammatory cells at that magnification.

Thank you for the reviewer's suggestion. In the revised manuscript, we have added details in the methods section regarding the calculation of the "relative fraction of collagen" (Page 19, Line 539-541). Specifically, the collagen volume fractions (stained blue) for individual sections were measured using ImageJ2 software. Additionally, we have marked the infiltrating inflammatory cells in the gingiva in the H&E images with black arrows shown in Figure 7-figure supplement 1B of the revised manuscripts.

(7) It appears that the in vivo experiment for metformin treatment was conducted with 6 animals per group, but this is not clear in the figures, main text, and methods.

Thank you for the reviewer's suggestion. In the revised manuscript, we have included the number of mice in each group for the in vivo experiments, specifying that there are 6 mice per group in the figures, main text, and methods sections.

(8) The methodology described for the bulk RNA-sequencing experiment in mice should describe the sequencing library characteristics and some reference to quality control thresholds that were implemented (mapped and aligned reads, sequencing depth and coverage, etc.).

In the bulk RNA-sequencing experiment, the sequencing library characteristics and quality control thresholds were listed as follows:

Sequencing Library Characteristics: We utilized the Illumina TruSeq RNA Library Construction Kit, generating libraries with an insert fragment length of approximately 400-500 bp.

Quality Control Standards include the following:

Alignment and Mapping Rates: The read data for all samples underwent preliminary quality control using FastQC (v0.11.9) and were aligned using HISAT2 (v2.2.1). The average mapping rate for each sample was over 90%.

Sequencing Depth and Coverage: Each sample had a sequencing depth of 30M-40M paired reads to ensure sufficient transcript coverage. Detailed alignment statistics have been provided in the supplementary materials.

Other Quality Control Measures: During the analysis, we also utilized RSeQC (v3.0.1) to evaluate the transcript coverage and GC bias of the sequencing data.

The corresponding method description and reference were added in the Page 19-20, Line 546-558 of the revised manuscripts.

(9) Patients with periodontitis are labeled as diagnosed with "chronic periodontitis". I would like to know how the authors defined this chronic state of the disease in their inclusion criteria.

Thank you very much for the reviewer’s question, which gives us the opportunity to further clarify the definition and diagnosis of chronic periodontitis. The diagnostic criteria for patients with chronic periodontitis in this study are based on the 1999 International Workshop for a Classification of Periodontal Diseases and Conditions (Reference 1). Chronic periodontitis is a type of periodontal disease distinct from aggressive periodontitis, and it is not diagnosed based on the rate of disease progression. Clinically, the diagnosis of chronic periodontitis is primarily based on clinical attachment loss (CAL) ≥ 4 mm or probing depth (PD) ≥ 5 mm as one of the criteria for diagnosis.

Reference

(1) Armitage G. C. (2000). Development of a classification system for periodontal diseases and conditions. Northwest dentistry, 79(6), 31–35.

(10) There is no detail about the age and sex of the donors for the healthy gingival fibroblast experiments. Are they some of the patients mentioned in Supplementary Table 1? Please clarify the source and number of independent primary cultures.

Thank you very much to the reviewer for allowing us to further clarify the source and number of independent primary cultures. In the cell experiments, we used gingival fibroblasts derived from gingival tissue of two healthy volunteers and two patients with periodontitis as experimental subjects. This information has been listed in the Supplementary Table 1. 

(11) Can the authors explain why their age inclusion criteria were different for the healthy and periodontitis groups according to their methods (healthy 18-50 years old: periodontitis 18-35 years old?)

Thank you very much to the reviewer for pointing this out. We noticed that there was an error in the age range indicated for the healthy and periodontitis groups in the inclusion criteria. Based on the original inclusion criteria information, we have corrected the age range of the included population. 18-65 years old individuals were included into the both healthy and periodontitis groups. (Page 14-15, Line 396-404 in the revised manuscripts)

(12) The methodology for inclusion is confusing and does not reflect the actual information of the recruited patients and samples thus analyzed. In the text, the healthy group appears to have included 8 young adult individuals and 8 middle-aged individuals. However, the list of recruited patients shows all healthy patients were in the young adult range (below 35 years of age) while all chronic periodontitis patients were middle-aged (above 50 years of age). Please clarify.

Thank you very much to the reviewer for pointing out the issues in the article. This study included 8 healthy periodontal patients and 8 patients with periodontitis (Page 14, Line 396-398 and Supplementary Table 1 in the revised manuscripts). Since periodontitis has a higher prevalence in middle-aged and elderly populations, the periodontitis samples included in this study were mostly from this demographic. In contrast, the healthy gingival samples were sourced from patients undergoing wisdom tooth extraction, which primarily involves younger individuals. Therefore, due to the limited sample size, we could not enforce strict age matching. To address this, we repeated the relevant experiments in more consistent mouse models, which confirmed the increase in senescent cells in periodontal tissues (Figure 1D in the revised manuscripts). In summary, although the clinical samples were limited, the experimental results from the mouse models still support our conclusions.

(13) The number of biological replicates for each group used in the bulk RNA-sequencing experiment is unclear. The methods state:" For those with biological duplication, we used DESeq2 [8] (version: 1.34.0) to screen differentially expressed gene sets between two biological conditions; for those without biological duplication, we used edgeR". Please clarify the number of mouse samples sequenced and the description of the groups.

Thank you very much to the reviewer for pointing out the errors in the article. In the transcriptome sequencing, we collected gingival tissues from 3 healthy mice and gingival tissues from 3 ligature-induced periodontitis mice. Therefore, we used the DESeq2 (version: 1.34.0) method to filter for differentially expressed genes. The corresponding descriptions were revised in Page 20, Line 554-555 in the revised manuscripts.

(14) Cluster group labels are misaligned in Figure 4C.

Thank you very much for the reviewer's suggestion. The cluster group labels in Figure 3C of the revised manuscripts have been aligned.

Reviewer #3 (Recommendations For The Authors):

Major Comments for the Authors:

(1) I do not find the immunohistochemical staining of p16 and p21 shown in Figures 2E and F to be particularly compelling. Especially as other stains of these markers used later in the manuscript are of higher quality (i.e. Figures 3F and G). Can this staining be improved to better reflect the quantifications in Figure 2G?

Thank you very much for the reviewer's suggestion. In the revised manuscript, we have provided more representative images in Figure 7C in the revised manuscripts to reflect the effect of metformin treatment on the number of p16-positive cells in periodontitis. In Figure 7-figure supplement 1D of the revised manuscripts, we have marked p21-positive cells with black arrows to help readers better identify the p21-positive cells. Additionally, we have also assessed the H3K9me3 marker, which is more specific, and the results similarly indicate that metformin treatment can alleviate the formation of senescent cells in periodontitis (Figure 7-figure supplement 1E of the revised manuscript).

(2) On line 140, Supplementary Figure 2C, D is quoted to show "...an increase in senescence characteristics of fibroblasts with the severity of periodontitis." This figure panel does not appear to support this statement. Please revise.

Thank you very much for pointing out the errors in the manuscript. In the revised version, we have corrected this part of the description and added that “The results showed a decline in fibroblast proportion along with increasing disease severity (Figure 2-figure supplement 1C and D)” (Page 6, Line 153-154 of the revised manuscript)

(3) I do not find the Western Blot experiment in Figure 4L to be particularly convincing. The text states that p21, p16, and CD81 increase in a context-dependent manner upon LPS stimulation, which doesn't appear to be very evident. I recommend repeating this experiment and showing both a representative blot alongside a blot density quantification where the bars have the error shown between experiments.

Thank you very much for the reviewer’s suggestion regarding this result. During subsequent repeated experiments, we found that the result was not reproducible, and we have removed the related results.

(4) The results state that metabolic profiling of senescent fibroblasts shows an increase in the biosynthesis of Linoleic acid, linolenic acid, arachidonic acid, and steroid. However, in Figure 5B only arachidonic acid and steroid biosynthesis appear to be elevated in CD81+ Fibroblasts, while Linoleic and linolenic acid appear to be decreased. Can the authors comment on this discrepancy? Moreover, in Figure 5C steroid biosynthesis is unchanged between healthy and periodontitis samples, contrary to the claimed increased trend in the results text. Please revise this section. Also, in Figures 5 B and C some of the terms are highlighted in a red or blue box. This is not discussed in the figure legend. Could the significance of this be explained or could these highlights be removed from the figure?

Thank you very much for the reviewer’s correction regarding the errors in the manuscript. In the Page 7-8, Line 186-194 of the revised manuscripts, “Pathways related to fatty acid biosynthesis, arachidonic acid metabolism, and steroid biosynthesis were significantly upregulated in CD81+ fibroblasts (Figure 4-figure supplement 1A)” was re-wrote. Moreover, we have removed the results from Figure 5C, and the highlights in Figures 5B and C of the previous manuscripts. Since the mechanism by which cellular metabolism regulates cellular senescence is not the core focus of this manuscript, we have moved the results of the metabolic analysis from the sc-RNA sequencing data to the figure supplement (Figure 4-figure supplement 1) and revised the related statements in the revised manuscript (Page 7-8, Line 186-194).

(5) The authors state that arachidonic acid can be converted to prostaglandins and leukotrienes through COXs (which are expressed in their CD81+ Fibroblasts), accentuating inflammatory responses. Have the authors profiled for the expression of prostaglandins and leukotrienes in their CD81+ Fibroblasts or between healthy and periodontitis samples? Such data would be a great inclusion in the manuscript.

Thank you very much for the reviewer’s suggestion. Our results indicated that CD81+ gingival fibroblasts expressed higher levels of PTGS1 and PTGS2 compared to other fibroblast subpopulations. These genes encode proteins that are COX-1 and COX-2, which are key enzymes in prostaglandin biosynthesis (Figure 4-figure supplement 1 of the revised manuscript). Additionally, previous studies have reported high levels of prostaglandins and leukotrienes in periodontal tissues, and these pro-inflammatory mediators contribute to tissue destruction in periodontitis (Reference 1 and 2).

Reference

(1) Van Dyke, T. E., & Serhan, C. N. (2003). Resolution of inflammation: a new paradigm for the pathogenesis of periodontal diseases. Journal of dental research, 82(2), 82–90.

(2) Hikiji, H., Takato, T., Shimizu, T., & Ishii, S. (2008). The roles of prostanoids, leukotrienes, and platelet-activating factor in bone metabolism and disease. Progress in lipid research, 47(2), 107–126.

(6) Lines 199 and 200 state "...the cellular senescence of CD81+ fibroblasts could be attributed to disturbances in lipid metabolism". While altered lipid metabolic profiles are shown in Figure 5 to correlate with senescent fibroblasts/periodontitis tissue, no evidence is shown to suggest that they are the driver or cause of fibroblast senescence. Could this sentence be amended to better reflect the conclusions that can be drawn from the data presented?

Thank you very much for the reviewer’s suggestion. We have revised the related statements and believed that “lipid metabolism might play a role in cellular senescence of the gingival fibroblasts” in the Page 7, Line 189 of the revised manuscripts.  

Minor Comments for the Authors:

(1) There are some sentences without references that I feel would warrant referencing: - Line 112 - "Metformin, an anti-aging drug has shown potential in inhibiting cell senescence in various disease models (REFERENCE)."

Thank you for the reviewer's suggestion. We have included the relevant references in the Page10, Line 267-271 of the revised manuscripts.

Reference

(1) Soukas, A. A., Hao, H., & Wu, L. (2019). Metformin as Anti-Aging Therapy: Is It for Everyone?. Trends in endocrinology and metabolism: TEM, 30(10), 745–755.

(2) Kodali, M., Attaluri, S., Madhu, L. N., Shuai, B., Upadhya, R., Gonzalez, J. J., Rao, X., & Shetty, A. K. (2021). Metformin treatment in late middle age improves cognitive function with alleviation of microglial activation and enhancement of autophagy in the hippocampus. Aging cell, 20(2), e13277.

- Line 210 - "Previous studies have demonstrated the importance of sustained neutrophil infiltration in the progression of periodontitis (REFERENCE)."

Thank you for the reviewer's suggestion. We have included the relevant references in the Page 8, Line 211-214 of the revised manuscripts.

Reference

(1) Song, J., Zhang, Y., Bai, Y., Sun, X., Lu, Y., Guo, Y., He, Y., Gao, M., Chi, X., Heng, B. C., Zhang, X., Li, W., Xu, M., Wei, Y., You, F., Zhang, X., Lu, D., & Deng, X. (2023). The Deubiquitinase OTUD1 Suppresses Secretory Neutrophil Polarization And Ameliorates Immunopathology of Periodontitis. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 10(30), e2303207.

(2) Kim, T. S., Silva, L. M., Theofilou, V. I., Greenwell-Wild, T., Li, L., Williams, D. W., Ikeuchi, T., Brenchley, L., NIDCD/NIDCR Genomics and Computational Biology Core, Bugge, T. H., Diaz, P. I., Kaplan, M. J., Carmona-Rivera, C., & Moutsopoulos, N. M. (2023). Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. The Journal of experimental medicine, 220(9), e20221751.

(3) Ando, Y., Tsukasaki, M., Huynh, N. C., Zang, S., Yan, M., Muro, R., Nakamura, K., Komagamine, M., Komatsu, N., Okamoto, K., Nakano, K., Okamura, T., Yamaguchi, A., Ishihara, K., & Takayanagi, H. (2024). The neutrophil-osteogenic cell axis promotes bone destruction in periodontitis. International journal of oral science, 16(1), 18.

(2) To improve the quality of several of the authors' claims I would recommend some further quantification of their experimental analyses. Namely:

- Figures 3 F and G

- Figures 4 I, J and K

- Figures 6 F and G

- Supplementary Figures 4 A, B, and C

Thank you for the reviewer's suggestion. We have supplemented the quantitative analysis results for some images based on the reviewer's recommendations, specifically in Figure. 2G, Figure. 3G, Figure 5-figure supplement 1A, B, Figure 5-figure supplement 2A and Figure 7figure supplement 3A-D in the revised manuscripts. 

(3) Figure 1L has missing x-axis annotation.

Thank you for the reminder from the reviewer. The X-axis label has been added in Figure 1-figure supplement 1D for the GO term annotation. 

(4) Line 117 is missing a reference for the experimental schematic shown in Figure 2A.

Thank you for the reminder from the reviewer. The experimental schematic shown in Figure 7A has been referenced in Page 10, Line 275-277.

(5) The "BV/TV ratio" and "CEJ-ABC distance" should be briefly explained in the results test (Lines 118 and 119).

Thank you for the reviewer's suggestion. We have added the explanation of "BV/TV ratio" and "CEJ-ABC distance." In Page 10-11, Line 279-281 in the revised manuscripts.

(6) Figure 2 could be improved by having some annotation for the anatomical regions shown.

Thank you for the reviewer’s valuable suggestion. We have labeled the relevant anatomical structures to enhance clarity in Figure 7 in the revised manuscripts. 

(7) The positive signal for p16 and p21 is difficult to interpret in Figure 2. Could the clarity of this be improved either by using more evident images or annotation with arrowheads indicating positive cells?

Thank you for the reviewer's suggestion. In the revised manuscript, we have provided more representative images in Figure. 7C in the revised manuscripts to reflect the effect of metformin treatment on the number of p16-positive cells in periodontitis. In Figure 7-figure supplement 1D of the revised manuscripts, we have marked p21-positive cells with black arrows to help readers better identify the p21-positive cells. Additionally, we have also assessed the H3K9me3 marker, which is more specific, and the results similarly indicate that metformin treatment can alleviate the formation of senescent cells in periodontitis (Figure 7-figure supplement 1E of the revised manuscript).

(8) Figure 2Gc, d, and e are not mentioned in the results text. Please include references to these panels at the appropriate points.

Thank you for the reminder. In the revised manuscripts, Figures 2G c, d, and e in the previous manuscripts have been mentioned in the text in the Page 11, Line 284-289 of the revised manuscript. 

(9) Scale bars are missing in Supplementary Figure 2E.

Thank you for the suggestion. The scale bar has been added in the Figure 7-figure supplement 2B in the revised manuscripts. 

(10) The order of the figure panels is not always mentioned in the order they are referred to in the text. For example, Figure 3 is presented in the order of A, B, D then C. Could this be changed to reflect the order in the results text?

Thank you for the feedback. We have renumbered the figures according to the order mentioned in the original manuscript (Page 6, Line 146-149, Figure 2 in the revised manuscripts).

(11) To improve reader clarity it would be good to briefly introduce the gene expression datasets analysed, such as GSE152042. I.e. what the experimental condition is from which it is derived.

Thank you for the suggestion. We have included a brief description of the information and sources of the samples from GSE152042 in Page 6, Line 140-142 of the revised manuscripts. 

(12) To improve reader clarity I would recommend signifying clearly in the figure if the data shown is from mouse or human samples. For example in Figure 3F and G.

Thank you for the suggestion. We have moved all the results from the mouse experiments to the figures supplement (Figure 5-figure supplement 1 and 2 in the revised manuscripts).

(13) The images shown in Figure 3H for SA-beta-Gal do not seem very convincing. Could this be improved?

Thank you for the suggestion. To further illustrate the differences in SA-beta-Gal results between the groups, we have provided images at higher magnification in the Figure 2-figure supplement 2 of the revised manuscripts.  

(14) Supplementary Figure 2E would benefit from small experimental schematics that would allow the reader to appreciate the timings of the treatment for this experiment.

Thank you for the suggestion. We have added a schematic diagram in Figure 7-figure supplement 2A of the revised manuscripts to illustrate the LPS treatment, metformin treatment, and the timing of the assessments. 

(15) Figure 4K would benefit from showing the merged image and single channels of each of the stains to better assess the degree of colocalisation.

Thank you for the suggestion. We have included each individual fluorescence channel in Figure 5-figure supplement 1C of the revised manuscripts. 

(16) The writing on the X-axis of Figure 6B is almost illegible to me, although this may just be a compression artefact. This makes the interpretation of the data quite difficult. Also, for Figures 6 B and C, the meaning of the (H) and (P) annotations should be clear on either the figure or figure legend. I surmise that they represent "Healthy" and "Periodontic" samples respectively.

Thank you for the suggestion. In the revised manuscript, we have enlarged Figure 6B in the previous manuscripts to better display the X-axis as shown in the Figure 5B of the revised manuscripts. Additionally, we have fully labeled "Healthy" and "Periodontitis" in Figure 5C of the revised manuscripts.

(17) MPO-positive cells are introduced on line 216, however, no explanation is provided for what population or state the expression of this protein marks. I surmise the authors are using it to detect Neutrophil populations. If so, could the authors briefly state this the first time it is used?

Thank you for the suggestion. In the revised manuscript, we have added an introduction to MPO. MPO, or myeloperoxidase, is considered one of the markers for neutrophils. (Page 9, Line 240-242 of the revised manuscripts)

(18) Supplementary Figure 3D does not appear to be mentioned or discussed in the results text.

Thank you for the reminder. We have referenced Supplementary Figure 3D in the previous manuscripts in Page 9, Line 240-242 shown as Figure 5-figure supplement 2C of the revised manuscript.  

(19) Figure 6E showing increased C3 expression in periodontic samples is not very convincing and differences in expression are not evident. Can the authors provide an image that more convincingly matches their quantification?

Thank you for the suggestion. In the revised manuscript, we have provided more representative images shown in Figure 5E of the revised manuscript.

(20) Figure 6I shows the expression of CD81 and SOD2 in healthy and periodontic tissue. The associated results texts (Lines 220 to 223) discuss the spatial coincidence of CD81 and MPO. Can the authors address this discrepancy in either the results text or the figure panel? Moreover, can Figure 6H and I be annotated to show the location of the gingival lamina propria to improve clarity?

Thank you for the reminder. We have revised the relevant statements in the text: "Interestingly, spatial transcriptomic analysis of gingival tissue revealed that the regions expressing CD81 and SOD2, a neutrophil marker, in periodontitis overlapped in the gingival lamina propria, showing a high spatial correlation" in Page 9, Line 223-226 of the revised manuscripts. Additionally, we have labeled the gingival lamina propria (LP) in Figure 5H of the revised manuscripts.

(21) I am confused about the purpose of Supplementary Figure 3E and what evidence it provides. Can the authors comment on this?

Thank you for the reminder. To avoid any potential misunderstanding by readers, we have deleted Supplementary Figure 3 image in the revised manuscripts

eLife Assessment

This is a valuable study showing that differentiated cells of the zebrafish skin form membrane protrusions called cytonemes that contact and likely transmit Notch signals to cells of the undifferentiated layer below. The data are convincing that cytoneme like protrusions from the periderm are required for proper periderm structure, proliferation, gene expression, and Notch signaling. Evidence that inflammatory signaling through IL-17 affects epidermal differentiation, Notch and cytoneme formation is solid, but whether these are through a single common or two parallel pathways requires further investigation.

Reviewer #1 (Public review):

Summary:

In this paper, Wang et al show that differentiated peridermal cells of the zebrafish epidermis extend cytoneme-like protrusions toward the less differentiated, intermediate layer below. They present evidence that expression of a dominant-negative cdc42, inhibits cytoneme formation and leads to elevated expression of a marker of undifferentiated keratinocytes, krtt1c19e, in the periderm layer. It is demonstrated that Delta-Notch signaling is involved in keratinocyte differentiation and that loss of cytonemes correlates with a loss of Notch signaling. Finally, changes in expression of the inflammatory cytokine IL-17 and its receptors is shown to affect cytoneme number and periderm structure in a manner similar to Notch and cdc42 perturbations.

Strengths:

Overall, the idea that differentiated cells signal to underlying undifferentiated cells via membrane protrusions in skin keratinocytes is interesting and novel, and it is clear that periderm cells send out thin membrane protrusions that contain a Notch ligand. Further, and perturbations that affect cytoneme number, Notch signaling and IL-17 expression clearly lead to changes in periderm structure and gene expression.

Weaknesses:

The mechanisms by which IL-17 affects cytoneme formation requires further investigation.

Reviewer #2 (Public review):

Summary:

The aim of the study was to understand how cells of the skin communicate across dermal layers. The research group has previously demonstrated that cellular connections called airinemes contribute to this communication. The current work builds upon this knowledge by showing that differentiated keratinocytes also use cytonemes, specialized signaling filopodia, to communicate with undifferentiated keratinocytes. They show that cytonemes are the more abundant type of cellular extension used for communication between the differentiated keratinocyte layer and the undifferentiated keratinocytes. Disruption of cytoneme formation led to expansion of the undifferentiated keratinocytes into the periderm, mimicking skin diseases like psoriasis. The authors go on to show that disruption of cytonemes results in perturbations in Notch signaling between the differentiated keratinocytes of the periderm and the underlying proliferating undifferentiated keratinocytes. Further the authors show that Interleukin-17, also known to drive psoriasis, can restrict formation of periderm cytonemes, possibly through the inhibition of Cdc42 expression. This work suggests that cytoneme mediated Notch signaling plays a central role in normal epidermal regulation. The authors propose that disruption of cytoneme function may be an underlying cause of various human skin diseases.

Strengths:

The authors provide strong evidence that periderm keratinocytes cytonemes contain the notch ligand DeltaC to promote Notch activation in the underlying intermediate layer to regulate accurate epidermal maintenance.

Weaknesses:

The impact of the study would be increased if the mechanism by which Interlukin-17 and Cdc42 collaborate to regulate cytonemes was defined. Experiments measuring Cdc42 activity, rather than just measuring expression, would strengthen the conclusions.

Comments on revisions:

The authors have sufficiently addressed my critiques from the initial round of evaluation. They have included useful representative images, clarified how they scored cytonemes and provided additional controls/experimental conditions that improve the rigor of the study. The results provided now support the key conclusions of the study.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Wang et al show that differentiated peridermal cells of the zebrafish epidermis extend cytoneme-like protrusions toward the less differentiated, intermediate layer below. They present evidence that expression of a dominant-negative cdc42, inhibits cytoneme formation and leads to elevated expression of a marker of undifferentiated keratinocytes, krtt1c19e, in the periderm layer. Data is presented suggesting the involvement of Delta-Notch signaling in keratinocyte differentiation. Finally, changes in expression of the inflammatory cytokine IL-17 and its receptors is shown to affect cytoneme number and periderm structure in a manner similar to Notch and cdc42 perturbations.

Strengths:

Overall, the idea that differentiated cells signal to underlying undifferentiated cells via membrane protrusions in skin keratinocytes is interesting and novel, and it is clear that periderm cells send out thin membrane protrusions that contain a Notch ligand. Further, perturbations that affect cytoneme number, Notch signaling, and IL-17 expression clearly lead to changes in periderm structure and gene expression.

Weaknesses:

More work is needed to determine whether the effects on keratinocyte differentiation are due to a loss of cytonemes themselves, or to broader effects of inhibiting cdc42. Moreover, more evidence is needed to support the claim that periderm cytonemes deliver Delta ligands to induce Notch signaling below. Without these aspects of the study being solidified, understanding how IL-17 affects these processes seems premature.

Reviewer #2 (Public Review):

Summary:

The aim of the study was to understand how cells of the skin communicate across dermal layers. The research group has previously demonstrated that cellular connections called airinemes contribute to this communication. The current work builds upon this knowledge by showing that differentiated keratinocytes also use cytonemes, specialized signaling filopodia, to communicate with undifferentiated keratinocytes. They show that cytonemes are the more abundant type of cellular extension used for communication between the differentiated keratinocyte layer and the undifferentiated keratinocytes. Disruption of cytoneme formation led to the expansion of the undifferentiated keratinocytes into the periderm, mimicking skin diseases like psoriasis. The authors go on to show that disruption of cytonemes results in perturbations in Notch signaling between the differentiated keratinocytes of the periderm and the underlying proliferating undifferentiated keratinocytes. Further, the authors show that Interleukin-17, also known to drive psoriasis, can restrict the formation of periderm cytonemes, possibly through the inhibition of Cdc42 expression. This work suggests that cytoneme-mediated Notch signaling plays a central role in normal epidermal regulation. The authors propose that disruption of cytoneme function may be an underlying cause of various human skin diseases.

Strengths:

The authors provide strong evidence that periderm keratinocytes cytonemes contain the notch ligand DeltaC to promote Notch activation in the underlying intermediate layer to regulate accurate epidermal maintenance.

Weaknesses:

The impact of the study would be increased if the mechanism by which Interlukin-17 and Cdc42 collaborate to regulate cytonemes was defined. Experiments measuring Cdc42 activity, rather than just measuring expression, would strengthen the conclusions.

Reviewer #3 (Public Review):

Summary:

Leveraging zebra fish as a research model, Wang et al identified "cytoneme-like structures" as a mechanism for mediating cell-cell communications among skin epidermal cells. The authors further demonstrated that the "cytoneme-like structures" can mediate Notch signaling, and the "cytoneme-like structures" are influenced by IL17 signaling.

Strengths:

Elegant zebrafish genetics, reporters, and live imaging.

Weaknesses: (minor)

This paper focused on characterizing the "cytoneme-like structures" between different layers and the NOTCH signaling. However, these "cytoneme-like structures" observed in undifferentiated KC (Figure 2B), although at a slightly lower frequency, were not interpreted. In addition, it is unclear if these "cytoneme-like structures" can mediate other signaling pathways than NOTCH.

We are currently investigating the role of cytoneme-like protrusions extended from undifferentiated keratinocytes and their role is still under investigation. We believe that addressing the function of undifferentiated keratinocyte cytonemes and exploring whether peridermal cytoneme can mediate other signaling pathways is beyond the scope of the current manuscript. However, we hope to publish our discoveries about them soon. It is worth noting that cytonemes mediate other morphogenetic signals, such as Hh, Wnt, Fgf, and TGFbeta in other contexts.

Overall, this is a solid paper with convincing data reporting the "cytoneme-like structures" in vivo, and with compelling data demonstrating the roles in NOTCH signaling and the regulation by IL17.

These findings provide a foundation for future work exploring the "cytoneme-like structures" in the mammalian system and other epithelial tissue types. This paper also suggests a potential connection between the "cytoneme-like structures" and psoriasis, which needs to be further explored in clinical samples.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points

- In general, representative images from each experiment should accompany the graphs shown. The inclusion of still frames from time-lapse imaging experiments in the main figures would help the reader understand the morphology and dynamics of these protrusions in control, cdc42, and IL-17 manipulations.

Thank you for the comments. We appreciate your suggestion to include representative images alongside the graphs to better illustrate the morphology and dynamics of these protrusions.

In response, we have made the following additions to our main figures.

Figure 3A now includes still images from time-lapse movies for both control and cdc42 manipulations.

Figure 5A and 6A,C now include still images for il17 manipulations.

- Data in Figure 3 is crucial as it demonstrates that cdc42DN selectively impairs cytoneme extensions without affecting other actin-based structures. It also shows that cdc42DN leads to upregulation of krtt1c19e in periderm. Therefore, these data should be presented in a comprehensive way. Still, frames of high mag views of time-lapse images from control and cdc42DN should be included in the figure. Similarly, a counter label (E-Cadherin, perhaps) showing the presence of all three layers and goblet cells at different focal planes capturing the different layers of the skin should be included. It is stated that the goblet cell number is unaffected, but they seem to be absent in the image shown in Figure 3B.

In this revised version, we have included magnified cross-sectional views. In addition to the images of the peridermal layer from the original version, we have now included the underlying intermediate and basal stem cell layers (Figure 3C-C”). We hope these data convincingly show that peridermal keratinocytes in cytoneme inhibited animals co-express krt4 and krtt1c19e markers, suggesting that peridermal keratinocytes are not fully differentiated.

We agree that the goblet cells in this particular image of experimental group appear largely absent, however, as we quantified many animals, the number of goblet cells was not significantly different between controls and experimental (Figure S2).

- The effects on periderm architecture upon broad cdc42 inhibition may not be directly due to a loss of cytonemes. Performing this experiment in a mosaic manner to determine if the effects are local and in the range of cytoneme protrusion would strengthen the conclusions. Adding a secondary perturbation to inhibit cytoneme formation in periderm cells would also strengthen the conclusions that defects are not related specifically to cdc42 inhibition, but cytonemes themselves.

Thank you for the suggestion. We confirmed that mosaic expression of cdc42DN in peridermal keratinocytes elicited local disorganization, and elevated krtt1c19e expression as we seen in transgenic lines. Also, the cdc42DN expressing cells exhibited significantly lower cytoneme extension frequency.

In addition, we found that like cdc42DN, rac1DN expressing keratinocytes exhibited significant decrease in cytoneme extension frequency, but rhoabDN show no effects (new Figure S3). These data suggest that cytoneme extension is regulated by cdc42 and rac1 but not rhoab. Further investigation is required however, at least these data suggest that the effects we observe is likely the loss of cytonemes not just specifically to cdc42 inhibition.

- Figure 4. The inclusion of an endogenous reporter of Notch activity, like Hes or Hey immunofluorescence, would strengthen the conclusion that the intermediate layer is Notch responsive.

Thank you for the suggestion. In this revised version, we have included immunostaining data in Figure 4D demonstrating that Her6 (the orthologous to human HES1) protein is expressed in the intermediate layer.

- It is not clear where along a differentiation trajectory Notch signaling and cytonemes are needed. What happens to the intermediate layer when Notch signaling or cdc42 is inhibited? Do the cells become more basal-like? Or failing to become periderm? Meaning - is Notch promoting the basal to intermediate fate transition, or the intermediate to periderm transition? A more comprehensive characterization of basal, intermediate, and periderm differentiation with markers selective to each layer would help define which step in the process is being altered.