Reviewer #1 (Public Review):

The work by Ohigashi and colleagues addresses the developmental and lineage relationship of a newly characterized thymus epithelial cell (TEC) progenitor subset. The authors take advantage of an elegant and powerful set of experimental approaches to demonstrate that CCL21-expressing TECs appear early in thymus organogenesis and that these cells, which are centrally located, go on to give rise to medullary (m)TECs. What makes the findings intriguing is that these CCL21-expressing mTECs are a distinct subset, which do not express RANK or AIRE, and transcriptomic and lineage tracing approaches point to these cells as potential mTEC progenitor-like cells. Of note, using in vitro and in vivo precursor-product cell transfer experiments, the authors show that this subset has a developmental potential to give rise to AIRE+ self-antigen-displaying mTECs, revealing that CCL21-expressing mTECs can give rise to distinct mTEC subsets. This functional duality provides an attractive rationale for the necessary function of mTECs, which is to attract CCR7+ thymocytes that have just undergone positive selection in the thymus cortex to enter the medulla to undergo tolerance-induction against self-antigen-displaying mTECs. Overall, the work is well supported and offers new insights into the diverse functions of the medullary compartment, and how two distinct subsets of mTECs can achieve it.

eLife assessment

This is an important study that provides new insights into the development and function of medullary thymus epithelial cells (mTEC). The authors provide compelling evidence to support their claims as to the differentiation and lineage outcomes of CCL21+ mTEC progenitors, which further our understanding of how central tolerance of T cells is enforced within the thymus.

Reviewer #2 (Public Review):

Summary:

The authors set out to discover a developmental pathway leading to functionally diverse mTEC subsets. They show that Ccl21 is expressed early during thymus ontogeny in the medullary area. Fate-mapping gives evidence for the Ccl21 positive history of Aire positive mTECs as well as of thymic tuft cells and postnatally of a certain percentage of cTECs. Therefore, the differentiation potential of Ccl21+ TECs is tested in reaggregate thymus experiments - using embryonic or postnatal Ccl21+ TECs. From these experiments, the authors conclude that at least embryonic mTECs in large part pass through a Ccl21 positive stage prior to differentiation towards an Aire expressing or tuft cell stage.

The authors are using Ccl21a as a marker for a bipotent progenitor that is detectable in the embryonic thymus and is still present at the adult stage mainly giving rise to mTECs. The choice of this marker gene is very interesting since Ccl21 expression can directly be linked to an important aspect in thymus biology: the expression of Ccl21 by cells in the thymic medulla allows trafficking of T cells into the medulla in order to undergo T cell selection.

Making use of the Ccl21 detection, the authors can nicely show that cells actively expressing Ccl21 are localized throughout the medulla at an embryonic stage but also in adult thymus tissue. This suggests, that this progenitor is not accumulating at a specific area inside the medulla. This is a new finding.

Moreover, the finding that a Ccl21+ progenitor population plays a functional role in thymocyte trafficking towards the medulla has not been described. Thus, Ccl21 expression may be used to localize a late bipotent progenitor in the thymic lobes.<br /> In addition, in Fig.8, the authors provide evidence that these progenitor cells have the potential to self-maintain as well as to differentiate in reaggregate experiments at E17 (not at 4 weeks of age). The first point is of great interest and importance since these cells in theory can be of therapeutic use.

Overall assessment:

The authors highlight a developmental pathway starting from a Ccl21-expressing TEC progenitor that contributes to a functionally diverse mTEC repertoire. This is a welcome addition to current knowledge of TEC differentiation.

Reviewer #3 (Public Review):

In this manuscript, the authors define the developmental trajectory resulting in a diverse mTEC compartment. Using a variety of approaches, including a novel CCL21-fate mapping model, data is presented to argue that embryonic CCL21-expressing thymocyte attracting mTECs naturally convert to into self-antigen displaying mTEC subsets, including Aire+ mTECs and thymic tuft cells. Perhaps somewhat surprisingly, a large fraction of cTECs were also marked for having expressed CCL21, suggesting that there exists some conversion of mTEC (progenitors) into cTEC, a developmentally interesting observation that could be followed up later. Overall, the experimental setup, writing, and conclusions, are all outstanding.

eLife assessment

This important study will provide evidence about a novel screen-triage-treat strategy for cervical cancer prevention. The trial will generate convincing evidence regarding the efficacy, effectiveness, cost-effectiveness, feasibility and acceptability in a range of geographically spread low-resource settings. The strategy should contribute to improving access to cervical cancer prevention to vulnerable women with low access to health care, and, therefore, at the highest risk of cervical cancer.

Reviewer #1 (Public Review):

Summary: A description of a modern protocol for cervical screening that likely could be used in any country of the world, based on self-sampling, extended HPV genotypinng and AI-assisted visual inspection - which is probably the best available combination today.

Strengths: Modern, optimised protocol, designed for global use. Innovative.

Weaknesses: The protocol is not clear. I could not even find how many women were going to be enrolled, the timelines of the study, the statistical methods ("comparing" is not statistics) or the power calculations.<br /> Tables 2 and 3 are too schematic - surely the authors must have an approximate idea of what the actual numbers are behind the green, red and yellow colors.<br /> Figure 1 comparing screening and vaccination is somewhat misleading. They screen 20 birth cohorts but vaccinate only 5 birth cohorts. Furthermore, the theoretical gains of screening has not really been attained in any country in practise. Modelling can be a difficult task and the commentary does not provide any detail on how to evaluate what was done. It just seems unnecessary to attack vaccination as a motivation on why screening needs to be modernised.

Reviewer #2 (Public Review):

Summary:

This manuscript describes the study protocol, structure and logic of the PAVE strategy. The PAVE study is a multicentric study to evaluate a novel cervical screen-triage-treat strategy for resource-limited settings as part of a global strategy to reduce cervical cancer burden. The PAVE strategy involves: 1) screening with self-sampled HPV testing; 2) triage of HPV-positive participants with a combination of extended genotyping and visual evaluation of the cervix assisted by deep-learning-based automated visual evaluation (AVE); and 3) treatment with thermal ablation or excision (Large Loop Excision of the Transformation Zone). The PAVE study has two phases: efficacy (2023-2024) and effectiveness (planned to begin in 2024-2025). The efficacy phase aims to refine and validate the screen-triage portion of the protocol. The effectiveness phase will examine few implementation of the PAVE strategy into clinical practice. In following phases implementation will further explored.

Strengths and weaknesses

The Pave Study develops and evaluates a novel strategy that combines HPV self-collection -that has been proven effective to increase screening coverage in different settings-, with genotyping and Automated Visual Evaluation as triage. The proposed strategy combined three key innovations to improve an important step in the cervical cancer care continuum. If the strategy is effective it will contribute to enhance cervical cancer prevention in low resource settings.

As authors mentioned, despite the existence of effective preventive technologies (e.g., HPV vaccine and HPV test) translation of the HPV prevention methods has not yet occurred in many Low-Middle-Income Countries. So, in this context, new screen-triage-treat strategies are needed and if PAVE strategy were effective, it could be a landmark for cervical cancer prevention.

The PAVE Study is a solid and important study that is aimed to be carried out in nine countries and recruit tens thousands of women. It is a study with a large and diverse sample that can provide useful information for the development of this new screen-triage-treat strategy. Another strength is the fact that the PAVE project is integrated into the screening activities placed in the selected countries that will allow to evaluate efficacy and effectiveness in real-word context.

The manuscript does not present results because its aim is to describe the study protocol, structure and logic of the PAVE strategy.

Phase 1 aims to evaluate efficacy of the strategy. Methods are well described and are consistent with the study aims.

Phase 2 aims to evaluate the implementation of the PAVE strategy in clinical practice. The inclusion of implementation evaluation in this type of studies is an important milestone in the field of cervical cancer prevention. It has been shown that many strategies that have proven to be effective in controlled studies face barriers when they are implemented in real life. In that sense, results of phase 2 are key to ensure the future implementation of the strategy.

Reviewer #3 (Public Review):

Summary: Despite being preventable and treatable, cervical cancer remains the second most common cause of cancer death globally. This cancer, and associated deaths, occur overwhelmingly in low- and middle-income countries (LMIC), reflecting a lack of access to vaccination, screening and treatment services. Cervical screening is the second pillar in the WHO strategy to eliminate cervical cancer as a public health problem and will be critical in delivering early gains in cervical cancer prevention as the impact of vaccination will not be realized for several decades. However, screening strategies implemented in high income countries are not feasible or affordable in LMICs. This ambitious multi-center study aims to address these issues by developing and systematically evaluating a novel approach to cervical screening. The approach, based on primary screening with self-collected specimens for HPV testing, is focused on optimizing triage of people in whom HPV is detected, so that sensitivity for the detection of pre-cancer and cancer is maximized while treatment of people without pre-cancer or cancer is minimized.

Strengths:

The triage proposed for this study builds on the authors' previously published work in designing the ScreenFire test to appropriately group the 13 detected genotypes into four channels and to develop automated visual evaluation (AVE) of images of the cervix, taken by health workers.

The move from mobile telephone devices to a dedicated device to acquire and evaluate images, overcomes challenges previously encountered whereby updates of mobile phone models required retraining of the AVE algorithm.

The separation of the study into two phases, an efficacy phase in which screen positive people will be triaged and treated according to local standard of care and the performance of AVE will be evaluated against biopsy outcomes will be followed by the second phase in which the effectiveness, cost-effectiveness, feasibility and acceptability will be evaluated.

The setting in a range of low resource settings which are geographically well spread and reflective of where the global cancer burden is highest.

Weaknesses:

Potential ascertainment bias due to the lack of specified biopsy (such as small four quadrant biopsies or small biopsies across the transformation zone) when aceto-white areas are not identified. This has the potential lead to lead to an over-estimate of sensitivity of the triage approach, particularly in the setting of VIA as compared to colposcopy. While the authors specify endocervical sampling in this setting, using curette or brush (for cytology), this may not be as sensitive unless clinicians are experienced in endocervical curette procedures.

eLife assessment

This study presents useful findings regarding the role of formin-like 2 in mouse oocyte meiosis. The submitted data are supported by incomplete analyses, and in some cases, the conclusions are overstated. If these concerns are addressed, this paper would be of interest to reproductive biologists.

Reviewer #1 (Public Review):

Summary:<br /> The presented study focuses on the role of formin-like 2 (FMNL2) in oocyte meiosis. The authors assessed FMNL2 expression and localization in different meiotic stages and subsequently, by using siRNA, investigated the role of FMNL2 in spindle migration, polar body extrusion, and distribution of mitochondria and endoplasmic reticulum (ER) in mouse oocytes.

Strengths:<br /> Novelty in assessing the role of formin-like 2 in oocyte meiosis.

Weaknesses:<br /> Methods are not properly described.<br /> Overstating presented data.<br /> It is not clear what statistical tests were used.

My main concern is that there are missing important details of how particular experiments and analyses were done. The material and methods section is not written in the way that presented experiments could be repeated - it is missing basic information (e.g., used mouse strain, timepoints of oocytes harvest for particular experiments, used culture media, image acquisition parameters, etc.). Some of the presented data are overstated and incorrectly interpreted. It is not clear to me how the analysis of ER and mitochondria distribution was done, which is an important part of the presented data interpretation. I'm also missing important information about the timing of particular stages of assessed oocytes because the localization of both ER and mitochondria differs at different stages of oocyte meiosis. The data interpretation needs to be justified by proper analysis based on valid parameters, as there is considerable variability in the ER and mitochondria structure and localization across oocytes based on their overall quality and stage.

Reviewer #2 (Public Review):

Summary:<br /> This research involves conducting experiments to determine the role of Fmnl2 during oocyte meiosis I.

Strengths:<br /> Identifying the role of Fmnl2 during oocyte meiosis I is significant.

Weaknesses:<br /> The quantitative analysis and the used approach to perturb FMNL2 function are currently incomplete and would benefit from more confirmatory approaches and rigorous analysis.

1- Most of the results are expected. The new finding here is that FMNL2 regulates cytoplasmic F-actin in mouse oocytes, which is also expected given the role of FMNL2 in other cell types. Given that FMNL2 regulates cytoplasmic F-actin, it is very expected to see all the observed phenotypes. It is already established that F-actin is required for spindle migration to the oocyte cortex, extruding a small polar body and normal organelle distribution and functions.

2-The authors used Fmnl2 cRNA to rescue the effect of siRNA-mediated knockdown of Fmnl2. It is not clear how this works. It is expected that the siRNA will also target the exogenous cRNA construct (which should have the same sequence as endogenous Fmnl2) especially when both of them were injected at the same time. Is this construct mutated to be resistant to the siRNA?

3-The authors used only one approach to knockdown FMNL2 which is by siRNA. Using an additional approach to inhibit FMNL2 would be beneficial to confirm that the effect of siRNA-mediated knockdown of FMNL2 is specific.

Reviewer #3 (Public Review):

Summary:<br /> The authors focus on the role of formin-like protein 2 in the mouse oocyte, which could play an important role in actin filament dynamics. The cytoskeleton is known to influence a number of cellular processes from transcription to cytokinesis. The results show that downregulation of FMNL2 affects spindle migration with resulting abnormalities in cytokinesis in oocyte meiosis I.

Weaknesses:<br /> The overall description of methods and figures is overall dismissively poor. The description of the sample types and number of replicate experiments is impossible to interpret throughout, and the quantitative analysis methods are not adequately described. The number of data points presented is unconvincing and unlikely to support the conclusions. On the basis of the data presented, the conclusions appear to be preliminary, overstated, and therefore unconvincing.

eLife assessment

Both reviewers positively received the manuscript, in general. The agreement was that the manuscript presented valuable findings, using solid techniques and approaches, that shed additional light into how the canine distemper virus hemagglutinin might engage cellular receptors and how that engagement impacts host tropism. While both reviewers appreciated the X-ray crystallographic data, they also felt that the AFM experiments could have been performed at a higher standard and that the interpretation of the results ensuing from those AFM experiments could have been explained more thoroughly and in simpler terms. An additional missed opportunity of the current manuscript is the lack of comparison of the crystal structure to that of the already published cryo-EM structure, for context.

Reviewer #1 (Public Review):

Summary:<br /> Fukuhara, Maenaka, and colleagues report a crystal structure of the canine distemper virus (CDV) attachment hemagglutinin protein globular domain. The structure shows a dimeric organization of the viral protein and describes the detailed amino-acid side chain interactions between the two protomers. The authors also use their best judgement to comment on predicted sites for the two cellular receptors - Nectin-4 and SLAM - and thus speculate on the CDV host tropism. A complementary AFM study suggests a breathing movement at the hemagglutinin dimer interface.

Strengths:<br /> The study of CDV and related Paramyxoviruses is significant for human/animal health and is very timely. The crystallographic data seem to be of good quality.

Weaknesses:<br /> While the recent CDV hemagglutinin cryo-EM structure is mentioned, it is not compared to the present crystal structure, and thus the context of the present study is poorly justified. Additionally, the results of the AFM experiment are not unexpected. Indeed, other paramyxoviral RBP/G proteins also show movement at the protomer interface.

Reviewer #2 (Public Review):

Summary:<br /> The authors solved the crystal structure of CDV H-protein head domain at 3,2 A resolution to better understand the detailed mechanism of membrane fusion triggering. The structure clearly showed that the orientation of the H monomers in the homodimer was similar to that of measles virus H and different from other paramyxoviruses. The authors used the available co-crystal strictures of the closely related measles virus H structures with the SLAM and Nectin4 receptors to map the receptor binding site on CDV H. The authors also confirmed which N-linked sites were glycosylated in the CDV H protein and showed that both wildtype and vaccine strains of CDV H have the same glycosylation pattern. The authors documented that the glycans cover a vast majority of the H surface while leaving the receptor binding site exposed, which may in part explain the long-term success of measles virus and CDV vaccines. Finally, the authors used HS-AFM to visualize the real-time dynamic characteristics of CDV-H under physiological conditions. This analysis indicated that homodimers may dissociate into monomers, which has implications for the model of fusion triggering.

The structural data and analysis were thorough and well-presented. However, the HS-AFM data, while very exciting, was not presented in a manner that could be easily grasped by readers of this manuscript. I have some suggestions for improvement.

1) The authors claim their structure is very similar to the recently published croy-EM structure of CDV H. Can the authors provide us with a quantitative assessment of this statement?

2) The results for the HS-AFM are difficult to follow and it is not clear how the authors came to their conclusions. Can the authors better explain this data and justify their conclusions based on it?

3) The fusion triggering model in Figure 8 is ambiguous as to when H-F interactions are occurring and when they may be disrupted. The authors should clarify this point in their model.

eLife assessment

This important work employed global proteomic and phosphorylation site analysis to examine adipose tissue and skeletal muscle samples collected at baseline from a sample of 10 women, including those with and without PCOS, both before and after 5 weeks of electrical stimulation treatment. This work significantly enhances our knowledge by demonstrating that women with PCOS who exhibit protein hyperandrogenicity have elevated extramyocellular lipid levels and a decreased number of oxidative insulin-sensitive type I muscle fibers. The convincing evidence supporting these conclusions makes this research of broad interest not only to scientists but also to clinicians.

Reviewer #1 (Public Review):

In the manuscript, the authors tried to explore the molecular alterations of adipose tissue and skeletal muscle in PCOS by global proteomic and phosphorylation site analysis. In the study, the samples are valuable, while there are no repeats for MS and there are no functional studies for the indicted proteins, phosphorylation sites. The authors achieved their aims to some extent, but not enough.

Reviewer #2 (Public Review):

This study provides the proteomic and phosphoproteomics data for our understanding of the molecular alterations in adipose tissue and skeletal muscle from women with PCOS. This work is useful for understanding of the characteristics of PCOS, as it may provide potential targets and strategies for the future treatment of PCOS. While the manuscript presents interesting findings on omics and phenotypic research, the lack of in-depth mechanistic exploration limits its potential impact.

The study primarily presents findings from omics and phenotypic research, but fails to provide a thorough investigation into the underlying mechanisms driving the observed results. Without a thorough elucidation of the mechanistic underpinnings, the significance and novelty of the study are compromised.

eLife assessment

This study presents a potentially valuable discovery which indicates that activation of the P2RX7 pathway can reduce the lung fibrosis after its establishment by inflammatory damage. If confirmed, the study could clarify the role of specific immune networks in the establishment and progression of lung fibrosis. However, the presented data and analyses are incomplete as they primarily rely on limited pharmacological treatments with modest effect sizes.

Reviewer #1 (Public Review):

In this revised preprint the authors investigate whether a presumably allosteric P2RX7 activating compound that they previously discovered reduces fibrosis in a bleomycin mouse model. They chose this particular model as publicly available mRNA data indicate that the P2RX7 pathway is downregulated in idiopathic pulmonary fibrosis patients compared to control individuals. In their revised manuscript, the authors use three proxies of lung damage, Ashcroft score, collagen fibers, and CD140a+ cells, to assess lung damage following the administration of bleomycin. These metrics are significantly reduced on HEI3090 treatment. Additional data implicate specific immune cell infiltrates and cytokines, namely inflammatory macrophages and damped release of IL-17A, as potential mechanistic links between their compound and reduced fibrosis. Finally, the researchers transplant splenocytes from WT, NLRP3-KO, and IL-18-KO mice into animals lacking the P2RX7 receptor to specifically ascertain how the transplanted splenocytes, which are WT for P2RX7 receptor, respond to HEI3090 (a P2RX7 agonist). Based on these results, the authors conclude that HEI3090 enhanced IL-18 production through the P2RX7-NLRP3 inflammasome axis to dampen fibrosis.

These findings could be interesting to the field, as there are conflicting results as to whether NLRP3 activation contributes to fibrosis and if so, at what stage(s) (e.g., acute damage phase versus progression). The revised manuscript is more convincing in that three orthogonal metrics for lung damage were quantified. However, major weaknesses of the study still include inconsistent and small effect sizes of HEI3090 treatment versus either batch effects from transplanted splenocytes or the effects of different genetic backgrounds. Moreover, the fundamental assumption that HEI3090 acts specifically and functionally through the P2RX7 pathway in this model cannot be directly tested, as the authors now provide results indicating that P2RX7 knockout mice do not establish lung fibrosis on bleomycin treatment.

In order to provide clear evidence that HEI3090 functions through P2RX7, a different lung fibrosis model that does not require P2RX7 would be necessary. For example, in such a system the authors could demonstrate a lack of HEI3090-mediated therapeutic effect on P2RX7 knockout. Molecularly, additional evidence on specificity, such as thermal proteome profiling and direct biophysical binding experiments, would also enhance the authors' argument that the compound indeed binds P2RX7 directly and specifically. Since all small molecules have some degree of promiscuity, the absence of an additional P2RX7 modulator, or direct recombinant IL-18 administration (as suggested by another reviewer), is needed to orthogonally validate the functional importance of this pathway. Another way the authors could probe pathway specificity would involve co-administering α-IL-18 with HEI3090 in several key experiments (similar to Figure 4L).

Reviewer #2 (Public Review):

In the study by Hreich et al, the potency of P2RX7-specific positive modulator HEI3090, developed by the authors, for the treatment of Idiopathic pulmonary fibrosis (IPF) was investigated. Recently, the authors have shown that HEI3090 can protect against lung cancer by stimulating dendritic cell P2RX7, resulting in IL-18 production that stimulates IFN-γ production by T and NK cells (DOI: 10.1038/s41467-021-20912-2). Interestingly, HEI3090 increases IL-18 levels only in the presence of high eATP. Since the treatment options for IPF are limited, new therapeutic strategies and targets are needed. The authors first show that P2RX7/IL-18/IFNG axis is downregulated in patients with IPF. Next, they used a bleomycin-induced lung fibrosis mouse model to show that the use of a positive modulator of P2RX7 leads to the activation of the P2RX7/IL-18 axis in immune cells that limits lung fibrosis onset or progression. Mechanistically, treatment with HEI3090 enhanced IL-18-dependent IFN-γ production by lung T cells leading to a decreased production of IL-17 and TGFβ, major drivers of IPF. The major novelty is the use of the small molecule HEI3090 to stimulate the immune system to limit lung fibrosis progression by targeting the P2RX7, which could be potentially combined with current therapies available. Overall, the study was well performed and the manuscript is clear. However, there is need for more details on the description and interpretation of the adoptive transfer experiments, as well as the statistical analyses and number of replicate independent experiments.

eLife assessment

The manuscript provides interesting evidence that miR-199b-5p regulates osteoarthritis and as such it may be considered as a potential therapeutic target. This finding may be useful to further advance the field. Although the study is considered potentially clinically relevant, the evidence provided was deemed insufficient and incomplete to support the conclusions drawn by the authors.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors observed that miR-199b-5p is elevated in osteoarthritis (OA) patients. They also found that overexpression of miR-199b-5p induced OA-like pathological changes in normal mice and inhibiting miR-199b-5p alleviated symptoms in knee OA mice. They concluded that miR-199b-5p is not only a potential micro-target for knee OA but also provides a potential strategy for the future identification of new molecular drugs.

Strengths:

The data are generated from both human patients and animal models.

Weaknesses:

The data presented in this manuscript is not solid enough to support their conclusions. There are several questions that need to be addressed to improve the quality of this study.

The following questions that need to be addressed to improve the quality of the study.

1. Exosomes were characterized by electron microscopy and western blot analysis (for CD9, 264 CD63, and CD81). However, figure S1 only showed two sample WB results and there is no positive and negative control as well as the confused not clear WB figure.

2. The sequencing of miRNAs in serum exosomes showed that 88 miRNAs were upregulated and 89 miRNAs were downregulated in KOA patients compared with the control group based on fold change > 1.5 and p < 0.05. Figure 2 legend did not clearly elucidate what those represent and why the authors chose those five miRNAs to further validate although they did mention it with several words in line 108 'based on the p-value and exosomal'.

3. In Figure 3 legend and methods, the authors did not mention how they performed the cell viability assay. What cell had been used? How long were they treated and all the details? Other figure legends have the same problem without detailed information.

4. The authors claimed that Gcnt2 and Fzd6 are two target genes of miR-199b-5p. However, there is no convincing evidence such as western blot to support their bioinformatics prediction.

5. To verify the binding site on 3'UTR of two potential targets, the authors designed a mouse sequence for luciferase assay, but not sure if it is the same when using a human sequence.

Reviewer #2 (Public Review):

Summary:

The authors identified miR-199b-5p as a potential OA target gene using serum exosomal small RNA-seq from human healthy and OA patients. Their RNA-seq results were further compared with publicly available datasets to validate their finding of miR-199b-5p. In vitro chondrocyte culture with miR-199b-5p mimic/inhibitor and in vivo animal models were used to evaluate the function of miR-199b-5p in OA. The possible genes that were potentially regulated by miR-199b-5p were also predicted (i.e., Fzd6 and Gcnt2) and then validated by using Luciferase assays.

Strengths:

1. Strong in vivo animal models including pain tests.<br /> 2. Validates the binding of miR-199b-5p with Fzd6 and binding of miR-199b-5p with Gcnt2.

Weaknesses:

1. The authors may overinterpret their results. The current work shows the possible bindings between miR-199b-5p and Fzd6 as well as bindings between miR-199b-5p and Gcnt2. However, whether miR-199b-5p truly functions through Fzd6 and/or Gcnt2 requires genetic knockdown of Fzd6 and Gcnt2 in the presence of miR-199b-5p.<br /> 2. In vitro chondrocyte experiments were conducted in a 2D manner, which led to chondrocyte de-differentiation and thus may not represent the chondrocyte response to the treatments.<br /> 3. There is a lack of description for bioinformatic analysis.<br /> 4. There are several errors in figure labeling.

Author Response

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

Response to Editor and Reviewers

Terzioglu et al, Mitochondrial temperature homeostasis resists external metabolic stresses

Editor:

We greatly appreciate the specific direction of the editors in guiding us as to what experiments are needed to strengthen the manuscript for publication. We here summarize how we have handled this advice (please refer to response to specific reviewer points, below, for the details). Changes to the text are indicated by red text and marginal red boxes numbered as per the responses below.

Benchmarking: we now include a direct calibration of MTY against temperature. Performing experiments on temperature probes localized to different subcellular and submitochondrial compartments would be interesting and potentially informative, but is a whole new study that would require a great deal of validation. Hopefully it will be implemented, but it would not change the basic conclusions from the current study.

Probe localization: In addition to referring to previously published literature, and the existing Figures 3B, 4 and S4 indicating that both MTY and mito-gTEMP are localized in mitochondria (the latter in the matrix), we have conducted some simple experiments to determine the intramitochondrial localization of MTY, applying standard subfractionation protocols. The findings confirm our previous assumption that MTY is inner membrane-associated.

Expected outcomes: Since, in most cases, it is not possible to do this simultaneously with fluorescence measurements, we rely mostly on previous literature which is fully cited, or on measurements conducted in parallel (e.g. respirometry, Fig. S5) or previously in our own laboratories (e.g. flow cytometry on TMRM-stained cells). We accept that specific inferences on causality, e.g. that the effect of anisomycin is mediated by decreased ATP usage, or that the effects of Gal medium are to enforce dependence on OXPHOS, are arguably an over-reach. We have therefore toned down these statements so as to focus on the mt temperature response to the treatments, rather than to the imputed downstream physiological effects thereof.

Confounding factors: We tested (and excluded) possible confounding factors affecting MTY and report the findings in an expanded supplementary figure.

Discussion of the model(s) proposed by Matta: We have now included this, as far as we considered appropriate for the eLife readership. However, not being theoretical physicists, we would greatly welcome a careful scrutiny of what we have written, by both the reviewer and handling editor.

Reviewer #1:

A1. Causality: We agree with the reviewer in that we cannot formally distinguish, in this study, whether metabolism is adjusted to maintain mitochondrial temperature, or whether mitochondrial temperature maintenance is a secondary consequence of metabolic changes induced by stress. We have added a note to the Discussion to this effect. On balance, we would argue that the many cases that we have documented here tend to favour the former assertion, although this does not constitute proof. Identification of a sensor of mitochondrial temperature changes and an associated signal transduction machinery to orchestrate responses to it would be needed to settle this, but we are obviously very far from this at present. We have added this point to the Discussion, as well.

A2. Metabolic correlates: We concede that the reviewer has a valid point, although exploring its ramifications in detail is not straightforward. The effects of AOX on respiration and resistance to OXPHOS inhibitors are documented previously and are also included in the paper as a check (Fig. S5). Our starting assumptions were that cells grown in low glucose/galactose would depend more upon mitochondrial as opposed to glycolytic ATP production, whilst net ATP production in anisomycin-treated cells should be attenuated, due to decreased ATP demand. Nevertheless, there are a number of ways this could be achieved, especially if our suggestion that altered ATP production is balanced by decreased or increased futile ATP turnover geared to maintenance of mitochondrial temperature. For example, measuring total oxygen consumption, P to O ratio or steady-state levels of ATP (or any other metabolite) would not be definitive. To accommodate the reviewer’s point, we have made clear that the various treatments we applied are predicted to alter metabolism in the specified ways, based upon theoretical arguments and previous data. To establish the exact details of the metabolic changes that accompany these treatments would require tracer-based metabolomics over time (see Jang 2018, 10.1016/j.cell.2018.03.055), followed up by measurements of specified enzyme activities. Whilst this would be very useful data that may illuminate our observations, it is obviously beyond the scope of the present paper. We hope that future studies will eventually unravel the relationship between metabolic adaptation and mitochondrial temperature.

A3. Combinations of inhibitors: We were (and remain) reluctant to cram the paper too full of unsubstantiated speculations. Most, though not all, of the combinations of OXPHOS inhibitors that failed to give a stable reading of MTY fluorescence involved oligomycin plus an inhibitor of respiration. Since we already know that a complete loss of membrane potential leads to leakage of the dye, we surmise that this is the most likely reason for the fluorescence instability. In the presence of oligomycin alone, the minimal respiratory electron flow sustained should suffice to maintain a membrane potential if balanced against proton leakage. Conversely, even when respiration is inhibited, ATP synthase alone should be able to generate a membrane potential. However, the membrane potential may collapse when both oligomycin and a respiratory chain inhibitor are simultaneously applied. We expanded our comment on this issue in the Discussion and referred to it, briefly, in the legend of Fig. S3A.

A4. Figure 4A: We added the panel indicators to the figure.

A5. Fig.7C: We have tried to tighten up the wording, for clarity. Yes, the blue trace was the relevant data, but we were comparing the effect of rotenone on cells treated with anisomycin for 1, 2….18 hours with cells not treated with anisomycin at all (i.e. blue trace, zero h time-point).

A6. Meaning of ‘control iMEFS’ (Fig. 7C): We meant iMEFs not expressing AOX. We have made the statement more precise, accordingly.

A7. Supplementary Movie S1: The movie was sent, to accompany the submission. If it is not accessible for review, please contact the handling editor.

Reviewer #2:

B1. Theoretical considerations (‘mitochondrial paradox’): Since we are not theoretical physicists, we have deferred to the reviewer’s expertise in these matters and quoted the suggested literature as succinctly as possible for the largely biological audience of eLife, sticking closely to the reviewer’s own words. In this light, we would invite the reviewer to scrutinize our added text (in a short additional section of the Discussion, for both this and point B3, below), and suggest any rewording that they consider appropriate.

B2. Biological implications: We appreciate the point, but since the Discussion section is already long, we have just referred the reader to the treatment of Fahimi et al. We hope to expand on these issues in a separate paper, to be published elsewhere.

B3. Theoretical considerations (Landauer’s principle and ATP synthase electrostatics): Once again, we have mentioned the issue as suggested, but would ask the reviewer to check the exact language we have used and propose any amendments they consider necessary.

Reviewer #3:

C1. Benchmark comparisons: We acknowledge that there are limitations to the use of each method of mitochondrial temperature assessment, and we now explain them more thoroughly in a new section of the Discussion. However, the fact that the two methods give approximately the same result constitutes a crucial validation. In addition, we verified the temperature-responsiveness of MTY fluorescence in free solution at physiological pH (see new supplementary figure panel, Fig. S2D), showing that the response is almost linear over the temperature range inferred in the experiments (35-65 ºC). Note, however, that the response curve generated cannot be used directly for calibration, due to the unknown contributions in vivo from cellular autofluorescence and quenching under OXPHOS-inhibited conditions, which may modify the signal, and will vary according to the amount of dye taken up in a given experiment. Because of this, the internal calibration used in each experiment is a far more reliable way of relating observed fluorescence changes to temperature. Note, however, that if the slight deviation from linearity seen at higher temperatures in the MTY fluorescence temperature-response curve (dotted line in Fig. S2D) reflects how the dye responds in vivo, MTY-based estimations of mitochondrial temperature may be over-estimated by ~2 ºC. This is now made clear in the text.

C2. Basal temperature: The basal mitochondrial temperature (no inhibitors) as inferred from the mitogTEMP calibration curve was already in the paper (zero time points for iMEF(P) and iMEF(AOX) cells, Fig. 7A, 7B.

C3. Other organelles: In principle, gTEMP could be targeted to other organelles, such as the nucleus, peroxisomes, ER, plasma membrane and so on, which would be highly informative in profiling intracellular temperature heterogeneities. However, this would require further rounds of recloning and expression, followed in each case by verification of intracellular targeting; obviously quite a large study beyond the scope of our present work. In any case, it would now best be undertaken using the improved, next-generation ratiometric probes (B-gTEMP), which is under way. We agree that this is an important question for future experimentation and have added a short extra section to the Discussion, accordingly.

C4. Variation with external temperature: We implemented additional experiments to test this, subjecting cells to a mild heat- or cold-shock, and tracking MTY fluorescence both before and after the subsequent addition of oligomycin, with final internal calibration as before. The results were again qualitatively reproducible, but suggested that the combination of external temperature shock and bioenergetic stress. We show the details of the results of these experiments here, for the reviewer and others to inspect and consider. However, since they are not straightforwardly interpretable, we feel that they should be reserved for a future study which investigates the effects of external temperature changes on intramitochondrial temperature and bioenergetics in much greater detail. For these reasons we show the data here only, and not in the revised paper.

Both cold shock (38→32 ºC) and heat shock (38→41 ºC) produced immediate shifts of mt temperature, but by lesser amounts than the external stresses applied, i.e. a cooling of 2-4 ºC in the first case and a warming of 0-2 ºC in the second. Over the following 10 min the mt temperature of the temperature-shocked cells held steady or drifted only slightly. These observations are broadly consistent with the general conclusions of the paper that mitochondrial temperature resists external stresses. However, the effect of then adding oligomycin was intriguingly different from that seen in control cells. In cold-shocked cells the mt temperature shift produced by oligomycin was several degrees less than in control cells and mitochondrial temperature then gradually readjusted upwards to near the starting value, suggesting the induction of thermogenic pathways to compensate for the decreased external temperature. In heat-shocked cells, the response to oligomycin was reproducibly triphasic: the initial cooling effect was less pronounced than in control cells, but was followed by rewarming and then by a prolonged and progressive cooling. This is obviously much harder to interpret, and will require substantial further studies to parse.

C5. Other factors: Although this point is addressed in previous literature, we measured effects directly in solution (for MTY). Note, however, that it is not feasible to measure membrane potential simultaneously, due to the spectral overlap between e.g. TMRM and MTY. Nevertheless we were able to test the effects on MTY fluorescence of incremental changes in Ca2+, pH and ROS within the physiological range (see doi: 10.1073/pnas.95.12.6803, doi: 10.1074/jbc.M610491200 and doi: 10.3390/antiox10050731). The results clearly indicate that changes in any of these parameters has no effect on MTY fluorescence (new supplementary figure panels S3E, S3F and S3G).

C6. Localization of probes: The existing Figures 3B, 4 and S4, as well as previous literature, indicate a mitochondrial localization both for MTY and mito-gTEMP. The matrix localization of proteins of the GFP reporter family tagged with the COX8 matrix-directed targeting signal used here is well established (e.g. see doi: 10.1016/S0076-6879(09)05016-2). To investigate the sub-mitochondrial localization of MTY we conducted a standard series of fractionation steps, using detergents, centrifugation and sonication. Whilst these do not provide absolute purity, they clearly indicate that MTY in energized mitochondria resides in or closely associated with the inner mitochondrial membrane. In two trials, in which mitochondria were fractionated into mitoplasts versus outer membrane/inter-membrane space fractions, an average 92% of the MTY fluorescence was retained in the mitoplast fraction (after subtracting autofluorescence from control samples not treated with MTY). After sonication, which should render most of the inner membrane pelletable as ‘inside out’ submitochondrial particles (SMPs), leaving most of the matrix contents in solution, 90% of the MTY fluorescence signal (again based on two trials, with background subtracted) was recovered in the SMP fraction, supporting the proposition that the dye is inner-membrane associated. These findings are now reported in the Results section and commented on in the appropriate section of the Discussion. We agree with the reviewer that it would be useful to target temperature probes, e.g. B-gTEMP, to specific sub- and extra-mitochondrial compartments (cytosol, MAMs, outer membrane, IMS, inner membrane or even specific protein complexes therein), so as to gauge the nature of intramitochondrial heat conduction between compartments and its radiation to the extramitochondrial environment. However, because it would be an extensive study in its own right, requiring careful validation of targeting, we feel this should be attempted as a follow-up study.

C7. Use of probes in isolated mitochondria: In principle we see no reason why this should not work, but any result would be non-physiological, since the external environment of isolated mitochondria is not the complex protein- and organelle-rich environment of the cytoplasm, which must play a crucial role in modulating heat diffusion from the organelle. Such an experiment may be useful to assess how much temperature buffering is provided by the rest of the cytoplasm, even though it does not directly address the internal temperature of mitochondria in vivo. Accordingly, we added a sentence to the Discussion foreshadowing such an experiment.

C8. Other probes and methods: See points C1 and C3 above. The reviewer’s suggestion could best be addressed using the superior B-gTEMP reporters engineered for specific expression in the nucleus and cytosol. This would be part of an extensive new study beyond the scope of the present work, but would of course be a further validation of its conclusions. We agree that multiple approaches are needed to address the issue of temperature differences within cells, in light of the surprising findings both of ourselves and of others, such as the study of Okabe et al (2012) to which the reviewer refers. This point too is now added to the Discussion.

C9. Theoretical considerations: The critiques referred to are now briefly addressed in the revised Discussion, along with those raised by Reviewer 2. However, since we are not theoretical physicists we do not feel qualified to enter the debate further. As Baffou and colleagues point out, in https://doi.org/10.1038/nmeth.3552, “In order for the community to come to a consensus, we believe some effort will be required to identify the actual origin of the signal measured in these studies, both theoretically and experimentally“. Our experimental findings provide source data for this debate but do not resolve it.

Author Response

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

eLife assessment

This study reports important findings regarding the systemic function of hemocytes controlling whole-body responses to oxidative stress. The evidence in support of the requirement for hemocytes in oxidative stress responses as well as the hemocyte single-nuclei analyses in the presence or absence of oxidative stress are convincing. In contrast, the genetic and physiological analyses that link the non-canonical DDR pathway to upd3/JNK expression and high susceptibility, and the inferences regarding the function of hemocytes in systemic metabolic control are incomplete and would benefit from more rigorous approaches. The work will be of interest to cell and developmental biologists working on animal metabolism, immunity, or stress responses.

We would like to thank the editorial team for these positive comments on our manuscript and the constructive suggestions to improve our manuscript. We are now happy to send you our revised manuscript, which we improved according to the suggestions and valuable comments of the referees.

Public Reviews:

Reviewer #1 (Public Review):

The study examines how hemocytes control whole-body responses to oxidative stress. Using single cell sequencing they identify several transcriptionally distinct populations of hemocytes, including one subset that show altered immune and stress gene expression. They also find that knockdown of DNA Damage Response (DDR) genes in hemocytes increases expression of the immune cytokine, upd3, and that both upd3 overexpression in hemocytes and hemocyte knockdown of DDR genes leads to increased lethality upon oxidative stress.

Strengths

1. The single cell analyses provide a clear description of how oxidative stress can cause distinct transcriptional changes in different populations of hemocytes. These results add to the emerging them in the field that there functionally different subpopulations of hemocytes that can control organismal responses to stress.

2. The discovery that DDR genes are required upon oxidative stress to limit cytokine production and lethality provides interesting new insight into the DDR may play non-canonical roles in controlling organismal responses to stress.

We are grateful to referee 1 to point out the importance and novelty of our snRNA-seq data and our findings on the role of DNA damage-modulated cytokine release by hemocytes during oxidative stress. We further extended these analyses in the revised manuscript by looking deeper into the transcriptomic alterations in fat body cells upon oxidative stress (Figure 4, Figure S4). We further provide additional data to support the connection of DNA damage signaling and regulation of upd3 release from hemocytes (Figure 6F). Here we show that upd3-deficiency can abrogate the increased susceptibility of flies with mei41 and tefu knockdown in hemocytes. In line with this finding, we also show that upd3null mutants show a reduced but not abolished susceptibility to oxidative stress overall (Figure 6F), underlining the role of upd3 as a mediator of oxidative stress response.

Weaknesses

1. In some ways the authors interpretation of the data - as indicated, for example, in the title, summary and model figure - don't quite match their data. From the title and model figure, it seems that the authors suggest that the DDR pathway induces JNK and Upd3 and that the upd3 leads to tissue wasting. However, the data suggest that the DDR actually limits upd3 production and susceptibility to death as suggested by several results:

According to the referee’s suggestion, we revised the manuscript and adjusted our title, abstract and graphical summary to be more precise that DNA damage signaling seem to have a modulatory or regulatory effect on upd3 release. Furthermore, we provide now additional data to support the connection between DNA damage signaling and upd3 release. For example, we added several genetic “rescue” experiments to strengthen the epistasis that modulation of DNA damage signaling and the higher susceptibility of the fly is connected to altered upd3 levels (Figure 6F). We now provide additional data showing that the loss of upd3 rescues the susceptibility to oxidative stress in flies, which are deficient for DDR components in hemocytes.

a. PQ normally doesn't induce upd3 but does lead to glycogen and TAG loss, suggesting that upd3 isn't connected to the PQ-induced wasting.

Even though in our systemic gene expression analysis of upd3 expression, we could not detect a significant induction of upd3 upon PQ feeding. However, we found upd3 expression within our snRNAseq data in a distinct cluster of immune-activated hemocytes (Figure 3B, Cluster 6). Upon knockdown of the DNA damage signaling in hemocytes, the levels then increase to a detectable level in the whole fly. This supports our assumption that upd3 is needed upon oxidative stress to induce energy mobilization from the fat body, but needs to be tightly controlled to balance tissue wasting for energy mobilization. Furthermore, we found evidence in our new analysis of the snRNA-seq data of the fat body cells, that indeed we can find Jak/STAT activation in one cell cluster here, which could speak for an interaction of Cluster 6 hemocytes with cluster 6 fat body cells. A hypothesis we aim to explore in future studies.

b. knockdown of DDR upregulates upd3 and leads to increased PQ-induced death. This would suggest that activation of DDR is normally required to limit, rather than serve as the trigger for upd3 production and death.

Our data support the hypothesis that DDR signaling in hemocytes “modulates” upd3 levels upon oxidative stress. We now carefully revised the text and the graphical summary of the manuscript to emphasize that oxidative stress causes DNA damage, which subsequently induces the DNA damage signaling machinery. If this machinery is not sufficiently induced, for example by knockdown of tefu and mei-41, non-canonical DNA damage signaling is altered which induces JNK signaling and induces release of pro-inflammatory cytokines, including upd3. Whereas DNA damage itself is only slightly increase in the used DDR deficient lines (Figure 5C) and hemocytes do not undergo apoptosis (unaltered cell number on PQ (Figure 5B)), we conclude that loss of tefu, mei-41, or nbs1 causes dysregulation of inflammatory signaling cascades via non-canonical DNA damage signaling. However, oxidative stress itself seems to also induce upd3 release and DNA damage signaling in the same cell cluster, as shown by our snRNA-seq data (Figure 3B). Hence, we think that DNA damage signaling is needed as a rate-limiting step for upd3 release.

c. hemocyte knockdown of either JNK activity or upd3 doesn't affect PQ-induced death, suggesting that they don't contribute to oxidative stress-induced death. It’s only when DDR is impaired (with DDR gene knockdown) that an increase in upd3 is seen (although no experiments addressed whether JNK was activated or involved in this induction of upd3), suggesting that DDR activation prevents upd3 induction upon oxidative stress.

Whereas the double knockdown of upd3 or bsk and DDR genes was resulting in insufficient knockdown efficiencies, we added a rescue experiment where we combined upd3null mutants with knockdown of tefu and mei-41 in hemocytes and found a reduced susceptibility of DDR-deficient flies to oxidative stress.

1. The connections between DDR, JNK and upd3 aren't fully developed. The experiments show that susceptibility to oxidative stress-induced death can be caused by a) knockdown of DDR genes, b) genetic overexpression of upd3, c) genetic activation of JNK. But whether these effects are all related and reflect a linear pathway requires a little more work. For example, one prediction of the proposed model is that the increased susceptibility to oxidative stress-induced death in the hemocyte DDR gene knockdowns would be suppressed (perhaps partially) by simultaneous knockdown of upd3 and/or JNK. These types of epistasis experiments would strengthen the model and the paper.

As mentioned before, we had some technical difficulties combining the knockdown of bsk or upd3 with DDR genes. However, we added a new experiment in which we show that upd3null mutation can rescue the higher susceptibility of hemocytes with tefu and mei41 knockdown.

1. The (potential) connections between DDR/JNK/UPD3 and the oxidative stress effects on depletion of nutrient (lipids and glycogen) stores was also not fully developed. However, it may be the case that, in this paper, the authors just want to speculate that the effects of hemocyte DDR/upd3 manipulation on viability upon oxidative stress involve changes in nutrient stores.

In the revised version of the manuscript, we now provide a more thorough snRNA-seq analysis in the fat body upon PQ treatment to give more insights on the changes in the fat body upon PQ treatment. We added additional histological images of the abdominal fat body on control food and PQ food, to demonstrate the elimination of triglycerides from fat body with Oil-Red-O staining (Figure S1). We also analyzed now hemocyte-deficient (crq-Gal80ts>reaper) flies for their levels of triglycerides and carbohydrates during oxidative stress, to support our hypothesis that hemocytes are key players in the regulation of energy mobilization during oxidative stress. Loss of hemocytes (and therefore also their regulatory input on energy mobilization from the fat body) results in increased triglyceride storage in the fat body during steady state with a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization, which is mostly done in muscle, is not altered in these flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies, which could be due to insufficient energy mobilization from the fat body and subsequently results in a higher susceptibility of these flies on oxidative stress (Figure 1K). Additionally, we aim to point out here that “functional” hemocytes are needed for effective response to oxidative stress, but this response has to be tightly balanced (see also new graphical abstract).

Reviewer #2 (Public Review):

Hersperger et al. investigated the importance of Drosophila immune cells, called hemocytes, in the response to oxidative stress in adult flies. They found that hemocytes are essential in this response, and using state-of-the-art single-cell transcriptomics, they identified expression changes at the level of individual hemocytes. This allowed them to cluster hemocytes into subgroups with different responses, which certainly represents very valuable work. One of the clusters appears to respond directly to oxidative stress and shows a very specific expression response that could be related to the observed systemic metabolic changes and energy mobilization. However, the association of these transcriptional changes in hemocytes with metabolic changes is not well established in this work. Using hemocyte-specific genetic manipulation, the authors convincingly show that the DNA damage response in hemocytes regulates JNK activity and subsequent expression of the JAK/STAT ligand Upd3. Silencing of the DNA damage response or excessive activation of JNK and Upd3 leads to increased susceptibility to oxidative stress. This nicely demonstrates the importance of tight control of JNK-Upd3 signaling in hemocytes during oxidative stress. However, it would have been nice to show here a link to systemic metabolic changes, as the authors conclude that it is tissue wasting caused by excessive Upd3 activation that leads to increased susceptibility, but metabolic changes were not analyzed in the manipulated flies.

We thank the referee for the suggestion to better connect upd3 cytokine levels to energy mobilization from the fat body. We agree that this is an important point to support our hypothesis. First, we added now a detailed analysis of fat body cells in our snRNA-seq data to evaluate the changes induced in the fat body upon oxidative stress. We further added additional metabolic analyses of hemocyte-deficient flies (crq-Gal80ts>reaper) to support our hypothesis that hemocytes are key players in the regulation of energy mobilization during oxidative stress (see also answer to referee 1). Loss of the regulatory role of hemocytes in the energy mobilization and redistribution leads to a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization from muscle, is not affected in hemocyte-deficient flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies compared to controls, which could be due to insufficient energy mobilization from the fat body resulting in a higher susceptibility to oxidative stress (Figure 1K). This data supports our assumption that “functional” hemocytes are needed for effective response to oxidative stress, but this response has to be tightly balanced (see also new graphical summary).

The overall conclusion of this work, as presented by the authors, is that Upd3 expression in hemocytes under oxidative stress leads to tissue wasting, whereas in fact it has been shown that excessive hemocyte-specific Upd3 activation leads to increased susceptibility to oxidative stress (whether due to increased tissue wasting remains a question). The DNA damage response ensures tight control of JNK-Upd3, which is important. However, what role naturally occurring Upd3 expression plays in a single hemocyte cluster during oxidative stress has not been tested. What if the energy mobilization induced by this naturally occurring Upd3 expression during oxidative stress is actually beneficial, as the authors themselves state in the abstract - for potential tissue repair? It would have been useful to clarify in the manuscript that the observed pathological effects are due to overactivation of Upd3 (an important finding), but this does not necessarily mean that the observed expression of Upd3 in one cluster of hemocytes causes the pathology.

We agree with the referee that the pathological effects and increased susceptibility to oxidative stress are mediated by over-activated hemocytes and enhanced cytokine release, including upd3 during oxidative stress. We edited the revised manuscript accordingly to imply a “regulatory” role of upd3, which we suspect and suggest as an important mediator for inter-organ communication between hemocytes and fat body. Whereas our used model for oxidative stress (15mM Paraquat feeding) is a severe insult from which most of the flies will not recover, we could not account and test how upd3 might influence tissue repair after injury, insults and infection. We believe that this is an important factor, we aim to explore in future studies.

Reviewer #3 (Public Review):

In this study, Kierdorf and colleagues investigated the function of hemocytes in oxidative stress response and found that non-canonical DNA damage response (DDR) is critical for controlling JNK activity and the expression of cytokine unpaired3. Hemocyte-mediated expression of upd3 and JNK determines the susceptibility to oxidative stress and systemic energy metabolism required for animal survival, suggesting a new role for hemocytes in the direct mediation of stress response and animal survival.

Strength of the study:

1. This study demonstrates the role of hemocytes in oxidative stress response in adults and provides novel insights into hemocytes in systemic stress response and animal homeostasis.

2. The single-cell transcriptome profiling of adult hemocytes during Paraquat treatment, compared to controls, would be of broad interest to scientists in the field.

We are grateful to these positive comments on our data and are excited that the referee pointed out the importance of our provided snRNA-seq analysis of hemocytes and other cell types during oxidative stress. In the revised, version we now extended this analysis and looked not only into hemocytes but also highlighted induced changes in the fat body (Figure 4).

Weakness of the study:

1. The authors claim that the non-canonical DNA damage response mechanism in hemocytes controls the susceptibility of animals through JNK and upd3 expression. However, the link between DDR-JNK/upd3 in oxidative stress response is incomplete and some of the descriptions do not match their data.

In the revised manuscript, we aimed to strengthen the weaknesses pointed out by the referee. We now included additional genetic crosses to validate the connection of DDR signaling in hemocytes with upd3 release. For example, we added now survival studies where we show that upd3null mutation can rescue the higher susceptibility of flies with tefu and mei41 knockdown in hemocytes during oxidative stress. Furthermore, we added additional data to highlight the importance of hemocytes themselves as essential regulators of susceptibility to oxidative stress. We analyzed the hemocyte-deficient flies (crq-Gal80ts>reaper) for their triglyceride content and carbohydrate levels during oxidative stress (Figure 1 I-L). As outlined above, loss of hemocytes leads to a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization from muscle, is not affected in hemocyte-deficient flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies, which could be due to insufficient energy mobilization from the fat body resulting in a higher susceptibility to oxidative stress (Figure 1K).

1. The schematic diagram does not accurately represent the authors' findings and requires further modifications.

We carefully revised the text throughout the manuscript describing our results and edited the graphical abstract to display that upd3 levels and hemocytes are essential to balance and modulate response to oxidative stress.

Reviewer #1 (Recommendations For The Authors):

The summary doesn't say too much about what the specific discoveries and results of the study are. The description is limited to just one sentence saying, "Here we describe the responses of hemocytes in adult Drosophila to oxidative stress and the essential role of non-canonical DNA damage repair activity in direct "responder" hemocytes to control JNK-mediated stress signaling, systemic levels of the cytokine upd3 and subsequently susceptibility to oxidative stress" which doesn't provide sufficient explanation of what the results were.

In the revised version of our manuscript, we now provide further information for the reader to outline the findings of our study in a concise way in the summary.

Reviewer #2 (Recommendations For The Authors):

1. To strengthen the conclusion that the DDR response suppresses JNK, and thus Upd3, rescue of DDR by upd3 null mutation would help (knockdown by Hml>upd3IR might not work, RNAi seems problematic).

We would like to thank the referee for this suggestion and included now a genetic experiment where we combined upd3null mutants with hemocyte-specific knockdown of mei-41 and tefu to test their susceptibility to oxidative stress. Our data indeed provide evidence that loss of upd3 rescues the higher susceptibility of flies with hemocyte-specific knockdown for tefu and mei-41 (Figure 6F). Furthermore, we see that upd3null mutants show a diminished susceptibility to oxidative stress compared to control flies (Figure 6F).

1. To link the observed effects to systemic metabolic changes, it would be useful to measure glycogen and triglycerides in these flies as well:

2. crq-Gal80ts>reaper to see what role hemocytes play in the observed metabolic changes.

3. Hml-Upd3 overexpression and Upd3 null mutant (Upd3 RNAi seems to be problematic, we have similar experiences) to see if Upd3 overexpression leads to even more profound changes as suggested, and if Upd3 mutation at least partially suppresses the observed changes.

We agree with the referee that analyzing the connection of hemocyte activation to metabolic changes should be demonstrated in our manuscript to support our claim that hemocytes are important regulators of energy mobilization during oxidative stress. Hence, we analyzed triglycerides and carbohydrate levels in hemocyte-deficient flies (crq-Gal80ts>reaper) during oxidative stress. Indeed, we found substantial differences in energy mobilization in these flies supporting the assumption that the higher susceptibility of hemocyte-deficient flies could be caused by substantial decrease in free glucose and inefficient lysis of triglycerides from the fat body (Figure 1I-K).

1. To test whether the cause of the increased susceptibility to oxidative stress is due to Upd3 overactivation induced by DDR silencing, the authors should attempt to rescue DDR silencing with an Upd3 null mutation.

The suggestion of the reviewer was included in the revised manuscript and as outlined above we now added this data set to our manuscript (Figure 6F). Indeed, we can now provide evidence that upd3null mutation rescues the higher susceptibility of flies with DDR knockdown in hemocytes.

1. Lethality after PQ treatment varies widely (sometimes from 10 to 90%! as in Figure 5D) - is this normal? In some experiments the variability was much lower. In particular, Figure 5D is very problematic and for example the result with upd3 null mutant compared to control is not very convincing. This could be an important result to test whether Upd3, with normal expression likely coming from cluster 6, actually plays a beneficial role, whereas overexpression with Hml leads to pathology.

We agree with the referee that it would be more convincing if the variation cross of survival experiments would be less. However, we included a lot of flies and vials in many individual experiments to test our hypothesis and variation in these survivals was always the case. These effects can be caused by many factors for example the amount of food intake by the flies, genetic background or inserted transgenes. The n-number is quite high across our survivals; so that we are convinced, the seen effects are valid. This reflects also the power of using Drosophila melanogaster as a model organism for such survivals. The high n-number in our data falls into a normal Gauss distribution with a distinct mean susceptibility between the genotypes analyzed.

1. I like the conclusion at the end of the results: line 413: "We show that this oxidative stressmediated immune activation seems to be controlled by non-canonical DNA damage signaling resulting in JNK activation and subsequent upd3 expression, which can render the adult fly more susceptible to oxidative stress when it is over-activated." This is actually a more appropriate conclusion, but in the summary, introduction and discussion along with the overall schematic illustration, this is not actually stated as such, but rather as Upd3 released from cluster 6 causes the pathology. For example: line 435 "Hence, we postulate that hemocyte-derived upd3, most likely released by the activated plasmatocyte cluster C6 during oxidative stress in vivo and subsequently controlling energy mobilization and subsequent tissue wasting upon oxidative stress."

We thank the referee for this suggestion and edited our manuscript and conclusions accordingly.

Reviewer #3 (Recommendations For The Authors):

1. In Figure 2, the authors claim showed that PQ treatment changes the hemocyte clusters in a way that suppresses the conventional Hml+ or Pxn+ hemocytes (cluster1) while expanding hemocyte clusters enriched with metabolic genes such as Lpin, bmm etc. It is not clear whether these cells are comparable to the fat body and if these clusters express any of previously known hemocyte marker genes to claim that these are bona fide hemocytes.

We now included a new analysis of our snRNA-seq data in Figure S4, where we clearly show that all identified hemocyte clusters do not have a fat body signature and are hemocytes, which seem to undergo metabolic adaptations (Figure S4A). Furthermore, we show that the identified fat body cells have a clear fat body signature (Figure S4B) and do not express specific hemocyte markers (Figure S4C).

1. In Figure 4C, the authors showed that comet assays of isolated hemocytes result in a statistically significant increase in DNA damage in DDR-deficient flies before and after PQ treatment. However, the authors conclude that, in lines 324-328, the higher susceptibility of DDR-deficient flies is not due to an increase in DNA damage. To explicitly conclude that "non-canonical" DNA damage response, without any DNA damage, is specifically upregulated during PQ treatment, the authors require further support to exclude the potential activation of canonical DDR.

The referee is correct that we do not provide direct evidence for non-canonical DNA damage signaling. Therefore, we also decided to tune down our statement here a bit and removed that claim from the title. Increase in DNA damage can of course also increase the non-canonical DNA damage signaling pathway, loss of DNA damage signaling genes such as tefu and mei-41 seem to only have minor impacts on the overall amount of DNA damage acquired in hemocytes by oxidative stress. We therefore concluded that the induction in immune activation is most unlikely only caused by increased DNA damage but might be connected to dysregulation in non-canonical DNA damage signaling. Canonical DNA damage signaling leads essentially to DDR, which could be slow in adult hemocytes because they post-mitotic, or to apoptosis, which we could not observe in the analyzed time window in our experiments. Hemocyte number remained stable over the 24h PQ treatment without reduction in cell number (Figure 1H).

1. From Figure 4D-F, the authors showed that loss of DDR in hemocytes induces the expression of unpaired 2 and 3, Socs36E, which represent the JAK/STAT pathway, and thor, InR, Pepck in the InR pathway, and a JNK readout, puc. These results indicate that the DDR pathway normally inhibits the upd-mediated JAK/STAT activation upon PQ treatment, compared to wild-type animals during PQ treatment in Figure 1B-C, which in turn protects the animal during oxidative stress responses. However, the authors claim that "enhanced DNA damage boosts immune activation and therefore susceptibility to oxidative stress (lines 365-366); we show that this oxidative stress-mediated immune activation seems to be controlled by non-canonical DNA damage signaling resulting in JNK activation and subsequent upd3 expression (line 413-416)". These conclusions are not compatible with the authors' data and may require additional data to support or can be modified.

In the revised manuscript, we carefully revised now the text and our statements that it seems that DNA damage signaling in hemocytes has regulatory or modulatory effect on the immune response during oxidative stress. Accordingly, we also adjusted our graphical summary. We agree with the referee and used the term “non-canonical” DNA damage signaling more carefully throughout the manuscript. The slight increase in DNA damage seen after PQ treatment can contribute to immune activation but seems to be not correlative to the induced cytokine levels or the susceptibility of the flies to oxidative stress.

1. In Fig 1I, the authors showed that genetic ablation of hemocytes using UAS-repear induces susceptibility to PQ treatment. It is possible that inducing cell death in hemocytes itself causes the expression of cytokine upd3 or activates the JNK pathway to enhance the basal level of upd3/JNK even without PQ treatment. If this phenotype is solely mediated by the loss of hemocytes, the results should be repeated by reducing the number of hemocytes with alternative genetic backgrounds.

In the different genotypes analyzed across our manuscript we did not detect cell death of hemocytes or a dramatic reduction in hemocytes number (see Figure 1H, Figure 5B, Figure 6C). The higher susceptibility if hemocyte-deficient flies during oxidative stress is most likely caused by the loss of their regulatory role during energy mobilization. We tested triglyceride levels in hemocyte-deficient flies and found a decreased triglyceride consumption (lipolysis), with reduced levels of circulating glucose levels. This findings support our hypothesis that hemocytes are needed to balance the response to oxidative stress. In contrast, the flies with DDR-deficient hemocytes show higher systemic cytokine levels, which most likely enhance energy mobilization from the fat body and therefore result in a higher susceptibility of the fly to oxidative stress. Hence, we claim that hemocytes and their regulation of systemic cytokine levels are important to balance the response to oxidative stress and guarantee the survival of the organism.

1. Lethality of control animals in PQ treatment is variable and it is hard to estimate the effect of animal susceptibility during 15mM PQ feeding. For example, Fig1A shows that control animals exhibit ~10% death during 15mM PQ which is further enhanced by crq-Gal80>reaper expression to 40% (Fig 1I). However, in Fig 5D-E, the basal lethality of wild-type controls already reaches 40~50%, which makes them hard to compare with other genetic manipulations. Related to this, the authors demonstrated that the expression of upd3 in hemocytes is sufficient to aggravate animal survival upon PQ treatment; however, upd3 null mutants do not rescue the lethality, which indicates that upd3 is not required for hampering animal mortality. These data need to be revisited and analyzed.

As outlined above, we find the variability of susceptibility to oxidative stress across all of our experiments. This could be due to different effects such as food intake but also transgene insertion and genetic background. Crq-gal80ts>reaper flies are healthy, but show a shortened life span on normal food (Kierdorf et al., 2020) due to enhanced loss of proteostasis in muscles. We show in the revised manuscript that these flies have a higher susceptibility to oxidative stress and that this effect could be mediated by defects in energy mobilization and redistribution as shown by less triglyceride lysis from the fat body and decreasing levels in free glucose. This would explain the high mortality rate of these flies at 7 days after eclosion. Paraquat treatment (15mM) is a severe inducer of oxidative stress, which results in death of most flies when they are maintained for longer time windows on PQ food. Hence, it is a model, which is not suitable to examine and monitor recovery from this detrimental insult. upd3null mutants were extensively reexamined in this manuscript, and even though we could not see a full protection of these flies from oxidative stress induced death, we found a reduced susceptibility compared to control flies (Figure 6F). Furthermore, when we combined upd3null mutants with flies deficient for tefu and mei-41 in hemocytes, the increased susceptibility to oxidative stress was rescued.

eLife assessment

This study elucidates the role of a specific hemocyte subpopulation in oxidative damage response by establishing connections between DNA damage response and the JNK-JAK/STAT axis to regulate energy metabolism. The identification of this distinct hemocyte subpopulation through single-cell RNA sequencing analysis and the finding of hemocytes that respond to oxidative stress are important. The method for single-cell RNA sequencing and related analyses are convincing and experiments linking oxidative stress to DNA damage and energy expenditure are solid. The finding of stress-responsive immune cells capable of influencing whole-body metabolism adds insights for cell biologists and developmental biologists in the fields of immunology and metabolism.

Reviewer #1 (Public Review):

The study examines how hemocytes control whole-body responses to oxidative stress. Using single cell sequencing they identify several transcriptionally distinct populations of hemocytes, including one subset that show altered immune and stress gene expression. They also find that knockdown of DNA Damage Response (DDR) genes in hemocytes increases expression of the immune cytokine, upd3, and that both upd3 overexpression in hemocytes and hemocyte knockdown of DDR genes leads to increased lethality upon oxidative stress. And they find that the PQ-induced lethality seen when the DDR is disrupted can be rescued in upd3 null background, suggesting links between proper regulation of DDR in hemocytes, modulation of systemic upd3 signaling, and the control of oxidative stress survival.

The paper has two key strengths:

1, The single cell analyses provide a clear description of how oxidative stress can cause distinct transcriptional changes in different populations of hemocytes. These results add to the emerging them in the field that there functionally different subpopulations of hemocytes that can control organismal responses to stress.<br /> 2, The discovery that DDR genes are required upon oxidative stress to modulate upd3 cytokine production and lethality provides interesting new insight into the DDR may play non-canonical roles in controlling organismal responses to stress.

Reviewer #2 (Public Review):

Hersperger et al. investigated the importance of Drosophila immune cells, called hemocytes, in the response to oxidative stress in adult flies. They found that hemocytes are essential in this response, and using state-of-the-art single-cell transcriptomics, they identified expression changes at the level of individual hemocytes. This allowed them to cluster hemocytes into subgroups with different responses, which certainly represents very valuable work. One of the clusters appears to respond directly to oxidative stress and shows a very specific expression response that could be related to the observed systemic metabolic changes and energy mobilization.

Using hemocyte-specific genetic manipulation, the authors convincingly show that the DNA damage response in hemocytes regulates JNK activity and subsequent expression of the JAK/STAT ligand Upd3. Silencing of the DNA damage response or excessive activation of JNK and Upd3 leads to increased susceptibility to oxidative stress. This nicely demonstrates the importance of tight control of JNK-Upd3 signaling in hemocytes during oxidative stress. The treatment the authors used is quite harsh, and in such a situation it is simply better not to use upd3 signaling, but it is still worth bearing in mind that upd3 signaling may have a protective role under milder stresses, but Upd3 could require very tight control - this could be an interesting objective for future studies.

The authors demonstrate that hemocytes play an important role in energy mobilization during oxidative stress, suggesting that control of energy mobilization by hemocytes is essential for the response. They further postulate that "hemocyte-derived upd3 is most likely released by the activated plasmatocyte cluster C6 during oxidative stress in vivo and is subsequently controlling energy mobilization and subsequent tissue wasting upon oxidative stress." It is important to note here that the association of upd3 with the observed changes in energy metabolism has not been tested, and the subsequent tissue wasting allegedly caused by excessive upd3 as a cause of death remains an open question.

Reviewer #3 (Public Review):

In this study, Kierdorf and colleagues investigated the function of hemocytes in oxidative stress response and found that non-canonical DNA damage response (DDR) is critical for controlling JNK activity and the expression of cytokine unpaired3. Hemocyte-mediated expression of upd3 and JNK determines the susceptibility to oxidative stress and systemic energy metabolism required for animal survival, suggesting a new role for hemocytes in the direct mediation of stress response and animal survival.

In the revised manuscript, the authors provide additional evidence to support the role of DNA damage-modulated cytokine release by hemocytes during oxidative stress responses and strengthen the connection between DNA damage and the regulation of upd3 release from hemocytes. The authors have also included new analyses to emphasize the significance of hemocytes in the regulation of energy during oxidative stress. Following the reviewers' suggestions, the authors made improvements to the manuscript and the graphical abstract to better display their findings. Overall, the revised manuscript makes it easier to understand the main points, flows better, and is supported by convincing data and analysis throughout.

eLife assessment

This important study uses cutting-edge miniature two-photon microscopy to follow the structural dynamics of microglia in the somatosensory cortex of freely-moving mice across the sleep/wake cycle. Solid evidence revealed the brain-state-dependent regulation of microglial activity, highlighting alterations in microglial morphology during REM and NREM sleep phases compared to wakefulness. Furthermore, this study provides evidence for a critical role of norepinephrine from the locus coeruleus as a modulator of microglial morphology through the β2-adrenergic receptor (b2AR). Overall, the article is an impressive technical feat to bridge a crucial gap in understanding sleep state-induced dynamics of microglia and its modulation by norepinephrine signaling.

eLife assessment

Based on the observation of an increase in miR-182-5p in diabetic patients, the authors propose that miR-182-5p and its target gene LRP6 may play a role in dysregulated glucose tolerance and fatty acid metabolism in obese type 2 diabetics. The use of human livers complemented by supporting data in mice and cells are strengths, but the evidence presented remains incomplete. Nonetheless, the findings provide valuable insights into the role of miRNAs in the regulation of liver metabolism and insulin sensitivity in individuals with diabetes and fatty liver disease.

Reviewer #2 (Public Review):

Summary:

In this study, Christin Krause et al mapped the hepatic miRNA-transcriptome of type 2 diabetic obese subjects, and identified miR-182-5p and its target genes LRP6 as potential drivers of dysregulated glucose tolerance and fatty acid metabolism in obese T2-diabetics.

Strengths:

This study contains some interesting findings and is valuable for the understanding of the key regulatory role of miRNAs in the pathogenesis of T2D.

Weaknesses:

The authors didn't systemically investigate the function of miR-182 in T2DM or NAFLD.

Reviewer #1 (Public Review):

Summary:

This study demonstrated a novel exciting link between the conserved miRNA-target axis of miR-182-Lrp6 in liver metabolism which causatively contributes to type 2 diabetes and NAFLD in mice and, potentially, humans.

Strengths:

The direct interaction and inhibition of Lrp6 by miR-182 are convincingly shown. The effects of miR-182-5p on insulin sensitivity are also credible for the in vivo and in vitro gain-of-function experiments.

Weaknesses:

However, the DIO cohorts lack key assays for insulin sensitivity such as ITT or insulin-stimulated pAKT, as well as histological evidence to support their claims and strengthen the link between miR-182-5p and T2D or NAFLD. Besides, the lack of loss-of-function experiments limits its aptitude as a potential therapeutic target.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Krause and colleagues identify miR-182 as diabetes-associated microRNA: miR-182 is increased in bariatric surgery patients with versus without T2D; miR-182 was the only microRNA associated with three metabolic traits; miR-182 levels were associated with increased body weight in mice under different dietary manipulations; overexpression in Hep-G2 led to a decrease in LRP6; and overexpression in HFD fed mice led to increased insulin and liver TG. The manuscript provides a potentially useful resource for microRNA expression in human livers, though the functional importance of miR-182 remains unclear.

Strengths:

The use of human tissues and good sample sizes is strong.

Weaknesses:

The study is primarily correlative; the in vivo overexpression is non-physiological; and the mechanisms by which miR-182 exerts its effects are not rigorously tested.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, Chamness and colleagues make a pioneering effort to map epistatic interactions among mutations in a membrane protein. They introduce thousands of mutations to the mouse GnRH Receptor (GnRHR), either under wild-type background or two mutant backgrounds, representing mutations that destabilize GnRHR by distinct mechanisms. The first mutant background is W107A, destabilizing the tertiary fold, and the second, V276T, perturbing the efficiency of cotranslational insertion of TM6 to the membrane, which is essential for proper folding. They then measure the surface expression of these three mutant libraries, using it as a proxy for protein stability, since misfolded proteins do not typically make it to the plasma membrane. The resulting dataset is then used to shed light on how diverse mutations interact epistatically with the two genetic background mutations. Their main conclusion is that epistatic interactions vary depending on the degree of destabilization and the mechanism through which they perturb the protein. The mutation V276T forms primarily negative (aggravating) epistatic interactions with many mutations, as is common to destabilizing mutations in soluble proteins. Surprisingly, W107A forms many positive (alleviating) epistatic interactions with other mutations. They further show that the locations of secondary mutations correlate with the types of epistatic interactions they form with the above two mutants.

Strengths:<br /> Such a high throughput study for epistasis in membrane proteins is pioneering, and the results are indeed illuminating. Examples of interesting findings are that: (1) No single mutation can dramatically rescue the destabilization introduced by W107A. (2) Epistasis with a secondary mutation is strongly influenced by the degree of destabilization introduced by the primary mutation. (3) Misfolding caused by mis-insertion tends to be aggravated by further mutations. The discussion of how protein folding energetics affects epistasis (Fig. 7) makes a lot of sense and lays out an interesting biophysical framework for the findings.

Weaknesses:<br /> The major weakness comes from the potential limitations in the measurements of surface expression of severely misfolded mutants. This point is discussed quite fairly in the paper, in statements like "the W107A variant already exhibits marginal surface immunostaining" and many others. It seems that only about 5% of the W107A makes it to the plasma membrane compared to wild-type (Figures 2 and 3). This might be a low starting point from which to accurately measure the effects of secondary mutations.

Still, the authors claim that measurements of W107A double mutants "still contain cellular subpopulations with surface immunostaining intensities that are well above or below that of the W107A single mutant, which suggests that this fluorescence signal is sensitive enough to detect subtle differences in the PME of these variants". I was not entirely convinced that this was true. Firstly, I think it would be important to test how much noise these measurements have and how much surface immunostaining the W107A mutant displays above the background of cells that do not express the protein at all. But more importantly, it is not clear if under this regimen surface expression still reports on stability/protein fitness. It is unknown if the W107A retains any function or folding at all. For example, it is possible that the low amount of surface protein represents misfolded receptors that escaped the ER quality control. The differential clustering of epistatic mutations (Fig. 6) provides some interesting insights as to the rules that dictate epistasis, but these too are dominated by the magnitude of destabilization caused by one of the mutations. In this case, the secondary mutations that had the most interesting epistasis were exceedingly destabilizing. With this in mind, it is hard to interpret the results that emerge regarding the epistatic interactions of W107A. Furthermore, the most significant positive epistasis is observed when W107A is combined with additional mutations that almost completely abolish surface expression. It is likely that either mutation destabilizes the protein beyond repair. Therefore, what we can learn from the fact that such mutations have positive epistasis is not clear to me. Based on this, I am not sure that another mutation that disrupts the tertiary folding more mildly would not yield different results.

With that said, I believe that the results regarding the epistasis of V276T with other mutations are strong and very interesting on their own.

Additionally, the study draws general conclusions from the characterization of only two mutations, W107A and V276T. At this point, it is hard to know if other mutations that perturb insertion or tertiary folding would behave similarly. This should be emphasized in the text.

Some statistical aspects of the study could be improved:

1. It would be nice to see the level of reproducibility of the biological replicates in a plot, such as scatter or similar, with correlation values that give a sense of the noise level of the measurements. This should be done before filtering out the inconsistent data.

2. The statements "Variants bearing mutations within the C- terminal region (ICL3-TMD6-ECL3-TMD7) fare consistently worse in the V276T background relative to WT (Fig. 4 B & E)." and "In contrast, mutations that are 210 better tolerated in the context of W107A mGnRHR are located 211 throughout the structure but are particularly abundant among residues 212 in the middle of the primary structure that form TMD4, ICL2, and ECL2 213 (Fig. 4 C & F)." are both hard to judge. Inspecting Figures 4B and C does not immediately show these trends, and importantly, a solid statistical test is missing here. In Figures 4E and F the locations of the different loops and TMs are not indicated on the structure, making these statements hard to judge.

3. The following statement lacks a statistical test: "Notably, these 98 variants are enriched with TMD variants (65% TMD) relative to the overall set of 251 variants (45% TMD)." Is this enrichment significant? Further in the same paragraph, the claim that "In contrast to the sparse epistasis that is generally observed between mutations within soluble proteins, these findings suggest a relatively large proportion of random mutations form epistatic interactions in the context of unstable mGnRHR variants". Needs to be backed by relevant data and statistics, or at least a reference.

eLife assessment

The important study describes exhaustive deep mutational scanning (DMS) of the gonadotropin-releasing hormone wild-type receptor and for two single point mutations reported to impact its folding and structure, monitoring how plasma membrane expression levels are affected by mutations. This important work is pioneering in exploring the interaction between mutations (epistasis) in a membrane protein, with a potential for explaining membrane protein evolution and genetic diseases. The evidence provided for some mutations is convincing, but it remains incomplete and harder to interpret for others without further validation of folding and stability properties of the mutants.

Reviewer #1 (Public Review):

Summary:<br /> The paper carries out an impressive and exhaustive non-sense mutagenesis using deep mutational scanning (DMS) of the gonadotropin-releasing hormone receptor for the WT protein and two single point mutations that I) influence TM insertion (V267T) and ii) influence protein stability (W107A), and then measures the effect of these mutants on correct plasma membrane expression (PME).

Overall, most mutations decreased mGnRHR PME levels in all three backgrounds, indicating poor mutational tolerance under these conditions. The W107A variant wasn't really recoverable with low levels of plasma membrane localisation. For the V267T variant, most additional mutations were more deleterious than WT based on correct trafficking, indicating a synergistic effect. As one might expect, there was a higher degree of positive correlation between V267T/W107A mutants and other mutants located in TM regions, confirming that improper trafficking was a likely consequence of membrane protein co-translational folding. Nevertheless, context is important, as positive synergistic mutants in the V27T could be negative in the W107A background and vice versa. Taken together, this important study highlights the complexity of membrane protein folding in dissecting the mechanism-dependent impact of disease-causing mutations related to improper trafficking.

Strengths:<br /> This is a novel and exhaustive approach to dissecting how receptor mutations under different mutational backgrounds related to co-translational folding, could influence membrane protein trafficking.

Weaknesses:<br /> The premise for the study requires an in-depth understanding of how the single-point mutations analysed affect membrane protein folding, but the single-point mutants used seem to lack proper validation. Furthermore, plasma membrane expression has been used as a proxy for incorrect membrane protein folding, but this not necessarily be the case, as even correctly folded membrane proteins may not be trafficked correctly, at least, under heterologous expression conditions. In addition, mutations can affect trafficking and potential post-translational modifications, like glycosylation.

eLife assessment

This valuable study identifies an Ephrin type-B Receptor 2 (EPHB2) interactor, MYCBP2, as a potential regulator of EPHB2 stability and function. In contrast to expectations, based on MYCBP2 function in the ubiquitin pathway, loss of function of MYCBP2 resulted in less EPHB2 receptor and defective EPHB2 function. The paper is supported by a largely convincing set of biochemical, cell culture and in vivo experiments.

Reviewer #1 (Public Review):

The Eph receptor tyrosine kinase family plays a critical function in multiple physiological and pathophysiological processes. Hence, understating the regulation of these receptors is a highly important question. Through extensive experiments in cell lines and cultured neurons Chang et.al show that the signaling hub protein, MYCBP2 positively regulates the overall stability of a specific member of the family, EPHB2, and by that the cellular response to ephrinBs.<br /> Overall, this work sheds light on the divergent in the regulatory mechanisms of the Eph receptors family. Although the physiological importance of this new regularly mechanism in mammals awaits to be discovered, the authors provide genetic evidence using C.elegans that it is evolutionarily conserved.

Reviewer #2 (Public Review):

Members of the EphB family of tyrosine kinase receptors are involved in a multitude of diverse cellular functions, ranging from the control of axon growth to angiogenesis and synaptic plasticity. In order to provide these diverse functions, it is expected that these receptors interact in a cell-type specific manner with a diverse variety of downstream signalling molecules.

The authors have used proteomics approaches to characterise some of these molecules in further detail. This molecule, myc-binding protein 2 (MYCBP2) is also known as highwire, has been identified in the context of establishment of neural connectivity. Another molecule coming up on this screen was identified as FBXO45.

The authors use classical methods of co-IP to show a kinase-independent binding of MYCBP2 to EphB2. They further showed that FBXO45 within a ternary complex increased the stability of the EphB2/MYCBP2 complex.

To define the interacting domains, they used clearly designed swapping experiments to show that the extracellular and transmembrane domains are necessary and sufficient for the formation of the ternary complex.

Using a cellular contraction assay, the authors showed the necessity of MYCBP2 in mediating the cytoskeletal response of EphB2 forward signalling. Furthermore, they used the technically challenging stripe assay of alternating lanes of ephrinB-Fc and Fc to show that also in this migration-based essay MYCBP2 is required for EphB mediated differential migration pattern.

MYCBP2 in addition is necessary to stabilize EphB2, that is in the absence of MYCBP2, EphB2 is degraded in the lysosomal pathway.

Interestingly, the third protein in this complex, Fbxo45, was further characterized by overexpression of the domain of MYCBP2, known to interact with Fbxo45. Here the authors showed that this approach led to the disruption of the EphB2 / MYCBP2 complex, and also abolished the ephrinB mediated activation of EphB2 receptors and their differential outgrowth on ephrinB2-Fc / Fc stripes.

Finally, the authors demonstrated an in vivo function of this complex using another model system, C elegans where they were able to show a genetic interaction.

Data show in a nice set of experiments a novel level of EphB2 forward signalling where a ternary complex of this receptor with multifunctional MYCBP2 and Fbxo45 controls the activity of EphB2, allowing a further complex regulation of this important receptors. Additionally, the authors challenge pre-existing concepts of the function of MYCBP2 which might open up novel ways to think about this protein.<br /> Of interest is this work also in terms of development of the retinotectal projection in zebrafish where MYCBP2/highwire plays a crucial role, and thus might lead to a better understanding of patterning along the DV axis, for which it is known that EphB family members are crucial.

Overall, the experiments are classical experiments of co-immunoprecipitations, swapping experiments, collapse assays, and stripe assays which all are well carried out and are convincing.

Reviewer #3 (Public Review):

In this improved version of the manuscript, Chang et al set out to find direct interactions with the Eph-B2 receptor, as our knowledge of its function/regulation is still incomplete. Using proteomic analysis of Hela cells expressing EPHB2, they identified MYCBP2 a potential binder, which they then confirm using extensive biochemical analyses, an interaction that seems to be negatively affected by binding of ephrin-B2 (but not B1). Furthermore, they find that FBXO45, a known MYCBP2 interaction, strongly facilitates its binding to EPHB2. Intriguingly, these interactions depend on the extracellular domains of EPHB2, suggesting the involvement of additional proteins as MYCBP2 is thought to be a cytoplasmic protein. Finally, they find that, in contrast to what could be expected given the known function of MYCBP2 as a ubiquitin E3 ligase, it actually positively regulates EPHB2 protein stability, and function.

The strength of this manuscript is the extensive biochemical analysis of the EPHB2/MYCBP2/FBXO43 interactions. The vast majority of the conclusions are supported by the data.

The attempt to extend the study to an in vivo animal using the worm is important, however the additive insight is, unfortunately, minimal.

eLife assessment

This important study describes a method to decouple the mechanisms supporting pancreatic progenitor self-renewal and expansion from feed-forward mechanisms promoting their differentiation allowing in vitro expansion of hPSC-derived pancreatic progenitors. The strength of evidence is convincing in that the authors use appropriate and validated methodology in line with current state-of-the-art. The work will be of interest to the field of beta cell replacement therapy in diabetes.

eLife assessment

This valuable study seeks to disentangle the different selective forces shaping the evolutionary dynamics of transposable elements (TEs) in the wild grass Brachypodium distachyon. Using haplotype-length metrics, and genetic and environmental differentiation tests, the authors present in large parts convincing evidence that positive selection on TE polymorphisms is rare, and that the distribution of TE ages points to purifying selection being the main force acting on TE evolution in this species. A caveat of this study, as of other studies that seek to assess TE insertion polymorphisms with short reads, is that the rates of false negatives and false positives are difficult to estimate, which may have major effects on the interpretation. This study will be relevant for anyone interested in the role of TEs in evolution and adaptation.

Reviewer #1 (Public Review):

The study presented in this manuscript presents very convincing evidence that purifying selection is the main force shaping the landscape of TE polymorphisms in B. distachyon, with only a few putatively adaptive variants detected, even though most conclusions are based on the 10% of polymorphisms contributed by retrotransposons. That first conclusion is not novel, however, as it had already been clearly established in natural A. thaliana strains (Baduel et al. Genome Biol 2021) and in experimental D. simulans lines (Langmüller et al. NAR 2023), two studies that the authors do not mention, or improperly mention. In contrast to the conclusions reached in A. thaliana, however, Horvath et al. report here a seemingly deleterious effect of TE insertions even very far away from genes (>5kb), a striking observation for a genome of relatively similar size. If confirmed, as a caveat of this study is the lack of benchmarking of the TE polymorphisms calls by a pipeline known for a high rate of false positives (see detailed Private Recommendations #1), this set of observations would make an important addition to the knowledge of TE dynamics in the wild and questioning our understanding of the main molecular mechanisms through which TEs can impact fitness.

Reviewer #2 (Public Review):

Summary:<br /> Transposable elements are known to have a strong potential to generate diversity and impact gene regulation, and they are thought to play an important role in plant adaptation to changing environments. Nevertheless, very few studies have performed genome-wide analyses to understand the global effect of selection on TEs in natural populations. Horvath et al. used available whole-genome re-sequencing data from a representative panel of B. distachyon accessions to detect TE insertion polymorphisms (TIPs) and estimate their time of origin. Using a thorough combination of population genomics approaches, the authors demonstrate that only a small amount of the TE polymorphisms are targeted by positive selection or potentially involved in adaptation. By comparing the age-adjusted population frequencies of TE polymorphisms and neutral SNPs, the authors found that retrotransposons are affected by purifying selection independently of their distance to genes. Finally, using forward simulations they were able to quantify the strength of selection acting on TE polymorphisms, finding that retrotransposons are mainly under moderate purifying selection, with only a minority of the insertions evolving neutrally.

Strengths:<br /> Horvath et al., use a convincing set of strategies, and their conclusions are well supported by the data. I think that incorporating polymorphism's age into the analysis of purifying selection is an interesting way to reduce the possible bias introduced by the fact that SNPs and TEs polymorphisms do not occur at the same pace. The fact that TE polymorphisms far from genes are also under purifying selection is an interesting result that reinforces the idea that the trans-regulatory effect of TE insertions might not be a rare phenomenon, a matter that may be demonstrated in future studies.

Weaknesses:<br /> TEs from different classes and orders strongly differ in multiple features such as size, the potential impact of close genes upon insertion, insertion/elimination ratio (ie, MITE/TIR excision, solo-LTR formation), or insertion preference. Given such diversity, it is expected that their survival rates on the genome and the strength of selection acting on them could be different. The authors differentiate DNA transposons and retrotransposons in some of the analyses, the specificities of the most abundant plant TE types (ie, LTR/Gypsy, LTR/Copia, MITE DNA transposons) are not considered.

The authors used a short-read-based approach to detect TIPs and TAPs. It is known that detecting TE polymorphisms is challenging and can lead to false negatives, depending on the method used and the sequencing coverage. The methodology used here (TEPID) has been previously applied to other species, but it is unclear if the sensitivity of the TIP/TAP caller is equivalent to that of the SNP caller and how these potential differences may affect the results.

Reviewer #1 (Public Review):

The regulation of motor autoinhibition and activation is essential for efficient intracellular transport. This manuscript used biochemical approaches to explore two members in the kinesin-3 family. They found that releasing UNC-104 autoinhibition triggered its dimerization whereas unlocking KLP-6 autoinhibition is insufficient to activate its processive movement, which suggests that KLP-6 requires additional factors for activation, highlighting the common and diverse mechanisms underlying motor activation. They also identified a coiled-coil domain crucial for the dimerization and processive movement of UNC-104. Overall, these biochemical and single-molecule assays were well performed, and their data support their statements. The manuscript is also clearly written, and these results will be valuable to the field.

Reviewer #2 (Public Review):

The Kinesin superfamily motors mediate the transport of a wide variety of cargos which are crucial for cells to develop into unique shapes and polarities. Kinesin-3 subfamily motors are among the most conserved and critical classes of kinesin motors which were shown to be self-inhibited in a monomeric state and dimerize to activate motility along microtubules. Recent studies have shown that different members of this family are uniquely activated by to undergo transition from monomers to dimers.

Niwa and colleagues study two well-described members of the kinesin-3 superfamily, unc104 and KLP6, to uncover the mechanism of monomer to dimer transition upon activation. Their studies reveal that although both Unc104 and KLP6 are both self-inhibited monomers, their propensities for forming dimers are quite different. The authors relate this difference to a region in the molecules called CC2 which has a higher propensity for forming homodimers. Unc104 readily forms homodimers if its self-inhibited state is disabled while KLP6 does not.

The work suggests that although mechanisms for self-inhibited monomeric states are similar, variations in the kinesin-3 dimerization may present a unique forms of kinesin-3 motor regulation with implications on the forms of motility functions carried out by these unique kinesin-3 motors.

Reviewer #3 (Public Review):

In this work, Kita et al., aim to understand the activation mechanisms of the kinesin-3 motors KLP-6 and UNC-104 from C. elegans. As many other motor proteins involved in intracellular transport processes, KLP-6 and UNC-104 motors suppress their ATPase activities in the absence of cargo molecules. Relieving the autoinhibition is thus a crucial step that initiates directional transport of intracellular cargo. To investigate the activation mechanisms, the authors make use of mass photometry to determine the oligomeric states of the full length KLP-6 and the truncated UNC-104(1-653) motors at sub-micromolar concentrations. While full length KLP-6 remains monomeric, the truncated UNC-104(1-653) displays a sub-population of dimeric motors that is much more pronounced at high concentrations, suggesting a monomer-to-dimer conversion. The authors push this equilibrium towards dimeric UNC-104(1-653) motors solely by introducing a point mutation into the coiled-coil domain and ultimately unleash a robust processivity of the UNC-104 dimer. The authors find that the same mechanistic concept does not apply to the KLP-6 kinesin-3 motor, suggesting an alternative activation mechanism of the KLP-6 that remains to be resolved. The present study encourages further dissection of the kinesin-3 motors with the goal of uncovering the main factors needed to overcome the 'self-inflicted' deactivation.

Author Response

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

Reviewer #1

1) IR reduced mature spines (mushroom) but not immature spines (filopodia) in vitro at 14 days post-2 Gy IR. Please check previous reports by C. Limoli and J. Fike groups (in vivo dendritic spine characterization following proton or photon irradiation).

We appreciate the reviewer's comments. Although IR did not reduce filopodia in the previous study, there are no prior studies using the same time points as ours, 4 days post-2 Gy IR. Additionally, according to other previous studies, PAK3 inhibition led to an increase in filopodia (J Neurosci. 2004 Dec 1;24(48):10816-25), and IR increased thin-type spines and decreased mushroom-type spines at the 7 days after 2 Gy IR (PLoS One. 2012;7(7):e40844). Considering these findings, we believe that the increase in filopodia observed in our study is due to the short-term effects of IR and the consequent PAK3 downregulation. We added the description regarding time point in “Materials and Methods”.

Page 20, line 439-440; "In the analysis of molecular alterations, cultured neurons were sampled 4 days after irradiation."

2) Does IR (2 Gy or 5 x 2 Gy) affect the viability in vitro? This could be linked with reduced dendritic structure and F/G-actin ratios.

As the reviewer mentioned, we evaluated neuronal viability following 2 Gy IR exposure. Consequently, approximately 80% of the cells survived after the IR exposure (Fig. 4A). Although we agree that cognitive abilities may decrease due to the neuron death after IR, we identified that PAK3 overexpression restores the F/G-actin ratio in surviving neurons after IR, suggesting the IR-induced alterations at least in neuronal plasticity are mainly regulated by PAK3 rather than IR itself. Additionally, neurons that survive after IR maintain similar levels of NeuN, a mature neuron marker (Fig. S5A). We added the description regarding additional experiments in “Results”.

Page 10, line 206-209; "IR decreased neuronal viability in human differentiated neurons, with approximately 80% survival (Fig 4A). However, IR did not alter the mature neuronal marker, NeuN (Fig S5A). These results indicate that IR-induced disruption of PAK3 signaling occurs in surviving neurons following irradiation. Consistent with previous murine neuron data, IR reduced the F/G-actin ratio (Fig. 4B)."

3) The authors state, "Overall, these results indicated that IR could induce cognitive impairment by disrupting dendritic spine maturation." Dendritic spine damage may not be the only factor contributing to cognitive dysfunction (neural circuit function, neuroinflammation, astrogliosis, etc., needs to be discussed).

We agree with the reviewer's comment that dendritic spine damage may not be the only factor contributing to cognitive impairment. Since our study has only confirmed the effects on dendritic spines as part of the complex impact of radiation, we added the description of the necessity for further research on various factors related to IR-induced cognitive dysfunction in “Discussion”.

Page 15, line 317-324; >The dendritic spine is one of the major factors influencing cognitive function. In our study, we observed changes in dendritic spines due to radiation exposure, followed by subsequent cognitive impairment. Additionally, we established that regulating PAK3, which affects dendritic spine maturation, can modulate radiation-induced cognitive dysfunction. However, considering that radiation can impact the entire nervous system and that neural circuit function, neuroinflammation, and astrogliosis can also influence cognitive function (Makale et al., 2017), future studies is needed to investigate the mechanisms of factors beyond dendritic spine changes caused by radiation.>

4) Fig 2 and Suppl Fig S2. The in vivo results should be placed in the manuscript Fig 2 as this would provide relevant physiological information on PAK3 downregulation and reduced dendritic spines and cognition.

We appreciated the reviewer's comment. As the reviewer mentioned, we rearranged Fig S2C to Fig 2H.

Page 33, line 825-827; "(H) Left: the protein levels of phosphorylated LIMK1, LIMK1, phosphorylated cofilin, and cofilin after IR in frontal cortex and hippocampus. Right: each western blot bands are quantified by ImageJ."

5) miR-206-3p expression was found to be elevated post-IR in the human and mouse neurons in vitro. This was correlated with IR-induced downregulation of PAK3 using an antagonist miR experiment, wherein PAK3, LIMK1, and downstream makers were restored in the irradiated neurons. MiR-206-3p upregulation data should also be confirmed in vivo using an irradiated mouse brain to correlate the cognitive dysfunction timepoint.

We observed IR-induced miR-206-3p upregulation (Fig 6D) and consequent PAK3 downregulation (Fig 6G) in vivo at 4 days after IR. Considering that the antagomiR significantly restores cognitive dysfunction (Fig 6E) at 1-3 days after IR, we suppose the expression of miR-206-3p would be consistently increased by IR, suppressing the PAK3 signaling pathway and leading to cognitive dysfunction.

Page 33, line 825-827; "(H) Left: the protein levels of phosphorylated LIMK1, LIMK1, phosphorylated cofilin, and cofilin after IR in frontal cortex and hippocampus. Right: each western blot bands are quantified by ImageJ."

6) Fig 5 shows that in vivo administration of antago-miR-206 reversed IR-induced upregulation of miR-206, reductions in PAK3 and downstream markers, and, importantly, reversed cognitive deficits induced by IR. This data should be supported by in vivo staining for important dendritic markers, including cofillin/p-cofilin, PSD-95, F- and G-actin within the hippocampal and PFC regions.

We appreciated the reviewer's comment. Based on previous studies on intranasal administration, the substance is delivered to the PFC and hippocampus through the olfactory pathway in both humans and mice (Exp Neurobiol. 2020 Dec 31;29(6):453-469, Stem Cells. 2021 Dec;39(12):1589-1600). Even though we did not show direct evidence that antagomiR-206 is delivered to both regions, we confirmed its actual delivery to the brain using Cy5 fluorescence and examined PAK3 signaling (Fig. 6G) and the F/G-actin ratio (Fig. 6H) in both regions. To show the reliability of the tissue separation, we added a detailed description of the tissue separation method in “Materials and Methods”.

Page 19, line 410-423; "Dissection of prefrontal cortex and hippocampus. The dissection of mouse brain regions was performed following a previous study (Spijker, 2011). First, to obtain the hippocampal region, we gently held the brain and opened the forceps, slowly separating the cortical halves. Once an opening had been created along the midline for approximately 60%, we directed the forceps (in the closed position) counterclockwise by 30–40° to expose the left cortex from the hippocampus, repeatedly opening the forceps as necessary. We then repeated the same procedure for the right cortex by pointing the forceps in a 30–40° clockwise direction until the upper part of the hippocampus became visible. At the most caudal part of the hippocampus/cortex boundary, we moved the small forceps through the cortex and used them to separate the hippocampus from the fornix. After removing the hippocampus, we used the large forceps to fold the cortex back into its original position. Subsequently, we placed the brain with the dorsal side and cut coronal sections to reveal the prefrontal cortex and striatum at different levels. Using a sharp razor blade, we made the first cut to remove the olfactory bulb and cut the section containing the prefrontal cortex."

7) Does this change in the F/G actin ratios, Cofillin, and/or p-Cofillin impact any particular neuronal subtypes, including excitatory, inhibitory or any particular layers of major neurons? This point can't be appreciated from the WB data.

The excitatory and inhibitory neurons do play crucial roles in cognitive function. In terms of response to radiation, excitatory neurons are more likely to be responsive. A previous study showed that spike firing and excitatory synaptic input were reduced by cranial irradiation, while inhibitory input was increased (Neural Regen Res. 2022 Oct;17(10):2253-2259). Additionally, PSD-95 is localized to dense specialized regions within the dendritic spines of excitatory synapses and is associated with synaptic plasticity (Neuron. 2001 Aug 2;31(2):289-303). Indeed, IR decreases the mRNA level of PSD-95 in differentiated human neurons (Fig S5A). Considering the previous research and our data, IR-induced PAK3 downregulation may occur primarily in excitatory neurons.

8) Discussion: "In this study, we investigated the effect of cranial irradiation on cognitive function and the underlying mechanisms in a mouse model." Please change this statement to "....underlying neuronal mechanisms using in vivo and in vitro models."

We appreciate the reviewer’s comment. We replaced ‘mechanisms in a mouse model’ with ‘neuronal mechanisms using in vivo and in vitro models.’ in the manuscript.

Page 14, line 283; "In this study, we investigated the effect of cranial irradiation on cognitive function and the underlying neuronal mechanisms using in vivo and in vitro models."

9) Discussion: "Furthermore, our study identifies a potential mechanism underlying the cognitive impairment associated with cranial irradiation, which downregulates PAK3 expression." This statement should be supported by the in vivo immunofluorescence data for the synaptic markers, including cofilin, p-cofillin, PSD-95, and F/G-actin staining.

Even though we did not show the in vivo immunofluorescence data for the synaptic markers, we examined PAK3 signaling (Fig. 6G) and the F/G-actin ratio (Fig. 6H) in the hippocampal and PFC regions. Additionally, according to The Allen Mouse Brain Atlas, PAK3 is mainly expressed in the PFC and hippocampus regions (Fig S2A), suggesting that IR-induced PAK3 downregulation in both regions may have a significant impact on the cognitive impairment. Considering these data, we strongly believe that cranial irradiation downregulates PAK3 levels in the PFC and hippocampus, thus inducing cognitive impairment.

10) miR modulate function by affecting multiple targets. The other potential neuronal and non-neuronal targets for miR-206-3p were not discussed. This possibility should be confirmed using relevant markers.

According to the reviewer’s comment, we performed real-time PCR to examine whether miR-206-3p affects the expressions of neuronal and non-neuronal markers (Fig S5A and S5B). As a result, the post-synaptic marker, PSD-95, was reduced by miR-206-3p treatment. However, a mature neuronal marker (NeuN) and non-neuronal markers (GFAP and IBA-1) did not change upon miR-206-3p treatment. We added the related description in “Results”.

Page 12, line 240-243; "Additionally, the post-synaptic marker, PSD-95, was decreased by miR-206-3p treatment. However, a mature neuronal marker (NeuN) and non-neuronal markers (GFAP and IBA-1) were not alterd upon miR-206-3p treatment (Fig. S5A and S5B)."

11) Irradiation procedure: Please confirm that sham (0 Gy)-irradiated mice were also anesthetized for a similar procedure carried out for the 2 Gy or fractionated irradiation.

According to the reviewer's comment, we added a description of sham (0 Gy)-irradiated mice in “Materials and Methods”.

Page 17, line 359-360; "All mice, including those in the sham (0 Gy) group, were anesthetized with an intraperitoneal (i.p.) injection of zoletil (5 mg/10 g) daily for five days."

12) 24 mL volume (antagomir treatment) via intra-nasal delivery is a rather unusually high volume. Please clarify if such a procedure was approved by the regulatory committee and if 24 mL volume led to any hemodilution.

We appreciate the reviewer's comment. We referred to the protocol of intranasal administration from a previous study (Mol Ther. 2021 Dec 1;29(12):3465-3483), and made an error in specifying the miRNA unit. We corrected it from mL to μL.

Page 19, line 399-402; "According to the manufacturer’s instructions and previous study (Zhou et al., 2021), 40 nmol of antagomiR-206-3p (sequence: 5’-CCACACACUUCCUUACAUUCCA -3’) or antagomiR-NC (the antagomiR negative control, its antisense chain sequence: 5’-UCUACUCUUUCUAGGAGGUUGUGA-3’) was dissolved in 1 mL of RNase-free water."

Page 19, line 402-403; "A total of 24 μL of the solution (1 nmol per one mouse) was instilled with a pipette, alternately into the left and right nostrils (1 μL/time), with an interval of 3–5 min."

Reviewer #2

1) To show the relevance of PAK3 in Radiation-induced neurocognitive decrements, I suggest using 10 Gy WBI, group of 15-16 animals and long-term follow up >2 months post-RT.

We appreciate the reviewer's comment. Biologically Effective Dose (BED) represents the most accurate quantitative prediction of biological effects of radiation. However, our study aimed to analyze the mechanisms underlying cognitive dysfunction induced not by a total dose of 10 Gy but rather by repeating 2 Gy fractions, which is used in clinical practice such as prophylactic cranial irradiation. In this regard, the administration of 2 Gy fractions holds significant relevance in our research.

In statistical analysis, a larger sample size tends to be more accurate. However, we determined the sample size based on ethical considerations in animal research, taking into account the parameter (Effect size: 1.2 / alpha value: 0.05 / Group: 3 groups), resulting in a total sample size of 15, five mice per group (G Power 3.1 software). Despite the relatively small sample size, radiation exposure significantly reduced PAK3 expression with marginal variance, thereby inducing cognitive impairment.

As the reviewer mentioned, the long-term effect (>2 months) of WBI may show more severe cognitive impairment, considering results from the previous studies. Nevertheless, previous research has revealed a correlation between mouse age and human age, suggesting that 2 months in mice is roughly equivalent to 5 years in humans (Life Sci. 2020 Feb 1;242:117242). Due to the substantial difference in biological time between humans and mice, 2 months in mice might be an excessive long-term period. Additionally, our study aims to investigate short-term changes rather than long-term effects. It is clear that IR-induced PAK3 downregulation induces cognitive impairment at least in the short-term period, and we believe that our findings may contribute to preventing serious neuronal dysfunction as the long-term side effects of PCI.

eLife assessment

The study investigates the functional impact of cranial irradiation in mouse and proposes PAK3 as molecular element involved in radiation-induced cognitive decrement. The significance of the findings is useful for fields covering radiation, brain tumor and cognition. The strength of evidence is solid, although the referees expressed divergent views on the manuscript.

Reviewer #1 (Public Review):

Summary:

Exposure to cranial irradiation (IR) leads to cognitive deficits in the survivors of brain cancer. IR upregulates miR-206-3p, which in turn reduces the PAK3-LIMK1 axis leading to the loss of F and G-actin ratio and, thereby, mature dendritic spine loss. Silencing miR-206-3p reverses these degenerative consequences.

Strengths:<br /> The authors show compelling data indicating a clear correlation between PAK3 knockdown and the loss of mature dendritic spine density. In contrast, overexpression of PAK3 in the irradiated neurons restored mature spine types and recovered the F/G ratio. These in vitro results support the authors' hypotheses that PAK3 and LIMK1-mediated downstream signaling impact neuronal structure and reorganization in vitro. These data were supported by similar experiments using differentiated human neurons. Importantly, silencing miR-206-30 using antagonist miR also reverses IR-induced downregulation of the PAK3-LIMK1 axis, preventing spine loss and cognitive deficits.

Weaknesses:

All the miR-206-3p data are presented from in vitro cortical neurons or human stem cell-derived neuron cultures. This data (IR-induced elevation of miR-206-3p) should also be confirmed in vivo using an irradiated mouse brain to correlate the cognitive dysfunction timepoint.

Antago-miR-206-3p reversed Ir-induced upregulation of miR-206 (in vitro), and prevent reductions in PAK3 and downstream markers. Importantly, it reversed cognitive deficits induced by IR. This data should be supported by in vivo staining for important dendritic markers, including cofillin, p-cofilin, PSD-95, F- and G-actin within the hippocampal and PFC regions.

Other neuronal and non-neuronal targets of miR-206-3p should be discussed and looked into as a downstream impact of IR-induced functional and physiological impairments in the brain.

Reviewer #2 (Public Review):

Summary:<br /> The paper entitled "PAK3 downregulation induces cognitive 1 impairment following cranial irradiation" by Lee et al. aimed at investigating the functional impact of cranial irradiation in mouse and propose PAK3 as molecular element involved in radiation-induced cognitive decrement. The results provided in this paper are problematic as both the irradiation paradigm (5X2 Gy) as well as the timing of investigation (3 to 8 days post-IR) are completely irrelevant to investigate radiation induced neurocognitive impairment. This testifies to the team's lack of knowledge in radiobiology/radiotherapy and the methodology to explore radiation induced neurocognitive damages. It precludes any further relevance of the molecular results.

Weaknesses:

First and according to the BED equation a single dose of 10 Gy cannot not be approximated by 5 fractions of 2 Gy, as fractionation is known to decrease normal tissue toxicity. Note that in radiobiology/radio-oncology, the BED stands for "Biologically Effective Dose." This equation is used to compare the effects of different radiation treatments on biological tissues, taking into account the dose, fractionation, and the overall biological response of the tissue to radiation.<br /> The BED equation is commonly used to calculate the equivalent dose of a fractionated radiation treatment, which is the dose that would produce the same biological effect as a single, higher dose delivered in a single fraction.<br /> The general formula for BED is:BED = D * (1 + d / α/β)<br /> D is the total physical dose of radiation delivered in Grays (Gy)<br /> d is the dose per fraction in Gy<br /> α/β is the tissue-specific ratio of the linear (α) and quadratic (β) components of the radiation response. It is measured in Gy and describes how the tissue responds to different fractionation schedules (usually equal to 3 for the normal brain).<br /> Please refer to radiobiology/radiotherapy textbooks by Hall or Joiner.

Second, the brain is a late responding organ. GBM patients treated with 60 Gy exhibit progressive and debilitating impairments in memory, attention and executive function several month post-irradiation. In mice, neurocognitive decrements after a single dose of 10 Gy delivered to the whole brain does occur at late time point, usually > 2 months post-exposure. Multiple publications such as the one by Limoli C lab, Rossi S lab, Britten R lab or earlier Fike J lab and Robin M lab support this. Next, 5 fractions of 2 Gy will be more protective than a single dose of 10 Gy and neurocognitive decrements will require at least 5-6 months to occur if they ever occur. In Figure 1, the decrement reported is marginal, the number of animals included (4 to 5 at most?) The number of animals is not specified) is too low to draw any significant conclusions. In addition to the timing issue, the strategy described for NOR analysis shows methodological issues with the habituation period being too short and exploration level being very low.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

“Peng et al develop a computational method to predict/rank transcription factors (TFs) according to their likelihood of being pioneer transcription factors--factors that are capable of binding nucleosomes--using ChIP-seq for 225 human transcription factors, MNase-seq and DNase-seq data from five cell lines. The authors developed relatively straightforward, easy to interpret computational methods that leverage the potential for MNase-seq to enable relatively precise identification of the nucleosome dyad. Using an established smoothing approach and local peak identification methods to estimate positions together with identification of ChIP-seq peaks and motifs within those peaks which they referred to as "ChIP-seq motifs", they were able to quantify "motif profiles" and their density in nucleosome regions (NRs) and nucleosome free regions (NFRs) relative to their estimated nucleosome dyad positions. Using these profiles, they arrived at an odd-ratio based motif enrichment score along with a Fisher's exact test to assess the odds and significance that a given transcription factor's ChIP-seq motifs are enriched in NRs compared to NFRs, hence, its potential to be a pioneer transcription factor. They showed that known pioneer transcription factors had among the highest enrichment scores, and they could identify 32 relatively novel pioneer TFs with high enrichment scores and relatively high expression in their corresponding cell line. They used multiple validation approaches including (1) calculating the ROC-AUC associated with their enrichment score based on 16 known pioneer TFs among their 225 TFs which they used as positives and the remaining TFs (among the 225) as negatives; (2) use of the literature to note that known pioneer TFs that acted as key regulators of embryonic stem cell differentiation had a highest enrichment scores; (3) comparison of their enrichments scores to three classes of TFs defined by protein microarray and electromobility shift assays (1. strong binder to free and nucleosomal DNA, 2. weak binder to free and nucleosomal DNA, 3. strong binding to free but not nucleosomal DNA); and (4) correlation between their calculated TF motif nucleosome end/dyad binding ratio and relevant data from an NCAP-SELEX experiment. They also characterize the spatial distribution of TF motif binding relative to the dyad by (1) correlating TF motif density and nucleosome occupancy and (2) clustering TF motif binding profiles relative to their distance from the dyad and identifying 6 clusters.

The strengths of this paper are the use of MNase-seq data to define relatively precise dyad positions and ChIP-seq data together with motif analysis to arrive at relatively accurate TF binding profiles relative to dyad positions in NRs as well as in NFRs. This allowed them to use a relatively simple odds ratio based enrichment score which performs well in identifying known pioneer TFs. Moreover, their validation approaches either produced highly significant or reasonable, trending results.

The weaknesses of the paper are relatively minor. The most significant one is that they used ROC-AUC to assess the prediction accuracy of their enrichment score on a highly imbalanced dataset with 16 positives and 209 negatives. ROC-AUC is known to be a misleading prediction measure on highly imbalanced data. This is mitigated by the fact that they find an AUC = 0.94 for their best case. Thus, they're likely to find good results using a more appropriate performance measure for imbalanced data. Another minor point is that they did not associate their enrichment score (focus of Figure 2) with their correlation coefficients of TF motif density and nucleosome occupancy (focus of Figure 3). Finally, while the manuscript was clearly written, some parts of the Methods section could have been made more clear so that their approaches could be reproduced. The description of the NCAP-SELEX method could have also been more clear for a reader not familiar with this approach.”

Reviewer #2 (Public Review):

“In this study, the authors utilize a compendium of public genomic data to identify transcription factors (TF) that can identify their DNA binding motifs in the presence of nuclosome-wrapped chromatin and convert the chromatin to open chromatin. This class of TFs are termed Pioneer TFs (PTFs). A major strength of the study is the concept, whose premise is that motifs bound by PTFs (assessed by ChIP-seq for the respective TFs) should be present in both "closed" nucleosome wrapped DNA regions (measured by MNase-seq) as well as open regions (measured by DNAseI-seq) because the PTFs are able to open the chromatin. Use of multiple ENCODE cell lines, including the H1 stem cell line, enabled the authors to assess if binding at motifs changes from closed to open. Typical, non-PTF TFs are expected to only bind motifs in open chromatin regions (measured by DNaseI-seq) and not in regions closed in any cell type. This study contributes to the field a validation of PTFs that are already known to have pioneering activity and presents an interesting approach to quantify PTF activity.

For this reviewer, there were a few notable limitations. One was the uncertainty regarding whether expression of the respective TFs across cell types was taken into account. This would help inform if a TF would be able to open chromatin. Another limitation was the cell types used. While understandable that these cell types were used, because of their deep epigenetic phenotyping and public availability, they are mostly transformed and do not bear close similarity to lineages in a healthy organism. Next, the methods used to identify PTFs were not made available in an easy-to-use tool for other researchers who may seek to identify PTFs in their cell type(s) of interest. Lastly, some terms used were not defined explicitly (e.g., meaning of dyads) and the language in the manuscript was often difficult to follow and contained improper English grammar.”

Reviewer #3 (Public Review):

Peng et al. designed a computational framework for identifying pioneer factors using epigenomic data from five cell types. The identification of pioneer factors is important for our understanding of the epigenetic and transcriptional regulation of cells. A computational approach toward this goal can significantly reduce the burden of labor-intensive experimental validation. Nevertheless, there are several caveats in the current analysis which may require some modification of the computational methods and additional analysis to maximize the confidence of the pioneer factor prediction results.

A key consideration that arises during this review is that the current analysis anchors on H1 ESC and therefore may have biased the results toward the identification of pioneer factors that are relevant to the four other differentiated cell types. The low ranking of Yamanaka factors and known pioneer factors of NFYs and ESRRB may be due to the setup of the computational framework. Analysis should be repeated by using each of every cell type as an anchor for validating the reproducibility of the pioneer factors found so far and also to investigate whether TFs related to ESC identity (e.g. Yamanaka factors, NFYs and ESRRB) would show significant changes in their ranking. Given the potential cell type specificity of the pioneer factors, the extension to more cell types appears to be important for further demonstrating the utility of the computational framework.

Author Response: We thank all reviewers for their thoughtful and constructive comments and suggestions, which helped us to strengthen our paper. Following the suggestions, we have performed additional analysis to address the reviewer’s comments and the detailed responses are itemized below.

Reviewer #1 (Recommendations For The Authors):

1. The authors should generate precision-recall curves in addition to (or replacing) the ROC-AUC curves shown Figure 2c. They should also calculate the precision-recall AUC and use that as their measure of enrichment score predication accuracy. Precision-recall curves and AUC are more appropriate for imbalanced positive-negative data as is the case in this study.

Response: Following the reviewer’s suggestion, we have performed precision-recall analysis and calculated Matthews correlation coefficients (MCC) (Figure 2). We have further expanded our validation set to 32 known pioneer transcription factors (Supplementary Table 5) and compared the performance of enrichment score using different test sets (Supplementary Table 10). We have attained the highest ROC = 0.71, pr-ROC-AUC = 0.37 and MCC = 0.31 for Test set1 and ROC = 0.92, pr-ROC-AUC = 0.45 and MCC=0.49 for Test set2 (Supplementary Table 11).

1. The authors should generate scatter plots of their TF enrichment scores (focus of Figure 2) and motif-density nucleosome occupancy Pearson correlation coefficients (focus of Figure 3) and calculate the corresponding correlation coefficient and p-value.

Response: We observed a weak but statistically significant correlation between the enrichment scores and the correlation coefficient values (R=0.32 and p-value=1e-9)).

1. The authors should write their computational methods in the Methods section in such a way that a skilled bioinformatician could reproduce their results. This does not require a major rewrite. They are very close. One example of this is that a minimum distance between neighboring local maxima of the smoothed dyad counts was set to 150 bps. How was this algorithmically done? Suppress/ignore weaker local maxima that are within 150bp of other stronger local maxima?

Response: We have revised the Methods section to make it easier to follow and to reproduce the results. For identifying the local maxima, we have used the bwtool with the parameters ‘‘find local-extrema -maxima -min-sep=150’’ so that local maxima located within 150 bp of another neighboring maxima was ignored to avoid local clusters of extrema.

1. Describe the NCAP-SELEX method more clearly so that a reader not familiar with this approach doesn't have to look it up. This can be brief.

Response: Following the reviewer’s suggestion, we have added a detailed description of the NCAP-SELEX method.

Reviewer #2 (Recommendations For The Authors):

To improve the manuscript:

1. The grammar in the manuscript should be read for accuracy to improve readability and clarify the exact meaning.

Response: We have improved the grammar and have clarified the meaning of terms.

1. The exact meaning of dyads needs to be defined up front. In some places seems to mean pairs of reads and others seems to refer to nucleosome positioning.

Response: The meaning of “dyads” has been clarified. The dyad positions were determined by the midpoints of the mapped reads in MNase-seq data and refer to the center of the nucleosomal DNA.

1. Meaning of NCAP-SELEX needs to be defined before use of acronym.

Response: We have defined it in the manuscript.

Reviewer #3 (Recommendations For The Authors):

1. The authors found that Yamanaka factors and several other known pioneer factors (e.g. NFY-A, NFY-B, and ESRRB) are lowly ranked in their pioneer factor analysis. Since the analysis was performed by anchoring on H1 ESCs and comparing them to the other four cell lines, the results may only be relevant to differentiated cell types. It is therefore not unexpected that the Yamanaka factors which are important for iPSC reprogramming and the NFYs which have been experimentally shown to replace nucleosomes for maintaining ESC identity from differentiation (PMID: 25132174; PMID: 31296853) would not be enriched in the analysis. I suggest the authors repeat their analysis by anchoring on differentiated cell types and validate the reproducibility of the pioneer factors found so far and also investigate whether TFs related to ESC identity (e.g. Yamanaka factors, NFYs, and ESRRB) would show significant changes in their ranking as pioneer factors.

Response: Following reviewer’s suggestions, we have repeated the enrichment analysis by redefining differentially open regions as those closed in differentiated cell lines (HepG2, HeLa-S3, MCF-7 and K562) and open in H1 embryonic cell line (Supplementary Figure 6). The results indicate that most known PTFs still showed significantly higher enrichment scores compared with other TFs especially for FOXA, GATA and CEBPB families. Interestingly, ESSRB and Yamanaka pioneer factor POU5F1 (OCT4) have also shown significantly high enrichment scores in this analysis (Supplementary Figure 6). This could be explained by the roles of Yamanaka factors in cellular reprogramming – they reprogram somatic differentiated cells into induced pluripotent stem cells.

1. The authors mentioned the cell-type-specificity of TFs been pioneer factors and the example of CTCF was given. This point relates closely to above point 1 and, in particular, the correlation analysis of Yamanaka factors and NFYs supports their binding to nucleosomes. Together, these results highlight potential caveats of the current analysis in that the analysis is likely to be limited to the available cell types and may be affected by which cell type was used as the anchor cell type.

Response: Differentiated and embryonic cell lines were used to ask specific question about the functional roles of PTFs for cell differentiation and stem cell reprogramming. In the revised manuscript, we have clarified this point and separated our data set into three different sets of PTFs with different functions (Supplementary table 10). We agree with the reviewer, it would be nice to have more data from other cell lines but unfortunately the matching between different Chip-seq, DNAase-seq and Mnase-seq data sets imposes strict limitations.

1. The differential and conserved open chromatin regions are defined based on overlaps found between five cell types using their DNase-seq mapping profiles. The limitation of this definition is its lack of quantitativeness. For example, a chromatin region can have more than 80% overlaps between H1 and another cell type but the level of accessibility (e.g. number of reads mapped to this region) can be quite different between cell types. In such a case, I think it is still more appropriate to define such a region as a differential open chromatin region. The author should explore whether using a more quantitative definition would improve the identification and categorization of differential and conserved open chromatin regions.

Response: we thank the reviewer for these suggestions. In the revised version, we have clarified the definition and further explored different thresholds in defining the differentially and conserved open chromatin regions in enrichment analysis (Supplementary Figure 8). Our results were not significantly affected when different thresholds are applied.

1. While it is mentioned that H3K27ac and H3K4me1 ChIP-seq data from the five human cell lines were used in the study, the information on how enhancers are mapped/defined in these cell types is lacking.

Response: We have clarified the definition in the text. The enhancer regions were identified as the open chromatin regions overlapped with both H3K27ac and H3K4me1 ChIP-seq narrow peaks. We have elucidated the how enhancers are defined in the methods sections. In addition, we have performed additional enrichment analysis using NRs located on differentially active enhancer regions and NDRs located on conserved active enhancer regions (Supplementary Figure 7) between H1 embryonic cell line and any other differentiated cell lines and the performance of enrichment scores in PTF classification was slightly worse compared with those calculated from differentially and conserved open chromatin regions

1. The description of "genome-wide mapping of transcription factor binding sites" is unclear. For example, what does it mean by "In total, ChIP-seq data for 225 transcription factors could be matched with MNase-seq data" and why is this step needed? I would assume that a typical approach for mapping TF binding sites in the five cell types is to obtain the ChIP-seq data for each TF in each cell type and perform sequence alignment to the reference genome. The procedure described by the authors needs a clearer motivation and justification.

Responses: This sentence refers to matching between the ChIP-seq and MNase-seq data from the same cell type. We explain in detail how ChIP-seq data is processed. We have clarified this in the paper.

1. I also suggest the authors clearly justify the use of ROC analyses given that only a ground truth of positive (e.g. 16 known pioneer factors) is available and the "other transcription factors" considered as negative in the analysis in fact are expected to contain unknown pioneer factors and their identification should not be minimized (which lead to the maximization of ROC) by the analysis procedure.

Responses: (This is also pointed by review 1). The fact that unknown transcription factors are treated as negatives actually leads to the lower reported ROC scores (more hits considered to be false positives), not to their maximization. That is the reason we mentioned in the paper that the obtained ROC scores can be considered as lower bound estimates. In addition, we have expanded our validation sets to 32 known pioneer factors and compiled three sets of PTFs for validations. Following the reviewers’ suggestions, we have further performed precision-recall (PR) analysis and calculated the Matthews correlation coefficient (MCC) using three sets of PTFs for validation (Supplementary Table 11 and Supplementary Figure 2).

1. The analysis of pioneer transcription factor binding sites lacks insight. What can we learn these this analysis other than TFs from the same families are likely to be clustered in the same group?

Responses: We thank the review for pointing out it and have added a more detailed discussion of these results in the revised manuscript. Very few PTF-nucleosome structural complexes have currently been solved so far and the binding modes of majority of PTFs with nucleosomes still remain unknow. Our analysis has identified six distinctive clusters of TF binding profiles with nucleosomal DNA, which could provide insight into the binding modes of PTFs with nucleosome. These clusters point to the diversity of binding motifs where transcription factors belonging to the same cluster may also exhibit potential competitive binding.

Reviewer #1 (Public Review):

Peng et al develop a computational method to predict/rank transcription factors (TFs) according to their likelihood of being pioneer transcription factors--factors that are capable of binding nucleosomes--using ChIP-seq for 225 human transcription factors, MNase-seq and DNase-seq data from five cell lines. The authors developed relatively straightforward, easy to interpret computational methods that leverage the potential for MNase-seq to enable relatively precise identification of the nucleosome dyad. Using an established smoothing approach and local peak identification methods to estimate positions together with identification of ChIP-seq peaks and motifs within those peaks which they referred to as "ChIP-seq motifs", they were able to quantify "motif profiles" and their density in nucleosome regions (NRs) and nucleosome depleted regions (NDRs) relative to their estimated nucleosome dyad positions. Using these profiles, they arrived at an odd-ratio based motif enrichment score along with a Fisher's exact test to assess the odds and significance that a given transcription factor's ChIP-seq motifs are enriched in NRs compared to NDRs, hence, its potential to be a pioneer transcription factor. They showed that known pioneer transcription factors had among the highest enrichment scores, and they could identify a number of relatively novel pioneer TFs with high enrichment scores and relatively high expression in their corresponding cell line. They used multiple validation approaches including (1) calculating the ROC-AUC and Matthews correlation coefficient (MCC) and generating ROC and precision-recall curves associated with their enrichment score based on 32 known pioneer TFs among their 225 TFs which they used as positives and the remaining TFs (among the 225) as negatives; (2) use of the literature to note that known pioneer TFs that acted as key regulators of embryonic stem cell differentiation had a highest enrichment scores; (3) comparison of their enrichment scores to three classes of TFs defined by protein microarray and electromobility shift assays (1. strong binder to free and nucleosomal DNA, 2. weak binder to free and nucleosomal DNA, 3. strong binding to free but not nucleosomal DNA); and (4) correlation between their calculated TF motif nucleosome end/dyad binding ratio and relevant data from an NCAP-SELEX experiment. They also characterize the spatial distribution of TF motif binding relative to the dyad by (1) correlating TF motif density and nucleosome occupancy and (2) clustering TF motif binding profiles relative to their distance from the dyad and identifying 6 clusters.

The strengths of this paper are the use of MNase-seq data to define relatively precise dyad positions and ChIP-seq data together with motif analysis to arrive at relatively accurate TF binding profiles relative to dyad positions in NRs as well as in NDRs. This allowed them to use a relatively simple odds ratio based enrichment score which performs well in identifying known pioneer TFs. Moreover, their validation approaches either produced highly significant or reasonable, trending results.

The weaknesses of the paper are relatively minor, and the authors do a good job describing the limitations of the data and approach.

Reviewer #2 (Public Review):

In this study, the authors utilize a compendium of public genomic data to identify transcription factors (TF) that can identify their DNA binding motifs in the presence of nuclosome-wrapped chromatin and convert the chromatin to open chromatin. This class of TFs are termed Pioneer TFs (PTFs). A major strength of the study is the concept, whose premise is that motifs bound by PTFs (assessed by ChIP-seq for the respective TFs) should be present in both "closed" nucleosome wrapped DNA regions (measured by MNase-seq) as well as open regions (measured by DNAseI-seq) because the PTFs are able to open the chromatin. Use of multiple ENCODE cell lines, including the H1 stem cell line, enabled the authors to assess if binding at motifs changes from closed to open. Typical, non-PTF TFs are expected to only bind motifs in open chromatin regions (measured by DNaseI-seq) and not in regions closed in any cell type. This study contributes to the field a validation of PTFs that are already known to have pioneering activity and presents an interesting approach to quantify PTF activity.

For this reviewer, there were a few notable limitations. One was the uncertainty regarding whether expression of the respective TFs across cell types was taken into account. This would help inform if a TF would be able to open chromatin. Another limitation was the cell types used. While understandable that these cell types were used, because of their deep epigenetic phenotyping and public availability, they are mostly transformed and do not bear close similarity to lineages in a healthy organism. Next, the methods used to identify PTFs were not made available in an easy-to-use tool for other researchers who may seek to identify PTFs in their cell type(s) of interest. Lastly, some terms used were not define explicitly (e.g., meaning of dyads) and the language in the manuscript was often difficult to follow and contained improper English grammar.

eLife assessment

This valuable study aims to identify pioneer transcription factors - which are defined as transcription factors that compete with nucleosomes for DNA binding. The authors provide methods for identifying pioneer transcription factors on a cell type basis, using nucleosome positioning and motif information across different cell lines. The evidence to support the claims is largely solid. This work will be of interest to computational and molecular biologists working on transcription factors.

Reviewer #3 (Public Review):

Peng et al. designed a computational framework for identifying pioneer factors using epigenomic data from five cell types. The identification of pioneer factors is important for our understanding of the epigenetic and transcriptional regulation of cells. A computational approach toward this goal can significantly reduce the burden of labor-intensive experimental validation.

The authors have addressed my previous comments.

The main issue identified in this re-review is based on the authors' additional experiments to investigate the reproducibility of the pioneer factors identified in the previously analysis that anchored on H1 ESCs.

The additional analysis that uses the other four cell types (HepG2, HeLa-S3, MCF-7, and K562) as anchors reveals the low reproducibility/concordance and high dependence on the selection of anchor cell type in the computational framework. In particular, now several stem cell related TFs (e.g. ESRRB, POU5F1) are ranked markedly higher when H1 ESC is not used as the anchor cell type as shown in Supplementary Figure 5.

Of note, the authors have now removed the shape labels that denote Yamanaka factors in Figure 2c (revised manuscript) that was presented in the main Figure 2a in the initial submission. The NFYs and ESRRB labels in Supplementary 4a are also removed and the boxplot comparing NFYs and ESRRB with other TF are also removed in this figure. Removing these results effectively hides the issues of the computational framework we identified in this revision. Please justify why this was done.

In summary, these new results reveal significant limitations of the proposed computational framework for identifying pioneer factors. The current identifications appear to be highly dependent on the choice of cell types.

Author Response

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

The co-authors and I would like to thank you for overseeing the review, and to thank you and the reviewers for your constructive feedback about the manuscript. Below, we have summarized each suggestion for improving the manuscript and provided our response. In addition, the abstract was revised to include findings from physiological studies of mice with a single Numb cKO and to provide a more concise and conservative concluding statement.

Reviewer #1 (Recommendations for The Authors):

1. While the specificity of the observed muscle phenotypes seems clear, the subsequent molecular analysis of Numb protein interactors does not seem to consider the potential involvement of Numb-like. The authors should demonstrate the relative expression levels of Numb and Numb-like in the models used, and establish the specificity of the antibodies used in IP, western and staining experiments.

Response: Perhaps the most convincing evidence that the anti-Numb antibody did not pull down Numb-like is that this protein was not detected among immunoprecipitated protein complexes pulled down by the anti-Numb antibody used. The antibody used in the immunoprecipitation was validated by the supplier and was previously reported to immunoprecipitate Numb [1, 2]. We previously demonstrated that a morpholino against Numb mRNA almost completely eliminated the band detected by this antibody and that this band was at the expected molecular weight [ref]. In our hands, mRNA levels for Numb-like in skeletal muscle are 5-10-fold lower than those for Numb [3]. We have been unable to detect Numb-like protein in healthy adult skeletal muscle by immunoblotting or immunofluorescence staining. Taking all of these findings together, it seems unlikely that the antibodies used for immunoprecipitating Numb-protein complexes pulls down Numb-like.

1. The authors use PCR to investigate Numb isoform expression and conclude that p65 is likely the dominant protein isoform expressed. While this agrees with the single band observed in Supp Figure 4A, a positive control for exon 9 excluded and included isoforms in the PCR reactions would strengthen this conclusion.

Response: The amplicons shown in Supplemental 4 were sequenced. The clones corresponded to the isoforms with the exon 3 present or removed. No amplicons containing exon 9 were detected. The following sentence was added to the Analysis of Splice Variants section of Methods to address this point: “PCR products were cloned using the TOPO TA cloning system (ThermoFisher) and multiple resulting clones were sequenced to confirm that the expected products were generated.”

1. PCR analysis of total Numb and Numb-like expression levels are not shown. This is important given the specificity of the Numb antibodies used for AP-MS experiments are not described and some Numb antibodies are well known to also recognize Numb-like. Two different Numb antibodies were used for Western and immunoprecipitation but the specificity for Numb and Numb-like is not described. In particular, does the antibody used in the AP-MS experiment recognize both Numb and Numb-like? Supplementary Table 1 does not list Numb or Numb-like, but presumably peptides were identified?

Response: As noted above, the specificity of anti-Numb antibodies was confirmed in previous studies [3]. Importantly, Numb-like mRNA levels are 5-10-fold lower than Numb mRNA, and NumbL protein is undetectable in healthy adult skeletal muscle by Western. The physiology data reported in this manuscript supports the conclusion that a single KO of Numb is sufficient to recapitulate the physiological phenotype of Numb/Numb-like KO . We therefore reason that the majority, if not all, of the physiological contribution of these proteins to muscle contractility due to Numb (Fig. 1).

1. The validation experiment used the same Numb antibody for immunoprecipitation, immunoblotted with Septin 7. A reciprocal IP of Septin 7 and blotted with Numb should be performed. In addition, a Numb-like IP or immunoblot would also be useful to demonstrate the specificity of the interaction. Efforts to map the interaction between Numb and Septin 7 would be useful to demonstrate specificity of the interaction and strategies to establish the biological relevance of the interaction.

Response: We agree with the reviewer and attempted several IPs with anti-Septin7 antibodies. These were unsuccessful. In a new collaboration, Dr. Italo Cavini (University of Sao Paulo) has used machine-learning-based approaches to model binding between Numb and several septins, including Septin 7. The analysis suggests that binding of Numb with septins involves a domain of Numb that has not yet been ascribed a function in protein-protein interactions. These computational predictions require experimental validation but provide rational starting point for experiments to define the domains responsible for these interactions. Such experiments were included in our recent NIH R01 renewal application. We hope to be able to report on results of confirmatory experiments of these computational models in the future.

1. Other septins were identified in the AP-MS experiment and might have been anticipated to also be disrupted by Numb/Numb-like deletion. Are these septins known to interact in a complex?

Response: This is an excellent question. Septins have conserved motifs providing a clear reason to imagine that many different mammalian septins could directly interact with Numb. Septins form heterooligomers consisting of complexes formed by 3, 6 or 8 septins [4]. It is likely that when Numb binds to one septin, antibodies against Numb pull down other septins present in the septin oligomer to which Numb is bound. The following paragraph was added to the discussion: “Our findings suggest that Numb may also interact with other septins such as septins 2, 9 and 10, which were also identified with a high level of confidence as Numb interacting proteins by our LC/MS/MS analysis. Our data to not allow us to determine if Numb binds directly to these septins. Septins contain highly conserved regions, and, consequently, if one such region of septin 7 interacts with Numb, then many septins would be expected to directly bind Numb through the same domain. However, because septins self-oligomerize, is possible that when Numb binds to one septin, antibodies against Numb could also pull down other septins present in the septin oligomer to which Numb is bound regardless of whether or not they are also bound by Numb. “

1. The text for Figure 5 describes analysis of Septin localization in inducible Numb/Numb-like cKO muscle, but the figure indicates only Numb is knocked out. Please clarify.

Response: We apologize for this oversight on our part. The Legend to Figure 5 has been corrected.

1. Supplementary Figure 2 seems to show that TAM treatment increases Numb expression. Please clarify. Also, please correct reference 9.

Response: The figure was incorrectly labeled. We apologize for this oversight and have corrected the figure in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

Overall, the manuscript is well written. I do have a few minor issues/concerns, which are detailed below.

Abstract: Please be a little more specific regarding which where the tissue came from (i.e. humans, mice, cell) when referring to your previous studies.

Response: The abstract has been revised as requested.

Introduction: Please be more specific regarding the technique used for detecting ultrastructural changes. I assume it was done with TEM, but the reference is listed as an "invalid citation" in your reference list.

Response: The introduction was revised as requested and the citation was updated to reference a valid citation.

Methods / Numb Co-Immunoprecipitation: Please indicated the level of confluency of the C2C12 cells as this will alter gene expression.

Response: As indicated in the updated Methods section, confluent C2C12 cells were switched to differentiation media (low serum) for seven days. When harvested, the cells had differentiated and fused into myotubes.

Methods / Immunohistochemical Staining: The first sentence needs to be edited regarding plurality and grammar.

Response: Thank you for this comment. The text was revised accordingly.

Results / GWAS and WGS Identify...: Please spell out phosphodiesterase (I assume) for PDE4D

Response: This change was incorporated in the text.

References cited:

1. Wu, M., et al., Epicardial spindle orientation controls cell entry into the myocardium. Dev Cell, 2010. 19(1): p. 114-25.

2. Garcia-Heredia, J.M. and A. Carnero, The cargo protein MAP17 (PDZK1IP1) regulates the immune microenvironment. Oncotarget, 2017. 8(58): p. 98580-98597.

3. De Gasperi, R., et al., Numb is required for optimal contraction of skeletal muscle. J Cachexia Sarcopenia Muscle, 2022.

4. Neubauer, K. and B. Zieger, The Mammalian Septin Interactome. Front Cell Dev Biol, 2017. 5: p. 3.

eLife assessment

This convincing study demonstrates a potentially important role for the factor Numb in skeletal muscle excitation-contraction coupling. Numb knockout reduced contractile force. The authors thus demonstrate a novel role for Numb in calcium release in skeletal muscle.

Reviewer #1 (Public Review):

The authors investigate the function of the PTB domain containing adaptor protein Numb in skeletal muscle structure and function. In particular, the effects of reduced Numb expression in aging muscle is proposed as a mechanism for reduced contractile function associated with sarcopenia. Using ex-vivo analysis of conditional Numb and Numblike knockout muscle the authors demonstrate that loss of Numb but not the related Numblike gene expression perturbs muscle force generation. In order to explore the molecular mechanisms involved, Numb interacting proteins were identified in C2C12 cell cultured myotubes by immunoprecipitation and LC-MS/MS. The authors identify Septin 7 as well as Septin 2, 9 and 10 as a Numb binding proteins and demonstrate that loss of Numb/Numblike in myofibers causes changes in Septin 7 subcellular localization. Of note, whether additional septins form a complex or are also disrupted by Numb/Numblike loss remains an interesting area for further investigation. Additional investigation of the specificity and mapping of the Numb-Septin 7 (or another Septin) interaction would be of interest and provide an approach for future studies to demonstrate the biological relevance and specificity of the Numb-Septin 7 interaction in skeletal muscle

Reviewer #2 (Public Review):

Summary:

The main purpose of this investigation was to 1) compare the effects of a single knockout (sKO) of Numb or a double knockout (dKO) of Numb and NumbL on ex-vivo physiological properties of the extensor digitorium longus (EDL) muscle in C57BL/6NCrl mice; and 2) analyze protein complexes isolated from C2C12 myotubes via immunoprecipitation and LC/MS/MS for potential Numb binding partners. The main findings are 1) the muscles from sKO and dKO were significantly weaker with little difference between the sKO and dKO lines, indicating the reduced force is mainly due to the inactivation of the Numb gene; and 2) there were 11 potential Numb binding proteins that were identified and cytoskeletal specific proteins including Septin 7.

Strengths:

Straight-forward yet elegant design to help determine the important role the Numb has in skeletal muscle.

Weaknesses:

There were a limited number of samples (3-6) that were used for the physiological experiments; however, there was a very large effect size in terms of differences in muscle tension development between the induced KO models and the controls.

Author Response

The following is the authors’ response to the previous reviews

Reviewer #1 (Public Review):

In countries endemic for P vivax the need to administer a primaquine (PQ) course adequate to prevent relapse in G6PD deficient persons poses a real dilemma. On one hand PQ will cause haemolysis; on the other hand, without PQ the chance of relapse is very high. As a result, out of fear of severe haemolysis, PQ has been under-used.

In view of the above, the Authors have investigated in well-informed volunteers, who were kept under close medical supervision in hospital throughout the study, two different schedules of PQ administration: (1) escalating doses (to a total of 5-7 mg/kg); (2) single 45 mg dose (0.75 mg/kg).

It is shown convincingly that regimen (1) can be used successfully to deliver within 3 weeks, under hospital conditions, the dose of PQ required to prevent P vivax relapse.

As expected, with both regimens acute haemolytic anaemia (AHA) developed in all cases. With regimen (2), not surprisingly, the fall in Hb was less, although it was abrupt. With regimen (1) the average fall in Hb was about 4 G. Only in one subject the fall in Hb mandated termination of the study.

Since the data from the Chicago group some sixty years ago, there has been no paper reporting a systematic daily analysis of AHA in so many closely monitored subjects with G6PD deficiency. The individual patient data in the Supplementary material are most informative and more than precious.

Having said this, I do have some general comments.

1. Through their remarkable Part 1 study, the Authors clearly wish to set the stage for a revision of the currently recommended PQ regimen for G6PD deficient patients. They have shown that 5-7 mg/kg can be administered within 3 weeks, whereas the currently recommended regimen provides 6 mg/kg over no less than 8 weeks.

We state in the abstract: “The aim was to explore shorter and safer primaquine radical cure regimens compared to the currently recommended 8-weekly regimen (0.75 mg/kg once weekly), potentially obviating the need for G6PD testing”. This is the primary goal of the study.

1. Part 2 aims to show that, as was known already, even a single PQ dose of 0.75 mg/kg causes a significant degree of haemolysis: G6PD deficiency-related haemolysis is characteristically markedly dose-dependent. Although they do not state it explicitly in these words (I think they should), the Authors want to make it clear that the currently recommended regimen does cause AHA.

We also wanted to compare the extent of haemolysis following single dose with the extent of haemolysis following the ascending dose regimens, in the same patients.

1. Regulatory agencies like to classify a drug regimen as either SAFE or NOT-SAFE; they also like to decide who is 'at risk' and who is 'not at risk'. A wealth of data, including those in this manuscript, show that it is not correct to say that a G6PD deficient person when taking PQ is at risk of haemolysis: he or she will definitely have haemolysis. As for SAFETY, it will depend on the clinical situation when PQ is started and on the severity of the AHA that will develop.

We agree completely. Haemolysis following primaquine is inevitable. What matters is the rate and extent of haemolysis, and the compensatory response. Importantly the extent of the haemolysis, even within a specific genotype and for a given drug dose, appears to be highly variable.

The above three issues are all present in the discussion, but I think they ought to be stated more clearly.

We have tried to clarify these points in a revised discussion.

Finally, by the Authors' own statement on page 15, the main limitation is the complexity of this approach. The authors suggest that blister packed PQ may help; but to me the real complexity is managing patients in the field versus the painstaking hospital care in the hands of experts, of which volunteers in this study have had the benefit. It is not surprising that a fall in Hb of 4 g/dl is well tolerated by most non-anaemic men; but patients with P vivax in the field may often have mild to moderate to severe anaemia; and certainly they will not have their Hb, retics and bilirubin checked every day. In crude approximation, we are talking of a fall in Hb of 4 G with regimen (1), as against a fall in Hb of 2 G with regimen (2), that is part of the currently recommended regimen: it stands to reason that, in terms of safety, the latter is generally preferable (even though some degree of fall in Hb will recur with each weekly dose). In my view, these difficult points should be discussed deliberately.

As above we have tried to clarify these important points in a revised discussion

Reviewer #1 (Recommendations For The Authors):

Page 2 para 3. The decreased haemolysis upon continued PQ administration (that originally was named the 'resistance phase' is explained by two additive factors. First, the reticulocytosis (cells with higher G6PD activity pour into circulation from the bone marrow); second, the early doses of PQ has caused selective haemolysis of the oldest red cells, that had the lowest G6PD activity. This dual phenomenon is hinted at, but I think it should be stated clearly.

Thank you. We have added to the Introduction (fourth paragraph in revised version):

“Continued primaquine administration to G6PD deficient subjects resulted in "resistance" to the haemolytic effect. The selective haemolysis of the older red cells resulted in a compensatory increase in the number of reticulocytes. Thus, the red cell population became progressively younger and increasing resistant to oxidant stress, so overall haemolysis decreased and a steady state was reached.”

Page 4 and elsewhere. In the 'Hillmen scale' for haemoglobinuria a value >6 was named a 'paroxysm'; but any value of 2 and above is already frank haemoglobinuria. Incidentally, the chart was published not in ref 17, but in NEJM 350:552, 2004.

We have changed the reference (now ref 19) to the 2004 paper by Hillmen. We used the value of 6 as clinical criterion for stopping primaquine. While >2 is detectable in dilute urine, >6 refers to clearly red/black urine.

In Table 1 and throughout the paper I am surprised that retics are given as %: absolute retic counts are more informative.

We showed these as % counts as the majority of measurements were taken from blood slide readings where it is not possible to get an absolute count.

Page 10, Attenuated hemolysis with continued or recurrent doses of PQ was shown convincingly for G6PD A-. There is also one report in which the time course of AHA was extensively investigated upon deliberate administration of PQ to a subject with G6PD Mediterranean (Blood 25: 92, 1965): there was little or no evidence for a 'resistance phase'.

We agree that this suggests it might not be possible to attenuate haemolysis with the Mediterranean variant (or variants of similar severity) as even the youngest circulating red cells may be susceptible to haemolysis. More evidence is needed.

S6, S7. Reticulocytes remain high until PQ is stopped; they return to normal some 17 days after stopping PQ. This should be stated in the main text.

This has been added to the main text (section “Haemolysis and reticulocyte response”):

“It took around 2 weeks for the reticulocyte counts to re-normalise.”

In subject 11 haemoglobinuria was slight on day 12; what was it before?

We have changed the caption of this Figure (Appendix 5) to:

“Day 10 urine sample from subject 11 showing slight haemoglobinuria (Hillmen score of 4). The subject had a maximum Hillmen score varying between 2 and 3 on days 4 to 9.”

I found individual patient data in S5 and S6 most interesting, especially since the G6PD variant was identified in each case. It would be helpful if in each case the total PQ dose were also shown, and in the interest of visual comparability the abscissa scale ought to be the same for all cases.

We have amended Figures S5 and S6 to make them consistent with each other (now Appendix 5). We also amended the figures showing the individual subject data for consistency.

eLife assessment

This manuscript addresses an important question, that in countries endemic for P vivax the need to administer a primaquine (PQ) course adequate to prevent relapse in G6PD deficient persons poses a real dilemma. On one hand PQ will cause haemolysis; on the other hand, without PQ the chance of relapse is very high. As a result, out of fear of severe haemolysis, PQ has been under-used. This manuscript is convincing that regimen (1) can be used successfully to deliver within 3 weeks, under hospital conditions, the dose of PQ required to prevent P vivax relapse.

Joint Public Review:

In countries endemic for P vivax the need to administer a primaquine (PQ) course adequate to prevent relapse in G6PD deficient persons poses a real dilemma. On one hand PQ will cause haemolysis; on the other hand, without PQ the chance of relapse is very high. As a result, out of fear of severe haemolysis, PQ has been under-used.

In view of the above, the authors have investigated in well-informed volunteers, who were kept under close medical supervision in hospital throughout the study, two different schedules of PQ administration: (1) escalating doses (to a total of 5-7 mg/kg); (2) single 45 mg dose (0.75 mg/kg).

It is shown convincingly that regimen (1) can be used successfully to deliver within 3 weeks, under hospital conditions, the dose of PQ required to prevent P vivax relapse.

As expected, with both regimens acute haemolytic anaemia (AHA) developed in all cases. With regimen (2), not surprisingly, the fall in Hb was less, although it was abrupt. With regimen (1) the average fall in Hb was about 4 G. Only in one subject the fall in Hb mandated termination of the study.

Since the data from the Chicago group some sixty years ago, there has been no paper reporting a systematic daily analysis of AHA in so many closely monitored subjects with G6PD deficiency. The individual patient data in the Supplementary material are most informative and more than precious.

Comments on the revised version:

In my view this important paper is further improved in this revised version (R2), particularly with respect to clarity in the discussion. All the points I had previously raised have been tackled.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors identified compound heterozygous mutations in CFAP52 recessively cosegregating with male infertility status in a non-consanguineous family. The Cfap52-mutant patient exhibits a mixed acephalic spermatozoa syndrome (ASS) and multiple morphological abnormalities of the sperm flagella (MMAF) phenotype. The influence of mutations on CFAP52 protein function is well validated by in vitro cell experiments and immunofluorescence staining. Cfap52-KO mice are further constructed and perfectly resemble the Cfap52-mutant patient's infertile phenotype, also showing a mixed ASS and MMAF phenotype. The phenotype and underlying mechanisms of the disruption of sperm head-tail connection and flagella development are carefully analyzed by TEM, Western blotting, and immunofluorescence staining. The data presented revealed a prominent role for CFAP52 in sperm development, suggesting that CFAP52 is a novel diagnostic target for male infertility with defects of sperm head-tail connection and flagella development.

Thank you for your positive comments.

Reviewer #2 (Public Review):

Summary:

The authors tried to identify the genetic factors for asthenoteratozoospermia. Using whole-exome sequencing, they analyzed a family with an infertile male and identified CFAP52 variants. They further knockout mouse Cfap52 gene and the homozygous mice phenocopied the patient. CFAP52 interacts with several other sperm proteins to maintain normal sperm morphology. Finally, CFAP52-associated male infertility in humans and mice could be overcome by using intracytoplasmic sperm injections (ICSI).

Strengths:

The major strength of this study is to identify genetic factors contributing to asthenoteratozoospermia, and to generate a mouse knockout model to validate the factor.

Thank you for your positive comments.

Weaknesses:

The authors did not use the OMICS to dissect the potential mechanisms. Instead, they took the advantage of direct co-IP experiment to fish the binding partners. They also did not discuss in detail why other motile cilia have different behavior.

Dear reviewer, thank you for your comments and we tried to answer your two questions as follows.

In this study, we did not choose omics technologies to explore the binding partners for CFAP52 (e.g., IP-MS) and differentially expressed proteins after the loss of CFAP52 (e.g., proteomics). For IP-MS, we feel sorry that all available antibodies of CFAP52 could not be used to perform protein immunoprecipitation experiments. Another reason is that there are only dozens of proteins that have been reported to regulate the head-tail coupling apparatus (HTCA) of sperm. Accordingly, we used Western blotting to examine the expression of ten acephalic sperm syndrome (ASS)-associated proteins and found that only SPATA6 expression was significantly reduced in the testis protein lysates of Cfap52-KO mice (Fig. 6A). We further carefully examined the regulation of the stability of SPATA6 by its binding partner CFAP52 (Fig. 6 and Figure 6—figure supplement 2).

In addition to male infertility, Cfap52-KO mice suffered from hydrocephalus; the ependymal cilia was sparse under SEM observation and disrupted axonemal structures were identified by TEM analysis (Figure 4—figure supplement 2). However, no obvious abnormalities of tracheal cilia were identified by SEM and TEM analyses (Figure 4—figure supplement 2). Although flagella and motile cilia exhibit quite similar “9+2” axoneme structure, they have some their unique proteins and the requirement of some axonemal proteins may be different. For example, IQUB expression is detected in tissues other than the testis, such as the lung and brain; however, IQUB deletion only affects beating of sperm flagella but not respiratory cilia (Cell Rep, 2022). Cfap43-KO mice exhibited both sperm flagella disordor and early-onset hydrocephalus (Dev Biol, 2020), and CFAP206 is required for sperm motility, mucociliary clearance of the airways and brain development (Development, 2020).

Reviewer #3 (Public Review):

Summary:

In this study, Jin et al. report the first evidence of CFAP52 mutations in human male infertility by identifying deleterious compound heterozygous mutations of CFAP52 in infertile human patients with acephalic and multiple morphological abnormalities in flagella (MMAF) phenotypes but without other abnormalities in motile cilia. They validated the pathogenicity of the mutations by an in vitro minigene assay and the absence of proteins in the patient's spermatozoa. Using a Cfap52 knockout mouse model they generated, the authors showed that the animals are hydrocephalic and the sperm have coupling defects, head decapitation, and axonemal structure disruption, supporting what was observed in human patients.

Strengths:

The major strengths of the study are the rigorous phenotypic and molecular analysis of normal and patient spermatozoa and the demonstration of infertility treatment by ICSI. The authors demonstrated the interaction between CFAP52 and SPATA6, a head-tail coupling regulator and structural protein, and showed that CFAP52 can interact with components of the microtubule inner protein (MIP), radial spoke, and outer dynein arm proteins.

Thank you for your positive comments.

Weaknesses:

The weakness of the study is some inconsistency in the localization of the CFAP52 protein in human spermatozoa in the figures and the lack of such localization information completely missing in mouse spermatozoa. Putting their findings in the context of the newly available structural information from the recent series of unambiguous and unequivocal identification of CFAP52 as an MIP in the B tubule will not only greatly benefit the interpretation of the study, but also resolve the inconsistent sperm phenotypes reported by an independent study. Since the mouse model is not designed to exactly recapitulate the human mutations but a complete knockout and the knockout mice show hydrocephaly phenotype as well, some of the claims of causality and ICSI as a treatment need to be tempered. Discussing the frequency of acephaly and MMAF in primary male infertility will be beneficial to justify CFAP52 as a practical diagnostic tool.

Dear reviewer, thank you for your comments and we tried to answer your questions as follows.

By immunofluorescence staining, we showed that CFAP52 was localized at both HTCA and full-length flagella from the normal control; in contrast, CFAP52 signals were barely detected in the patient’s spermatozoa (Figure 3F). Given that CFAP52 staining did not occur in other figures, no inconsistency exists in the localization of the CFAP52 protein in human spermatozoa in the figures. We did not perform the CFAP52 staining in mouse spermatozoa; however, we have shown that CFAP52 protein was completely absent in the Cfap52-KO testes compared with the WT testes (Figure 4C).

We appreciate the reviewer’s suggestion to put our findings of CFAP52 in the context of the newly available axoneme architecture. Given that these cryo-EM studies focus on doublet microtubules (DMTs), a broader expression pattern of CFAP52 in cilia/flagella could not be excluded. In mammals, CFAP52 seems to interact with a broad range of axonemal proteins, including MIP (CFAP45), ODAs (DNAI1 and DNAH11), and DRC (DRC10) (Dougherty et al., 2020). We have mentioned that ‘a lack of FAP52 in Chlamydomonas causes an instability of microtubules and detachment of the B-tubule from the A-tubule and shortened flagella are observed in Chlamydomonas when both FAP52 and FAP20 are absent (Owa et al., 2019). Unlike a specific regulation of the stability of B-tubules by FAP52 in Chlamydomonas (Owa et al., 2019), Cfap52-KO mice and CFAP52-mutant patient showed a serious disorder of the axoneme and its accessory structures.’

Before our study, Cfap52-KO mice have not yet been generated. To explore the physiological roles of CFAP52, we decided to construct Cfap52-KO mice. During our manuscript is under preparation, an independent group also generated the Cfap52-KO mice and explored their phenotype (Wu et al., 2023). We quite agree with this reviewer that Cfap52-mutant mice will be exact models to recapitulate the human variants. Cfap52-mutant mice were not included in our current manuscript due to i) the two identified variants were ‘nonsense’ variant and ‘frameshift’ variants, respectively, which are expected to damage the CFAP52 expression and function; ii) the influence of two variants on CFAP52 protein function has been well validated by in vitro cell experiments and iii) research funding is limited for us. The assisted reproductive technology (ART) outcomes were also reported for the CFAP52-mutant patient and Cfap52-KO mice, which will be potential useful for further clinical studies. However, it is not suggested to be over-interpreted because it is only a case study.

Quantitative analyses showed that the decapitated spermatozoa, abnormal head-tail connecting spermatozoa, and spermatozoa with deformed flagella accounted for approximately 40%, 25%, and 30% of the total spermatozoa in Cfap52-KO mice, respectively (Figure 4I). Regarding the CFAP52-mutant patient, the frequency of acephaly and MMAF were not counted and now we feel sorry that we don’t have enough samples (repeats) to perform quantitative analyses.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Major concerns:

1. In lines 41-43, there seems to be some confusion about the terminology regarding "sporadic ALS". ALS is subdivided into familial and sporadic forms. Familial ALS simply indicates that the patient has a family history of ALS and presumably has a genetic predisposition for developing this disease. In many families, the identity of the mutation remains unknown. Sporadic ALS patients do not have a family history of this disease. However, this does not imply that they lack mutations that caused disease. In fact, 5-10% of these patients have the hexanucleotide repeat expansion in C9orf72. This mutation is also found in about 40% of familial ALS cases.

We have now amended the manuscript to be more accurate in our description of underlying genetics of ALS. This changes to this section are as follows:

Lines 39-47:

"...The median survival time in ALS, from initial onset of symptoms to death, typically as a result of respiratory complications, is only 20-48 months Chiò et al. (2009) and ALS has an estimated global mortality of 30,000 patients per year Mathis et al. (2019).

ALS is typically classified into either familial (fALS) or sporadic (sALS) forms of the disease, based on whether or not patients have an identified family history of the disease; between 5-10% of total ALS cases fall into the former category, fALS, with the remaining 90-95% consisting of sALS cases Mathis et al. (2019). To date, over 20 monogenic mutations that cause ALS have been identified, however these still only account for 45% of fALS cases and only 7% of sALS cases Mejzini et al. (2019)..."

1. In Fig. 4-supplement 1, 7DD and 5DD are not defined. I assume one is the fast-firing and one is the slow-firing motor neurons. I am also a bit confused as to why the 5DD neurons produce greater muscle force than the 7DD neurons when electrically stimulated. It seems to suggest that there is some difference between the two types of neurons or the groups of mice used to test them.

We have now defined these terms and the amended figure legend now reads as follows:

"(A) Fast-firing motor neurons (produced using a 7-day differentiation protocol thus labelled as “7DD”) or slow-firing ChR2+ motor neurons (produced using a 5-day differentiation protocol thus labelled as “5DD”) were engrafted in age matched SOD1G93A mice… Our expectation was that fast-firing motor neurons, which normally innervate larger numbers (>100) of stronger fast-twitch muscle fibres per motor unit would elicit significantly greater contractile force when optically stimulated, compared to slow-firing motor neurons that innervate small numbers (<10) of weaker, slow-twitch muscle fibres per motor unit. Surprisingly, our data did not show any difference when using grafts consisting of fast-firing motor neurons, versus slow-firing motor neurons, at least in response to optical stimulation. The factors underlying this surprising result, and the apparent discrepancy between electrically-evoked muscle contractions in nerves that had bene engrafted with either fast or slow firing motor neurons, are likely to be highly complex; we hope to further explore this as part of a separate follow up study."

1. Along those lines, do these two subpopulations of motor neurons innervate the same set of muscle fibers? More generally, are certain types of muscle fibers preferentially innervated by this approach? Answering these questions could point to additional ways to enhance the effectiveness of this treatment approach. This should be discussed.

This point is partially addressed in our response to Point 2 above, but to further extrapolate: certainly, the phenotype of individual muscle fibres is largely dictated by the firing properties of the motor neuron that innervates it. Slow-twitch muscle fibres tend to produce less contractile force but are more fatigue resistant, whereas fast-twitch muscle fibres produce more force but fatigue rapidly. There is evidence that expression of the chemorepellent molecule ephrin-A3 prevents the inappropriate innervation of slow-twitch muscle fibres by fast-firing motor neurons, which express the cognate receptor EphA8 [PMID: 26644518]. Importantly, fast-firing motor neurons are preferentially susceptible to disease mechanisms in ALS and the fast-twitch muscle fibres that they innervate are therefore more likely to undergo denervation and atrophy. Surprisingly, in this study we clearly show that grafts consisting of slow-firing motor neurons are able to innervate all regions of the triceps surae muscle group, including the normally exclusively fast-twitch superficial regions of the gastrocnemius and the exclusively slow-twitch soleus muscle. This finding strongly suggests that the normal developmental pairing of motor neuron and muscle fibre properties is not essential in this therapeutic context. Indeed, the use of more disease-resistant slow-firing motor neurons may provide some advantages. Again, we hope to be able to further explore this relationship in forthcoming follow-up studies.

1. The authors state that exercise programs are likely to accelerate disease progression. This is not supported by the current body of clinical data. In fact, current guidelines are for moderate (not strenuous) exercise, and mouse studies have demonstrated a protective effect of moderate exercise on disease progression.

We apologise for the lack of clarity on this point, as it was not our intention to imply that voluntary exercise accelerates disease progression. We have now amended the manuscript to specify “ENS-based exercise programs” to avoid any confusion.

1. It is unclear what the experimental endpoint is. Page 25 defines it as 135 days of age, but ranges are given the figure legends, suggesting that some other criteria were used. It also seems unclear at what determined the age at which each animal was treated since they were also not treated at the same age.

We hope that our response in the Public Reviews section above has fully addressed this point.

1. I am a little confused by Figure 5 - figure supplement 5, panel D. Why do the authors give specific p-values here but not in the other panels? The sample sizes in D are very low, in some cases with only 1 animal in a group, and performing statistical tests under these conditions seems futile. The statistical power is nearly zero.

For the purposes of consistency, we have now replaced the specific p-values in panel D with “ns”. The low n-values for the MUNE analysis data is due to the extremely difficult nature of identifying the contribution of individual motor units to the total muscle contractile response, when the maximal muscle force is extremely weak. In the absence of optical stimulation training, the extremely weak force elicited by acute optical stimulation precluded our ability to separate out the contribution of individual motor units and, often, in animals where this was not possible, we did not always perform electrically-evoked MUNE analysis. Unfortunately, we are not currently in a position to increase the n-values for this component of the study. Our ongoing research to enhance the amplitude of the muscle response to optical stimulation will hopefully help to more clearly address this in the future.

1. One concern about this approach is that the procedure could accelerate the denervation of the target muscle. Figure 5 - figure supplement 6, panel B, indicates a significant reduction in force on the ipsilateral side relative to the contralateral side, at least under electrical stimulation of the nerve. This would be consistent with the hypothesis that the procedure does enhance disease progression in the treated limb. Is there a reduction in voluntary motor activity in these animals, such as in grip strength or the position of the foot while walking?

We hope that this important point has been satisfactorily addressed in the Public Reviews section. Unfortunately, we did not undertake any behavioural analysis relating to voluntary motor function of the engrafted (or contralateral) hindlimbs, which may have provided useful data to address this point. As described above, the most likely explanation for this finding is due to physical nerve damage caused by the intraneural injection procedure; in our efforts to refine our strategy and move it towards clinical translation, we will take this into consideration in our future research.

1. Based on Fig. 6D, it seems that the vast majority of innervated NMJs at endpoint are innervated by cells from the graft. And yet, electrical stimulation evokes substantially greater muscle force. This may suggest that optical control of engrafted motor neurons will not yield enough force for routine tasks or that the few remaining endogenous motor neurons are much more effective at generating force. These potential limitations and ways to overcome them should be discussed.

There appears to be a slight misunderstanding, since our aim here was to sample a sufficiently powered number of motor end-plates innervated by YFP+ for statistical analysis. To do this we specifically chose regions of interest containing at least 1 YFP+ NMJ and the adjacent muscle fibres were included at random, whatever their innervation status. Had we sampled regions of interest at random, we would have been likely to capture only a very few YFP+ terminal as they occupy a very small volume of the total muscle section and the maximum scanning area for each high-resolution z-confocal stack is relatively small, so we feel that this selection was warranted.

Minor comments:

1. The donor mouse strain should be described as 129S1/SvImJ.

We have now corrected this.

1. The first time the supplementary figures show up in the manuscript, they seem to have two titles each, such as "Figure 1-figure supplement 1. (Figure 4 - figure supplement 1)". The second seems to be the correct one.

This was caused by an issue with the Latex template, which has now been resolved.

1. PCB is not defined the first time it is used (page 8, line 332).

We have now defined this term on first use: printed circuit board (PCB)

1. CNI is not defined in the text (page 12, line 432).

We have now defined this abbreviation at the first usage on Page 4, Line 158

1. Some of the fonts on the graphs are very small, such as Fig. 5J.

We have increased the font size as much as possible for Fig. 5.

1. Figure 6 - figure supplement 1 does not include a key to indicate which antigens are stains and which color refers to which antigen. This is also needed for the videos.

We have now included a key on this figure supplement to indicate the relevant antigens and stain and we have also done the same for the videos.

1. Video 5 seems to indicate that there is a dead zone in the back of the chamber. Does this raise any concerns about the consistency of training from animal to animal?

This is an extremely astute observation. However, the intermittent activation of the implantable LED devices is not due to a dead zone; rather, it is due to the orientation of the power receiving coil within the device and it’s alignment with the resonance frequency chamber that transmits the power to the device. As the animals move around, and particularly when they rear up, the power receiving coil occasionally becomes misaligned and fails to receive sufficient power to activate the LED. Since the pulses are delivered every 2 seconds, for 1 hour per day, we feel that the animals, on average, receive sufficient numbers of pulses to implement the training regimen. Indeed, we feel that the results speak for themselves.

Reviewer #1 (Public Review):

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder leading to the loss of innervation of skeletal muscles, caused by the dysfunction and eventual death of lower motor neurons. A variety of approaches have been taken to treat this disease. With the exception of three drugs that modestly slow progression, most therapeutics have failed to provide benefit. Replacing lost motor neurons in the spinal cord with healthy cells is plagued by a number of challenges, including the toxic environment, inhibitory cues that prevent axon outgrowth to the periphery, and proper targeting of the axons to the correct muscle groups. These challenges seem to be well beyond our current technological approaches. Avoiding these challenges altogether, Bryson et al. seek to transplant the replacement motor neurons into the peripheral nerves, closer to their targets. The current manuscript addresses some of the challenges that will need to be overcome, such as immune rejection of the allograft and optimizing maturation of the neuromuscular junction.

Reviewer #2 (Public Review):

The authors provide convincing evidence that optogenetic stimulation of ChR2-expressing motor neurons implanted in muscles effectively restore innervation of severely affected skeletal muscles in the aggressive SOD1 mouse model of ALS, and concluded that this method can be applied to selectively control the function of implicated muscles, which was supported by convincing data presented in the paper.

Author Response

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

We appreciate the reviewers’ detailed corrections and insightful comments. We have revised our manuscript per reviewers’ recommendations by including new data and clarifications/expansion of the discussion on our findings. Please see below for details.

Reviewer #1 (Recommendations For The Authors):

1. The introduction notes that CD1d KO mice show reduced levels of Va3.2 T cells (Ruscher et al.), which is interesting because innate memory T cell development in the thymus often requires IL-4 production by NKT cells. Have the authors explored QFL T cells in CD1d KO and/or IL-4 KO mice? Since their QFL TCR Tg mice still develop QFL T cells (and these animals likely have very few thymic NKT cells), NKT cells may not be required for the intrathymic development of QFL T cells?

Answer: We agree that investigation on the role of NKT cells or IL-4 in QFL T cell development will greatly further our understanding of these cells.

We validated the finding that expression of the QFL TCR transgene largely repressed the expression of endogenous TCRα, as indicated by the low levels of endogenous Vα2 on mature CD8SP T cells in both thymus and spleen. However, the frequencies of Vα2 usage in CD4 SP thymocytes and splenocytes from QFL transgenic mice were similar to non-transgenic mice, confirming that they underwent positive selection using endogenous TCR rather than the QFL TCR. We thus do not exclude the possible presence of NKT cells in QFLTg mouse and their potential involvement in the QFL T cells development. Our manuscript here is mainly focused on investigating the peripheral phenotype of QFL T cells and their association with the gut microbiota environment. Investigations into the role of CD1d/IL-4 will be best addressed in our future studies.

1. The finding that Qa-1 expression is not required for the development of QFL T cells raises questions about other MHC products that may be involved. In this context, it is interesting that TAP-deficient mice develop few QFL T cells, for reasons that are unclear, but the authors may speculate a bit. In this context, it may be helpful for the authors to note whether TAP is required for QFL presentation to QFL T cells. Since Qa-1 is not required, and CD1d is still expressed in TAP KO mice, what then could be responsible for their defect in QFL T cell development?

Answer: This is a great point. Figure 2 (from (Valerio et al., 2023) on the development of QFL T cells) tested whether QFL TCR cross-react with other MHC I molecules.

We assessed the activation of pre-selection QFLTg thymocytes in response to various MHC I deficient DC2.4 cell lines. While the QFL thymocytes showed partially reduced activation when stimulated with Qa-1b deficient APCs, triple knock-out (KO) of Qa-1b, Kb, and Db in DC2.4 cells reduced activation close to background levels. However, double knock-out of Qa-1b with either Kb, or Db led to stimulation that was intermediate between the triple KO and Qa-1b-KO cell lines. These data suggest that Kb and Db may contribute to the positive selection of QFL T cells in Qa-1b-KO mice.

TAP is required for FL9 peptide presentation and is very likely needed for presentation of the yet unidentified MHC Ia presented peptide(s) that are essential to QFL T positive selection. While CD1d/NKT cells/IL-4 may be involved in supporting the maturation of QFL T cells, we think in the TAP-KO mice the absence of TAP led to deletion/altered selection of the QFL T population at early developmental stage. We have added clarification on this point in the revised manuscript (line 412~418).

1. It may be worthwhile for the authors to note that Qa-1 was also dispensable for the intrathymic selection of another Qa-1-restricted TCR (Doorduijn et al. 2018. Frontiers Immunol.), although this is presumably not the case for others (Sullivan et al. 2002. Immunity 17, 95).

Answer: We appreciate this recommendation. We have noted this point in the resubmitted manuscript (line 412~418).

1. Lines 122-124: The sentence "Interesting ..." seemed confusing to me; are the numbers (60 and 30%) correct?

Answer: The numbers 60% and 30% were referring to the largest number we have detected for percentages of Va3.2 QFL T cells and Va3.2 CD8 T cell respectively. Here in the revised version, we replaced these numbers with average percentages (20.1% and <10%) to avoid confusion (line 134).

1. Qa-1/peptide complexes may also be recognized by CD94/NKG2 receptors, which may complicate the interpretation of the data (e.g., staining of the dextramers). From their previous work, it appears that Qa-1/QFL does not bind CD94/NKG2, which would be helpful to note in the text.

Answer: We have noted this point in the revised manuscript (line 117~121).

1. It would be helpful to add a few comments about the potential relevance to HLA-E.

Answer: We have included discussion on this point (line 391~401).

1. Figure legends: Most legends note the total number of replicates, which is usually quite high. It would also be helpful to indicate the total number of independent experiments performed and, when relevant, that the data are pooled from multiple independent experiments.

Answer: Thank you for raising the concern. We have clarified the experimental repeats in figure legends.

Reviewer #2 (Recommendations For The Authors):

1. The work of Nilabh Shastri was the foundation of the present study. Unfortunately, he passed away in 2021. Since he can no longer assume the responsibilities of a senior author, I wonder if it would be more appropriate to dedicate this paper to him than to list him as a co-author.

Answer: We have removed Dr. Shastri’s name as a co-senior author and have dedicated this work to his memory.

1. The official symbol for ERAAP is Erap1.

Answer: We have replaced ERAAP with ERAP1.

1. Please refrain from editorializing. For example, "strikingly" appears eight times and "interestingly" 9 times in the manuscript. Most readers believe they do not need to be said when something is striking or interesting.

Answer: We appreciate the Reviewer’s suggestion and have removed ‘strikingly’ and ‘interestingly’ from the manuscript.

1. In WT mice, are there some cell types that express Qa-1b but not Erap1 and could therefore present the FL9 peptide?

Answer: This is a great question. Using our highly sensitive QFL T cell hybridoma line BEko8Z (sensitivity shown in Fig. 6b), we have so far not been able to detect steady-state FL9 presentation by cells isolated from the spleen, lymph nodes, various gut associated lymphoid tissues or intestinal epithelial cells (Supplementary Fig. 8 a left panel). However, we do not exclude the possibility of FL9 peptide being transiently presented under certain conditions (i.e. ER stress/transformed cells) at particular locations or within certain time windows, which is of great importance for understanding the function of these cells but is beyond the scope of this study.

1. Since you have not tested substitutions at other positions, could you explain your reasoning that P4 and P6 are the critical residues (lines 271-272)?

Answer: Thank you for raising the concern. We have expanded on explanation of our strategy for determining peptide homology (line 272~313) in the revised manuscript. We have also included data on the structure the QFL TCR: FL9-Qa-1b complex predicted by Alphafold2, conformation alignment of FL9 and Qdm (Figure 6. a, b) and the NetMHCpan prediction of Qa1b binding of Qdm, FL9 and various FL9 mutant peptides (Supplementary Fig. 8 c) to help readers visualize the reasoning behind our strategy.

1. Readers might appreciate having a Figure summarizing the differences between spleen and gut QFL T cells.

Answer: This is a great suggestion. We have added a table summarizing the characteristic features of the splenic and IEL QFL T cells (Table 1).

1. In the discussion, readers would like to know what plan you might have to elucidate the function of QFL T cells.

Answer: We appreciate the recommendation. We have elaborated on our opinions and future directions in the resubmitted manuscript (line 393~401, 446~455).  

Reviewer #3 (Public Review):

1. For most of the report, the authors use a set of phenotypic traits to highlight the unique features of QFL-specific CD8+ T cells - specifically, CD44high, CD8aa+ve, CD8ab-ve. In Supp. Fig. 4, however, completely distinct phenotypic characteristics are presented, indicating that IEL QFL-specific T cells are CD5low, Thy-1low. No explanation is provided in the text about whether this is a previously reported phenotype, whether any elements of this phenotype are shared with splenic QFL T cells, what significance the authors ascribe to this phenotype (and to the fact that Qa1-deficiency leads to a more conventional Thy-1+ve, CD5+ve phenotype), and whether this altered phenotype is also seen in ERAAP-deficient mice. At least some explanation for this abrupt shift in focus and integration with prior published work is needed. On a related note, CD5 expression is measured in splenic QFL-specific CD8+ T cells from GF vs SPF mice (Supp. Fig. 9), to indicate that there is no phenotypic impact in the GF mice - but from Supp. Fig. 4, it would seem more appropriate to report CD5 expression in QFL-specific cells from the IEL, not the spleen.

Answer: Expression of CD8αα and lack of CD4, CD8αβ, CD5 and CD90 expression was indeed reported as the characteristic phenotype of natIELs. We have clarified this point in the resubmitted manuscript (line 80). The CD8αα+ IEL QFL T cells have consistently showed CD5CD90- phenotype. While CD8αα expression was sufficient to describe their natIEL phenotype, we showed the CD5-CD90- data in Supplementary figures only to provide additional evidence.

The CD5 molecule by itself reflects the TCR signaling strength and high CD5 level is associated with self-reactivity of T cells (Azzam et al., 2001; Fulton et al., 2015). The implication of CD5 expression on QFLTg cells is discussed in our other manuscript where we investigate the development of these cells (Valerio et al., 2023). In Supplementary Fig. 9, because the donor splenic QFLTg cell have consistently showed comparable CD5 level between the GF and SPF group, we reasoned that it would not interfere with our interpretation of the CD44 expression.

1. The authors suggest the finding that QFL-specific cells from ERAAP-deficient mice have a more "conventional" phenotype indicates some form of negative selection of high-affinity clones (this result being somewhat unexpected since ERAAP loss was previously shown to increase the presentation of Qa-1b loaded with FL9, confirmed in this report). It is not clear how this argument aligns with the data presented, however, since the authors convincingly show no significant reduction in the number of QFL-specific cells in ERAAP-knockout mice (Fig. 3a), and their own data (e.g. Fig. 2a) do not suggest that CD44 expression correlates with QFL-multimer staining (as a surrogate for TCR affinity/avidity). Is there some experimental basis for suggesting that ERAAP-deficient lacks a subset of high affinity QFL-specific cells?

Answer: We think the presence of QFL T cells in ERAAP-KO mice is a result of the unconventional developmental mechanism of these cells which is better addressed in our complementary manuscript on the development of QFL T cells(Valerio et al., 2023). Valerio et al. found that the most predominant QFL T clone which expresses Vα3.2Jα21, Vβ1Dβ1Jβ2-7 received relatively strong TCR signaling and underwent agonist selection during thymic development, indicating that the QFL ligand is involved in selection of the innate-like QFL T population.

We agree that there is so far no direct evidence showing the QFL T cells that were absent in the ERAAP-KO mice were high-affinity clones. We have removed ‘high-affinity’ from the manuscript (line 180). While CD44 expression has been associated the antigen-experiences phenotype of T cells, it is yet unclear whether expression level of this molecule directly reflects TCR affinity/avidity. identification of clones of different affinities/avidities require high precision technologies that are not currently available to the research community. While we do have zMovi, a newly developed (developing) technology, in the lab claimed to measure relative avidity/affinity of different cell types for ligands, during the past two years working with this instrument has taught us that the technology is not yet advanced enough; it can only produce reliable data on extreme differences of single clones, i.e., high numbers of homogeneous cell types expressing very high affinity receptors.

1. The rationale for designing FL9 mutants, and for using these data to screen the proteomes of various commensal bacteria needs further explanation. The authors propose P4 and P6 of FL9 are likely to be "critical" but do not explain whether they predict these to be TCR or Qa-1b contact sites. Published data (e.g., PMID: 10974028) suggest that multiple residues contribute to Qa-1b binding, so while the authors find that P4A completely lost the ability to stimulate a QFL-specific hybridoma, it is unclear whether this is due to the loss of a TCR- or a Qa-1-contact site (or, possibly, both). This could easily be tested - e.g., by determining whether P4A can act as a competitive inhibitor for FL9-induced stimulation of BEko8Z (and, ideally, other Qa-1b-restricted cells, specific for distinct peptides). Without such information, it is unclear exactly what is being selected in the authors' screening strategy of commensal bacterial proteomes. This, of course, does not lessen the importance of finding the peptide from P. pentosaceus that can (albeit weakly) stimulate QFL-specific cells, and the finding that association with this microbe can sustain IEL QFL cells.

Answer: Thank you for raising the concern. We have expanded on explanation of our strategy for determining peptide homology (line 272~313) in the revised manuscript. We have also included data on the structure the QFL TCR: FL9-Qa-1b complex predicted by Alphafold2, conformation alignment of FL9 and Qdm (Figure 6. a, b) and the NetMHCpan prediction of Qa1b binding of Qdm, FL9 and various FL9 mutant peptides (Supplementary Fig. 8 c) to help readers visualize the reasoning behind our strategy.

References

Azzam, H.S., DeJarnette, J.B., Huang, K., Emmons, R., Park, C.S., Sommers, C.L., El-Khoury, D., Shores, E.W., and Love, P.E. (2001). Fine tuning of TCR signaling by CD5. J Immunol 166, 5464- 5472.10.4049/jimmunol.166.9.5464, PMID:11313384

Fulton, R.B., Hamilton, S.E., Xing, Y., Best, J.A., Goldrath, A.W., Hogquist, K.A., and Jameson, S.C. (2015). The TCR's sensitivity to self peptide-MHC dictates the ability of naive CD8(+) T cells to respond to foreign antigens. Nat Immunol 16, 107-117.10.1038/ni.3043, PMID:25419629

Valerio, M.M., Arana, K., Guan, J., Chan, S.W., Yang, X., Kurd, N., Lee, A., Shastri, N., Coscoy, L., and Robey, E.A. (2023). The promiscuous development of an unconventional Qa1b-restricted T cell population. bioRxiv, 2022.2009.2026.509583.10.1101/2022.09.26.509583,

eLife assessment

This is an important study that investigates the role of commensal microbes and molecules in the antigen presentation pathway affecting the development and phenotype of an unusual population of T lymphocytes. The authors provide compelling evidence to identify a population of unconventional T cells that exist in the small intestinal epithelium, which appear to depend on commensal microbes, and show that a single commensal microbe (that encodes an antigen capable of weakly stimulating these cells) is sufficient to maintain this T cell population.

Reviewer #1 (Public Review):

Guan et al. explored the mechanisms responsible for the development, maintenance, and functional properties of a specific subset of unconventional T cells expressing a Va3.2 T cell receptor that recognizes a peptide, QFL, presented by the class Ib protein Qa-1. Prior studies from this group showed that cells from mice deficient in the ER protease ERAAP elicit responses in wild-type animals enriched for Qa-1-restricted CD8 T cells. They further showed that a significant proportion of these responses were directed against the QFL peptide derived from a conserved protein with incompletely understood functions. Many of these so-called QFL T cells expressed Va3.2-Ja21, were present in the spleen of wild-type mice, and exhibited a memory-like phenotype. Due to their relatively low frequency and weak staining with Qa-1 tetramers, analyzing QFL T cells has been challenging. Therefore, the authors generated dextramers, which permitted them to more rigorously identify these cells. They confirmed some of their previous findings and further showed that Va3.2+ and Va3.2- QFL T cells were present in the intestinal epithelium, where they also express CD8alpha homodimers, a characteristic of most small intestinal intraepithelial lymphocytes (siIELs), and most similar to the so-called natural siIELs that acquire their innate functions in the thymus. The authors show that TAP but not Qa-1 or ERAAP expression are required for the development of these cells, and both Qa-1 and ERAAP are required for the natural siIEL phenotype. Some of these findings were confirmed using a new TCR transgenic mouse expressing the QFL TCR. They further show that retention but not homing of QFL T cells to the intestinal epithelium involves commensal microorganisms, and using in silico approaches, they identify a commensal that contains a peptide similar to QFL that can activate QFL T cells. Finally, they show that this organism, P. pentosaceus, can promote gut retention of QFL T cells when it is introduced into germ-free mice. From these findings, the authors conclude that the microbiota influence the maintenance of Qa-1-restricted T cells.

Comments:

1. The authors employ a number of new reagents and elegant approaches to explore the development, maintenance and functional properties of QFL T cells.<br /> 2. Generally, conclusions made are well supported by the data presented.<br /> 3. One limitation of the work is that the immunological functions of QFL T cells remain unclear.<br /> 4. In their revised manuscript, the authors present additional data that have appropriately addressed the reviewer comments.

Reviewer #2 (Public Review):

Summary: CD8+ QFL T cells recognize a peptide, FYAEATPML (FL9), presented on Erap1-deficient cells. QFL T cells are present at a high frequency in the spleen of naïve mice. They express an antigen-experienced phenotype, and about 80% express an invariant TCRα chain Vα3.2Jα21.

Here, Guan and coll. report that QFL T cells are present not only in the spleen but also in the intestinal epithelium, where they display several phenotypic and functional peculiarities. The establishment of spleen and gut Vα3.2+ QFL T cells is TAP-dependent, and their phenotype is regulated by the presence/absence of Qa-1b and Erap1. Maintenance of gut Vα3.2+ QFL T cells depends on the gut microbiota and is associated with colonization by Pediococcus pentosaceus.

Strengths:

This article contains in-depth studies of a peculiar and interesting subset of unconventional CD8 T cells, based partly on generating two novel TCR-transgenic models.

The authors discovered a clear relation between the gut microbiome and the maintenance of gut QFL T cells. One notable observation is that monocolonization of the gut with Pediococcus pentosaceus is sufficient to sustain gut QFL T cells.

Weaknesses:

In the absence of immunopeptidomic analyses, the presence or absence of the FL9 peptide on various cell types is inferred based on indirect evidence. Hence, whether the FL9 peptide is presented by some cells that express Qa-1b but not Erap1 remains unknown.

Analyses of the homology between the FL9 and bacterial peptides were limited to two amino acid residues (P4 and P6). This limitation is mitigated in part by the justifications provided by the authors in the revised preprint.

The potential function of QFL T cells remains elusive. The present article should provide an incentive for further functional studies.

Reviewer #3 (Public Review):

The authors investigate the role of commensal microbes and molecules in the antigen presentation pathway in the development and phenotype of CD8 T cells specific for the Qa-1b-restricted peptide FL9 (QFL). The studies track both endogenous QFL-specific T cells and utilize a recently generated TCR transgenic model. The authors confirm that QFL-specific T cells in the spleen and small intestine intraepithelial lymphocyte (IEL) pool show an antigen-experienced phenotype as well as unique phenotypic and innate-like functional traits, especially among CD8+ T cells expressing Va3.2+ TCRs. They find that deficiency in the TAP transporter leads to almost complete loss of QFL-specific T cells but that loss of either Qa1 or the ERAAP aminopeptidase does not impact QFL+ T cell numbers but does cause them to maintain a more conventional, naïve-like phenotype. In germ-free (GF) mice, the QFL-specific T cells are present at similar numbers and with a similar phenotype to SPF animals, but in older animals (>18w) there is a notable loss of IEL QFL-specific cells. This drop can be avoided by neonatal colonization of GF mice with the commensal microbe Pediococcus pentosaceus but not a different commensal, Lactobacillus johnsonii, and the authors show that P. pentosaceus encodes a peptide that weakly stimulates QFL-specific T cells, while the homologous peptide from L. johnsonii does not stimulate such cells.

This study provides new insights into the way in which the differentiation, phenotype, and function of CD8+ T cells specific for Qa-1b/FL9 is regulated by peptide processing and Qa1 expression, and by interactions with the microbiota. The approaches are well designed, the data compelling, and the interpretation, for the most part, appropriate.

The response to several of my concerns involved reference to a different manuscript from the authors (which has not been through peer review), and for point #3, it would have been useful to provide experimental evidence (e.g., competitive inhibition assays) to justify their hypothesis that P4 serves as a TCR contact while P6 may be a Qa-1b contact residue. Nevertheless, the authors have made considerable efforts to clarify their approaches and interpretation, which strengthens the manuscript.

eLife assessment

This manuscript offers a valuable contribution to studying wildlife responses during and after COVID-19 lockdowns. It convincingly demonstrates that bird species in urban areas respond differently to human activity changes. What sets this study apart from others on avian responses to COVID-19 lockdowns is its use of passive acoustic monitoring. By concurrently measuring anthropogenic noise, a crucial reflection of changes in human activity due to COVID-19 lockdowns, this study reveals rare local-scale variations in bird responses to human activity. Only one study so far has used vocalization recordings to assess the effects of COVID-19 lockdowns on a bird species.

Reviewer #1 (Public Review):

Summary:

This study is one of several around the world to investigate how urban wildlife responded to changes in human activity during the lockdowns associated with the COVID-19 pandemic. Unlike several other studies on the topic that used observational data from citizen science programs, this project relied on passive acoustic monitoring to record bird vocalizations during and after stringent lockdown periods in an urban environment. The authors focused on three species that differ in their level of adaptation to human presence, providing an ecologically relevant comparison that highlights the importance of micro-habitats for species living in close proximity to humans.

Strengths:

The element that sets this study apart from most others examining avian responses to COVID-19 lockdowns is the use of passive acoustic monitoring. As the authors describe, this method offers several advantages over other methods (though, it does come with some limitations on what questions can be addressed). Perhaps the most relevant advantage is that it offers the ability to concurrently measure anthropogenic noise in the environment, which is one of the most likely mechanisms for effects on wildlife from changes to human activity. These authors were, therefore, able to show local-scale differences in bird responses to human activity measured at the same scale. To my knowledge, only one other study (Derryberry et al. Science. 2020) has used recordings of vocalizations to examine the influence of COVID-19 lockdowns on a bird species.

It was encouraging to see a study that focused on the local-scale impacts of lockdowns, with methods that could investigate effects within micro-habitats. Logistics prevented many other projects from operating at such fine scales, making the results from this study particularly useful for the examination of rapid changes in bird behavior. This does mean that comparisons between this study and others examining the effects of COVID-19 lockdowns on birds should be done with care, as the effects described here may have been the result of different processes, operating at different spatial and temporal scales. However, that also means this study fills an important gap in our knowledge of how wildlife reacts to human activity in urban spaces.

Weaknesses:

One drawback of the approach is that the acoustic sampling only occurred during the pandemic: samples were taken during several lockdown periods in the early spring (March through early May) of 2020 and then for a period of 10 days after the end of the final lockdown period in late May of 2020. Unfortunately, this means that the interpretation of the effect of lockdowns could have been affected by any shifts in the birds' vocal behavior that resulted from unmodeled environmental factors or normal seasonal phenology during that three-month period. However, the authors chose focal species that would be less prone to seasonal changes in vocal behavior and their approach did account for several factors to minimize any such effects.

Reviewer #2 (Public Review):

In this study, the authors tried to gauge the effect of human activity on three species, (1) the Hooded grow, an urban exploiter, (2) the Rose ring parakeet, an invasive, alien species that has adapted to exploit human resources, and (3) the Graceful prinia, an urban adapter, which is relatively shy of humans. A goal of the study was to increase awareness of the importance of urban parks.

Strengths:<br /> Strengths of the study include the fact that it was conducted at 17 different sites, including parks, roads and residential areas, and included three species with different habitat preferences. Each species produced relatively loud and repeatable vocalizations. To avoid the effect of seasonal changes, sounds were sampled within a 10 day period of the lockdown as well as post-lockdown. The analysis included a comparison of the number of sound files, binary values indicating emission of a common syllable, and also the total number of syllables emitted as a measurement of bird activity. Ambient temperatures and sound levels of human activity were also recorded. All of these factors speak to the comprehensive approach and analysis adopted in this study. The results are based on a rigorous statistical analysis, ruling out the effects of various extraneous parameters.

Weaknesses:<br /> Most significant changes may occur near the ambient noise levels and this could lead to a different conclusion, but the authors authors acknowledge this possibility and clarify that they only analyzed vocalizations with high signal-to-noise signals to avoid ambiguity. In the revised version, they also replaced the previous ambient noise parameter with an estimate of ambient noise under 1kHz, assuming that it reflects most anthropogenic noise (not restricted to human speech). This seems reasonable and this new model gave very similar results to the previous one.

In interpreting the data, the authors mention the effect of human activity on bird vocalizations in the context of inter-species predator-prey interactions; however, the presence of humans could also modify intraspecies interactions by acting as triggers for communication of warning and alarm, and/or food calls (as may sometimes be the case) to conspecifics. The behavioral significance of the syllables used to monitor animal activity could be informative in this context; however, the authors acknowledge this possibility in the Discussion. Most importantly, the authors acknowledge the possibility of the above-noted bias, and the potential of a transient nature of the observed effects.

Conclusion:<br /> In general, the authors achieved their aim of illustrating the complexity of the affect of human activity on animal behavior notwithstanding the caveats noted above. Their study also makes it clear that estimating such affects is not simple given the dynamics of animal behavior. For example, seasonality, temperature changes, animal migration and movement, as well as interspecies interactions, such as those related to predator-prey behavior, and inter/intra-species competition in other respects can all play into site-specific changes in the vocal activity of a particular species.

eLife assessment

This important study combines experimental and computational data to address crucial aspects of RNA methylation by a vital RNA methyltransferase (MTase). The authors have provided compelling, strong evidence, utilizing well-established techniques, to elucidate aspects of the methyl transfer mechanism of methyltransferase-like protein 3 (METTL3), which is a part of the METTL3-14 complex. This work will be of broad interest to biochemists, biophysicists, and cell biologists alike.

Reviewer #1 (Public Review):

Summary:<br /> This important study nicely integrates a breadth of experimental and computational data to address fundamental aspects of RNA methylation by an important for biology and health RNA methyltransferases (MTases). 



Strengths:<br /> The authors offer compelling and strong evidence, based on carefully performed work with appropriate and well-established techniques to shed light on aspects of the methyl transfer mechanism of the methyltransferase-like protein 3 (METTL3), which is part of the methyltransferase-like proteins 3 & 14 (METTL3-14) complex. 


Weaknesses:<br /> 
The significance of this foundational work is somewhat diminished mostly due to mostly efficient communication of certain aspects of this work. Parts of the manuscript are somewhat uneven and don't quite mesh well with one another. The manuscript could be enhanced by careful revision and significant textual and figure edits. 

Examples of recommended edits that would improve clarity and allow accessibility to a broader audience are highlighted in some detail below.

Reviewer #2 (Public Review):

Summary:<br /> Caflisch and coworkers investigate the methyltransferase activity of the complex of methyltransferase-like proteins 3 and 14 (METTL3-14). To obtain a high-resolution description of the complete catalytic cycle they have carefully designed a combination of experiments and simulations. Starting from the identification of bisubstrate analogues (BAs) as binders to stabilise a putative transition state of the reaction, they have determined multiple crystal structures and validated relevant interactions by mutagenesis and enzymatic assays.

Using the resolved structure and classical MD simulations they obtained a kinetic picture of the binding and release of the substrates. Of note, they accumulated very good statistics on these processes using 16 simulation replicates over a time scale of 500 ns. To compare the time scale of the release of the products with that of the catalytic step they performed state-of-the-art QM/MM free energy calculations (testing multiple levels of theory) and obtained a free energy barrier that indicates how the release of the product is slower than the catalytic step.

Strengths:<br /> All the work proceeds through clear hypothesis testing based on a combination of literature and new results. Eventually, this allows them to present in Figure 10 a detailed step-by-step description of the catalytic cycle. The work is very well crafted and executed.

Weaknesses:<br /> To fulfill its potential of guiding similar studies for other systems as well as to allow researchers to dig into their vast work, the authors should share the results of their simulations (trajectories, key structures, input files, protocols, and analysis) using repositories like Zenodo, the plumed-nest, figshare or alike.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Coberski et al describes a combined experimental and computational study aimed to shed light on the catalytic mechanism in a methyltransferase that transfers a methyl group from S-adenosylmethionine (SAM) to a substrate adenosine to form N6-methyladenosine (m6A).

Strengths:<br /> The authors determine crystal structures in complex with so-called bi-substrate analogs that can bridge across the SAM and adenosine binding sites and mimic a transition state or intermediate of the methyl-transfer reaction. The crystal structures suggest dynamical motions of the substrate(s) that are examined further using classical MD simulations. The authors then use QM/MM calculations to study the methyl-transfer process. Together with biochemical assays of ligand/substrate binding and enzyme turnover, the authors use this information to suggest what the key steps are in the catalytic cycle. The manuscript is in most places easy to read.

Weaknesses:<br /> My main suggestion for the authors is that they show better how their conclusions are supported by the data. This includes how the electron density maps for example support the key interactions and water molecules in the active site and a better error analysis of the computational analyses.

Reviewer #1 (Public Review):

The manuscript by Dr. Shinkai and colleagues is about the posttranslational modification of a highly important protein, MT3, also known as the growth inhibitory factor. Authors postulate that MT3, or generally all MT isoforms, are sulfane sulfur binding proteins. The presence of sulfane sulfur at each Cys residue has, according to the authors, a critical impact on redox protein properties and almost does not affect zinc binding. They show a model in which 20 Cys residues with sulfane sulfur atoms can still bind seven zinc ions in the same clusters as unmodified protein. They also show that recombinant MT3 (but also MT1 and MT2) protein can react with HPE-IAM, an efficient trapping reagent of persulfides/polysulfides. This reaction performed in a new approach (high temperature and high reagent concentration) resulted in the formation of bis-S-HPE-AM product, which was quantitatively analyzed using LC-MS/MS. This analysis indicated that all Cys residues of MT proteins are modified by sulfane sulfur atoms. The authors performed a series of experiments showing that such protein can bind zinc, which dissociates in the reaction with hydrogen peroxide or SNAP. They also show that oxidized MT3 is reduced by thioredoxin. It gives a story about a new redox-dependent switching mechanism of zinc/persulfide cluster involving the formation of cystine tetrasulfide bridge.

The whole story is hard to follow due to the lack of many essential explanations or full discussion. What needs to be clarified is the conclusion (or its lack) about MT3 modification proven by mass spectrometry. Figure 1B shows the FT-ICR-MALDI-TOF/MS spectrum of recombinant MT3. It clearly shows the presence of unmodified MT3 protein without zinc ions. Ions dissociate in acidic conditions used for MALDI sample preparation. If the protein contained all Cys residues modified, its molecular weight would be significantly higher. Then, they show the MS spectrum (low quality) of oxidized protein (Fig. 1C), in which new signals (besides reduced apo-MT3) are observed. They conclude that new signals come from protein oxidation and modification with one or two sulfur atoms. If the conclusion on Cys residue oxidation is reasonable, how this protein contains sulfur is unclear. What is the origin of the sulfur if apo-MT does not contain it? Oxidized protein was obtained by acidification of the protein, leading to zinc dissociation and subsequent neutralization and air oxidation. Authors should perform a detailed isotope analysis of the isotopic envelope to prove that sulfur is bound to the protein. They say that the +32 mass increase is not due to the appearance of two oxygen donors. They do not provide evidence. This protein is not a sulfane sulfur binding protein, or its minority is modified. Moreover, it is unacceptable to write that during MT3 oxidation are "released nine molecules of H2". How is hydrogen molecule produced? Moreover, zinc is not "released", it dissociates from protein in a chemical process.

Another important point is a new approach to the HPE-IAM application. Zinc-binding MT3 was incubated with 5 mM reagent at 60oC for 36 h. Authors claim that high concentration was required because apoMT3 has stable conformation. Figure 2B shows that product concentration increases with higher temperature, but it is unclear why such a high temperature was used. Figure 1D shows that at 37oC, there is almost no reaction at 5 mM reagent. Changing parameters sounds reasonable only when the reaction is monitored by mass spectrometry. In conclusion, about 20 sulfane sulfur atoms present in MT3 would be clearly visible. Such evidence was not provided. Increased temperature and reagent concentration could cause modification of cysteinyl thiol/thiolates as well, not only persulfides/polysulfides. Therefore, it is highly possible that non-modified MT3 protein could react with HPE-IAM, giving false results. Besides mass spectrometry, which would clearly prove modifications of 20 Cys, authors should use very important control, which could be chemically synthesized beta- or alfa-domain of MT3 reconstituted with zinc (many protocols are present in the literature). Such models are commonly used to test any kind of chemistry of MTs. If a non-modified chemically obtained domain would undergo a reaction with HPE-IAM under such rigorous conditions, then my expectation would be right.

- The remaining experiments provided in the manuscript can also be applied for non-modified protein (without sulfane sulfur modification) and do not provide worthwhile evidence. For instance, hydrogen peroxide or SNAP may interact with non-modified MTs. Zinc ions dissociate due to cysteine residue modification, and TCEP may reduce oxidized residue to rescue zinc binding. Again, mass spectrometry would provide nice evidence.

- The same is thioredoxin (Fig. 7) and its reaction with oxidized MT3. Nonmodified and oxidized MT3 would react as well.

- If HPE-IAM reacts with Cys residues with unmodified MT3, which is more likely the case under used conditions, the protein product of such reaction will not bind zinc. It could be an explanation of the cyanolysis experiment (Fig. 6).

- Figure 4 shows the reactivity of (pol)sulfides with TCEP and HPE-IAM. What are redox potentials? Do they correlate with the obtained results?

- Raman spectroscopy experiments would illustrate the presence of sulfane sulfur in MT3 only if all Cys were modified.

- The modeling presented in this study is very interesting and confirms the flexibility of metallothioneins. MT domains are known to bind various metal ions of different diameters. They adopt in this way to larger size the ions. The same mechanism could be present from the protein site. The presence of 9 or 11 sulfur atoms in the beta or alfa domain would increase the size of the domains without changing the cluster structure.

- Comment to authors. Apo-MT is not present in the cell. It exists as a partially metallated species. The term "apo-MT" was introduced to explain that MTs are not fully saturated by metals and function as a metal buffer system. Apo-MT comes from old ages when MT was considered to be present only in two forms: apo-form and fully saturated forms.

Reviewer #3 (Public Review):

Summary:<br /> The authors were trying to show that a novel neuronal metallothionein of poorly defined function, GIF/MT3, is actually heavily persulfidated in both the Zn-bound and apo (metal-free) forms of the molecule as purified from a heterologous or native host. Evidence in support of this conclusion is compelling, with both spectroscopic and mass spectrometry evidence strongly consistent with this general conclusion. The authors would appear to have achieved their aims.

Strengths:<br /> The analytical data are compelling in support of the author's primary conclusions are strong. The authors also provide some modeling evidence that strongly supports the contention that MT3 (and other MTs) can readily accommodate sulfane sulfur on each of the 20 cysteines in the Zn-bound structure, with little perturbation of the structure. This is not the case with Cys trisulfides, which suggests that the persulfide-metallated state is clearly positioned at lower energy relative to the immediately adjacent thiolate- or trisulfidated metal coordination complexes.

Weaknesses:<br /> The biological significance of the findings is not entirely clear. On the one hand, the analytical data are clearly solid (albeit using a protein derived from a bacterial over-expression experiment), and yes, it's true that sulfane S can protect Cys from overoxidation, but everything shown in the summary figure (Fig. 8D) can be done with Zn release from a thiol by ROS, and subsequent reduction by the Trx/TR system. In addition, it's long been known that Zn itself can protect Cys from oxidation. I view this as a minor weakness that will motivate follow-up studies. Fig. 1 was incomplete in its discussion and only suggests that a few S atoms may be covalently bound to MT3 as isolated. This is in contrast to the sulfate S "release" experiment, which I find quite compelling.

Impact:<br /> The impact will be high since the finding is potentially disruptive to the metals in the biology field in general and the MT field for sure. The sulfane sulfur counting experiment (the HPE-IAM electrophile trapping experiment) may well be widely adopted by the field. Those of us in the metals field always knew that this was a possibility, and it will interesting to see the extent to which metal-binding thiolates broadly incorporate sulfate sulfur into their first coordination shells.

eLife assessment

This study provides useful evidence that GIF/MT-3 harbors sulfane sulfur, which may play a role in zinc coordination. The study includes a variety of well-designed assays to support the authors' hypothesis, revealing that sulfane sulfur is released from MT-3. The analysis and conclusions could benefit from a more rigorous approach to analyzing sulfur and zinc content in recombinant MT3 protein, leaving the evidence in parts incomplete.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors reveal that GIF/MT-3 regulates zinc homeostasis depending on the cellular redox status. The manuscript technically sounds, and their data concretely suggest that the recombinant MTs, not only GIF/MT-3 but also canonical MTs such as MT-1 and MT-2, contain sulfane sulfur atoms for the Zn-binding. The scenario proposed by the authors seems to be reasonable to explain the Zn homeostasis by the cellular redox balance.

Strengths:<br /> The data presented in the manuscript solidly reveal that recombinant GIF/MT-3 contains sulfane sulfur.

Weaknesses:<br /> It is still unclear whether native MTs, in particular, induced MTs in vivo contain sulfane sulfur or not.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript describes some biochemical experiments on the crucial virulence factor EsxA (ESAT-6) of Mycobacterium tuberculosis. EsxA is secreted via the ESX-1 secretion system. Although this system is recognized to be crucial for virulence the actual mechanisms employed by the ESX-1 substrates are still mostly unknown. The EsxA substrate is attracting the most attention as the central player in virulence, especially phagosomal membrane disruption. EsxA is secreted as a dimer together with EsxB. The authors show that EsxA is also able to form homodimers and even tetramers, albeit at very low pH (below 5). Furthermore, the addition of a nanobody that specifically binds EsxA blocks intracellular survival, as well as if the nanobody is produced in the cytosol of the infected macrophages.

Strengths:<br /> -Decent biochemical characterization of EsxA and identification of a new and interesting tool to study the function of EsxA (nanobody).

-The manuscript is well-written.

Weaknesses:<br /> The findings are not critically evaluated using extra experiments or controls.

For instance, tetrameric EsxA in itself is interesting and could reveal how EsxA works. But one would say that this is a starting point to make small point mutations that specifically affect tetramer formation and then evaluate what the effect is on phagosomal membrane lysis. Also one would like to see experiments to indicate whether these structures can be produced under in vitro conditions, especially because it seems that this mainly happens when the pH is lower than 5, which is not normally happening in phagosomes that are loaded with M. tuberculosis.

Also, the fact that the addition of the nanobody, either directly to the bacteria or produced in the cytosol of macrophages is interesting, but again it is the starting point for further experimentation. As a control, one would like to see the effect on an Esx-1 secretion mutant. Furthermore, does cytosolic production or direct addition of the nanobody affect phagosomal escape? What happens if an EsxA mutant is produced that does not bind the nanobody?

Finally, it is a bit strange that the authors use a non-native version of esxA that has not only an additional His-tag but also an additional 12 amino acids, which makes the protein in total almost 20% bigger. Of course, these additions do not have to alter the characteristics, but they might. On the other hand, they easily discard the natural acetylation of EsxA by mycobacteria itself (proven for M. marinum) as not relevant for the function because it might not happen in (the close homologue) M. tuberculosis.

eLife assessment

This useful study investigates two secreted Mycobacterium tuberculosis proteins, ESAT-6 and CFP10, using biochemical assays, including a Biolayer Interferometry assay. Solid experimental evidence demonstrates that ESAT-6 forms a tight interaction with CFP10 as a heterodimer at neutral pH and that ESAT-6 also forms a homodimer at acidic pH. Additional, more definitive evidence is required to describe how these proteins disrupt the phagosomal membrane.

Reviewer #1 (Public Review):

Summary:<br /> The authors sought to establish a biochemical strategy to study ESAT-6 and CFP-10 biochemistry. They established recombinant reagents to study these protein associations in vitro revealing an unexpected relationship at low pH. They next develop much-needed reagents to study these proteins in an infection context and reveal that treatment with an ESAT-6 nanobody enhances Mtb control.

Strengths:<br /> The biochemical conclusions are supported by multiple configurations of the experiments. They combine multiple approaches to study a complex problem.

Weaknesses:<br /> It would be valuable to understand if the nanobody is disrupting the formation of the ESAT6-CFP10 complex. It is unclear how the nanobody is functioning to enhance control in the infection context. More detail or speculation in the discussion would have been valuable. Where is the nanobody in the cell during infection?

Reviewer #2 (Public Review):

Summary:<br /> Bates TA. et al. studied the biochemical characteristics of ESAT-6, a major virulence factor of Mycobacterium tuberculosis (Mtb), as part of the heterodimer with CFP10, a molecular chaperon of ESAT-6, as in homodimer and in homotetramer using recombinant ESAT-6 and CFP10 expressed in E. coli by applying several biochemical assays including Biolayer Interferometry (BLI) assay. The main findings show that ESAT-6 forms a tight interaction with CFP10 as a heterodimer at neutral pH, and ESAT-6 forms homodimer and even tetramer-based larger molecular aggregates at acidic pH. Although the discussion of the potential problems associated with the contamination of ESAT-6 preparations with ASB-14 during the LPS removal step is interesting, this research does not test the potential impact of residual ASB-14 contaminant on the biochemical behavior ESAT-6-CFP10 heterodimer and ESAT-6 homodimer or tetramer and their hemolytic activity in comparison with the ones without ASB-14. The main strength of this study is the generation of ESAT-6 specific nanobodies and the demonstration of its anti-tuberculosis efficiency in THP-1 cell lines infected with Mtb strains with reporter genes.

Strengths:<br /> Generation and demonstration of the anti-ESAT-6 nanobodies against tuberculosis infection in a cell line based Mtb infection model.

Weaknesses:<br /> Although the biochemistry studies provide quantitative data about the interactions of ESAT-6 with its molecular chaperon CFP10 and the interaction of ESAT-6 homodimer and tetramers, the novel information from these studies is minimal.

eLife assessment

In this important study, chromatin is simulated as a polymer at the scale of genes and 3D organization is analyzed at nucleosome resolution. There is convincing evidence for the emergence of microdomains due to the action of transcription factors, based on the simulation incorporating well-known biophysical properties of DNA, of nucleosomes, of linker histones, and of the transcription factor pair Myc:Max, as well as the 3D organization resulting from bending and looping of DNA. The work improves our knowledge of how the joint action of transcription factors and chromatin features affects chromatin structure and accessibility, which is of interest to anyone studying gene regulation.

Reviewer #1 (Public Review):

In this study, authors performed multiple sets of mesoscale chromatin simulations at nucleosome resolution to study the effects of TF binding on chromatin structures. Through simulations at various conditions, authors performed systemically analysis to investigate how linker histone, tail acetylation, and linker DNA length can operate together with TFs to regulate chromatin architecture. Using gene Eed as one example, authors found that binding of Myc:Max could repress the gene expression by increasing fiber folding and compaction and this repression can be reversed by the linker histone. Understanding how transcription factors bind to regulatory DNA elements and modulate chromatin structure and accessibility is an essential question in epigenetics. Through modelling of TF binding to chromatin structures at nucleosome levels, authors demonstrated that TF binding could create microdomains that are visible in the ensemble-based contact maps and short DNA linkers prevent the formation microdomains. It has also been shown that tail acetylation and TF binding have opposite effects on chromatin compaction and linker histone can compete for the linker DNA with TF binding to impair the effect of TF binding. This study improves our knowledge on how TFs collaborate with different epigenetic marks and chromatin features to regulate chromatin structure and accessibility, which will be of broad interest to the community.

For this reviewer, there were a few notable limitations. One was the implicit model of TF binding, which is modelled by adding harmonic restraints at two DNA beads. The model is very simple and it lacks kind of validation of how the results can be extended to many other TFs. In addition, the results of TF binding creating microdomains are very interesting but it requires further quantitative analysis of how microdomains was affected under different conditions. Also, some definitions and protocols demand further elucidation.

Reviewer #2 (Public Review):

Summary: In this paper, Portillo-Ledesma et al. study chromatin organization in the length scale of a gene, simulating the polymer at nucleosome resolution. The authors have presented an extensive simulation study with an excellent model of chromatin. The model has linker DNA and nucleosomes with all relevant interactions (electrostatics, tails, etc). Authors simulate 10 to 26 kb chromatin with varying linker lengths, linker histones (LH), and acetylated tails. The authors then study the effect of a transcription factor (TF) Myc: Max binding. The critical physical feature of the TF in the model is that it binds to the linker region and bends the DNA to make loops/intra-chromatin contacts. Authors systematically investigate the interplay between different variables such as linker DNA length, LH density, and the TF concentration in determining chromatin compaction and 3D organization.

Strengths: The manuscript is well-written and is a relevant study with many useful results. The biggest strength of the work is the fact that the authors start with a relevant model that incorporates well-known biophysical properties of DNA, nucleosomes, linker histones, and the transcription factor Myc:Max. One of the novel results is the demonstration of how linker lengths play an important role in chromatin compaction (measured by computing packing ratio) in the presence of DNA-bending TFs. As the TF concentration increases, chromatin with short linker lengths does not compact much (only a small change in packing ratio). If the linker lengths are long, a higher percentage of TFs leads to an increase in packing ratio (higher compaction). Authors further show that TFs are able to compact Life-like chromatin fiber with linker length taken from a realistic distribution. The authors compute inter-nucleosomal contact maps from their simulated configurations and show that the map has features similar to what is observed in Hi-C/Micro-C experiments. Authors study the compaction of the Eed gene locus and show that TF binding leads to the formation of small domains known as micro-domains. Authors have predicted many relevant and testable quantities. Many of the results agree with known experiments like the formation of the micro-domains. Hence, the conclusions made in this study are justified - they follow from the simulation results.

Weaknesses: (1) While this has the advantage of a minimal model (model with minimal factors incorporated), it is a disadvantage for predicting in vivo organization; one might need to incorporate the action of many other proteins (for example, PRC, HP1, etc) and several other histone modifications to predict in vivo organization. (2) While this forward model produces features of relevant contact maps, one would need to tune some of the intra-chromatin interaction parameters to obtain an accurate contact map and radius of gyration.

Reviewer #1 (Public Review):

Summary:

This revised study follows up on previous work showing a female-specific enhancer region of PAX1 is associated with adolescent idiopathic scoliosis (AIS). This new analysis combines human GWAS analysis from multiple countries to identify a new AIS-associated coding variant in the COL11A1 gene (COL11A1P1335L). Using a Pax1 knockout mouse they go on to find that PAX1 and Collagen XI protein are expressed in the intervertebral discs (IVDs) and robustly in the growth plate, showing that COL11A1 expression is reduced in Pax1 mutant growth plate. Moreover, other AIS-associated genes, Gpr126 and Sox6, were also reduced in Pax1 mutant mice, suggesting a common pathway is involved in AIS.

Using SV40 immortalized costal cartilage cells, derived from floxed Col11a1 mice primary rib cage cartilage, they go to show that removal of Col11a1 leads to reduction of Mmp3 expression. In this context, the expression of wild-type Col11a1 restored regular levels of Mmp3 expression, while expression of the AIS-associated Col11a1P1335L allele failed to restore normal Mmp3 expression. This supports a model that the AIS-associated Col11a1P1335L allele leads to the dysregulation of ECM in vivo.

Using this culture system, they go on to test the role of the estrogen receptor ESR2, showing that loss of this receptor leads to reduced Mmp3 and Pax1 expression, and increased Col11a1 expression. They support this by showing similar gene expression changes and estrogen receptor function in Rat cartilage endplate cell culture.

Altogether, this study nicely brings together an impressive number of human genetic data from multi-ethnic AIS cohorts and controls from across the globe and functionally tests these findings in cell culture and animal models. This study wonderfully integrates other findings from other human and mouse work in AIS and supports a new molecular mechanism by which estrogen can interact and synergize with COL11A1/PAX1/MMP3 signaling to change ECM development and dynamics, thus providing a tangible model for mutations and dysregulation of this pathway can increase the susceptibility of scoliosis.

Strengths:

This work integrates a large cohort of human genetic data from AIS patient and control from diverse ethnic backgrounds, across the globe. This work attempts to functionally test their findings in vivio and by use of cell culture.

Weaknesses:

Many of the main functional work was done in cell culture and not in vivo.

eLife assessment

This valuable study analyzes a large cohort of Adolescent Idiopathic Scoliosis (AIS) patients, identifying an association with a variant in COL11A1 (Pro1335Leu). Experimental testing of this potentially pathogenic variant in vitro suggests a connection between Pax1, Col11a1, Mmp3, and estrogen signaling, thus providing solid support for the proposed link between hormonal and matrix components in the development of AIS.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Yu and colleagues sought to identify new susceptibility genes for adolescent idiopathic scoliosis (AIS). Significance for this work is high, especially given the still large knowledge gap of the mechanistic underpinnings for AIS. In this multidisciplinary body of work, the authors first performed a genetic association study of AIS case-control cohorts (combined 9,161 cases and 80,731 controls) which leveraged common SNPs in 1027 previously defined matrisome genes. Two nonsynonymous variants were found to be significantly associated with AIS: MMP14 p.Asp273Asn and COL11A1 p.Pro1153Leu, the latter of which had the more robust association and remained significant when females were tested independent of males. Next, the authors followed a series of functional validation experiments to support biological involvement of COL11A1 p.Pro1153Leu in AIS through expression, biochemical, and histological studies in physiologically relevant cell and mouse models. Together, the authors propose a hitherto unreported model for AIS that involves the interplay of the COL11A1 susceptibility locus with estrogen signaling to alter a Pax1-Col11a1-Mmp3 signaling axis at the growth plate.

Strengths:

The manuscript is clearly written and follows a series of logical steps toward connecting multiple matrisome genes and putative AIS effectors in a new framework of pathomechanism. The multidisciplinary nature of the work makes it a strong body of work wherein multiple models offer multiple lines of supportive data.

Weaknesses:

This manuscript remains an important multidisciplinary study of the genetic and functional basis of adolescent idiopathic scoliosis (AIS). To the benefit of the overall manuscript quality, the reviewers have addressed most concerns to satisfaction. I have a few remaining suggestions:

1. Regarding the genetic association of the common COL11A1 variant rs3753841, p.Pro1335Leu, please soften this statement to indicate that the variant could be a "risk locus" rather than "causal" in the following sentence on page 7-8: "These observations suggested that rs3753841 itself could be causal, although our methods would not detect deep intronic variants that could contribute to the overall association signal."

2. Include the list of three rare missense variants mentioned in the response to reviewers as a supplementary table. Please also include methods for the SKATO rare variant burden analysis.

3. Thank you for addressing the question of whether p.Pro1335Leu is a loss of function, gain of function, or dominant negative variant. The rationale in the response to reviewers was helpful, so please include this line of reasoning, and that there remains uncertainty, in the Discussion of the main text of the manuscript.

Reviewer #3 (Public Review):

Summary:

This article demonstrates a Pax1-Col11a1-Mmp3 signaling axis associated with adolescent idiopathic scoliosis and finds that estrogen affects this signaling axis. In addition, the authors have identified a new COL11A1 mutation and verified its effect on the Pax1-Col11a1-Mmp3 axis.

Strengths:

1. Col11a1P1335L is verified in multicenter cohorts with high confidence.

2. The article identified a potential pathogenesis of AIS.

Weaknesses:

The SV40-immortalized cell line established from Col11a1fl/fl mouse rib cartilage was applied in the study, and overexpression system was used to confirm that P1335L variant in COL11A1 perturbs its regulation of MMP3. However, due to the absence of P1335L point mutant mice, it cannot be confirmed whether P1335L can actually cause AIS, and the pathogenicity of this mutation cannot be directly verified.

eLife assessment

This paper provides useful information about how the ionome of Arabidopsis thaliana adapts to very high CO2-levels, backed up by solid evidence and carefully designed studies. However, the broader claims of the paper about climate change and food security - heavily emphasized in the abstract, introduction, and discussion - are inappropriate, as there is no direct link to the presented work.

Reviewer #1 (Public Review):

Summary:<br /> The study's abstract, introduction, and conclusions are not supported by the methods and results conducted. In fact, the results presented suggest that Arabidopsis could easily adapt to an extremely high CO2 environment.

This study offers good evidence pointing to a genetic basis for Arabidopsis thaliana's response to elevated CO2 (eCO2) levels and its subsequent impact on the leaf ionome. The natural variation analyses in the study support the hypothesis that genetic factors, rather than local adaptation, guide the influence of eCO2 on the ionome of rosette leaves in Arabidopsis. However, the manuscript's claim regarding its role in "the development of biofortified crops adapted to a high-CO2 world" (line 23) is overstated, especially given the absence of any analysis on the influence of eCO2 on the seed ionome and Arabidopsis is a poor model for harvest index for any crop. The manuscript, in its current form, necessitates massive revisions, particularly in clarifying its broader implications and in providing more substantial evidence for some of its assertions.

Major Drawbacks and Questions:

1. Evidence for the Central Premise:<br /> The foundational premise of the study is the assertion that rising atmospheric CO2 levels result in a decline in plant mineral content. This phenomenon is primarily observed in C3 plants, with C4 plants seemingly less affected. The evidence provided on this topic is scant and, in some instances, contradicts the authors' own references. The potential reduction of certain minerals, especially in grains, can be debated. For instance, reduced nitrogen (N) and phosphorus (P) content in grains might not necessarily be detrimental for human and animal consumption. In fact, it could potentially mitigate issues like nitrogen emissions and phosphorus leaching. Labeling this as a "major threat to food security" (line 30) is exaggerated. While the case for microelements might be more compelling, the introduction fails to articulate this adequately. Furthermore, the introduction lacks any discussion on how eCO2 might influence nutrient allocation to grains, which would be crucial in substantiating the claim that eCO2 poses a threat to food security. A more comprehensive introduction that clearly delineates the adverse effects of eCO2 and its implications for food security would greatly enhance the manuscript.

2. Exaggerated Concerns:<br /> The paper begins with the concern that carbon fertilization will lead to carbon dilution in our foods. While we indeed face numerous genuine threats in the coming decades, this particular issue is manageable. The increase in CO2 alone offers many opportunities for boosting yield. However, the heightened heat and increased evapotranspiration will pose massive challenges in many environments.

Figure 4 in fact suggests that 43% of the REGMAP panel (cluster 3) is already pre-adapted to very high CO2 levels. This suggests annual species could adapt very rapidly.

3. Assumptions on CO2 Levels:<br /> The assumption of 900ppm seems to be based on a very extreme climate change scenario. Most people believe we will overshoot the 1.5{degree sign}C scenario, however, it seems plausible that 2.5 to 3{degree sign}C scenarios are more likely. This would correspond to around 500ppm of CO2. https://www.nature.com/articles/s41597-022-01196-7/tables/4

4. Focus on Real Challenges:<br /> We have numerous real challenges, such as extreme heat and inconsistent rainfall, to address in the context of climate change. However, testing under extreme CO2 conditions and then asserting that carbon dilution will negatively impact nutrition is exaggerated.

In contrast, the FACE experiments are fundamental and are conducted at more realistic eCO2 levels. Understanding the interaction between a 20% increase in CO2 and new precipitation patterns is key for global carbon flux prediction.

As I look at the literature on commercial greenhouse tomato production, 1000ppm of eCO2 is common, but it also looks like the breeders and growers have already solved for flavor and nutrition under these conditions.

Conclusion:<br /> While the study provides valuable insights into the genetic underpinnings of Arabidopsis thaliana's response to elevated CO2 levels, it requires an entirely revised writeup, especially in its abstract, broader claims and implications. The manuscript would benefit from a more thorough introduction, a clearer definition of its scope, and a clear focus on the limits of this study.

Reviewer #2 (Public Review):

Strengths:<br /> The authors have conducted a large, well-designed experiment to test the response to eCO2. Overall, the experimental design is sound and appropriate for the questions about how a change in CO2 affects the ionome of Arabidopsis. Most of the conclusions in this area are well supported by the data that the authors present.

Weakness:<br /> While the authors have done good experiments, it is a big stretch from Arabidopsis grown in an arbitrary concentration of CO2 to relevance to human and animal nutrition in future climates. Arabidopsis is a great model plant, but its leaves are not generally eaten by humans or animals.

The authors don't justify their choice of a CO2 concentration. Given the importance of the parameter for the experiment, the rationale for selecting 900 ppm as elevated CO2 compared to any other concentration should be addressed. And CO2 is just one of the variables that plants will have to contend with in future climates, other variables will also affect elemental concentrations.

Given these concerns, I think the emphasis on biofortification for future climates is unwarranted for this study.

Additionally, I have trouble with these conclusions:

-Abstract "Finally, we demonstrate that manipulating the function of one of these genes can mitigate the negative effect of elevated CO2 on the plant mineral composition. "<br /> -Discussion "Consistent with these results, we show that manipulating TIP2;2 expressions with a knock-out mutant can modulate the Zn loss observed under high CO2."

The authors have not included the data to support this conclusion as stated. They have shown that this mutant increases the Zn content of the leaves when compared to WT but have not demonstrated that this response is different than in ambient CO2. This is an important distinction: one way to ameliorate the reduction of nutrients due to eCO2 is to try to identify genes that are involved in the mechanism of eCO2-induced reduction. Another way is to increase the concentration of nutrients so that the eCO2-induced reduction is not as important (i.e. a 10% reduction in Zn due to eCO2 is not as important if you have increased the baseline Zn concentration by 20%). The authors identified tip2 as a target from the GWAS on difference, but their validation experiment only looks at eCO2.

Reviewer #2 (Public Review):

Summary:

In this article, the authors employed modified CRISPR screens ["guide-only (GO)-CRISPR"] in the attempt to identify the genes which may mediate cancer cell dormancy in the high grade serous ovarian cancer (HGSOC) spheroid culture models. Using this approach, they observed that abrogation of several of the components of the netrin (e.g., DCC, UNC5Hs) and MAPK pathways compromise the survival of non-proliferative ovarian cancer cells. This strategy was complemented by the RNAseq approach which revealed that a number of the components of the netrin pathway are upregulated in non-proliferative ovarian cancer cells and that their overexpression is lost upon disruption of DYRK1A kinase that has been previously demonstrated to play a major role in survival of these cells. Perampalam et al. then employed a battery of cell biology approaches to support the model whereby the Netrin signaling governs the MEK-ERK axis to support survival of non-proliferative ovarian cancer cells. Moreover, the authors show that overexpression of Netrins 1 and 3 bolsters dissemination of ovarian cancer cells in the xenograft mouse model, while also providing evidence that high levels of the aforementioned factors are associated with poor prognosis of HGSOC patients.

Strengths:

Overall it was thought that this study is of potentially broad interest inasmuch as it provides previously unappreciated insights into the potential molecular underpinnings of cancer cell dormancy, which has been associated with therapy resistance, disease dissemination, and relapse as well as poor prognosis. Notwithstanding the potential limitations of cellular models in mimicking cancer cell dormancy, it was thought that the authors provided sufficient support for their model that netrin signaling drives survival of non-proliferating ovarian cancer cells and their dissemination. Collectively, it was thought that these findings hold a promise to significantly contribute to the understanding of the molecular mechanisms of cancer cell dormancy and in the long term may provide a molecular basis to address this emerging major issue in the clinical practice.

Weaknesses:

Several issues were observed regarding methodology and data interpretation. The major concerns were related to the reliability of modelling cancer cell dormancy. To this end, it was relatively hard to appreciate how the employed spheroid model allows to distinguish between dormant and e.g., quiescent or even senescent cells. This was in contrast to solid evidence that netrin signaling stimulates abdominal dissemination of ovarian cancer cells in the mouse xenograft and their survival in organoid culture. Moreover, the role of ERK in mediating the effects of netrin signaling in the context of the survival of non-proliferative ovarian cancer cells was found to be somewhat underdeveloped.

eLife assessment

Perampalam and colleagues provide solid evidence that Netrin signaling drives survival of non-proliferating ovarian cancer cells and their dissemination. These valuable findings were thought to provide unique insights into the molecular underpinnings of ovarian cancer spread and thus to be of significant interest to cancer biologists. However, the incomplete evidence supporting the role of the described Netrin-dependent mechanism in cancer dormancy was found to be a major shortcoming of the study.

Reviewer #1 (Public Review):

Summary:

Perampalam et al. describe novel methods for genome-wide CRISPR screening to identify and validate genes essential for HGSOC spheroid viability. In this study, they report that Netrin signaling is essential for maintaining disseminated cancer spheroid survival, wherein overexpression of Netrin pathway genes increases tumor burden in a xenograft model of ovarian cancer. They also show that high netrin expression correlates with poor survival outcomes in ovarian cancer patients. The study provides insights into the biology of netrin signaling in DTC cluster survival and warrants development of therapies to block netrin signaling for treating serous ovarian cancer.

Strengths:

- The study identifies Netrin signaling to be important in disseminated cancer spheroid survival<br /> - A Novel GO-CRISPR methodology was used to find key genes and pathways essential for disseminated cancer cell survival

Weaknesses:

- The term dormancy is not fully validated and requires additional confirmation to claim the importance of Netrin signaling in "dormant" cancer survival.<br /> - Findings shown in the study largely relate to cancer dissemination and DTS survival rather than cancer dormancy.

Reviewer #3 (Public Review):

Summary:

In this study, Warfvinge and colleagues use CITE-seq to interrogate how CML stem cells change between diagnosis and after one year of TKI therapy. This provides important insight into why some CML patients are "optimal responders" to TKI therapy while others experience treatment failure. CITE-seq in CML patients revealed several important findings. First, substantial cellular heterogeneity was observed at diagnosis, suggesting that this is a hallmark of CML. Further, patients who experienced treatment failure demonstrated increased numbers of primitive cells at diagnosis compared to optimal responders. This finding was validated in a bulk gene expression dataset from 59 CML patients, in which it was shown that the proportion of primitive cells versus lineage-primed cells correlates to treatment outcome. Even more importantly, because CITE-seq quantifies cell surface protein in addition to gene expression data, the authors were able to identify that BCR/ABL+ and BCR/ABL- CML stem cells express distinct cell surface markers (CD26+/CD35- and CD26-/CD35+, respectively). In optimal responders, BCR/ABL- CD26-/CD35+ CML stem cells were predominant, while the opposite was true in patients with treatment failure. Together, these findings represent a critical step forward for the CML field and may allow more informed development of CML therapies, as well as the ability to predict patient outcomes prior to treatment.

Strengths:

This is an important, beautifully written, well-referenced study that represents a fundamental advance in the CML field. The data are clean and compelling, demonstrating convincingly that optimal responders and patients with treatment failure display significant differences in the proportion of primitive cells at diagnosis, and the ratio of BCR-ABL+ versus negative LSCs. The finding that BCR/ABL+ versus negative LSCs display distinct surface markers is also key and will allow for a more detailed interrogation of these cell populations at a molecular level.

Weaknesses:

CITE-seq was performed in only 9 CML patient samples and 2 healthy donors. Additional samples would greatly strengthen the very interesting and notable findings.

eLife assessment

This study, utilizing CITE-Seq to explore CML, is considered a useful contribution to our understanding of treatment response. However, the reviewers express concern about the incomplete evidence due to the small sample size and recommend addressing these limitations. Strengthening the study with additional patient samples and validation measures would enhance its significance.

Reviewer #1 (Public Review):

Summary:

This manuscript by Warfvinge et al. reports the results of CITE-seq to generate single-cell multi-omics maps from BM CD34+ and CD34+CD38- cells from nine CML patients at diagnosis. Patients were retrospectively stratified by molecular response after 12 months of TKI therapy using European Leukemia Net (ELN) recommendations. They demonstrate heterogeneity of stem and progenitor cell composition at diagnosis, and show that compared to optimal responders, patients with treatment failure after 12 months of therapy demonstrate increased frequency of molecularly defined primitive cells at diagnosis. These results were validated by deconvolution of an independent previously published dataset of bulk transcriptomes from 59 CML patients. They further applied a BCR-ABL-associated gene signature to classify primitive Lin-CD34+CD38- stem cells as BCR:ABL+ and BCR:ABL-. They identified variability in the ratio of leukemic to non-leukemic primitive cells between patients, showed differences in the expression of cell surface markers, and determined that a combination of CD26 and CD35 cell surface markers could be used to prospectively isolate the two populations. The relative proportion of CD26-CD35+ (BCR:ABL-) primitive stem cells was higher in optimal responders compared to treatment failures, both at diagnosis and following 3 months of TKI therapy.

Strengths:

The studies are carefully conducted and the results are very clearly presented. The data generated will be a valuable resource for further studies. The strengths of this study are the application of single-cell multi-omics using CITE-Seq to study individual variations in stem and progenitor clusters at diagnosis that are associated with good versus poor outcomes in response to TKI treatment. These results were confirmed by deconvolution of a historical bulk RNAseq data set. Moreover, they are also consistent with a recent report from Krishnan et al. and are a useful confirmation of those results. The major new contribution of this study is the use of gene expression profiles to distinguish BCR-ABL+ and BCR-ABL- populations within CML primitive stem cell clusters and then applying antibody-derived tag (ADT) data to define molecularly identified BCR:ABL+ and BCR-ABL- primitive cells by expression of surface markers. This approach allowed them to show an association between the ratio of BCR-ABL+ vs BCR-ABL- primitive cells and TKI response and study dynamic changes in these populations following short-term TKI treatment.

Weaknesses:

One of the limitations of the study is the small number of samples employed, which is insufficient to make associations with outcomes with confidence. Although the authors discuss the potential heterogeneity of primitive stem, they do not directly address the heterogeneity of hematopoietic potential or response to TKI treatment in the results presented. Another limitation is that the BCR-ABL + versus BCR-ABL- status of cells was not confirmed by direct sequencing for BCR-ABL. The BCR-ABL status of cells sorted based on CD26 and CD35 was evaluated in only two samples. We also note that the surface markers identified were previously reported by the same authors using different single-cell approaches, which limits the novelty of the findings. It will be important to determine whether the GEP and surface markers identified here are able to distinguish BCR-ABL+ and BCR-ABL- primitive stem cells later in the course of TKI treatment. Finally, although the authors do describe differential gene expression between CML and normal, BCR:ABL+ and BCR:ABL-, primitive stem cells they have not as yet taken the opportunity to use these findings to address questions regarding biological mechanisms related to CML LSC that impact on TKI response and outcomes.

Reviewer #2 (Public Review):

Summary:

The authors use single-cell "multi-comics" to study clonal heterogeneity in chronic myeloid leukemia (CML) and its impact on treatment response and resistance. Their main results suggest 1) Cell compartments and gene expression signatures both shared in CML cells (versus normal), yet 2) some heterogeneity of multiomic mapping correlated with ELN treatment response; 3) further definition of s unique combination of CD26 and CD35 surface markers associated with gene expression defined BCR::ABL1+ LSCs and BCR::ABL1- HSCs. The manuscript is well-written, and the method and figures are clear and informative. The results fit the expanding view of cancer and its therapy as a complex Darwinian exercise of clonal heterogeneity and the selective pressures of treatments.

Strengths:

Cutting-edge technology by one of the expert groups of single-cell 'comics.

Weaknesses:

Very small sample sizes, without a validation set.<br /> The obvious main problem with the study is that an enormous amount of results and conjecture arise from a very small data set: only nine cases for the treatment response section (three in each of the ELN categories), only two normal marrows, and only two patient cases for the division kinetic studies. Thus, it is very difficult to know the "noise" in the system - the stability of clusters and gene expression and the normal variation one might expect, versus patterns that may be reproducibly study artifact, effects of gene expression from freezing-thawing, time on the bench, antibody labeling, etc. This is not so much a criticism as a statement of reality: these elegant experiments are difficult, time-consuming, and very expensive. Thus in the Discussion, it would be helpful for the authors to just frankly lay out these limitations for the reader to consider. Also in the Discussion, it would be interesting for the authors to consider what's next: what type of validation would be needed to make these studies translatable to the clinic? Is there a clever way to use these data to design a faster/cheaper assay?

Reviewer #1 (Public Review):

Summary:

Mainali and colleagues provide evidence for Itaconate stabilising Cpt1a via a decrease in ubiquitination. This in turn likely regulates fatty acid oxidation which in turn would appear to be involved in thermoregulation in the context of sepsis.

Strengths:

These findings add to our knowledge of the role of Itaconate in sepsis and its rather complex effects on metabolism, specifically lipid metabolism.

Weaknesses:

1. This is a complex paper and would benefit from a schematic depicting the key findings.

2. The paper would benefit from additional supporting evidence. Would it be possible to measure fatty acid oxidation by metabolic tracing here, in IRG-deficient cells or in response to 4-OI? Although changes in protein level for Cpt1A are seen, this is correlated with fatty acid oxidation rather than direct demonstration. This may be challenging but would strengthen the manuscript.

3. The aspect concerning body temperature regulation is confusing. Would Itaconate not promote fatty acid oxidation to increase or maintain body temperature? Itaconate must therefore not be involved in the hypothermic response? Bringing UCP1 into the finding is confusing and needs to be better explained. Again a diagram would help, but enhanced BAT fatty acid oxidation and UCP1 expression appear linked here, with both being affected by Itaconate. This needs clarifying.

eLife assessment

This work describes a connection between inflammation and metabolism, in which itaconate stabilizes the mitochondrial fatty acid uptake enzyme Cpt1a to enhance fatty acid oxidation. The mechanism for itaconate action may be generalizable to other protein targets. This is an important advance, which is supported by solid experimental data.

Reviewer #2 (Public Review):

Summary:

This manuscript provides important new findings regarding the connection between inflammation and metabolism. It also identifies a new type of post-translational modification and its connection to protein stability. This finding is expected to be generalizable to other protein targets. In vitro evidence is solid. In vivo evidence needs some additional controls.

Strengths:

A new connection between inflammation and metabolism.

A novel type of PTM was identified.

Findings would be of broad interest and the mechanisms are likely generalizable to related control systems.

In vitro data are well-supported.

The authors successfully demonstrated that treatment with 4-octyl Itaconate (4-OI), a prodrug form of itaconate, reduces neutral lipid accumulation in the AML12 cell line and primary hepatocytes. They show that 4-OI promotes fatty acid beta-oxidation through increased stability of CPT1a protein, the rate-limiting step in this process.

Weaknesses:

Some conclusions involving the Irg1 knockout mice require important controls and clarifications to be fully convincing and some controls are missing.

eLife assessment

The effect of Vitamin D supplementation in reducing asthma via anti-inflammatory mechanisms is a topic of wide interest, with somewhat conflicting published data. Here, bioinformatic approaches help to identify a role of VDR in inducing the expression of the key regulator Ikzf3, which possibly suppresses the IL-2/STAT5 axis, consequently blunting the Th2 response and mitigating allergic airway inflammation. The evidence is solid and the findings are important for researchers in the field.

Reviewer #1 (Public Review):

The association of vitamin D supplementation in reducing Asthma risk is well studied, although the mechanistic basis for this remains unanswered. In the presented study, Kilic and co-authors aim to dissect the pathway of Vitamin D-mediated amelioration of allergic airway inflammation. They use initial leads from bioinformatic approaches, which they then associate with results from a clinical trial (VDAART) and then validate them using experimental approaches in murine models. The authors identify a role of VDR in inducing the expression of the key regulator Ikzf3, which possibly suppresses the IL-2/STAT5 axis, consequently blunting the Th2 response and mitigating allergic airway inflammation.

The major strength of the paper lies in its interdisciplinary approach, right from hypothesis generation, and linkage with clinical data, as well as in the use of extensive ex vivo experiments and in vivo approaches using knock-out mice. The study presents some interesting findings including an inducible baseline absence/minimal expression of VDR in lymphocytes, which could have physiological implications and needs to be explored in future studies.<br /> However, the study presents a potential for further dissection of relevant pathophysiological parameters using additional techniques, to explain certain seemingly associative results, and allow for a more effective translation.

Several results in the study suggest multiple factors and pathways influencing the phenotype seen, which remain unexplored. The inferences of this study also need to be read in the context of the different sub-phenotypes and endotypes of Asthma, where the Th2 response may not be predominant. While this does not undermine the importance of this elegant study, it is essential to emphasise a holistic picture while interpreting the results.

Reviewer #2 (Public Review):

Summary:<br /> This study seeks to advance our knowledge of how vitamin D may be protective in allergic airway disease in both adult and neonatal mouse models. The rationale and starting point are important human clinical, genetic/bioinformatic data, with a proposed role for vitamin D regulation of 2 human chromosomal loci (Chr17q12-21.1 and Chr17q21.2) linked to the risk of immune-mediated/inflammatory disease. The authors have made significant contributions to this work specifically in airway disease/asthma. They link these data to propose a role for vitamin D in regulating IL-2 in Th2 cells implicating genes associated with these loci in this process.

Strengths:<br /> Here the authors draw together evidence form. multiple lines of investigation to propose that amongst murine CD4+ T cell populations, Th2 cells express high levels of VDR, and that vitamin D regulates many of the genes on the chromosomal loci identified to be of interest, in these cells. The bottom line is the proposal that vitamin D, via Ikfz3/Aiolos, suppresses IL-2 signalling and reduces IL-2 signalling in Th2 cells. This is a novel concept and whilst the availability of IL-2 and the control of IL-2 signalling is generally thought to play a role in the capacity of vitamin D to modulate both effector and especially regulatory T cell populations, this study provides new data.

Weaknesses:<br /> Overall, this is a highly complicated paper with numerous strands of investigation, methodologies etc. It is not "easy" reading to follow the logic between each series of experiments and also frequently fine detail of many of the experimental systems used (too numerous to list), which will likely frustrate immunologists interested in this. There is already extensive scientific literature on many aspects of the work presented, much of which is not acknowledged and largely ignored. For example, reports on the effects of vitamin D on Th2 cells are highly contradictory, especially in vitro, even though most studies agree that in vivo effects are largely protective. Similarly other reports on adult and neonatal models of vitamin D and modulation of allergic airway disease are not referenced. In summary, the data presentation is unwieldy, with numerous supplementary additions, that makes the data difficult to evaluate and the central message lost. Whilst there are novel data of interest to the vitamin D and wider community, this manuscript would benefit from editing to make it much more readily accessible to the reader.

Wider impact: Strategies to target the IL-2 pathway have long been considered and there is a wealth of knowledge here in autoimmune disease, transplantation, GvHD etc - with some great messages pertinent to the current study. This includes the use of IL-2, including low dose IL-2 to boost Treg but not effector T cell populations, to engineered molecules to target IL-2/IL-2R.

Reviewer #2 (Public Review):

Summary:

This manuscript from Liu et al. examines the role of Fat and Dachsous, two transmembrane proto-cadherins that function both in planar cell polarity and in tissue growth control mediated by the Hippo pathway. The authors developed a new method for measuring growth of the wing imaginal disc during late larval development and then used this approach to examine the effects of disruption of Fat/Dachsous function on disc growth. The authors show that during mid to late third instar the wing imaginal disc normally grows in a linear rather than exponential fashion and that this occurs due to slowing of the mitotic cell cycle as the disc grows during this period. Consistent with their known role in regulating Hippo pathway activity, this slowing of growth is disrupted by loss of Fat/Dachsous function. The authors also observed a previously unreported gradient of Fat protein across the wing blade. However, graded expression of Fat or Dachsous is not necessary for proper growth regulation in the late third instar because ectopic Dachsous expression, which affects gradients of both Dachsous and Fat, has no growth phenotype.

Strengths:

Although the role of the Hippo pathway in growth control has been extensively studied, our understanding of how the pathway controls growth during normal development remains relatively weak. This work addresses this question by examining normal growth of the wing imaginal disc during part of its development in the larva and characterizing the effects of Fat/Dachsous manipulation on that growth. The authors developed tools for measuring wing growth by measuring wing volume, an approach that could be useful in future studies of tissue growth.

Weaknesses:

1) Although the approach used to measure volume is new to this study, the basic finding that imaginal disc growth slows at the mid-third instar stage has been known for some time from studies that counted disc cell number during larval development (Fain and Stevens, 1982; Graves and Schubiger, 1982). Although these studies did not directly measure disc volume, because cell size in the disc is not known to change during larval development, cell number is an accurate measure of tissue volume. However, it is worth noting that the approach used here does potentially allow for differential growth of different regions of the disc.

2) Related to point 1, a main conclusion of this study, that cell cycle length scales with growth of the wing, is based on a developmentally limited analysis that is restricted to the mid-third instar larval stage and later (early third instar begins at 72 hr - the authors' analysis started at 84 hr). The previous studies cited above made measurements from the beginning of the 3rd instar and combined them with previous histological analyses of cell numbers starting at the beginning of the 2nd instar. Interestingly, both studies found that cell number increases exponentially from the start of the 2nd instar until mid-third instar, and only after that point does the cell cycle slow resulting in the linear growth reported here. The current study states that growth is linear due to scaling of cell cycle with disc size as though this is a general principle, but from the earlier studies, this is not the case earlier in disc development and instead applies only to the last day of larval life.

3) The analysis of the roles of Fat and Dachsous presented here has weaknesses that should be addressed. It is very curious that the authors found that depletion of Fat by RNAi in the wing blade had essentially no effect on growth while depletion of Dachsous did, given that the loss of function overgrowth phenotype of null mutations in fat is more severe than that of null mutations in dachsous (Matakatsu and Blair, 2006). An obvious possibility is that the Fat RNAi transgene employed in these experiments is not very efficient. The authors tried to address this by doubling the dose of the transgene, but it is not clear to me that this approach is known to be effective. The authors should test other RNAi transgenes and additionally include an analysis of growth of discs from animals homozygous for null alleles, which as they note survive to the late larval stages.

4) It is surprising that the authors detect a gradient of Fat expression that has not been seen previously given that this protein has been extensively studied. It is also surprising that they find that expression of Nubbin Gal4 is graded across the wing blade given that previous studies indicate that it is uniform (ie. Martín et al. 2004). These two surprising findings raise the possibility that the quantification of fluorescence could be inaccurate. The curvature of the wing blade makes it a challenging tissue to image, particularly for quantitative measurements.

5) Overall, in my view the impact of these findings is limited. The focus on growth solely at the end of larval development, when there are a number of potentially confounding variables (for example hormonal cues), makes the generality of the findings reported here difficult to judge. Additionally, the functional analysis of Fat/Dachsous function in this process is limited - for example does disruption of other Hippo pathway components have a similar effect?

eLife assessment

This valuable work develops a new approach to measure imaginal disc growth in Drosophila. With this approach, the roles of two protocadherins (Fat and Dachsous), in late larval development is explored, and there is novel data on the scaling of their protein gradients. The evidence supporting the authors' findings overall are solid, though the genetic analysis of Fat and Dachsous function is incomplete and would benefit from further experiments.

Reviewer #1 (Public Review):

Summary and Strengths:

The manuscript presents novel results on the regulation of Drosophila wing growth by the protocadherins Ds and Fat. The manuscript performs a more careful analysis of disc volume, larval size, and the relationship between the two, in normal and mutant larvae, and after localized knockdown or overexpression of Fat and Ds. Not all of the results are equally surprising given the previous work on Fat, Ds, and their regulation of disc growth, pupariation, and the Hippo pathway, but the presentation and detail of the presented data is new. The most novel results concern the scaling of gradients of Fat and Ds protein during development, a largely unstudied gradient of Fat protein, and using overexpression of Ds to argue that changes in the Ds gradient do not underlie the slowing and halting of cell divisions during development.

Weaknesses:

Below I list questions and suggestions about the methodology, the presentation, and the interpretation of the data.

1) Pouch growth: division or recruitment? The study chooses to examine growth only in the prospective wing blade (the "pouch") rather than the wing disc as a whole. This can create biases, as fat and ds manipulations often cause stronger effects on growth, and on Hippo signaling targets, in the adjacent hinge regions of the disc. So I am curious about this choice.

The limitation to the wing region also creates some problems for the measurements themselves. The division between wing and pouch is not a strict lineage boundary, and thus cells can join or leave this region, creating two different reasons for changes in wing pouch size; growth of cells already in the region, or recruitment of cells into or out of the region. The authors do not discuss the second mechanism.

It is not at all clear that the markers for the pouch used by the authors are stable during development. One of these is Vg expression, or the Vg quadrant enhancer. But the Vg-expressing region is thought to increase by recruitment over late second and third instar through a feed-forward mechanism by which Vg-expressing cells induce Vg expression in adjacent cells. In fact, this process is thought to be driven in part by Fat and Ds (Zecca et al 2010). So when the authors manipulate Fat and Ds are they increasing growth or simply increasing Vg recruitment? I would prefer that this limitation be addressed.

The second pouch marker the authors use is epithelial folding, but this also has problems, as Fat and Ds manipulations change folding. Even in wild type, the folding patterns are complex. For instance, to make folding fit the Vg-QE pattern at late third the authors appear to be jumping in the dorsal pouch between two different sets of folds (Fig 1S2A). The authors also do not show how they use folding patterns in younger, less folded discs, nor provide evidence that the location of the folds are the same and do not shift relative to the cells. They also do not explain how they use folds and measure at later wpp and bpp stages, as the discs unfold and evert, exposing cells that were previously hidden in the folds.

Finally, the authors limit their measurements to cells with exposed apical faces and thus a measurable area but apparently ignore the cells inside the folds. At late third, however, a substantial amount of the prospective wing blade is found within the folds, especially where they are deepest near the A/P compartment boundary. Using the third vein sensory organ precursors as markers, the L3-2 sensillum is found just distal to the fold, the L3-1 and the ACV sensilla are within the fold, and the GSR of the distal hinge is found just proximal to the fold. That puts the proximal half of the central wing blade in the fold, and apparently uncounted in their assays. These cells will however be exposed at wpp and especially bpp stages. How are the authors adjusting for this?

2) Stabilizing and destabilizing interactions between Fat and Ds- The authors describe a distal accumulation of Fat protein in the wing, and show that this is unlikely to be through Fat transcription. They further try to test whether the distal accumulation depends on destabilization of proximal Fat by proximal Ds by looking at Fat in ds mutant discs.

However, the authors do not describe how they take into account the stabilizing effects of heterophilic binding between the extracellular domains (ECDs) of Fat and Ds; without one, the junctional levels and stability of the other is reduced (Ma et al., 2003; Hale et al. 2015). So when they show that the A-P gradient of Fat is reduced in a ds mutant, is this because of the loss of a destabilizing effect of Ds on Fat, as they assume, or is it because all junctional Fat has been destabilized by loss of extracelluarlar binding to Ds? The description of the Fat gradient in Ds mutants is also confusing (see note 6 below), making this section difficult for the reader to follow.

The authors do not propose or test a mechanism for the proposed destabilization. Fat and Ds bind not only through their ECDs, but binding has now also been demonstrated through their ICDs (Fulford et al. 2023)

3) Ds gradient scales by volume, rather than cell number - This is an intriguing result, but the authors do not discuss possible mechanisms.

4) Autonomous effects on growth- Fat and Ds are already known to have autonomous effects on growth and Hippo signaling from clonal analyses and localized knockdowns. One novelty here is showing that localized knockdown does not delay pupariation in the way that whole animal knockdown does, although the mechanism is not investigated. Another novelty is that the authors find stronger wing pouch overgrowth after localized ds RNAi or whole disc loss of fat than after localized fat RNAi, the latter being only 11% larger. The fat RNAi result would have strengthened by testing different fat RNAi stocks, which vary in their strength and are commonly weaker than null mutations, or stronger drivers such as the ap-gal4 they used for some of their ds-RNAi experiments or use of UAS-dcr2. Another reason for caution is that Garoia (2005) found much stronger overgrowth in fat mutant clones, which were about 75% larger than control clones.

5) Flattening of Ds gradients does not slow growth. One model suggests that the flattening of the Ds gradient, and thus polarized Ds-Fat binding, account for slowed growth in older discs. The difficulty in the past has been that two ways of flattening the Ds gradient, either removing Ds or overexpressing Ds uniformly, give opposite results; the first increases growth, while the latter slows it. Both experiments have the problem of not just flattening the gradient, but also altering overall levels of Ds-Fat binding, which will likely alter growth independent of the gradients. Here, the authors instead use overexpression to create a strong Ds gradient (albeit a reversely oriented one) that does not flatten, and show that this does not prevent growth from slowing and arresting.

To make sure that this is not some effect caused by using a reverse gradient, one might instead induce a more permanent normally oriented Ds gradient and see if this also does not alter growth; there is a ds Trojan gal4 line available that might work for this, and several other proximal drivers.

Another possible problem is that, unlike previous studies, the authors have not blocked the Four-jointed gradient; Fj alters Fat-Ds binding and might regulate polarity independently of Ds expression. A definitive test would be to perform the tests above in four-joined mutant discs.

The Discussion of these data should be improved. The authors state in the Discussion "The significance of these dynamics is unclear, but the flattening of the Fat gradient is not a trigger for growth cessation." While the Discussion mentions the effects of Ds on Fat distribution in some detail, this is the only phrase that discusses growth, which is surprising given how often the gradient model of growth control is mentioned elsewhere. The reader would be helped if details are given about what experiment supports this conclusion, the effect on not only growth cessation but cell cycle time, and why the result differs from those of Rogjula 2008 and Willecke 2008 using Ds and Fj overexpression.

6) Discussion of Dpp. The authors spend much of the discussion speculating on the possibility that Fat and Ds control growth by changing the wing's sensitivity to the BMP Dpp. As the manuscript contains no new data on Dpp, this is somewhat surprising. The discussion also ignores Schwank (2011), who argues that Fat and Dpp are relatively independent. There have also been studies showing genetic interactions between Fat and signaling pathways such as Wg (Cho and Irvine 2004) and EGF (Garoia 2005).

eLife assessment

This manuscript introduces two valuable new metrics - "variant vulnerability" and "drug applicability" - that would be of use to identify candidate drugs for treating infections while considering longer-term, evolution-based treatment outcomes. Despite the intuitive appeal of the metrics and their potential, the study remains incomplete, as it fails to demonstrate the generality of the approach. The work could be improved by analysing a broader range of data in a systematic way and directly tying the metrics to outcomes, which would make it possible to better assess their impact and utility.

Reviewer #1 (Public Review):

The manuscript by Geurrero and colleagues introduces two new metrics that extend the concept of "druggability"- loosely speaking, the potential suitability of a particular drug, target, or drug-target interaction for pharmacological intervention-to collections of drugs and genetic variants. The study draws on previously measured growth rates across a combinatoriality complete mutational landscape involving 4 variants of the TEM-50 (beta lactamase) enzyme, which confers resistance to commonly used beta-lactam antibiotics. To quantify how growth rate - in this case, a proxy for evolutionary fitness - is distributed across allelic variants and drugs, they introduce two concepts: "variant vulnerability" and "drug applicability".

Variant vulnerability is the mean vulnerability (1-normalized growth rate) of a particular variant to a library of drugs, while drug applicability measures the mean across the collection of genetic variants for a given drug. The authors rank the drugs and variants according to these metrics. They show that the variant vulnerability of a particular mutant is uncorrelated with the vulnerability of its one-step neighbors, and analyze how higher-order combinations of single variants (SNPs) contribute to changes in growth rate in different drug environments.

The work addresses an interesting topic and underscores the need for evolution-based metrics to identify candidate pharmacological interventions for treating infections. The authors are clear about the limitations of their approach - they are not looking for immediate clinical applicability - and provide simple new measures of druggability that incorporate an evolutionary perspective, an important complement to the orthodoxy of aggressive, kill-now design principles.

As I said in my initial review, I think the work could be improved with additional analysis that tie the new metrics to evolutionary outcomes. Without this evidence, or some other type of empirical or theoretical support for the utility of these metrics, I am not fully convinced that these concepts have substantial impact. The new metrics could indeed be useful--and they have intuitive appeal--but the current revisions stop short of demonstrating that these intuitive notions hold up under "realistic" conditions (whether in simulation, theory, or experiment).

Reviewer #2 (Public Review):

In the main text, the authors apply their metrics to a data set that was published by Mira et al. in 2015. The data consist of growth rate measurements for a combinatorially complete set of 16 genetic variants of the antibiotic resistance enzyme beta-lactamase across 10 drugs and drug combinations at 3 different drug concentrations, comprising a total of 30 different environmental conditions. In my previous report I had asked the authors to specify why they selected only 7 out of 30 environments for their analysis, with only one concentration for drug, but a clear explanation is still lacking. In the Data section of Material and Methods, the authors describe their criterion for data selection as follows: "we focus our analyses on drug treatments that had a significant negative effect on the growth of wildtype/TEM-1 strains". However, in Figure 2 it is seen that, even for the selected data sets, not all points are significant compared to wild type (grey points). So what criterion was actually applied?

In effect, for each chosen drug or drug combination, the authors choose the data set corresponding to the highest drug concentration. As a consequence, they cannot assess to what extent their metrics depend on drug concentration. This is a major concern, since Mira et al. concluded in their study that the differences between growth rate landscapes measured at different concentrations were comparable to the differences between drugs. I argued before that, if the new metrics display a significant dependence on drug concentration, this would considerably limit their usefulness. The authors challenge this, saying in their rebuttal that "no, that drug concentration would<br /> be a major actor in the value of the metrics does not limit the utility of the metric. It is simply another variable that one can consider when computing the metrics." While this is true in principle, I don't think any practicing scientist would disagree with the statement that the existence of additional confounding factors (in particular if they are unknown) reduces the usefulness<br /> of a quantitative metric.

As a consequence of the small number of variant-drug-combinations that are used, the conclusions that the authors draw from their analysis are mostly tentative. For example, on line 123 the authors write that the observation that<br /> the treatment of highest drug applicability is a combination of two drugs "fits intuition". In the Discussion this statement is partly retracted with reference to the piperacillin/tazobactam-combination which has low drug applicability. Being based on only a handful of data points, both observations are essentially anecdotal and it is unclear what the reader is supposed to learn.

To assess the environment-dependent epistasis among the genetic mutations comprising the variants under study, the authors decompose the data of Mira et al. into epistatic interactions of different orders. This part of the analysis is incomplete in two ways. First, in their study, Mira et al. pointed out that a fairly large fraction of the fitness differences between variants that they measured were not statistically significant. This information has been removed in the depiction of the Mira et al. fitness landscapes in Figure 1 of the present manuscript, and it does not seem to be reflected in the results of the interaction analysis in Figure 4. Second, the interpretation of the coefficients obtained from the epistatic decomposition depends strongly on the formalism that is being used. In a note added on page 15 of the revised manuscript, the authors write that they have used the LASSO regression for their analysis and refer the reader to a previous publication (Guerrero et al. 2019) which however (as far as I could see) also does not fully explain how the method works. To give an example of the difficulty of interpreting the data in Figure 4 without further information: The substitution C (G238S) is well known to have a strong positive effective in cefotaxime, but the corresponding coefficient is essentially zero. So whatever the LASSO regression does, it cannot simply measure the effect on growth.

Reviewer #3 (Public Review):

The authors introduce two new concepts for antimicrobial resistance borrowed from pharmacology, "variant vulnerability" (how susceptible a particular resistance gene variant is across a class of drugs) and "drug applicability" (how useful a particular drug is against multiple allelic variants). They group both terms under an umbrella term "drugability". They demonstrate these features for an important class of antibiotics, the beta-lactams, and allelic variants of TEM-1 beta-lactamase. In the revised version, they investigate a second drug class that targets dihydrofolate reductase in Plasmodium (the causative agent of malaria).

The strength of the result is in its conceptual advance and that the concepts seem to work for beta-lactam resistance and DHFR inhibitors in a protozoan. However, I do not necessarily see the advance of lumping both terms under "drugability", as this adds an extra layer of complicaton in my opinion.

I think that the utility of the terms will be more comprehensively demonstrated by using examples across a breadth of drug classes classes and/or resistance genes. For instance, another good bacterial model with published data might have been trimethoprim resistance, which arises through point mutations in the folA gene (although, clinical resistance tends to be instead conferred by a suite of horizontally acquired dihydrofolate reductase genes, which are not so closely related as the TEM variants explored here).

The impact of the work on the field depends on a more comprehensive demonstration of the applicability of these new concepts to other drugs. This would be demonstrated in future work.

eLife assessment

The authors used ribosome profiling in conjunction with standard biochemical approaches to investigate the role of eIF2A in translation initiation in yeast under optimal growth conditions or stress. The convincing data demonstrate that eIF2A does not play a substantial role in translation initiation in yeast. These important findings challenge the current view that eIF2A substitutes for eIF2 under stress and are thus anticipated to spur future investigation on the role(s) of eIF2A. Considering the broad scope of cellular functions attributed to eIF2A, this study should be of interest to a wide spectrum of biomedical researchers ranging from those studying fundamental mechanisms of translation regulation to virologists and cancer biologists.

Reviewer #1 (Public Review):

Summary:<br /> The study follows the role of yeast eIF2A protein as a potential translation initiation factor engaged in the non-canonical translation initiation under stress conditions and as a substitute for eIF2. Using ribosome profiling, RNA-Seq and reporter-based assays authors evaluated the role of eIF2A protein under regular or stress conditions (cells starved for branched amino acids). The authors found that yeast cells depleted of eIF2A protein do not change significantly their translation initiation, or translation in general. In contrast to previously reported data for human homolog, yeast eIF2A does not significantly contribute to the regulation of the uORFs, regardless of whether they start with canonical AUG or near cognate start codons. eIF2A is not involved in the repression of IRES element in the URE2 gene or has a role in purine biosynthesis. It appears that in yeast eIF2A contributes to the regulation of a very limited number of mRNAs (32 with significant changes in translation efficiency), where only 17 of such messages indeed are consistent with eIF2A deletion, and single mRNA (HKR1) could be validated in reporter assay.

Strengths:<br /> The main strength of the manuscript is a complete analysis and unbiased approach using genomic analysis methods (ribosome profiling and RNA-seq) as well as reporter validation studies. Additional strengths of the manuscript are scientific rigor and statistics associated with data analyses, clear data presentation, and discussion of the results in the context of the previous studies and results.

Weaknesses:<br /> No weaknesses were noted by this reviewer.

Reviewer #2 (Public Review):

Summary:<br /> Gaikwad et al. investigated the role of eIF2A in translational response to stress in yeast. For this purpose, the authors conducted ribosome profiling under SM treatment in an eIF2A-depleted strain. Data analysis revealed that eIF2A did not influence translation from mRNAs bearing uORFs or cellular IRESes, in the stress condition, broadly. The authors found that only a small number of mRNAs were supported by eIF2A. The data should be helpful for researchers in the field.

Major points:<br /> 1. The weakness of this work is the lack of clarification on the function of eIF2A in general. The novelty of this study was limited.

2. Related to this, it would be worth investigating common features in mRNAs selectively regulated (surveyed in Figure 3A). Also, it would be worth analyzing the effect of eIF2A deletion on elongation (ribosome occupancy on each codon and/or global ribosome footprint distribution along CDS) and termination/recycling (footprint reads on stop codon and on 3′ UTR).

3. Regarding Figure 3D, the reporters were designed to include promoter and 5′ UTR of the target genes. Thus, it should be worth noting that reporter design was based on the assumption that eIF2A-dependency in translation regulation was not dependent on 3′ UTR or CDS region. The reason why the effects on ribosome profiling-supported mRNAs could not be recapitulated in reporter assay may originate from this design. This should be also discussed.

4. Related to the point above, the authors claimed that eIF2A affects "possibly only one" (HKR1) mRNA. However, this was due to the reporter assay which is technically variable and could not allow some of the constructs to pass the authors' threshold. Alternative wording for this point should be considered.

5. For Figure 3D, it would be worth considering testing the #-marked genes (in Figure 3C) in this set up.

6. In box plots, the authors should provide the statistical tests, at least where the authors explained in the main text.

Reviewer #3 (Public Review):

Summary:<br /> The authors have undertaken a study to rigorously characterize the possible role of eIF2A in regulating translation in yeast. The authors test for the role of eIF2A in the absence or presence of cellular stress and conclude that eIF2A does not play any significant role in regulating translation initiation in yeast.

Strengths:<br /> The authors have used rigorous experimental approaches, including genome-wide ribosome profiling analysis in the absence or presence of stress, to show that eIF2A does not function in translation initiation on most mRNAs in yeast. Interestingly, the authors do identify a small number of mRNAs that possess some eIF2A dependency, so they constructed reporters to rigorously test them. One mRNA, HKR1, appears to possess a degree of eIF2A-dependent translation regulation.

Weaknesses:<br /> While no role of eIF2A in translation initiation is apparent, the authors do not determine what function eIF2A does play in yeast. Whether it plays a role in regulating translation in a different stress response is not determined.

eLife assessment

In this valuable study, the discovery and subsequent design of the AF03-NL chimeric antibody yielded a tool for studying filoviruses and provides a possible blueprint for future therapeutics. However, the data are incomplete and not presented clearly, which obscures flaws in the analyses and leaves unexplained phenomena. The work will be of interest to virologists studying antibodies.

Reviewer #1 (Public Review):

Summary and Strengths:

Zhang et al. conducted a study in which they isolated and characterized a Marburg virus (MARV) glycoprotein-specific antibody, AF-03. The antibody was obtained from a phage-display library. The study shows that AF-03 competes with the previously characterized MARV-neutralizing antibody MR78, which binds to the virus's receptor binding site. The authors also performed GP mutagenesis experiments to confirm that AF-03 binds near the receptor binding site. In addition, the study confirmed that AF-03, like MR78, can neutralize Ebola viruses with cleaved glycoproteins. Finally, the authors demonstrated that NPC2-fused AF-03 was effective in neutralizing several filovirus species.

Weaknesses:

1. The main premise of this study is unclear. Flyak et al. in 2015 described the isolation and characterization of a large panel of neutralizing antibodies from a Marburg survivor (Flyak et al., Cell, 2015). Based on biochemical and structural characterization, Flyak proposed that the Marburg neutralizing antibodies bind to the NPC1 receptor binding side. In the same study, it has been shown that several MARV-neutralizing antibodies can bind to cleaved Ebola glycoproteins that were enzymatically treated to remove the mucin-like domain and glycan cap. In the following study, it has been shown that the bispecific-antibody strategy can be used to deliver Marburg-specific antibodies into the endosome, where they can neutralize Ebola viruses (Wec et al., Science 2016). Finally, the use of lysosome-resident protein NPC2 to deliver antibody cargos to late endosomes has been previously described (Wirchnianski et al., Front. Immunol, 2021)

The above-mentioned studies are not referenced in the introduction. The authors state that "there is no licensed treatment or vaccine for Marburg [virus] infection." While this is true, there are human antibodies that recognize neutralizing epitopes - that information can't be excluded while providing the rationale for the study. Furthermore, the authors use the word "novel" to describe the AF-03 antibody. How novel is AF-03 if multiple Marburg-neutralizing antibodies were previously characterized in multiple studies? Since AF-03 competes with previously characterized MR78, it binds to the same antigenic region as MR78. AF-03 also has comparable neutralization potency as MR78.

2. Without the AF-03-MARV GP crystal structure, it's unclear how van der Waals interactions, H-bonds, and polar and electrostatic interactions can be evaluated. While authors use computer-guided homology modeling, this technique can't be used to determine critical interactions. Furthermore, Flyak et al. reported that binding to the NPC1 receptor binding site is the main mechanism of Marburg virus neutralization by human monoclonal antibodies. Since both AF-03 (this study) and MR78 (Flyak study) competed with each other, that information alone was sufficient for GP mutagenesis experiments that identified the NPC1 receptor binding site as the main region for mutagenesis.

3. The AF-03-GP affinity measurements were performed using bivalent IgG molecules and trimeric GP molecules. This format does not allow accurate measurements of affinity due to the avidity effect. The reported KD value is abnormally low due to avidity effects. The authors need to repeat the affinity experiments by immobilizing trimeric GPs and then adding monovalent AF-03 Fab.

Reviewer #2 (Public Review):

Summary:

The authors describe the discovery of a filovirus neutralizing antibody, AF03, by phage display, and its subsequent improvements to include NPC2 that resulted in a greater breadth of neutralization. Overall, the manuscript would benefit from considerable grammatical review, which would improve the communication of each point to the reader. The authors do not convincingly map the AF03 epitope, nor do they provide any strong support for their assumption that AF03 targets the NPC1 binding site. However, the authors do show that AF03 competes for MR78 binding to its epitope, and provides good support for the internalization of AF03-NL as the mechanism for improved breadth over the original AF03 antibody.

Strengths:

This study shows convincing binding to Marburgvirus GP and neutralization of Marburg viruses by AF03, as well as convincing neutralization of Ebolaviruses by AF03-NL. While there are no distinct populations of PE-stained cells shown by FACS in Figure 5A, the cell staining data in Figure 5C are compelling to a non-expert in endosomal staining like me. The control experiments in Figure 7 are compelling showing neutralization by AF03-NL but not AF03 or NPC2 alone or in combination. Altogether these data support the internalisation and stabilisation mechanism that is proposed for the gain in neutralization breadth observed for Ebolaviruses by AF03-NL over AF03 alone.

Weaknesses:

Overall, this reviewer is of the opinion that this paper is constructed haphazardly. For instance, the neutralization of mutant pseudoviruses is shown in Figure 2 before the concept of pseudovirus neutralization by AF03 is introduced in Figure 3. Similarly, the control experiments for AF03+NPC2 are described in Figure 7 after the data for breadth of neutralization are shown in Figure 6. GP quality controls are shown in Figure 2 after GP ELISAs / BLI experiments are done in Figure 1. This is disorienting for the reader.

Figure 1: The visualisation of AF03 modelling and docking endeavours is extremely difficult to interpret. Firstly, there is no effort to orient the non-specialist reader with respect to the Marburgvirus GP model. Secondly, from the figures presented it is impossible to tell if the Fv docks perfectly onto the GP surface, or if there are violent clashes between the deeply penetrating AF03 CDRs and GP. This information would be better presented on a white background, perhaps showing GP in surface view from multiple angles and slices. The authors attempt to label potential interactions, but these are impossible to read, and labels should be added separately to appropriately oriented zoomed-in views.

Figure 2: The neutralization of mutant pseudoviruses cannot be properly assessed using bar graphs. These data should be plotted as neutralization curves as they were done for the wild-type neutralization data in Figure 3. The authors conclude that Q128 & N129 are contact residues, but the neutralization data for this mutant appear odd as the lowest two concentrations of AF03 show higher neutralization than the second highest AF03 concentration. Neutralization of T204/Q205/T206 (green), Y218 (orange), K222 (blue), or C226 (purple) appears to be better than neutralization of the wild-type MARV. The authors do not discuss this oddity. What are the IC50's? The omission of antibody concentrations on the x-axis and missing IC50 values give a sense of obscuring the data, and the manuscript would benefit from greater transparency, and be much easier to interpret if these were included. I am intrigued that the Q128S/N129S mutant is reported as having little effect on the neutralization of MR78. The bar graph appears to show some effect (difficult to interpret without neutralization curves and IC50 data), and indeed PDB:5UQY seems to suggest that these amino acids form a central component of the MR78 epitope (Q128 forms potential hydrogen bonds with CDRH1 Y35 and CDRL3 Y91, while N129 packs against the MR78 CDRH3 and potentially makes additional polar contact with the backbone). Lastly, since neutralization was tested in both HEK293T cells and Huh7 cells in Figure 3, the authors should clarify which cells were used for neutralization in Figure 2.

Figure 3: The first two images in Figure 3C showing bioluminescent intensity from pseudovirus-injected mice pretreated with either 10mg/kg or 3mg/kg AF03 are identical images. This is apparent from the location, shape, and intensity of the bioluminescence, as well as the identical foot placement of each mouse in these two panels. Currently, this figure is incomplete and should be corrected to show the different mice treated with either 10mg/kg or 3mg/kg of AF03.

Figure 4 would benefit from a control experiment without antibodies comparing infection with GP-cleaved and GP-uncleaved pseudoviruses. The paragraph describing these data was also difficult to read and would benefit from additional grammatical review.

Figure 5: The authors should clarify in the methods section that the "mock" experiment included the PE anti-human IgG Fc antibody. Without this clarification, the lack of a distinct negative population in the FACS data could be interpreted as non-specific staining with PE. If the PE antibody was added at an equivalent concentration to all panels, what does the directionality of the arrowheads in Figure 5A (labelled PE) and 5B (labelled pHrodo Red) indicate?

Figure 6B: These data would benefit from the inclusion of IC50, transparency of antibody concentrations used, and consistency in the direction of antibody concentrations (increasing to the right or left of the x-axis) when compared to Figure 2.

eLife assessment

This valuable study employs a diverse array of techniques encompassing cell biological manipulations, biophysical measurements, and mouse models to elucidate the impact of target cell stiffness on CD8+ cytotoxic T cell activation, with a particular focus on the actin nucleator protein WASP. The finding that WASP is essential for the stiffness-dependent phosphorylation of ZAP70 in CD8 T cells is convincing. However, the data regarding the role of WASP in mechanosensing within CD8 T cell-mediated anti-tumor immunity is incomplete and would benefit from a more rigorous study design. This work would be of interest to cell biologists and investigators studying mechanosensing within the immune system.

Reviewer #1 (Public Review):

Summary:

Mandal et al build upon their earlier work in CD 4 T cells to address the role of WASP in cytotoxic T cell mechanosensing. As shown previously by this group and others, the authors present evidence that tumour cell lysis is stiffness dependent and requires CTL WASP expression. They proceed to show that CTLs engaging targets form actin-rich foci, that the formation of these structures is dependent upon tumour cell stiffness and WASP dependent actin nucleation. Traction force measurements show that WASP is involved in force generation, and evidence that WASP plays a role in mechanosensing comes from studies showing that stiffness dependent phosphorylation of early TCR signalling intermediates (but not the later stages of T cell activation) is WASP dependent, as is phosphorylation of the tension sensor CasL. Finally, the authors provide in vivo data that WASP-deficient T cells kill tumours inefficiently.

Strengths:

The paper is well-written and brings together a range of well-established techniques for measuring T cell stiffness responses, force production, signalling, and effector function. Although some of the findings are necessarily correlative, the authors have largely achieved their aims. One particularly interesting observation is that stiffness dependent phosphorylation of ZAP70 requires WASP expression. Evidence that ZAP70 phosphorylation is WASP dependent is important, as it suggests that forces exerted by WASP are needed for some of the earliest stages of TCR signalling, perhaps TCR deformation itself. This observation, made in CD8 T cells, is particularly interesting given that previous work from this group [Kumari et al eLife 2015] showed that ZAP70 phosphorylation was intact in WASP-/- CD4 T cell blasts. In that study, the first clear differences in TCR signaling were seen at the level of PLCγ phosphorylation. This could represent an interesting difference between CD4 and CD8 T cells, but supplemental data from Figure S2 also show WASP dependence for CD3ζ and ZAP70 phosphorylation in naïve CD4 T cells. Unfortunately, this interesting issue was not discussed or pursued experimentally.

Weaknesses:

While the study is well executed, it is rather limited in scope, and many of the observations have been reported previously in other systems. These weaknesses limit the impact of the study. In particular, the authors have previously shown in CD4 T cells that the nucleation promoting activity of WASP is responsible for the formation of actin foci, for early TCR signalling events associated with T cell activation, for traction force generation and for CasL phosphorylation [Kumari et al eLife 2015, Kumari et al EMBO J 2020]. It could be argued that this paper extends findings made originally in CD4 cells to include CD8 T cells. But the authors did not make this clear, and the advance is rather incremental. Moreover, similar studies have been done in CD8 T cells by other labs. Most notably, the Huse group has conducted highly relevant work investigating the mechanobiology of CTL function in vitro and in vivo [Basu et al Cell 2016, Wang et al Nat Comms 2022, Tamzalit et al Sci Immunol 2019, Tello-Lafoz et al Immunity 2021, de Jesus et al bioRxiv Preprint 2023]. Indeed, one study showed that WASP depletion impairs the formation of protrusions that deform the target cell surface and promote target lysis [Tamzalit et al Sci Immunol 2019]. Mandal et al cite this work and argue that what they show differs from the mechanopotentiation shown in Tamzalit et al, but they don't explore the issue further. They also fail to cite work from Tello-Lafoz et al showing that regulated changes in target cell stiffness contribute to CTL vulnerability. Finally, Mandal et al. fail to deal with evidence that WASP participates in many phases of the CTL response, including adhesion, migration, granule release, and serial killing. All of these are likely contributors to the in vivo phenotypes shown in Figure 4.

Reviewer #2 (Public Review):

Summary:

Mandal et al. use WASP-deficient T cells to study the role of WASP in T cell signaling and activation and tying WASP to mechanosensing in T cells. Using both CD8 and CD4 T cells from WASP-deficient animals, the authors show defects in T cell signaling and function as well as defects in mechanosensing in activated CD8 T cells.

Strengths:

Confirming findings from many previous studies, Mandal et al. demonstrate that WASP-deficiency in T cells leads to defective T cell function (Figs 1, 2, 3, and 4). Fig 3 shows direct effects of mechanical stress on CD8 T cell signaling in the absence of WASP.

Weaknesses:

The title does not reflect the data presented as the only data demonstrating a role for WASP in mechanosensing in this manuscript doesn't directly connect WASP mechanosensing with tumors (Fig 3). The results shown in Fig 1 using an actin inhibitor doesn't directly connect WASP with mechanosensing. Fig 4 uses WASP-deficient animals in a tumor model, but doesn't demonstrate any role for mechanosensing in the WASP-deficient animals. The title should reflect the lack of data connecting WASP in mechanosensing to a tumor context.

One major oversight is the absence of discussion of a previous publication demonstrating a direct role of WASP in mechanosensing to the actin cytoskeleton in dendritic cells and naive CD4 and CD8 T cells (Gaertner et al. Dev Cell 2022). There should be a discussion of how the findings in Gaertner et al. shed light on the results from this manuscript.

The use of Myca to disrupt the actin cytoskeleton as a "modulator of stiffness" is problematic. While one of the potential effects of disrupting the actin cytoskeleton is changing stiffness, as shown in Figure 1, many other functions are simultaneously disturbed also. The use of B16 tumor cells is simply for antigen presentation, and not in a tumor context, so generalized statements about "stiffness" or "softness" and "tumor cells" in reference to Figure 1 should be changed to account for these alternative explanations.

Fig S2 shows Myca treatment of BMDCs leads to decreased functionality of OTII CD4s. Interpretation in the manuscript claims "This indicates that leaching of Myca from treated cells does not cause inhibition of bystander cells". This would not be my interpretation of the data. An alternative interpretation is that if Myca is remaining in the media, then effects on APCS (either BMDCs or B16s) could lead to decreased CD4 or CD8 T cell activation and thus be responsible for effects seen in Fig 1. This possibility should be considered.

Fig 4 claims that high rigidity leads to downstream effects of WASP-/- T cell function. But there is no demonstration of the role of mechanosensing in Figure 4. To make this claim, the authors would need to compare high and low rigidity conditions.

Fig 4 also shows that WASP-/- showed higher tumor growth in an implanted tumor model. For 4F, since WASP is deficient in all hematopoietic cells, the finding in 4G may not be due to T cells. In 4H-J, because implantation of tumors occurs within 1 day of lymphodepletion and assessing tumor growth prior to reconstitution of the hematopoietic compartment, there should be control experiments shown to demonstrate that other hematopoietic cell types that remain are not function and thus do not participate in the differences seen in tumor growth. Also, statistical tests need to be done to show the significance of the differences between groups in Fig 4I and 4J (also 4G).

Reviewer #3 (Public Review):

The manuscript from Mandal et al. aims to show that the actin cytoskeleton is the key mechanosensitive element in cytotoxic T lymphocytes, enabling them to discriminate between target cells of different cortical stiffness. They further examine whether WASP activation is sensitive to substrate stiffness, and thus modulates actin polymerization and early T cell signaling in a mechanosensitive manner. Overall, the mechanosensitivity of CTLs has attracted a lot of attention in the last few years and this study explores new and interesting facets. The manuscript asks an important question regarding the mechanisms underlying the stiffness dependent response observed in T cells. The authors have used a variety of techniques ranging from mouse models and in vivo studies, cell biological manipulations and biophysical measurements which is commendable. Their work suggests that the actin cytoskeleton regulated by WASP plays a key role in mechanosensitivity - which is an intriguing finding.

While this manuscript has wide-ranging experiments and interesting results, a number of points need to be carefully addressed to support the central claims.

The first major issue is that the irreversible actin inhibitor myca can have a number of non-specific effects on CTL activation. It is not clear that the effects observed are due to the change in stiffness alone. Since Myca depolymerizes actin, the B16 target cells would have altered MHC mobility or impaired receptor-ligand engagement - which might affect actin foci formation and signaling. There is also no gain of function experiment, wherein the stiffness of the target cell is enhanced. Moreover, there are two populations in both the control and myca-treated Young's modulus histograms for B16 cells. Are these sub-populations fundamentally different in their cytoskeletal organization? This can also confound or introduce variability in results on stiffness-dependence of CTL function, given the second sub-population of Myca-treated cells overlaps with the first sub-population of control cells. The authors need to provide a justification for these.

Secondly, the WASP knockout still shows mechanosensitivity but at reduced force levels (Fig. 3B). Similarly, other measures (Fig. 3) still show increases with stiffness. Thus, it is not clear whether WASP is necessary for mechanosensing but simply for maintaining force levels and (expectedly) lower actin levels and foci in the WASP knockout. In fact, Fig 3 implies WASP is required for signaling and not for mechanosensing, undermining the main claim of the paper. At the very least, ANOVA or factor analysis (stiffness x WASP) needs to be done to demonstrate the requirement of WASP for CTL mechanosensitivity.

Third, there are some concerns regarding the traction force microscopy. The authors do not present key details in the manuscript about the methods used. Secondly, the traction values are entirely too high compared to reported values in the literature for CTLs. A back-of-the-envelope calculation of the total force yields ~30 nN for wild-type cells) on 10 kPa gels, which is about an order of magnitude higher than reported values (Tamzalit et al. 2020, Hui et al. 2017, Bashour et al. 2014, Pathni et al. 2022). The authors should clearly demonstrate and justify that their measured values are reasonable and accurate. The lack of representative movies and displacement maps used for the traction force measurements make it hard to evaluate the results. Typical bead displacements for CTLs on softer gels are on the order of 1 micron (Mustapha et al. 2022), which should decrease to 0.1 micron or less on 50 kPa gels. These would make the tractions hard to estimate accurately. The authors should evaluate and show the displacements underneath the cell and outside the cell boundaries to give estimates of the noise floor for tractions. Finally, there is no discussion of how the tractions were calculated from the displacements - was Fourier Transform or Finite element method used? What is the noise level of the measurements and how were the traction estimates regularized?

Fourth, many of the plots in the manuscripts are not accompanied by representative images to show how these aspects (distribution of actin and signaling markers for example) change qualitatively under different conditions (e.g. stiffness). Details of analysis and quantification need to be provided for a clearer understanding of the results and interpretations. All figures and captions should include information about the number of cells and experiments. Along these lines, there is very little detail in the methods, statistical power, calculations are not mentioned, there is little description of the pmel-1 knockout mouse, all of which make it hard to evaluate the soundness of the results.

Finally, the study as presented, doesn't conclusively show that WASP is required for mechanosensitive CTL function. The results presented show that WASP is required for early and longer-term signaling events and cytolytic activity, and that knocking out WASP reduces early TCR signaling, actin foci formation in response to substrate stiffness. To make the claim of WASP-mediated regulation of CTL mechanosensitivity stronger, it would be helpful to see how WASP knockout affects CTL killing in response to softened and (possibly) stiffened B16 targets.

eLife assessment

This important work by Park et al. introduces an open-top two-photon light sheet microscopy (OT-TP-LSM) for lesser invasive evaluation of intraoperative 3D pathology. The authors provide convincing evidence for the effectiveness of this technique in investigating various human cancer cells. The paper needs some minor corrections and has the potential to be of broad interest to biologists and, specifically, pathologists utilizing 3D optical microscopy.

Reviewer #1 (Public Review):

Summary:

This manuscript presents the development of a new microscope method termed "open-top two-photon light sheet microscopy (OT-TP-LSM)". While the key aspects of the new approach (open-top LSM and Two-photon microscopy) have been demonstrated separately, this is the first system of integrating the two. The integration provides better imaging depth than a single-photon excitation OT-LSM.

Strengths:

- The use of liquid prism to minimize the aberration induced by index mismatching is interesting and potentially helpful to other researchers in the field.<br /> - The use of propidium iodide (PI) provided a deeper imaging depth.

Weaknesses:

- Details are lacking on imaging time, data size, the processing time to generate large-area en face images, and inference time to generate pseudo H&E images. This makes it difficult to assess how applicable the new microscope approach might be in various pathology applications.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors developed an open-top two-photon light sheet microscopy (OT-TP-LSM) that enables high-throughput and high-depth investigation of 3D cell structures. The data presented here shows that OT-T-LSM could be a complementary technique to traditional imaging workflows of human cancer cells.

Strengths:

High-speed and high-depth imaging of human cells in an open-top configuration is the main strength of the presented study. An extended depth of field of 180 µm in 0.9 µm thickness was achieved together with an acquisition of 0.24 mm2/s. This was confirmed by 3D visualization of human cancer cells in the skin, pancreas, and prostate.

Weaknesses:

The complementary aspect of the presented technique in human pathological samples is not convincingly presented. The traditional hematoxylin and eosin (H&E) staining is a well-established and widely used technique to detect human cancer cells. What would be the benefit of 3D cell visualization in an OT-TP-LSM microscope for cancer detection in addition to H&E staining?

eLife assessment

This paper represents important findings when identifying untargeted metabolomics and its differences between metabolomes of different biological samples. GromovMatcher is the fantasy name for the soft development. The main idea behind it is built on the assumption of featuring and matching complex datasets. Although the manuscript reflects a solid analysis, it remains incomplete for validation with putative non-curated datasets.

Reviewer #1 (Public Review):

Summary:

The authors have implemented the Optimal Transport algorithm in GromovMatcher for comparing LC/MS features from different datasets. This paper gains significance in the proteomics field for performing meta-analysis of LC/MS data.

Strengths:

The main strength is that GromovMatcher achieves significant performance metrics compared to other existing methods. The authors have done extensive comparisons to claim that GromovMatcher performs well.

Weaknesses:

There are two weaknesses.

1. When the number of features is reduced the precision drops to ~0.8.<br /> 2. How applicable is the method for other non-human datasets?

Reviewer #2 (Public Review):

Summary:

The goal of untargeted metabolomics is to identify differences between metabolomes of different biological samples. Untargeted metabolomics identifies features with specific mass-to-charge ratio (m/z) and retention time (RT). Matching those to specific metabolites based on the model compounds from databases is laborious and not always possible, which is why methods for comparing samples on the level of unmatched features are crucial.

The main purpose of the GromovMatcher method presented here is to merge and compare untargeted metabolomes from different experiments. These larger datasets could then be used to advance biological analyses, for example, for the identification of metabolic disease markers. The main problem that complicates merging different experiments is m/z and RT vary slightly for the same feature (metabolite).

The main idea behind the GromovMatcher is built on the assumption that if two features match between two datasets (that feature i from dataset 1 matches feature j from dataset 2, and feature k from dataset 1 matches feature l from dataset 2), then the correlations or distances between the two features within each of the datasets (i and k, and j and l) will be similar. The authors then use the Gromov-Wasserstein method to find the best matches matrix from these data.

The variation in m/z between the same features in different experiments is a user-defined value and it is initially set to 0.01 ppm. There is no clear limit for RT deviations, so the method estimates a non-linear deviation (drift) of RT between two studies. GromovMatcher estimates the drift between the two studies and then discards the matching pairs where the drift would deviate significantly from the estimate. It learns the drift from a weighted spline regression.

The authors validate the performance of their GromovMatcher method by a validation experiment using a dataset of cord blood. They use 20 different splits and compare the GromovMatcher (both its GM and GMT iterations, whereby the GMT version uses the deviation from estimated RT drift to filter the matching matrix) with two other matching methods: M2S and metabCombiner.

The second validation was done using a (scaled and centered) dataset of metabolics from cancer datasets from the EPIC cohort that was manually matched by an expert. This dataset was also used to show that using automatic methods can identify more features that are associated with a particular group of samples than what was found by manual matching. Specifically, the authors identify additional features connected to alcohol consumption.

Strengths:

I see the main strength of this work in its combination of all levels of information (m/z, RT, and higher-order information on correlations between features) and using each of the types of information in a way that is appropriate for the measure. The most innovative aspect is using the Gromov-Wasserstein method to match the features based on distance matrices.

The authors of the paper identify two main shortcomings with previously established methods that attempt to match features from different experiments: a) all other methods require fine-tuning of user-defined parameters, and, more importantly, b) do not consider correlations between features. The main strength of the GromovMatcher is that it incorporates the information on distances between the features (in addition to also using m/z and RT).

Weaknesses:

The first, minor, weakness I could identify is that there seem not to be plenty of manually curated datasets that could be used for validation. The second is also emphasized by the authors in the discussion. Namely, the method as it is set up now can be directly used only to compare two datasets.

eLife assessment

This useful study describes a single set of label-chase mass spectrometry experiments to confirm the molecular function of YafK as a peptidoglycan hydrolase, and to describe the timing of its attachment to the peptidoglycan. Confirmation of the molecular function of YafK will be helpful in further studies to examine the function and regulation of the outer membrane-peptidoglycan link in bacteria. The evidence supporting the molecular function of YafK and that lpp molecules are shuffled on and off the peptidoglycan is solid, however, data supporting conclusions relating to the locations of lpp-peptidoglycan attachment are incomplete. The work will be of interest to microbiologists studying the bacterial cell wall.

Reviewer #1 (Public Review):

Summary:

Liang et. al., uses a previously devised full isotope labeling of peptidoglycan followed by mass spec to study the kinetics of Lpp tethering to PG and the hydrolysis of this bond by YafK.

Strengths:

-The labeling and mass spec analysis technique works very well to discern differentially labelled Tri-KR muropeptide containing new and old Lpp and PG.

Weaknesses:

-Only one line of experimentation using mass spec based analysis of labeled PG-Lpp is used to make all conclusions in the paper. The evidence is also not enough to fully deleanate the role of YafK.<br /> -Only one mutant (YafK) is used to make the conclusion.<br /> -The paper makes a lot of 'implications' with minimal proof to support their hypothesis. Other lines of experimentations must be added to fully delineate their claims.<br /> -Time points to analyse Tri-KR isotopologues in Wt (0,10,20,40,60 min) and yafK mutant (0,15, 25, 40, 60 min) are not the same.<br /> -Experiments to define physiological role of YafK are also missing.

Reviewer #2 (Public Review):

Summary:

The authors of this study have sought to better understand the timing and location of the attachment of the lpp lipoprotein to the peptidoglycan in E. coli, and to determine whether YafK is the hydrolase that cleaves lpp from the peptidoglycan.

Strengths:

The method is relatively straightforward. The authors are able to draw some clear conclusions from their results, that lpp molecules get cleaved from the peptidoglycan and then re-attached, and that YafK is important for that cleavage.

Weaknesses:

However, the authors make a few other conclusions from their data which are harder to understand the logic of, or to feel confident in based on the existing data. They claim that their 5-time point kinetic data indicates that new lpp is not substantially added to lipidII before it is added to the peptidoglycan, and that instead lpp is attached primarily to old peptidoglycan. I believe that this conclusion comes from the comparison of Fig.s 3A and 3C, where it appears that new lpp is added to old peptidoglycan a few minutes before new lpp is added to new peptidoglycan. However, the very small difference in the timing of this result, the minimal number of time points and the complete lack of any presentation of calculated error in any of the data make this conclusion very tenuous. In addition, the authors conclude that lpp is not significantly attached to septal peptidoglycan. The logic behind this conclusion appears to be based on the same data, but the authors do not provide a quantitative model to support this idea.

This work will have a moderate impact on the field of research in which the connections between the OM and peptidoglycan are being studied in E. coli. Since lpp is not widely conserved in gram negatives, the impact across species is not clear. The authors do not discuss the impact of their work in depth.

eLife assessment

Sulphur atoms derived from cysteine are thought to play significant roles in maintaining redox homeostasis in Mycobacterium tuberculosis, which encounters stresses associated with immune cell interactions. In this valuable manuscript, the authors provide solid evidence that the genes encoding cysteine biosynthetic enzymes (cysM and cysK2) are required to maintain full viability of M. tuberculosis under in vitro stress conditions, macrophage infections, and within the lung tissues of mice. The manuscript presents transcriptomic and metabolomic evidence to support the hypothesis that CysM and CysK2 play distinct roles in maintaining cysteine-derived metabolite pools under stress conditions. The work will be of interest to microbiologists in general.

Reviewer #1 (Public Review):

Summary:

Khan et. al., investigated the functional redundancy of the non-canonical L-cysteine synthases of M. tuberculosis, CysM and CysK2, focussing on their role in mitigating the effects of host-derived stress. They found that while deletion mutants of the two synthases (Rv∆cysM, Rv∆cysK2) have similar transcriptomes under standard conditions, their transcriptional response to oxidative stress is distinct. The impact of deleting the synthases also differentially affected the pools of L-cysteine-derived metabolites. They show that the mutants (Rv∆cysM, Rv∆cysK2) have impaired survival in peritoneal macrophages and in a mouse model of infection. Importantly, they show that the survival of the mutants increases when the host is defective in producing reactive oxygen and nitrogen species, linking the phenotype to a defect in combating host-derived stress. Finally, they show that compounds inhibiting L-cysteine synthases reduce the intracellular survival of M. tuberculosis.

Strengths:

1. The distinct transcriptome of the Rv∆cysM and Rv∆cysK2 mutants in the presence of oxidative stress provides solid evidence that these mutants are distinct in their response to oxidative stress, and suggests that they are not functionally redundant.<br /> 2. The use of macrophages from phox-/- and INF-/- mice and an iNOS inhibitor for the intracellular survival assays provides solid evidence that the survival defect seen for the Rv∆cysM and Rv∆cysK2 mutants is related to their reduced ability to combat host-derive oxidative and nitrosative stress. This is further supported by the infection studies in phox-/- and INF-/- mice.

Weaknesses:

1. There are several previous studies looking at the transcriptional response of M. tuberculosis to host-derived stress, however, the authors do not discuss initial RNA-seq data in the context of these studies. Furthermore, while several of the genes in sulfur assimilation and L-cysteine biosynthetic pathway genes are upregulated by more than one stress condition, the data does not support the statement that it is the "most commonly upregulated pathway in Mtb exposed to multiple host-like stresses".<br /> 2. For the quantification of the metabolites, it isn't clear how the abundance was calculated (e.g., were standards for each metabolite used? How was abundance normalised between samples?), and this information should be included to strengthen the data. Furthermore, labelling with L-methionine was performed to determine the rate of synthesis of the L-cysteine-derived metabolites. L-cysteine is produced from L-methionine via the transsulfuration pathway, which is independent of CysM and CysK2. It is therefore difficult to interpret this experiment, as the impact of deleting CysM and CysK2 on the transsulfuration pathway is likely indirect.

3. The ability of L-cysteine to rescue the survival defect of the Rv∆cysM and Rv∆cysK2 mutants in macrophages is interpreted as exogenous L-cysteine being able to compensate for reduced intracellular levels. However, there is no evidence that L-cysteine is being taken up by the mutants and an alternate explanation is that L-cysteine functions as an antioxidant within cells i.e., it reduces intracellular ROS.

The authors sought to investigate the functional redundancy of the non-canonical L-cysteine synthases CysM and CysK2. While their distinct transcriptional response to oxidative stress suggests distinct physiological roles, the study did not explore these differences and therefore provides only preliminary insight into the underlying reasons for this observation. In the context of drug development, this work suggests that while L-cysteine synthase inhibitors do not have high potency for killing intracellular M. tuberculosis, they have the potential to decrease the pathogen's survival in the presence of host-derive stress.

Reviewer #2 (Public Review):

Summary:

The paper examines the role L-cysteine metabolism plays in the biology of Mycobacterium tuberculosis. The authors have preliminary data showing that Mycobacterium tuberculosis has two unique pathways to synthesize cysteine. The data showing new compounds that act synergistically with INH is very interesting.

Strengths:

RNAseq data is interesting and important.

Weaknesses:

The paper would be strengthened if the authors were to add further detail to their genetic manipulations.

The authors provide evidence that they have successfully made a cysK2 mutant by recombineering. This data looks promising, but I do not see evidence for the cysM deletion. It is also important to state what sort of complementation was done (multicopy plasmid, integration proficient vector, or repair of the deletion). Since these mutants are the basis for most of the additional studies, these details are essential. It is important to include complementation in mouse studies as unexpected loss of PDIM could have occurred.

Reviewer #3 (Public Review):

In this work, the authors conduct transcriptional profiling experiments with Mtb under various different stress conditions (oxidative, nitrosative, low pH, starvation, and SDS). The Mtb transcriptional responses to these stress conditions are not particularly new, having been reported extensively in the literature over the past ~20 years in various forms. A common theme from the current work is that L-cysteine synthesis genes are seemingly up-regulated by many stresses. Thus, the authors focused on deleting two of the three L-cysteine synthesis genes (cysM and cysK2) in Mtb to better understand the roles of these genes in Mtb physiology.

The cysM and cysK2 mutants display fitness defects in various media (Sautons media, starvation, oxidative and nitrosative stress) noted by CFU reductions. Transcriptional profiling studies with the cysM and cysK2 mutants revealed that divergent gene signatures are generated in each of these strains under oxidative stress, suggesting that cysM and cysK2 have non-redundant roles in Mtb's oxidative stress response which likely reflects the different substrates used by these enzymes, CysO-L-cysteine and O-phospho-L-serine, respectively. Note that these studies lack genetic complementation and are thus not rigorously controlled for the engineered deletion mutations.

The authors quantify the levels of sulfur-containing metabolites (methionine, ergothioneine, mycothiol, mycothionine) produced by the mutants following exposure to oxidative stress. Both the cysM or cysK2 mutants produce more methionine, ergothioneine, and mycothionine relative to WT under oxidative stress. Both mutants produce less mycothiol relative to WT under the same condition. These studies lack genetic complementation and thus, do not rigorously control for the engineered mutations.

Next, the mutants were evaluated in infection models to reveal fitness defects associated with oxidative and nitrosative stress in the cysM or cysK2 mutants. In LPS/IFNg activated peritoneal macrophages, the cysM or cysK2 mutants display marked fitness defects which can be rescued with exogenous cysteine added to the cell culture media. Peritoneal macrophages lacking the NADPH oxidase (Phox) or IFNg fail to produce fitness phenotypes in the cysM or cysK2 mutants suggesting that oxidative stress is responsible for the phenotypes. Similarly, chemical inhibition of iNOS partly abrogated the fitness defect of the cysM or cysK2 mutants. Similar studies were conducted in mice lacking IFNg and Phox establishing that cysM or cysK2 mutants have fitness defects in vivo that are dependent on oxidative and nitrosative stress.

Lastly, the authors use small molecule compounds to inhibit cysteine synthases. It is demonstrated that the compounds display inhibition of Mtb growth in 7H9 ADC media. No evidence is provided to demonstrate that these compounds are specifically inhibiting the cysteine synthases via "on-target inhibition" in the whole Mtb cells. Additionally, it is wrongly stated in the discussion that "combinations of L-cys synthase inhibitors with front-line TB drugs like INH, significantly reduced the bacterial load inside the host". This statement suggests that the INH + cysteine synthase inhibitor combinations reduce Mtb loads within a host in an infection assay. No data is presented to support this statement.

eLife assessment

This valuable contribution studies factors that impact molecular exchange between dense and dilute phases of biomolecular condensates through continuum models and coarse-grained simulations. The authors provide solid evidence that interfacial resistance can cause molecules to bounce off the interface and limit mixing. Results like these can inform how experimental results in the field of biological condensates are interpreted.

Reviewer #1 (Public Review):

Summary:<br /> In this paper by Zhang, the authors build a physical framework to probe the mechanisms that underlie the exchange of molecules between coexisting dense and dilute liquid-like phases of condensates. They first propose a continuum model, in the context of a FRAP-like experiment where the fluorescently labeled molecules inside the condensate are bleached at t=0 and the recovery of fluorescence is measured. Through this model, they identify how the key timescales of internal molecular mixing, replenishment from dilute phase, and interface transfer contribute to molecular exchange timescale. Motivated by a recent experiment reported by some of the co-authors previously (Brangwynne et al. in 2019) finding strong interfacial resistance in in-vitro protein droplets of LAF-1, they seek to understand the microscopic features contributing to the interfacial conductance (inversely proportional to the resistance). To check, they perform coarse-grained MD simulations of sticker-spacer self-associative polymers and report how conductance varies significantly even across the few explored sequences. Further, by looking at individual trajectories, they postulate that "bouncing" - i.e., molecules that approach the interface but are not successfully absorbed - is a strong contributor to this mass transfer limitation. Consistent with their predictions, sequences that have more free unbound stickers (i.e., for example through imbalance sequence sticker stoichiometries) have higher conductances and they show a simple linear scaling between the number of unbound stickers and conductance. Finally, they predict a droplet-size-dependent transition in recovery time behavior.

Strengths:<br /> 1. This paper is well-written overall and clear to understand.

2. By combining coarse-grained simulations, continuum modeling, and comparison to published data, the authors provide a solid picture of how their proposed framework relates to molecular exchange mechanisms that are dominated by interface resistance and LAF-1 droplets.

3. The choice of different ways to estimate conductance from simulation and reported data are thoughtful and convincing in their near agreement (although a little discussion of why and when they differ would be merited as well).

Weaknesses:<br /> 1. Almost the entirety of this paper is motivated by a previously reported FRAP experiment on a particular LAF-1 droplet in vitro. There are a few major concerns I have with how the original data is used, how these results may generalize, and the lack of connection of predictions with any other experiments (published or new).

a. The mean values of cdense, cdilute, diffusivities, etc. are taken from Taylor et al. to rule in the importance of interfacial mass transfer limits. While this may be true, the values originally inferred (in the 2019 paper that this paper is strongly built off) report extremely large confidence intervals/inferred standard errors. The authors should accordingly report all their inferences with correct standardized errors or confidence intervals, which in turn, allow us to better understand these data.

b. The generalizability of this model is hard to gauge when all comparisons are made to a single experiment reported in a previous paper.<br /> i. Conceptually, the model is limited to single-component sticker-spacer polymers undergoing phase separation which is already a very simplified model of condensates - for e.g., LAF1 droplets in the cell have no perceptible interfacial mass limitations, also reported in Taylor et al. 2019 - so how these mechanisms relate to living systems as opposed to specific biochemistry experiments. So the authors need to discuss the implications and limitations of their model in the living context where there are multiple species, finite-size effects, and active processes at play.

ii. Second, can the authors connect their model to make predictions of the impact of perturbations to LAF-1 on exchange timescales? For example, are mutants (which change the number or positioning of "stickers") expected to show particular trends in conductances or FRAP timescales? Since LAF-1 is a relatively well-studied protein in vitro, can the authors further contrast their expectations with already published datasets that explore these perturbations, even if they don't generate new data?

iii. A key prediction of the interface limitation model is the size-dependent crossover in FRAP dynamics. Can the authors reanalyze published data on LAF-1 (albeit of different-size droplets) to check their predictions? At the least, is the crossover radius within experimentally testable limits?

c. The authors nicely relate the exchange timescale to various model parameters. Is LAF-1 the only protein for which the various dilute/dense concentrations/diffusivities are known? Given the large number of FRAP and other related studies, can the authors report on a few other model condensate protein systems? This will help broaden the reach of this model in the context of other previously reported data. If such data are lacking, a discussion of this would be important.

2. The reported sticker-spacer simulations, while interesting, represent a very small portion of the parameter space. Can the authors - through a combination of simulation, analyses, or physical reasoning, comment on how the features of their underlying microscopic model (sequence length, implicit linker length, relative stoichiometry of A/B for a given length, overall concentration, sequence pattern properties like correlation length) connect to conductance? This will provide more compelling evidence relating their studies beyond the cursory examination of handpicked sequences. A more verbose description of some of the methods would be appreciated as well, including specifically how to (a) calculate the bond lifetime of isolated A-B pair, and (b) how equilibration/convergence of MD simulations is established.

3. A lot of the main text repeats previously published models (continuum ones in Taylor et al. 2019 and Hubsatch et al., 2021, amongst others) and the idea of interface resistance being limiting was already explored quantitatively in Taylor 2019 (including approximate estimates of mass transfer limitations) - this is fine in context. While the authors do a good job of referring to past work in context, the main results of this paper, in my reading, are:<br /> - a simplified physical form relating conductance timescales.<br /> - sticker-spacer simulations probing microscopic origins.<br /> - analysis of size-dependent FRAP scaling.

I am stating this not as a major weakness, but, rather - I would recommend summarizing and categorizing the sections to make the distinctions between previously reported work and current advances sufficiently clear.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors have obtained an analytical expression that provides intuition about regimes of interfacial resistance that depend on droplet size. Additionally, through simulations, the authors provide microscopic insight into the arrangement of sticky and non-sticky functional groups at the interface. The authors introduce bouncing dynamics for rationalizing quantity recovery timescales.

I found several sections that felt incomplete or needed revision and additional data to support the central claim and make the paper self-contained and coherent.

First, the analytical theory operates with diffusion coefficients for dilute and dense phases. For the dilute phase, this is fine. For the dense phase, I have doubts that dynamics can be described as diffusive. Most likely, dynamics is highly subdiffusive due to crowded, entangled, and viscoelastic environments of densely packed interactive biomolecules. Some explanation and justification are in order here.

The second major issue is that I did not find a clean comparison of simulations with the derived analytical expression. Simulations test various microscopic properties on the value of k, which is important. But how do we know that it is the same quantity that appears in the expressions? Also, how can we be sure that analytical expressions can guide simulations and experiments as claimed? The authors should provide sound evidence of the predictive aspect of their derived expressions.

Are the plots in Figure 4 coming from experiment, theory, and simulation? I could not find any information either in the text or in the caption.

eLife assessment

This important study identifies differential Orsay virus infection of C. elegans when animals are fed on different bacteria. The evidence for this is however, incomplete, as experiments to control for feeding rate and bacterial pathogenicity are needed as well as direct quantification of viral load.

Reviewer #1 (Public Review):

Summary:<br /> This manuscript explores the importance of food type on virus infection dynamics using a nematode virus as a model system. The authors demonstrate that susceptibility to viral infection can change by several orders of magnitude based on the type of bacterial food that potential hosts consume. They go on to show that, for the bacterial food source that reduces susceptibility, the effect is modulated by quorum sensing molecules that the bacteria produce.

Strengths:<br /> This manuscript shows convincingly that nematode susceptibility to viral infection changes by several orders of magnitude (i.e. doses must be increased by several orders of magnitude to infect the same fraction of the population) depending on the bacterial food source on which hosts are reared. The authors then focus on the bacteria that reduce host susceptibility to viral infection and demonstrate that certain bacterial quorum-sensing compounds are required to see this effect of reduced susceptibility. Overall, sample sizes are large, methods are generally rigorous, experiments are repeated, and patterns are clear.

Weaknesses:<br /> Although the molecular correlate of reduced susceptibility is identified (i.e. quorum sensing compounds) the mechanisms underlying this effect are missing. For example, there are changes in susceptibility due to altered nutrition, host condition, the microbiome, feeding rate, mortality of infected hosts, etc. In addition, the authors focus almost entirely on the reduction in susceptibility even though I personally find the increased susceptibility generated when reared on Ochrobactrum to be much more exciting.

I was a bit surprised that there was no data on basic factors that could have led to reductions in susceptibility. In particular, data on feeding rates and mortality rates seem really important. I would expect that feeding rates are reduced in the presence of Pseudomonas. Reduced feeding rates would translate to lower consumed doses, and so even though the same concentration of virus is on a plate, it doesn't mean that the same quantity of virus is consumed. Likewise, if Pseudomonas is causing mortality of virus-infected hosts, it could give the impression of lower infection rates. Perhaps mortality rates are too small in the experimental setup to explain this pattern, but that isn't clear in the current version of the manuscript. Is mortality greatly impacted by knocking out quorum-sensing genes? Also, the authors explored susceptibility to infection, but completely ignored variation in virus shedding.

I was also curious why the authors did not further explore the mechanism behind the quorum-sensing effect. Not sure whether this is possible, but would it be possible to add spent media to the infection plates where the spent media was from Pseudomonas that produce the quorum sensing compound but the plates contain OOP50, Pseudomonas, or the quorum sensing knockout of Pseudomonas? That would reveal whether it is the compound itself vs. something that the compound does.

In addition, I was surprised by how much focus there was on the attenuation of infection and how little there was on the enhancement of infection. To me, enhancement seems like the more obvious thing to find a mechanism for -- is the bacteria suppressing immunity, preventing entry to gut cells, etc?

I was a bit concerned about the "arbitrary units", which were used without any effort to normalize them. David Wang and Hongbing Jiang have developed a method based on tissue culture infectious dose 50 (TCID50) that can be used to measure infectious doses in a somewhat repeatable way. Without some type of normalization, it is hard to imagine how this study could be repeated. The 24-hour time period between exposure and glowing suggests very high doses, but it is still unclear precisely how high. Also, it is clear that multiple batches of virus were used in this study, but it is entirely unclear how variable these batches were.

The authors in several places discuss high variability or low variability in incidence as though it is a feature of the virus or a feature of the host. It isn't. For infection data (or any type of binomial data) results are highly variable in the middle (close to 50% infection) and lowly variable at the ends (close to 0% or 100% infection). This is a result that is derived from a binomial distribution and it should not be taken as evidence that the bacteria or the host affect randomness. If you were to conduct dose-response experiments, on any of your bacterial food source treatments, you would find that variability is lowest at the extremely high and extremely low doses and it is most variable in the middle when you are at doses where about 50% of hosts are infected.

Reviewer #2 (Public Review):

Summary and Major Findings/Strengths:

Across diverse hosts, microbiota can influence viral infection and transmission. C. elegans is naturally infected by the Orsay virus, which infects intestinal cells and is transmitted via the fecal-oral route. Previous work has demonstrated that host immune defense pathways, such as antiviral RNAi and the intracellular pathogen response (IPR), can influence host susceptibility to virus infection. However, little is known about how bacteria modulate viral transmission and host susceptibility.

In this study, the authors investigate how diverse bacterial species influence Orsay virus transmission and host susceptibility in C. elegans. When C. elegans is grown in the presence of two Ochrobactrum species, the authors find that animals exhibit increased viral transmission, as measured by the increased proportion of newly infected worms (relative to growth on E. coli OP50). The presence of the two Ochrobactrum species also resulted in increased host susceptibility to the virus, which is reflected by the increased fraction of infected animals following exposure to the exogenous Orsay virus. In contrast, the presence of Pseudomonas lurida MYb11, as well as Pseudomonas PA01 or PA14, attenuates viral transmission and host susceptibility relative to E. coli OP50. For growth in the presence of P. aeruginosa PA01 and PA14, the attenuated transmission and susceptibility are suppressed by mutations in regulators of quorum sensing and the gacA two-component system. The authors also identify six virulence genes in P. aeruginosa PA14 that modulate host susceptibility to virus and viral transmission, albeit to a lesser extent. Based on the findings in P. aeruginosa, the authors further demonstrate that deletion of the gacA ortholog in P. lurida results in loss of the attenuation of viral transmission and host susceptibility.

Taken together, these findings provide important insights into the species-specific effects that bacteria can have on viral infection in C. elegans. The authors also describe a role for Pseudomonas quorum sensing and virulence genes in influencing viral transmission and host susceptibility.

Major weaknesses:

The manuscript has several issues that need to be addressed, such as insufficient rigor of the experiments performed and questions about the reproducibility of the data presented in some places. In addition, confounding variables complicate the interpretations that can be made from the authors' findings and weaken some of the conclusions that are stated in the manuscript.

1. The authors sometimes use pals-5p::GFP expression to indicate infection, however, this is not necessarily an accurate measure of the infection rate. Specifically, in Figures 4-6, the authors should include measurements of viral RNA, either by FISH staining or qRT-PCR, to support the claims related to differences in infection rate.

2. In several instances, the experimental setup and presentation of data lack sufficient rigor. For example, Fig 1D and Fig 2B only display data from one experimental replicate. The authors should include information from all 3 experimental replicates for more transparency. In Fig 3B, the authors should include a control that demonstrates how RNA1 levels change in the presence of E. coli OP50 for comparison with the results showing replication in the presence of PA14. In order to support the claim that "P. aeruginosa and P. lurida MYb11 do not eliminate Orsay virus infection", the authors should also measure RNA1 fold change in the presence of PA01 and P. lurida in the context of exogenous Orsay virus. Additionally, the authors should standardize the amount of bacteria added to the plate and specify how this was done in the Methods, as differing concentrations of bacteria could be the reason for species-specific effects on infection.

3. The authors should be more careful about conclusions that are made from experiments involving PA14, which is a P. aeruginosa strain (isolated from humans), that can rapidly kill C. elegans. To eliminate confounding factors that are introduced by the pathogenicity of PA14, the authors should address how PA14 affects the health of the worms in their assays. For example, the authors should perform bead-feeding assays to demonstrate that feeding rates are unaffected when worms are grown in the presence of PA14. Because Orsay virus infection occurs through feeding, a decrease in C. elegans feeding rates can influence the outcome of viral infection. The authors should also address whether or not the presence of PA14 affects the stability of viral particles because that could be another trivial reason for the attenuation of viral infection that occurs in the presence of PA14.

eLife assessment

This useful work argues that claims on travelling waves in the cortex must be backed up with more careful experimental and theoretical work. Through simulations and neural recordings, the authors provide solid evidence to show that differences in activation timings can superficially appear as a travelling wave. This work has implications for how the field might need to revisit how travelling waves are methodologically characterised.

Joint Public Review:

Summary:<br /> In this interesting work, the authors investigated an important topical question: when we see travelling waves in cortical activity, is this due to true wave-like spread, or due to sequentially activated sources? In simulations, it is shown that sequential brain module activation can show up as a travelling wave - even in improved methods such as phase delay maps - and a variety of parameters is investigated. Then, in ex-vivo turtle eye-brain preparations, the authors show that visual cortex waves observable in local field potentials are in fact often better explained as areas D1 and D2 being sequentially activated. This has implications for how we think about travelling wave methodology and relevant analytical tools.

Strengths:<br /> I enjoyed reading the discussion. The authors are careful in their claims, and point out that some phenomena may still indeed be genuine travelling waves, but we should have a higher evidence bar to claim this for a particular process in light of this paper and Zhigalov & Jensen (2023) (ref 44). Given this careful discussion, the claims made are well-supported by the experimental results. The discussion also gives a nice overview of potential options in light of this and future directions.

The illustration of different gaussian covariances leading to very different latency maps was interesting to see.

Furthermore, the methods are detailed and clearly structured and the Supplementary Figures, particularly single trial results, are useful and convincing.

Weaknesses:<br /> The details of the sequentially activated Gaussian simulations give some useful results, but the fundamental idea still appears to be "sequential activation is often indistinguishable from a travelling wave", an idea advanced e.g. by Zhigalov & Jensen (2023). It takes a while until the (in my opinion) more intriguing experimental results.

One of the key claims is that the spikes are more consistent with two sequentially activated modules rather than a continuous wave (with Fig 3k and 3l key to support this). Whilst this is *more* consistent, it is worth mentioning that there seems to be stochasticity to this and between-trial variability, especially for spikes.

Reviewer #3 (Public Review):

Summary:<br /> Here, Osnes et al examine the population dynamics of Neisseria gonorrhoeae. They develop new methodologies to deal with the issue of recombination, as well as using ancestral state reconstruction approaches to quantify the number of import and export transmission events occurring in different regions in the world. Overall, they provide a framework for understanding intercontinental transmission that could be applied to other microbial pathogens.

Strengths:<br /> A major strength of this study is the incredibly large number of genomes analysed, which span a wide temporal range with significant geographical diversity. The use of ancestral state reconstruction to quantitatively determine the number of import and export events of N. gonorrhoeae in densely sampled Norway and Victoria, Australia, is an interesting application of this well-known method and could be applied to other bacterial species that have been well-sampled.

Weaknesses:<br /> The methods development to deal with the issue of recombination in their dataset to ensure that the recombination signal does not affect their dating estimates and effective population size analysis is thorough but has likely not been able to remove all bias. Additionally, the authors discuss the utility of using the identified transmission lineages in this study to better type N. gonorrhoeae as there are issues with traditional typing, such as MLST, due to the highly recombinogenic nature of this species. However, no method seems to be provided to enable future researchers to easily assign their genomes to the transmission lineages identified in this study.

eLife assessment

This important work provides a detailed analysis of the population dynamics of an important human bacterial pathogen, Neisseria gonorrhoeae, using a large global collection of genomes with geographic, temporal, and demographic metadata. A unique facet of this work is the focus on both importation and exportation of N. gonorrhoeae; whilst quantifying importation is important to national public health efforts, quantifying exportation is relevant on an international level. However, the evidence supporting the conclusions is incomplete, due to potential inaccuracies in the method used for phylogenetic analysis, which forms the basis of this work. With the phylogenetic analysis strengthened, this paper would be of interest to epidemiologists and public health officials working on N. gonorrhoeae epidemiology and interventions.

Reviewer #1 (Public Review):

Summary:<br /> Osnes et al., describe a large and impressive study of the population and transmission dynamics of Neisseria gonorrhoeae using a global dataset of 9,732 genomes. This included dense sampling from Norway and the state of Victoria, Australia. Understanding the transmission dynamics of this increasingly drug-resistant pathogen is crucial for designing optimal public health interventions. This study provides useful insights into the differing transmission dynamics between two well-sampled populations.

The authors have also developed novel techniques to address the size and complexity of the dataset, including an approach to account for recombination when building large phylogenies. While the authors have made significant efforts to account for sampling biases in the data, it is not clear that this has been sufficient to address the problem. The use of non-standard analysis techniques also requires further validation.

Strengths:<br /> The size of the dataset and the comparisons between densely sampled regions are major strengths of the manuscript. While sampling biases may limit the generalizability of the results, as acknowledged by the authors, the characterization of local and inter-country transmission, will help to inform future studies into N. gonorrhoeae.

Weaknesses:<br /> Sampling bias:<br /> The authors have gone to considerable efforts to acknowledge and account for biases in the sampling between different locations. Despite this, comparisons are still frequently made in the manuscript between populations with very different sampling profiles, which are likely to dominate the import, export and local transmission signals.

To determine the sensitivity of their results to sampling, the authors randomly took subsamples of each population at varying sizes. While this would address issues with the overall number of genomes being considered, it is not obvious that it would account for biases in sampling including the differing dates over which each population was sampled.<br /> Randomly subsampling tips of the tree is unlikely to change the overall population structure of each dataset much. For example, subsampling a single outbreak would result in highly similar genomes each time. Subsampling clades would provide a better indication of how sensitive the results are to particular clusters within each population. Simulations would also help to determine under what conditions the inferred asymptotes for import and export fractions are likely to hold.

The text states that Europe and the USA have 'older' transmission lineages than Norway and Victoria. Norway is also found to export more lineages than Victoria, which is likely to be heavily influenced by biases in the distribution of the 'rest of world' samples. Although the impact of sampling bias is acknowledged by the authors in cases such as these, it would be better to avoid making direct comparisons in the first place.

Recombination detection and filtering:<br /> The authors introduce a novel pipeline for masking recombination before building phylogenetic trees, based on randomly subsetting the dataset and running the Gubbins algorithm. While I appreciate it is challenging to account for recombination in a dataset of this size, further verification needs to be done to demonstrate the effectiveness of this approach.

In particular, this approach resulted in ~ 10% of sites being filtered out from a diverse set of genomes. This is considerably less than a previous publication that considered ~400 diverse gonococcal genomes, where just under 50% of sites were removed using the Gubbins algorithm (Sánchez-Busó et al., 2019).

One reason for this is that the new approach requires recombination events called by Gubbins to meet additional filtering requirements before they are masked from the alignment. This may exclude rarer recombination events, which could subsequently impact the length of branches in the final phylogeny.

Transmission clustering:<br /> The use of LineageHomology and ancestral state reconstruction to determine transmission clusters may be susceptible to biases in sampling between locations. As noted by the authors, locations with sparse sampling, such as the USA, are likely to have older ancestral nodes that are exclusive to that location. Biases in the sampling of countries that transmit to and from each location will also heavily impact the size of the inferred clusters.

This could potentially explain the occurrence of larger 'mixed outbreaks' in Victoria when compared to Norway, as these clusters may be older and driven by a lack of observed isolates in the 'rest of world' subset.

While it would not solve the problem entirely, a SNP-based cut-off as used in the original study of Victorian isolates by Williamson et al., is less likely to be as heavily biased.

Import and export estimates:<br /> Using LineageHomology to define import and export estimates may have a similar problem with sampling biases. This is acknowledged by the authors and nicely described in Supplementary Figure 6. The authors make a comparison with the analysis of SARS-CoV-2 genomes by du Plessis et. al. (2021). However, in the analysis of SARS-CoV-2, the sampling times were far more consistent than those observed in the gonococcal dataset. To address this, the authors could compare their results to an analysis restricted to samples observed in a similar time period. This could most easily be achieved by cutting the inferred phylogeny at a particular date and re-running the LineageHomology analyses.

Reviewer #2 (Public Review):

Summary:<br /> This work analyses the historical spread and evolution, termed 'population dynamics', of a human bacterial pathogen, Neisseria gonorrhoeae, the cause of the sexually transmitted infection, gonorrhoea. N. gonorrhoeae is classified as a high priority pathogen by the World Health Organisation, due to infections numbering in the tens of millions annually, with high levels of antibiotic resistance and no vaccine available, meaning treating and preventing infections is becoming increasingly more difficult. To implement interventions effectively, important resistant lineages and their transmission routes must be identified on a national and international level.

In this work, Osnes et al. use genomic data, coupled with geographic, temporal and demographic metadata, to analyse the global population dynamics N. gonorrhoeae using 9,732 genomes. The study also includes a granular analysis of transmission between and within four regions of different sizes with high levels of data coverage: USA, Europe, Norway, and Victoria state in Australia.<br /> The authors built a phylogenetic tree including all genomes using a novel computationally efficient method for removing genome regions resulting from recombination, which would otherwise result in incorrect branch lengths and tree topology. Using the tree, the authors show that the effective population size of N. gonorrhoeae, describing population size and diversity, decreased in the period from 2010 to present day, and was not entirely an artefact of sampling bias. The authors then stratified the tree based on isolates that contained alleles that are associated with resistance to antibiotics commonly used to treat gonorrhoea. The authors found resistance was associated with particular lineages, of which most, but not all, underwent shrinking in effective population size in the last decade.<br /> Using the tree, the authors then inferred likely importation, exportation, and local transmission events, finding notable differences in the contribution of imports to local incidence between locations, as well as the likelihood of exportation. As inference of these events relies on sampling density, the authors used a novel method for identifying whether sampling was representative of the population diversity of a given location. Using this approach, they found that the densely sampled regions, Norway and Victoria, were likely representative of the local N. gonorrhoeae population diversity, whilst the larger, less densely sampled regions, Europe and USA, were not. Finally, they investigated the contribution of specific transmission networks to the spread gonorrhoea, finding that the frequency of males within a transmission network may play a role in the rate of N. gonorrhoeae transmission in Norway, but not Victoria.<br /> This work introduces several novel approaches to the analysis of pathogen population dynamics, and highlights notable differences in N. gonorrhoeae transmission between and within distinct geographic locations.

Strengths:<br /> • The authors have collated a large global collection of N. gonorrhoeae genomes with associated metadata, and in some cases generated assemblies themselves. A dataset of this size and detail is a valuable asset to the public health community, enabling analysis of both national and international population dynamics.<br /> • The stratification of the phylogenetic tree by antimicrobial resistance gene alleles enables the study of how antibiotic usage has shaped global and regional N. gonorrhoeae populations. Analysis of changes in the effective population size of clades harbouring resistance alleles is particularly impactful, as this can be used to show how changes in treatment patterns affect the growth or decline of drug-resistant pathogen populations. This analysis also enables the determination of the frequency of multiple resistance alleles being present in single isolates, important for determining the scale of multidrug resistance within the N. gonorrhoeae global population.<br /> • The use of ancestral trait reconstruction to quantify importation, exportation and local transmission is an important contribution to public health efforts tackling N. gonorrhoeae spread. Understanding the differences in transmission networks within and between different geographic locations provides public health researchers with crucial information to model and implement effective targeted interventions on regional and international scales.

Weaknesses:<br /> • The method used to generate the phylogenetic tree and mask regions of recombination is likely flawed. The authors repeatedly down-sampled the whole population to 500 genomes, using Gubbins to identify regions that have recombined and therefore would not follow the clonal history of the N. gonorrhoeae population. This small sample size will result in the same ancient internal nodes being sampled repeatedly, whilst more recent internal nodes will not. Therefore, more recent recombination events would not be identified by this method and were therefore likely included in the whole genome alignment used to build the tree. Furthermore, Gubbins was designed to identify recombination between closely related genomes, not across a whole species, where the background mutation rate will be too high to differentiate between recombined regions and the clonal frame. Both of these factors will mean that the amount of the genome predicted to have recombined will likely be underestimated, resulting in inflated branch lengths and incorrect tree topology. This effect is potentially the cause of the observed drop in N. gonorrhoeae effective population size between 2010-present day in Figure 2, which does not align with gonorrhoea incidence, and the elevated estimated mutation rate of 7.41x10-6 substitutions per site per year, which is higher than previous estimates based on N. gonorrhoeae global populations. The result of underestimation of recombined regions will be two-fold. Inclusion of recombined regions in the alignment will result in inflated branch lengths, which will impact all estimates of effective population size in the study. Furthermore, tree topology may be incorrect, which will impact ancestral trait reconstruction and result in incorrect inference of import, export and local transmission events in Figures 3, 4 and 5. Additionally, the clade-specific resistance gene analyses will be affected in Figure 2, as certain isolates may be incorrectly included or excluded within stratified clades. Therefore, the conclusions made about the changes in effective population size for the global population, and individual clades, as well as the differences in transmission dynamics between locations, are likely to be incorrect.<br /> • The method used to identify sampling bias, shown in Figure 4, is a novel and interesting take on the problem. However, it is not clear whether the effect being measured is the presence of sampling bias or an artefact of differences in N. gonorrhoeae diversity between locations. The results in Figure 4 do align with what is known about the population datasets; the data from Norway and Victoria is more comprehensive than that of the USA and Europe due to the difference in size of the respective human populations, meaning the likelihood of sampling bias will be lower in the smaller population. However, with increased human population size, we would also expect a greater amount of pathogen diversity, due to increased within-region transmission and greater numbers of importation events. Supporting this, we see in Figure 3 that the transmission lineages in the USA and Europe are estimated to have emerged earlier than Norway or Victoria, indicative of a greater amount of standing population diversity. Therefore, the reason why convergence is observed when up-sampling from smaller populations may be because a vast majority of isolates will sit within a small part of the tree, whilst from a larger, more diverse population, isolates will be placed all across the tree and so convergence will never be observed. In effect, it is unknown whether increasing the sample size of the USA and Europe to be truly representative of their respective N. gonorrhoeae populations would ever result in convergence between the two methods of up-sampling. Testing this method using simulations could be used to determine whether it is sensitive to sampling bias, or population diversity.<br /> • In Figure 5, a significant difference in transmission lineage size was only found between male-dominated and mixed lineages in Norway and not Victoria. Therefore, the conclusion that sex distribution within transmission networks affects the size of transmission lineages is not supported by the data, and could also be due to geographical and other demographic differences between the datasets which were not accounted for.

Author Response

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

Reviewer 1

Public Review

R1.1) Randomized clinical trials use experimental blinding and compare active and placebo conditions in their analyses. In this study, Fassi and colleagues explore how individual differences in subjective treatment (i.e., did the participant think they received the active or placebo treatment) influence symptoms and how this is related to objective treatment. The authors address this highly relevant and interesting question using a powerful method by (re-)analyzing data from four published neurostimulation studies and including subjective treatment in statistical models explaining treatment response. The major strengths include the innovative and important research question, the inclusion of four different studies with different techniques and populations to address this question, sound statistical analyses, and findings that are of high interest and relevance to the field.

We thank the reviewer for this summary and the overall appreciation for our work.

R1.2) My main suggestion is that authors reconsider the description of the main conclusion to better integrate and balance all findings. Specifically, the authors conclude that (e.g., in the abstract) "individual differences in subjective treatment can explain variability in outcomes better than the actual treatment", which I believe is not a consistent conclusion across all four studies as it does not appropriately consider important interactions with objective treatment observed in study 2 and 3. In study 2, the greatest improvement was observed in the group that received TMS but believed they received sham. While subjective treatment was associated with improvement regardless of objective active or sham treatment, improvement in the objective active TMS group who believed they received sham suggests the importance of objective treatment regardless of subjective treatment. In Study 3, including objective treatment in the model predicted more treatment variance, further suggesting the predictive value of objective treatment.

We thank the reviewer for this comment and agree that the interpretation of findings requires a more nuanced and balanced description. We, therefore, implemented changes in both the abstract and discussion of the manuscript, as reported below (additions are highlighted in grey and deletions are shown in strikethrough):

Abstract

“Our findings consistently show that the inclusion of subjective treatment can provide a better model fit when accounted for alone or in an interaction term with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive and behavioural outcomes. Based on these findings, We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.” (p. 3)

Discussion

“We demonstrate that participants’ subjective beliefs about receiving the active vs control (sham) treatment are an important factor that can explain variability in the primary outcome and, in some cases, fits the observed data better than the actual treatment participants received during the experiment.” (p. 21)

“We demonstrate that participants’ subjective beliefs about receiving the active vs control (sham) treatment are an important factor that can explain variability in the primary outcome and, in some cases, fits the observed data better than the actual treatment participants received during the experiment. Specifically, in Studies 1, 2 and 4, the fact that participants thought to be in the active or control condition explained variability in clinical and cognitive scores to a more considerable extent than the objective treatment alone. Notably, the same pattern of results emerged when we replaced subjective treatment with subjective dosage in the fourth experiment, showing that subjective beliefs about treatment intensity also explained variability in research results better than objective treatment. In contrast to Studies 1 and 4, Studies 2 and 3 showed a more complex pattern of results. Specifically, in Study 2 we observed an interaction effect, whereby the greatest improvement in depressive symptoms was observed in the group that received the active objective treatment but believed they received sham. Differently, in Study 3, the inclusion of both subjective and objective treatment as main effects explained variability in symptoms of inattention. Overall, these findings suggest the complex interplay of objective and subjective treatment. The variability in the observed results could be explained by factors such as participants’ personality, type and severity of the disorder, prior treatments, knowledge base, experimental procedures, and views of the research team, all of which could be interesting avenues for future studies to explore.” (p. 22)

R1.3) In addition to updating the conclusions to better reflect this interaction, I suggest authors include the proportion of participants in each subjective treatment group that actually received active or sham treatment to better understand how much of the subjective treatment is explained by objective treatment. I think it is particularly important to better integrate and more precisely communicate this finding, because the conclusions may otherwise be erroneously interpreted as improvements after treatment only being an effect of subjective treatment or sham.

We thank the reviewer for this comment. The information about how many participants are included in each group is provided in the every each codebooks under the section “Count of Participants by Treatment Condition and Their Subjective Guess” which is in the project’s OSF link (https://osf.io/rztxu/). Additionally, we added these tables to the supplementary material in tables S1, S8, S15, and S18, and we referred to these tables throughout the Methods section. Further, we added this information to the manuscript results, as follows:

• “Further details on participant groupings based on objective treatment and their subjective treatment can be found in the codebook corresponding to each of the four studies as well as S1.” (p. 8).

• “The breakdown of participants to objective treatment and subjective treatment in the sample can be found in S8.” (p. 13).

• “The breakdown of participants to objective treatment and subjective treatment in the sample can be found in S15.” (p. 17).

• “The breakdown of participants to objective treatment and subjective treatment in the sample can be found in S18.” (p. 19).

R1.4) The paper will have significant impact on the field. It will promote further investigation of the effects of sham vs active treatment by the introduction of the terms subjective treatment vs objective treatment and subjective dosage that can be used consistently in the future. The suggestions to assess the expectation of sham vs active earlier on in clinical trials will advance the understanding of subjective treatment in future studies. Overall, I believe the data will substantially contribute to the design and interpretation of future clinical trials by underscoring the importance of subjective treatment.

We thank the reviewer for this positive comment.

Review for authors

R1.4) Abstract

"Here we show that individual differences in subjective treatment.. can explain variability in outcomes better than the actual treatment". "Our findings consistently show that the inclusion of subjective treatment provides a better model fit than objective treatment alone" - these two statements could be interpreted as two different conclusions, authors should be more consistent.

We thank the reviewer for this comment and have now changed the abstract to be consistent, as also highlighted in R1.1:

Abstract

“Our findings consistently show that the inclusion of subjective treatment can provides a better model fit when accounted for alone or in an interaction term with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive and behavioural outcomes. Based on these findings, We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.” (p. 3)

R1.5) Introduction

This is an odd sentence given it is 2023: "As a result, the global neuromodulation device industry is expected to grow to $13.3 billion in 2022 (Colangelo, 2020)."

We have now removed this sentence as indeed not applicable and instead added a reference for the previous sentence:

“In recent years, neuromodulation has been studied as one of the most promising treatment methods (De Ridder et al., 2021).”

Reference

De Ridder, D., Maciaczyk, J., & Vanneste, S. (2021). The future of neuromodulation: Smart neuromodulation. Expert Review of Medical Devices, 18(4), 307–317. https://doi.org/10.1080/17434440.2021.1909470

R1.6) Figures

• Lines of Figure 1 are vague.

• Figure 5 color scheme is confusing. It would be better to use green/blue colors for one, (e.g.) sham in both subjective and objective treatment and orange/red colors for active treatment.

• For Figure 6 it would be better to use the same color for sham as subjective dosage none.

• Relatedly, it would be easier to keep color scheme consistent across the paper and for example use green/blue colors for sham throughout.

We thank the reviewer for this comment. Following these comments, all the figures of the paper has remade for better clarity.

• Figure 1, the individual lines are now shown stronger, there is also a connecting line between the averages.

• Figure 5, sham is now on cold colours (blue and green), and active treatment on warm colours (red and orange)

• Figure 6, the same colour for sham as subjective dosage none is now applied.

Further, we also edited Figures 2 and 4 by removing the percentages between 0% and 100% on the y-axis. Given that the outcome variable was binary coded, we implemented this change to avoid confusion.

Reviewer 2

Public Review

R2.1) This manuscript focuses on the clinical impact of subjective experience or treatment with transcranial magnetic stimulation and transcranial direct current stimulation studies with retrospective analyses of 4 datasets. Subjective experience or treatment refers to the patient level thought of receiving active or sham treatments. The analyses suggest that subjective treatment effects are an important and under appreciated factor in randomized controlled trials. The authors present compelling evidence that has significance in the context of other modalities of treatment, treatment for other diseases, and plans for future randomized controlled trials. Other strengths included a rigorous approach and analyses. Some aspects of the manuscript are underdeveloped and the findings are over interpreted. Thank you for your efforts and the opportunity to review your work.

We thank the reviewer for their overall appreciation of this work. We address the comment on the overinterpretation of findings in response to reviewer 1 (see R1.2) above, and we expand on the underdeveloped explanation of sham procedures (see R2.2) below.

Review for authors

R2.2) One concern is that the findings are consistently over interpreted and presented with a polarizing framework. This is a complicated area of study with many variables that are not understood or captured. For example, subjective experience effects likely varies with personality dimensions, disease, prior treatments, knowledge base, view of the research team, and disease severity. Framing subjective experience with a more balanced tone, as an important consideration for future trial design and study execution would enhance the impact of the paper.

We thank the reviewer for this comment. We reframed our interpretation of results in both the manuscript abstract and discussion, as highlighted in response to reviewer 1 (see R1.2) above.

R2.3) The discussion of sham approaches for transcranial magnetic stimulation and transcranial direct current stimulation is underdeveloped. There are approaches that are not discussed. The tilt method is seldom used for modern studies for example.

We thank the reviewer for this comment, and we now rewrote a paragraph elaborating more on different practices to apply sham procedures in the introduction section:

“Participants that take part in TMS and tES studies consistently report various perceptual sensations, such as audible clicks, visual disturbances, and cutaneous sensations (Davis et al., 2013) Consequently, they can discern when they have received the active treatment, making subjective beliefs and demand characteristics potentially influencing performance (Polanía et al., 2018). To account for such non-specific effects, sham (placebo) protocols have been employed. For transcranial direct current stimulation (tDCS), the most common form of tES, various sham protocols exist. A review by Fonteneau et al., 2019 shows 84% of 173 studies used similar sham approaches to an early method by Gandiga et al., 2005. This initial protocol had a 10s ramp-up followed by 30s of active stimulation at 1mA before cessation, differently from active stimulation that typically lasts up to 20 minutes.. However, this has been adapted in terms of intensity and duration of current, ramp-in/out phases, and the number of ramps during stimulation. Similarly, in sham TMS, the TMS coil may be tilted or replaced with purpose-built sham coils equipped with magnetic shields, which produce auditory effects but ensure no brain stimulation (Duecker & Sack, 2015). By using surface electrodes, the somatosensory effects of actual TMS are also mimicked. Overall, these types of sham stimulation aim to mimic the perceptual sensations associated with active stimulation without substantially affecting cortical excitability (Fritsch et al., 2010; Nitsche & Paulus, 2000). As a result, sham treatments should allow controlling for participants’ specific beliefs about the type of stimulation received.” (p.6)

References

Fonteneau, C., Mondino, M., Arns, M., Baeken, C., Bikson, M., Brunoni, A. R., Burke, M. J., Neuvonen, T., Padberg, F., Pascual-Leone, A., Poulet, E., Ruffini, G., Santarnecchi, E., Sauvaget, A., Schellhorn, K., Suaud-Chagny, M.-F., Palm, U., & Brunelin, J. (2019). Sham tDCS: A hidden source of variability? Reflections for further blinded, controlled trials. Brain Stimulation, 12(3), 668–673. https://doi.org/10.1016/j.brs.2018.12.977

Gandiga, P. C., Hummel, F. C., & Cohen, L. G. (2006). Transcranial DC stimulation (tDCS): A tool for double-blind sham-controlled clinical studies in brain stimulation. Clinical Neurophysiology, 117(4), 845–850. https://doi.org/10.1016/j.clinph.2005.12.003

Joint Public Review:

Randomized clinical trials use experimental blinding and compare active and placebo conditions in their analyses. In this study, Fassi and colleagues explore how individual differences in subjective treatment (i.e., did the participant think they received the active or placebo treatment) influence symptoms and how this is related to objective treatment. Authors address this highly relevant and interesting question using a powerful method by (re-)analyzing data from four published neurostimulation studies and including subjective treatment in statistical models explaining treatment response. The major strengths include the innovative and important research question, the inclusion of four different studies with different techniques and populations to address this question, sound statistical analyses, and findings that are of high interest and relevance to the field.

The paper will have significant impact on the field. It will promote further investigation of the effects of sham vs active treatment by the introduction of the terms subjective treatment vs objective treatment and subjective dosage that can be used consistently in the future. The suggestions to assess the expectation of sham vs active earlier on in clinical trials will advance the understanding of subjective treatment in future studies. Overall, I believe the data will substantially contribute to the design and interpretation of future clinical trials by underscoring the importance of subjective treatment.

eLife assessment

This is an important report that has implications for both the brain stimulation field and beyond. The strength of evidence provided is quite convincing. The major strength of this work is the recognize the importance of participant expectation in brain stimulation studies.

Author Response

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

eLife assessment:

This study reports a meta-analysis of published data to address an issue that is topical and potentially useful for understanding how the sites of initiation of DNA replication are specified in human chromosomes. The work focuses on the role of the Origin Recognition Complex (ORC) and the Mini-Chromosome Maintenance (MCM2-7) complex in localizing origins of DNA replication in human cells. While some aspects of the paper are of interest, the analysis of published data is in parts inadequate to allow for the broad conclusion that, in contrast to multiple observations with other species, sites in the human genome for binding sites for ORC and MCM2-7 do not have extensive overlap with the location of origins of DNA replication.

Public Reviews:

Reviewer #1 (Public Review):

In the best genetically and biochemically understood model of eukaryotic DNA replication, the budding yeast, Saccharomyces cerevisiae, the genomic locations at which DNA replication initiates are determined by a specific sequence motif. These motifs, or ARS elements, are bound by the origin recognition complex (ORC). ORC is required for loading of the initially inactive MCM helicase during origin licensing in G1. In human cells, ORC does not have a specific sequence binding domain and origin specification is not specified by a defined motif. There have thus been great efforts over many years to try to understand the determinants of DNA replication initiation in human cells using a variety of approaches, which have gradually become more refined over time.

In this manuscript Tian et al. combine data from multiple previous studies using a range of techniques for identifying sites of replication initiation to identify conserved features of replication origins and to examine the relationship between origins and sites of ORC binding in the human genome. The authors identify a) conserved features of replication origins e.g. association with GC-rich sequences, open chromatin, promoters and CTCF binding sites. These associations have already been described in multiple earlier studies. They also examine the relationship of their determined origins and ORC binding sites and conclude that there is no relationship between sites of ORC binding and DNA replication initiation. While the conclusions concerning genomic features of origins are not novel, if true, a clear lack of colocalization of ORC and origins would be a striking finding.

Response: Thank you. That is where the novelty of the paper lies.

However, the majority of the datasets used do not report replication origins, but rather broad zones in which replication origins fire. Rather than refining the localisation of origins, the approach of combining diverse methods that monitor different objects related to DNA replication leads to a base dataset that is highly flawed and cannot support the conclusions that are drawn, as explained in more detail below.

Response: We are using the narrowly defined SNS-seq peaks as the gold standard origins and making sure to focus in on those that fall within the initiation zones defined by other methods. The objective is to make a list of the most reproducible origins. Unlike what the reviewer states, this actually refines the dataset to focus on the SNS origins that have also been reproduced by the other methods in multiple cell lines. We have changed the last box of Fig. 1A to make this clearer: Shared origins = reproducible SNS-seq origins that are contained in initiation zones defined by Repli-seq, OK-seq and Bubble-seq. This and the Fig. 2B (as it is) will make our strategy clearer.

Methods to determine sites at which DNA replication is initiated can be divided into two groups based on the genomic resolution at which they operate. Techniques such as bubble-seq, ok-seq can localise zones of replication initiation in the range ~50kb. Such zones may contain many replication origins. Conversely, techniques such as SNS-seq and ini-seq can localise replication origins down to less than 1kb. Indeed, the application of these different approaches has led to a degree of controversy in the field about whether human replication does indeed initiate at discrete sites (origins), or whether it initiates randomly in large zones with no recurrent sites being used. However, more recent work has shown that elements of both models are correct i.e. there are recurrent and efficient sites of replication initiation in the human genome, but these tend to be clustered and correspond to the demonstrated initiation zones (Guilbaud et al., 2022).

These different scales and methodologies are important when considering the approach of Tian et al. The premise that combining all available data from five techniques will increase accuracy and confidence in identifying the most important origins is flawed for two principal reasons. First, as noted above, of the different techniques combined in this manuscript, only SNS-seq can actually identify origins rather than initiation zones. It is the former that matters when comparing sites of ORC binding with replication origin sites if a conclusion is to be drawn that the two do not co-localise.

Response: We agree. So the reviewer should agree that our method of finding SNS-seq peaks that fall within initiation zones actually refines the origins to find the most reproducible origins. We are not losing the spatial precision of the SNS-seq peaks.

Second, the authors give equal weight to all datasets. Certainly, in the case of SNS-seq, this is not appropriate. The technique has evolved over the years and some earlier versions have significantly different technical designs that may impact the reliability and/or resolution of the results e.g. in Foulk et al. (Foulk et al., 2015), lambda exonuclease was added to single stranded DNA from a total genomic preparation rather than purified nascent strands), which may lead to significantly different digestion patterns (ie underdigestion). Curiously, the authors do not make the best use of the largest SNS-seq dataset (Akerman et al., 2020) by ignoring these authors separation of core and stochastic origins. By blending all data together any separation of signal and noise is lost. Further, I am surprised that the authors have chosen not to use data and analysis from a recent study that provides subsets of the most highly used and efficient origins in the human genome, at high resolution (Guilbaud et al., 2022).

Response: 1) We are using the data from Akerman et al., 2020: Dataset GSE128477 in Supplemental Table 1. We have now separately examined the core origins defined by the authors to check its overlap with ORC binding (Supplementary Fig. S8b).

2) To take into account the refinement of the SNS-seq methods through the years, we actually included in our study only those SNS-seq studies after 2018, well after the lambda exonuclease method was introduced. Indeed, all 66 of SNS-seq datasets we used were obtained after the lambda exonuclease digestion step. To reiterate, we recognize that there may be many false positives in the individual origin mapping datasets. Our focus is on the True positives, the SNS-seq peaks that have some support from multiple SNS-seq studies AND fall within the initiation zones defined by the independent means of origin mapping (described in Fig. 1A and 2B). These True positives are most likely to be real and reproducible origins and should be expected to be near ORC binding sites.

We have changed the last box of Fig. 1A to make this clearer: Shared origins = reproducible SNS-seq origins that are contained in initiation zones defined by Repli-seq, OK-seq or Bubble-seq.

Ini-seq by Torsten Krude and co-workers (Guillbaud, 2022) does NOT use Lambda exonuclease digestion. So using Ini-seq defined origins is at odds with the suggestion above that we focus only on SNS-seq datasets that use Lambda exonuclease. However, Ini-seq identifies a much smaller subset of SNS-seq origins, so, as requested, we have also done the analysis with just that smaller set of origins, and it does show a better proximity to ORC binding sites, though even then the ORC proximate origins account for only 30% of the Ini-seq2 origins (Supplementary Fig. S8d). Note Ini-seq2 identifies DNA replication initiation sites seen in vitro on isolated nuclei.

References:

Akerman I, Kasaai B, Bazarova A, Sang PB, Peiffer I, Artufel M, Derelle R, Smith G, Rodriguez-Martinez M, Romano M, Kinet S, Tino P, Theillet C, Taylor N, Ballester B, Méchali M (2020) A predictable conserved DNA base composition signature defines human core DNA replication origins. Nat Commun, 11: 4826

Foulk MS, Urban JM, Casella C, Gerbi SA (2015) Characterizing and controlling intrinsic biases of lambda exonuclease in nascent strand sequencing reveals phasing between nucleosomes and G-quadruplex motifs around a subset of human replication origins. Genome Res, 25: 725-735

Guilbaud G, Murat P, Wilkes HS, Lerner LK, Sale JE, Krude T (2022) Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation. Nucleic Acids Res, 50: 7436-7450

Reviewer #2 (Public Review):

Tian et al. perform a meta-analysis of 113 genome-wide origin profile datasets in humans to assess the reproducibility of experimental techniques and shared genomics features of origins. Techniques to map DNA replication sites have quickly evolved over the last decade, yet little is known about how these methods fare against each other (pros and cons), nor how consistent their maps are. The authors show that high-confidence origins recapitulate several known features of origins (e.g., correspondence with open chromatin, overlap with transcriptional promoters, CTCF binding sites). However, surprisingly, they find little overlap between ORC/MCM binding sites and origin locations.

Overall, this meta-analysis provides the field with a good assessment of the current state of experimental techniques and their reproducibility, but I am worried about: (a) whether we've learned any new biology from this analysis; (b) how binding sites and origin locations can be so mismatched, in light of numerous studies that suggest otherwise; and (c) some methodological details described below.

Major comments:

• Line 26: "0.27% were reproducibly detected by four techniques" -- what does this mean? Does the fragment need to be detected by ALL FOUR techniques to be deemed reproducible?

Response: If the reproducible SNS-seq peaks are included in the reproducible initiation zones found by the other methods, then we consider it reproducible across datasets. The strategy is to focus our analysis on the most reproducible SNS-seq peaks that happen to be in reproducible initiation zones. It is the best way to confidently identify a very small set of true positive origins. We have re-stated this in the abstract: “only 0.27% were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by each of three other techniques (20,250 shared origins),...”

And what if the technique detected the fragment is only 1 of N experiments conducted; does that count as "detected"?

Response: A reproducible SNS-seq origin has been reproduced above a statistical threshold of 20 reproductions of SNS-seq datasets. A threshold of reproduction in 20 datasets out of 66 SNS-seq datasets gives an FDR of <0.1. This is explained in Fig. 2a and Supplementary Fig. S2. For the initiation zones, we considered a Zone even if it appears in only 1 of N experiments, because N is usually small. This relaxed method for selecting the initiation zones gives the best chance of finding SNS-seq peaks that are reproduced by the other methods.

Later in Methods, the authors (line 512) say, "shared origins ... occur in sufficient number of samples" but what does sufficient mean?

Response: “Sufficient” means that SNS-seq origin was reproducibly detected in ≥ 20 datasets and was included in any initiation zone defined by three other techniques.

Then on line 522, they use a threshold of "20" samples, which seems arbitrary to me. How are these parameters set, and how robust are the conclusions to these settings? An alternative to setting these (arbitrary) thresholds and discretizing the data is to analyze the data continuously; i.e., associate with each fragment a continuous confidence score.

Response: We explained Fig. 2a and Supplementary Fig. S2 on line 192 as follows: The occupancy score of each origin defined by SNS-seq (Supplementary Fig. 2a) counts the frequency at which a given origin is detected in the datasets under consideration. For the random background, we assumed that the number of origins confirmed by increasing occupancy scores decreases exponentially (see Methods and Supplementary Table 2). Plotting the number of origins with various occupancy scores when all SNS-seq datasets published after 2018 are considered together (the union origins) shows that the experimental curve deviates from the random background at a given occupancy score (Fig. 2a). The threshold occupancy score of 20 is the point where the observed number of origins deviates from the expected background number (with an FDR < 0.1) (Fig. 2a).

In the Methods: We have revised the section, “Identification of shared origins” to better describe our strategy. The number of observed origins with occupancy score greater than 20 (out of 66 measures) is 10 times more than expected from the background model. This approach is statistically sound and described by us in (Fang et al. 2020).

• Line 20: "50,000 origins" vs "7.5M 300bp chromosomal fragments" -- how do these two numbers relate? How many 300bp fragments would be expected given that there are ~50,000 origins? (i.e., how many fragments are there per origin, on average)? This is an important number to report because it gives some sense of how many of these fragments are likely nonsense/noise. The authors might consider eliminating those fragments significantly above the expected number, since their inclusion may muddle biological interpretation.

Response: We confused the reviewer by the way we wrote the abstract. The 50,000 origins that are mentioned in the abstract is the hypothetical expected number of origins that have to fire to replicate the whole 6x10^9 nt diploid genome based on the average inter-origin distance of 100 kb (as determined by molecular combing). The 7.5M 300 bp fragments are the genomic regions where the 7.5M union SNS-seq-defined origins are located. Clearly, that is a lot of noise, some because of technical noise and some due to the fact that origins fire stochastically. Which is why our paper focuses on a smaller number of reproducible origins, the 20,250 shared origins. Our analysis is on the 20,250 shared origins, and not on all 7.5M union origins. Thus, we are not including the excess of non-reproducible (stochastic?) origins in our analysis.

The revised abstract in the revised paper will say: “Based on experimentally determined average inter-origin distances of ~100 kb, DNA replication initiates from ~50,000 origins on human chromosomes in each cell-cycle. The origins are believed to be specified by binding of factors like the Origin Recognition Complex (ORC) or CTCF or other features like G-quadruplexes. We have performed an integrative analysis of 113 genome-wide human origin profiles (from five different techniques) and 5 ORC-binding site datasets to critically evaluate whether the most reproducible origins are specified by these features. Out of ~7.5 million union origins identified by all the SNS-seq datasets, only 0.27% were reproducibly obtained in at least 20 independent SNS-seq datasets and contained in initiation zones identified by any of three other techniques (20,250 shared origins), suggesting extensive variability in origin usage and identification in different circumstances.”

• Line 143: I'm not terribly convinced by the PCA clustering analysis, since the variance explained by the first 2 PCs is only ~25%. A more robust analysis of whether origins cluster by cell type, year etc is to simply compute the distribution of pairwise correlations of origin profiles within the same group (cell type, year) vs the correlation distribution between groups. Relatedly, the authors should explain what an "origin profile" is (line 141). Is the matrix (to which PCA is applied) of size 7.5M x 113, with a "1" in the (i,j) position if the ith fragment was detected in the jth dataset?

Response: The reviewer is correct about how we did the PCA and have now included the description in the Methods. We have now done the pairwise correlations the way the reviewer suggests, and it is clear that each technique correlates best with itself (though there are some datasets that do not correlate as well as the others even with the same technique) (Supp. Fig. S3). We have also done the PCA by techniques (Fig. 1c), by cell types for all techniques (Supp. Fig. S1c), by cell-types for SNS-seq only (Supp. Fig. S1d), and by year of publication of SNS-seq data (Supp. Fig. S1e). Our conclusions remain the same: in general, origins defined from the same cell lineage are more similar to each other than across lineages, though this similarity within a lineage is more pronounced when we focus on SNS-seq alone. However, even when we look at SNS-seq alone, there is not a perfect overlap of origins determined by different studies on the same lineage. Finally, although we looked only at SNS-seq data after 2018, by which time lamda exonuclease had become the accepted way of defining SNS-seq, there is surprising clustering around each year.

• It's not clear to me what new biology (genomic features) has been learned from this meta-analysis. All the major genomic features analyzed have already been found to be associated with origin sites. For example, the correspondence with TSS has been reported before:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320713/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547456/

So what new biology has been discovered from this meta-analysis?

Response: The new biology can be summarized as: (a) We can identify a set of reproducible (in multiple datasets and in multiple cell lines) SNS-seq origins that also fall within initiation zones identified by completely independent methods. These may be the best origins to study in the midst of the noise created by stochastic origin firing. (b) The overlap of these Shared origins (True Positive Origins) with known ORC binding sites is tenuous. So either all the origin mapping data, or all the ORC binding data has to be discarded, or this is the new biological reality in mammalian cancer cells: on a genome-wide scale the most reproduced origins are not in close proximity to ORC binding sites, in contrast to the situation in yeast. (c) Several of the features reported to define origins (CTCF binding sites, G quadruplexes etc.) could simply be from the fact that those features also define transcription start sites (TSS), and the origins may prefer to locate to these parts of the genome because of the favorable chromatin state, instead of the sequence or the structural features of CTCF binding sites or G quadruplexes specifically locating the origins.

• Line 250: The most surprising finding is that there is little overlap between ORC/MCM binding sites and origin locations. The authors speculate that the overlap between ORC1 and ORC2 could be low because they come from different cell types. Equally concerning is the lack of overlap with MCM. If true, these are potentially major discoveries that butts heads with numerous other studies that have suggested otherwise. More needs to be done to convince the reader that such a mis-match is true. Some ideas are below:

Idea 1) One explanation given is that the ORC1 and ORC2 data come from different cell types. But there must be a dataset where both are mapped in the same cell type. Can the authors check the overlap here? In Fig S4A, I would expect the circles to not only strongly overlap but to also be of roughly the same size, since both ORC's are required in the complex. So something seems off here.

Response: We agree with the reviewer that there is something “off here”. Either the techniques that report these sites are all wrong, or the biology does not fit into the prevailing hypothesis. As shown in Supplementary Fig. S6C, we do not have ORC1 and ORC2 ChIP-seq data from the same cell-type. We have ORC1 ChIP-seq and SNS-seq data from HeLa cells and ORC2 ChIP seq and origins from K562 cells, and so have now done the overlap of the binding sites to the shared origins in the same cell-type in the new Figure S5e and S5f. Out of 9605 shared origins in K562 cells, 12.8% overlap with ORC2 and 5.4% overlap with MCM3-7 binding sites also defined in K562 cells. Out of 8305 shared origins in HeLa cells, 4.4% overlap with ORC1 binding sites defined in HeLa cells.

There is nothing in the Literature that shows that various ORC subunits ChiP-seq to the same sites, and we have unpublished data that shows very poor overlap in the CHiP binding sites of different ORC subunits. The poor overlap between the binding sites of subunits of the same complex either suggests that the subunits do not always bind to the chromatin as a six-subunit complex or that all the ORC subunit ChIP-seq data in the Literature is suspect. We provide in the supplementary figure S6A examples of true positive complexes (SMARCA4/ARID1A, SMC1A/SMC3, EZH2/SUZ12), whose subunits ChIP-seq to a large fraction of common sites.

Idea 2) Another explanation given is that origins fire stochastically. One way to quantify the role of stochasticity is to quantify the overlap of origin locations performed by the same lab, in the same year, in the same experiment, in the same cell type -- i.e., across replicates -- and then compute the overlap of mapped origins. This would quantify how much mis-match is truly due to stochasticity, and how much may be due to other factors.

Response: A given lab may have superior reproducibility with its own results compared to the entire field, and the finding that origins published in the same year tend to be clustered together could be because a given lab publishes a number of origin sets in a single paper in a given year. But the notion of stochasticity is well accepted in the field because of this observation: the average inter-origin distance measured by single molecule techniques like molecular combing is ~100 kb, but the average inter-origin distance measure on a population of cells (same cell line) is ~30 kb. The only explanation is that in a population of cells many origins can fire, but in a given cell on a given allele, only one-third of those possible origins fire. This is why we did not worry about the lack of reproducibility between cell-lines, labs etc, but instead focused on those SNS-seq origins that are reproducible over multiple techniques and cell lines.

Idea 3) A third explanation is that MCMs are loaded further from origin sites in human than in yeast. Is there any evidence of this? How far away does the evidence suggest, and what if this distance is used to define proximity?

Response: MCMs, of course, have to be loaded at an origin at the time the origin fires because MCMs provide the core of the helicase that starts unwinding the DNA at the origin. Thus, the lack of proximity of MCM binding sites with origins can be because the most detected MCM sites (where MCM spends the most time in a cell-population) does not correspond to where it is first active to initiate origin firing. This has been discussed. MCMs may be loaded far from origin site, but because of their ability to move along the chromatin, they have to move to the origin-site at some point to fire the origin.

Idea 4) How many individual datasets (i.e., those collected and published together) also demonstrate the feature that ORC/MCM binding locations do not correlate with origins? If there are few, then indeed, the integrative analysis performed here is consistent. But if there are many, then why would individual datasets reveal one thing, but integrative analysis reveal something else?

Response: In the revised manuscript we have now discussed Dellino, 2013; Kirstein, 2021; Wang, 2017; Mas, 2023. None of them have addressed what we are addressing, which is whether the small subset of the most reproducible origins proximal to ORC or MCM binding sites, but the discussion is essential.

Idea 5) What if you were much more restrictive when defining "high-confidence" origins / binding sites. Does the overlap between origins and binding sites go up with increasing restriction?

Response: We have made SNS-seq origins more restrictive by selecting those reproduced by 30, 40, or 50 datasets, in addition to the FDR-determined cutoff of 20. The number of origins fall, but when we do not see any significant increase in the % of origins that overlap with or are proximal to with all ORC or MCM binding sites or Shared ORC or MCM binding sites. This analysis is now included in Supp. Fig. S9 and discussed.

Overall, I have the sense that these experimental techniques may be producing a lot of junk. If true, this would be useful for the field to know! But if not, and there are indeed "unexplored mechanisms of origin specification" that would be exciting. But I'm not convinced yet.

• It would be nice in the Discussion for the authors to comment about the trade-offs of different techniques; what are their pros and cons, which should be used when, which should be avoided altogether, and why? This would be a valuable prescription for the field.

Response: Thanks for the suggestion. We have done what the reviewer suggested in the new Supp. Fig. S4.

Among the 20,250 high-confidence shared origins, 9,901 (48.9%) overlapped with SNS-seq origins in K562; 3,872 (19.1%) overlapped with OK-seq IZs; 1,163 (5.7%) overlapped with Repli-seq IZs.

In the reciprocal direction, we asked which method best picks out the highly reproducible shared origins. 2.7% of SNS-seq origins, 17.2% of OK-seq initiation zones and 7.7% of Repli-seq initiation zones overlapped with the 20,250 shared origins

Thus SNS-seq identifies more of the reproducible origins, but it comes with a high false positive rate.

ORC ChIP-seq and MCM ChIP-seq data do not define origins: they define the binding sites of these proteins. Thus we have discussed why the ChIP-seq sites of these protein complexes should not be used to define origins.

Reviewer #3 (Public Review):

Summary: The authors present a thought-provoking and comprehensive re-analysis of previously published human cell genomics data that seeks to understand the relationship between the sites where the Origin Recognition Complex (ORC) binds chromatin, where the replicative helicase (Mcm2-7) is situated on chromatin, and where DNA replication actually beings (origins). The view that these should coincide is influenced by studies in yeast where ORC binds site-specifically to dedicated nucleosome-free origins where Mcm2-7 can be loaded and remains stably positioned for subsequent replication initiation. However, this is most certainly not the case in metazoans where it has already been reported that chromatin bindings sites of ORC, Mcm2-7, and origins do not necessarily overlap, likely because ORC loads the helicase in transcriptionally active regions of the genome and, since Mcm2-7 retains linear mobility (i.e., it can slide), it is displaced from its original position by other chromatin-contextualized processes (for example, see Gros et al., 2015 Mol Cell, Powell et al., 2015 EMBO J, Miotto et al., 2016 PNAS, and Prioleau et al., 2016 G&D amongst others). This study reaches a very similar conclusion: in short, they find a high degree of discordance between ORC, Mcm2-7, and origin positions in human cells.

Strengths: The strength of this work is its comprehensive and unbiased analysis of all relevant genomics datasets. To my knowledge, this is the first attempt to integrate these observations and the analyses employed were suited for the questions under consideration.

Response: Thank you for recognizing the comprehensive and unbiased nature of our analysis. The fact that the major weakness is that the comprehensive view fails to move the field forward, is actually a strength. It should be viewed in the light that we cannot find evidence to support the primary hypothesis: that the most reproducible origins must be near ORC and MCM binding sites. This finding will prevent the unwise adoption of ORC or MCM binding sites as surrogate markers of origins and will stimulate the field to try and improve methods of identifying ORC or MCM binding until the binding sites are found to be proximal to the most reproducible origins. The last possibility is that there are ORC- or MCM-independent modes of defining origins, but we have no evidence of that.

Weaknesses: The major weakness of this paper is that this comprehensive view failed to move the field forward from what was already known. Further, a substantial body of relevant prior genomics literature on the subject was neither cited nor discussed. This omission is important given that this group reaches very similar conclusions as studies published a number of years ago. Further, their study seems to present a unique opportunity to evaluate and shape our confidence in the different genomics techniques compared in this study. This, however, was also not discussed.

Response: We have done what the reviewer suggested: use K562 cell type-specific data where origins have been defined by three methods and reporting the percent of shared origins identified by each method (Supp. Fig. S4). Thanks for the suggestion. We have discussed now that SNS-seq identifies more of the reproducible origins, but it comes with a high false positive rate. ORC ChIP-seq and MCM ChIP-seq data do not define origins: they define the binding sites of these proteins. Thus, we have discussed that the ChIP-seq sites of these protein complexes as we now have them should not be used to define origins.

We do not cite the SNS-seq data before 2018 because of the concerns discussed above about the earlier techniques needing improvement. We have discussed other genomics data that we failed to discuss.

We have cited the papers the reviewer names:

Gros, Mol Cell 2015 and Powell, EMBO J. 2015 discuss the movement of MCM2-7 away from ORC in yeast and flies and will be cited. MCM2-7 binding to sites away from ORC and being loaded in vast excess of ORC was reported earlier on Xenopus chromatin in PMC193934, and will also be cited.

Miotto, PNAS, 2016: publishes ORC2 ChIP-seq sites in HeLa (data we have used in our analysis), but do not measure ORC1 ChIP-seq sites. They say: “ORC1 and ORC2 recognize similar chromatin states and hence are likely to have similar binding profiles.” This is a conclusion based on the fact that the ChIP seq sites in the two studies are in areas with open chromatin, it is not a direct comparison of binding sites of the two proteins.

Prioleau, G&D, 2016: This is a review that compared different techniques of origin identification but has no primary data to say that ORC and MCM binding sites overlap with the most reproducible origins. It has now been referenced in the context of epigenetic marks and origins.

Reviewing Editor:

While there is some disagreement between the reviewers about the analysis performed, there are relevant concerns about the data analyzed (reviewers 1 and 2) and the biological significance of the observation (all three reviewers). There is also concern raised about the ORC ChIP-Seq data and the lack of overlap between published data for ORC1 and ORC2, which, if they were in a complex, the overlap in binding sites should be much better that reported.

Given the high overlap of ChIP-seq data for subunits of three other complexes shown in Supp. Fig. S6A, the most likely explanation is that ORC1 and ORC2 do not necessarily bind to DNA only as part of a complex. In other words, other protein complexes that contain one subunit or the other also bind DNA. This is not entirely unexpected. Biochemically the ORC2-3-4-5 complex is more stable and more abundant than the six subunit ORC.

Reviewer #2 (Recommendations For The Authors):

Minor comments:

• Line 44, missing spaces near references: "origins(Hu". Repeated issue throughout the manuscript.

• Line 82: "Notably any technical biases are uniquely associated with each assay" -- how do you know the biases are unique to each assay and orthogonal to each other?

• Line 135: typo: "using pipeline"

• Line 136: "All the 113 datasets" -> "Each of the 113 datasets"?

• Line 156: "differences among different techniques" -> "different" can be removed.

• Figure 4F: I don't see any difference in 4F amongst shared *. What is the y-axis anyways?

We have addressed these issues in the revised manuscript.

Reviewer #3 (Recommendations For The Authors):

The most significant omission is a contextualization of the results in the discussion and an explanation of why these results matter for the biology of replication, disease, and/or our confidence in the genomic techniques reported on in this study. As written, the discussion simply restates the results without any interpretation towards novel insight. I suggest that the authors revise their discussion to fill this important gap.

A second important, unresolved point is whether replication origins identified by the various methods differ due to technical reasons or because different cell types were analyzed. Given the correlation between TSS and origins (reported in this study but many others too), it is somewhat expected that origins will differ between cell types as each will have a distinct transcriptional program. This critique is partly addressed in Figure S1C. However, given the conclusion that the techniques are only rarely in agreement (only 0.27% origins reproducibly detected by the four techniques), a more in-depth analysis of cell type specific data is warranted. Specifically, I would suggest that cell type-specific data be reported wherever origins have been defined by at least two methods in the same cell type, specifically reporting the percent of shared origins amongst the datasets. This type of analysis may also inform on whether one or more techniques produces the highest (or lowest) quality list of true origins.

We have done what has been suggested: used K562 cell type-specific data because here the origins have been defined by at least two methods in the same cell type, and reported the percent of shared origins amongst the datasets (Supp. Fig. S4).

Other MINOR comments include:

• Line 215: the authors show that shared origins overlap with TF binding hotspots more often than union origins, which they claim suggests "that they are more likely to interact with transcription factors." As written, it sounds like the authors are proposing that ORC may have some direct physical interaction with transcription factors. Is this intended? If so, what support is there for this claim?

The reviewer is correct. We have rephrased because we have no experimental support for this claim.

• In the text, Figure 3G is discussed before Figure 3F. I suggest switching the order of these panels in Figure 3.

Done.

• It's not clear what Figure 5H to Figure 6 accomplishes. What specifically is added to the story by including these data? Is there something unique about the high confidence origins? If there is nothing noteworthy, I would suggest removing these data.

We want to keep them to highlight the small number of origins that meet the hypothesis that ORC and MCM must bind at or near reproducible origins. These would be the origins that the field can focus in on for testing the hypothesis rigorously. They also show the danger of evaluating proximity between ORC or MCM binding sites with origins based on a few browser shots. If we only showed this figure we could conclude that ORC and MCM binding sites are very close to reproducible origins.

• Line 394: "Since ORC is an early factor for initiating DNA replication, we expected that shared human origins will be proximate to the reproducible ORC binding sites." This is only expected if one disbelieves the prior literature that shows that ORC and origins are not, in many cases, proximal. This statement should be revised, or the previous literature should be cited, and an explanation provided about why this prior work may have missed the mark.

We do not know of any genome-wide study in mammalian cell lines where ORC binding sites and MCM binding have been compared to highly reproducible origins, or that show that these binding sites and highly reproducible origins are mostly not proximal to each other. Most studies cherry pick a few origins and show by ChIP-PCR that ORC and/or MCM bind near those sites. Alternatively, studies sometimes show a selected browser shot, without a quantitative measure of the overlap genome wide and without doing a permutation test to determine if the observed overlap or proximity is higher than what would be expected at random with similar numbers of sites of similar lengths. In the revised manuscript we have discussed Dellino, 2013; Kirstein, 2021; Wang, 2017; Mas, 2023. None of them have addressed what we are addressing, is the small subset of the most reproducible origins proximal to ORC or MCM binding sites?

• Line 402-404: given the lack of agreement between ORC binding sites and origins the authors suggest as an explanation that "MCM2-7 loaded at the ORC binding sites move much further away to initiate origins far from the ORC binding sites, or that there are as yet unexplored mechanisms of origin specification in human cancer cells". The first part of this statement has been shown to be true (Mcm2-7 movement) and should be cited. But what do the authors mean by the second suggestion of "unexplored mechanisms"? Please expand.

We have addressed this point in the revised manuscript.

• The authors should better reference and discuss the previous literature that relates to their work, some of these include Gros et al., 2015 Mol Cell, Powell et al., 2015 EMBO J, Miotto et al., 2016 PNAS, but likely there are many others.

We have addressed this point in the revised manuscript.

Reviewer #2 (Public Review):

Tian et al. performed a meta-analysis of 113 genome-wide origin profile datasets in humans to assess the reproducibility of experimental techniques and shared genomics features of origins. Techniques to map DNA replication sites have quickly evolved over the last decade, yet little is known about how these methods fare against each other (pros and cons), nor how consistent their maps are. The authors show that high-confidence origins recapitulate several known features of origins (e.g., correspondence with open chromatin, overlap with transcriptional promoters, CTCF binding sites). However, surprisingly, they find little overlap between ORC/MCM binding sites and origin locations.

Overall, this meta-analysis provides the field with a good assessment of the current state of experimental techniques and their reproducibility, but I am worried about: (a) whether we've learned any new biology from this analysis; (b) how binding sites and origin locations can be so mismatched, in light of numerous studies that suggest otherwise; and (c) some methodological details described below.

-- I understand better the inclusion/exclusion logic for the samples. But I'm still not sure about the fragments. As the authors wrote, there is both noise and stochasticity; the former is not important but the latter is essential to include. How can these two be differentiated, and what may be the expected overlap as a function of different stochasticity rates?

-- Many of the major genomic features analyzed have already been found to be associated with origin sites. For example, the correspondence with TSS has been reported before:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320713/<br /> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547456/

-- Line 250: The most surprising finding is that there is little overlap between ORC/MCM binding sites and origin locations. The authors speculate that the overlap between ORC1 and ORC2 could be low because they come from different cell types. Equally concerning is the lack of overlap with MCM. If true, these are potentially major discoveries that butts heads with numerous other studies that have suggested otherwise.

The key missing dataset is ORC1 and ORC2 CHiP-seq from the same cell type. This shouldn't be too expensive to perform, and I hope someone performs this test soon. Without this, I remain on the fence about how much existing datasets are "junk" vs how much the prevailing hypothesis about replication needs to be revisited. Nonetheless, the authors do perform a nice analysis showing that existing techniques should be carefully used and interpreted.

Reviewer #3 (Public Review):

Summary: The authors present a thought-provoking and comprehensive re-analysis of previously published human cell genomics data that seeks to understand the relationship between the sites where the Origin Recognition Complex (ORC) binds chromatin, where the replicative helicase (Mcm2-7) is loaded, and where DNA replication actually beings (origins). The view that these should coincide is influenced by studies in yeast where ORC binds site-specifically to dedicated nucleosome-free origins where Mcm2-7 can be loaded and remains stably positioned for subsequent replication initiation. However, this is most certainly not the case in metazoans where it has already been reported that chromatin bindings sites of ORC and Mcm2-7 do not necessarily overlap, nor do they always overlap with origins. This is likely due to Mcm2-7 possessing linear mobility on DNA (i.e., it can slide) such that other chromatin-contextualized processes can displace it from the site in which it was originally loaded. Additionally, Mcm2-7 is loaded in excess and thus only a fraction of Mcm2-7 would be predicted to coincide with replication start sites. This study reaches a very similar conclusion of these previous studies: they find a high degree of discordance between ORC, Mcm2-7, and origin positions in human cells.

Strengths: The strength of this work is its comprehensive and unbiased analysis of all relevant genomics datasets. To my knowledge, this is the first attempt to integrate these observations. It also is an important cautionary tale to not confuse replication factor binding sites with the genomic loci where replication actually begins, although this point is already widely appreciated in the field.

Weaknesses: The major weakness of this paper is the lack of novel biological insight and that the comprehensive approach taken failed to provide any additional mechanistic insight regarding how and why ORC, Mcm2-7, and origin sites are selected or why they may not coincide.

Reviewer #1 (Public Review):

In the best genetically and biochemically understood model of eukaryotic DNA replication, the budding yeast, Saccharomyces cerevisiae, the genomic locations at which DNA replication initiates are determined by a specific sequence motif. These motifs, or ARS elements, are bound by the origin recognition complex (ORC). ORC is required for loading of the initially inactive MCM helicase during origin licensing in G1. In human cells, ORC does not have a specific sequence binding domain and origin specification is not specified by a defined motif. There have thus been great efforts over many years to try to understand the determinants of DNA replication initiation in human cells using a variety of approaches, which have gradually become more refined over time.

In this manuscript Tian et al. combine data from multiple previous studies using a range of techniques for identifying sites of replication initiation to identify conserved features of replication origins and to examine the relationship between origins and sites of ORC binding in the human genome. The authors identify a) conserved features of replication origins e.g. association with GC-rich sequences, open chromatin, promoters and CTCF binding sites. These associations have already been described in multiple earlier studies. They also examine the relationship of their determined origins and ORC binding sites and conclude that there is no relationship between sites of ORC binding and DNA replication initiation. While the conclusions concerning genomic features of origins are not novel, if true, a clear lack of colocalization of ORC and origins would be a striking finding. However, the majority of the datasets used do not report replication origins, but rather broad zones in which replication origins fire. Rather than refining the localisation of origins, the approach of combining diverse methods that monitor different objects related to DNA replication leads to a base dataset that is highly flawed and cannot support the conclusions that are drawn, as explained in more detail below.

Methods to determine sites at which DNA replication is initiated can be divided into two groups based on the genomic resolution at which they operate. Techniques such as bubble-seq, ok-seq can localise zones of replication initiation in the range ~50kb. Such zones may contain many replication origins. Conversely, techniques such as SNS-seq and ini-seq can localise replication origins down to less than 1kb. Indeed, the application of these different approaches has led to a degree of controversy in the field about whether human replication does indeed initiate at discrete sites (origins), or whether it initiates randomly in large zones with no recurrent sites being used. However, more recent work has shown that elements of both models are correct i.e. there are recurrent and efficient sites of replication initiation in the human genome, but these tend to be clustered and correspond to the demonstrated initiation zones (Guilbaud et al., 2022).

These different scales and methodologies are important when considering the approach of Tian et al. The premise that combining all available data from five techniques will increase accuracy and confidence in identifying the most important origins is flawed for two principal reasons. First, as noted above, of the different techniques combined in this manuscript, only SNS-seq can actually identify origins rather than initiation zones. It is the former that matters when comparing sites of ORC binding with replication origin sites, if a conclusion is to be drawn that the two do not co-localise.

Second, the authors give equal weight to all datasets. Certainly, in the case of SNS-seq, this is not appropriate. The technique has evolved over the years and some earlier versions have significantly different technical designs that may impact the reliability and/or resolution of the results e.g. in Foulk et al. (Foulk et al., 2015), lambda exonuclease was added to single stranded DNA from a total genomic preparation rather than purified nascent strands), which may lead to significantly different digestion patterns (ie underdigestion). Curiously, the authors do not make the best use of the largest SNS-seq dataset (Akerman et al., 2020) by ignoring these authors separation of core and stochastic origins. By blending all data together any separation of signal and noise is lost. Further, I am surprised that the authors have chosen not to use data and analysis from a recent study that provides subsets of the most highly used and efficient origins in the human genome, at high resolution (Guilbaud et al., 2022).

References

Akerman I, Kasaai B, Bazarova A, Sang PB, Peiffer I, Artufel M, Derelle R, Smith G, Rodriguez-Martinez M, Romano M, Kinet S, Tino P, Theillet C, Taylor N, Ballester B, Méchali M (2020) A predictable conserved DNA base composition signature defines human core DNA replication origins. Nat Commun, 11: 4826

Foulk MS, Urban JM, Casella C, Gerbi SA (2015) Characterizing and controlling intrinsic biases of lambda exonuclease in nascent strand sequencing reveals phasing between nucleosomes and G-quadruplex motifs around a subset of human replication origins. Genome Res, 25: 725-735

Guilbaud G, Murat P, Wilkes HS, Lerner LK, Sale JE, Krude T (2022) Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation. Nucleic Acids Res, 50: 7436-7450

Update in response to authors' comments on the original review:

While the authors have clarified their approach to some aspects of their analysis, I believe they and I are just going to have to disagree about the methodology and conclusions of this work. I do not find the authors responses sufficiently compelling to change my mind about the significance of the study or veracity of the conclusions. In my opinion, the method for identification of strong origins is not robust and of insufficient resolution. In addition, the resolution and the overlap of the MCM Chip-seq datasets is poor. While the conclusion of the paper would indeed be striking and surprising if true, I am not at all persuaded that it is based on the presented data.

Author Response

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

We are very grateful for your time and efforts spent on our manuscript. Your feedback has been very valuable. Please see below a point-by-point response to each suggestion and actions taken to address each point in the manuscript.

eLife assessment

In this fundamental study, the authors propose analytical methods for inferring evolutionary parameters of interest from sequencing data in healthy tissue relevant to hematopoiesis. By combining analyses of single cell and bulk sequencing data, the authors can use a stochastic process to inform different aspects of genetic heterogeneity. The strength of evidence in support of the authors' claim is thus compelling. The work will be of broad interest to cell biologists and theoretical biologists.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Authors propose mathematical methods for inferring evolutionary parameters of interest from bulk/single cell sequencing data in healthy tissue and hematopoiesis. In general, the introduction is well-written and adequately references the relevant and important previous literature and findings in this field (e.g. the power laws for well-mixed exponentially growing populations). The authors consider 3 phases of human development: early development, growth and maintenance, and mature phase. In particular, time-dependent mutation rates in Figure 2d is an intriguing and strong result, and the process underlying Figures 3 and 4 are generally wellexplained and convincing.

Thank you for your positive comments.

Notes & suggestions:

1. The explanation of Figure 2 in Lines 101 - 111 should be expanded for clarity. First, is Figure 2a derived from stochastic simulation (line 101 suggests) or some theoretical analysis? Second, the gradual transition from f-2 to f-1 is appreciated, but the shape of the intermediates is not addressed in detail. The power laws are straight lines, and the simulations provide curved lines -- please expand in what range (low or high frequency variants) the power law approximations apply.

Figure 2a was obtained from a numerical solution of equation 1, which describes the time dynamics of the expected VAF distribution. This is indeed unclear from the text, and we thank the reviewer for pointing out this discrepancy.

We thank the reviewer for this suggestion and have now adjusted this in the text (102-110):

“Numerical solutions of Eq.(1) show that the expected VAF distribution exhibits a gradual transition from the f-2 (growing population) to the f-1 (constant population) power law (Fig.2). These transitional states themselves do not adhere to some intermediate power-law (e.g. f for 1<<2), but instead present a sigmoidal shape, with the low frequency portion following f-1 and the high frequencies f-2 . Over time the shape changes as a wavelike front traveling from low to high frequency, with the constant-size equilibrium establishing earliest at the lowest frequencies and moving to higher frequency over time. Interestingly, the convergence towards equilibrium slows down over time -- for evenly-spaced observation times the solutions lie increasingly closer together -- further decreasing the speed at which the high frequency portion of the spectrum approaches equilibrium.”

We also changed the caption of Figure 2 to make this clearer as

“(a) Expected VAF distributions from evolving Eq1 to different time points for a population with an initial exponential growth phase and subsequent constant population phase (mature size N=103). Once the population reaches the maximum carrying capacity, the distribution moves from a 1/f2 growing population shape (purple) to a 1/f constant population shape (green). Note that the shift slows considerably at older age.”

In addition, we have also added annotations to Figure 2a and 2b to further clarify which line (green or purple) is f-1 and f-2.

Additionally, I do not understand the claim in line 108, that the transition is fast for low frequency variants, as the low frequency (on the left of the graph) lines are all close together, whereas the high frequency lines are far apart.

The lines are closer together in the low frequency portion (left of the plot) because they are already very close to the constant-size equilibrium (f-1/green line) and these frequencies approached equilibrium very fast. On the contrary, in the high frequency portion (right side of plot) they are still very far from equilibrium and approached equilibrium much slower.

It would be helpful to reiterate in this paragraph that these power laws are derived based on exponentially growing populations and are expected to break down under homeostatic conditions.

We have adjusted the relevant paragraph in the text to make the validity of the power laws clearer (90-94):

“For a well-mixed exponentially growing population without cell death the VAF spectrum 𝑣(𝑓) is given by 2𝜇/(𝑓 + 𝑓2 )$ (a 𝑓−2 power law) and is independent of time. In contrast, for a population of constant size – i.e. where birth and death rates are equal – the spectrum obeys 𝑣(𝑓) ∝ 2𝜇/ 𝑓 (a 𝑓−1 power law; see also SI), though this solution is only valid at sufficiently long times.”

1. The sample vs population (blue vs orange) in Figure 3 is under-explained. How is it that the mutational burden and inferred mutation rate in A and B roughly match, but the VAF distributions in C are so different? How was the sampled set chosen? Perhaps this is an unimportant distinction based on the particular sample set, but the divergence of the two in C may serve as a distraction, here.

This is an important question, and the answer was perhaps underemphasized in the caption. The sampling was performed as a uniform random sampling with replacement, and the same sample set was used for both the mutational burden and the VAF distribution. The reason for this stark contrast is that while the expectation of the burden distribution is not affected by sampling (i.e. sampling only affects the resolution/amount of stochasticity), the expectation of the VAF distribution changes due to sampling. While this was discussed in the section "Sparse sampling, single cell derived VAF spectra and evolutionary inferences", we have added note of this (indeed surprising) effect in the caption as well:

“(b) Distribution of estimated mutation rates from 10'000 individual simulations, obtained from burden distributions of the complete populations (blue) as well as sampled sets of cells (orange). Because the expected mutational burden distribution is unaltered by sampling, the expected estimate of the mutation rate from (5) remains unchanged: 𝐸(𝜇̃𝑝𝑜𝑝) = 𝐸(𝜇̃𝑠𝑎𝑚𝑝𝑙𝑒). However, sampling increases the noise on the observed burden distribution, which results in a higher errormargin of the estimate: 𝜎(𝜇̃𝑝𝑜𝑝) < 𝜎(𝜇̃𝑠𝑎𝑚𝑝𝑙𝑒).”

“(c) VAF spectra measured in the complete population (blue) and a sampled set of cells (orange). In contrast with the mutational burden distribution, strong sampling changes the shape of the expected distribution. A single simulation result is shown (diamonds) alongside the theoretically predicted expected values for both the total and sampled populations (Eqs. (1) and (6))(dashed line) and the average across 100 simulations (solid line).”

1. The comparison of results herein to claims by Mitchell (ref. 12) are quite important results within the paper. I appreciate the note in the final paragraph of the discussion, and I suggest adding a sentence referencing the result noted in line 248-249 to the abstract, as well.

We agree with the reviewer. We have extended the abstract now to reference the result in more detail:

“However, the single cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations suggesting. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell intrinsic variation.”

Reviewer #2 (Public Review):

Summary: The authors provide a nice summary on the possibility to study genetic heterogeneity and how to measure the dynamics of stem cells. By combining single cell and bulk sequencing analyses, they aim to use a stochastic process and inform on different aspects of genetic heterogeneity.

Strengths: Well designed study and strong methods

Thank you for your positive comments.

Weaknesses: Minor

Further clarification to Figure 3 legend would be good to explain the 'no association' of number of samples and mutational burden estimate as per line 180-182 p.8.

We have added a note to the caption of Figure 3b to explain more clearly how sampling affects the burden distribution and the mutation rate inferred from it (see also previous response to Reviewer 1):

“Because the expected mutational burden distribution is unaltered by sampling, the expected estimate of the mutation rate from (5) remains unchanged: 𝐸(𝜇̃𝑝𝑜𝑝) = 𝐸(𝜇̃𝑠𝑎𝑚𝑝𝑙𝑒). However, sampling increases the noise on the observed burden distribution, which results in a higher errormargin of the estimate: 𝜎(𝜇̃𝑝𝑜𝑝) < 𝜎(𝜇̃𝑠𝑎𝑚𝑝𝑙𝑒).”

Reviewer #1 (Recommendations For The Authors):

Minor/editorial suggestions:

1. Equation 1, please define \partial_t and \partial_K, for clarity.

These have now been defined in the text (between line 85-86): “where 𝜅 = 𝑓𝑁(𝑡) denotes the number of cells sharing a variant (the variant frequency f times the total population size N), 𝛿(x) is the Dirac impulse function, 𝜕𝑡 and 𝜕𝜅 are the partial derivatives with respect to time and variant size.”

1. Figure 2: It would be helpful to label the green and purple lines with the corresponding 1/f and 1/f^2 rule, in addition to the growing/fixed label, for clarity.

We agree and have now added the corresponding labels to each line.

Reviewer #2 (Recommendations For The Authors):

Minor suggestions are given below:

It would be nice for the authors to comment on whether the results could be extended/modified to account for possible fitness advantage of mutations which would be clinically relevant, for instance in the case of CHIP mutations and difference in time to myeloid malignancies transformation between CHIP/No CHIP individuals.

This is an important point. We agree with the reviewer that CHIP mutations play an important role in shaping mutational diversity especially in older individuals. Evidence is now emerging that CHIP mutations are almost universally present in individuals 60+. Interestingly, in individuals younger than 60, a neutral model (as presented here), does capture the observed effective dynamics well. For the purpose of the analysis underlying this manuscript, a neutral model seems reasonable.

The techniques we use here can be adjusted to include selection. How the results extend or modify will critically depend on the actual model of selection (rare or frequent CHIP mutations, strong vs weak selection etc.) that is realized in human hematopoiesis. Here we would say, the underlying biology currently is mostly unknown and is subject to (by others and in part by us) ongoing investigations, which extend beyond the scope of this manuscript.

We now make note of this point in the manuscript and added a small paragraph in page 11 to the discussion:

“Another open question is the role of selection and how it shapes intra-tissue genetic heterogeneity. Evidence is emerging that positively selected variants in blood are almost universally present in individuals above 60, while the effective observable dynamics in younger individuals is well described by neutral dynamics. How results presented here generalize or modify will critically depend on the model of selection realized in human hematopoiesis, e.g. a models of rare or frequent driver events. Details of the underlying biology are currently unknown.”

It would be nice to see if any significant differences in parameter estimates occur between loci with/without linkage disequilibrium, for instance HLA region. Could the number of single-cell samples be 'more' relevant when studying the VAF distribution in HLA region?

This is a good suggestion. We might be wrong or missing an important point, but somatic evolution as we use it in our modeling here is solely driven by asexual reproduction of cells. As such the entire genome of the cell is in linkage disequilibrium, independent of the precise genomic region (somatic evolution is in first approximation blind to germline mutations, as they are present in every single cell of the organism and therefore do not carry any information on the somatic evolutionary dynamics).

We thank all editors and reviewers again for your constructive comments.

Author Response

I would like to express my thorough gratitude to the editors and reviewers, for the helpful comments and valuable suggestions, which provided us an opportunity to further address our research. Prior to submitting our final revision, here we provide our preliminary responses for the comments. Please find our detailed responses to the reviewers’ recommendations below.

Reviewer #1 (Public Review):

Summary:

The authors were trying to understand the relationship between the development of large trunks and longirrostrine mandibles in bunodont proboscideans of Miocene, and how it reflects the variation in diet patterns.

Strengths:

The study is very well supported, written, and illustrated, with plenty of supplementary material. The findings are highly significant for the understanding of the diversification of bunodont proboscideans in Asia during Miocene, as well as explaining the cranial/jaw disparity of fossil lineages. This work elucidates the diversification of paleobiological aspects of fossil proboscideans and their evolutionary response to open environments in the Neogene using several methods. The authors included all Asian bunodont proboscideans with long mandibles and I suggest that they should use the expression "bunodont proboscideans" instead of gomphotheres.

Weaknesses:

I believe that the only weakness is the lack of discussion comparing their results with the development of gigantism and long limbs in proboscideans from the same epoch.

Response: Thank you for your comprehensive review and positive feedback on our study regarding the co-evolution of feeding organs in bunodont proboscideans during the Miocene. We appreciate your suggestion, and have decided to use the term "bunodont elephantiforms" (for more explicit clarification, we use elephantiforms to exclude some early proboscideans, like Moeritherium, ect.) instead of "gomphotheres," and we will make this change in our revised manuscript. We also appreciate the potential weakness you mentioned regarding the lack of discussion comparing our results with the development of gigantism and long limbs in proboscideans from the same epoch. We agree with the reviewer’s suggestion, and we are aware that gigantism and long limbs are potential factors for trunk development. Gigantism resulted in the loss of flexibility in elephantiforms, and long limbs made it more challenging for them to reach the ground. A long trunk serves as compensation for these limitations. limb bones were rare to find in our material, especially those preserved in association with the skull.

Reviewer #2 (Public Review):

This study focuses on the eco-morphology, the feeding behaviors, and the co-evolution of feeding organs of longirostrine gomphotheres (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) which are characterised by their distinctive mandible and mandible tusk morphologies. They also have different evolutionary stages of food acquisition organs which may have co-evolve with extremely elongated mandibular symphysis and tusks. Although these three longirostrine gomphothere families were widely distributed in Northern China in the Early-Middle Miocene, the relative abundances and the distribution of these groups were different through time as a result of the climatic changes and ecosysytems.

These three groups have different feeding behaviors indicated by different mandibular symphysis and tusk morphologies. Additionally, they have different evolutionary stages of trunks which are reflected by the narial region morphology. To be able to construct the feeding behavior and the relation between the mandible and the trunk of early elephantiformes, the authors examined the crania and mandibles of these three groups from the Early and Middle Miocene of northern China from three different museums and also made different analyses.

The analyses made in the study are:

1. Finite Element (FE) analysis: They conducted two kinds of tests: the distal forces test, and the twig-cutting test. With the distal forces test, advantageous and disadvantageous mechanical performances under distal vertical and horizontal external forces of each group are established. With the twig-cutting test, a cylindrical twig model of orthotropic elastoplasity was posed in three directions to the distal end of the mandibular task to calculate the sum of the equivalent plastic strain (SEPS). It is indicated that all three groups have different mandible specializations for cutting plants.

2. Phylogenetic reconstruction: These groups have different narial region morphology, and in connection with this, have different stages of trunk evolution. The phylogenetic tree shows the degree of specialization of the narial morphology. And narial region evolutionary level is correlated with that of character-combine in relation to horizontal cutting. In the trilophodont longirostrine gomphotheres, co-evolution between the narial region and horizontal cutting behaviour is strongly suggested.

3. Enamel isotopes analysis: The results of stable isotope analysis indicate an open environment with a diverse range of habitats and that the niches of these groups overlapped without obvious differentiation.

The analysis shows that different eco-adaptations have led to the diverse mandibular morphology and open-land grazing has driven the development of trunk-specific functions and loss of the long mandible. This conclusion has been achieved with evidence on palaecological reconstruction, the reconstruction of feeding behaviors, and the examination of mandibular and narial region morphology from the detailed analysis during the study.

All of the analyses are explained in detail in the supplementary files. The 3D models and movies in the supplementary files are detailed and understandable and explain the conclusion. The conclusions of the study are well supported by data.

Response: We appreciate your detailed and insightful review of our study. Your summary accurately captures the essence of our research, and we are pleased to note that multiple research methods were used to demonstrate our conclusions. Your recognition of the evidence-based conclusions from palaeoecological, feeding behavior reconstruction, and morphological analyses reinforces the validity of our findings. Once again, we appreciate your time and thoughtful reviews.

Author Response

Reviewer #1 (Public Review):

Summary:

This study presents careful biochemical experiments to understand the relationship between LRRK2 GTP hydrolysis parameters and LRRK2 kinase activity. The authors report that incubation of LRRK2 with ATP increases the KM for GTP and decreases the kcat. From this, they suppose an autophosphorylation process is responsible for enzyme inhibition. LRRK2 T1343A showed no change, consistent with it needing to be phosphorylated to explain the changes in G-domain properties. The authors propose that phosphorylation of T1343 inhibits kinase activity and influences monomer-dimer transitions.

Strengths: The strengths of the work are the very careful biochemical analyses and the interesting result for wild-type LRRK2.

Weaknesses:

A major unexplained weakness is why the mutant T1343A starts out with so much lower activity--it should be the same as wild-type, non-phosphorylated protein. Also, if a monomer-dimer transition is involved, it should be either all or nothing. Other approaches would add confidence to the findings.

We thank the reviewer for these suggestions. We are aware that the T1343A has generally a lower activity compared to the wild type. Therefore, we would like to emphasize that this mutant is the only one not showing an increase in Km values after ATP treatment. Other mutants, also having lower kcat values like T1503A, still show this characteristic change in Km. Our favored explanation for the lower kcat of T1343A is that this mutation lays within a critical region, the so-called ploop, of the Roc domain and is very likely structurally not neutral. Concerning the dimer-monomer transition, we are convinced that there is more than one factor involved in this equilibrium. Most likely, including, but not limited to other LRRK2 domains (e.g. the WD40 domain), binding of co-factors (e.g. Rab29/Rab32 or 14-3-3) and membrane binding. Consistently, also n with stapled peptides targeting the Roc or Cor domains we were not able to shift the equilibrium completely to the monomer (Helton et al., ACS Chem Biol. 2021, 16:2326-2338; Pathak et al. ACS Chem Neurosci. 2023, 14(11):1971-1980) We will address these points in a revised version of the manuscript.

Reviewer #2 (Public Review):

This study addresses the catalytic activity of a Ras-like ROC GTPase domain of LRRK2 kinase, a Ser/Thr kinase linked to Parkinson's disease (PD). The enzyme is associated with gain-of-function variants that hyper-phosphorylate substrate Rab GTPases. However, the link between the regulatory ROC domain and activation of the kinase domain is not well understood. It is within this context that the authors detail the kinetics of the ROC GTPase domain of pathogenic variants of LRRK2, in comparison to the WT enzyme. Their data suggest that LRRK2 kinase activity negatively regulates the ROC GTPase activity and that PD variants of LRRK2 have differential effects on the Km and catalytic efficiency of GTP hydrolysis. Based on mutagenesis, kinetics, and biophysical experiments, the authors suggest a model in which autophosphorylation shifts the equilibrium toward monomeric LRRK2 (locked GTP state of ROC). The authors further conclude that T1343 is a crucial regulatory site, located in the P-loop of the ROC domain, which is necessary for the negative feedback mechanism. Unfortunately, the data do not support this hypothesis, and further experiments are required to confirm this model for the regulation of LRRK2 activity.

Specific comments are below:

• Although a couple of papers are cited, the rationale for focusing on the T1343 site is not evident to readers. It should be clarified that this locus, and perhaps other similar loci in the wider ROCO family, are likely important for direct interactions with the GTP molecule.

To clarify this point: We, have not only have focused on this specific locus, but instead systematically mutated all known auto-phosphorylation sites with the RocCOR domain (see. supplemental information). Furthermore, it has been shown that this site, at least in the RCKW (Roc to WD40) construct, is quantitatively phosphorylated (Deniston et al., Nature 2020, 588:344-349). We are aware that the T1343 residue is located within the p-loop and that this can impact nucleotide binding capacities (see response to reviewer 1). We will clarify and address these points in a revised version of the manuscript.

• Similar to the above, readers are kept in the dark about auto-phosphorylation and its effects on the monomer/dimer equilibrium. This is a critical aspect of this manuscript and a major conceptual finding that the authors are making from their data. However, the idea that auto-phosphorylation is (likely) to shift the monomer/dimer equilibrium toward monomer, thereby inactivating the enzyme, is not presented until page 6, AFTER describing much of their kinetics data. This is very confusing to readers, as it is difficult to understand the meaning of the data without a conceptual framework. If the model for the LRRK2 function is that dimerization is necessary for the phosphorylation of substrates, then this idea should be presented early in the introduction, and perhaps also in the abstract. If there are caveats, then they should be discussed before data are presented. A clear literature trail and the current accepted (or consensus) mechanism for LRRK2 activity is necessary to better understand the context for these data.

We agree on the reviewer’s opinion. We will address this point in a revised version of the manuscript.

• Following on the above concepts, I find it interesting that the authors mention monomeric cytosolic states, and kinase-active oligomers (dimers??), with citations. Again here, it would be useful to be more precise. Are dimers (oligomers?) only formed at the membrane? That would suggest mechanisms involving lipid or membrane-attached protein interactions. Also, what do the authors mean by oligomers? Are there more than dimers found localized to the membrane?

There are multiple studies that have shown that LRRK2 is mainly monomeric in the cytosol while it forms mainly dimeric or higher oligomeric states at membrane (James et al., Biophys. J. 2012, 102, L41–L43; Berger et al., Biochemistry, 2010, 49, 5511–5523). However, we agree with the reviewer that it remains to be determined if the dimeric form is the most active state at the membrane, or a higher oligomeric state. Especially since a recent study shows that LRRK2 can form active tetramers only when bound to Rab29 (Zhu et al., bioRxiv, 2022, DOI: 10.1101/2022.04.26.489605). We will clarify and address these points in the introduction of a revised version of the manuscript.

• Fig 5 is a key part of their findings, regarding the auto-phosphorylation induced monomer formation of LRRK2. From these two bar graphs, the authors state unequivocally that the 'monomer/dimer equilibrium is abolished', and therefore, that the underlying mechanism might be increased monomerization (through maintenance of a GTP-locked state). My view is that the authors should temper these conclusions with caveats. One is that there are still plenty of dimers in the auto-phosphorylated WT, and also in the T1343A mutant. Why is that the case? Can the authors explain why only perhaps a 10% shift is sufficient? Secondly, the T1343A mutant appears to have fewer overall dimers to begin with, so it appears to readers that 'abolition' is mainly due to different levels prior to ATP treatment at 30 deg. I feel these various issues need to be clarified in a revised manuscript, with additional supporting data. Finally, on a minor note, I presume that there are no statistically significant differences between the two sets of bar graphs on the right panel. It would be wise to place 'n.s.' above the graphs for readers, and in the figure legend, so readers are not confused.

Starting with the monomer-dimer equilibrium we are convinced that there is more than the phosphorylation of T1343 (see response to reviewer 1). Therefore a 10% shift in our assay most likely underestimate the effect seen in cells.

Consistently, the T1343A mutants show a similar increase in Rab10 phosphorylation assay as the G2019S mutant. This thus shows that the identified feedback mechanism plays an important role in a cellular context. We will explain this in more detail in a revised version of the manuscript. Concerning the bar diagram, we will add the “n.s.” indication in a future version of the manuscript.

• Figure 6B, Westerns of phosphorylation, the lanes are not identified and it is unclear what these data mean.

We apologize for this mistake and will add the correct labeling in a revised version of the manuscript.

eLife assessment

This study presents a useful method for the extraction of behaviour-related activity from neural population recordings based on a specific deep learning architecture - a variational autoencoder. However, the evidence supporting the scientific claims resulting from the application of this method is incomplete as the results may stem, in part, from its properties. The authors should: (1) improve how they benchmark their method, by comparing against additional relevant techniques, and (2) reframe their results considering what observations may be a byproduct of their method, and which do constitute new scientific observations.

Reviewer #1 (Public Review):

This work seeks to understand how behaviour-related information is represented in the neural activity of the primate motor cortex. To this end, a statistical model of neural activity is presented that enables a non-linear separation of behaviour-related from unrelated activity. As a generative model, it enables the separate analysis of these two activity modes, here primarily done by assessing the decoding performance of hand movements the monkeys perform in the experiments. Several lines of analysis are presented to show that while the neurons with significant tuning to movements strongly contribute to the behaviourally-relevant activity subspace, less or un-tuned neurons also carry decodable information. It is further shown that the discovered subspaces enable linear decoding, leading the authors to conclude that motor cortex read-out can be linear.

Strengths:

In my opinion, using an expressive generative model to analyse neural state spaces is an interesting approach to understanding neural population coding. While potentially sacrificing interpretability, this approach allows capturing both redundancies and synergies in the code as done in this paper. The model presented here is a natural non-linear extension of a previous linear model (PSID) and

Weaknesses:

First, the model in the paper is almost identical to an existing VAE model (TNDM) that makes use of weak supervision with behaviour in the same way [1]. This paper should at least be referenced. If the authors wish they could compare their model to TNDM, which combines a state space model with smoothing similar to LFADS. Given that TNDM achieves very good behaviour reconstructions, it may be on par with this model without the need for a Kalman filter (and hence may achieve better separation of behaviour-related and unrelated dynamics).

Second, in my opinion, the claims regarding identifiability are overstated - this matters as the results depend on this to some extent. Recent work shows that VAEs generally suffer from identifiability problems due to the Gaussian latent space [2]. This paper also hints that weak supervision may help to resolve such issues, so this model as well as TNDM and CEBRA may indeed benefit from this. In addition however, it appears that the relative weight of the KL Divergence in the VAE objective is chosen very small compared to the likelihood (0.1%), so the influence of the prior is weak and the model may essentially learn the average neural trajectories while underestimating the noise in the latent variables. This, in turn, could mean that the model will not autoencode neural activity as well as it should, note that an average R2 in this case will still be high (I could not see how this is actually computed). At the same time, the behaviour R2 will be large simply because the different movement trajectories are very distinct. Since the paper makes claims about the roles of different neurons, it would be important to understand how well their single trial activities are reconstructed, which can perhaps best be investigated by comparing the Poisson likelihood (LFADS is a good baseline model). Taken together, while it certainly makes sense that well-tuned neurons contribute more to behaviour decoding, I worry that the very interesting claim that neurons with weak tuning contain behavioural signals is not well supported.

Third, and relating to this issue, I could not entirely follow the reasoning in the section arguing that behavioural information can be inferred from neurons with weak selectivity, but that it is not linearly decodable. It is right to test if weak supervision signals bleed into the irrelevant subspace, but I could not follow the explanations. Why, for instance, is the ANN decoder on raw data (I assume this is a decoder trained fully supervised) not equal in performance to the revenant distilled signals? Should a well-trained non-linear decoder not simply yield a performance ceiling? Next, if I understand correctly, distilled signals were obtained from the full model. How does a model perform trained only on the weakly tuned neurons? Is it possible that the subspaces obtained with the model are just not optimally aligned for decoding? This could be a result of limited identifiability or model specifics that bias reconstruction to averages (a well-known problem of VAEs). I, therefore, think this analysis should be complemented with tests that do not depend on the model.

Finally, a more technical issue to note is related to the choice to learn a non-parametric prior instead of using a conventional Gaussian prior. How is this implemented? Is just a single sample taken during a forward pass? I worry this may be insufficient as this would not sample the prior well, and some other strategy such as importance sampling may be required (unless the prior is not relevant as it weakly contributed to the ELBO, in which case this choice seems not very relevant). Generally, it would be useful to see visualisations of the latent variables to see how information about behaviour is represented by the model.

Summary:

This paper presents a very interesting analysis, but I have several concerns as to well the analysis supports the main conclusions. I think the work could benefit from an additional complementary analysis that seeks to confirm with another method if weakly tuned neurons indeed show an encoding that differs qualitatively from the strongly tuned ones.

[1] Hurwitz, Cole, et al. "Targeted neural dynamical modeling." Advances in Neural Information Processing Systems 34 (2021): 29379-29392.<br /> [2] Hyvarinen, Aapo, Ilyes Khemakhem, and Hiroshi Morioka. "Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning." arXiv preprint arXiv:2303.16535 (2023).

Reviewer #2 (Public Review):

Li et al present a method to extract "behaviorally relevant" signals from neural activity. The method is meant to solve a problem which likely has high utility for neuroscience researchers. There are numerous existing methods to achieve this goal some of which the authors compare their method to, though there are notable omissions. However, I do believe that d-VAE is a promising approach that has its own advantages.

That being said, there are issues with the paper as-is. This could have been a straightforward "methods" paper describing their approach and validating it on different ground truth and experimental datasets. Instead, the authors focus on the neuroscientific results and their implications for brain mechanisms. Unfortunately, while the underlying method seems sound and performs well relative to the assessed competition, the scientific results and presentation they put forward were not sufficiently strong to support these claims, especially given the small amount of data (recordings of one monkey per task, with considerable variability between them).

Specific comments<br /> - Is the apparently increased complexity of encoding vs decoding so unexpected given the entropy, sparseness, and high dimensionality of neural signals (the "encoding") compared to the smoothness and low dimensionality of typical behavioural signals (the "decoding") recorded in neuroscience experiments? This is the title of the paper so it seems to be the main result on which the authors expect readers to focus.

- I take issue with the premise that signals in the brain are "irrelevant" simply because they do not correlate with a fixed temporal lag with a particular behavioural feature hand-chosen by the experimenter. As an example, the presence of a reward signal in motor cortex [1] after the movement is likely to be of little use from the perspective of predicting kinematics from time-bin to time-bin using a fixed model across trials (the apparent definition of "relevant" for behaviour here), but an entire sub-field of neuroscience is dedicated to understanding the impact of these reward-related signals on future behaviour. Is there method sophisticated enough to see the behavioural "relevance" of this brief, transient, post-movement signal? This may just be an issue of semantics, and perhaps I read too much into the choice of words here. Perhaps the authors truly treat "irrelevant" and "without a fixed temporal correlation" as synonymous phrases and the issue is easily resolved with a clarifying parenthetical the first time the word "irrelevant" is used. But I remain troubled by some claims in the paper which lead me to believe that they read more deeply into the "irrelevancy" of these components.

- The authors claim the "irrelevant" responses underpin an unprecedented neuronal redundancy and reveal that movement behaviors are distributed in a higher-dimensional neural space than previously thought." Perhaps I just missed the logic, but I fail to see the evidence for this. The neural space is a fixed dimensionality based on the number of neurons. A more sparse and nonlinear distribution across this set of neurons may mean that linear methods such as PCA are not effective ways to approximate the dimensionality. But ultimately the behaviourally relevant signals seem quite low-dimensional in this paper even if they show some nonlinearity may help.

- Relatedly, I would like to note that the exercise of arbitrarily dividing a continuous distribution of a statistic (the "R2") based on an arbitrary threshold is a conceptually flawed exercise. The authors read too much into the fact that neurons which have a low R2 w.r.t. PDs have behavioural information w.r.t. other methods. To this reviewer, it speaks more about the irrelevance, so to speak, of the preferred direction metric than anything fundamental about the brain.

- there is an apparent logical fallacy that begins in the abstract and persists in the paper: "Surprisingly, when incorporating often-ignored neural dimensions, behavioral information can be decoded linearly as accurately as nonlinear decoding, suggesting linear readout is performed in motor cortex." Don't get me wrong: the equivalency of linear and nonlinear decoding approaches on this dataset is interesting, and useful for neuroscientists in a practical sense. However, the paper expends much effort trying to make fundamental scientific claims that do not feel very strongly supported. This reviewer fails to see what we can learn about a set of neurons in the brain which are presumed to "read out" from motor cortex. These neurons will not have access to the data analyzed here. That a linear model can be conceived by an experimenter does not imply that the brain must use a linear model. The claim may be true, and it may well be that a linear readout is implemented in the brain. Other work [2,3] has shown that linear readouts of nonlinear neural activity patterns can explain some behavioural features. The claim in this paper, however, is not given enough

- I am afraid I may be missing something, as I did not understand the fano factor analysis of Figure 3. In a sense the behaviourally relevant signals must have lower FF given they are in effect tied to the temporally smooth (and consistent on average across trials) behavioural covariates. The point of the original Churchland paper was to show that producing a behaviour squelches the variance; naturally these must appear in the behaviourally relevant components. A control distribution or reference of some type would possibly help here.

- The authors compare the method to LFADS. While this is a reasonable benchmark as a prominent method in the field, LFADS does not attempt to solve the same problem as d-VAE. A better and much more fair comparison would be TNDM [4], an extension of LFADS which is designed to identify behaviourally relevant dimensions.

[1] https://doi.org/10.1371/journal.pone.0160851<br /> [2] https://doi.org/10.1101/2022.03.31.486635<br /> [3] https://doi.org/10.1038/s41593-017-0028-6<br /> [4] Hurwitz et al, Targeted Neural Dynamical Modeling, NeurIPS 2021.

Reviewer #3 (Public Review):

The authors develop a variational autoencoder (VAE), termed d-VAE (or distill VAE) that aims to tease apart the behaviorally relevant and irrelevant sections of each neuron's firing rate. The input to the VAE is the population activity for a given time step, and the output is the inferred behaviorally relevant section of the population activity at that time step. The residual is referred to as behaviorally irrelevant: total neural activity = behaviorally relevant + behaviorally irrelevant (x = x_r + x_i). The mapping from the raw neural signals (x) to the bottlenecked latent in the autoencoder (called z, z=f(x)) and back to the inferred behaviorally relevant single-neuron activities (x_r = g(z)) is applied per time step (does not incorporate any info from past/future time steps) and, critically, it is nonlinear (f and g are nonlinear feedforward neural networks). The key technical novelty that encourages x_r to encode behaviorally relevant information is a term added to the loss, which penalizes bad linear behavior decoding from the latent z. Otherwise the method is very similar to a prior method called pi-VAE, which should be explained more thoroughly in the manuscript to clearly highlight the technical novelty.

The authors apply their method to 3 non-human primate datasets to infer behaviorally relevant signals and contrast them with the raw neural signals and the residual behaviorally irrelevant signals. As a key performance metric, they compute the accuracy of decoding behavior from the inferred behaviorally relevant signals (x_r) using a linear Kalman filter (KF) or alternatively using a nonlinear feed forward neural network (ANN). They highlight 3 main conclusions in the abstract: first, that single neurons from which behavior is very poorly decodable do encode considerable behavior information in a nonlinear manner, which the ANN can decode. Second, they conclude from various analyses that behavior is occupying a higher dimensional neural space than previously thought. Third, they find that linear KF decoding and nonlinear ANN decoding perform similarly when provided with the inferred behaviorally relevant signals (x_r), from which they conclude that a linear readout must be performed in motor cortex.

The paper is well-written in many places and has high-quality graphics. The questions that it aims to address are also of considerable interest in neuroscience. However, unfortunately, several main conclusions, including but not limited to all 3 conclusions that are highlighted in the abstract, are not fully supported by the results due to confounds, some of which are fundamental to the method. Several statements in the text also seem inaccurate due to use of imprecise language. Moreover, the authors fail to compare with some more relevant existing methods that are specifically designed for extracting behaviorally relevant signals. In addition, for some of the methods they compare with, they do not use an appropriate setup for the benchmark methods, rendering the validation of the proposed method unconvincing. Finally, in many places imprecise language that is not accompanied with an operational definition (e.g., smaller R2 [of what], similar [per what metric]) makes results hard to follow, unless most of the text is read very carefully. Some key details of the methods are also not explained anywhere.

eLife assessment

In this potentially useful study, the authors use deep learning models to provide solid evidence that epithelial wounding triggers bursts of cell division at a characteristic distance away from the wound. The usefulness of the methods to the community will depend on documenting their robustness toward variability in temporal resolution and/or mitotic event duration and demonstrating their overall superiority over existing approaches.

Reviewer #1 (Public Review):

The authors present a number of deep-learning models to analyse the dynamics of epithelia. In this way, they want to overcome the time-consuming manual analysis of such data and also remove a potential operator bias. Specifically, they set up models for identifying cell division events and cell division orientation. They apply these tools to the epithelium of the developing Drosophila pupal wing. They confirm a linear decrease of the division density with time and identify a burst of cell division after the healing of a wound that they had induced earlier. These division events happen a characteristic time after and a characteristic distance away from the wound. These characteristic quantities depend on the size of the wound.

Strength:<br /> The methods developed in this work achieve the goals set by the authors and are a very helpful addition to the toolbox of developmental biologists. They could potentially be used on various developing epithelia. The evidence for the impact of wounds on cell division is solid.

Weakness:<br /> Some aspects of the deep-learning models remained unclear, and the authors might want to think about adding details. First of all, for readers not being familiar with deep-learning models, I would like to see more information about ResNet and U-Net, which are at the base of the new deep-learning models developed here. What is the structure of these networks? How many parameters do you use? What is the difference between validating and testing the model? Do the corresponding data sets differ fundamentally? How did you assess the quality of the training data classification?

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

In general, novelty over previous work does not seem particularly important. From a methodological point of view, the models are based on generic architectures of convolutional neural networks, with minimal changes, and on ideas already explored in general. The authors seem to have missed much (most?) of the literature on the specific topic of detecting mitotic events in 2D timelapse images, which has been published in more specialized journals or Proceedings. (TPMAI, CCVPR etc., see references below). Even though the image modality or biological structure may be different (non-fluorescent images sometimes), I don't believe it makes a big difference. How the authors' approach compares to this previously published work is not discussed, which prevents me from objectively assessing the true contribution of this article from a methodological perspective.

On the contrary, some competing works have proposed methods based on newer - and generally more efficient - architectures specifically designed to model temporal sequences (Phan 2018, Kitrungrotsakul 2019, 2021, Mao 2019, Shi 2020). These natural candidates (recurrent networks, long-short-term memory (LSTM), gated recurrent units (GRU), or even more recently transformers), coupled to CNNs are not even mentioned in the manuscript, although they have proved their generic superiority for inference tasks involving time series (Major point 2). Even though the original idea/trick of exploiting the different channels of RGB images to address the temporal aspect might seem smart in the first place - as it reduces the task of changing/testing a new architecture to a minimum - I guess that CNNs trained this way may not generalize very well to videos where the temporal resolution is changed slightly (Major point 1). This could be quite problematic as each new dataset acquired with a different temporal resolution or temperature may require manual relabeling and retraining of the network. In this perspective, recent alternatives (Phan 2018, Gilad 2019) have proposed unsupervised approaches, which could largely reduce the need for manual labeling of datasets.

Regarding the other convolutional neural networks described in the manuscript:

1) the one proposed to predict the orientation of mitosis performs a regression task, predicting a probability for the division angle. The architecture, which must be different from a simple Unet, is not detailed anywhere, so the way it was designed is difficult to assess. It is unclear if it also performs mitosis detection, or if it is instead used to infer orientation once the timing and location of the division have been inferred by the previous network.

2) the one proposed to improve the quality of cell boundary images before segmentation is nothing new, it has now become a classic step in segmentation, see for example Wolny et al. eLife 2020.

As a side note, I found it a bit frustrating to realise that all the analysis was done in 2D while the original images are 3D z-stacks, so a lot of the 3D information had to be compressed and has not been used. A novelty, in my opinion, could have resided in the generalisation to 3D of the deep-learning approaches previously proposed in that context, which are exclusively 2D, in particular, to predict the orientation of the division.

Concerning the biological application of the proposed methods, I found the results interesting, showing the potential of such a method to automatise mitosis quantification for a particular biological question of interest, here wound healing. However, the deep learning methods/applications that are put forward as the central point of the manuscript are not particularly original.

Major point 1: generalisation potential of the proposed method.

The neural network model proposed for mitosis detection relies on a 2D convolutional neural network (CNN), more specifically on the Unet architecture, which has become widespread for the analysis of biology and medical images. The strategy proposed here exploits the fact that the input of such an architecture is natively composed of several channels (originally 3 to handle the 3 RGB channels, which is actually a holdover from computer vision, since most medical/biological images are gray images with a single channel), to directly feed the network with 3 successive images of a timelapse at a time. This idea is, in itself, interesting because no modification of the original architecture had to be carried out. The latest 10-channel model (U-NetCellDivision10), which includes more channels for better performance, required minimal modification to the original U-Net architecture but also simultaneous imaging of cadherin in addition to histone markers, which may not be a generic solution.

Since CNN-based methods accept only fixed-size vectors (fixed image size and fixed channel number) as input (and output), the length or time resolution of the extracted sequences should not vary from one experience to another. As such, the method proposed here may lack generalization capabilities, as it would have to be retrained for each experiment with a slightly different temporal resolution. The paper should have compared results with slightly different temporal resolutions to assess its inference robustness toward fluctuations in division speed.

Another approach (not discussed) consists in directly convolving several temporal frames using a 3D CNN (2D+time) instead of a 2D, in order to detect a temporal event. Such an idea shares some similarities with the proposed approach, although in this previous work (Ji et al. TPAMI 2012 and for split detection Nie et al. CCVPR 2016) convolution is performed spatio-temporally, which may present advantages. How does the authors' method compare to such an (also very simple) approach?

Major point 2: innovatory nature of the proposed method.

The authors' idea of exploiting existing channels in the input vector to feed successive frames is interesting, but the natural choice in deep learning for manipulating time series is to use recurrent networks or their newer and more stable variants (LSTM, GRU, attention networks, or transformers). Several papers exploiting such approaches have been proposed for the mitotic division detection task, but they are not mentioned or discussed in this manuscript: Phan et al. 2018, Mao et al. 2019, Kitrungrotaskul et al. 2019, She et al 2020.

An obvious advantage of an LSTM architecture combined with CNN is that it is able to address variable length inputs, therefore time sequences of different lengths, whereas a CNN alone can only be fed with an input of fixed size.

Another advantage of some of these approaches is that they rely on unsupervised learning, which can avoid the tedious relabeling of data (Phan et al. 2018, Gilad et al. 2019).

References :<br /> Ji, S., Xu, W., Yang, M., & Yu, K. (2012). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231. >6000 citations

Nie, W. Z., Li, W. H., Liu, A. A., Hao, T., & Su, Y. T. (2016). 3D convolutional networks-based mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 55-62).

Phan, H. T. H., Kumar, A., Feng, D., Fulham, M., & Kim, J. (2018). Unsupervised two-path neural network for cell event detection and classification using spatiotemporal patterns. IEEE Transactions on Medical Imaging, 38(6), 1477-1487.

Gilad, T., Reyes, J., Chen, J. Y., Lahav, G., & Riklin Raviv, T. (2019). Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy. Bioinformatics, 35(15), 2644-2653.

Mao, Y., Han, L., & Yin, Z. (2019). Cell mitosis event analysis in phase contrast microscopy images using deep learning. Medical image analysis, 57, 32-43.

Kitrungrotsakul, T., Han, X. H., Iwamoto, Y., Takemoto, S., Yokota, H., Ipponjima, S., ... & Chen, Y. W. (2019). A cascade of 2.5 D CNN and bidirectional CLSTM network for mitotic cell detection in 4D microscopy image. IEEE/ACM transactions on computational biology and bioinformatics, 18(2), 396-404.

Shi, J., Xin, Y., Xu, B., Lu, M., & Cong, J. (2020, November). A Deep Framework for Cell Mitosis Detection in Microscopy Images. In 2020 16th International Conference on Computational Intelligence and Security (CIS) (pp. 100-103). IEEE.

Wolny, A., Cerrone, L., Vijayan, A., Tofanelli, R., Barro, A. V., Louveaux, M., ... & Kreshuk, A. (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. Elife, 9, e57613.

eLife assessment

This paper investigates host and viral factors influencing transmission of alpha and delta SARS-CoV-2 variants in the Syrian hamster model and fundamentally increases knowledge regarding transmission of the virus via the aerosol route. The strength of evidence is solid and could be improved with a clearer presentation of the data.

Reviewer #1 (Public Review):

In the submitted manuscript, Port et al. investigated the host and viral factors influencing the airborne transmission of SARS-CoV-2 Alpha and Delta variants of concern (VOC) using a Syrian hamster model. The authors analyzed the viral load profiles of the animal respiratory tracts and air samples from cages by quantifying gRNA, sgRNA, and infectious virus titers. They also assessed the breathing patterns, exhaled aerosol aerodynamic profile, and size distribution of airborne particles after SARS-CoV-2 Alpha and Delta infections. The data showed that male sex was associated with increased viral replication and virus shedding in the air. The relationship between co-infection with VOCs and the exposure pattern/timeframe was also tested. This study appears to be an expansion of a previous report (Port et al., 2022, Nature Microbiology). The experimental designs were rigorous, and the data were solid. These results will contribute to the understanding of the roles of host and virus factors in the airborne transmission of SARS-CoV-2 VOCs.

Reviewer #2 (Public Review):

This manuscript by Port and colleagues describes rigorous experiments that provide a wealth of virologic, respiratory physiology, and particle aerodynamic data pertaining to aerosol transmission of SARS-CoV-2 between infected Syrian hamsters. The data is particularly significant because infection is compared between alpha and delta variants, and because viral load is assessed via numerous assays (gRNA, sgRNA, TCID) and in tissues as well as the ambient environment of the cage. The paper will be of interest to a broad range of scientists including infectious diseases physicians, virologists, immunologists and potentially epidemiologists. The strength of evidence is relatively high but limited by unclear presentation in certain parts of the paper.

Important conclusions are that infectious virus is only detectable in air samples during a narrow window of time relative to tissue samples, that airway constriction increases dynamically over time during infection limiting production of fine aerosol droplets, that variants do not appear to exclude one another during simultaneous exposures and that exposures to virus via the aerosol route lead to lower viral loads relative to direct inoculation suggesting an exposure dose response relationship.

While the paper is valuable, I found certain elements of the data presentation to be unclear and overly complex.

Reviewer #1 (Public Review):

Summary of what the author was trying to achieve:<br /> In this study, the author aimed to develop a method for estimating neuronal-type connectivity from transcriptomic gene expression data, specifically from mouse retinal neurons. They sought to develop an interpretable model that could be used to characterize the underlying genetic mechanisms of circuit assembly and connectivity.

Strengths:<br /> The proposed bilinear model draws inspiration from commonly implemented recommendation systems in the field of machine learning. The author presents the model clearly and addresses critical statistical limitations that may weaken the validity of the model such as multicollinearity and outliers. The author presents two formulations of the model for separate scenarios in which varying levels of data resolution are available. The author effectively references key work in the field when establishing assumptions that affect the underlying model and subsequent results. For example, correspondence between gene expression cell types and connectivity cell types from different references are clearly outlined in Tables 1-3. The model training and validation are sufficient and yield a relatively high correlation with the ground truth connectivity matrix. Seemingly valid biological assumptions are made throughout, however, some assumptions may reduce resolution (such as averaging over cell types), thus missing potentially important single-cell gene expression interactions.

Weaknesses:<br /> The main results of the study could benefit from replication in another dataset beyond mouse retinal neurons, to validate the proposed method. Dimensionality reduction significantly reduces the resolution of the model and the PCA methodology employed is largely non-deterministic. This may reduce the resolution and reproducibility of the model. It may be worth exploring how the PCA methodology of the model may affect results when replicating. Figure 5, 'Gene signatures associated with the two latent dimensions', lacks some readability and related results could be outlined more clearly in the results section. There should be more discussion on weaknesses of the results e.g. quantification of what connectivity motifs were not captured and what gene signatures might have been missed.

The main weakness is the lack of comparison against other similar methods, e.g. methods presented in<br /> Barabási, Dániel L., and Albert-László Barabási. "A genetic model of the connectome." Neuron 105.3 (2020): 435-445.<br /> Kovács, István A., Dániel L. Barabási, and Albert-László Barabási. "Uncovering the genetic blueprint of the C. elegans nervous system." Proceedings of the National Academy of Sciences 117.52 (2020): 33570-33577.<br /> Taylor, Seth R., et al. "Molecular topography of an entire nervous system." Cell 184.16 (2021): 4329-4347.

Appraisal of whether the author achieved their aims, and whether results support their conclusions:<br /> The author achieved their aims by recapitulating key connectivity motifs from single-cell gene expression data in the mouse retina. Furthermore, the model setup allowed for insight into gene signatures and interactions, however could have benefited from a deeper evaluation of the accuracy of these signatures. The author claims the method sets a new benchmark for single-cell transcriptomic analysis of synaptic connections. This should be more rigorously proven. (I'm not sure I can speak on the novelty of the method)

Discussion of the likely impact of the work on the field, and the utility of methods and data to the community :<br /> This study provides an understandable bilinear model for decoding the genetic programming of neuronal type connectivity. The proposed model leaves the door open for further testing and comparison with alternative linear and/or non-linear models, such as neural network-based models. In addition to more complex models, this model can be built on to include higher resolution data such as more gene expression dimensions, different types of connectivity measures, and additional omics data.

Reviewer #2 (Public Review):

Summary:<br /> In this study, Mu Qiao employs a bilinear modeling approach, commonly utilized in recommendation systems, to explore the intricate neural connections between different pre- and post-synaptic neuronal types. This approach involves projecting single-cell transcriptomic datasets of pre- and post-synaptic neuronal types into a latent space through transformation matrices. Subsequently, the cross-correlation between these projected latent spaces is employed to estimate neuronal connectivity. To facilitate the model training, connectomic data is used to estimate the ground-truth connectivity map. This work introduces a promising model for the exploration of neuronal connectivity and its associated molecular determinants. However, it is important to note that the current model has only been tested with Bipolar Cell and Retinal Ganglion Cell data, and its applicability in more general neuronal connectivity scenarios remains to be demonstrated.

Strengths:<br /> This study introduces a succinct yet promising computational model for investigating connections between neuronal types. The model, while straightforward, effectively integrates single-cell transcriptomic and connectomic data to produce a reasonably accurate connectivity map, particularly within the context of retinal connectivity. Furthermore, it successfully recapitulates connectivity patterns and helps uncover the genetic factors that underlie these connections.

Weaknesses:<br /> 1. The study lacks experimental validation of the model's prediction results.<br /> 2. The model's applicability in other neuronal connectivity settings has not been thoroughly explored.<br /> 3. The proposed method relies on the availability of neuronal connectomic data for model training, which may be limited or absent in certain brain connectivity settings.

Author Response:

Reviewer #1 (Public Review):

[...] Major concerns/weakness:

1) All the results in Fig. 2 utilized two glioma lines SF188 and Res259. The authors should repeat all these experiments in a couple of H3.3K27M DMG lines by deleting the H3.3K27M mutation first.

We thank the referee for his/her comments that will help us to strengthen our conclusions.

The reviewer's proposal is interesting, but this approach to deletion of the K27M mutation rather answers the question of the role of the BMP pathway in maintaining the phenotype of DMG cells. Our aim in the first part of this article (with Res and SF188) is rather to study how the BMP pathway can participate in installing a particular cellular state at the time of expression of the K27M mutation. In other words, the underlying idea is to define the phenotypic changes specifically associated with activation of the BMP pathway when epigenetic modifications are induced by expression of the K27M mutation. We have chosen the SF188 and Res259 models to remain in a glial context, but it would indeed be interesting to test the effect of this synergy in other models, closer to the cells of origin of DMG. In any case, these models should make it possible to answer the question of the cellular state transition at the moment of K27M expression, even if the reciprocal question of the reversibility of this state proposed by the reviewer is also of interest for understanding the oncogenic synergy between BMP/K27M.

2) Fig. 3. The experiments of BMP2 treatment should be repeated in other H3.3K27M DMG lines using H3.1K27M ACVR1 mutant tumor lines as controls.

We will provide the results of these experiments in a revised version. The use of mutant ACVR1 lines is interesting, but their control status seems questionable, as the addition of BMPs could have a cumulative effect on the effect of the mutation, notably by activating other receptors in the pathway.

Minor concerns:

Fig.2A. BMP2 expression increased in H3.3K27M SF188 cells. Therefore, the statement "whereas BMP2 and BMP4 expressions are not significantly modified (Figure 2A and Figure 2-figure supplement A-B)" is not accurate.

The referee is absolutely right and we will correct this statement in the revised version.

Reviewer #2 (Public Review):

[...] The paper is well-written and easy to follow with a robust experimental plan and datasets supporting the claims. While previous work (acknowledged by the authors) indicated activation of BMP in H3K27M tumors, wild type for the ACVR1 mutation this paper is a nice addition and provides further mechanistic cues as to the importance of the BMP pathway and specific members in these deadly brain cancers. The effect of these BMPs in quiescence and invasion is of particular interest.

We thank the referee for his/her supportive comments.

A few suggestions to clarify the message are provided below:

1- In thalamic diffuse midline gliomas, the BMP pathway should not be activated as it is in the pons. The authors should identify thalamic tumors in the datasets they explored and patients-derived cell lines from thalamic tumors available to investigate whether this pathway is active across all H3.3K27M mutants in the brain midline or specifically in tumors from the pons.

The referee's question is an interesting one, and we will try to see if we can determine tumor’s location from the public data we've used. We will nevertheless try to determine whether the inter-patient variability observed in the level of activation of the BMP pathway may be due, in particular, to different tumor locations.

2 - There are ~20% H3.3K27M tumors that carry an ACVR1 mutation and similar numbers of H3.1K27M that are wild type for this gene. Can the authors identify these outliers in their datasets and assess the activation of BMP2 and 7 or other BMP pathway members in this context?

Indeed, defining the level of activation of the pathway in this type of H3.3K27M ACVR1 mutant or H3.1K27M ACVR1 wt tumors would be extremely interesting, but no samples of this type are a priori included in the datasets analyzed. Instead, we will try to define the phenotype of cell lines of this type in response to BMP.