DOI: 10.1016/j.xpro.2025.103945

Resource: RRID:Addgene_12259

Curator: @scibot

SciCrunch record: RRID:Addgene_12259

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DOI: 10.1016/j.xpro.2025.103945

Resource: None

Curator: @sonofthor

SciCrunch record: RRID:SCR_003089

What is this?

DOI: 10.1016/j.xcrm.2025.102257

Resource: (IMSR Cat# JAX_000651,RRID:IMSR_JAX:000651)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:000651

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Prueba de anotación

Author response:

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

Reviewer #1 (Recommendations for the authors):

Line 122: There were a number of qualitative descriptors in the paper. For instance, if the authors want to say massive campaign, how massive? How rapid? These are relative terms in this context.

We have revised the text to minimize qualitative descriptors and to provide concrete numbers where possible. The revised sentence (line 121) now reads “We began our structural investigation of nitrogenase evolutionary history by conducting on a large-scale structure prediction analysis of 5378 protein structures, a more than threefold increase compared to available nitrogenase structures in the PDB. We then analyzed our phylogenetic dataset to identify notable structural changes.”

Line 179: "massively scale up" How massive?

We agree with the reviewer’s observation, in response, we have removed the phrase “massively scale up” and revised the text.

Line 182: "no compromise on alignment depth and negligible cost to prediction accuracy". How do you know this? Is this shown somewhere? Was there a comparison between known structures and the predicted structure for those nitrogenases that have structures?

In response to this comment, we have made several clarifications and revisions in the manuscript:

We modified Figure S1, which now shows the pLDDT (per-residue confidence metric from Alphafold) values of all our predictions. These scores are consistently high (over 90 for the D and K subunits, and approximetly 90 for the H subunits) regardless of whether the recycling protocol or the bona-fide protocol was used.

The reviewer’s comment demonstrated to us that the Figure S1 needed to more clearly representing these values, we therefore updated it accordingly.

To prevent any misinterpretation of our claims about the accuracy and cost of the method , we have revised the text at line 179, as follows:

“In total, 2,689 unique extant and ancestral nitrogenase variants were targeted. All structures were generated in approximately 805 hours, including GPU computations and MMseqs2 alignments performed using two different protocols: one for extant or most likely ancestral sequences, and another for ancestral variants.”

To support our analyses further, Figure S10A compares our model predictions with available PDB structures for nitrogenases.

Additionally, Figure S10B compare our predicted structures with the experimental structures reported in this article. In all cases, we observe low RMSD values.

Line 220: "fall within 2 angstroms" instead of "fall 2A"?

We have updated it in the text.

Line 315: It is not clear how the binding affinities and other measurements in Figure 4 and S6C were measured, and it is not discussed in the material and methods.

We thank the reviewer for pointing out this lack of clarity. The binding affinity estimations were performed using Prodigy. We have updated the main text (see line 322) to explicitly state that binding affinities were estimated using Prodigy. In addition, we have expanded the Materials and Methods section to include additional information about the structure characterization methods (lines 745-749). Previously, these details were only noted in Supplementary Table S6.

Line 510-511: "Subtle, modular structural adjustments away from the active site were key to the evolution and persistence of nitrogenases over geologic time". This seems like a bit of an overstatement. While the authors see structural differences in the ancestral nitrogenase and speculate these differences could be involved in oxygen protection, there is no evidence that the ancestral nitrogenase is more sensitive to oxygen than the extant nitrogenase.

We appreciate the reviewer’s comment. Our intention was to emphasize that subtle, modular structural adjustments might have contributed to oxygen protection rather than to assert that ancestral nitrogenases are more oxygen-sensitive than their extant counterparts. We have revised the text to clarify.

Reviewer #2 (Recommendations for the authors):

What is the reference for the measured RMSDs in Fig 2A? What is the value on the y-axis? The range of 'Count' is unclear, given that there are 5000 structures predicted in the study.

Figure 2A presents a histogram of RMSD values from all pairwise alignments among 769 structures (385 extant and 384 ancestral DDKK), totaling 591,361 comparisons. We excluded ancestral DDKK variants due to computational limitations.  

Similarly, what is the sequence identity in Figure 2B calculated relative to?

In Figure 2B, sequence identities are derived from pairwise comparisons across all structures in our dataset. Each value represents the identity between two specific structures, rather than being measured against a single reference.

The claim that 'structural analysis could reproduce sequence-based phylogenetic variation' should probably be tempered or qualified, given that the RMSD differences calculated are so low.

We hope to have addressed the concerns about the low RMSD values in the previous comments. We have revised the text (line 204), which now reads: “it still strongly correlates with sequence identity (Figure 2B), indicating that even minor structural variations can recapitulate sequence-based phylogenetic distinctions.”

How are binding affinities (Figure 4) calculated?

We have now clarified the binding affinity calculations in the main text. The model used is now detailed at line 322, with additional information provided in the Methods section.

Presumably, crystallized proteins (Anc1A, Anc1B, Anc2) were also among those whose structures were predicted with AF. A comparison should be provided of the predicted and crystallized structures, as this is an excellent opportunity to further comment on the reliability of AlphaFold.

In the revised manuscript, Figure S10 now present structural comparisons between the crystallized proteins and their AlphaFold-predicted counterparts.

The labels in Figure 5B are not clear. Are the 3rd and 4th panels also comparative RMSD values? But only one complex name is provided.

We appreciate this feedback and now revised the Figure 5B for clarity.

Page 9 line 220, missing word: 'varaints fall within/under 2angstroms'

We thank the reviewer for the correction, we have updated the text.

Este concepto subraya la importancia de la claridad en la definición del problema de investigación. Al formular el problema con precisión, el investigador establece una base sólida que facilita la identificación de objetivos, preguntas y métodos, reduciendo ambigüedades y enfocando el estudio hacia resultados concretos. Esto refuerza la necesidad de dedicar tiempo a la revisión de literatura y al análisis del contexto para garantizar que el problema sea comprensible y relevante.

El título actúa como una "tarjeta de presentación" del proyecto, condensando la esencia de la investigación. Incluir especificidad, espacialidad y temporalidad asegura que el título sea claro y delimite el alcance del estudio. Por ejemplo, un título como "Conocimientos sobre COVID-19 en estudiantes de la UVG, 2022" define claramente qué, dónde y cuándo, ayudando a los lectores a comprender inmediatamente el enfoque y contexto del trabajo.

Este punto destaca la importancia de contextualizar el problema para darle relevancia. Describir quiénes se ven afectados y las implicaciones (causas y consecuencias) permite al investigador justificar la pertinencia del estudio y conectar con las necesidades reales de una población o situación. Esto refuerza que un buen enunciado no solo describe el problema, sino que lo sitúa en un marco social, cultural o práctico significativo.

Este principio resalta la importancia de la reproducibilidad en la investigación científica. Detallar los recursos materiales (como software, equipos o documentos) asegura que el estudio sea transparente y verificable. Este aprendizaje refuerza que una investigación bien planificada considera no solo la ejecución, sino también la posibilidad de que otros puedan replicarla para validar los resultados.

Usar un formato estandarizado como APA asegura que las fuentes sean citadas de manera clara y profesional, facilitando la trazabilidad de la información. Este aprendizaje refuerza la importancia de la integridad académica, ya que citar correctamente no solo da crédito a los autores originales, sino que también permite a otros investigadores acceder a las fuentes para profundizar en el tema.

El modelo SMART es una herramienta clave para garantizar que los objetivos sean prácticos y efectivos. Por ejemplo, un objetivo como "Demostrar los conocimientos de los estudiantes sobre Check4Covid en 2022" es específico (conocimientos), medible (a través de encuestas), alcanzable (dentro del contexto de la UVG), relevante (para la prevención de COVID-19) y temporal (en 2022). Este enfoque refuerza la importancia de diseñar objetivos que guíen la investigación sin desviarse.

Las preguntas auxiliares son esenciales para desglosar el problema en aspectos manejables. Esta pregunta específica guía la investigación hacia la evaluación de la efectividad de una herramienta, promoviendo un análisis crítico de sus fortalezas y limitaciones. Aprender a formular preguntas claras y enfocadas, como esta, ayuda a estructurar la investigación y a mantener el rumbo hacia el objetivo general.

La justificación es el "corazón" persuasivo de la investigación, ya que conecta el problema con su relevancia práctica o teórica. Al explicar por qué el estudio es necesario, el investigador no solo motiva su realización, sino que también convence a otros (como financiadores o académicos) de su valor. Este aprendizaje enfatiza la necesidad de alinear el proyecto con necesidades reales o vacíos de conocimiento.

Este concepto subraya la importancia de la claridad en la definición del problema de investigación. Al formular el problema con precisión, el investigador establece una base sólida que facilita la identificación de objetivos, preguntas y métodos, reduciendo ambigüedades y enfocando el estudio hacia resultados concretos. Esto refuerza la necesidad de dedicar tiempo a la revisión de literatura y al análisis del contexto para garantizar que el problema sea comprensible y relevante.

El título actúa como una "tarjeta de presentación" del proyecto, condensando la esencia de la investigación. Incluir especificidad, espacialidad y temporalidad asegura que el título sea claro y delimite el alcance del estudio. Por ejemplo, un título como "Conocimientos sobre COVID-19 en estudiantes de la UVG, 2022" define claramente qué, dónde y cuándo, ayudando a los lectores a comprender inmediatamente el enfoque y contexto del trabajo.

El modelo SMART es una herramienta clave para garantizar que los objetivos sean prácticos y efectivos. Por ejemplo, un objetivo como "Demostrar los conocimientos de los estudiantes sobre Check4Covid en 2022" es específico (conocimientos), medible (a través de encuestas), alcanzable (dentro del contexto de la UVG), relevante (para la prevención de COVID-19) y temporal (en 2022). Este enfoque refuerza la importancia de diseñar objetivos que guíen la investigación sin desviarse.

Las preguntas auxiliares son esenciales para desglosar el problema en aspectos manejables. Esta pregunta específica guía la investigación hacia la evaluación de la efectividad de una herramienta, promoviendo un análisis crítico de sus fortalezas y limitaciones. Aprender a formular preguntas claras y enfocadas, como esta, ayuda a estructurar la investigación y a mantener el rumbo hacia el objetivo general.

Este punto destaca la importancia de contextualizar el problema para darle relevancia. Describir quiénes se ven afectados y las implicaciones (causas y consecuencias) permite al investigador justificar la pertinencia del estudio y conectar con las necesidades reales de una población o situación. Esto refuerza que un buen enunciado no solo describe el problema, sino que lo sitúa en un marco social, cultural o práctico significativo.

Este principio resalta la importancia de la reproducibilidad en la investigación científica. Detallar los recursos materiales (como software, equipos o documentos) asegura que el estudio sea transparente y verificable. Este aprendizaje refuerza que una investigación bien planificada considera no solo la ejecución, sino también la posibilidad de que otros puedan replicarla para validar los resultados.

La justificación es el "corazón" persuasivo de la investigación, ya que conecta el problema con su relevancia práctica o teórica. Al explicar por qué el estudio es necesario, el investigador no solo motiva su realización, sino que también convence a otros (como financiadores o académicos) de su valor. Este aprendizaje enfatiza la necesidad de alinear el proyecto con necesidades reales o vacíos de conocimiento.

Usar un formato estandarizado como APA asegura que las fuentes sean citadas de manera clara y profesional, facilitando la trazabilidad de la información. Este aprendizaje refuerza la importancia de la integridad académica, ya que citar correctamente no solo da crédito a los autores originales, sino que también permite a otros investigadores acceder a las fuentes para profundizar en el tema.

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

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Reply to the reviewers

1. Response to reviewers

We would like to thank the reviewers for carefully reading our manuscript and for their valuable comments in support for the publication of our investigation of rapid promoter evolution of accessory gland genes between Drosophila species and hybrids. We are glad to read that the reviewers find our work interesting and that it provides valuable insights into the regulation and divergence of genes through their promoters. We are encouraged by their acknowledgement of the overall quality of the work and the importance of our analyses in advancing the understanding of cis-regulatory changes in species divergence.

2. Point-by-point description of the revisions

Reviewer #

Reviewer Comment

Author Response/Revision

Reviewer 1

The authors test the hypothesis that promoters of genes involved in insect accessory glands evolved more rapidly than other genes in the genome. They test this using a number of computational and experimental approaches, looking at different species within the Drosophila melanogaster complex. The authors find an increased amount of sequence divergence in promoters of accessory gland proteins. They show that the expression levels of these proteins are more variable among species than randomly selected proteins. Finally, they show that within interspecific hybrids, each copy of the gene maintains its species-specific expression level.

We thank Reviewer 1 for their detailed review and positive feedback on our manuscript, and for their helpful suggestions. We have now fully addressed the points raised by Reviewer 1 and have provided the suggested clarifications and revisions to improve the flow, readability, and presentation of the data, which we believe have improved the manuscript significantly.

The work is done with expected standards of controls and analyses. The claims are supported by the analysis. My main criticism of the manuscript has to do not with the experiments or conclusion themselves but with the presentation. The manuscript is just not very well written, and following the logic of the arguments and results is challenging.

The problem begins with the Abstract, which is representative of the general problems with the manuscript. The Abstract begins with general statements about the evolution of seminal fluid proteins, but then jumps to accessory glands and hybrids, without clarifying what taxon is being studied, and what hybrids they are talking about. Then, the acronym Acp is introduced without explanation. The last two sentences of the Abstract are very cumbersome and one has to reread them to understand how they link to the beginning of the Abstract.

More generally, if this reviewer is to be seen as an "average reader" of the paper, I really struggled through reading it, and did not understand many of the arguments or rationale until the second read-through, after I had already read the bottom line. The paragraph spanning lines 71-83 is another case in point. It is composed of a series of very strongly worded sentences, almost all starting with a modifier (unexpectedly, interestingly, moreover), and supported by citations, but the logical flow doesn't work. Again, reading the paragraph after I knew where the paper was going was clearer, but on a first read, it was just a list of disjointed statements.

Since most of the citations are from the authors' own work, I suspect they are assuming too much prior understanding on the part of the reader. I am sure that if the authors read through the manuscript again, trying to look through the eyes of an external reader, they will easily be able to improve the flow and readability of the text.

We thank the reviewer for their detailed feedback and are glad that they acknowledge our work fully supports the claims of our manuscript. We also appreciate their helpful suggestions for improving the readability of the manuscript and have done our best to re-write the abstract and main text where indicated. In particular, the paragraph between lines 71-83 have been rewritten and we have taken care to write to non-expert readers.

1) In the analysis of expression level differences, it is not clear what specific stage / tissue the levels taken from the literature refer to. Could it be that the source of the data is from a stage or tissue where seminar fluid proteins will be expressed with higher variability in general (not just inter-specifically) and this could be skewing the results? Please add more information on the original source of the data and provide support for their validity for this type of comparison.

These were taken from publicly available adult male Drosophila datasets, listed in the data availability statement and throughout the manuscript. We have provided more detail on the tissue used for analysis of Acp gene expression levels.

2) The sentence spanning lines 155-157 needs more context.

We have added more context to lines 155-157.

3) Line 203-204: What are multi-choice enhancers?

We replaced the sentence with "... such as rapidly evolving enhancers or nested epistasis enhancer networks"

4) Figure 1: The terminology the authors use, comparing the gene of interest to "Genome" is very confusing. They are not comparing to the entire genome but to all genes in the genome, which is not the same.

We have changed the word "genome" to "all genes in the genome" on the reviewer's suggestion.

5) Figure 2: Changes between X vs. Y is redundant (either changes between X and Y or changes in X vs. Y).

We assume that the reviewer is referring to Fig. 2B, which does not measure changes between X and Y, but changes in distribution between Acps and the control group. We have explained this in the figure legend.

The manuscript addresses a general question in evolutionary biology - do control regions diverge more quickly protein coding regions. The answer is that yes, they do, but this is actually not very surprising. The work is probably thus of more interest to people interested in the copulatory proteins or in the evolution of mating systems, than to people interested in broader evolutionary questions.

We appreciate this reviewer's recognition of the significance of our work and would like to point out that there are very few studies looking at promoter evolution as detailed in the introduction. Of particular relevance, our study using Acp genes allows us to directly test the impact of promoter mutations on the expression by comparing two alleles in male accessory glands of Drosophila hybrids. Male accessory glands consist of only two secretory cell types allowing us to study evolution of gene expression in a single cell type (Acps are either expressed in main cells or secondary cells). Amid this unique experimental set up we can conclude that promoter mutations can act dominant, in contrast to mutations in protein coding regions, which are generally recessive. Thus, our study is unique in pointing out a largely overseen aspect of gene evolution.

Reviewer 2

This manuscript explores promoter evolution of genes encoding seminal fluid proteins expressed in the male accessory gland of Drosophila and finds cis-regulatory changes underlie expression differences between species. Although these genes evolve rapidly it appears that the coding regions rarely show signs of positive selection inferring that changes in their expression and hence promoter sequences can underlie the evolution of their roles within and among species.

We thank Reviewer 2 for their thorough review, positive feedback on the importance of our work, and suggestions for improving the manuscript. We have addressed all points raised by the reviewer, including analysis of Acp coding region evolution, additional analyses of hybrid expression data, and improved the clarity of the text.

Figure 1 illustrates evidence that the promoter regions of these gene have accumulated more changes than other sampled genes from the Drosophila genome. While this convinces that the region upstream of the transcription start site has diverged considerably in sequence (grey line compared to black line), Figure 1A also suggests the "Genespan" region which includes the 5'UTR but presumably also part of the coding region is also highly diverged. It would be useful to see how the pattern extends into the coding region further to compare further to the promoter region (although Fig 1H does illustrate this more convincingly).

The reviewer raises an interesting point, and certainly all parts of genes evolve. Fig. 1A shows the evolutionary rates of Acps compared to the genome average from phyloP27way scores calculated from 27 insect species. Since these species are quite distant it is unsurprising that they show divergence in coding regions as well as promoter regions. In fact, we addressed whether promoter regions evolve fast in closely related Drosophila species in Fig. 1H compared to coding regions. We have included an additional analysis of coding region evolution in Figure 1B.

Figure 2 presents evidence for significant changes in (presumably levels of) expression of male accessory gland protein (AcP) genes and ribosomal proteins genes between pairs of species, which is reflected in the skew of expression compared to randomly selected genes.

Correct, we have rephrased the statement for clarity.

Figure 3 shows detailed analysis for 3 selected AcP genes with significantly diverged expression. The authors claim this shows 'substitution' hotspots in the promoter regions of all 3 genes but this could be better illustrated by extending the plots in B-D further upstream and downstream to compare to these regions.

We picked the 300-nucleotide promoter region for this analysis as it accumulated significant changes as shown in Fig. 1E-H, and extending the G plots (Fig. 3B-D) to regions with lower numbers of sequence changes would not substantially change the conclusion. Specifically, this analysis identifies sequence change hotspots within fast-evolving promoter regions, rather than comparing promoter regions to other genomic regions, as we previously addressed. The plot is based on a cumulative distribution function and the significant positive slope in the upstream region where promoters are located identifies a hotspot for accumulation of substitutions. There could be other hotspots, but the point being made is that significant hotspots consistently appear in the promoter region of these three genes.

Figure 4 shows the results of expression analysis in parental lines of each pair of species and F1 hybrids. However the results are very difficult to follow in the figure and in the relevant text. While the schemes in A, C. E and G are helpful, the gel images are not the best quality and interpretations confusing. An additional scheme is needed to illustrate hypothetical outcomes of trans change, cis change and transvection to help interpret the gels. On line 169 (presumably referring to panels D and F although C and D are cited on the next line) the authors claim that Obp56f and CG11598 'were more expressed in D. melanogaster compared to D. simulans' but in the gel image the D. sim band is stronger for both genes (like D. sechellia) compared to the D. mel band. The authors also claim that the patterns of expression seen in the F1s are dominant for one allele and that this must be because of transvection. I agree this experiment is evidence for cis-regulatory change. However the interpretation that it is caused by transvection needs more explanation/justification and how do the authors rule out that it is not a cis X trans interaction between the species promoter differences and differences in the transcription factors of each species in the F1? Also my understanding is that transvection is relatively rare and yet the authors claim this is the explanation for 2/4 genes tested.

We appreciate the reviewer's comments on Figure 4 and the opportunity to improve its clarity. To address these concerns, we have carefully checked the figure citations and corrected any inconsistencies.

The reviewer raises an important point about our interpretation of transvection. We have expanded our discussion of this result to consider why transvection is a plausible explanation for the observed dominance patterns and also consider cis x trans interactions between species-specific promoters and transcription factor binding. While rare, transvection likely has more relevance in hybrid regulatory contexts involving homologous chromosome pairing which we discuss this in the revised text.

Line 112 states that the melanogaster subgroup contains 5 species - this is incorrect - while this study looked at 5 species there are more species in this subgroup such as mauritiana and santomea.

We have corrected the statement about the number of species in the melanogaster subgroup.

Lines 131-134 could explain better what the conservation scores and their groupings mean and the rationale for this approach.

We have clarified what the conservation scores and their groupings mean and the rationale for this approach.

Line 162 - the meaning of the sentence starting on this line is unclear - it sounds very circular.

We have rephrased the statement for more clarity.

Line 168 should cite Fig 4 H instead of F.

We have amended citation of Fig 4F to H.

Reviewer 3

In this study, McQuarrie et al. investigate the evolution of promoters of genes encoding accessory gland proteins (Acps) in species within the D. melanogaster subgroup. Using computational analyses and available genomic and transcriptomic datasets, they demonstrate that promoter regions of Acp genes are highly diverse compared to the promoters of other genes in the genome. They further show that this diversification correlates with changes in gene expression levels between closely related species. Complementing these computational analyses, the authors conduct experiments to test whether differences in expression levels of four Acp genes with highly diverged promoter regions are maintained in hybrids of closely related species. They find that while two Acp genes maintain their expression level differences in hybrids, the other two exhibit dominance of one allele. The authors attribute these findings to transvection. Based on their data, they conclude that rapid evolution of Acp gene promoters, rather than changes in trans, drives changes in Acp gene expression that contribute to speciation.

We thank Reviewer 3 for their thorough review and suggestions. We further thank the reviewer for acknowledging the importance of our findings and for pointing out that it contributes to our understanding of speciation. We have thoroughly addressed all comments from the reviewer and significantly revised the manuscript. We believe that this has greatly improved the manuscript.

Unfortunately, the presented data are not sufficient to fully support the conclusions. While many of the concerns can be addressed by revising the text to moderate the claims and acknowledge the methodological limitations, some key experiments require repetition with more controls, biological replicates, and statistical analyses to validate the findings.

Specifically, some of the main conclusions heavily rely on the RT-PCR experiments presented in Figure 4, which analyze the expression of four Acp genes in hybrid flies. The authors use PCR and RFLP to distinguish species-specific alleles but draw quantitative conclusions from what is essentially a qualitative experiment. There are several issues with this approach. First, the experiment includes only two biological replicates per sample, which is inadequate for robust statistical analysis. Second, the authors did not measure the intensity of the gel fragments, making it impossible to quantify allele-specific expression accurately. Third, no control genes were used as standards to ensure the comparability of samples.

The gold standard for quantifying allele-specific expression is using real-time PCR methods such as TaqMan assays, which allow precise SNP genotyping. To address this major limitation, the authors should ideally repeat the experiments using allele-specific real-time PCR assays. This would provide a reliable and quantitative measurement of allele-specific expression.

If the authors cannot implement real-time PCR, an alternative (though less rigorous) approach would be to continue using their current method with the following adjustments:

• Include a housekeeping gene in the analysis as an internal control (this would require identifying a region distinguishable by RFLP in the control).

• Quantify the intensity of the PCR products on the gel relative to the internal standard, ensuring proper normalization.

• Increase the sample size to allow for robust statistical analysis.

These experiments could be conducted relatively quickly and would significantly enhance the validity of the study's conclusions.

We thank the reviewer for their detailed suggestions for improving the conclusions in Fig. 4. Indeed, incorporating a housekeeping gene as a control supports our results for qualitative analysis of gene expression in hybrids assessing each allele individually (Fig 4), and improves interpretation for non-experts. We have also quantified differential gene expression in hybrids between species alleles and the log2 fold change from D. melanogaster. In addition, we have included an additional analysis in the new Fig. 5 which analyses RNA-seq expression changes in D. melanogaster x D. simulans hybrid male accessory glands. We believe these additions have significantly improved the manuscript and its conclusions.

While the following comments are not necessarily minor, they can be addressed through revisions to the text without requiring additional experimental work. Some comments are more conceptual in nature, while others concern the interpretation and presentation of the experimental results. They are provided in no particular order.

1. A key limitation of this study is the use of RNA-seq datasets from whole adult flies for interspecies gene expression comparisons. Whole-body RNA-seq inherently averages gene expression across all tissues, potentially masking tissue-specific expression differences. While Acp genes are likely restricted to accessory glands, the non-Acp genes and the random gene sets used in the analysis may have broader expression profiles. As a result, their expression might be conserved in certain tissues while diverging in others- an aspect that whole-body RNA-seq cannot capture. The authors should acknowledge that tissue-specific RNA-seq analyses could provide a more precise understanding of expression divergence and potentially reveal reduced conservation when considering specific tissues independently.

We have added a section discussing the limitations in gene expression analysis in the discussion. In addition, we have included an additional Figure analysing gene expression in hybrid male accessory glands (Fig. 5).

1. The statement in line 128, "Consistent with this model," does not accurately reflect the findings presented in Figures 2A and B. Specifically, the data in Figure 2A show that Acp gene expression divergence is significantly different from the divergence of non-Acp genes or a random sample only in the comparison between D. melanogaster and D. simulans. However, when these species are compared to D. yakuba, Acp gene expression divergence aligns with the divergence patterns of non-Acp genes or random samples. In contrast, Figure 2B shows that the distribution of expression changes is skewed for Acp genes compared to random control samples when D. melanogaster or D. simulans are compared to D. yakuba. However, this skew is absent when the two D. melanogaster and D. simulans are compared. Therefore, the statement in line 128 should be revised to accurately reflect these nuanced results and the trends shown in Figure 2A and B.

We have updated the statement for clarity. Here, the percentage of Acps showing significant gene expression changes is greater between more closely related species, but the distribution of expression changes increases between more distantly related species.

1. The statement in lines 136-138, "Acps were enriched for significant expression changes in the faster evolving group across all species," while accurate, overlooks a key observation. This trend was also observed in other groups, including those with slower evolving promoters, in some of the species' comparisons. Therefore, the enrichment is not unique to Acps with rapidly evolving promoters, and this should be explicitly acknowledged in the text.

This is a valid point, and we have updated this statement as suggested.

1. It would be helpful for the authors to explain the meaning of the d score at the beginning of the paragraph starting in line 131, to ensure clarity for readers unfamiliar with this metric.

This scoring method is described in the methods sections, and we have now included reference to thorough explanation of how d was calculated at the indicated section.

1. In Figure 2C-E - the title of the Y-axis does not match the text. If it represents the percentage of genes with significant expression changes, as in Figure 2A, the discrepancies between the percentages in this figure and those in Figure 2A need to be addressed.

We have updated the method used to categorise significant changes in gene expression in the text and the figure legend for clarity.

1. The experiment in Figure 3 needs a better explanation in the text. What is the analysis presented in Figure 3B-D. How many species were compared?

We have added additional details in the results section and an explanation of how sequence change hotspots were calculated in the results section is available.

1. The concept of transvection should be omitted from this manuscript. First, the definition provided by the authors is inaccurate. Second, even if additional experiments were to convincingly show that one allele in hybrid animals is dominant over the other, there are alternative explanations for this phenomenon that do not involve transvection. The authors may propose transvection as a potential model in the discussion, but they should do so cautiously and explicitly acknowledge the possibility of other mechanisms.

We have updated the text to more conservatively discuss transvection, moving this to the discussion section with additional possibilities discussed.

1. The statement at the end of the introduction is overly strong and would benefit from more cautious phrasing. For instance, it could be reworded as: "These findings suggest that promoter changes, rather than genomic background, play a significant role in driving expression changes, indicating that promoter evolution may contribute to the rise of new species."

We have reworded this line following the reviewer's suggestion.

1. Line 32 of the abstract: The term "Acp" is introduced without explaining what it stands for. Please define it as "Accessory gland proteins (Acp)" when it first appears.

We have updated the manuscript to define Acp where it is first mentioned.

1. Line 61: The phrase "...through relaxed,..." is unclear. Specify what is relaxed (e.g., "relaxed selective pressures").

We have included description of relaxed selective pressures.

1. The sentence in lines 74-76, starting in "Interestingly,...." Needs revision for clarity.

We have removed the word interestingly.

1. Line 112: Revise "we focused on the melanogaster subgroup which is made up of five species" to: "we focused on the melanogaster subgroup, which includes five species."

We have made this change in the text.

1. In line 144 use the phrase "promoter conservation" instead of "promoter evolution"

We have updated the phrasing.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this study, McQuarrie et al. investigate the evolution of promoters of genes encoding accessory gland proteins (Acps) in species within the D. melanogaster subgroup. Using computational analyses and available genomic and transcriptomic datasets, they demonstrate that promoter regions of Acp genes are highly diverse compared to the promoters of other genes in the genome. They further show that this diversification correlates with changes in gene expression levels between closely related species. Complementing these computational analyses, the authors conduct experiments to test whether differences in expression levels of four Acp genes with highly diverged promoter regions are maintained in hybrids of closely related species. They find that while two Acp genes maintain their expression level differences in hybrids, the other two exhibit dominance of one allele. The authors attribute these findings to transvection. Based on their data, they conclude that rapid evolution of Acp gene promoters, rather than changes in trans, drives changes in Acp gene expression that contribute to speciation.

Major comments:

Unfortunately, the presented data are not sufficient to fully support the conclusions. While many of the concerns can be addressed by revising the text to moderate the claims and acknowledge the methodological limitations, some key experiments require repetition with more controls, biological replicates, and statistical analyses to validate the findings.

Specifically, some of the main conclusions heavily rely on the RT-PCR experiments presented in Figure 4, which analyze the expression of four Acp genes in hybrid flies. The authors use PCR and RFLP to distinguish species-specific alleles but draw quantitative conclusions from what is essentially a qualitative experiment. There are several issues with this approach. First, the experiment includes only two biological replicates per sample, which is inadequate for robust statistical analysis. Second, the authors did not measure the intensity of the gel fragments, making it impossible to quantify allele-specific expression accurately. Third, no control genes were used as standards to ensure the comparability of samples.

The gold standard for quantifying allele-specific expression is using real-time PCR methods such as TaqMan assays, which allow precise SNP genotyping. To address this major limitation, the authors should ideally repeat the experiments using allele-specific real-time PCR assays. This would provide a reliable and quantitative measurement of allele-specific expression.

If the authors cannot implement real-time PCR, an alternative (though less rigorous) approach would be to continue using their current method with the following adjustments:

• Include a housekeeping gene in the analysis as an internal control (this would require identifying a region distinguishable by RFLP in the control).
• Quantify the intensity of the PCR products on the gel relative to the internal standard, ensuring proper normalization.
• Increase the sample size to allow for robust statistical analysis. These experiments could be conducted relatively quickly and would significantly enhance the validity of the study's conclusions.

Minor comments

While the following comments are not necessarily minor, they can be addressed through revisions to the text without requiring additional experimental work. Some comments are more conceptual in nature, while others concern the interpretation and presentation of the experimental results. They are provided in no particular order. 1. A key limitation of this study is the use of RNA-seq datasets from whole adult flies for interspecies gene expression comparisons. Whole-body RNA-seq inherently averages gene expression across all tissues, potentially masking tissue-specific expression differences. While Acp genes are likely restricted to accessory glands, the non-Acp genes and the random gene sets used in the analysis may have broader expression profiles. As a result, their expression might be conserved in certain tissues while diverging in others- an aspect that whole-body RNA-seq cannot capture. The authors should acknowledge that tissue-specific RNA-seq analyses could provide a more precise understanding of expression divergence and potentially reveal reduced conservation when considering specific tissues independently. 2. The statement in line 128, "Consistent with this model," does not accurately reflect the findings presented in Figures 2A and B. Specifically, the data in Figure 2A show that Acp gene expression divergence is significantly different from the divergence of non-Acp genes or a random sample only in the comparison between D. melanogaster and D. simulans. However, when these species are compared to D. yakuba, Acp gene expression divergence aligns with the divergence patterns of non-Acp genes or random samples. In contrast, Figure 2B shows that the distribution of expression changes is skewed for Acp genes compared to random control samples when D. melanogaster or D. simulans are compared to D. yakuba. However, this skew is absent when the two D. melanogaster and D. simulans are compared. Therefore, the statement in line 128 should be revised to accurately reflect these nuanced results and the trends shown in Figure 2A and B. 3. The statement in lines 136-138, "Acps were enriched for significant expression changes in the faster evolving group across all species," while accurate, overlooks a key observation. This trend was also observed in other groups, including those with slower evolving promoters, in some of the species' comparisons. Therefore, the enrichment is not unique to Acps with rapidly evolving promoters, and this should be explicitly acknowledged in the text. 4. It would be helpful for the authors to explain the meaning of the d score at the beginning of the paragraph starting in line 131, to ensure clarity for readers unfamiliar with this metric. 5. In Figure 2C-E - the title of the Y-axis does not match the text. If it represents the percentage of genes with significant expression changes, as in Figure 2A, the discrepancies between the percentages in this figure and those in Figure 2A need to be addressed. 6. The experiment in Figure 3 needs a better explanation in the text. What is the analysis presented in Figure 3B-D. How many species were compared? 7. The concept of transvection should be omitted from this manuscript. First, the definition provided by the authors is inaccurate. Second, even if additional experiments were to convincingly show that one allele in hybrid animals is dominant over the other, there are alternative explanations for this phenomenon that do not involve transvection. The authors may propose transvection as a potential model in the discussion, but they should do so cautiously and explicitly acknowledge the possibility of other mechanisms. 8. The statement at the end of the introduction is overly strong and would benefit from more cautious phrasing. For instance, it could be reworded as: "These findings suggest that promoter changes, rather than genomic background, play a significant role in driving expression changes, indicating that promoter evolution may contribute to the rise of new species."

Text edits:

Throughout the manuscripts there are incomplete sentences and sentences that are not clear. Below is a list of corrections:

1. Line 32 of the abstract: The term "Acp" is introduced without explaining what it stands for. Please define it as "Accessory gland proteins (Acp)" when it first appears.
2. Line 61: The phrase "...through relaxed,..." is unclear. Specify what is relaxed (e.g., "relaxed selective pressures").
3. The sentence in lines 74-76, starting in "Interestingly,...." Needs revision for clarity.
4. Line 112: Revise "we focused on the melanogaster subgroup which is made up of five species" to: "we focused on the melanogaster subgroup, which includes five species."
5. In line 144 use the phrase "promoter conservation" instead of "promoter evolution"

Significance

This study addresses an important question in evolutionary biology: how seminal fluid proteins achieve rapid evolution despite showing limited adaptive changes in their coding regions. By focusing on accessory gland proteins (Acps) and examining their promoter regions, the authors suggest promoter-driven evolution as a potential mechanism for rapid seminal fluid protein diversification. While this hypothesis is intriguing and can contribute to our understanding of speciation, more rigorous analysis and experimental validation would be needed to support the conclusions. The revised manuscript can be of interest to fly geneticists and to scientists in the fields of gene regulation and evolution.

Keywords for my expertise: Enhancers, transcriptional regulation, development, evolution, Drosophila.

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Referee #2

Evidence, reproducibility and clarity

Summary

This manuscript explores promoter evolution of genes encoding seminal fluid proteins expressed in the male accessory gland of Drosophila and finds cis-regulatory changes underlie expression differences between species. Although these genes evolve rapidly it appears that the coding regions rarely show signs of positive selection inferring that changes in their expression and hence promoter sequences can underlie the evolution of their roles within and among species.

Major comments

Figure 1 illustrates evidence that the promoter regions of these gene have accumulated more changes than other sampled genes from the Drosophila genome. While this convinces that the region upstream of the transcription start site has diverged considerably in sequence (grey line compared to black line), Figure 1A also suggests the "Genespan" region which includes the 5'UTR but presumably also part of the coding region is also highly diverged. It would be useful to see how the pattern extends into the coding region further to compare further to the promoter region (although Fig 1H does illustrate this more convincingly).

Figure 2 presents evidence for significant changes in (presumably levels of) expression of male accessory gland protein (AcP) genes and ribosomal proteins genes between pairs of species, which is reflected in the skew of expression compared to randomly selected genes.

Figure 3 shows detailed analysis for 3 selected AcP genes with significantly diverged expression. The authors claim this shows 'substitution' hotspots in the promoter regions of all 3 genes but this could be better illustrated by extending the plots in B-D further upstream and downstream to compare to these regions.

Figure 4 shows the results of expression analysis in parental lines of each pair of species and F1 hybrids. However the results are very difficult to follow in the figure and in the relevant text. While the schemes in A, C. E and G are helpful, the gel images are not the best quality and interpretations confusing. An additional scheme is needed to illustrate hypothetical outcomes of trans change, cis change and transvection to help interpret the gels. On line 169 (presumably referring to panels D and F although C and D are cited on the next line) the authors claim that Obp56f and CG11598 'were more expressed in D. melanogaster compared to D. simulans' but in the gel image the D. sim band is stronger for both genes (like D. sechellia) compared to the D. mel band. The authors also claim that the patterns of expression seen in the F1s are dominant for one allele and that this must be because of transvection. I agree this experiment is evidence for cis-regulatory change. However the interpretation that it is caused by transvection needs more explanation/justification and how do the authors rule out that it is not a cis X trans interaction between the species promoter differences and differences in the transcription factors of each species in the F1? Also my understanding is that transvection is relatively rare and yet the authors claim this is the explanation for 2/4 genes tested.

Minor comments

Line 112 states that the melanogaster subgroup contains 5 species - this is incorrect - while this study looked at 5 species there are more species in this subgroup such as mauritiana and santomea.

Lines 131-134 could explain better what the conservation scores and their groupings mean and the rationale for this approach.

Line 162 - the meaning of the sentence starting on this line is unclear - it sounds very circular.

Line 168 should cite Fig 4 H instead of F.

Significance

This paper is generally well written although some sections would benefit from more explanation. The paper demonstrates cis-regulatory changes between the promoters of orthologs of male accessory gland genes underlie expression differences but that the species differences are not always reflected in hybrids, which the authors interpret as being caused by transvection although there could be other explanations. Overall this provides new insights into the regulation and divergence of these interesting genes. The paper does not explore the consequences of these changes in gene expression although this is discussed to some extent in the Discussion section.

Ubicación

où sont les nav demandés dans la branche P3C1-solution?

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Reply to the reviewers

Below is a point-by-point response to reviewers concerns.

Main changes are colored in red in the revised manuscript.

Reviewer #1 (Significance (Required)):

General assessment:

This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

Audience: broad interests; including DNA replication, 3D genome structure, and basic research

Expertise: DNA replication and DNA damage repair within the chromatin environment.

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

By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks.

This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation.

We thank Reviewer 1 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

However, the following revisions could strengthen the manuscript:

Major:

Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.

We agree with the reviewer that testing the model in higher eukaryotes would be highly informative. The availability of Repli-HiC on one hand and higher resolution microscopy on the other could enable insightful quantitative analyses. With our formalism, it is in principle already possible to capture realistic 1D replication dynamics as the integrated mathematical formalism (by Arbona et al. ref. [63]) was already used to model human genome S-phase. In addition, the formalism developed for chain duplication is generic and can be contextualized to any species. However, when addressing the problem in 3D, we would likely require including other crucial structural features such as TADs or compartments. Such a model would require an extensive characterization worthy of its own publication. These considerations are now mentioned in the Discussion as exciting future perspectives (Page 17).

On the other hand, we would like to highlight that, while very minimal in many aspects, our model includes many layers of complexity (explicit replication, different forks interactions, stochastic 1D replication dynamics, physical constraints at the nuclear level). In addition, addressing this problem in budding yeast offers the great advantage of simultaneously capturing at the same time both the local and global spatio-temporal properties of DNA replication and to focus first only on those aspects and not on the interplay with other mechanisms like A/B compartmentalization (absent in yeast) that may add confusions in the data analysis and comparison with experimental data . Studying such an interplay is a very important and challenging question that, we believe, goes beyond the scope of the present work.

Validation with Repli-HiC or Time-Resolved Techniques

The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.

We thank the reviewer for the suggestion. Unfortunately, corroborating our HiC results using Repli-HiC or HiChIP would require developing and adapting the protocols to budding yeast which is well beyond the scope of this work mainly focused on computational modelling. In addition, we believe that the signature found in our Hi-C data is clear and significant enough to demonstrate the effect.

However, we included in the Discussion (Page 15) a more detailed description on how our work compares with the Repli-HiC study in mammals. In particular, we added a new supplementary figure (new Fig. S23) where we discuss our prediction on how Repli-HiC maps would appear in yeast in both scenarios of sister-forks interaction. Interestingly, we find that:

1) Fountain signals are strongly enhanced when sister forks interact.

2) Only mild replication dependent enrichment is detected when diverging forks do not interact.

These two results imply that disrupting putative sister-forks interaction would have a drastic effect on Repli-HiC if compared to HiC.

Interactions Between Convergent Forks

The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?

We thank the reviewer for this suggestion. We included in our Hi-C analysis aggregate plots around termination sites. Interestingly, no clear signature of coupling between convergent forks was detected (such as type II fountains in mammals) in vivo and in silico. Similarly, from visual inspection of individual termination sites, no fountains were clearly observed. These results can be found in the new Fig. S24 and possible mechanistic explanations are described more in detail in the Discussion (Page 15).

Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts.

In Fig.3A, a schematic illustrating the cell treatment would improve clarity. In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include:

Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks).

Altered chromatin mobility in mutants, enhancing Hi-C signal resolution.

Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included).

A scheme illustrating the experimental protocol for degron systems (CDC45-miniAID & SCC1-V5-AID) with the corresponding western blots and cell-cycle progression are shown in Fig. S26. Note that for Ctf4, we are using a KO cell line where the gene was deleted.

We do agree with the reviewer that there exist several possible explanations explaining the differences between WT fountains and those observed in mutants. In the revised manuscript, we discussed some of them in Section 2 II B (Page 8):

(1) As already suggested in the paper, asynchronization of cells may impact the intensity of the fountains due a dilution effect mediated by the cells still in G1. Therefore, possible differences in the fractions of replicating/non-relicating cells between the different experiments (new Fig. S7C) would also result in differences in the signal. Moreover, it is important to highlight that aggregate plots are normalized (Observed/Expected) by the average signal (P(s)). Therefore, as Scc1-depleted cells do not exhibit cohesin-mediated loop-extrusion (see aggregate plots around CARs in new Fig. S7B), we may expect an enhancement of signal at origins due to dividing each pixel by a lower contact frequency with respect to the one found in WT.

(2) In the new Fig. S10, we plotted the relative enrichment of Hi-C reads around origins. While we already used the same approach to compare replicon sizes between simulations and experiments (see Fig S7A and response to comment n°9 of Reviewer 3), this analysis is instructive also when comparing different experimental conditions. While we find that the experiment in WT and Scc1-depleted cells show very similar replicon sizes, we do observe a small increase in the peak height for the cohesin mutant. This may also partially motivate differences in the intensity of the fountain. For ctf4Δ, we observe significantly smaller replicons. We speculate that such a mutant might exhibit slower replication and consequently might be enriched in sister-forks contacts.

(3) Compensatory mechanisms: we now briefly discussed this in the Discussion (Page 15).

Inconsistent Figure References

Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

We thank the reviewer for this observation. We have now corrected the mismatches.

Minor:

Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.

Loops of approx 20-40 kb are found in interphase in budding yeast but only after the onset of S-phase ( ref. [52-61]). For this reason, our G1 model of yeast without loops well captures the experimental P(s) curves (Fig. S2). See also answer to point 12 of reviewer 2 .

In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

The initial use of aggregate plots was motivated by the fact that fountains are quite difficult to observe at the single origin level in the experimental Hi-C due to the strong intensity of surrounding contacts (along the diagonal). However, when dividing early-S phase maps by the corresponding G1 map, we can now observe clear correlation between origin and fountain positions on such normalized maps. We now added an example for chromosome 7 in Fig.3 indicating early/late origins.

In Fig. S8 and S9 (where we also included termination sites), we show that fountains are prominently found at origins during S-phase and are lost in G2/M.

Reviewer #2 (Significance (Required)):

The topic is relevant and the problem being addressed is very interesting. While there has been some earlier work in this area, the polymer simulation approach used here is novel. The simulation methodology is technically sound and appropriate for the problem. Results are novel. The authors compare their simulations with experimental data and explore both interacting and non-interacting replication forks. Most conclusions are supported by the data presented. Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The manuscript by D'Asaro et al. investigates the relationship between DNA replication and chromatin organization using polymer simulations. While this is primarily a simulation-based study, the authors also present relevant comparisons with experimental data and explore mechanistic aspects of replication fork interactions.

We thank Reviewer 2 for her/his positive evaluation of our work and her/his suggestions that help us to clarify many aspects in our manuscript.

The primary weakness is that many aspects are not clear from the manuscript. Below is a list of questions that the authors must clarify:

In the Model and Methods section, it is written "Arbitrarily, we choose the backbone to be divided into two equally long arms, in random directions." It is unclear what is meant by "backbone to be divided" and "two equally long arms." Does this refer to replication?

We agree with the reviewer that the term backbone may be ambiguous. In the context of the initialization of the polymer, it refers to the L/4 initial bonds used to recursively build an unknotted polymer chain of final size L using the Hedgehog algorithm (see refs [101,109]). As shown in the Fig S1A, these initial L/4 bonds define the initial backbone of each chromosome before they are recursively grown to their final size. We chose to divide them into two branches (called “arms” in the old version of the manuscript) of equal length (L/8) and with random orientations. To avoid any ambiguity between the term arm used in that context and the chromosome arms in a biological sense (sequences on the left and right with respect to centromeres), we changed it to “linear branches” to improve clarity. We highlighted in Fig. S1A two examples of such a “V-shaped” backbone.

As stated in the text, these initial configurations are artificial and just aim to generate unknotted, random structures. After initiating the structures, we then added the geometrical constraints to the centromeric, telomeric and rDNA beads. This, combined with the tendency of the polymer to explore and fill the spherical volume, determine the relaxed G1-like state (see Fig. S2) obtained after an equilibration stage (corresponding to 10^7 MCS). Only after that initialization protocol, DNA replication is activated.

In chromosome 12, since the length inside the nucleolus (rDNA) is finite, the entry and exit points should be constrained. Have the authors applied any relevant constraint in the model?

Indeed, we did not introduce any specific constraint on the relative distance between rDNA boundary monomers in our model. They can therefore freely diffuse, independently from each other, on the nucleolus surface. This point is now clarified in the text. Note that, in this paper, we did not aim to finely describe the rDNA organization and its interactions with the rest of the genome, that is why we did not explicitly model rDNA. Moreover, to the best of our knowledge, there is not available experimental data to potentially tune such additional restraints.

Previous models such as Tjong et al. (ref. [66]) and Di Stefano et al. (ref [67]) have used very similar approximations than us. In the works of Wong et al. (ref.[61]) and Arbona et al. (ref.[63]), rDNA is explicitly modelled via larger/thicker beads/segments, and thus accounts for some generic polymer-based constraints between rDNA boundary elements.

However, note that all these different models, including ours, still correctly predict the strong depletion of contacts between rDNA boundaries, indicating that there exists a spatial separation between the two boundary elements that is qualitatively well captured by our model (See Fig. S1 D and Fig. 1B).

What is the rationale for normalizing the experimental and simulation results by dividing by the respective P_intra(s = 10 kb)?

This normalization was used in Fig. 1 to obtain a rescaling between experiments and simulations. This approach assumes that simulated and experimental Hi-C maps are proportional by a factor that, in Fig 1B, was set to P_exp(s=16kb)/P_sim(s=16kb). Similar strategies are used in a number of modeling studies (for example ref. [103,106]).

We use the average contact frequency (P_intra) at this genomic scale (s in the order of 10s of kb) because our polymer simulations well capture the experimental P(s) decay above this scale. This method allows to plot the two signals with the same color scale and to give a qualitative, visual intuition on the quality of the modeling. Note that normalization has no impact on the Pearson correlation given in text. More generally, it allows to semi-quantitatively compare predicted and experimental Hi-C data.

In Fig 1D, we instead normalize the average signal between pairs of centromeres (inter-chromosomal aggregate plot off-diagonal) by the average P_intra(s=10kb). This method allows estimating how frequently centromeres of different chromosomes are in contact relative to intra-chromosomal contacts at the chosen scale (10 kb). In the new paragraph “Comparison with in vivo HiC maps in G1” (Page 22) , we describe more in detail the quantitative insights that can be recovered from such analysis.

As a comparison, such normalization is not required when computing Observed/Expected maps (Fig. 1C or aggregate plots in Fig. 2 and Fig. 3) as simulation and experimental maps are normalized by their own P(s) curves. We now clarify this aspect in the Materials in Methods under the paragraph “Comparison between on diagonal aggregate plots” (Page 22).

In the sentence "For instance, chromosomes are strictly bound by the strong potential to localize between 250 and 320 nm from the SPB," is it 320 or 325 nm? Is there a typo?

We confirm that the upper bound is indeed 325 nm as stated in Eq.2 and not 320 nm.

Please list the number of beads in each chromosome and the location of the centromere beads.

A new table (Table S2) was included to highlight beads number and centromere positions.

In Eq. 7, when the Euclidean distance between the sister forks d_ij > 50 nm, the energy becomes more and more negative. This implies that the preferred state of sister forks is at distances much greater than 50 nm. Then how is "co-localization of sister forks" maintained?

We corrected the typo sign in Eq.7. The corrected equation without the minus sign - consistently with what simulated - implies that sister forks tend to minimize their 3D distance. The term goes to zero when their distance is within 40 nm (2 nearest-neighbouring sites).

The section on "non-specific fork interactions" is unclear. You state that the interaction is between "all the replication forks in the system," but f_ij is non-zero only for second nearest-neighbors. The whole subsection needs clarification.

We corrected the text, specifying that the energy is non-zero for both first and second neighbours. In practice, two given forks do not experience any attractive energy unless their 3D distance is less than 2 nearest-neighbours. To clarify this aspect, we articulated more in the methods how non-specific fork interactions are implemented in the lattice during the KMC algorithm. We also included a new supplementary image (Fig. S15), where we schematize how forks move in 3D and how changes in their position update the table that tracks the number of forks around each lattice site.

Eq. 6 has no H_{sister-forks}. Is this a typo?

We confirm that it is a typo and the formula was corrected to H_{sister-forks}.

While discussing the published work, the authors may cite the recent paper [https://doi.org/10.1103/PhysRevE.111.054413].

The reference is now included when discussing previous polymer models of DNA replication.

It is not clear how the authors actually increase the length of new DNA in a time-dependent manner. For example, when a new monomer is added near the replication origin (green bead in Fig. 3C), what happens to the red and blue polymer segments? Do they get shifted? How do the authors take into account self-avoidance while adding a new monomer? These details are not clear.

The detailed description of the chain duplication algorithm and its systematic analysis was performed in our previous study (ref. [25]).

However, we agree with the reviewer that to improve self-consistency more details must be included in the present manuscript (see also answer to comment 1 of Reviewer 3). In particular, we now highlight in Materials and Methods that self-avoidance is indeed temporarily broken when we add a newly replicated monomer on top of the site where the fork is. Such double occupancy in the lattice rapidly vanishes due to 3D local moves. We refer to our PRX work (ref [25] and in particular to the following figure (extracted from FIG. S1 in ref.[25]) which illustrates how the bonds/segments of the two sister chromatids are consistently maintained.

How do the authors ensure that monomers get added at a rate corresponding to velocity v? The manuscript mentions "1 MCS = 0.075 msec," but in how many MC steps is a new monomer added? How is it decided?

Similarly to origin firing, replication by fork movement along the genome occurs stochastically, with a rate which we derive by converting the physiological fork speed in yeast 2.2 kb/min (ref. [41]) into a rate in (number of monomer/MCS) units. In practice, we generate a random number that, if smaller than such a rate, leads to forks duplication. We clarify this aspect in the Materials and Methods, also referring to our previous work for a more detailed summary.

The authors stress the relevance of loop extrusion. However, in their polymer simulation, the newly replicated chromatin does not form any loops. Is this consistent with what is known?

Indeed, our simulations do not have any concurrent extrusion mechanism such as cohesin-mediated loops. This choice was purposely made to isolate and characterize replication-dependent effects.

That is why we compare our predictions on chromatin fountain patterns (Fig. 3) with data obtained for the Scc1 mutant strain where cohesin is absent in order to disentangle the possible interference with loop-extruding cohesin. For subsection C where microscopy data are available only in WT condition, we cannot rule out that the observed discrepancies between experiments and predictions cannot be due to missing mechanisms including loop extrusion. It was already mentioned in the Discussion (Page 16). It is however unclear whether sparse and small loops between CARs (see Fig. S7B) in S-phase, could be sufficient to recapitulate the microscopy estimates on the sizes of replication foci and no clear signature of inter-origin loops (possibly mediated by loop extrusion) are observed in Hi-C data in WT and Scc1 deficient conditions.

Moreover, as mentioned in the Discussion, the poorly characterized mechanisms behind forks/extruding-cohesin encounters does not allow for a straightforward modelling of such processes whose accurate description/simulation would require its own study.

Please add a color bar to Fig. 4B.

The color bar was included.

In the MSD plot (Fig. 6), even though it appears to be a log-log plot, the exponents are not computed. Typically, exponents define the dynamics.

We plot the expected 0.5 exponent at smaller time-scales as mentioned in the main text in Fig. 6, previously included only in new Fig. S19A.

The dynamics will depend on the precise nature of interactions, such as the presence or absence of loop extrusion. If the authors present dynamics without extrusion, is it likely to be correct?

The reviewer is correct in highlighting how our model does not capture the potential decrease in dynamics due to cohesin mediated loop extrusion. However, our model does capture the expected Rouse regime (see Fig. 6A, S19A and ref [83]), which justify our timemapping strategy. In comment 16 of reviewer 3, we discuss more in detail the robustness of our results with respect to variation in such a mapping. In the specific context of Fig. 6A, we predict the gradual decrease in dynamics due to sister chromatids intertwining independently of any cohesin-associated activity (both loop-extruding and cohesive). As loop extrusion is also decreasing chromatin mobility overall (ref. [87]), if such a decrease in mobility is observed in WT in vivo, it may be indeed difficult to assign such a decrease to replication rather than loop extrusion. That is why in the Discussion (Page 16), we propose to compare our prediction to experiments in cohesin-depleted cells. In the context of Fig.6B&C, we don’t expect loop extrusion to be a confounding effect as the predicted decrease in dynamics is specific to forks.

Reviewer #3 (Significance (Required)):

The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interest to biophysics and molecular biology researchers; the manuscript is written such that it would suit an interdisciplinary basic research audience.

We thank Reviewer 3 for her/his enthusiasm and her/his comments that help us to greatly improve the manuscript.

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

The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.

We now have expanded the introduction to address some of these aspects. In particular, also as a response to comment 1 of Reviewer 4, we included additional background on the eukaryotic replication time program. We address in more detail its known interplay and correlation with crucial 3D structural features such as compartments and TADs. Finally, we add a sentence to clarify how the current work is distinct from the prior implementation and the novelty introduced here.

In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.

As very briefly summarized in the mentioned paragraph, the yeast genome is organized in the so-called Rabl organization where chromosome arms are all connected via the centromeres at the Spindle Pole Body (SPB). This is analogous to the definition of a polymer brush where several branches (the arms in this case), are grafted to a surface or to another polymer (see new Inset panel in Fig S1B). We refer in the main text to the scheme in Fig. S1B where we also include the snapshot of a single chromosome and the physical constraints that characterize this large-scale organization and extend the caption to clarify the analogy. A typical emerging feature at the single chromosome level is described in Fig. 1 B and C.

On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.

These Pearson correlations are obtained for our own modeling. We correct the values in the main text and more clearly indicate the specific correspondence with the maps used. We describe now in the Materials and Methods (new paragraph “Comparison with in vivo HiC maps in G1” and Table S2) how these values were obtained.

In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification why it will not likely change the results).

We now state more clearly in the main text the limitation of our model regarding the doubling of DNA content without any increase of nuclear size. As mentioned in the Discussion, we do not expect this approximation to strongly impact our results, which mainly focus on early S-phase.

We now also included in the Discussion how the detection of the “replication wave” should be qualitatively independent of the density regime. In fact, even in the case of growing nuclei and constant density, the polarity induced by the Rabl organization and replication timing are the main drivers of such fork redistribution.

Regarding the slowdowning in diffusion due to sister chromatids intertwinings (see response to comment 13), we instead verified that the effect is indeed density independent (new Fig S21).

Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.

This is now corrected.

In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.

We reformulate the description in the caption to make clearer that the time indicated above the plots indicate the time window used for the computation. As mentioned more in detail in the response to comment 17 below (and comment 3 of Reviewer 2), we included in the Material and Methods a more precise description on the normalization used in the case of on-diagonal aggregate plots (observed-over-expected).

In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rationale behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.

In the experimental setup, cells are arrested in G1 with alpha-factor and then released in S-phase (see Fig S26 with corresponding scheme). The release from G1 synchronisation is not immediate, and staging of cells by flow-cytometry every 5 minutes for 30 minutes after release (data not shown in the main text but provided below) proved 20 minutes to be an adequate early S-phase timepoint (Page 17 in the Materials and Methods). As a consequence, the times indicated when describing the in vivo experiment, do not correspond to the ones indicated in our in silico system, for which the onset of replication is well defined. For these reasons, we have to determine which time window among the ones used in Fig 2E, is the most appropriate to compare with the experiment (see response to comment 9 for more details).

Fig.R1: Cell cycle progression monitored by flow cytometry after the release. For the first 15 minutes, cells are still mainly in G1 and only start replicating ~20 minutes after the release.

Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.

Taken together, the G1, Cdc20 (metaphase-arrested cells), and Cdc45-depleted (early S cells but not replicated) conditions suggest that fountains reflect ongoing replication. Namely, G1-arrest shows that fountains require S-phase entry; Cdc45-depletion shows that fountains require origin firing and is not due to another S-phase event; and metaphase-arrested cells show that fountains are not permanent structures established by replication, but a transient replication-dependent structure.

This demonstrates that the emerging signal is not trivially dependent on (1) the presence of the second sister chromatids; or on (2) potential overlaps between origin positions and barriers (CARs) to loop extrusion (see also comment 12 of Reviewer 2). A sentence at the end of II-a was added to clarify the different information gained with the two strains.

We discuss again the cdc20 and cdc45 mutants in II-b to highlight how the results in II-a do not exclude potential interplay between cohesin-mediated loop-extrusion in presence forks progression. These considerations motivated our experiment in Scc1-depleted cells during early S-phase.

At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.

As mentioned above in comment 7, we cannot clearly define a “t=0” when replication starts in vivo as the release from the G1-arrest is not immediate and perfectly synchronous. On the other hand, the times indicated within the text are those following the onset of polymer self-duplication in our simulations. Note that the mean replication time (MRT) shown in Fig.2B does not represent an absolute time, but rather an average relative timing along S-phase (signal rescaled between 0 and 1).

For all these considerations, we think that the most reliable strategy to compare fountains in vivo and in silico is to look at the replicon size via the enrichment in raw contacts around early origins, as illustrated in Fig S7A. In practice, looking at the relative counts of contacts around early origins we have a proxy for the average replicon size that we can match by computing the same analysis on simulated signals (Fig S7A). As a result, we find that the best simulated time window is between 5 and 7.5 minutes, compatible with early-S phase and with an approximate duration of G1 after release of 15 minutes as observed in other studies (ref. [61]).

Note that our conclusions are robust with respect to modulating this mapping method. In particular in Fig. S7, we thoroughly investigated how several confounding factors (such as time window used or partial synchronization) may impact the quantitative nature of our prediction without affecting the qualitative insights.

We included a more precise reference to the Supplementary Materials, where the approach is described and clarified.

In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).

This issue seems to appear using the default PDF viewer on Mac OS. We have corrected the problem and no more black boxes should appear in the main text and in the Supplementary Material.

In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?

We included a more accurate description in the main text about why Saner et al. (ref [20]) make these assumptions in their estimates. We highlight possible inconsistencies such as the presence of termination events which, in our formalism, break sister forks interactions and lead to single forks to be detected. We also clarify the latter point when describing Fig 5B and describe in more detail replication bubbles merging events in the Materials and Methods.

Fig 6B and C, it would be useful if the same scale was used on both plots.

We now use the same scale when plotting Fig 6B and C.

Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.

We included a small paragraph to better explain how intertwining of sister chromatids occurs, and more clearly refer to a snapshot in supplementary figure S19D (Page 14). As correctly mentioned by the reviewer, replication bubbles by construction are always unknotted during their growth (see example in Fig. 2C). As we thoroughly characterize in our previous work (ref. [25]), when several replication bubbles merge, the random orientation of sister chromatids potentially lead to catenation points and intertwined structures. We show below a scheme from our previous work (ref [25]). While in this past work, we demonstrated that the center of mass of the two sister chromatids show subdiffusive behaviour due to the additional topological constraints of their intertwining, this new analysis in the present work suggests that possible effects may also be observed when tracking the MSD (mean square displacement at the locus level) in a more realistic scenario where we included correct replication timing, chromosome sizes and Rabl-organization.

On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?

To the best of our knowledge, there is no other candidate protein of the replisome that has been directly associated to sister-fork pairing in previous studies (as Ctf4). However, components of the replisome such as Cdt1, that have the capacity to oligomerize/self-interact, could be good candidates. We now mention this possibility in the Discussion (Page 15).

As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.

We rephrased the mentioned passages to clarify the differences in detecting such “replication wave” at the population vs single cell level. In video S3 and S4, we can still observe an enrichment of forks at the SPB and later in S-phase a shift towards the equatorial plane. However, the stochasticity of polymer dynamics and 1D replication strongly hinder the ability to clearly visualize such redistribution.

In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.

Indeed, we choose to map the experimental MSD measured in ref [83] by simulating a homopolymer 5% volume fraction and in periodic boundary conditions for consistency to previous work in the group (ref. [102-106]) and our previous replication model (ref.[25]). Moreover, this intermediate density regime also lies in between the minimal (3%) and maximal (6%) densities present in our system. When redoing the time mapping with the G1 MSD plotted in Fig 6A and new Fig S19A, we obtain a very similar value of approx. 1MC=0.6ms. Note that the time mapping aims to obtain a rough estimation of real times as several factors, such as active processes, non-constant density, cell-cycle progression may all contribute to chromatin diffusion in vivo (see also comment 15 to Reviewer 2). In the context of our formalism, differences in time mapping do not affect the 1D replication dynamics as all the parameters to model the 1D process are rescaled by the same factor. Moreover, as we characterized in more depth in our previous work (ref [25]), a crucial aspect that defines self-replicating polymers is the relationship between fork progression and the polymer relaxation dynamics. In physiological conditions, we remain in the regime where forks progress almost quasi-statically to allow the bubbles to re-equilibrate. Therefore, small discrepancies in the time mapping will not modify this regime and our results should remain robust.

On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?

In the raw - unbalanced - data, we observe an artificial increase in contacts around origins in S-phase for both simulation and experiments. This is simply due to the presence of the second Sister chromatids and the fact that contacts between distinct DNA segments are mapped to a single bin.

In the new Fig. S25, we illustrate this effect by computing aggregate plots around early origins using single-chromosome simulations. We demonstrate that the ICE normalization corrects for the variations in copy number due to replication and thus for such artificial increases in contacts during S-phase. We show that such a normalization is equivalent to explicitly divide each bin by the average copy-number of the corresponding segments.

We have now included a sentence in the Materials and Methods to clarify this. Moreover, a detailed description of the other alternative strategies used to compare experiments and simulations were presented in response to comment 3 to Reviewer 2 and two new paragraphs were added in the Materials and Methods.

The references section has an unusual formatting with journal names underlined.

We updated the formatting.

Reviewer #4 (Significance (Required)):

D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condensin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

We thank Reviewer 4 for her/his positive evaluation of our work and her/his comments that help us to greatly improve the manuscript.

Reviewer Comments / Significance

In their manuscript, "Genome-wide modeling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes," authors D’Asaro et al perform computational modeling analyses to address an important open question in the chromatin field: how is DNA replication timing coupled to 3D genome architecture? Over the past ten years, the convergence of high-resolution replication timing (RT) analysis with high-resolution 3D genome mapping (e.g. 'Hi-C' technology) has resulted in the discovery that replication timing domains overlap considerably with 3D genomic domains such as topologically associating domains (TADs). How and why this happens both remain unknown, and advances in 3D genome mapping technology have provided even more data to model the problem of both 1) scheduling replication from distinct series of origins / initiation zones, and 2) modeling how 3D genome architecture is altered by the progression of replication forks, which inherently destroy chromatin structure before faithfully reforming G1 structures on daughter chromatids. As such, the problem being tackled by this computational manuscript is interesting.

D’Asaro et al focus on the problem of how genome structure is altered by the progression of replisomes through S-phase in the budding yeast S. cerevisiae. The authors employ computational polymer modeling of G1 chromosomes, then implement a hierarchical model of replication origin firing along these polymers to examine how the G1 chromosome structural state is perturbed by replisome progression. Their results indicate that replication origins create 'fountains' - Hi-C map features that other groups have demonstrated are likely to originate from symmetric extrusion by condesin / cohesin complexes originating at a fixed point. These 'fountains' appear to be cohesin-independent, as revealed by depletion Hi-C experiments. Finally, the authors provide evidence from their model of a 'replication wave' that emanates from the spindle pole body. This is an interesting manuscript that raises some exciting questions for the field to follow up on.

Major Comments

There is a tremendous amount of work coupling RT domains to 3D genome architecture, especially deriving from the ENCODE and 4D Nucleome consortia. These studies are not adequately highlighted in the introduction and discussion of this manuscript, and this treatment of the literature would ideally be amended in any revised manuscript.

We include new sentences in the introduction to discuss more in detail the correlation between 3D genome architecture and replication timing program, and advancement in this field in the last decades. We also included additional citations to reviews and publications (ref [8-16]). These references were also included at the end of the Discussion where we address the exciting perspective of employing our model in higher eukaryotes and potentially tackle the complex interplay between 3D nuclear compartmentalization and replication dynamics (see also response 1 to Reviewer 1).

S. cerevisiae origins of replication differ from metazoan origins of replication in that they are sequence-defined and are known to fire in a largely deterministic pattern (see classic study PMID11588253). From the methods of the authors it is not clear that the known deterministic firing pattern is being used here, but instead a stochastic sampling method? Please clarify in the manuscript. Specifically, it would be good to understand how the Initiation Probability Landscape Signal correlates with what is already known about origin firing timing.

In our model, the positions of origins are stochastically sampled proportionally to the IPLS which was inferred directly from experimental MRT (ref. [63]) and RFD (ref. [44]). This modeling approach allows reproducing with a very high accuracy the known replication timing data (correlation of 0.96) and Fork directionality data (correlation of 0.91) (see ref. [71]). Origins were defined as the peaks in the IPLS signal. In Fig S3, we extensively compare these origins and the known ARS positions from the Oridb database. For example, most of our early origins (96%) are located close to known, confirmed ARS. Moreover, even if our algorithm is stochastic for origin firing, we remark that each early origin will fire in 90 % of the simulations, coherent with the quasi-deterministic pattern of origin firing and experimental MRT and RFD data. We now have added such statistics of firing in the revised manuscript (Page 4).

It seems possible that experimental sister chromatid Hi-C data (PMID32968250) and nanopore replicon data (PMID35240057) could be used to further ascertain the validity of some of the findings of this paper. Specifically, could the authors demonstrate evidence in sister chromatid Hi-C data that the replisome is in fact extruding sister chromatids? Moreover, are the interactions being measured specifically in cis (as opposed to trans sister contacts)? For the nanopore replicon data, how do replicon length, replication timing, and position along the replication 'wave' correlate?

We thank the reviewer for the suggestions.

Hopelessly there is currently no Sister-C data available during S-phase. In the seminal study (PMID32968250), cells were arrested in G2/M via nocodazole treatment. For a different unpublished work, we already analysed in detail the SisterC dataset and we did not observe clear fountain-like signature, consistent with our own G2/M Hi-C maps (cdc20) where fountains were absent. Note that, in the present work, in order to compare our predictions with standard HiC data, we included all contacts (cis and trans chromatids), mapping pairwise contacts from distinct replicated sequences/monomers to a single bin (see also response to comment 17 to Reviewer 3 and new Fig. S25).

We now mention in the Discussion that Sister-C data during S-phase could help monitoring the role of replisomes on relative sister-chromatids organization (Page 15).

Main results from the nanopore replicon data study include the observed high symmetry between sister forks and their linear progression, as the density of replicons appears to be uniform with respect to their length. Since these two specific constraints are already present in the framework of Arbona et al. (ref. [63]), our model is able to reproduce these features of DNA replication captured by the nanopore data.

Moreover, as we model with very high accuracy replication timing data (see response to comment 2) and forks positioning, we can assume that our formalism well captures replicon positioning and lengths observed in vivo.

As this study does not include any additional exploration or variation of the parameters inferred by Arbona et al. (ref. [63]), we consider a quantitative comparison with the nanopore replicon data to be beyond the scope of this paper.

Minor Comments:

The paper is in most places easy to follow. However, Section C bucked this trend and in general was quite difficult to follow. We would recommend that the authors try to revise this section to make clearer the actual physical parameters that govern a 'replication wave' and the formation of replication foci - how many forks, the extent to which the sisters are coordinated, etc for early vs. late replicating regions.

We now state more clearly with a sentence in the main text the driving forces behind the formation of such a “replication wave”. We believe that the several additions and clarifications following the various comments, improved the clarity of the manuscri

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Referee #3

Evidence, reproducibility and clarity

The paper "Genome-wide modelling of DNA replication in space and time confirms the emergence of replication specific patterns in vivo in eukaryotes" by D'Asaro et. al presents new computational and experimental results on the dynamics of genome replication in yeast. The authors present whole-nucleus scale simulations using a kinetic Monte Carlo polymer physics model. New HiC data for synchronised yeast samples with different protein knock-downs are also presented.

The main questions which the paper addresses are whether sister forks remain associated during replication, whether there is more general clustering of replication forks, and whether replication occurs in a 'spatial wave' through the nucleus. While the authors' model data are not able to conclusively show whether sister forks remain co-localised, the work provides some important insights which will be of high interest to the field.

I have no major issues with the paper, only some minor comments and suggestions to improve the readability of the manuscript or provide additional detail which will be of interest to readers. I list these here in the order in which they appear in the paper. There are also a number of typos and grammatical issues through the text, so I recommend thorough proofreading.

1. The paper seems to be aimed at a broad interdisciplinary audience of biophysicists and molecular biologists. For this reason, the introduction could be expanded slightly to include some more background on DNA replication, the key players and terminology. Also, it seems that this work builds on previous modelling work (Ref. 19), so a bit more detail of what was done there, and what is new here would be helpful. The final paragraph the introduction mentions chromosome features such as TADs and loops, which should be explained in more detail.
2. In the first results section, end of p2, the "typical brush-like architecture" is mentioned. This is not well explained, some additional detail or a diagram might help.
3. On p3-4, some previous work is described, with Pearson correlations of 0.86 and 0.94 are mentioned. What cases these two different values correspond to is not clear.
4. In section II-A-2, on the modelling details, it should be made clearer that the nucleus volume is kept constant, and that this is an approximation since typically the nucleus grows during S-phase. This is discussed in the Methods section, but it would be useful to also mention it here (and give some justification was to why it will not likely change the results).
5. Fig 2. The text in Fig 2B is much smaller than other panels and difficult to read. Also Fig 3B, Fig 6.
6. In 2E, are the times given above each map the range which is averaged over? This could be clearer in the caption. In the caption it stated that these are 'observed over expected'; what the 'expected' is could be clearer.
7. In section II-B-2, the authors state that the cells are fixed 20 mins after release from S-phase. Can they comment on the rational behind this choice, since from Fig 2 their simulations predict that the fountain pattern will no-longer be visible by that time.
8. Section II-B-2(b) could be clearer. I don't understand what the conclusion the authors take from the metaphase arrest maps is. I'm not sure why they discuss again the Cdc45-depleted cells here, since this was already covered in the previous section.
9. At the start of p8 (II-B-3) there is a discussion of the mapping to times to the early-S stage experiments. This could have more explanation. I don't follow what the issue is, or the process which has been used to do the mapping. From Fig 2B, it seems that the simulation time is already mapped well to real time.
10. In Fig 4A above each plot there is a cartoon showing the fork scenario. The left-hand cartoon is rendered properly, but the right-hand one has overlapping black boxes which I don't think should be there. These black boxes are present in many other figures (4B, 3B, 2E etc).
11. In II-C-2(b) it is mentioned that the number of forks within RFis is always assumed to be even. This discussion could be clearer. In particular, the authors state that under both fork scenarios, in the simulations they can detect odd numbers of forks within RFis - how can this happen in the case where sister forks are held together?
12. Fig 6B and C, it would be useful if the same scale was used on both plots.
13. Section II-D-1. There is a discussion on the presence of catenated chains; I did not understand how the replicated DNA becomes catenated, and what this actually means in this context. The way the process is described and the snapshots in Fig2C do not suggest that the chains are catenated. Some further discussion or a diagram would be useful here.
14. On p14 (section III) there is a section discussing possible mechanisms for sister fork interactions, and that result that Ctf4 might not play a role in this, as previously suggested. Are there any other candidate proteins which could be tested in the future?
15. As on p14, second paragraph: there is a sentence "replication wave [51] cannot be easily visualised at the single cell level.", which seems to contradict the discussion on p9 "such a "wave" can also be observed at the level of an individual trajectory (Video S3,4) even if much more stochastic." I think more explanation is needed here.
16. In the methods section, p18, it is mentioned that the volume fraction is 3%. I assume this is before replication, and so after replication is complete this will increase to 6%. This should be stated more explicitly, with also a comment on the 5% volume fraction used in the time-scale mapping discussed on p17.
17. On p20, processing of simulated HiC using cooltools is discussed. For readers unfamiliar with this software, a bit more detail should be given. Specifically, how does the normalisation account for having some segments which have been replicated and some which have not. Later on the same page (IV-C-2) two different strategies for comparing HiC maps are given; why are two different methods required, and what is the reasoning in each case?
18. The references section has an unusual formatting with journal names underlined.

Significance

The work has been conducted thoroughly, and in general the paper is well written with good attention to detail. As far as I am aware, this is the first study where replication is simulated in a whole nucleus context, and the scale of the simulations is impressive. This allows the authors to address questions on replication foci and the spatiotemporal organisation of replication which would not be possible with more limited simulations, and to compare the model with previous experimental work. This, together with the new HiC data, I think this makes this a strong paper which will be of interested to biophysics and molecular biology researchers; the manuscript is written such that it would suit a interdisciplinary basic research audience.

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Referee #1

Evidence, reproducibility and clarity

By developing a new genome-wide 3D polymer simulation framework, D'Asaro et al. investigated the spatiotemporal interplay between DNA replication and chromatin organization in budding yeast: (1) T The simulations recapitulate fountain-like chromatin patterns around early replication origins, driven by colocalized sister replication forks. These findings align with Repli-HiC observations in human and mouse cells, yet the authors advance the field by demonstrating that these patterns are independent of Cohesin and Ctf4, underscoring replication itself as the primary driver. (2) Simulations reveal a replication "wave" where forks initially cluster near the spindle pole body (SPB) and redistribute during S-phase. While this spatial reorganization mirrors microscopy-derived replication foci (RFis), discrepancies in cluster sizes compared to super-resolution data suggest unresolved mechanistic nuances. (3) Replication transiently reduces chromatin mobility, attributed to sister chromatid intertwining rather than active forks. This work bridges replication timing, 3D genome architecture, and chromatin dynamics, offering a quantitative framework to dissect replication-driven structural changes. This work provides additional insights into how replication shapes nuclear organization and vice versa, with implications for genome stability and regulation. However, the following revisions could strengthen the manuscript:

Major:

1. Generalizability to Other Species While the model successfully recapitulates yeast replication, its applicability to larger genomes (e.g., mammals) remains unclear. Testing the model against (Repli-HiC/ in situ HiC, and Repli-seq) data from other eukaryotes (particularly in mammalian cells) could enhance its broader relevance.
2. Validation with Repli-HiC or Time-Resolved Techniques The Hi-C data in early S-phase supports the model, but the intensity of replication-specific chromatin interactions is faint, which could be further validated using Repli-HiC, which captures interactions around replication forks. Alternatively, ChIA-PET or HiChIP targeting core component(s) (eg. PCNA or GINS) of replisomes may also solidify the coupling of sister replication forks.
3. Interactions Between Convergent Forks The study focuses on sister-forks but overlooks convergent forks (forks moving toward each other from adjacent origins), whose coupling has been observed in Repli-HiC. Could the simulation detect the coupling of convergent fork dynamics?
4. Unexpected Increase in Fountain Intensity in Cohesin/Ctf4 Knockouts In Fig.3A, a schematic illustrating the cell treatment would improve clarity.

In Sccl- and Ctf4-depleted cells, fountain signals persist or even intensify (Fig. 3A). This counterintuitive result warrants deeper investigation. Could the authors provide any suggestions or discussions? Potential explanations may include: Compensatory mechanisms (e.g., other replisome proteins stabilizing sister-forks). Altered chromatin mobility in mutants, enhancing Hi-C signal resolution. Artifacts from incomplete depletion (western blots for Sccl/Ctf4 levels should be included). 5. Inconsistent Figure References Several figure citations are mismatched. For instance, Fig. S1A has not been cited in the manuscript. Moreover, there is no Fig.1E in figure 1, while it has been cited in the text. All figure/panel references must be cross-checked and corrected.

Minor:

1. Page2: "While G1 chromosomes lack of structural features such as TADs or loops [3]" However, Micro-C captures chromatin loops, although much smaller than those in mammalian cells, within budding yeast.
2. In figure 2E, chromatin fountain signals can be readily observed in the fork coupling situation and movement can also be observed. However, the authors should indicate the location of DNA replication termination sites and show some examples at certain loci but not only the aggregated analysis.

Significance

General assessment:

This study provides a valuable computational framework for investigating the dynamic interplay between DNA replication and 3D genome architecture. While the current implementation focuses on Saccharomyces cerevisiae, whose genome organization differs significantly from mammalian systems.

Advance: providing the first in vivo experimental evidence in investigating the role(s) of Cohesin and Ctf4 in the coupling of sister replication forks.

Audience: broad interests; including DNA replication, 3D genome structure, and basic research

Expertise: DNA replication and DNA damage repair within the chromatin environment.

14:12 Ich möchte eher, dass es auch den Menschen, die mich irgendwie ohne Grund, also ad-hominem irgendwie in Kakao ziehen oder diskreditieren, dass es denen auch gut geht, obwohl die mir gegenüber feindlich eingestellt sind. Und meistens ist es so, wenn man mit solchen Vorteilen gegenüber Menschen auftritt, nur weil sie ein T-Shirt tragen, ist es meistens wahrscheinlich ja, weil sie halt "intellektuell limitiert" sind. Da habe ich schon mal großes Mitleid, und möchte dann gerne denen auch helfen. Da habe ich so ein Helfersyndrom natürlich, dass es Menschen, die vielleicht physisch oder auch vor allem dann mental nicht so fit sind, dass die das dann auch erkennen, worum die Reise geht. Deswegen versuche ich auch Bücher zu schreiben, die alle verstehen.

Und ich war ja vorhin am Bahnhof in Köln. Ich glaube, die meisten hätten das T-Shirt gar nicht verstanden, weil die nicht Englisch konnten, die ich dort gesehen habe. Die Armut ist da wirklich krasierend, finde ich. Und wenn jemand so mir entgegenkommt oder feindlich eingestellt ist, also es gibt Menschen, die sagen manchmal was so "ööh Trump Fan" oder was, ne? Dann gehe ich gerne mit denen in die Diskussion und ich merke relativ schnell, die Gegenargumente sind relativ flach. Also die haben keine Argumente. Also dann kommt dann irgendwie ja, "Trump ist doch ein Nazi". Also ich bin jetzt kein Trump Fan. Ich ich bin als erst ich finde gut, dass Trump gewonnen hat. Es war sehr erfrischend. Ich finde auch sehr amüsant oftmals. Ich finde vieles auch total schlecht, was er macht. Ja, aber immer gleich alle über einen Kamm zu ziehen ist zu armselig.

Also lasst uns doch wieder in den Diskurs gehen, weil die Debattenkultur in Deutschland ist komplett unter die Räder gekommen und viele Sachen, die ich ja immer wieder gesagt habe, wo ich dann auch böse angegriffen wurde, ne? Also wir wissen der Zwischenzeit z.B., dass die Coronaimpfung eben doch nicht ohne Nebenwirkungen war, ja? Und vor allem, wenn eine Regierung intransparent ist, wenn wir geschwärzte RKI Protokolle haben, wenn wir geschwärzte Protokolle zum Atomausstieg von Habbeck haben, ne, wenn wir die Maskendeals von Spahn haben, die auch komplett geschwärzt sind. Also, wenn jemand alles schwärzt, z.B. also eine Regierung vor allem gegenüber ihren Bürgern, dann haben die was zu verbergen. Sorry. Ja, also ist ganz offensichtlich.

idioten erkennt man recht zuverlässig an ihrem klischeehaften denken "alle X sind gleich", "alle rechten sind gleich", "alle trump fans sind gleich", "es gibt nur schwarz und weiss", ... die challenge hier ist die paarung von dummen und schlauen menschen, so dass die schlauen körperliche arbeit delegieren können an die dummen, und dass die dummen geistige arbeit delegieren können an die schlauen. das kann man nicht erzwingen, das muss freiwillig ("natürlich") passieren. siehe auch mein buch: Pallas. Wer sind meine Freunde. Gruppenaufbau nach Persönlichkeitstyp

Author response:

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

Reviewer #1 (Public review):

Munday, Rosello, and colleagues compared predictions from a group of experts in epidemiology with predictions from two mathematical models on the question of how many Ebola cases would be reported in different geographical zones over the next month. Their study ran from November 2019 to March 2020 during the Ebola virus outbreak in the Democratic Republic of the Congo. Their key result concerned predicted numbers of cases in a defined set of zones. They found that neither the ensemble of models nor the group of experts produced consistently better predictions. Similarly, neither model performed consistently better than the other, and no expert's predictions were consistently better than the others. Experts were also able to specify other zones in which they expected to see cases in the next month. For this part of the analysis, experts consistently outperformed the models. In March, the final month of the analysis, the models' accuracy was lower than in other months and consistently poorer than the experts' predictions. 

A strength of the analysis is the use of consistent methodology to elicit predictions from experts during an outbreak that can be compared to observations, and that are comparable to predictions from the models. Results were elicited for a specified group of zones, and experts were also able to suggest other zones that were expected to have diagnosed cases. This likely replicates the type of advice being sought by policymakers during an outbreak. 

A potential weakness is that the authors included only two models in their ensemble. Ensembles of greater numbers of models might tend to produce better predictions. The authors do not address whether a greater number of models could outperform the experts. 

The elicitation was performed in four months near the end of the outbreak. The authors address some of the implications of this. A potential challenge to the transferability of this result is that the experts' understanding of local idiosyncrasies in transmission may have improved over the course of the outbreak. The model did not have this improvement over time. The comparison of models to experts may therefore not be applicable to the early stages of an outbreak when expert opinions may be less welltuned. 

This research has important implications for both researchers and policy-makers. Mathematical models produce clearly-described predictions that will later be compared to observed outcomes. When model predictions differ greatly from observations, this harms trust in the models, but alternative forms of prediction are seldom so clearly articulated or accurately assessed. If models are discredited without proper assessment of alternatives then we risk losing a valuable source of information that can help guide public health responses. From an academic perspective, this research can help to guide methods for combining expert opinion with model outputs, such as considering how experts can inform models' prior distributions and how model outputs can inform experts' opinions. 

Reviewer #2 (Public review):

Summary: 

The manuscript by Munday et al. presents real-time predictions of geographic spread during an Ebola epidemic in north-eastern DRC. Predictions were elicited from individual experts engaged in outbreak response and from two mathematical models. The authors found comparable performance between experts and models overall, although the models outperformed experts in a few dimensions. 

Strengths: 

Both individual experts and mathematical models are commonly used to support outbreak response but rarely used together. The manuscript presents an in-depth analysis of the accuracy and decision-relevance of the information provided by each source individually and in combination. 

Weaknesses: 

A few minor methodological details are currently missing.

We thank the reviewers for taking the time to consider our paper and for their positive reflections and suggestions for our study. We recognise and endorse their characterisation of the study in the public reviews and are greatful for their interest and support for this work. 

Reviewer #1 (Recommendations For The Authors): 

I initially found Table 1 difficult to interpret. In the final two columns, the rows relate to each other but in the other columns, rows within months don't relate to each other. Could this be made clearer? 

Thank you for your helpful suggestion. We agree that this is a little confusing and have now added vertical dividers to the table to indicate which parts of the table relate to each other.

In Figure 1A, the colours are the same as in the colour-bar for Figure 1B but don't have the same meaning. Could different colours be used or could Figure 1A have its own colour-bar to aid clarity? 

Thank you for your query. The colours are not the same pallette, but we appreciate that they look very similar. To help the reader we have changed the colour palette of panel A and added a legend to the left.  

In Figure 3, can labels for each expert be aligned horizontally, rather than moving above and below the timeline each month? 

Thank you for your perspective on this. We made the concious dicision to desplay the experts in this way as it allows the timeline to be presented in a shorter horizontal space. We appreciate that others may prefer a different design, but we are happy with this one. 

On lines 292 and 293, the authors state that experts were less confident that case numbers would cross higher thresholds. It seems that this would be inevitable given the number of cases is cumulative. Could this be clarified, please? 

Thank you for raising this point. We agree that this wording is confusing. We have now reworked the entire section in response to another reviewer. The equivalent section now reads: 

Experts correctly identified Mabalako as the highest-risk HZ in December. They attributed an average 82% probability of exceeding 2 cases; Mabalako reported 38 cases that month, exceeding all thresholds, although the probability assigned to exceeding the higher thresholds was similar to that of Beni (3 cases)

Reviewer #2 (Recommendations For The Authors): 

(1) Some methodological details seem to be missing. Most importantly, the results present multiple ensembles (experts, models, and both), but I can't seem to find anywhere in the Methods that details how these ensembles are calculated. Also, I think it would be useful to define the variables in each equation. It would have been easier to connect the equations to the description if the variables were cited explicitly in the text. 

Thank you for pointing out these omissions. We have included the following paragraph to detail how ensemble forecasts were calculated. 

“Enslemble forecasts

Ensemble forecasts were calculated as an average of the probabilities attributed by the members of the ensemble. For the expert ensemble the arithmetic mean was calculated across all experts with equal weighting. Similarly the model ensemble used the unweighted mean of the model forecasts. For the mixed (model and expert) ensemble, the mean was weighted such that the combined weight of the experts forecasts and the combined weight of the models forecasts were equal.”

(2) Overall, I think the results provide a strong analysis of model vs. expert performance. However, some sections were highly detailed (e.g., the text usually discusses results for every month and all health zones), which clouded my ability to see the salient points. For example, I found it difficult to follow all the details about expert/model predictions vs. observations in the "Expert panel and health zones..." subsection; instead, the graphical illustration of predictions vs. observations in Figure 4 was much easier to interpret. Perhaps some of these details could be trimmed or moved to the supplementary material. 

Thank you for your honest feedback on this point. We have shortened this section to highlight the key points that we feel are the most important. We have also simplified the text where we discuss the health zones nominated by experts. 

(3) Figure 5C is a nice visualization of the fallibility of relying on a single individual expert (or model). I wonder if it would be useful to summarize these results into the probability that a randomly selected expert outperforms a single model. Is it the case that a single expert is more unreliable than a single model? The discussion emphasizes the importance of ensembles and compares a single model to an ensemble of experts, but eliciting predictions from multiple experts may not always be possible. 

Thank you for raising this. We agree that this is an important point that eliciting expert opinions is not a trivial task and should not be taken for granted. We agree with the principle of your suggestion that it would be useful to understand how the models compare to indevidual experts. We don’t however believe that an additional analysis would add sufficiently more information than already shown in Figure 5, which already displays the full distribution of indevidual experts for each month and threshold. If you would like to try this analysis yourself, the relevant data (the indevidual score for each combination of expert, threshold, heal zone and month) is included in the github repo (https://github.com/epiforecasts/Ebola-Expert-Elicitation/blob/main/outputs/indevidual_results_with_scores.csv).

Minor comments: 

(1) Figure 2: the color scales in each panel are meant to represent different places, correct? The figure might be easier to interpret if the colors used were different.  

Thank you for bringing this to our attention. We have now changed the palette of panel A to differ from panel B.  

(2) Equation 7: is o(c>c_thresh) meant to be the indicator function (i.e. 1 if c>c_thresh) and 0 otherwise)? 

Thanks for raising this. The function o is the same as in the previous equation – an observation count function. We appreciate that this is not immediately clear so have added a sentence to explain the notation after the equation.

(3) Table 1: a brief description of the column headers would be useful.  

Thank you for the suggestion. We have now extended the table caption to include more description of the columns. 

“Table 1: Experts and health zones included in each round of the survey. The left part of the table details the experts interviewed (highlighted in green) the health zones included in the main survey in each month. In addition, the right part of the table details the health zones nominated by experts and the number of experts that nominated each one.”

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Quétin et al "Transient hypoxia followed by progressive reoxygenation is required for efficient skeletal muscle repair through Rev-ERBα modulation" describes the nature of muscle stem cell (MuSC) differentiation within its hypoxic niche using in vivo, ex vivo and in vitro methodologies. Approaches to limit oxygen in a regenerating model of muscle injury showed that muscle oxygenation is necessary for proper muscle repair. They found that the lack of oxygen is associated with the formation of hypotrophic myofibers, due to the inability of MuSCs to differentiate and fuse. Their findings show that the phenotype was independent of HIF-1α. However, RNA-seq of MuSCs 7 day post injury from prolonged hypoxia was shown to have significantly increased circadian clock gene Rev-erbα expression. Pharmacological inhibition of Rev-erbα during hypoxia rescued the myogenic phenotype. Contrarily, the use of Rev-erbα agonist in normoxia impaired the fusion capacity of MuSCs and decreases the number of large mature myofibres. This manuscript is well written and very easy to follow. Though, there are certain shortcomings outlined below. Sometimes the evidence provided does not support the conclusions made. For example, more rigour should be performed to state that there is a self-renewal phenotype.

Major issues

1. In Figure 1, why were these timepoints chosen? Is the hypoxia more severe between days 0 and 5 (i.e. when MuSCs begin their activation).
2. "From 5 to 28 dpi, pimonidazole adduct intensity gradually declined, demonstrating a progressive reoxygenation after transient hypoxia during muscle repair (Fig. 1E and 1F) that correlates with progressive restoration of the vascular network (Fig. 1C) and MuSC return into quiescence (Fig. 1B and 1D)." For this statement, correlating these events to MuSC returning to quiescence might not be appropriate. As Figure 1D shows all the Pax7+ cells, it does not reflect whether they are quiescent. Thus, the timelines might not actually match up with the proportion of self-renewed MuSCs?
3. The manuscript cites far too many review articles (at least half) and not primary sources. Also, some citations are misrepresented. For example: Reference #13 does not show that HIF-1alpha level increases during muscle injury in rodents, Reference #15 shows fusion is impaired in hypoxic c2c12 cells, not promotion of quiescence, Reference #22 does not support the claim that hypoxia induces myostatin expression, only that myostatin inhibits MyoD expression.
4. Figure 1E and 1F, does the dye intensity change with it being more accessible to the muscle during early injury as opposed to later recovery. Also, when using the probe for hypoxia determination, the whole tissue is fluorescing intensely suggesting potential non specificity. It would be prudent to use markers of hypoxia on western blots or gene expression to corroborate this data.
5. a) It is well known that CTX injury does not cause damage to the vasculature but directly to the muscle (Tatsumi et al doi:10.1002/stem.2639; Ramadasan-Nair et al doi:10.1074/jbc.M113.493270; Ohtsubo et al. doi: 10.1016/j.biocel.2017.02.005; Wang et al doi: 10.3390/ijms232113380). How do the authors reconcile their findings that there is vasculature damage with CTX (Fig. 1C).

b) Moreover, the endothelial cell staining (Fig. 1B) appears to be unchanged in the time course of injury. To prove vascular damage this data should be corroborated, for example with lectin perfusion. 6. Problems with Figure 3J. There are data points with zero clusters/isolated myofibres suggesting that the hypoxic environment caused MuSCs to not activate from quiescence. There are several outliers for example at 1% there is a zero reading that makes the data significant. 7. In Figure 1G, Loxl2 after 14 days appears to be significant, as the error bars at 0 and 14 days do not overlap and thus it does not return to normal. An n=3 is not sufficient, as one of the data points at 14 days appears to be an outlier (the data stretching from 1500 to 3000). 8. In Fig. 2C and 2D, there are no control CSA and myofiber diameter experiments for keeping the mice in hypoxia over 14 and 28 days without injury. 9. For Figure 3K, how can self-renewing MuSCs be distinguished from MuSCs that never activated? Especially in the 1% O2 condition where few clusters formed. How does hypoxia influence activation? A 4hr or 8hr timepoint is necessary, as well as 24hrs. Also, for Figure 5E and 5F, it is possible that HIFcKO allowed the cells to activate normally, thus explaining the shift from quiescence to activation in the read-outs. This further highlights the importance of analyzing earlier timepoints. One cannot state that these cells are self-renewing or returning to quiescence without performing experiments on earlier timepoints. 10. The data for Figure 4 does not suggest that transient reoxygenation is required "for proper skeletal muscle repair" as stated by the authors only that reoxygenation has rescued the phenotype in the primary myoblasts. There is no hypoxia in the control (8% O2) for regeneration to occur (Fig. 2B). 11. One cannot rule out metabolic dysregulation. It's true that glycolytic fibers are generally larger than oxidative, it is likely that that alone does not explain the difference in fiber size. However, the fact that the fibers are more glycolytic does suggest a metabolic shift in the muscle (which was the aim of the experiment), which could also shift MuSC character altering their behaviour. How are MuSCs metabolically responding to hypoxia? 12. In Figure 2, how can one be sure that reoxygenation is blocked by the hypoxic chamber? Reduced O2 levels will induce hypoxia, but one cannot state that it blocks reoxygenation without further validation such as using pimonidazole as in Fig. 1E. If reoxygenation is blocked, then pimonidazole staining should remain consistent throughout the injury. 13. For Figure 3G, is a sum appropriate for the graph? Proportions would be more appropriate as cell number is not equal as shown in figure 3E. Can Pax7+/MyoD+ be defined as differentiated? By day 7, many MuSCs will have fused and be expressing MyoG, which is not accounted for by these definitions. Did systemic hypoxia increase self-renewal or impair activation? How can you distinguish these two? 14. In Figure 6A, while it is interesting that Pax7 levels are elevated in hypoxia and differentiation and fusion markers are down at 7days, it does not necessarily mean that self-renewal is increased. It might suggest that the hypoxic cells might have never activated or might have differentiated precociously. Are any cell cycle genes down regulated? Any other genes involved in quiescence altered? 15. The use of pimonidazole in Fig. 1E shows the staining within fibers (many with centrally located nuclei). These nuclei are differentiating and not representative of expanding MuSCs. How do the authors reconcile these MuSCs as part of their population.

Minor Problems

1. In the introduction, the line "Vascular alterations result in reduced oxygen (O2) levels, disrupting cell homeostasis and contributing to many diseases" is not always true as vascular alterations do not always result in reduced oxygen levels. For example, in angiogenesis there is no reduction of O2. This line should better reflect this.
2. In the introduction, Paragraph 2, line 9 change "quiescence thought HIF-1α" to "quiescence through HIF-1α".
3. Paragraph 3, line 8: "lead" instead of "leads"
4. It is not sure how important the connection between capillary density and Pax7+ cell number is. Both are presumed to occur at the same time in muscle, so both will recover concurrently. To state that it is a coupled response is overstating the evidence presented.
5. Figure 1B the colour-labels for Pax7 and Dapi over lap with the border.
6. In the Introduction, the following sentence does not follow the previous sentence: "In vivo, Majmundar and colleagues show that HIF-1a in MuSCs negatively regulates myogenesis by decreasing myogenic differentiation".
7. In the Introduction, the following statement is not accurate "Hypoxia can also alter myogenic differentiation and myotube formation by inhibiting p21 (as known as p21 and CDKN1A) that leads to an accumulation of the retinoblastoma protein Rb24", for what was found in the reference. The authors should correct this statement.
8. Paragraph 3, line 5: "as known as p21 and CDKN1A" should perhaps read "also known as CDKN1A"
9. The following statement is not supported by the results: "Strikingly, the most abundant and intense pimonidazole staining is detected on CTX-injured TAs at 5 dpi, indicating that myogenic cell expansion is initiated in a hypoxic environment in situ (Fig. 1D-1F)." MuSCs are activated and expanding from time zero to 5 days according to Figure 1D.
10. "....Since glycolytic fibers are larger than oxidative fibers, ...." citation missing
11. An inconsistent finding is that the authors show that protein synthesis rates are normal between normoxia and hypoxia of regenerating muscle (suppl. Fig. 1E), yet the capacity of protein synthesis is found to be higher in oxidative muscle fibres compared to glycolytic fibers (Van Wessel et al, doi: 10.1007/s00421-010-1545-0), which are formed during regeneration (Fig. 2G and 2H).
12. Some figure legends that describe graphs do not denote the number of samples or mice used.
13. In Figure 1C, 1D and 1F what is being compared to obtain statistical significance?
14. The font size of many figures is too small to follow.
15. Confusion for the results of figure 3G. Labels in the text do not reflect the labels in figure (which cannot be read anyway because the font is too small). Why is Ki67 used as a marker for activation versus proliferation.
16. The physiological O2 concentration is 8%, do the authors know what the hypoxic O2 concentration is in the injured environment. Why did they choose hypoxic O2 concentration at 1% for ex vivo and invitro experiments? Why did they choose 10% for the in vivo experiment?
17. For Figure 2H it is not appropriate to state that type IIA ratio was reduced with hypoxia, as the results show no statistical significance.
18. For Figure legend 3K, are the cell number/fiber the sums per one mouse or the sum from all mice combined for each condition?
19. For Figure 3B and 3E "concomitantly with their proliferation peak" seems to imply that hypoxia in Pax7+ cells peaks alongside proliferation, but the evidence doesn't support that conclusion. More timepoints would be needed to show that 5 dpi is truly the peak of hypoxia in Pax7+ cells.
20. For Figure legend 4E, should read "MHC" not "MCH"
21. In Figure 4C there is no gap between the significance bar.
22. In Figure legend 5G, "Experience design" should read "Experimental design"
23. Representative images Fig 3I and 5E are poor quality.
24. Confusing statement "In the same way, this presence of smaller myofibers under prolonged hypoxia could not be explain by the glycolytic fiber-type switch from type-IIA to type-IIB, as observed in pathological context of COPD or peripheral arterial disease (PAD), since type-IIB are the largest myofibers in mice."

Referees cross-commenting

I agree with the thoughtful reviews and issues raised by Reviewers 1 and 2. I do not have anything more to add.

Significance

General Assessment: This manuscript is well written and easy to follow. It rigorously investigates the influence of oxygenation on MuSC behaviour. The authors utilize in vivo, ex vivo, and in vitro models to support their study and executed their work to a high degree. A limitation is that all experiments are only performed in mice and might not be applicable in humans. In addition, some claims made by the authors were over-reaching. The study can be improved by further validating some of the authors' claims, as has been suggested in the review.

Advance: This study is the first to report the effect of hypoxia on MuSCs in an ex vivo culture and in vivo injury model using a hypoxia chamber. This study helps clarify the role of HIF-1α on MuSC behaviour by suggesting that it does have a role in MuSC fate decisions. Finally, the authors make a novel link between circadian rhythm and MuSC behaviour in hypoxia.

Audience: A specialized audience that is interested in myogenesis, muscle stem cells, and/or hypoxia will be interested in this study. It highlights the important role of oxygen in muscle regeneration and may help researchers understand the role of oxygen in MuSC fate decisions.

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Referee #2

Evidence, reproducibility and clarity

The manuscript, Transient hypoxia followed by progressive reoxygenation is required for efficient skeletal muscle repair through Rev-ERBa modulation, revisits the role of hypoxia in skeletal muscle regeneration after acute injury. They first nicely demonstrate, using the pimonidazole hypoxia probe, that during regeneration skeletal muscle is transiently hypoxic at 5 days post injury (DPI). Then they show skeletal muscle regeneration is impaired in mice housed in a hypoxic (10% 02) chamber; the regenerated muscle mass is smaller, due to smaller regenerated myofibers and there is a shift in myofiber type so that there are more IIB myofibers. In addition, at 7 DPI when mice are raised in a hypoxic environment there is a shift in muscle stem cells so that they are more proliferative and fewer have differentiated. Ex vivo experiments culturing muscle stem cells in association with EDL myofibers in 1% 02, as compared with 8% 02, also led to fewer differentiated Pax7-MyoD+ cells, but could be restored if 02 was subsequently increased to 8%. They also found that low oxygen inhibited myoblast fusion in vitro. They then tested, via Pax7CreERT2/+;HIF-1afl/fl, whether HIF-1a signaling mediated the response of muscle stem cells to hypoxia in vivo. Surprisingly, they found that loss of HIF-1 did not impair myofiber regeneration in normoxic or hypoxic conditions, but they do provide some data suggesting that HIF-1a is required for the hypoxic-induced increase in Pax7+MyoD- muscle stem cells. Bulk RNA-seq analysis of 7 DPI muscle from mice housed in normoxic versus hypoxic conditions uncovered the interesting mis-regulation of circadian rhythm associated genes - in particular, the circadian clock repressor Rev-ERBa. Using a pharmacological antagonist of Rev-ERBa they show in culture that blocking Rev-ERBa (in contrast to loss of HIF-1a) rescues the fusion defect of muscle stem cells cultured in 1% 02. Conversely, they show that a Rev-ERBa agonist inhibits fusion in 8% 02. Altogether, the paper provides interesting new data on the controversial role of hypoxia and HIF-1a as well as data suggesting a connection between hypoxia and circadian rhythm genes. The data is logical and well presented, and the paper will be of strong interest to the regeneration and skeletal muscle research communities. I have two major comments and a list of smaller suggestions to improve the manuscript.

Major comments:

1. In vivo experiments (presented in Figures 2, 3, 5, 6, 7) house mice in hypoxic (10% oxygen) chambers, and the authors suggest that this blocks the progressive reoxygenation of skeletal muscle during regeneration. Surprisingly, the authors do not test when the mice are in hypoxic chambers whether, in fact, skeletal muscle is hypoxic at homeostasis and whether during regeneration muscle experiences prolonged hypoxia. The obvious experiment would be to use the pimonidazole probe on skeletal muscle sections of muscle at homeostasis and at 0, 5, 6, 14, and 28 DPI CTX injury in mice housed in hypoxic chambers. Without some demonstration that skeletal muscle oxygenation is changed when the mice are housed in hypoxic chambers, it is impossible to interpret these experiments.

2. The authors claim that reducing reoxygenation by maintaining the mice under systemic hypoxia impairs skeletal muscle repair by limiting the differentiation and fusion capacity of MuSCs in HIF-1a-independent manner, while it favors their return into quiescence through HIF-1a activation. They provide some in vitro evidence that Hif1ais required for the high levels Pax7+MyoD- muscle stem cells in 1% O2. They should also show that the elevated levels of Pax7+ muscle stem cells at 7 DPI (seen in Fig. 3D-G) requires HIF1a via analysis of Pax7CreERT2/+;HIF-1afl/fl mice.

Minor comments:

1. Please provide a reference for the pimonidazole probe. Reference 26, Hardy et al., is not the right one.

2. Please provide references that Loxl-2, Pdgfb, and Ang2 are HIF-inducible target genes.

3. Fig. 2C shows changes in average myofiber diameter. How was this calculated? Is this the largest diameter? Is there a reason that cross-sectional area was not measured (the more standard measurement)? Also, generally this type of data is shown as bar graphs - which is how these data are shown in Fig. 5C. Please also show the data in Fig. 2C as bar graphs.

4. Please provide reference for 8% 02 being physioxia in culture.

5. Fig.5 should also quantify the number of centronuclei/myofiber (as in Fig. 2I) for Pax7CreERT2/+;HIF-1afl/fl mice 14 and 28 DPI - to further demonstrate that differentiation defects in hypoxia are HIF-1a independent.

6. Please provide a graphical model of your research findings.

7. There are many typos and verb tense issues. Please fix these. The most amusing is Stinkingly in the Discussion.

Referees cross-commenting

I think several important issues are raised by myself and reviewer 3. First, the authors need to explain and support their use of 10% O2 hypoxia in vivo chambers and 1% O2 for hypoxic in vitro experiments. Second, the authors have not demonstrated that reoxygenation of muscle is prevented in mice raised in hypoxic chamber. There are questions about how well the pimonidazole probe is working (the widespread expression at 5 dpi in Fig. 1E suggests there may be specificity issues) and this probe is also not shown for muscle from mice living in hypoxic chambers. Another method of demonstrating hypoxia in muscle tissue would be useful.

Significance

The paper provides interesting new data on the controversial role of hypoxia and HIF-1a as well as data suggesting a connection between hypoxia and circadian rhythm genes.

This paper will be of interest to researchers studying the role of hypoxia on regeneration and also to researchers studying muscle regeneration.

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Referee #1

Evidence, reproducibility and clarity

SUMMARY

Quétin et al investigated the dynamics of oxygen levels during the skeletal muscle regeneration following sterile damage and its impact on muscle repair. They combined in vivo and ex-vivo model systems, together with genetic and pharmacological manipulations. They found results consistent with the fact that a dynamic oxygeneation process, hypoxia during the early phase followed by reoxygenation, are involved in muscle repair. Prolonged hypoxia leads to defective myogenesis and muscle repair. These activities apper to be meadiated by modulation of Rev-ERBα levels. Collectively, the study provide intriguing insight regarding the role of oxygen in muscle repair.

MAJOR COMMENTS

1. In Figure 1, the 5 days post CTX injury is too late to claim that "myogenic cell expansion is initiated in a hypoxic environment". Indeed, at day 5 myofibers are already regenerated, although immature. To support their claim, the authors should perform analyses and quantification of Pax7+, Pax7+Ki67+ and hypoxia at earlier timepoints.

2. In Figure 2B, a larger number of mononuclear cells is present in hypoxia mice. Is hypoxia affecting the number/activity of extra-muscular cells important for muscle regeneration like for example FAPs, macrophages, etc?

3. In Figure 5H, the myotubes formed by HIF-1α cKO appear thinner than control myotubes. Is myotube size affected by lack of HIF1 α?

4. The choice of the 7 days post CTX for the RNA-seq is odd. Indeed, at that timepoint there are obvious histological abnormalities in hypoxia mice. Hence, it is highly likely that many DEGs are simply secondary to the defect in regeneration and not directly linked to hypoxia exposure. This is probably the reason why the authors found so many (close to 4K) DEGs. To focus on the genes closely-associated to the primary defect, the authors should have performed the RNA-seq at an earlier timepoint, in which minimal histological defects were present. While repeating the RNA-seq would be costly and time consuming, the authors could at least address this issue by RT-qPCR. Are muscle stem cell fate, repair, and circadian clock genes significantly altered 3 and 5 days after CTX injury in hypoxia vs normoxia?

5. Given that compounds have frequently off-target effects, the authors must independently support their Rev-ERBα findings by performing genetic manipulations, at least ex-vivo.

6. A recent study (PMID: 38333911), which was not cited by the authors, reports muscle atrophy and weakness, impaired muscle regeneration, and increased fibrosis in hypoxia exposed mice. Intriguingly, this was due to impaired MuSC proliferation and differentiation following HIF-2α stabilization under hypoxia. Hence, the authors should investigate if HIF-2α plays any role in the phenotypes they describe. For example, is HIF-2α a regulator of circadian clock genes expression?

Referees cross-commenting

The other reviewers raised very relevant issues and I fully agree with their comments. In particular, I concur with Reviewer #3 that in several instances the evidence provided by the authors does not support the conclusions made.

Significance

SIGNIFICANCE

There is a limited knowledge regarding the role of oxygen supply during tissue differentiation and repair. In the muscle field, there are conflicting reports in the literature. This study combines genetic, pharmacological and oxygen manipulations both in vivo and ex-vivo to investigate the role of oxygen during regeneration following sterile skeletal muscle injury. The results are very intriguing and potentially relevant both for muscle, but possibly also for other tissue repair. Aspects of the study that must be improved concern the role of HIF-1a and HIF-2α in the process, and the characterization of the molecular mechanism through which Rev-ERBα is regulated by oxygen and regulates muscle repair.

• AUDIENCE: specialized, basic research, translational research; results could potentially extend beyond the muscle field.

• FIELD OF EXPERTISE: muscle differentiation, muscular dystrophy, gene expression regulation.

Reviewer #2 (Public review):

Summary:

This manuscript presents compelling evidence for a novel anti-inflammatory function of glycoprotein non-metastatic melanoma protein B (GPNMB) in chondrocyte biology and osteoarthritis (OA) pathology. Through a combination of in vitro, ex vivo, and in vivo models, including the destabilization of the medial meniscus (DMM) surgery in mice, the authors demonstrate that GPNMB expression is upregulated in OA-affected cartilage and that recombinant GPNMB treatment reduces the expression of key catabolic markers (MMPs, Adamts-4, and IL-6) without impairing anabolic gene expression. Notably, DBA/2J mice lacking functional GPNMB exhibit exacerbated cartilage degradation post-injury. Mechanistically, GPNMB appears to mitigate inflammation via the MAPK/ERK pathway. Overall, the work is thorough, methodologically sound, and significantly advances our understanding of GPNMB as a protective modulator in osteoarthritic joint disease. The findings could open pathways for therapeutic development.

Strengths:

(1) Clear hypothesis addressing a well-defined knowledge gap.

(2) Robust and multi-modal experimental design: includes human, mouse, cell-line, explant, and surgical OA models.

(3) Elegant use of DBA/2J GPNMB-deficient mice to mimic endogenous loss-of-function.

(4) Mechanistic insight provided through MAPK signaling analysis.

(5) Statistical analysis appears rigorous, and figures are informative.

Weaknesses:

(1) Clarify the strain background of the DBA/2J GPNMB+ mice: While DBA/2J GPNMB+ is described as a control, it would help to explicitly state whether these are transgenically rescued mice or another background strain. Are they littermates, congenic, or a separate colony?

(2) Provide exact sample sizes and variance in all figure legends: Some figures (e.g., Figure 2 panels) do not consistently mention how many replicates were used (biological vs. technical) for each experimental group. Standardizing this across all panels would improve reproducibility.

(3) Expand on potential sex differences: The DMM model is applied only in male mice, which is noted in the methods. It would be helpful if the authors added 1-2 lines in the discussion acknowledging potential sex-based differences in OA progression and GPNMB function.

(4) Visual clarity in schematic (Figure 7): The proposed mechanism is helpful, but the text within the schematic is somewhat dense and could be made more readable with spacing or enlarged font. Also, label the MAPK/ERK pathway explicitly in panel B.

DOI: 10.1038/s41586-025-09303-5

Resource: RRID:Addgene_14887

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_14887

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DOI: 10.2337/db25-0326

Resource: None

Curator: @scibot

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What is this?

DOI: 10.1158/2159-8290.CD-24-1409

Resource: (MBL International Cat# TS-5001-2C, RRID:AB_3090188)

Curator: @scibot

SciCrunch record: RRID:AB_3090188

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DOI: 10.1016/j.celrep.2025.116082

Resource: (Cell Signaling Technology Cat# 9727, RRID:AB_561095)

Curator: @scibot

SciCrunch record: RRID:AB_561095

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DOI: 10.1007/s12035-025-04861-1

Resource: (R and D Systems Cat# AF-493-NA, RRID:AB_354506)

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DOI: 10.1186/s12860-025-00547-7

Resource: None

Curator: @sonofthor

SciCrunch record: RRID:AB_2239761

What is this?

Reviewer #3 (Public review):

Summary:

The authors' research here was to understand the role of hypoxia and hypoxia-induced transcription factor Hif-1a in the epicardium. The authors noted that hypoxia was prevalent in the embryonic heart, and this persisted into neonatal stages until postnatal day 7 (P7). Hypoxic regions in the heart were noted in the outer layer of the heart, and expression of Hif-1a coincided with the epicardial gene WT1. It has been documented that at P7, the mouse heart cannot regenerate after myocardial infarction, and the authors speculated that the change in epicardial hypoxic conditions could play a role in regeneration. The authors then used genetic and pharmacological tools to increase the activity of Hif genes in the heart and noted that there was a significant improvement in cardiac function when Hif-1a was active in the epicardium. The authors speculated that the presence of Hif-1a improved cell survival.

Strengths:

A focus on hypoxia and its effects on the epicardium in development and after myocardial infarction. This study outlines the potential to extend the regenerative time window in neonatal mammalian hearts.

Weaknesses:

While the observations of improved cardiac function are clear, the exact mechanism of how increased Hif-1a activity causes these effects is not completely revealed. The authors mention improved myocardium survival, but do not include studies to demonstrate this.

There is an indication that fibrosis is decreased in hearts where Hif activity is prolonged, but there are no studies to link hypoxia and fibrosis.

eliminar esta FAQ porque esta cuenta no está retribuida

te ayudan a regular niveles de glucosa en un rango de 80-100mg/dL. Por fin un tratamiento que te da lo que tu cuerpo necesita.

Enfatiza que os princípios apresentados devem ser considerados de forma conjunta com outra normas (inclusive àquelas de auditoria operacional).

Dropped cases against LA protesters reveal false claims from federal agents<br /> by [[Sam Levin]] for | Los Angeles | The Guardian accessed on 2025-07-29T08:16:14

Author response:

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

Reviewer #1 (Public review):

This study investigates how ant group demographics influence nest structures and group behaviors of Camponotus fellah ants, a ground-dwelling carpenter ant species (found locally in Israel) that build subterranean nest structures. Using a quasi-2D cell filled with artificial sand, the authors perform two complementary sets of experiments to try to link group behavior and nest structure: first, the authors place a mated queen and several pupae into their cell and observe the structures that emerge both before and after the pupae eclose (i.e., "colony maturation" experiments); second, the authors create small groups (of 5,10, or 15 ants, each including a queen) within a narrow age range (i.e., "fixed demographic" experiments) to explore the dependence of age on construction. Some of the fixed demographic instantiations included a manually induced catastrophic collapse event; the authors then compared emergency repair behavior to natural nest creation. Finally, the authors introduce a modified logistic growth model to describe the time-dependent nest area. The modification introduces parameters that allow for age-dependent behavior, and the authors use their fixed demographic experiments to set these parameters, and then apply the model to interpret the behavior of the colony maturation experiments. The main results of this paper are that for natural nest construction, nest areas, and morphologies depend on the age demographics of ants in the experiments: younger ants create larger nests and angled tunnels, while older ants tend to dig less and build predominantly vertical tunnels; in contrast, emergency response seems to elicit digging in ants of all ages to repair the nest.

We sincerely thank Reviewer #1 for the time and effort dedicated to our manuscript's detailed review and assessment. The revision suggestions were constructive, and we have provided a point-by-point response to address them.

Reviewer #2 (Public review):

I enjoyed this paper and the approach to examining an accepted wisdom of ants determining overall density by employing age polyethism that would reduce the computational complexity required to match nest size with population (although I have some questions about the requirement that growth is infinite in such a solution). Moreover, the realization that models of collective behaviour may be inappropriate in many systems in which agents (or individuals) differ in the behavioural rules they employ, according to age, location, or information state. This is especially important in a system like social insects, typically held as a classic example of individual-as-subservient to whole, and therefore most likely to employ universal rules of behaviour. The current paper demonstrates a potentially continuous age-related change in target behaviour (excavation), and suggests an elegant and minimal solution to the requirement for building according to need in ants, avoiding the invocation of potentially complex cognitive mechanisms, or information states that all individuals must have access to in order to have an adaptive excavation output.

We sincerely thank reviewer #2 for the time and effort dedicated to our manuscript's detailed review and assessment. We have provided a point-by-point response to the reviewer's comments, which we have incorporated into the revised version of the manuscript.

The only real reservation I have is in the question of how this relationship could hold in properly mature colonies in which there is (presumably) a balance between the birth and death of older workers. Would the prediction be that the young ants still dig, or would there be a cessation of digging by young ants because the area is already sufficient? Another way of asking this is to ask whether the innate amount of digging that young ants do is in any way affected by the overall spatial size of the colony. If it is, then we are back to a problem of perfect information - how do the young ants know how big the overall colony is? Perhaps using density as a proxy? Alternatively, if the young ants do not modify their digging, wouldn't the colony become continuously larger? As a non-expert in social insects, I may be misunderstanding and it may be already addressed in the citations used.

We thank the reviewer for this interesting question. We find that the nest excavation is predominantly performed by the younger ants in the nest, and the nest area increase is followed by an increase in the population. However, if the young ants dig unrestricted, this could result in unnecessary nest growth as suggested by reviewer #2. Therefore, we believe that the innate digging behavior of ants could potentially be regulated by various cues such as;

(a) Density-based: If the colony becomes less dense as its area expands, this could serve as a feedback signal for young ants to reduce or stop digging, as described in references (25, 29, 30).

(b) Pheromone depositions: If the colony reaches a certain population density, pheromone signals could inhibit further digging by young ants, references (25, 29), or space usage as a proxy for the nest area. 

Thus, rather than perfect information, decentralized control, and digging-based local cues probably regulate the level of age-dependent digging, without the ants needing to estimate the overall colony size or nest area.

In any case, this is an excellent paper. The modelling approach is excellent and compelling, also allowing extrapolation to other group sizes and even other species. This to me is the main strength of the paper, as the answer to the question of whether it is younger or older ants that primarily excavate nests could have been answered by an individual tracking approach (albeit there are practical limitations to this, especially in the observation nest setup, as the authors point out). The analysis of the tunnel structure is also an important piece of the puzzle, and I really like the overall study.

We thank the reviewer for the comments. We completely agree that individual tracking of ants within our experimental setup would have been the ideal approach, but we were limited by technical and practical limitations of the setup, as pointed out by the reviewer, such as; 

(a) Continuous tracking of ants in our nests would have required a camera to be positioned at all times in front of the nest, which necessitates a light background. Since Camponotus fellah ants are subterranean, we aimed to allow them to perform nest excavation in conditions as close to their natural dark environment as possible. Additionally, implementing such a system in front of each nest would have reduced the sample sizes for our treatments.

(b) The experimental duration of our colony maturation and fixed demographics experiments extended for up to six months (unprecedented durations in these kinds of measurements). These naturally limited our ability to conduct individual tracking while maintaining the identity of each ant based on the current design.

These details are described in detail within the revised version of the manuscript.

Reviewer #3 (Public review):

Summary:

In this study, Harikrishnan Rajendran, Roi Weinberger, Ehud Fonio, and Ofer Feinerman measured the digging behaviours of queens and workers for the first 6 months of colony development, as well as groups of young or old ants. They also provide a quantitative model describing the digging behaviours and allowing predictions. They found that young ants dig more slanted tunnels, while older ants dig more vertically (straight down). This finding is important, as it describes a new form of age polyethism (a division of labour based on age). Age polyethism is described as a "yes or no" mechanism, where individuals perform or not a task according to their age (usually young individuals perform in-nest tasks, and older ones foraging). Here, the way of performing the task is modified, not only the propensity to carry it or not. This data therefore adds in an interesting way to the field of collective behaviours and division of labour.

The conclusions of the paper are well supported by the data. Measurements of the same individuals over time would have strengthened the claims.

We sincerely thank reviewer #3 for the time and effort dedicated to our manuscript's detailed review and assessment. We completely agree with the reviewer’s comments on the measurements of the same individuals over time, however, we were limited by the technical and experimental limitations as described above and pointed out by reviewer #2.

Strengths:

I find that the measure of behaviour through development is of great value, as those studies are usually done at a specific time point with mature colonies. The description of a behaviour that is modified with age is a notable finding in the world of social insects. The sample sizes are adequate and all the information clearly provided either in the methods or supplementary.

We thank reviewer #3  for this assessment.

Weaknesses:

I think the paper is failing to take into consideration or at least discuss the role of inter-individual variabilities. Tasks have been known to be undertaken by only a few hyper-active individuals for example. Comments on the choice to use averages and the potential roles of variations between individuals are in my opinion lacking. Throughout the paper wording should be modified to refer to the group and not the individuals, as it was the collective digging that was measured. Another issue I had was the use of "mature colony" for colonies with very few individuals and only 6 months of age. Comments on the low number of workers used compared to natural mature colonies would be welcome.

Regarding the main comment 1

We completely agree with the reviewer’s comment on considering inter-individual variability based on activity levels. We have discussed how individual morphological variability could influence digging behavior (references: 28, 31), and we will elaborate further on this aspect in future revisions.

Regarding the main comment 2:

The term ‘colony maturation’ in our study refers to the progressive development of colonies from a single queen, distinguishing it from experiments that begin with pre-established, demographically stable colonies. We provide a detailed explanation for this terminology in the revised version of the manuscript. We were practically limited by the continuation of the experiments for more than 6 months of age, predominantly due to the stability of nests, as they were made with a sand-soil mix. We also acknowledge that the colony sizes attained in our maturation experiments may be smaller than those of naturally matured colonies. This trend was observed generally in lab-reared colonies and could be attributed to differences in microclimatic conditions, foraging opportunities, space availability, and other factors. We have explicitly described these details in the revised version of the manuscript.

Reviewer #1 (Recommendations for the authors):

The experimental design is fantastic. The large quasi-2D should allow for the direct visualization of the movements of individuals and the creation of the nest, and the inclusion of non-workers (specifically, a mated queen and pupae) is new and important. However, I have some questions and concerns about the results, as outlined below. Also, I found the paper difficult to read, and the connections between the various experiments and the model were not always clear. 

We thank the reviewer for the time and effort dedicated to reviewing our manuscript. We have modified the manuscript substantially to address the comments and readability. 

The assumption that the digging rate is constant across ants may be a strong one. Previous work (see, for instance, Aguilar, et al, Science 2018) has demonstrated a very heterogeneous workload distribution among ants. I am not sure what implications that may have for the results here, but the authors should comment on this choice. Related to the point above, given a constant digging rate, the variation in digging is attributed to an age-dependent "desired target area". Can the authors comment on the implications of this, specifically in contrast to a variable digging rate? The distinction between digging rate differences and target area differences seems to be important for the authors. However, the way this is presented, it is difficult to fully understand or appreciate this importance and its implications. What is the consequence of this difference, and why is this important?

We apologize to the reviewer for the confusion.

Our model does not assume that the digging rate (da/dt, Equation 1) remains constant throughout the experiment. Instead, we only treat the basal digging rate (r) as a constant.

The variable digging rate (da/dt, Equation 1) is derived by multiplying the basal rate constant (r) by the term (1 - a/a_age), which accounts for deviations from the age-dependent target area that the ants aim to achieve. This makes the actual digging rate dynamic, as it responds to changes in excavated area (e.g., expansion or rapid collapse)

For example, according to our model (Equation 1), two ants with the same basal digging rate (r) may exhibit markedly different actual digging rates at a given time if they differ in age. This occurs because the variable digging rate (da/dt) depends not only on ‘r’ but also on the age-dependent term (1 - a/a_age). Also, we emphasize that the use of a basal digging rate constant aligns with prior studies (refs. 24, 29, 30).

In our work, we demonstrate that after a collapse event, ants of all ages dig at rates comparable to those observed in the initial (pre-collapse) phase of the experiment. This occurs because the ants are far from their age-dependent target area, effectively resetting their digging behavior. By comparing maximum digging rates pre- and post-collapse, we provide strong empirical evidence that this rate is age-independent (SI Fig. 6A, 6B), supporting the conclusion that the basal digging rate constant (r) is a fundamental property of the ants' behavior, unaffected by age.

We agree with the reviewer that individual tracking of ants within our experimental setup would have been the ideal approach. Then, we could have taken the inter-individual variability of the digging activity into account. However, we were limited to doing so by the technical and practical limitations of the setup, such as; 

(a) Continuous tracking of ants in our nests would have required a camera to be positioned at all times in front of the nest, which necessitates a light background. Since Camponotus fellah ants are subterranean, we aimed to allow them to perform nest excavation in conditions as close to their natural dark environment as possible. Additionally, implementing such a system in front of each nest would have reduced the sample sizes for our treatments.

(b) The experimental duration of our colony maturation experiments extended for up to six months (unprecedented durations in these kinds of measurements). These naturally limited our ability to conduct individual tracking while maintaining the identity of each ant based on the current design.

In light of these points, the following lines are added to the discussion (line numbers: 283-295), signifying the above points:

“Our age-dependent model demonstrates that the digging behavior in Camponotus fellah is governed by a basal digging rate constant (r) modulated by the age-dependent feedback (1 − a/aage). Crucially, we show that after a collapse, the maximum digging rates return to their pre-collapse levels, suggesting that this basal rate ’r’ represents an age-independent ceiling on how fast ants can dig, regardless of age or context (SI Fig. 6 A, B). Previous studies have demonstrated both homogeneous and heterogeneous workload distribution, with varying digging rates among ants (24, 29, 30, 35). Studies showing heterogeneous workload distribution relied on continuous individual tracking of ants to quantify digging rates (35). However, this approach was not feasible in our current design due to the experimental durations of both our colony maturation and fixed demographics experiments. Additionally, sample size requirements naturally limited our ability to conduct continuous individual tracking during nest construction in our study. Thus, based on empirical measurements from our fixed-demographics experiments and supported by the age-independent post-collapse digging rates, we adopted a constant basal digging rate for simulating our age-dependent model—an assumption aligned with both prior literature and the collective dynamics observed in our system (24,29,30)”.

Model: as presented, the model seems to lack independent validation. The model seems to have built-in that there is an age-dependent target area, and this is what is recovered from the model. I am failing to see what is learned from the model that the experiments do not already show. Also, the model has no ant interactions, though ants are eusocial and group size is known to have a large effect on behavior (this is acknowledged by the authors at the beginning of the discussion). Can the authors comment on this?My recommendation would be to remove the model from this paper or improve the text to address the above comments.

We did not draw the conclusion of the age-dependent target area from our model. We used the fixed demographics experiments to quantify the age-dependent area target as a function of the age of individuals. We then used this age-dependent area target in our model to quantify the excavation dynamics of the colony maturation experiments, where ants span a variety of ages, as the nest population changes over time, resulting in natural variation in the ages of individuals within the nest.  These results could not have been obtained by performing any of the individual experiments, whether colony maturation or the fixed demographics, young or old, on their own. The need for different age demographics was crucial to quantify the age-dependent effects in nest excavation, which were lacking in previous studies. 

First, the age-dependent model provides a very good estimate for the natural growth of the nest.  More importantly, after fixing an age threshold of 56 days (mean + standard deviation of the young ant age), the model provides an estimate of which ants are doing the majority of the digging during natural nest expansion. This teaches us that during natural expansion, the older ants are far from their density target and therefore do not engage in any substantial digging, which is shown in Figure 4. C. 

On the other hand, the younger ants are close to their area targets and induced to dig. Indeed, the target area fitted for the age-independent model closely approximates the empirically measured age-dependent target when extrapolated to very young ants. This provides further support for the idea that, in the colony maturation experiments, the youngest ants are responsible for most of the digging.

Our model is a simple analytical model, inspired by earlier models that used a fixed area target (such as density models) for nest construction. However, because we knew the precise age of workers in our experiments, we were able to obtain age-dependent area targets, thereby challenging the use of a constant area target (as employed in prior studies) in light of our findings from the fixed demographics of young and old colonies.

Empirically Quantifiable Parameters: We wanted our model to have empirically quantifiable parameters. Since we did not continuously record the experiment, we could not quantify agent-agent interactions, pheromonal depositions, or similar factors.

Minimal Model Design: We aimed to keep the model as minimal as possible, which is why we did not include complex interactions such as those found in continuous tracking experiments.

However, the model does set up some interesting hypotheses that could easily be tested with the experimental setup (e.g., marking the ants / tracking individual activity levels). For instance, it is hypothesized that older ants dig less often, but when they do dig, they do so at the same rate. Given the 2D setup, the authors could track individual ants and test this hypothesis. Also, if the desired target area does decrease with age, the authors could verify this hypothesis by placing older ants into arenas with different-sized pre-formed nests to observe how structure is changed to achieve the desired area/ant.

We thank the reviewer for this comment.

We believe that the confusion with the usage of a constant basal digging rate is resolved now. To briefly reiterate, ants dig at variable rates that can be decomposed to a (constant on short time scales but age-dependent) basal rate times the (variable) distance from the density target. The suggested experiments are beyond the scope of our current study, and further studies could utilize the suggested experimental design with better time-resolved imaging for individual ant tracking that could verify the predictions from our model. 

Specific comments:

Title:

The title suggests a broad result, yet the study focuses on one ant species. Please modify the title to more accurately reflect the scope of the work.

We thank the reviewer for the comment.

The title is modified as “Colony demographics shape nest construction in Camponotus fellah ants.”

Introduction:

Important information and context are missing about this ant species. For instance, please add the following about this species in the introduction:

What is their natural habitat and substrate? How does the artificial soil compare?

What is their (rough) colony size? [later, discuss experiment group size choice and potential insights/limitations of results when applied to the natural system].

The details have been added to the introduction (line numbers : 49-55) and the materials and methods section (Study species).

“Camponotus fellah ants are native to the Near East and North Africa, particularly found in countries like Israel, Egypt, and surrounding arid and semi-arid regions, where they prefer to nest in moist, decaying wood, including tree trunks, branches, or stumps (49,50). The species lives in monogynous colonies with tens to thousands of individuals. Nests are commonly found in a sand-loamy mix, which is a combination of sand, soil, clay, or gravel, providing structural stability and moisture retention (51). They are typically found under rocks, in the crevices of dried vegetation, or dry, sandy soils, sometimes in areas with loose gravel, with a colony size ranging from tens to thousands of workers”.

What is the natural life expectancy of a worker? A queen? [later, discuss fixed demographic age choices in this context and/or why were age ranges chosen for experiments?].

The lifespan of ants, including both queens and workers, varies significantly based on caste, species, and environmental conditions.

(1) Queen Longevity: From the literature, Camponotus fellah queens can live up to 20 years, with one documented case reaching 26 years (50). 

(2) Worker Longevity: In contrast to queens, the lifespan of workers is much shorter. Lab studies on Camponotus fellah (82) and other Camponotus species (83) suggest that workers can live for several months depending on environmental conditions, colony health, and caste-specific roles (e.g., minor vs. major workers)

(3) Laboratory vs. Natural Conditions: Worker longevity is highly variable between laboratory and natural conditions

Therefore, in the context of the old worker lifespan in our experiments, ~200 days (roughly 6–7 months), we strongly believe that the worker lifespan used in our experiments represents a substantial portion of a worker's expected life. While exact figures for C. fellah workers are unavailable, inferences from related species suggest that workers nearing 200 days are approaching the latter stages of their lifespan, making them meaningfully "old". 

The details are added to the main text (line numbers: 124-127) and discussion (line numbers: 278-282).

Why was this species chosen? Convenience, or is there something special about this species that the readers should know? Specifically, is there something that might make the results more general or of broader interest?

Camponotus fellah was chosen for this study because it is native to Israel, making it convenient to collect and maintain in the lab. Additionally, its nuptial flights occur close to the study location, ensuring a steady supply of colonies. We were able to provide them with a nesting substrate similar to what they naturally use, as their nests are typically found in a sand-loamy mix, similar to the sand-soil mix in our artificial nests. This was possible because we had the opportunity to observe their habitat and nesting behavior in the wild, allowing us to gather preliminary information on their natural nesting conditions.

Results:

Line 60: "several brood items" - how many exactly? Was this consistent across experiments? Do mated queens ever produce more pupae during the experiments?

Yes, the number of brood items (5) was added consistently across the experiments. Additionally, the mated queen did produce pupae during the course of the experiments, which was evident from the noticeable increase in the number of workers in the nest. This was significantly higher than the number of brood items present at the start of the study.

The above points are added to the section (line numbers : 68-69).

Figure 1: Panel A - The food ports are never mentioned in the text. Are the ants fed during the experiments? If so, what? With what frequency? Is the water column replenished/maintained? If so, how and how often? panel C - how long did this experiment last?

We thank the reviewer for pointing this out. We have now updated the nest maintenance section in the Materials and Methods (line numbers : 349-354) part to include all the necessary details and clarifications.

“We provided food to the ants ad libitum through three separate tubes containing water, 20 % sucrose water, and protein food. The protein mixture included egg powder, tuna, prawns, honey, agar, and vitamins. Each of the three tubes was filled with 5 ml of their respective contents and sealed with a cotton stopper to prevent overflow. The tubes were positioned at a slight angle and connected using a custom-made plexiglass adapter to facilitate the flow of liquids. These tubes were replenished once depleted, and regularly replaced once the nest maintenance was carried out bi-weekly.”

Line 76: "...excavation was commenced by the founding queen". How were the queen and pupae introduced into the system?

We initiated colony maturation experiments by introducing a single mated queen and several brood items (pupae) at random positions on the soil layer of the nest (line numbers : 68-69)

Line 87: Please provide bounds for 11cm2/ant value. Is there any biological or physical justification for this number?

We thank the reviewer for the suggestion. We have now provided the bounds as requested (line numbers : 97-101). 

We were unable to pinpoint a specific biological justification based solely on this treatment. However, on extrapolating the age-dependent area fit we derived from the fixed demographics experiment, we found that at the age of 1 day, an ant has a target area of approximately 11.17 cm², which is the largest age-dependent area target possible within our experimental setup.

From the colony maturation experiment, we obtained the value of  11.6 (±1.15) cm² as the area per ant. The consistency between the area per ant obtained from two completely different treatments across different colonies yielded similar results. We propose that under standardized conditions, a 1-day-old ant has a theoretical maximum target area of 11.17 cm²—the highest value observed in our experimental framework.

Lines 98-99: "one straightforward possibility would be that newborn ants are the ones that dig". This statement contradicts the results presented in Figures 1 and S1 - the population increase seems to occur at least a few days before increased excavation in nearly all cases.

We apologize for any confusion caused by our initial phrasing. To clarify, we proposed that a lag likely exists between population growth and nest area expansion. This lag could arise from two sequential processes: (1) newborn ants require time to mature and become active (first delay), and (2) digging to expand the nest takes additional time (second delay; estimated at ~10 days from the cross-correlation analysis). Thus, our results suggest that it is not the population that lags behind the area, but rather the area that lags behind the population, as demonstrated in Figures 2D and SI. Figure. S1.

The sentence “one straightforward possibility would be that newborn ants are the ones that dig” is modified as below (line numbers : 112-119) to prevent further confusion.

“One possible explanation is that, although all ants are capable of digging, it is primarily the newly emerged ants who perform this task. In this case, nest expansion would lag behind colony growth due to two delays: first, the time needed for young ants to mature enough to begin digging, and second, the physical time required to excavate additional space (e.g., around 10 days). This mechanism could eliminate the need for ants to assess overall colony density, as each new group of active workers simply enlarges the nest as they become ready. An alternative possibility is that all ants, regardless of age, respond to increased density by initiating excavation. In that scenario, nest expansion would follow more immediately after the emergence of new individuals, making delays less prominent (24, 29, 30)”.

Line 105: How do group sizes compare to natural colony size? Line 106: How do "young" and "old" classifications compare to natural life expectancy?

We have already addressed this question in an earlier comment. The details are added to the main text (line numbers: 124-127) and discussion (line numbers: 278-282).

Line 118-119: How are nests artificially collapsed?

We have added a new section in the Materials and Methods section that describes the nest collapsing procedure (Nest artificial collapse - line numbers : 386-399).

Figure 2 Panel A: The white dotted line is nearly impossible to see. Please use a more visible color.

We thank the reviewer for the comment.

We changed the solid circles to violet and the dotted line color to continuous white.

Figure 3: The use of circle markers as post-collapse recovery in young and old as well as old pre-collapse is confusing. Use different symbols for old pre-collapse vs young and old post-collapse.

We thank the reviewer for pointing out the confusion. We have revised the figure markers as suggested and modified the main text accordingly.

• Young; pre-collapse : star

• Young; post-collapse : diamond

• Old; pre-collapse : circle

• Old; post-collapse: triangle.

Figure 3 Panel C: Indicate that fixed demographic values here are pre-collapse. Also, as presented, it appears that there is a large group-size dependence that is not commented on. Previous results (Line 87 and Figure 2C) suggest a constant excavation area per ant of 11cm2/ant. Figure 3, panel C appears to suggest a group-size dependence. If these values are divided by group size, is excavated area per ant nearly constant across groups? How does the numerical value compare to the slope from Figure 2C?

We thank the reviewer for their insightful comments.

First, we would like to clarify that the area target of 11.1 (±1) cm²/ant, as described in Line 87, was obtained from the colony maturation experiments. In these experiments, we were unable to track the age of each individual ant, so the area target was calculated by normalizing the total excavated area by the number of ants.

We normalized the excavated area by the group size for both young and old colonies as suggested, and found that the area per ant was not significantly different across the group sizes (see new SI Fig. 5A). This indicates that the excavated area per ant remains relatively constant within each demographic group. Moreover, this shows that the total excavated area is proportional to group size, in agreement with previous works (24, 29, and 30). 

We have explicitly described the above information in the line numbers: 142-146

Regarding the slope comparisons, the slope of Figure 2C (10.71), from the colony maturation experiments, is the largest, followed by the area per ant from the short-term young (8.79 ± 0.98) cm²/ant, and short-term old experiments (5.16 ± 0.44) cm²/ant.

Lines 128-129: "...younger ants aim to approach a higher target area". Seems hard to know what they "aim" to do... rephrase to report what they are observed to do.

We thank the reviewer for the comment. The sentence is rephrased as suggested (line numbers : 158-161).

“In the previous sections, we showed that in fixed-demographics experiments, younger ants excavated a significantly larger nest area compared to older ants (Fig. 3. C).  This difference emerged despite similar temporal patterns in digging rates across age groups, with excavation activity peaking within the first 7 days before asymptotically decaying as nest expansion approached saturation (SI Fig. 8).”

Lines 133-141: The model description is not clear. Specifically, what parameters are ant-dependent? How does A relate to a?

We appreciate the reviewer's request for clarification. In our model:

(1) Equation 1 describes the change in the excavated area due to the digging activity of a single ant. Here, the variable 'a' represents the area excavated by one ant. This formulation allows us to capture the individual digging behavior and its impact on the excavation process.

(2) Equation 2 extends this concept to the total area excavated in the nest, denoted by 'A'. Specifically, 'A' is the sum of the areas excavated by all ants present in the nest. In other words, it aggregates the individual contributions of each ant, linking the microscopic digging behavior to the macroscopic excavation dynamics.

Therefore, the relationship between 'a' and 'A' is as follows:

●     'a' = Area excavated by a single ant.

●     'A' = ∑ 'a' (Summed over all ants in the nest).

We have explicitly mentioned this in the line numbers “ 161-179”, and describe the model assumptions and parameters in detail.

Figure 4:

Figure 4, Panel A: The equation quoted in the caption does not match the data in the figure. The equation has a positive slope and negative intercept, while the figure has a negative slope and a positive intercept. Please provide the correct equation and bounds on fit parameters.

We thank the reviewer for spotting this typing mistake.

The equation was already updated in the reviewed preprint published online. The correct equation and the fit bound are provided in the figure caption.

“Target areas decrease linearly with the ant age (y = −0.032x + 11.22 , 95 % CI (Intercept : (-0.035,-0.027), Slope : (10.53,11.91)), R2 = 0.96 ).”

Figure 4, Panel A: There seem to be three "fixed target area per ant values" in the paper: around 11cm2/ant (line 87), 11.6 cm2/ant (SI Figure 2), and linearly dependent value from fit to Figure 4A. The distinctions between these values and their significance are hard to keep track of. Can the authors add a discussion somewhere that helps the reader better understand? Is there a way to connect/rationalize/explain these different values in terms of demographics?

We thank the reviewer for the suggestion.We have added a paragraph in the discussion (line numbers : 270-277) describing the area targets.

“In our colony maturation experiments, we found that area per ant was highest when the workers were youngest, with values around 11.1–11.6 (±1–1.15). This aligns with observations from naturally growing nests, where newly eclosed ants dominate the population and nest volumes are relatively large. Supporting this, fixed-demographics experiments showed that the area excavated per ant declines linearly with worker age, indicating that the youngest ants contribute most to excavation. Notably, the target area we fit for the age-independent model (11.6 ± 1.15) closely matches the extrapolated value for very young workers (Fig. 4. A), reinforcing the idea that young ants are the primary excavators during early colony growth. In contrast, during events like collapses or displacement, when space is urgently needed, ants of all ages participate in excavation.”

Figure 4, Panel A: What are various symbols and colors for data with error bars? If consistent with Figure 3, then this panel and subsequent model confound two factors: (1) the age dependence and (2) the behavioral differences pre- and post-collapse (structures are different pre-and post-collapse, according to SI Figure 6; line 120: "...colonies ceased digging when they recovered 93{plus minus}3% of the area lost by the manual collapse..."; lines 201-202: "We find significant quantitative and qualitative differences between nests constructed within this natural context and nests constructed in the context of an emergency") and behavior is different (according to SI Figure 7 and line 119: "...all ants dig after collapse...")). Therefore, without further supporting evidence, it does not seem that these data should be used to fit a single line that defines a model parameter a_age for each ant in equation 2.

The symbols are the area per ant quantified from the fixed demographics of young, and old experiments. The symbols show the following;

A.  Star - Young, pre-collapse

B.  Diamond - Young, post-collapse 

C.  Circle - Old, pre-collapse

D.  Triangle - Old, post-collapse.

The details are clearly described in the figure caption. 

We apologize to the reviewer for the confusion. We argue that the data can be fit by a single line to quantify the parameter ‘a_age’ as follows. 

A. All data presented in Figure 4A were obtained from the same fixed-demographics experiments (containing only young and old ants) under experimental collapse conditions, pre- and post-collapse. These results, therefore, exclusively reflect emergency nest-building behaviors during emergency scenarios and do not include any observations from natural colony maturation processes.

B. Age-dependent excavation differences: As correctly noted by the reviewer, the observed difference in excavated area before versus after collapse reflects the natural aging of ants in our experimental colonies. While colonies recovered >90% of lost area post-collapse, the residual variation was not negligible—instead, it systematically correlated with colony age structure. By tracking colonies across this demographic transition, we obtained additional data points spanning a broader developmental spectrum. This extended range strengthened our ability to detect and quantify the linear relationship between worker age and excavation output.

C.The quoted sentence (lines 201-202, submitted version) refers to comparisons across all three experimental cases: (1) fixed-demographics young ants, (2) fixed-demographics old ants, and (3) the natural scenario (mixed-age colonies). Importantly, these comparisons are based on pre-collapse steady-state excavation areas, ensuring a consistent baseline across treatments. We highlight quantitative and qualitative differences between these distinct experimental groups, not between pre- and post-collapse phases within the same treatment. The pre- and post-collapse data within fixed-demographics groups were analyzed separately to avoid conflating aging effects with emergency responses.

To avoid confusion, the whole paragraph in the discussion (line numbers : 253-260) is rephrased.

In lines 201-202; “We find significant quantitative and qualitative differences between nests constructed within this natural context and nests constructed in the context of an emergency”. 

Here, by natural context, we mean the nests excavated in the colony maturation experiments. We believe that it could have been confusing, and the sentence is modified as answered for the previous question. 

Figure 4, Panel B: This uses the model with a_age determined by from Figure 4A and the life table (as shown in the supplemental), whereas the supplemental Figure SI 8 uses the fixed blue line a_age value for the model, which comes from the colony maturation experiments. The age-independent model in the supplemental fits the data better, yet the authors claim the supplemental model cannot be applied to the data because of their experimentally determined age-dependent target area. Given the age-independent target area model fits better, additional evidence/justification is needed to support the choice of the model.

We agree with the reviewer that the age-independent model fits the data well. However, we believe that the fixed area target cannot be used to explain the excavation dynamics for the following reasons.

We make an important assumption in our model: that the ants rely on local cues and that individual ants can not distinguish between the fixed demographics and colony maturation experiments (line numbers : 161-166). Given this assumption, the ants cannot change their behavior between experiments, meaning the same model should fit all of our results. However, the fixed demographics experiments revealed a significant difference in the areas excavated by young vs. old cohorts, despite having the same group size. If the ants regulated the excavated area based on an age-independent constant density target model, then the excavated area in the fixed demographics of young and old colonies would have been similar. This discrepancy indicates that the target area per ant is not constant, as assumed in the age-independent density model (SI. Fig. 8). We emphasize that while the age-independent model provides a better fit for the excavated area in colony maturation experiments, the age-dependence of excavation is empirically supported by fixed-demographics experiments. Therefore, we implemented this age-dependence through a variable target area within the age-dependent model framework to explain excavation dynamics in the colony maturation experiments.

These details are explicitly mentioned in the main text (line numbers : 187 - 198)

Figure 4, Panel C: Is this plot entirely from the model, or are the data points measured from experiments? Please label this more clearly.

We apologize to the reviewer for the confusion.

The Figure 4C is based on the age-dependent digging model. We applied the model to population data from the long-term experiments (n = 22). By setting an age threshold of 56 days (since ants used in the short-term young experiment had an average age of 40 ± 16 days), we categorized the ants into young and old groups. We then quantified the area dug by the young ants, the queen, and the old ants in terms of the percentage of the total area excavated. We hypothesized that, because young ants have a lower digging threshold, they would perform the majority of the digging. We indeed confirm this in Figure 4C.

This information is added to the main text and described in detail (line numbers: 200 - 208).

Lines 162-165: "...Furthermore, we quantified the area dug by each ant in the normal colony growth experiment as estimated from the age-dependent model and found that all ants excavated more or less the same amount...". Figure 4D shows a distribution with significant values ranges from 1-16 cm2... how is this interpreted as "more or less the same amount" and what is the significance of this?

We apologise to the reviewer for the confusion.

We quantified the percentage contribution to the excavated area of each histogram bin (provided in the new SI table: 4), and found that the area excavated between 5 cm² and 13 cm² accounts for 73.76% of the total excavated area. This indicates that most ants dug within this range rather than exhibiting extreme variations. Additionally, the mean excavation amount is 7.84 cm², with a standard deviation of 3.44 cm², meaning that most values fall between 4.4 cm² and 11.28 cm², which aligns well with the 5–13 cm² range. Since the majority of the excavation is concentrated within this narrow interval, and the mean is well centered within it, this suggests that ants excavated more or less the same amount, rather than forming distinct groups with highly different excavation behaviors.

We have modified the main text (line numbers: 209-216) to include these points.

The biological significance of this finding is that since all ants in the colony maturation experiments are born inside the nest, we hypothesize that they should excavate similar amounts. To test this, we quantified the area contribution of each ant over the entire duration of the experiment using the age-dependent digging model as described above and found that they indeed excavated more or less the same amount. From our analysis of fixed demographics experiments, we showed that the youngest ants excavate the largest area. Since the majority of the youngest ants participated in the colony maturation experiments, this further supports our hypothesis.

Figure 5.

Figure 5, Panels A-C: Please provide a scale bar. 

The scale bar is provided in the figure as suggested. The algorithm for the cutoffs for tunnel vs wide tunnels is described in detail in the section “Nest skeletonization, segmentation, and orientation.”

Figure 5, Panel E: Why does the chamber error bar for 5 ants go to zero?

In Figure 5, E, we plot the standard error, as described in the figure caption. In the experiments, the chamber area contributions were (0,0,39.94,0) respectively. The mean of the 4 numbers is 9.985, the standard deviation is 19.97, and the standard error is 9.985. So, the mean and the standard error are the same, so the lower error bar goes to zero, and the upper error bar goes to 19.97. This implies that in these experiments, the chamber area is often zero.

Figure 5, Panel I: Why are there no chambers for young colonies in I when they are in the histogram in E?

We apologize to the reviewer for the confusion. We initially missed adding the chamber orientation data of the young colonies to Panel I, but it has now been included.

Line 212: "...densities of ants never become too high...". What is too high? Is there some connection to biological or physical constraints?

Under normal growth conditions, nest volume is kept proportional to the number of ants, ensuring that the density remains within a specific range. This prevents overcrowding, which could otherwise lead to excessively high densities.

Yes, we believe there is likely a connection to both biological and physical constraints. The proportional relationship between nest volume and the number of ants is likely driven by factors such as:

(1) Biological Constraints:

Ant Colony Size: Ants typically adjust their behavior and social structure to maintain an optimal population size relative to available resources and space.Overcrowding could lead to potentially a breakdown in colony function.

Colony Health: High densities can lead to faster epidemic spread, leading to negative effects on reproduction, foraging efficiency, and overall colony health. By maintaining density within a specific range, the colony can thrive without these adverse effects.

(2) Physical Constraints:

Spatial Limitations: The physical space within the nest limits how many ants can occupy it before space becomes constrained. The nest’s structure and size must physically accommodate the ants, and the volume must be large enough to prevent overcrowding, and efficient resource distribution.

Lines 272 and 302: How often were photos taken? These two statements seem to suggest different data collection rates.

As stated in line 272, photos were taken every 1 to 3 days. During each photo session, four photos were taken, with each photo separated by 2 seconds, as mentioned in line 302. To avoid confusion, we rephrased the sentence (line numbers: 359-361).

“We photographed the nest development every 1-3 days. During each photography session, four pictures of the nest were taken, with a 2-second interval between each.”

Reviewer #2 (Recommendations for the authors):

Some more minor points/questions/clarifications:

This might be pedantic, but I don't think the nest serves as the skeleton of the superorganism, while it does change and grow, the analogy becomes weak beyond that point. The skeleton serves to protect the internal organs of the organism, facilitates movement and muscle attachment, and creates new blood cells. I would be more comfortable with a statement that the nest can grow or shrink according to need.

We sincerely thank the reviewer for their time and effort in providing a detailed review and assessment of our manuscript. A point-by-point response to the comments is provided below.

The analogy of treating a nest structure to the skeleton of a superorganism was based on the following points;

(a) Protection: A nest protects the colony on a collective scale. This is analogous to protecting "organs" by a skeletal framework.

(b) Organization and Division of Space: The skeletal structure organizes the body's internal layout, just as nest structures are organized into various spatial compartments for various colony functions, with specific regions designated for brood chambers, food storage, and waste disposal.

Thus, we believe that the analogy can still be valid in a metaphorical way.

Does this statement need justification with a citation, or is that information contained in the subsequent clause? "However, for more complex structures where ants congregate in specific chambers, workers are less likely to assess the overall nest density." The idea that workers do (or do not) assess overall density touches on many issues, including that of perfect information and adaptive responses, that it seems it needs to be well founded in previous work to be stated in such unequivocal terms.

We thank the reviewer for this comment. The references for this argument are provided in the next sentence. We have now moved these references to the relevant sentence (reference number: 24, 29,30; line number : 30-31 ) 

Can you give some more information on this statement? "Experiments were terminated either when the queen died or when she became irreversibly trapped after a structural collapse." Why was this collapse irreversible and therefore unlike treatment 2? Did the queen die in these instances? Was this event more likely than in natural colonies? And if so, was there something inherently different about your experiments that limit interpretation under natural conditions (e.g. the narrow nature of the observation setup? The consistency of the sand?)

Our nest excavation experiments were terminated under two primary scenarios: (1) the queen died of natural causes, reflecting the baseline mortality expected when queens are brought into laboratory conditions, or (2) the nest experienced a structural collapse that left the queen irreversibly trapped. The second scenario is further elaborated below:

Irreversible Collapses: These collapses were classified as irreversible because the queen could not be rescued alive. This occurred when the structural stability of the nest failed, burying the queen in a manner that prevented recovery. In some cases, the collapse resulted in the queen's immediate death, while in others, she was trapped beyond reach, and any rescue attempt risked further structural damage.

Collapse and Experimental Context: These collapses were not uniquely associated with natural colonies or fixed-demographic experiments; rather, they occurred across various experimental setups.

The sentence is modified as below to improve clarity (line numbers : 70-72 ).

“In all instances where a collapse resulted in the queen's death or her being irreversibly trapped in the nest, the experiment was excluded from analysis starting from the point of the collapse, as such events did not reflect normal colony dynamics.”

I want to make sure I understand the following statement: "Moreover, the area excavated by the young cohorts was similar to that excavated by naturally maturing colonies at the point in which they reached the same population size (Tukey's HSD; group size: 5; p = 0.61, group size: 10; p = 0.46, group size: 15; p = 0.20)." Do I have it right that this means a group of (e.g. 10) young ants excavates an area similar to that of a group of 10 naturally maturing ants at the same age as the young ants?

Yes, the interpretation provided is correct. We apologize to the reviewer for the confusion. We have rephrased the sentence for better readability (line numbers : 146-148).

“Furthermore, the area excavated by the young cohorts was comparable to that excavated by naturally maturing colonies when they reached the same population size (Tukey's HSD; group size: 5, p = 0.61; group size: 10, p = 0.46; group size: 15, p = 0.20)”

How old do ants get? Is the 'old' demographic (~200 days) meaningfully old in the context of the overall worker lifespan? While the results certainly demonstrate there is an age effect, I would like to understand how rapid this is in terms of overall lifespan.

The lifespan of ants, including both queens and workers, varies significantly based on caste, species, and environmental conditions.

(1) Queen Longevity: From the literature, Camponotus fellah queens can live up to 20 years, with one documented case reaching 26 years. This remarkable longevity underscores the queen's central role in maintaining the colony.

(2) Worker Longevity: In contrast to queens, the lifespan of workers is much shorter.

However, specific data on worker longevity in Camponotus fellah colonies are lacking. Studies on other Camponotus species (50, 82) suggest that workers can live for several months depending on environmental conditions, colony health, and caste-specific roles (e.g., minor vs. major workers).

(3) Laboratory vs. Natural Conditions: Worker longevity is highly variable between laboratory and natural conditions

Therefore, in the context of the old worker lifespan in our experiments of, ~200 days (roughly 6–7 months) we strongly believe that the worker lifespan used in our experiments represents a substantial portion of a worker's expected life. While exact figures for C. fellah workers are unavailable, inferences from related species suggest that workers nearing 200 days are approaching the latter stages of their lifespan, making them meaningfully "old."

These details are added to the main text (line numbers : 124 - 127) and to the discussion (line numbers : 278-282)

Reviewer #3 (Recommendations for the authors):

We sincerely thank the reviewer for their time and effort in providing a detailed review and assessment of our manuscript. A point-by-point response to the comments is provided below.

L10: "fixed demographics": I find this term unclear, what does it mean, it should specify if the groups are with or without a queen.

We thank the reviewer for the comment. The sentence is modified in the abstract, and definitions are later added in detail in the introduction (line numbers : 8-10) and the Materials and Methods section (Fixed demographics colonies). 

“We experimentally compared nest excavation in colonies seeded from a single mated queen and allowed to grow for six months to excavation triggered by a catastrophic event in colonies with fixed demographics, where the age of each individual worker, including the queen, is known”.

The details of the “fixed demographics” treatments were explained in the later portion of the text (line numbers: 58-61).

L36: I think it is documented that younger individuals are the ones who involved in nest construction in many species.

Previous studies on nest construction were predominantly performed on mature colonies of specific age demographics or rather mixed demographics, where age was not considered as a factor influencing nest construction. Some studies have speculated that young ants could be the most probable ones to dig, but this has not been experimentally verified to the best of our knowledge.

L50: I do not think the colony should be called mature after only 6 months, given that colonies reach thousands of workers.

The sentence is changed as suggested (line numbers : 56-57).

“The "Colony-Maturation" experiment observed the development of colonies up to six months, starting from a single fertile queen and progressing to colonies with established worker populations.” 

L60: Where was the queen introduced? It is specified in the Methods but a word here would be helpful.

The detail is added as suggested (line numbers : 68-69).

“We initiated colony maturation experiments by introducing a single mated queen and several brood items (n = 5, across all experiments) at random positions on the soil layer of the nest.”

L106: Young vs Old workers 40 vs 171 days. Maybe cite a reference or provide a reason for the selection of those ages?

Previous studies have shown that the Camponotus fellah queens can live up to 20 years, with one documented case reaching 26 years (50). To the best of our knowledge, specific data on worker longevity in Camponotus fellah colonies in natural conditions are lacking. Lab studies on Camponotus fellah (82) and other Camponotus species (50) suggest that workers can live for several months depending on environmental conditions, colony health, and caste-specific roles (e.g., minor vs. major workers). 

We intentionally selected workers from two distinct age groups: younger ants (40 ± 16 days old) and older ants (171.56 ± 20 days old). These ages represent functionally different life stages - the younger group had completed about 25% of their expected lifespan at the start of the experiment, while the older group had lived through most of theirs (50, 82). This 4-fold age difference allowed us to compare excavation behaviors across fundamentally different phases of adult life.

Our experiments lasted for 60-90 days, during which all participating workers continued to age. To ensure all ants remained alive throughout the experiments, and given the constraints of the experimental timeline, we selected young and old workers within the specified age range. 

These details are added to the main text (line numbers :  124 -127), and the discussion (line numbers  : 278-282)

L122-123: But usually ants can vary highly in their behaviours. Can the authors comment on their choice to consider an average, implying that all ants of the same age had the same digging rates?

We thank the reviewer for the comment.

In our experiments, we could not track each worker's activity over time. As described in the methods, we took snapshots of the nest structure over days and recorded the population size of the nest. Thus, we could not capture the activity of single ants in the nest as described in the response to major comments in the reviewed preprint.

We agree that individual tracking of ants within our experimental setup would have been the ideal approach. Then, we could have taken the inter-individual variability of the digging activity into account. However, we were limited to doing so by the technical and practical limitations of the setup, such as; 

(a) Continuous tracking of ants in our nests would have required a camera to be positioned at all times in front of the nest, which necessitates a light background. Since Camponotus fellah ants are subterranean, we aimed to allow them to perform nest excavation in conditions as close to their natural dark environment as possible. Additionally, implementing such a system in front of each nest would have reduced the sample sizes for our treatments.

(b)The experimental duration of our colony maturation and fixed demographics experiments extended for up to six months (unprecedented durations in these kinds of measurements). These naturally limited our ability to conduct individual tracking while maintaining the identity of each ant based on the current design.

To clarify this, we have added the following to the discussion (line numbers: 286-292).

“Previous studies have demonstrated both homogeneous and heterogeneous workload distribution, with varying digging rates among ants (24,29,30,35). Studies showing heterogeneous workload distribution relied on continuous individual tracking of ants to quantify digging rates (35). However, this approach was not feasible in our current design due to the experimental durations of both our colony maturation and fixed demographics experiments. Additionally, sample size requirements naturally limited our ability to conduct continuous individual tracking during nest construction in our study.”

L171: A line on how the nest structure was acquired and data extracted would be welcome here.

The algorithm for the nest structure segmentation, data extraction, and analysis is added in detail to the SI section: Nest skeletonization, segmentation, and orientation. The line is modified (line numbers : 221-224) in the main text as suggested.

“We compared nest architectures by segmenting raw nest images into chambers and tunnels (see SI Section: Nest Skeletonization, Segmentation, and Orientation). Chambers were identified as flat, horizontal structures, while tunnels were narrower and more vertical in orientation (see SI Fig. 9, SI Section: Nest Skeletonization, Segmentation, and Orientation)”.  

Figure 3: Where does the data of the mean in panel C come from: is it the mean of the first 30 days, before the collapse? How is it comparable with the rest?

We apologize to the reviewer for the confusion.

In panel C, the mean values (solid stars and circles) for fixed-demography colonies (young/old groups) represent pre-collapse excavation areas. For colony maturation experiments (where no collapses were induced), we instead plot the mean saturated excavation area for each group size. This allows direct comparison of mean excavated areas across experimental conditions at equivalent colony sizes.

To improve readability, the following sentences are added to the main text (line numbers : 139 - 146 ) 

“We compared the saturated excavation areas (pre-collapse) from fixed-demographics experiments (young and old groups) with those from colony maturation experiments of the same colony sizes (Fig. 3C). We find that, for a given age cohort (young or old), the saturation areas increase linearly with the colony size (GLMM, F(35,37); p < 0.0001) (Fig. 3 C, SI. Fig 7 A). The observed proportional scaling between excavated area and group size aligns with previous studies, even though those studies did not explicitly account for age demographics (24, 29, 30). After normalizing the pre-collapse excavated area by group size for both young and old colonies, we found no significant difference in area per ant across group sizes (SI Fig. 5. A). This indicates that the excavated area per ant remains relatively constant within each demographic group”.

L209-210: I would be more parsimonious in saying that the results presented prove that the target area decreases with age, as the individual behaviour of the ants was not monitored. Suggestion: rephrase to "the target of the group decreases with age".

The sentence is rephrased as suggested (line numbers : 265-266).

“Our results reveal that this target area of the group decreases linearly with age, such that young ants are more sensitive to shortages in space.”

L246: Are C.fellah colonies really found with such few workers?

Previous studies have speculated that mature Camponotus fellah colonies are a monogynous species typically founded by a single queen following nuptial flights (50,51,82), and can range from tens to thousands of workers. However, during the founding stage (as in our experiments), colonies naturally pass through smaller developmental sizes comparable to the matured colonies.

Author response:

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

Reviewer #1 (Public review): 

Summary:  

The Szczupak lab published a very interesting paper in 2012 (Rodriquez et al. J Neurophysiol 107:1917-1924) on the effects of the segmentally-distributed non-spiking (NS) cell on crawl-related motoneurons. As far as I can tell, the working model presented in 2012, for how the non-spiking (NS) cell impacts the crawling motor pattern, is the same functional model presented in this new paper. Unfortunately, the Discussion does not address any of the findings in the previous paper or cite them in the context of NS alterations of fictive crawling. Aside from different-looking figures and some new analyses, the results and conclusions are the same. 

Reviewers #1 and #2 called our attention to our failure to cite the Rodriguez et al. 2012 article in the context of the main goal of the present work. We do now explain how the present study is framed by the published work. See lines 74-79.

In Rodriguez et al. 2012, we hypothesized that the inhibitory signals onto NS were originated in the motoneuron firing. We now cite this reference in line 104. In the current manuscript we further investigated the connection between the inhibitory signals onto NS and the motoneuron activity (Figure 2) and proved that the hypothesis was wrong. Thus, the model presented here differs from the one proposed in Rodriguez et al. 2012.

In Rodriguez et al. 2012, we speculated that the inhibitory signals received by NS were transmitted to the motoneurons, but an important control was missing in that study. In the current study depolarization of NS during crawling is tested against a control series that allows to properly examine the hypothesis (lines 138-147). But, most important, because NS is so widely connected with the layer of motoneurons it was necessary to test the effect on other motoneurons during the fictive crawling cycle. We now explain this rationale in lines 249-257.

Strengths: 

The figures are well illustrated. 

Weaknesses:  

The paper is a mix of what appears to be two different studies and abruptly switches gears to examine how closely the crawl patterning is in the intact animal as compared to the fictive crawl patterning in the intact animal. Unfortunately, previous studies in other labs are not cited even though identical results have been obtained and similar conclusions were made. Thus, the novelty of the results is missing for those who are familiar with the leech preparation. The lack of appropriate citations and discussion of previous studies also deprives the scientific community of fully comprehending the impact of the data presented and the science it was built upon.  

The main aim of the manuscript is to learn the role of premotor NS neurons in the crawling motor pattern studied using spike sorting in extracellular nerve recordings. This readout allows to  simultaneously monitor a larger number of units  than in any previous study. This approach aims to determine whether and how a recurrent inhibitory peripheral circuit is involved in coordinating or modulating the rhythmic motor pattern.

Our rationale was that the known effect of NS on one particular motoneuron (DE-3) may have overlooked a more general effect on crawling (lines 253-257). Moreover, we wanted to investigate whether this effect was due to the recurrent inhibitory circuit or if other elements were involved, and to study whether the modulation was mediated by the recurrent synapse between NS and the motoneurons.

In the context of this aim we studied the rhythmic activity of cell DE-3, together with motoneurons that fire in-phase and anti-phase, in isolated ganglia (Figure 4). To reveal the effect of NS manipulation we applied a quantitative analysis that showed the phase-specific effect of NS (Figure 6). 

Given that this is the first study using a spike sorting algorithm to detect and describe the activity of motoneurons in nerve recordings we found it reasonable to compare these results with an in vivo study; thus, providing information to the general reader, that supports the correspondence between the ex vivo and the in vivo patterns.

(1) Results, Lines 167-170: "While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results present the first quantitative analysis of motor units activated throughout the crawling cycle. The In-Phase units are expected to control the contraction stage by exciting or inhibiting the longitudinal or circular muscles, respectively, and the Anti-Phase units to control the elongation stage by exciting or inhibiting the circular or longitudinal muscles, respectively."  

Reviewer: The first line above is misleading. The study by Puhl and Mesce (2008, J. Neurosci, 28:4192- 420) contains a comprehensive analysis of the motoneurons active during fictive crawling with the aim of characterizing their roles and phase relationships and solidifying the idea that the oscillator for crawling resides in a single ganglion. Intracellular recordings from a number of key crawl-related motoneurons were made in combination with extracellular recordings of motoneuron DE-3, a key monitor of crawling. In their paper, it was shown that motoneurons AE, VE-4, DI-1, VI-2, and CV were all correlated with crawl activity, and fired repeatedly either in phase or out-of-phase with DE-3. They were shown to be either excitatory or inhibitory. At a minimum, the above paper should be cited. 

The sentence in the submitted manuscript explicitly refers to the quantitative analysis of extracellular recordings, but we recognize that it may lead to confusion. We have now added a clarification (lines 197-199). 

The article by Puhl and Mesce 2008 shows very nice intracellular recordings of the AE, CV, VE-4, DE-3, DI-1, and Vi-2, accompanied by extracellular recordings of DE-3 in the DP nerve. In all cases, there is only one intracellular recording paired with the DP nerve recording.

While it is possible to perform up to 3-4 simultaneous intracellular recordings, these are technically challenging, and more so when the recordings have to last 10-20 minutes. Due to this difficulty, and because our objective was to record multiple units simultaneously in order to comprehensively describe the different crawling stages, we implemented the spike sorting analysis on multiple extracellular recordings. This approach enabled us to reliably obtain multiple units per experiment and thus execute a quantitative analysis of the activity of each identified unit.

The article by Puhl and Mesce 2008 mentions several quantitative aspects of the neurons that fire in-phase or out-of-phase with DE-3, but, as far as we understand, there is no figure that summarizes activity levels and span in the way Figures 4 and 6 do in the current manuscript. To the best of our knowledge, no previous work renders this information.

It is very important for us to emphasize that the work by Puhl and Mesce was seminal for our research. We cited it four times in the original manuscript and 10 times in the present version. But, like any important discovery, it sets the ground for further work that can refine certain measurements that in the original discovery were not central.

This is why we believe that the cited sentence in our manuscript is not misleading.  However, to comply with the requirement of Reviewer #1, we added a sentence preceding the mentioned paragraph (lines 185-187) that acknowledges the description made using intracellular recordings, and explains the need for implementing the approach we chose.

The submitted paper would be strengthened if some of these previously identified motoneurons were again recorded with intracellular electrodes and concomitant NS cell stimulation. The power of the leech preparation is that cells can be identified as individuals with dual somatic (intracellular) and axonal recordings (extracellular). 

Most of the motoneurons mentioned by Reviewer #1 are located on the opposite side (dorsal) of the ganglion to NS (ventral), and therefore, simultaneous intracellular recordings in the context of fictive crawling are challenging.

In the publication of Rodriguez et al. 2009, Mariano Rodriguez did manage to record NS from the dorsal side together with DE-3 and MN-L (!) and this led to the discovery that these motoneurons are electrically coupled, but the recurrent inhibitory circuit masks this interaction. Repeating this type of experiments during crawling, which requires stable recordings for around 15 minutes, is not a reasonable experimental setting.

Rodriguez et al. 2012 shows intracellular recordings of motoneurons AE and CV during crawling in conjunction with NS, and their activity presented the expected correlation. 

The shortfall of this aspect of the study (Figure 5) is that the extracellular units have not been identified here. 

The Reviewer is right in that the extracellular units have not been identified in terms of cell identity. As we explained earlier, most motoneurons are on the opposite side (ventral/dorsal) of the ganglion relative to NS. 

However, we do characterize the units in terms of the nerve through which they project to the periphery and their activity phase. In lines 345-349 we use this information and, based on published work, we propose possible cellular identities of the different units.

In xfact, these units might not even be motoneurons. 

We are surprised by this comment. The classical work of Ort and collaborators (1974) showed that spikes detected in extracellular nerve recordings were emitted by specific motoneurons, and several previous publications have validated extracellular nerve recordings as a means to study fictive motor patterns (Wittenberg & Kristan 1992, Shaw & Kristan 1997, Eisenhart et al. 2000).

For further reassurance, we only took in consideration units whose activity was locked to DE3; any non-rhythmical activity was filtered out (see lines 433-435). 

They could represent activity from the centrally located sensory neurons, dopamine-modulated afferent neurons or peripherally projecting modulatory neurons. 

Peripheral nerves also contain axons from sensory neurons. However, in a previous article, we studied the activity of mechanosensory neurons (Alonso et al. 2020) and showed that they remain silent during crawling. Moreover, the low-threshold T sensory neurons are inhibited in phase with DE-3 bursts and NS IPSPs (Kearney et al. 2022). Alonso et al. 2000 showed that spiking activity of T cells affects the crawling motor pattern, revealing the relevance of keeping them silent.

What does the Reviewer mean by “dopamine-modulated afferents”? We are not aware of this category of leech neurons.

The neuromodulatory Rz neurons project peripherally through the recorded nerves, but intracellular recordings of these neurons from our lab show no rhythmic activity in those cells during dopamine-induced crawling.

Essentially, they may not have much to do with the crawl motor pattern at all.

Does the Reviewer consider that neurons engaged in a coherent rhythmic firing could be unrelated to the pattern? As indicated above, the units reported in our manuscript were selected because dopamine evoked their rhythmic activity, locked to DE-3. 

Does the Reviewer consider that dopamine could evoke spurious neuronal activity?

(2) Results Lines 206-210: "with the elongation and contraction stages of in vivo behavior. However the isometric stages displayed in vivo have no obvious counterpart in the electrophysiological recordings. It is important to consider that the rhythmic movement of successive segments along the antero-posterior axis of the animal requires a delay signal that allows the appropriate propagation of the metachronal wave, and this signal is probably absent in the isolated ganglion." 

Reviewer: The so-called isometric stages, indeed, have an electrophysiological counterpart due in part to the overlapping activities across segments. This submitted paper would be considerably strengthened if it referred to the body of work that has examined how the individual crawl oscillators operate in a fully intact nerve cord, excised from the body but with all the ganglia (and cephalic ganglion) attached. Puhl and Mesce 2010 (J. Neurosci 30: 2373-2383) and Puhl et al. 2012 (J. Neurosci, 32:17646 -17657) have shown that "appropriate propagation of the metachronal wave" requires the brain, especially cell R3b-1. They also show that the long-distance projecting cell R3b-1 synapses with the CV motoneuron, providing rhythmic excitatory input to it.  

We would like to draw the Reviewer’s attention to the fact that Puhl and Mesce 2008, 2010 and Puhl et al. 2012 characterized crawling in intact (or nearly intact) animals considering the whole body. In our in vivo analysis, we studied the changes in length of the whole animal and of sections demarcated by the drawn points, as described in the Materials and Methods/Behavioral

Experiments. Because of this different analysis, we defined “isometric” stages as those in which a given section of the animal does not change its length. We now clarify this (line 230).

In the paragraph cited by the Reviewer, we intended to state that, in the context of our study, the intersegmental lag caused by the coordinating mechanisms has no counterpart “in the electrophysiological recordings of motoneurons in the isolated ganglia”. We have now completed this idea with the expression underlined in the previous sentence (line 231).

As the Reviewer indicates, in the intact nerve cord the behavioral isometric stages correspond to the “waiting time” between segments. We did refer to the metachronal order but did not cite the articles by Puhl and Mesce 2010 and Puhl et al. 2012; we now do so (lines 234).

For this and other reasons, the paper would be much more informative and exciting if the impacts of the NS cell were studied in a fully intact nerve cord. Those studies have never been done, and it would be exciting to see how and if the effects of NS cell manipulation deviated from those in the single ganglion.  

The Reviewer may consider that a systematic analysis of multiple nerves in several ganglia along the whole nerve cord would have been a different enterprise than the one we carried out. The Reviewer is right in recognizing the interest of such study, but in our opinion, the value of the present work lies in presenting a thorough quantitative analysis of multiple nerves to demonstrate its usefulness for the study of the network underlying leech crawling. In this manuscript, we used it to analyze the role of the premotor NS neuron. Without the recording of units firing in-phase and out-ofphase with DE-3, we would have been unable to assess the span of NS effects.

(3) Discussion Lines 322-324. "The absence of descending brain signals and/or peripheral signals are assumed as important factors in determining the cycle period and the sequence at which the different behavioral stages take place." 

Reviewer: The authors could strengthen their paper by including a more complete picture of what is known about the control of crawling. For example, Puhl et al. 2012 (J Neurosci, 32:17646-17657) demonstrated that the descending brain neuron R3b-1 plays a major role in establishing the crawlcycle frequency. With increased R3b-1 cell stimulation, DE-3 periods substantially shortened throughout the entire nerve cord. Thus, the importance of descending brain inputs should not be merely assumed; empirical evidence exists.  

We now strengthen the concept using “known descending brain signals” (line 358) and cite Puhl et al. 2012. We believe that extending the discussion to cell R3b-1 does not contribute meaningfully to the focus of this manuscript.

(4) Discussion Lines 325-327: "the sequence of events, and the proportion of the active cycle dedicated to elongation and contraction were remarkably similar in both experimental settings. This suggests that the network activated in the isolated ganglion is the one underlying the motor behavior." 

Reviewer: The results and conclusions drawn in the current manuscript mirror those previously reported by Puhl and Mesce (2008, J. Neurosci, 28:4192- 420) who first demonstrated that the essential pattern-generating elements for leech crawling were contained in each of the segmental ganglia comprising the nerve cord. Furthermore, the authors showed that the duty cycle of DE-3, in a single ganglion treated with dopamine, was statistically indistinguishable from the DE-3 duty cycle measured in an intact nerve cord showing spontaneous fictive crawling, in an intact nerve cord induced to crawl via dopamine, and in the intact behaving animal. What was statistically significant, however, was that the DE-3 burst period was greatly reduced in the intact animal (i.e., a higher crawl frequency), which was replicated in the submitted paper.  

There is no doubt that the article by Puhl and Mesce 2008 is seminal to the work we present here. The Reviewer seems to suggest that we do not recognize the value of this work. The contrary is true, all our related papers cite this important breakthrough. We cite the paper very early in the article in the Introduction (see lines 51 and 52-53). Likely, we would like the Reviewer to recognize the novelty of the current report. To clarify what has been shown and what is new in our manuscript, considerer the following:

i. Figures 1-6 in Puhl and Mesce 2008 provide representative intracellular recordings that describe neurons that fire in phase and out of phase relative to DE-3. Some general measurements are given in the text, but none of these figures quantify the relative activity of neurons that fire in different stages; only DE-3 activity was quantified. A quantitative description of multiple units active in phase and out of phase with DE-3 is presented here for the first time, are we wrong? This quantification is particularly relevant when assessing how a treatment affects the function of the circuit.

ii. Regarding the cycle period, we referred to the work from the Kristan lab, which reported this value long before the requested reference. We now cite Puhl and Mesce 2008 in lines 222 regarding in vivo measurements, and in line 221 regarding isolated ganglia.

iii. Regarding the duty cycle: 

Puhl and Mesce 2008 measured the duty cycle of DE-3 in three configurations: a. spontaneous whole cord, b. DA-mediated whole cord and c. DA mediated single ganglion crawling. However, it does not report the duty cycle of neurons out-of-phase with DE-3. Our current manuscript carried out this analysis. One could argue that the silence between DE-3 bursts captures that value, but this is a speculation that needed a proper measure.

Puhl and Mesce 2008 does not indicate the duty cycle of the contraction and elongation stages in vivo. Our current manuscript does. 

Therefore, the sentence cited by the Reviewer refers to data presented in this manuscript, and not in any prior manuscript. It is true that Puhl and Mesce 2008 inspire the intuition that the sentence is true, but does not present the data that the current manuscript does.

Finally, our study focused only on the body sections corresponding to the same segmental range used in the ex vivo experiments, rather than the whole animal. The comparison was made only to validate that the duty cycles of neurons firing in phase and out of phase with DE-3 matched the dynamic stages in the studied sections of the leech (line 364).

In my opinion, the novelty of the results reported in the submitted manuscript is diminished in the light of previously published studies. At a minimum, the previous studies should be cited, and the authors should provide additional rationale for conducting their studies. They need to explain in the discussion how their approach provided additional insights into what has already been reported.  

Throughout our reply, we have provided a detailed explanation of the rationale and necessity behind each experiment. Following the Reviewer’s suggestion, we have rephrased the research objectives, included what is known from our previously published work, and highlighted the substantial new data contributed by the present study. See lines 80-85. 

Additionally, we further cite our published article in lines 93, 104, 138, 146 and 250. 

Reviewer #2 (Public review):  

The paper is well-written overall. The findings are clearly presented, and the data seems solid overall. I do have, however, a few major and some minor comments representing some concerns.

My major comments are below. 

(1) This may seem somewhat semantic, yet, it has implications on the way the data is presented and moreover on the conclusions drawn - a single ganglion cannot show fictive crawling. It can demonstrate rhythmic patterns of activity that may serve in the (fictive) crawling motor pattern. The latter is a result of the intrinsic within single-ganglion connectivity AND the inter-ganglia connections and interactions (coupling) among the sequential ganglia. It may be affected by both short-range and long-range connections (e.g., descending inputs) along the ganglia chain. 

Semantics is not a trivial issue in science communication. It entails metaphors that enter the bibliography as commonly used “shortcuts” to a complex concept that are adopted by a community of researchers. And yes, indeed, they can be misleading.

However, if recording the activity in an isolated ganglion shows that a wide group of motoneurons, that control known muscle movements, presents a rhythmic output that maintains the appropriate cycle period and phase relationships, the “shortcut” is incomplete but could be valid (Puhl and Mesce 2008). If we were to include the phase lag component, a single ganglion cannot generate the fictive motor output.

Because any new study builds knowledge on the basis of the cited bibliography, the way we name concepts is a sensitive point. Adopting the terminology used by previous publications (Puhl and Mesce 2008) seems important to allow readers to follow the development of knowledge. However, attending the observation made by Reviewer #2, we included a sentence clarifying that the concept “fictive crawling” does not include intersegmental connectivity (lines 54-57)

(2) The point above is even more critical where the authors set to compare the motor pattern in single ganglia with the intact animals. It would have made much more sense to add a description of the motor pattern of a chain of interconnected ganglia. The latter would be expected to better resemble the intact animal. Furthermore, this project would have benefitted from a three-way comparison (isolated ganglion-interconnected ganglia-intact animal.  

As we answered to Reviewer #1, the present manuscript does not intend to present a thorough study on how the activity in the isolated nervous system compares with the animal behavior. To do so we would have needed to perform a completely different set of experiments. To better define the relevance of our comparison with the in vivo experiments we rephrased the objective of the behavioral analysis (lines 197-199).

The main aim of the manuscript is to learn the role of premotor NS neurons in the crawling motor pattern studied using a readout (spike sorting in extracellular nerve recordings) that allows simultaneous screening of a larger number of units than in any previous study, in order to determine whether and how a recurrent inhibitory peripheral circuit is involved in coordinating or modulating the rhythmic motor pattern.

Our rationale was that the known effect of NS on one particular motoneuron (DE-3) may have overlooked a more general effect on crawling (lines 253-257). Moreover, we wanted to investigate whether this effect was due to the recurrent inhibitory circuit or if other elements were involved, and to study whether the modulation was mediated by the recurrent synapse between NS and the motoneurons.

In the context of this aim we studied the rhythmic activity of cell DE-3, together with motoneurons that fire in-phase and anti-phase, in isolated ganglia (Figure 4). To reveal the effect of NS manipulation we applied a quantitative analysis that showed the phase-specific effect of NS (Figure 6). 

Given that this is the first study using a spike sorting algorithm to detect and describe the activity of motoneurons in nerve recordings we found it reasonable to compare these results with an in vivo study; thus, providing information to the general reader, that supports the correspondence between the ex vivo and the in vivo patterns.

(3) Two previous studies by the same group are repeatedly mentioned (Rela and Szczupak, 2003; Rodriguez et al., 2009) and serve as a basis for the current work. The aim of one of these previous studies was to assess the role of the NS neurons in regulating the function of motor networks. The other (Rodriguez et al., 2009) reported on a neuron (the NS) that can regulate the crawling motor pattern. LL 71-74 of the current report presents the aim of this study as evaluating the role of the known connectivity of the premotor NS neuron in shaping the crawling motor pattern. The authors should make it very clear what indeed served as background knowledge, what exactly was known about the circuitry beforehand, and what is different and new in the current study. 

Rela and Szczupak 2003 and Rodriguez et al. 2009 analyze the interactions of motoneurons with NS. We believe that Reviewer #2 refers here to Rodriguez et al. 2012. A similar observation was made by Reviewer #1. Below, we copy the answer previously stated:

Following the Reviewer’s suggestion, we have rephrased the research objectives, included what is known from our previously published work, and highlighted the substantial new data contributed by the present study. See lines 80-85. 

Additionally, we further cite our published article in lines 93, 104, 138, 146 and 250. 

Reviewer #1 (Recommendations for the authors):  

Please edit for correct word usage. 

Reviewer #2 (Recommendations for the authors):  

Minor Concerns 

(1) LL33-36: These lines are somewhat vague and non-informative. Why is the functional organization of motor systems an open question? What are the mechanisms at the level of the nerve cord that are an open question? Maybe be more explicit? 

We did as suggested (lines 30-32).

(2) L62: The homology between the NS neurons and the vertebrate Renshaw cells is mentioned already in the Abstract and here again. While a reference is provided (citing the lead author of this current work), the reader would benefit from some further short words of explanation regarding the alleged homology. 

We included a description of Renshaw cell connectivity (lines 64-65).

(3) LL90-92: The NS recording in Figure 1 (similar to Figure 3 in Rodriguez et al.) demonstrates clear distinct IPSPs. Could these be correlated with DE-3 spikes? 

We investigated this correlation in detail and the answer is that there is no strictly a 1:1 DE-3 spike to IPSP correlation. NS receives inputs from other dorsal and ventral excitors of longitudinal muscles, and the NS trace is too “noisy” to reflect any short-term correlation. Originally we proposed that the NS IPSPs were due to the polysynaptic interaction between the MN and NS (Rodríguez et al. 2012). However, the present work demonstrates that the IPSPs in NS are caused by a source upstream from the MNs. 

(4) LL145-145: Do you mean - inhibitory signals FROM NS premotor neurons? Not clear. 

We see the confusion, and we rewrote the sentence (lines 164). We hope it is clearer now: “…inhibitory signals onto NS premotor neurons were transmitted to DE-3 motoneurons via rectifying electrical synapses and counteracted their excitatory drive during crawling, limiting their firing frequency.”

(5) LL153-154: Why isn't AA included in Figure 4A? 

Reading our original text, the Reviewer #1 is right in expecting to see the AA recording. We changed the sentence: “we performed extracellular recordings of DP along with AA and/or PP root nerves” (lines 171-172).

We dissected the three nerves but, unfortunately, we did not always obtain good recordings from the three of them.

(6) LL237-238: The statistical significance (B- antiphase) is not clear. Furthermore, with N of 7-8, I'm not sure the parametric tests utilized are appropriate. 

Regarding the Reviewer's concern about the tests, please note that all the assumptions made for each model were tested (see now Materials and Methods lines 466-467).The information on each model is provided in Supplementary Table 2 under the column 'Model, random effect,' which specifies whether a Linear Mixed Model (LMM) or a Generalized Linear Mixed Model (GLMM) was implemented. For GLMMs, the corresponding distribution and link function are also specified. For the analysis of Max bFF of Anti-Phase motor units, we found a significant interaction between epoch and treatment, indicating a difference between treatments. This is indicated on the left of the y-axis (##). In control experiments, all three comparisons (pre-test, pre-post, test-post) show significant differences in Max bFF: this variable decreased (slightly but significantly) along the subsequent epochs, suggesting a change over time. We now corrected the text to indicate that these changes were small (line 268). In contrast, Max bFF in depo experiments remained stable between pre-test and pre-post, but significantly decreased between the depo and post epochs. Thus, in our view the comparison between control and the test supports the conclusion that NS depolarization was limited to counteracting this decrease (lines 270-273). Supplementary Table 2 provides the significance and modeled estimated ratio for each comparison in the column for pairwise simple contrasts.

Thanks to this question, we realized that the nomenclature used in the table for the epochs (pre - depo - post) needed to be changed to pre - test - post, and we have now corrected it.

(7) LL240-241: I fail to see a difference from Control. 

For the Relative HW of In-Phase units, we also found a significant interaction between epoch and treatment, indicating a difference between treatments, as denoted to the left of the y-axis (#). Then, the significance of the comparisons across epochs within each treatment are shown in the figure (*). What is important to notice is that obtaining the same significance for each treatment does not imply identical results, but we failed to describe this in our original text and we do now in lines 275-279.

(8) LL244-245: I must admit that Table 2 is beyond me. Maybe add some detail or point out to the reader what is important (if at all). 

We have now clarified what each column of the tables indicates in the corresponding legends. 

Here, we also share an insight into how the experiments were designed and analyzed:

To account for possible temporal drifts of the variables during the recordings that could mask or confuse the results, we compared two experimental series: one in which NS was subjected to depolarizing current pulses (depo), and another series (ctrl) in which the neurons were not depolarized.

The statistical analysis was made using Linear Mixed Models (LMMs) or Generalized Linear Mixed Models (GLMMs). In these analyses treatments and epochs are used as explanatory variables to evaluate the interaction between these factors. These models allow us to determine whether changes in each variable across epochs differ depending on the treatment. For example, whether the variation in firing frequency from pre to test to post differs between control experiments and those in which NS was depolarized.

A significant interaction between treatment and epoch indicates that NS depolarization affected the variable. In such cases, we performed pairwise comparisons between epochs (pre-test, test-post, pre-post) within each treatment. In contrast, the absence of a significant interaction can result from two possibilities: either the variable did not change across epoch in either treatment, or a similar temporal drift occurred in both cases.

(9) LL245-256: Move this paragraph to the discussion. 

Because we introduced a rationale for the experiments described in Figure 6 (lines 282-284) the paragraph was mostly removed, but the part that supports the methodological approach was left.

(10)  LL259-260: see my second minor point above. This is explained in LL270-272 for the first time. 

We amended according to comment (2).

(11) Figures: The quantitative analysis shown in Figure 3B is very useful. Why isn't this type of analysis utilized for the comparisons shown in Figures 4 and 6? 

We chose different ways of plotting the data based on their nature. In Figure 3B, we present data from an identified neuron (DE-3) recorded in different experiments. In contrast, in Figure 6 we analyze data from neurons classified into the same group based on their activity during the fictive crawling cycle, but their individual identity was not ascertained. Therefore, we consider it important to plot the results for each unit individually, to assess the effect of temporal drift and NS depolarization.

(12) Figures: Figure 7 is meant to be compared to Figure 1C; the point being the addition of an inhibitory connection onto the NS neuron. Why are other details of the figure also different (different colored M)? 

While Figure 1C illustrates the known connection between NS and both DE-3 and CV motoneurons, Figure 7 shows the connections between NS and the different groups of motor units described in this study. The units are represented in the circuit using the same colors that identify them in Figures 4 and 6. Since the CV motoneuron was not recorded in this study, the circuit represents the AntiPhase neurons but does not identify them with CV. Figure 7 legend now clarifies what the colors represent, and Figure 1C has been updated to match the same color scheme.

Author response:

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

Reviewer #1 (Public review):

This work addresses an important question in the field of Drosophila aggression and mating- prior social isolation is known to increase aggression in males by increased lunging, which is suppressed by group housing (GH). However, it is also known that single-housed (SH) males, despite their higher attempts to court females, are less successful. Here, Gao et al., developed a modified aggression assay, to address this issue by recording aggression in Drosophila males for 2 hours, over a virgin female which is immobilized by burying its head in the food. They found that while SH males frequently lunge in this assay, GH males switch to higher intensity but very low-frequency tussling. Constitutive neuronal silencing and activation experiments implicate cVA sensing Or67d neurons promoting high-frequency lunging, similar to earlier studies, whereas Or47b neurons promote low-frequency but higher intensity tussling. Using optogenetic activation they found that three pairs of pC1 neurons- pC1SS2 increase tussling. While P1a neurons, previously implicated in promoting aggression and courtship, did not increase tussling in optogenetic activation (in the dark), they could promote aggressive tussling in thermogenetic activation carried out in the presence of visible light. It was further suggested, using a further modified aggression assay that GH males use increased tussling and are able to maintain territorial control, providing them mating advantage over SI males and this may partially overcome the effect of aging in GH males.

Strengths

Using a series of clever neurogenetic and behavioral approaches, subsets of ORNs and pC1 neurons were implicated in promoting tussling behaviors. The authors devised a new paradigm to assay for territory control which appears better than earlier paradigms that used a food cup (Chen et al, 2002), as this new assay is relatively clutter-free, and can be eventually automated using computer vision approaches. The manuscript is generally well-written, and the claims made are largely supported by the data.

Thank you for your precise summary of our study, and being very positive on the novelty and significance of the study.

Weaknesses

I have a few concerns regarding some of the evidence presented and claims made as well as a description of the methodology, which needs to be clarified and extended further.

(1) Typical paradigms for assaying aggression in Drosophila males last for 20-30 minutes in the presence of nutritious food/yeast paste/females or all of these (Chen et al. 2002, Nilsen et al., 2004, Dierick et al. 2007, Dankert et al., 2009, Certel & Kravitz 2012). The paradigm described in Figure 1 A, while important and more amenable for video recording and computational analysis, seems a modification of the assay from Kravitz lab (Chen et al., 2002), which involved using a female over which males fight on a food cup. The modifications include a flat surface with a central food patch and a female with its head buried in the food, (fixed female) and much longer adaptation and recording times respectively (30 minutes, 2 hours), so in that sense, this is not a 'new' paradigm but a modification of an existing paradigm and its description as new should be appropriately toned down. It would also be important to cite these earlier studies appropriately while describing the assay.

We now toned down the description of the paradigm and cited more related references.

(2) Lunging is described as a 'low intensity' aggression (line 111 and associated text), however, it is considered a mid to high-intensity aggressive behavior, as compared to other lower-intensity behaviors such as wing flicks, chase, and fencing. Lunging therefore is lower in intensity 'relative' to higher intensity tussling but not in absolute terms and it should be mentioned clearly.

We have modified the description as suggested.

(3) It is often difficult to distinguish faithfully between boxing and tussling and therefore, these behaviors are often clubbed together as box, tussle by Nielsen et al., 2004 in their Markov chain analysis as well as a more detailed recent study of male aggression (Simon & Heberlein, 2020). Therefore, authors can either reconsider the description of behavior as 'box, tussle' or consider providing a video representation/computational classifier to distinguish between box and tussle behaviors.

Indeed, we could not faithfully distinguish boxing and tussling. To address this concern, we now made textual changes in the result section we occasionally observed the high-intensity boxing and tussling behavior in male flies, which are difficult to distinguish and hereafter simply referred to as tussling.

We also added this information in the Materials and Methods section Tussling is often mixed with boxing, in which both flies rear up and strike the opponent with forelegs. Since boxing is often transient and difficult to distinguish from tussling, we referred to the mixed boxing and tussling behavior simply as tussling.

(4) Simon & Heberlein, 2020 showed that increased boxing & tussling precede the formation of a dominance hierarchy in males, and lunges are used subsequently to maintain this dominant status. This study should be cited and discussed appropriately while introducing the paradigm.

We now cited this important study in both the Introduction and Discussion sections.

(5) It would be helpful to provide more methodological details about the assay, for instance, a video can be helpful showing how the males are introduced in the assay chamber, are they simply dropped to the floor when the film is removed after 30 minutes (Figures 1-2)?

We now provided more detailed description about behavioral assays and how we analyze them. For example All testers were loaded by cold anesthesia. After a 30-minute adaptation, the film was gently removed to allow the two males to fell into the behavioral chamber, and the aggressive behavior was recorded for 2 hours.

(6) The strain of Canton-S (CS) flies used should be mentioned as different strains of CS can have varying levels of aggression, for instance, CS from Martin Heisenberg lab shows very high levels of aggressive lunges. Are the CS lines used in this study isogenized? Are various genetic lines outcrossed into this CS background? In the methods, it is not clear how the white gene levels were controlled for various aggression experiments as it is known to affect aggression (Hoyer et al. 2008).

We used the wtcs flies from Baker lab in Janelia Research Campus, and are not sure where they are originated. We appreciate your concern on the use of wild-type strains as they may show different fighting levels, but this study mainly used wild-type strains to compare behavioral differences between SH and GH males. All flies tested in this study are in w+ background, based on w+ balancers flies but are not backcrossed. We have listed detailed genotypes of all tested flies in Table S1 in the revised manuscript.

(7) How important it is to use a fixed female for the assay to induce tussling? Do these females remain active throughout the assay period of 2.5 hours? Is it possible to use decapitated virgin females for the assay? How will that affect male behaviors?

We used a fixed female to restrict it in the center of food. These females remain active throughout the assay as their legs and abdomens can still move. Such design intends to combine the attractive effects from both female and food. One can also use decapitated females, but in this case, males can push the decapitated female into anywhere in the behavioral chamber. The logic to use fixed females has now been added in the Materials and Methods section of the revised manuscript.

(8) Raster plots in Figure 2 suggest a complete lack of tussling in SH males in the first 60 minutes of the encounter, which is surprising given the longer duration of the assay as compared to earlier studies (Nielsen et al. 2004, Simon & Heberlein, 2020 and others), which are able to pick up tussling in a shorter duration of recording time. Also, the duration for tussling is much longer in this study as compared to shorter tussles shown by earlier studies. Is this due to differences in the paradigm used, strain of flies, or some other factor? While the bar plots in Figure 2D show some tussling in SH males, maybe an analysis of raster plots of various videos can be provided in the main text and included as a supplementary figure to address this.

Indeed, tussling is very low in SH males in our paradigm, which may be due to different genetic backgrounds and behavioral assays. Since tussling behavior is a rare fighting form, it is not surprising to see variation between studies from different labs. Nevertheless, this study compared tussling behaviors in SH and GH males, and our finding that GH males show much more tussling behaviors is convincing. The longer duration of tussling in our paradigm may also be due to the modified behavioral paradigm, which also supports that tussling is a high-level fighting form.

(9) Neuronal activation experiments suggesting the involvement of pC1SS2 neurons are quite interesting. Further, the role of P1a neurons was demonstrated to be involved in increasing tussling in thermogenetic activation in the presence of light (Figure 4, Supplement 1), which is quite important as the role of vision in optogenetic activation experiments, which required to be carried out in dark, is often not mentioned. However, in the discussion (lines 309-310) it is mentioned that PC1SS2 neurons are 'necessary and sufficient' for inducing tussling. Given that P1a neurons were shown to be involved in promoting tussling, this statement should be toned down.

Thank you for this important comment. We now toned down the statement on pC1SS2 function.

(10) Are Or47b neurons connected to pC1SS2 or P1a neurons?

We conducted pathway analysis in the FlyWire electron microscopy database to investigate the connection between Or47b neurons and pC1 neurons. The results indicate that at least three levels of interneurons are required to establish a connection from Or47b neurons to pC1 neurons. Although the FlyWire database currently only contains neuronal data from female brains, they provide a reference for circuit connect in males.

(11) The paradigm for territory control is quite interesting and subsequent mating advantage experiments are an important addition to the eventual outcome of the aggressive strategy deployed by the males as per their prior housing conditions. It would be important to comment on the 'fitness outcome' of these encounters. For instance, is there any fitness advantage of using tussling by GH males as compared to lunging by SH males? The authors may consider analyzing the number of eggs laid and eclosed progenies from these encounters to address this.

Thank you for this suggestion. We agree with you and other reviewers that increased tussling behaviors correlate with better mating competition, but it is difficult for us to make a direct link between them. Thus, in the revised manuscript, we prefer to tone down this statement but not expanding on this part.

Reviewer #2 (Public review):

Summary

Gao et al. investigated the change of aggression strategies by the social experience and its biological significance by using Drosophila. Two modes of inter-male aggression in Drosophila are known lunging, high-frequency but weak mode, and tussling, low-frequency but more vigorous mode. Previous studies have mainly focused on the lunging. In this paper, the authors developed a new behavioral experiment system for observing tussling behavior and found that tussling is enhanced by group rearing while lunging is suppressed. They then searched for neurons involved in the generation of tussling. Although olfactory receptors named Or67d and Or65a have previously been reported to function in the control of lunging, the authors found that these neurons do not function in the execution of tussling, and another olfactory receptor, Or47b, is required for tussling, as shown by the inhibition of neuronal activity and the gene knockdown experiments. Further optogenetic experiments identified a small number of central neurons pC1[SS2] that induce the tussling specifically. In order to further explore the ecological significance of the aggression mode change in group rearing, a new behavioral experiment was performed to examine territorial control and mating competition. Finally, the authors found that differences in the social experience (group vs. solitary rearing) are important in these biologically significant competitions. These results add a new perspective to the study of aggressive behavior in Drosophila. Furthermore, this study proposes an interesting general model in which the social experience-modified behavioral changes play a role in reproductive success.

Strengths

A behavioral experiment system that allows stable observation of tussling, which could not be easily analyzed due to its low frequency, would be very useful. The experimental setup itself is relatively simple, just the addition of a female to the platform, so it should be applicable to future research. The finding about the relationship between the social experience and the aggression mode change is quite novel. Although the intensity of aggression changes with the social experience was already reported in several papers (Liu et al., 2011, etc), the fact that the behavioral mode itself changes significantly has rarely been addressed and is extremely interesting. The identification of sensory and central neurons required for the tussling makes appropriate use of the genetic tools and the results are clear. A major strength of the neurobiology in this study is the finding that another group of neurons (Or47b-expressing olfactory neurons and pC1[SS2] neurons), distinct from the group of neurons previously thought to be involved in low-intensity aggression (i.e. lunging), function in the tussling behavior. Further investigation of the detailed circuit analysis is expected to elucidate the neural substrate of the conflict between the two aggression modes.

Thank you for the acknowledgment of the novelty and significance of the study, and your suggestions for improving the manuscript.

Weaknesses

The experimental systems examining the territory control and the reproductive competition in Figure 5 are novel and have advantages in exploring their biological significance. However, at this stage, the authors' claim is weak since they only show the effects of age and social experience on territorial and mating behaviors, but do not experimentally demonstrate the influence of aggression mode change itself. In the Abstract, the authors state that these findings reveal how social experience shapes fighting strategies to optimize reproductive success. This is the most important perspective of the present study, and it would be necessary to show directly that the change of aggression mode by social experience contributes to reproductive success.

We agree that our data did not directly show that it is the change of aggression mode that results in territory and reproductive advantages in GH males. To address the concern, we have toned down the statement throughout the manuscript. For example, we made textual changes in the abstract as following

Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success, mitigating the disadvantages associated with aging. Our findings identify distinct sensory and central neurons for two fighting forms and suggest how social experience shapes fighting strategies to optimize reproductive success.

In addition, a detailed description of the tussling is lacking. For example, the authors state that the tussling is less frequent but more vigorous than lunging, but while experimental data are presented on the frequency, the intensity seems to be subjective. The intensity is certainly clear from the supplementary video, but it would be necessary to evaluate the intensity itself using some index. Another problem is that there is no clear explanation of how to determine the tussling. A detailed method is required for the reproducibility of the experiment.

Thank you for this important suggestion. We now analyzed duration of tussling and lunging, and found that a lunging event is often very short (less than 0.2s), while a tussling event may last from seconds to minutes. This new data is added as Figure 2G. In addition, we also provided more detailed methods regarding to tussling behavior

.<br /> Reviewer #3 (Public review):

In this manuscript, Gao et al. presented a series of intriguing data that collectively suggest that tussling, a form of high-intensity fighting among male fruit flies (Drosophila melanogaster) has a unique function and is controlled by a dedicated neural circuit. Based on the results of behavioral assays, they argue that increased tussling among socially experienced males promotes access to resources. They also concluded that tussling is controlled by a class of olfactory sensory neurons and sexually dimorphic central neurons that are distinct from pathways known to control lunges, a common male-type attack behavior.

A major strength of this work is that it is the first attempt to characterize the behavioral function and neural circuit associated with Drosophila tussling. Many animal species use both low-intensity and high-intensity tactics to resolve conflicts. High-intensity tactics are mostly reserved for escalated fights, which are relatively rare. Because of this, tussling in the flies, like high-intensity fights in other animal species, has not been systematically investigated. Previous studies on fly aggressive behavior have often used socially isolated, relatively young flies within a short observation duration. Their discovery that 1) older (14-days-old) flies tend to tussle more often than younger (2-days-old) flies, 2) group-reared flies tend to tussle more often than socially isolated flies, and 3) flies tend to tussle at a later stage (mostly ~15 minutes after the onset of fighting), are the result of their creativity to look outside of conventional experimental settings. These new findings are keys for quantitatively characterizing this interesting yet under-studied behavior.

Precisely because their initial approach was creative, it is regrettable that the authors missed the opportunity to effectively integrate preceding studies in their rationale or conclusions, which sometimes led to premature claims. Also, while each experiment contains an intriguing finding, these are poorly related to each other. This obscures the central conclusion of this work. The perceived weaknesses are discussed in detail below.

Thank you for the precise summary of the key findings and novelty of the study, and your insightful suggestions.

Most importantly, the authors' definition of "tussling" is unclear because they did not explain how they quantified lunges and tussling, even though the central focus of the manuscript is behavior. Supplemental movies S1 and S2 appear to include "tussling" bouts in which 2 flies lunge at each other in rapid succession, and supplemental movie S3 appears to include bouts of "holding", in which one fly holds the opponent's wings and shakes vigorously. These cases raise a concern that their behavior classification is arbitrary. Specifically, lunges and tussling should be objectively distinguished because one of their conclusions is that these two actions are controlled by separate neural circuits. It is impossible to evaluate the credibility of their behavioral data without clearly describing a criterion of each behavior.

Thank you for this very important suggestion. We now provided more detailed description of the two fighting forms in the Materials and Methods section. See below

Lunging is characterized by a male raising its forelegs and quickly striking the opponent, and each lunge typically lasts less than 0.2 seconds through detailed analysis. Tussling is characterized by both males using their forelegs and bodies to tumble over each other, and this behavior may last from seconds to minutes. Tussling is often mixed with boxing, in which both flies rear up and strike the opponent with forelegs. Since boxing is often transient and difficult to distinguish from tussling, we referred to the mixed boxing and tussling behavior simply as tussling. As we manually analyze tussling for 2 hours for each pair of males, it is possible that we may miss some tussling events, especially those quick ones.

It is also confusing that the authors completely skipped the characterization of the tussling-controlling neurons they claimed to have identified. These neurons (a subset of so-called pC1 neurons labeled by previously described split-GAL4 line pC1SS2) are central to this manuscript, but the only information the authors have provided is its gross morphology in a low-resolution image (Figure 4D, E) and a statement that "only 3 pairs of pC1SS2 neurons whose function is both necessary and sufficient for inducing tussling in males" (lines 310-311). The evidence that supports this claim isn't provided. The expression pattern of pC1SS2 neurons in males has been only briefly described in reference 46. It is possible that these neurons overlap with previously characterized dsx+ and/or fru+ neurons that are important for male aggressions (measured by lunges), such as in Koganezawa et al., Curr. Biol. 2016 and Chiu et al., Cell 2020. This adds to the concern that lunge and tussling are not as clearly separated as the authors claim.

Thank you very much for this important question. Indeed, there are many experiments that could do to better understand the function of pC1SS2 neurons, and we only provide the initial characterization of them due to the limited scope of this study. My lab has been focused on studying P1/pC1 function in both male and female flies and will continue to do so.

To partially address your concern, we made the following revisions

(1) We provided higher-resolution images of P1a and pC1SS2 (Figure 4C-4E). While their cell bodies are very close, they project to distinct brain regions, in addition to some shared ones.

(2) By staining these neurons with GFP and co-staining with anti-FruM or anti-DsxM antibodies, we showed that P1a neurons are partially FruM-positive and partially DsxM-positive, while pC1SS2 neurons are DsxM-positive and FruM-negative (Figure 5A-5D).

(3) As pC1SS2 neurons are DsxM-positive and FruM-negative, we also examined how DsxM regulates the development of these neurons. We found that knocking down DsxM expression in pC1SS2 neurons using RNAi significantly affected pC1 development regarding to both cell numbers (Figure 5G) and their projections (Figure 5H).

(4) We further found that DsxM in pC1SS2 neurons is crucial for executing their tussling-promoting function, as optogenetic activation of these neurons with DsxM knockdown failed to induce tussling behavior in the initial activation period, and a much lower level of tussling in the second activation period compared to control males (Figure 5I-5K).

(5) While it is very difficult to identify the upstream and downstream neurons of P1a and pC1SS2 neurons, we made an initial step by utilizing trans-tango and retro-Tango to visualize potential downstream and upstream neurons of P1a and pC1SS2 (Figure 4-figure supplement 2), which certainly needs future investigation.  

While their characterizations of tussling behaviors in wild-type males (Figures 1 and 2) are intriguing, the remaining data have little link with each other, making it difficult to understand what their main conclusion is. Figure 3 suggests that one class of olfactory sensory neurons (OSN) that express Or47b is necessary for tussling behavior. While the authors acknowledged that Or47b-expressing OSNs promote male courtship toward females presumably by detecting cuticular compounds, they provided little discussion on how a class of OSN can promote two different types of innate behavior. No evidence of a functional or circuitry relationship between the Or47b pathway and the pC1SS2 neurons was provided. It is unclear how these two components are relevant to each other.

It has been previously found that Or47b-expressing ORNs respond to fly pheromones common to both sexes, and group-housing enhances their sensitivity. Regarding to how Or47b ORNs promotes two different types of innate behaviors, a simple explanation is that they act on multiple second-order and further downstream neurons to regulate both courtship and aggression, not mentioning that neural circuitries for courtship and aggression are partially shared. We did not include this in the discussion as we would like to focus on aggression modes, and how different ORNs (Or47b and Or67d) mediate distinct aggression modes.

Regarding to the relationship between Or47b ORNs and pC1_SS2 neurons, or in general ORNs to P1/pC1, it is interesting and important to explore, but probably in a separate study. We tried to conduct pathway connection analyses from Or47b to pC1 using the FlyWire database, and found that Or47b neurons can act on pC1 neurons via three layers of interneurons. Although the FlyWire database currently only contains neuronal data from female brains, they can provide a certain degree of reference. We hope the editor and reviewers would agree with us that identifying these intermediate neurons involved in their connection is beyond this study.

Lastly, the rationale of the experiment in Figure 5 and the interpretation of the results is confusing. The authors attributed a higher mating success rate of older, socially experienced males over younger, socially isolated males to their tendency to tussle, but tussling cannot happen when one of the two flies is not engaged. If, for instance, a socially isolated 14-day-old male does not engage in tussling as indicated in Figure 2, how can they tussle with a group-housed 14-day-old male? Because aggressive interactions in Figure 5 were not quantified, it is impossible to conclude that tussling plays a role in copulation advantage among pairs as authors argue (lines 282-288).

Indeed, we do not have direct evidence to show it is tussling that makes socially experienced males to dominate over socially isolated males. To address your concern, we have made following revisions

(1) We toned down the statements about the relationship between fighting strategies and reproductive success throughout the manuscript. For example, in the abstract Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success.

(2)  Regarding to whether a SH male can engage in tussling with a GH male, we found that while two SH males rarely perform tussling, paired SH and GH males displayed similar levels of tussling like two GH males, although tussling duration from paired SH and GH males is significantly lower compared to that in two GH males (Figure 6-figure supplement 2).

(3) To support the potential role of tussling in territory control and mating competition, we performed additional experiments to silence Or47b or pC1SS2 neurons that almost abolished tussling, and paired these males with control males. We found that males with Or47b or pC1SS2 neurons silenced cannot compete over control males, further suggesting the involvement of tussling in territory control and mating competition.  

Despite these weaknesses, it is important to acknowledge the authors' courage to initiate an investigation into a less characterized, high-intensity fighting behavior. Tussling requires the simultaneous engagement of two flies. Even if there is confusion over the distinction between lunges and tussling, the authors' conclusion that socially experienced flies and socially isolated flies employ distinct fighting strategies is convincing. Questions that require more rigorous studies are 1) whether such differences are encoded by separate circuits, and 2) whether the different fighting strategies are causally responsible for gaining ethologically relevant resources among socially experienced flies. Enhanced transparency of behavioral data will help readers understand the impact of this study. Lastly, the manuscript often mentions previous works and results without citing relevant references. For readers to grasp the context of this work, it is important to provide information about methods, reagents, and other key resources.

Thank you very much for this comment and we almost totally agree.

(1) Our results suggest the involvement of distinct sensory neurons and central neurons for lunging and tussling, but do not exclude the possibility that they may also utilize shared neurons. For example, activation of P1a neurons promotes both lunging and tussling in the presence of light.

(2) We have now toned down the statements about the relationship between fighting strategies and reproductive success throughout the manuscript.

(3) We provided more detailed methods, genotypes of flies to improve transparency of the manuscript.

Reviewer #1 (Recommendations for the authors):

(1) Figure 1 Supplement 1 shows that increased aging has a linear and inverse relationship with the number of lunges, this is in contrast to a previous study from Dierick lab (Chowdhury, 2021), where using Divider assays they showed that aggressive lunges increased up to day 10 and subsequently decreased in 30-day old flies. Given that this study did not use 14-day-old flies, it might be useful to comment on this.

Thank you for this comment. Indeed, Chowdhury et al., suggested a decline of lunging after 10 days, which is not contradictory to our findings that lunging in 14d-old males is lower than that in 7d-old males. It is ideally to perform a time-series experiments to reveal the detailed relationship between ages and aggression (lunging or tussling) levels, but given our initial findings that 14d-old males showed stable tussling behavior, we prefer to use this time point for the rest of this study.

(2) For Figure 3, do various manipulations also affect the duration of tussling and boxing besides frequency and latency?

Thank you for this comment. We only analyzed latency and frequency, but not duration, as data analysis was performed manually rather than automatically on every fly pair for about 2 hours, which is very labor-consuming. We hope you could agree with us that the two parameters (frequency and latency) for tussling are representative for assaying this behavior.

(3) For Figure 3 A-F, the housing status of the males is not clearly mentioned either in the main text or the figure. What is the status of the tussling and lunging status when this housing condition is reversed when Or47b neurons are silenced, or the gene is knocked down? Do these manipulations overcome the effect of housing conditions similar to what is seen in NaChBac-mediated activation experiments?

Figure 3A-F used group-housed males and we have now added such information in the figure legends as well as Table S1.

We appreciate your suggestion on using different housing conditions. As silencing Or47b neurons or knocking down Or47b reduced tussling, it is reasonable to use GH males (as we did in Figure 3A-F) that performed stable tussling behavior, but not SH males that rarely tussle.

(4) The connections between Or47b neurons and pC1SS2 or P1a neurons can be addressed by available connectomic datasets or TransTango/GRASP approaches.

Thank you for this important suggestion. We used the FlyWire electron microscope database to analyze the pathway connections between these two types of neurons. The results indicated that there are at least three levels of interneurons for connecting Or47b and pC1 neurons. Although the FlyWire database currently only contains neuronal data from female brains, they can provide a certain degree of reference for males.

The lack of direct synaptic connection also suggests that it is challenging to resolve the connection between these two neuronal types using methods like trans-Tango/GRASP. To partially address this question, we utilized trans-Tango and retro-Tango techniques to visualize potential downstream and upstream neurons of P1a and pC1SS2 (Figure 4-figure supplement 2). Future investigations are certainly needed for clarifying functional connections between Or47b/Or67d and P1a/pC1SS2 neurons.

(5) Figure 5, 'Winning index' and 'Copulation advance index' while described in Material and Methods, should be referred to in the main text.

We now described these two indices briefly in the main manuscript, and in the Discussion section with more details.

(6) Figure 6 shows comparisons for territorial control and mating outcomes where four different housing and aging conditions are organized in a hierarchical sequence. It is not clear from the data in Figure 5, how this conclusion was arrived at. A supplementary table with various outcomes with statistical analysis would help with this.

We now added a supplementary table (Table S2) with various outcomes with statistical analysis.

Minor Comments

(1) Line 26 says that the courtship levels in SH and GH males are not different, however, unilateral wing extension is higher in SH males as compared to GH males (Pan & Baker, 2014; Inagaki et al., 2014), also it was shown that courtship attempts are higher in D. paulsitorium (Kim & Ehrman, 1998). It would be better to clarify this statement.

Indeed, it is found in some cases that SH males court more vigorously than GH males. We have added more references on this matter in the introduction.

(2) Figure 4, correct 'Tussing' to 'Tussling' or 'Box, Tussling' as appropriate.

Corrected.

(3) Duistermars, 2018 should be cited while discussing the role of vision in aggression (Figure 4). [A Brain Module for Scalable Control of Complex, Multi-motor Threat Displays]

We now cited this reference and added more discussion in the revised manuscript.

(4) Reviews on Drosophila aggression and social isolation can be cited in the introduction/discussion to incorporate recent literature e.g., Palavicino-Maggio, 2022 [The Neuromodulatory Basis of Aggression Lessons From the Humble Fruit Fly]; Yadav et al., 2024[Lessons from lonely flies Molecular and neuronal mechanisms underlying social isolation], etc.

We now cited these references in both the introduction and discussion sections.

(5) The concentration of apple juice agar should be mentioned in the methods.

We added this and other necessary information for materials in the Materials and Methods section of the study.

(6) Source of the LifeSongX software and, if available, a Github link would be helpful to include in the materials and methods section.

We now provided the source of the LifesongY software (website https//sourceforge.net/projects/lifesongy/), which is a Windows version of LifesongX (Bernstein, Adam S.et al., 1992).

Reviewer #2 (Recommendations for the authors):

(1) Major comment 1

As pointed out in the public review, the weakness of this study is that the relationship between the aggression strategy and reproductive success is an inference that is not based on experimental facts; I understand that the frequency of tussling is not so high, but at least tussling-like behavior can be observed in the territory control experiment shown in Video 3. Wouldn't it be possible to re-analyse data and examine the correlation between aggressive behavior and territory control? Even if the analysis of tussling itself in this setup is difficult, for example, additional experiments using Or47b knock-out fly or pC1[SS2]-inactivated fly could provide stronger support.

Indeed, we can only make a correlation between the type of aggressive behavior and territory control. We now toned down this statement throughout the manuscript. For example, in the abstract, we changed our conclusions as following

Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success. Our findings identify distinct sensory and central neurons for two fighting forms and suggest how social experience shapes fighting strategies to optimize reproductive success.

To further address the concern, we now performed additional experiments to silence Or47b or pC1SS2 neurons that almost abolished tussling, and paired these males with control males. We found that males with Or47b or pC1SS2 neurons silenced cannot compete over control males (Figure 6-figure supplement 3), further suggesting the involvement of tussling in territory control and mating competition.

In relation to the above, some of the text in the Abstract should be changed.Line 28 These findings "reveal" how social experience shapes fighting strategies to optimise reproductive success.

"suggest" is more accurate at this stage.

Changed as suggested.

(2) Major comment 2

The tussling is the central subject of this paper. However, neither the main text nor Materials and Methods section provides a clear explanation of how this aggression mode was detected. Did the authors determine this behavior manually? Or was it automatically detected by some kind of image analysis? In either case, the criteria and method for detecting the tussling should be clearly described.

The behavioral data analysis in this study was performed manually. We now provided more detailed description of the two fighting forms in the Materials and Methods section. See below

Lunging is characterized by a male raising its forelegs and quickly striking the opponent, and each lunge typically lasts less than 0.2 seconds through detailed analysis. Tussling is characterized by both males using their forelegs and bodies to tumble over each other, and this behavior may last from seconds to minutes. Tussling is often mixed with boxing, in which both flies rear up and strike the opponent with forelegs. Since boxing is often transient and difficult to distinguish from tussling, we referred to the mixed boxing and tussling behavior simply as tussling. As we manually analyze tussling for 2 hours for each pair of males, it is possible that we may miss some tussling events, especially those quick ones.

For the experimental groups where tussling cannot be observed, the latency is regarded as 120 min, but this is a value depending on the observation time. While it is reasonable to use the latency to evaluate the behavior such as the lunging that is observed at relatively early times, care should be taken when using it to evaluate the tussling. Since similar trends to those obtained for the latency are observed for Number of tussles and % of males performing tussling, it may be better to focus on these two indices.

We initially intended to provide all three statistical metrics. However, we found that using the "% of males performing tussling" would require a significantly larger sample size for subsequent statistical analysis (using chi-square tests), greatly increasing the workload. At the same time, we believe that the trend observed with "% of males performing tussling" is consistent with the other two indices, and the percentage information can also be derived from the individual sample scatter data of the other two metrics. Therefore, we opted to use "latency" and "numbers" as the statistical metrics, despite the caveat as you mentioned.

The authors repeatedly mention that tussling is less frequent but more vigorous. The low frequency can be understood from the data in Fig. 1 and Fig. 2, but there are no measured data on the intensity. As the authors mention in line 125, each tussling event appears to be sustained for a relatively long period, as can be seen from the ethogram in Fig. 2. For example, it would be possible to evaluate the intensity by measuring the duration of the tussling event.

Thank you for your valuable suggestion. We now analyzed duration of tussling and lunging, and found that a lunging event is often very short (less than 0.2s), while a tussling event may last from seconds to minutes, further supporting their relative intensities. This new data is added as Figure 2G.

(3) Minor comments

a) Line 117 How many flies were placed in one vial for group-rearing (GH)? Were males and females grouped together? Please specify in the Materials and Methods section.

We have added this information in the Materials and Methods section. In brief, 30-40 virgin males were collected after eclosion and group-housed in each food vial.

b) Line 174 The trans-Tango is basically a postsynaptic cell labeling technique. It is unlikely that the labeling intensity changes depending on neuronal activity. Do the authors want to say in this text the high activity of Or47b-expressing neurons under GH conditions? Or are they trying to show that the expression level of the Or47b gene, which is supposedly monitored by the expression of GAL4, is increased by GH conditions? The authors should clarify which is the case.

Although the primary function of the trans-Tango technique is to label downstream neurons, the original literature indicates that the signal strength in downstream neurons depends on the use of upstream neurons evidenced by age-dependent trans-Tango signals. Therefore, the trans-Tango technique can indirectly reflect the usage of upstream neurons. Our findings that GH males showed broader Or47b trans-Tango signals than SH males can indirectly suggest that group-housing experience acts on Or47b neurons. We made textually changes to clarify this.

c) Line 178 Which fly line labels the mushroom body; R19B03-GAL4?

Yes, we now provided the detailed genotypes for all tested flies in the Table S1.

d) Line 184 It was reported in Koganezawa et al., 2016 that some dsx-expressing pC1 neurons are involved in aggressive behavior. The authors should also refer to this paper as they include tussling in the observed aggressive behavior.

Thank you for this comment, and we now cited this reference in the revised manuscript.

e) Line 339 I think you misspelled fruM RNAi.

Thank you for pointing this out. fruMi refers to microRNAi targeting fruM, and we have now clearly stated this information in the main text.

f) Line 681 Is tussling time (%) the total duration of tussling occurrences during the observation time? Or is it the percentage of individuals observed tussling during the observation time? This needs to be clarified.

It is the former one. We now clearly stated this definition in the Materials and Methods section

Reviewer #3 (Recommendations for the authors):

For authors to support their conclusion that enhanced tussling among socially experienced flies allows them to better retain resources, it is necessary to quantify aggressive behaviors (mainly tussling and lunging) in Figure 5.

We agree that we can only make a correlation between enhanced tussling behavior and mating competition. We now toned down this statement throughout the manuscript. For example, in the abstract, we changed our conclusions as following Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success. Our findings identify distinct sensory and central neurons for two fighting forms and suggest how social experience shapes fighting strategies to optimize reproductive success.

To further address the concern, we now performed additional experiments to silence Or47b or pC1SS2 neurons that almost abolished tussling, and paired these males with control males. We found that males with Or47b or pC1SS2 neurons silenced cannot compete over control males (Figure 6-figure supplement 3), further suggesting the involvement of tussling in territory control and mating competition.

In contrast to the authors' data in Figure 4, movies in ref 36 clearly show instances of 2 flies exchanging lunges after the optogenetic activation of P1a neurons, like the examples shown in supplementary movies S1-S3. It is a clear discrepancy that requires discussion (and raises a concern about the lack of transparency about behavioral quantification).

In our study, optogenetic activation of P1^a neurons failed to induce obvious tussling behavior, and temperature-dependent activation of P1^a neurons can only induce tussling in the presence of light. These data are different from Hoopfer et al., (2015), but are generally consistent with a new study (Sten et al., Cell, 2025), in which pC1SS2 neurons but not P1a neurons promote aggression. Such discrepancy has now been discussed in the revised manuscript.

The authors often fail to cite relevant references while discussing previous results, which compromises the scholarship of the manuscript. Examples include (but are not limited to)

(1) Line 85-86 Simon and Heberlein, J. Exp. Biol. 223 jeb232439 (2020) suggested that tussling is an important factor for flies to establish a dominance hierarchy.

Reference added.

(2) Line 142-143 Cuticular compounds such as palmitoleic acid are characterized to be the ligands of Or47b by ref #18.

Reference added.

(3) Line 185-187 pC1SS1 and pC1SS2 are first characterized by ref #46. Expression data of this paper also implies that pC1SS1 and pC1SS2 label different neurons in the male brain.

We have now added this reference at the appropriate place in the revised manuscript. In addition, we have clarified that these two drivers exhibit sexually dimorphic expression patterns in the brain.

(4) Line 196-199 Cite ref #36, which describes the behavior induced by the optogenetic activation of P1a neurons.

Reference added.

(5) Line 233-235 The authors' observation that control males do not form a clear dominance directly contradicts previous observations by others (Nilsen et al., PNAS 10112342 (2002); Yurkovic et al., PNAS 10317519 (2006); also see Trannoy et al., PNAS 1134818 (2016) and Simon and Heberlein above). The authors must at least discuss why their results are different.

There is a misunderstanding here. We clearly state that there is a ‘winner takes all’ phenomenon. However, for wild-type males of the same age and housing condition, we calculated the winning index as (num. of wins by unmarked males – num. of wins by marked males)/10 encounters * 100%, which is roughly zero due to the randomness of marking.

(6) Line 251-254 The authors' observation that aged males are less competitive than younger males contradicts the conclusion in ref #18. Discussion is required.

We have now added a discussion on this matter. In brief, Lin et al., showed that 7d-old males are more competitive than 2d-old males, which is probably due to different levels of sexual maturity of males, but not a matter of age like our study that used up to 21d-old males.

(7) Line 274-275 It is unclear which "previous studies" "have found that social isolation generally enhances aggression but decreases mating competition in animal models". Cite relevant references.

Reference added.

(8) Line 309-310 The evidence supporting the statement that "there are only three pairs of pC1SS2 neurons". If there is a reference, cite it. If it is based on the authors' observation, data is required.

We have now provided additional data on the number of pC1SS2 neurons in Figure 5G of the revised manuscript.

Reviewer #1 (Public review):

The manuscript by Feng et al. reported that Endothelin B receptor (ETBR) expressed by the satellite glial cells (SGCs) in the dorsal root ganglions (DRG) acted to inhibit sensory axon regeneration in both adult and aged mice. Thus, pharmacological inhibition of ETBR with specific inhibitors resulted in enhanced sensory axon regeneration in vitro and in vivo. In addition, sensory axon regeneration significantly reduces in aged mice and inhibition of ETBR could restore such defect in aged mice. Moreover, the study provided some evidence that the reduced level of gap junction protein connexin 43 might act downstream of ETBR to suppress axon regeneration in aged mice. Overall, the study revealed an interesting SGC-derived signal in the DRG microenvironment to regulate sensory axon regeneration. It provided additional evidence that non-neuronal cell types in the microenvironment function to regulate axon regeneration via cell-cell interaction.

However, the molecular mechanisms by which ETBR regulates axon regeneration are unclear, and the structure of the manuscript is relatively not well organized, especially the last section. Some discussion and explanation about the data interpretation are needed to improve the manuscript.

(1) The result showed that the level of ETBR was not changed after the peripheral nerve injury. Does it mean that its endogenous function is to limit the spontaneous sensory axon regeneration? In other words, the results suggest that SGCs expressing ETBR or vascular endothelial cells expressing its ligand ET-1 act to suppress sensory axon regeneration. Some explanation or discussion about this are necessary. Moreover, does the protein level of ETBR or its ligand change during aging?

(2) In ex vivo experiments, NGF was added in the culture medium. Previous studies have shown that adult sensory neurons could initiate fast axon growth in response to NGF within 24 hours. In addition, dissociated sensory neurons could also initiate spontaneous regenerative axon growth without NGF after 48 hours. Some discussion or rationale is needed to explain the difference between NGF-induced or spontaneous axon growth of culture adult sensory neurons and the roles of ETBR and SGCs.

(3) In cultured dissociated sensory neurons, inhibiting ETBR also enhanced axon growth, which meant the presence of SGCs surrounding the sensory neurons. Some direct evidence is needed to show the cellular relationship between them in culture.

(4) In Figure 3, the in vivo regeneration experiments first showed enhanced axon regeneration either at 1 day or 3 days after the nerve injury. The study then showed that inhibiting ETBR could enhance sensory axon growth in vitro from uninjured naïve neurons or conditioning lesioned neurons. To my knowledge, in vivo sensory axon regeneration is relatively slow during the first 2 days after the nerve injury and then enter the fast regeneration mode in the 3rd day, representing the conditioning lesion effect in vivo. Some discussion is needed to compare the in vitro and the in vivo model of axon regeneration.

(5) In Figure 5, the study showed that the level of connexin 43 increased after ETBR inhibition in either adult or aged mice, proposing an important role of connexin 43 in mediating the enhancing effect of ETBR inhibition on axon regeneration. However, in the study there was no direct evidence supporting that ETBR directly regulate connexin 43 expression in SGCs. Moreover, there was no functional evidence that connexin 43 acted downstream of ETBR to regulate axon regeneration.

In the revised manuscript, most comments have been addressed with some new experiments or text revisions in the results or discussion. For representative images showing in vitro cultured DRG neurons, it would be much more convincing if several neurons in the same imaging field are shown, rather than a single neuron (Figure 2A, 3J).

Author response:

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

Reviewer #1 (Public Review): 

The manuscript by Feng et al. reported that the Endothelin B receptor (ETBR) expressed by the satellite glial cells (SGCs) in the dorsal root ganglions (DRG) acted to inhibit sensory axon regeneration in both adult and aged mice. Thus, pharmacological inhibition of ETBR with specific inhibitors resulted in enhanced sensory axon regeneration in vitro and in vivo. In addition, sensory axon regeneration significantly reduces in aged mice and inhibition of ETBR could restore such defect in aged mice. Moreover, the study provided some evidence that the reduced level of gap junction protein connexin 43 might act downstream of ETBR to suppress axon regeneration in aged mice. Overall, the study revealed an interesting SGC-derived signal in the DRG microenvironment to regulate sensory axon regeneration. It provided additional evidence that non-neuronal cell types in the microenvironment function to regulate axon regeneration via cell-cell interaction. 

However, the molecular mechanisms by which ETBR regulates axon regeneration are unclear, and the manuscript's structure is not well organized, especially in the last section. Some discussion and explanation about the data interpretation are needed to improve the manuscript. 

We thank the reviewer for the positive comments. We agree that the mechanisms by which ETBR signaling functions as a brake on axon growth and regeneration remain to be elucidated. We believe that unraveling the detailed molecular pathways downstream of ETBR signaling in SGCs that promote axon regeneration is beyond the scope of this manuscript. Answering these questions would first require cell specific KO of ETBR and Cx43 to confirm that this pathway is operating in SGCs to control axon regeneration. We would also need to identify how SGCs communicate with neurons to regulate axon regeneration, which is a large area of ongoing research that remains poorly understood. Our data showing that pharmacological inhibition of ETBR with specific FDA-approved inhibitors enhances sensory axon regeneration provide not only new evidence for non-neuronal mechanisms in nerve repair, but also a new potential clinical avenue for therapeutic intervention.

As suggested by the reviewer, we have extensively revised the organization of the manuscript, especially the last section of results. We have performed additional snRNAseq experiments to establish the impact of aging in DRG. We have also performed additional experiments to determine if blocking ETBR improves target tissue reinnervation. Following the reviewer’s suggestion, we have also expanded the Discussion section to discuss alternative mechanisms and o]er additional interpretation of our data. Below we describe how we address each point in detail.

(1) The result showed that the level of ETBR did not change after the peripheral nerve injury. Does this mean that its endogenous function is to limit spontaneous sensory axon regeneration? In other words, the results suggest that SGCs expressing ETBR or vascular endothelial cells expressing its ligand ET-1 act to suppress sensory axon regeneration. Some explanation or discussion about this is necessary. Moreover, does the protein level of ETBR or its ligand change during aging?  

We thank the reviewer for this point. Our results indeed indicate that one endogenous function of ETBR is to limit the extent of sensory axon regeneration. This may be a part of a mechanism to limit spontaneous sensory axon growth or plasticity and maladaptive neural rewiring after nerve injury. While the increased growth capacity of damaged peripheral axons can lead to reconnection with their targets and functional recovery, the increased growth capacity can also lead to axonal sprouting of the central axon terminals of injured neurons in the spinal cord, and to pain (see for example Costigan et al 2010, PMID: 19400724).  In the context of aging that we describe here, this protective mechanism may hinder beneficial recovery. Other mechanisms that slow axon regeneration have been reported, and include, for example, axonally synthesized proteins, which typically support nerve regeneration through retrograde signaling and local growth mechanisms. RNA binding proteins (RBP) are needed for this process. One such RBP, the RNA binding protein KHSRP is locally translated following nerve injury. Rather than promoting axon regeneration, KHSRP promotes decay of other axonal mRNAs and slows axon regeneration.  Another example includes the Rho signaling pathway, which was shown to function as an inhibitory mechanism that slows the growth of spiral ganglion neurites in culture. We have now included these examples in the Discussion section.

To address the reviewer’s second question, we have checked protein levels of ETBR and ET-1 in adult and aged DRG tissue. We observed a robust increase in ET-1 in aged DRG, while the levels of ETBR did not appear to change significantly. These results are now presented in Figure 4- Figure Supplement 1, and further support the notion that in aging, activation of the ETBR signaling hinders axon regeneration.

(2) In ex vivo experiments, NGF was added to the culture medium. Previous studies have shown that adult sensory neurons could initiate fast axon growth in response to NGF within 24 hours. In addition, dissociated sensory neurons could also initiate spontaneous regenerative axon growth without NGF after 48 hours. Some discussion or rationale is needed to explain the di]erence between NGF-induced or spontaneous axon growth of culture adult sensory neurons and the roles of ETBR and SGCs. 

We appreciate the reviewer’s suggestion. In adult DRG explant or dissociated cultures, NGF is not typically required for survival or axon outgrowth. However, in dissociated culture, the addition of NGF to the medium stimulates growth from more neurons compared to controls (Smith and Skene 1997). In the DRG explant, NGF does not promote significant e]ects on axon growth, but stimulates glial cell migration (Klimovich et al 2020). We opted to included NGF in our explant assay to increase the potential of stimulating axon regeneration with pharmacological manipulations of ETBR. We have now clarified these considerations in the Method section.

(3) In cultured dissociated sensory neurons, inhibiting ETBR also enhanced axon growth, which meant the presence of SGCs surrounding the sensory neurons. Some direct evidence is needed to show the cellular relationship between them in culture.  

We thank the reviewer for raising this point and have added new data, now presented in Figure 2B, to show that in mixed DRG cultures, SGCs labeled with Fabp7 are present in the culture in proximity to neurons labeled with TUJ1, but they do not fully wrap the neuronal soma. These results are consistent with prior findings reporting that as time in culture progresses, SGCs lose their adhesive contacts with neuronal soma and adhere to the coverslip (PMID: 22032231, PMID: 27606776).  While in some cases SGCs can maintain their association with neuronal soma in the first day in culture after plating, in our hands, most SGCs have left the soma at the 24h time point we examined. 

(4) In Figure 3, the in vivo regeneration experiments first showed enhanced axon regeneration either 1 day or 3 days after the nerve injury. The study then showed that inhibiting ETBR could enhance sensory axon growth in vitro from uninjured naïve neurons or conditioning lesioned neurons. To my knowledge, in vivo sensory axon regeneration is relatively slow during the first 2 days after the nerve injury and then enters the fast regeneration mode on the 3rd day, representing the conditioning lesion e]ect in vivo. Some discussion is needed to compare the in vitro and the in vivo model of axon regeneration. 

We agree that axon growth is relatively slow the first 2 days and enters a fast growth mode on day 3. This has been elegantly demonstrated in Shin et al Neuron 2012 (PMID: 22726832), where an in vivo conditioning injury 3 days prior increases axon growth one day after injury. In vitro, similar e]ects have been described: a prior in vivo injury accelerates growth capacity within the first day in culture, but a similar growth mode occurs in naive adult neurons after 2-3 days in vitro (Smith and Skene 1996). We also know that the neurite growth in culture is stimulated by higher cell density, likely because non-neuronal cells can secrete trophic factors (Smith and Skene 1996). Our in vitro results thus suggest that blocking ETBR in SGCs in these mixed cultures may alter the media towards a more growth promoting state. In vivo, our data show that Bosentan treatment for 3 days partially mimics the conditioning injury and potentiate the e]ect of the conditioning injury. One possible interpretation is that inhibition of ETBR alters the release of trophic factors from SGCs. Future studies will be required to unravel how ETBR signaling influence the SGCs secretome and its influence on axon growth. We have now included these discussions points in the Results and Discussion Section.

(5) In Figure 5, the study showed that the level of connexin 43 increased after ETBR inhibition in either adult or aged mice, proposing an important role of connexin 43 in mediating the enhancing e]ect of ETBR inhibition on axon regeneration. However, in the study, there was no direct evidence supporting that ETBR directly regulates connexin 43 expression in SGCs. Moreover, there was no functional evidence that connexin 43 acted downstream of ETBR to regulate axon regeneration.  

We thank the reviewer for this point and agree that we do not provide direct evidence that connexin 43 acts downstream of ETBR to regulate axon regeneration. To obtain such functional evidence would require selective KO of ETBR and Cx43 in SGCs, which we believe is beyond the scope of the current study. We have revised the Results and Discussion sections to emphasize that while we observe that ETBR inhibition increases Cx43 levels and Cx43 levels correlates with axon regeneration, whether Cx43 directly mediates the e]ect on axon regeneration remains to be established.  We also discuss potential alternative mechanisms downstream of ETBR in SGCs that could contribute to the observed e]ects on axon regeneration. Specifically, we discuss the possibility that  ETBR signaling may limit axon regeneration via regulating SGCs glutamate reuptake functions, because of the following reasons: 1) Similarly to astrocytes, glutamate uptake by SGCs is important to regulate neuronal function, 2) exposure of cultured cortical astrocytes to endothelin results in a decrease in glutamate uptake that correlates with a major loss of basal glutamate transporter expression (GLT-1 and1), 3) Both glutamate transporters are expressed in SGCs in sensory ganglia 4) GLAST and glutamate reuptake function is important for lesion-induced plasticity in the developing somatosensory cortex. 

Reviewer #2 (Public Review): 

Summary: 

In this interesting and original study, Feng and colleagues set out to address the e]ect of manipulating endothelin signaling on nerve regeneration, focusing on the crosstalk between endothelial cells (ECs) in dorsal root ganglia (DRG), which secrete ET-1 and satellite glial cells (SGCs) expressing ETBR receptor. The main finding is that ETBR signaling is a default brake on axon growth, and inhibiting this pathway promotes axon regeneration after nerve injury and counters the decline in regenerative capacity that occurs during aging. ET-1 and ETBR are mapped in ECs and SGCs, respectively, using scRNA-seq of DRGs from adult or aged mice. Although their expression does not change upon injury, it is modulated during aging, with a reported increase in plasma levels of ET-1 (a potent vasoconstrictive signal). Using in vitro explant assays coupled with pharmacological inhibition in mouse models of nerve injury, the authors demonstrate that ET-1/ETBR curbs axonal growth, and the ETAR/ETBR antagonist Bosentan boosts regrowth during the early phase of repair. In addition, Bosentan restores the ability of aged DRG neurons to regrow after nerve lesions. Despite Bosentan inhibiting both endothelin receptors A and B, comparison with an ETAR-specific antagonist indicates that the e]ects can be attributed to the ET-1/ETBR pathway. In the DRGs, ETBR is mostly expressed by SGCs (and a subset of Schwann cells) a cell type that previous studies, including work from this group, have implicated in nerve regeneration. SGCs ensheath and couple with DRG neurons through gap junctions formed by Cx43. Based on their own findings and evidence from the literature, the pro-regenerative e]ects of ETBR inhibition are in part attributed to an increase in Cx43 levels, which are expected to enhance neuron-SGC coupling. Finally, gene expression analysis in adult vs aged DRGs predicts a decrease in fatty acid and cholesterol metabolism, for which previous work by the authors has shown a requirement in SGCs to promote axon regeneration. 

Strengths: 

The study is well-executed and the main conclusion that "ETBR signaling inhibits axon regeneration after nerve injury and plays a role in age-related decline in regenerative capacity" (line 77) is supported by the data. Given that Bosentan is an FDA-approved drug, the findings may have therapeutic value in clinical settings where peripheral nerve regeneration is suboptimal or largely impaired, as it often happens in aged individuals. In addition, the study highlights the importance of vascular signals in nerve regeneration, a topic that has gained traction in recent years. Importantly, these results further emphasize the contribution of longneglected SGCs to nerve tissue homeostasis and repair. Although the study does not reach a complete mechanistic understanding, the results are robust and are expected to attract the interest of a broader readership. 

We thank the reviewer for the positive comments, especially in regard to the rigor and originality of our study.

Weaknesses: 

Despite these positive comments provided above, the following points should be considered: 

(1) This study examines the contribution of the ET-1 pathway in the ganglia, and in vitro assays are consistent with the idea that important signaling events take place there. Nevertheless, it remains to be determined whether the accelerated axon regrowth observed in vivo depends also on cellular crosstalk mediated by ET-1 at the lesion site. Are ECs along the nerve secreting ET-1? What cells are present in the nerve stroma that could respond and participate in the repair process? Would these interactions be sensitive to Bosentan? It may be di]icult to dissect this contribution, but it should at least be discussed.  

We thank the reviewer for this important point and agree that the in vivo e]ects observed cannot rule out the contribution of ECs or SCs at the lesion site in the nerve. Dissecting the contribution of ETBR expressing cells in the nerve would require cell-specific manipulations that go beyond the scope of this manuscript. We have revised the Discussion section to highlight the potential contribution of ECs, fibroblast and SCs in the nerve.  

(2) It is suggested that the permeability of DRG vessels may facilitate the release of "vascularderived signals" (lines 82-84). Is it possible that the ET-1/ETBR pathway modulates vascular permeability, and that this, in turn, contributes to the observed e]ects on regeneration?  

We thank the reviewer for raising this interesting point. ET-1 can have an impact on vascular permeability. It was indeed shown that in high glucose conditions, increased trans-endothelial permeability is associated with increased Edn1, Ednra and Ednrb expression and augmented ET1 immunoreactivity (PMID: 10950122). It is thus possible that part of the e]ects observed results from altered vascular permeability. We have included this point in the Discussion section. Future experiments will be required to test how injury and age a]ects vascular permeability in the DRG.

(3) Is the a]inity of ET-3 for ETBR similar to that of ET-1? Can it be excluded that ET-3 expressed by fibroblasts is relevant for controlling SGC responses upon injury/aging?  

We thank the reviewer for raising this point. ET-1 binds to ETAR and ETBR with the same a]inity, but ET3 shows a higher a]inity to ETBR than to ETAR (Davenport et al. Pharmacol. Rev 2016 PMID: 26956245). We attempted to examine ET-3 level in adult and aged DRG by western blot, but in our hands the antibody did not work well enough, and we could not obtain clear results. We thus cannot exclude the possibility that ET-3 released by fibroblasts contribute to the e]ects we observe on axon regeneration. Indeed, in cultured cortical astrocytes, application of either ET-1 or ET-3 leads to inhibition of Cx43 expression. We have revised the text in the Discussion section to highlight the possibility that both ET-1 and ET-3 could participate on the ETBRdependent e]ect on axon regeneration.

(4) ETBR inhibition in dissociated (mixed) cultures uncovers the restraining activity of endothelin signaling on axon growth (Figure 2C). Since neurons do not express ET-1 receptors, based on scRNA-seq analysis, these results are interpreted as an indication that basal ETBR signaling in SGC curbs the axon growth potential of sensory neurons. For this to occur in dissociated cultures, however, one should assume that SGC-neuron association is present, similar to in vivo, or to whole DRG cultures (Figure 2C). Has this been tested?

We thank the reviewer for this point. In dissociated DRG culture, neurons, SGCs and other nonneuronal cells are present, but SGCs do not retain the surrounding morphology as they do in vivo. Within 24 hours in culture, SGCs lose their adhesive contacts with neuronal soma and adhere to the coverslip (PMID: 22032231, PMID: 27606776).  We have included new data in Figure 2B to show that in our culture conditions, SGCs are present, but do not wrap neurons soma as they do in vivo. We also know from prior studies that the density of the culture a]ects axon growth, an e]ect that was attributed to trophic factors released from non-neuronal cells (Smith and Skene 1997). Therefore, although SGCs do not surround neurons, the signaling pathway downstream of ETBR may be present in culture and contribute to the release of trophic factors that influence axon growth. We have revised the Results section to better explain our in vitro results and their interpretation.

In both in vitro experimental settings (dissociated and whole DRG cultures) how is ETBR stimulated over up to 7 days of culture? In other words, where does endothelin come from in these cultures (which are unlikely to support EC/blood vessel growth)? Is it possible that the relevant ligand here derives from fibroblasts (see point #6)? Or does it suggest that ETBR can be constitutively active (i.e., endothelin-independent signaling)? Is there any chance that endothelin is present in the culture media or Matrigel? 

We thank the reviewer for raising this point.  Our single-cell data indicate that ET-1 is expressed by endothelial cells and ET-3 by fibroblasts. In dissociated DRG culture at 24h time point, all DRGs cells are present, including endothelial cells and fibroblasts, and could represent the source of ET-1 or ET-3. In the explant setting, it is also possible that both ET-1 and ET-3 are released by endothelial cells and fibroblasts during the 7 days in culture. According to information for the suppliers, endothelin is not present neither in the culture media nor in the Matrigel. While mutations can facilitate the constitutive activity of the ETBR receptor, we are not aware of data showing that endogenous ETBR can be constitutively active.  Because the molecular mechanisms governing ETBR -mediated signaling remain incompletely understood (see for example PMID: 39043181, PMID: 39414992) future studies will be required to elucidate the detailed mechanisms activating ETBR in SGCs and its downstream signaling mechanisms.  We have now expanded the Results and discussion sections to clarify these points. 

(5) The discovery that ET-1/ETBR signaling in SGC curtails the growth capacity of axons at baseline raises questions about the physiological role of this pathway. What happens when ETBR signaling is prevented over a longer period of time? This could be addressed with pharmacological inhibitors, or better, with cell-specific knock-out mice. The experiments would certainly be of general interest, although not within the scope of this story. Nevertheless, it could be worth discussing the possibilities. 

We agree that this is an interesting point. As mentioned above in response to point #1 of reviewer 1, the physiological role of this pathway could be to limit plasticity and prevent maladaptive neural rewiring that can happen after injury (Costigan et al 2009, PMID: 19400724), but can also hinder beneficial recovery after injury. Other mechanisms that limit axon regeneration capacity have been described and involve local mRNA translation and Rho signaling. We have revised the Discussion section to include these points. We agree that understanding the consequence of blocking ETBR over longer time periods is beyond the scope of the current study, but we now discuss the possibility that blocking ETBR with a cell specific KO approach could unravel its physiological function on target innervation and behavior. 

(6) Assessing Cx43 levels by measuring the immunofluorescence signal (Figure 5E-F) is acceptable, particularly when the aim is to restrict the analysis to SGCs. The modulation of Cx43 expression by ET-1/ETBR plays an important part in the proposed model. Therefore, a complementary analysis of Cx43 expression by quantitative RT-PCR on sorted SGCs would be a valuable addition to the immunofluorescence data. Is this attainable? 

We agree and have attempted to perform these types of experiments but encountered technical di]iculties. We attempted to sorting SGCs from transgenic mice in which SGCs are fluorescently labeled. However, the cells did not survive the sorting process and died in culture.  We think that increasing the viability of cells after sorting would require capillary- free fluorescent sorting approaches. However, we do not currently have access to such technology. We attempted this experiment with cultured SGCs, following a previously published protocol (Tonello et al. 2023 PMID: 38156033). In these experiments, SGCs are cultured for 8 days to obtain purity. We did not observe any di]erence in Cx43 protein or mRNA level upon treatment with ET-1 with or without BQ788. However, in these SGCs cultures, Cx43 displayed a di]use localization, rather than puncta as observed in vivo. Therefore, despite our multiple attempts, quantifying Cx43 on sorted or purified SGCs was not attainable.

(7) The conclusions "We thus hypothesize that ETBR inhibition in SGCs contributes to axonal regeneration by increasing Cx43 levels, gap junction coupling or hemichannels and facilitating SGC-neuron communication" (lines 303-305) are consistent with the findings but seem in contrast with the e]ect of aging on gap junction coupling reported by others and cited in line 210: "the number of gap junctions and the dye coupling between these cells increases (Huang et al., 2006)". I am confused by what distinguishes a potential, and supposedly beneficial, increase in coupling after ETBR inhibition, from what is observed in aging. 

We agree that the aging impact of Cx43 level and gap junction number appears contradictory. Procacci et al 2008 reported that Cx43 expression in SGCs decreases in the aged mice. Huang et al 2006 report that both the number of gap junctions and the dye coupling between these cells were found to increase with aging. Procacci et al suggested as a possible explanation for this apparent discrepancy that additional connexin types other than Cx43 may contribute to the gap junctions between SGCs in aged mice. Our snRNAseq data did not allow us to verify this hypothesis, because there were less SGCs in aged mice compared to adult, and connexin genes were detected in only 20% or less of SGCs.  Furthermore, our quantification did not look specifically at gap junctions, but just at Cx43 puncta. Cx43 can also form hemichannels in addition to gap junctions, and can also perform non-channel functions, such as protein interaction, cell adhesion, and intracellular signaling. Thus, more research examining the role of Cx43 in SGCs is necessary to address this discrepancy in the literature. We have expanded the Discussion section to include these points. 

(8) I find it di]icult to reconcile the results in Figure 5F with the proposed model since (1) injury increases Cx43 levels in both adult and aged mice, (2) the injured aged/vehicle group has a similar level to the uninjured adult group, (3) upon injury, aged+Bosentan is much lower than adult+Bosentan (significance not tested). It seems hard to explain the e]ect of Bosentan only through the modulation of Cx43 levels. Whether the increase in Cx43 levels following ETBR inhibition actually results in higher SGC-neuron coupling has not been assessed experimentally. 

We thank the reviewer for this point and agree that the e]ect of Bosentan is likely not exclusively through the modulation of Cx43 levels in SGCs, and that Cx43 levels may simply correlate with axon regenerative capacity. We have revised the manuscript to clarify this point.  We have also added the missing significance test in Figure 5F.

Cell specific KO of Cx43 and ETBR would allow to test this hypothesis directly but is beyond the scope of the current study. We have not tested SGCs-neuron coupling, as these experiments are currently beyond our area of expertise. Cx43 has also other functions beyond gap junction coupling, such as protein interaction, cell adhesion, and intracellular signaling. Investigating the precise function of Cx43 would require in depth biochemical and cell specific experiments that are beyond the scope of this study. Furthermore, as we now mentioned in response to reviewer #2 point 5, ETBR signaling may also have other downstream e]ects in SGCs, such as glutamate transporters expression, or a]ect other cells in the nerve during the regeneration process. We have revised the Discussion section to include these alternative mechanisms.

Reviewer #3(Public Review): 

Summary: 

This manuscript suggests that inhibiting ETBR via the FDA-approved compound Bosentan can disrupt ET-1-ETBR signalling that they found detrimental to nerve regeneration, thus promoting repair after nerve injury in adult and aged mice. 

Strengths: 

(1) The clinical need to identify molecular and cellular mechanisms that can be targeted to improve repair after nerve injury. 

(2) The proposed mechanism is interesting. 

(3) The methodology is sound. 

We thank the reviewer for highlighting the strengths of our study

Weaknesses: 

(1) The data appear preliminary and the story appears incomplete. 

We appreciate the reviewer’s point. We would like to emphasize that our results provide compelling evidence that ETBR signaling is a default brake on axon growth, and inhibiting this pathway promotes axon regeneration after nerve injury and counters the decline in regenerative capacity that occurs during aging. We also provide evidence that ETBR signaling regulates the levels of Cx43 in SGCs. Furthermore, our results document the use of an FDA approved compound to increase axon regeneration may be of interest to the broader readership, as there is currently no therapies to improve or accelerate nerve repair after injury. We agree that the detailed mechanisms operating downstream of ETBR will need to be elucidated. Answering these questions would first require cell specific KO of ETBR and Cx43 to confirm that this pathway is operating in SGCs to control axon regeneration. We would also need to identify how SGCs communicate with neurons to regulate axon regeneration, which is a large area of ongoing research that remains poorly understood. This extensive and highly complex set of experiments is beyond the scope of the current study. As we discussed in our response to reviewer #1 and #2 we attempted to perform numerous additional experiments to better define the role of ETBR signaling in SGCs in aging and have included additional results in Fig. 2B, Fig 3G-H,  Fig 5A-E, and Figure 4- Figure Supplement 1and Figure 5- Figure Supplement 1. We have expanded the

Discussion to acknowledge the limitation of our study and to discuss possible mechanisms.  

(2) Lack of causality and clear cellular and molecular mechanism. There are also some loose ends such as the role of connexin 43 in SGCs: how is it related to ET-1- ETBR signalling?  

We thank the reviewer for this point and agree that the molecular mechanisms downstream of ETBR remain to be elucidated. However, we believe that our manuscript reports an interesting potential of an FDA-approved compound in promoting nerve repair. We focused on Cx43 downstream of ETBR signaling because decreased Cx43 expression in SGCs in ageing was previously established, but the mechanisms were not elucidated. Furthermore, it was reported that ET1 signaling in cultured astrocytes, which share functional similarities with SGCs, leads to the closure of gap junctions and reduction in Cx43 expression. Our study thus provides a mechanism by which ETBR signaling in SGCs regulates Cx43 expression. Whether Cx43 directly impact axon regeneration remains to be tested. Cell specific KO of Cx43 and ETBR would be required to answer this question. We have revised the Introduction and Discussion section extensively to provide a link between ETBR and Cx43 and to acknowledge the lack of causality in Cx43 in SGCs, as well as to provide additional potential mechanisms by which ETBR inhibition may promote nerve repair.

Reviewer #2 (Recommendations For The Authors): 

In addition to the points listed in the Public Review section, please consider the following comments: 

(1) ETAR, which is high in mural cells, does not seem to be implicated in the reported proregenerative e]ects. Even so, can vasoconstriction be ruled out as an underlying cause of the age-dependent decline in axon regrowth potential and, more generally, in the e]ects of ET-1 inhibition on regeneration? This could be discussed. 

We agree that we can’t exclude a role in vasoconstriction or e]ect on vascular permeability in the age-dependent decline in axon regrowth potential. However, our in vitro and ex vivo experiments, in which vascular related mechanisms are unlikely, suggest that vasoconstriction may not be a major contributor to the e]ects we observed.

(2) The manuscript (e.g. line 287-288) would benefit from a discussion of the role that blood vessels play in the peripheral nervous system, and possibly CNS, repair. Vessels were shown to accompany regenerating fibers and instruct the reorganization of the nerve tissue to favor repair potentially through the release of pro-regenerative signals acting on stromal cells, glia, and other cellular components. Highlighting these processes will help put the current findings into perspective. 

We agree and have revised the Discussion section to better explain the role of blood vessels in orientating Schwann cells migration and guiding axon regeneration.

(3) The vast majority of the cells that are sequenced and shown in the UMAP in Figure 1C are from adult (3-month-old) mice [16,923 out of 18,098]. It would be useful to include the UMAP split (or color-coded) by timepoint to appreciate changes in cell clustering that may occur with aging.  

We apologize for this misunderstanding, Figure 1C had all cells from all ages. However, the number of cells we obtained from the age group was insu]icient to perform in depth analysis of each cell type. We have thus revised this section and Figure 1, now only presenting the data from adult mice.  

It is not discussed why fewer cells were sequenced at later stages. Additionally, I do not know how to interpret the double asterisks next to the labeling "18,098 samples" in Figure 1C. 

Since our original sequencing of adult and aged mice using 10x yielded so few cells from the aged DRG, we tested and optimized a new technology for single cell preparation of DRG using Illumina Single Cell 3’ RNA Prep. This preparation creates templated emulsions using a vortex mixer to capture and barcode single-cell mRNA instead of a microfluidics system. This method yielded much better results for nuclei recovery from aged DRG, with more nuclei and better quality of nuclei. Thus, we now present in Figure 5 and Figure 5- Figure Supplement 1 the results from snRNA-sequencing of aged and adult DRG using the Illumina single cell kit. The results of the snRNA-sequencing show a decreased abundance of SGCs in aged mice, consistent with the results from our morphology analysis with EM. We were also able to perform SGCs-specific pathway analysis because of the increased number of nuclei captured in the aged SGCs, which we included in the manuscript.

(4) The in vivo studies are designed to examine the e]ects of ETBR inhibition during the first phase of axon regrowth after nerve injury (1-3 days post-injury, dpi). Is there a reason why later stages have not been studied? It would be interesting to understand whether ETBR inhibition improves long-term recovery or is only e]ective at boosting the initial growth of axons through the lesion. It is possible that early inhibition will be enough for long-term recovery. If so, these experiments would define a sensitivity window with therapeutic value. 

We agree that assessing functional recovery requires proper behavioral tests or morphological evaluations of reinnervation. To determine if Bosentan treatment has long-term e]ects on recovery, we administered Bosentan or vehicle for 3 weeks (daily for 1 week, and then once a week for the subsequent 2 weeks) after sciatic nerve crush. At 24 days after SNC, we assessed intraepidermal nerve fiber density (IENFD) in the injured paw and saw a trend towards increased fibers/mm in the treated animals (new Figure 3G,H). Future studies will examine how long-term Bosentan treatment a]ects functional recovery and innervation at later time points. Additionally, behavior assays will be needed to determine if these morphological changes relate to behavioral improvements using IENFD and behavior assays.

(5) I am unsure if the gene expression analysis shown in Figure 6 fits well into this story. It is interesting per se and in line with previous work from this group showing the relevance of fatty acid metabolism in SGCs for axon regeneration. Nevertheless, without a mechanistic link to endothelin signaling and Cx43/gap junction modulation, the observations derived from DEG analysis are not well integrated with the rest and may be more distracting than helpful. One limitation is that there is no cell-type information for the DEGs due to the small number of cells recovered from aged mice. For instance, if ETBR inhibition rescued gene downregulation associated with fatty acid/cholesterol metabolism, then the DGE results would become more relevant for understanding the cellular basis of the pro-regenerative e]ect, which at this point remains quite speculative (lines 264-265; lines 318-319).  

We agree and have added new snRNA sequencing data to replace these findings (see above response to point #4, new Figure 5 and Figure 5- Figure Supplement 1. The new data shows a decreased abundance of SGCs in aged mice, consistent with our TEM results. Pathway analysis revealed that aging triggers extensive transcriptional reprogramming in SGCs, reflecting heightened demands for structural integrity, cell junction remodeling, and glia–neuron interactions within the aged DRG microenvironment.  

(6) It would be interesting to determine whether Bosentan increases SGC coverage of neuronal cell bodies in aged mice (Figures 6A-C). 

We agree that this would be very interesting, but will require extensive EM analysis at di]erent time points and is beyond the scope of the current manuscript.

(7) Finally, adding a summary model would help the readers. 

We agree and have made a summary model, now presented in Figure 6F.

Reviewer #3 (Recommendations For The Authors): 

Longer time points post-injury and assessment of functional recovery after Bosentan would be of great value here. 

We agree that assessing functional recovery requires proper behavioral tests or morphological evaluations of reinnervation. To determine if Bosentan treatment has long-term e]ects on recovery, we administered Bosentan or vehicle for 3 weeks (daily for 1 week, and then once a week for the subsequent 2 weeks) after sciatic nerve crush. At 24 days after SNC, we assessed intraepidermal nerve fiber density in the injured paw and saw a trend towards increased fibers/mm in the treated animals (Fig 3). While the results do not reach significance, we decided to include this new data as it provides evidence that Bosentan treatment may also improves long term recovery. Future studies will be required examine how long-term Bosentan treatment a]ects functional recovery and innervation at later time points. Additionally, behavior assays will be needed to determine if these morphological changes relate to behavioral improvements.

It would be important to know how ET-1- ETBR signalling axis promotes the regeneration of axons:this remains unaddressed. What are the cells that are specifically involved? Endothelial cellsSGC- neurons- SC? There are no experiments addressing the role of any of these? 

We agree that the molecular and cellular mechanisms by which ETBR signaling in SGCs promote axon regeneration remains to be elucidated.  Answering these questions would first require cell specific KO of ETBR and Cx43 to confirm that this pathway is operating in SGCs to control axon regeneration. We would also need to identify how SGCs communicate with neurons to regulate axon regeneration, which is a large area of ongoing research that remains poorly understood. While these are important experiments, because of numerous technical and temporal constrains, we believe they are beyond the scope of the current manuscript. 

How does connexin 43 in SGCs related to ET-1- ETBR signalling? 

The relation between connexin 43 and ETBR signaling stems from observations made in astrocytes. ET1 signaling in cultured astrocytes, which share functional similarities with SGCs, was shown to lead to the closure of gap junctions and the reduction in Cx43 expression. Because Cx43 expression, a major connexin expressed in SGCs as in astrocytes, was previously shown to be reduced at the protein level in SGCs from aged mice, we decided to explore it this ETBR-Cx43 mechanism also operates in SGCs. We have revised the Introduction and Discussion section extensively to acknowledge the lack of causality in Cx43 expression SGCs and to provide additional potential mechanisms by which ETBR inhibition may promote nerve repair.

Reviewer #2 (Public review):

In the presented manuscript, Teplenin and colleagues use both electrical pacing and optogenetic stimulation to create a reproducible, controllable source of ectopy in cardiomyocyte monolayers. To accomplish this, they use a careful calibration of electrical pacing characteristics (i.e., frequency, number of pulses) and illumination characteristics (i.e., light intensity, surface area) to show that there exists a "sweet spot" where oscillatory excitations can emerge proximal to the optogenetically depolarized region following electrical pacing cessation, akin to pacemaker cells. Furthermore, the authors demonstrate that a high-frequency electrical wave-train can be used to terminate these oscillatory excitations. The authors observed this oscillatory phenomenon both in vitro (using neonatal rat ventricular cardiomyocyte monolayers) and in silico (using a computational action potential model of the same cell type). These are surprising findings and provide a novel approach for studying triggered activity in cardiac tissue.

The study is extremely thorough and one of the more memorable and grounded applications of cardiac optogenetics in the past decade. One of the benefits of the authors' "two-prong" approach of experimental preps and computational models is that they could probe the number of potential variable combinations much deeper than through in vitro experiments alone. The strong similarities between the real-life and computational findings suggest that these oscillatory excitations are consistent, reproducible, and controllable.

Triggered activity, which can lead to ventricular arrhythmias and cardiac sudden death, has been largely attributed to sub-cellular phenomena, such as early or delayed afterdepolarizations, and thus to date has largely been studied in isolated single cardiomyocytes. However, these findings have been difficult to translate to tissue and organ-scale experiments, as well-coupled cardiac tissue has notably different electrical properties. This underscores the significance of the study's methodological advances: the use of a constant depolarizing current in a subset of (illuminated) cells to reliably result in triggered activity could facilitate the more consistent evaluation of triggered activity at various scales. An experimental prep that is both repeatable and controllable (i.e., both initiated and terminated through the same means).

The authors also substantially explored phase space and single-cell analyses to document how this "hidden" bi-stable phenomenon can be uncovered during emergent collective tissue behavior. Calibration and testing of different aspects (e.g., light intensity, illuminated surface area, electrical pulse frequency, electrical pulse count) and other deeper analyses, as illustrated in Appendix 2, Figures 3-8, are significant and commendable.

Given that the study is computational, it is surprising that the authors did not replicate their findings using well-validated adult ventricular cardiomyocyte action potential models, such as ten Tusscher 2006 or O'Hara 2011. This may have felt out of scope, given the nice alignment of rat cardiomyocyte data between in vitro and in silico experiments. However, it would have been helpful peace-of-mind validation, given the significant ionic current differences between neonatal rat and adult ventricular tissue. It is not fully clear whether the pulse trains could have resulted in the same bi-stable oscillatory behavior, given the longer APD of humans relative to rats. The observed phenomenon certainly would be frequency-dependent and would have required tedious calibration for a new cell type, albeit partially mitigated by the relative ease of in silico experiments.

For all its strengths, there are likely significant mechanistic differences between this optogenetically tied oscillatory behavior and triggered activity observed in other studies. This is because the constant light-elicited depolarizing current is disrupting the typical resting cardiomyocyte state, thereby altering the balance between depolarizing ionic currents (such as Na+ and Ca2+) and repolarizing ionic currents (such as K+ and Ca2+). The oscillatory excitations appear to later emerge at the border of the illuminated region and non-stimulated surrounding tissue, which is likely an area of high source-sink mismatch. The authors appear to acknowledge differences in this oscillatory behavior and previous sub-cellular triggered activity research in their discussion of ectopic pacemaker activity, which is canonically expected more so from genetic or pathological conditions. Regardless, it is exciting to see new ground being broken in this difficult-to-characterize experimental space, even if the method illustrated here may not necessarily be broadly applicable.

En la resolución tendrá que realizarse una valoración racional de cada prueba, sin que sea válido el argumento que se valoró conforme a la íntima convicción del juzgador.

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What is this?

"keine Lebensversicherung"... versicherungen sind nur für die idioten, die sonst überhaupt nicht mit geld umgehen können... die einzig stabile lösung ist auswandern, fragt sich nur wohin, weil europa = scheisse.

17:49 Was machst du persönlich, um durch den "Winter" zu kommen? Ja, auf jeden Fall immer schön Feuer, damits schön warm ist, Tee trinken und Sport. Nein, Spaß beiseite. Ähm ja, man muss natürlich sich darauf vorbereiten, wie vorhin schon erwähnt, das muss mental passieren und monetär. Und mental ist das allerwichtigste, erstmal verstehen, wie funktionieren die Zyklen, und wie funktioniert unser Geldsystem. Na, weil nur wenn man das weiß, wenn man dieses Wissen Intus hat, dann kann man sich auch aktiv dagegen wehren und schützen natürlich, vor Enteignung, vor Entwertung.

Und noch mal, ich kann so sagen, also ein Allzeithoch bei Bitcoin bedeutet nicht, dass der Bitcoin Preis steigt, sondern dass der Euro immer tiefer fällt. Das ist ganz wichtig zu verstehen, weil ein Bitcoin ist ein Bitcoin, 1 Kilo Gold ist 1 Kilo Gold, und ähm, was ich mache natürlich ist Diversifikation. Also ich versuche mein Geld, das ich verdiene, in sichere Häfen zu bringen, die durch die Inflation nicht enteignet werden, weil wir leben jetzt einer inflationären Welt und in dieser inflationären Welt musst du raus aus Papier werden, musst raus aus deinem Tagesgeld und musst rein in limitierte Werte. Und das sind natürlich alles, was durch Natur limitiert ist. von Edelmetallen, Edelsteinen, Land und so weiter, Aktien, hin auch zu äh limitierten Werten, die limitiert sind durch die Mathematik wie z.B. Bitcoin. Und da würde ich auf jeden Fall reinskalieren.

19:00 Ich würde wenig Geld auf dem Konto lassen, keine Lebensversicherung, kein Bausparvertrag, keine Anleihen, um Gottes Willen, der Anleihenmarkt ist in meiner Ansicht nach jetzt im Bärenmarkt, nach 40 Jahren Bullenmarkt.

Und dann natürlich baue ich auch schon halt Plan B, Plan C, also Strategien auch, um auch im Ausland ein Standbein zu haben außerhalb Deutschlands, außerhalb die EU. ähm, sowohl privat als auch natürlich mit dem Unternehmen, um dann halt auch zu gehen, wenn die wirklich einen Krieg vom Zaun brechen oder wenn die Kommunisten in Berlin einziehen oder wenn ein Vermögensregister oder Transparenzregister kommt durch die EU und so weiter, weil da sollte man einfach dann schon ein Plan B in der Schubladte haben.

19:40 Und was machst du in deinem privaten Umfeld, Freunde, Familie? Weil das ist ja auch was, was mich oft erreicht, wo ich halt merk, ich beschäftige mich jetzt seit mehreren Jahren damit, wir haben eine Community, wir wir helfen Leuten dabei damit umzugehen, aber oft kommen halt dann irgendwann Leute so aus dem privaten Umfeld auf einem zu, die sich halt die letzten Jahre nie damit befasst haben, jetzt vielleicht irgendwie mal merken "oh da kommt was" und irgendwie ist das halt dann super super schwierig. Was gibst du den Menschen mit, die jetzt hal Freunde, Familie sind und halt irgendwie jetzt vielleicht auch langsam aufwachen?

20:08 Mhm. Ja, genau. Also, es gibt ja keine Schubladenlösung, das sind ja immer maßgeschneiderte Lösungen, wenn man dann irgendwie eine Exit Strategie baut und für viele ist es halt auch nicht vorstellbar, ne? Weil, die sind hier verankert, die haben Freundeskreis, sind im Verein, arbeiten hier sind Angestellte, nicht jeder kann irgendwie sagen "macht's gut, ihr Idioten, macht euren Scheiß alleine, ich hau ab". Ja, aber bei mir z.B. in der Honorarberatung ist es gerade in JEDER Beratung wird gefragt, wo kann mein Geld hin flüchten und wo kann ich hin flüchten im Notfall, ne?

Und es sind unterschiedliche Gründe natürlich, warum die gehen möchten. Also bei den einen ist die Intention Angst vor Krieg, bei den anderen halt Enteignung, bei den anderen irgendwie Deutschland ist unsicher geworden, ich fühle mich nicht mehr sicher, hier Schwimmbad-Grapscher, Messerstecher etc. pp. Also völlig legitime Gründe allesamt, ne?

Und dann muss man halt überlegen, welche Sprache spreche ich, wo möchte ich hin? Aber wenn man sagt, man möchte mit der Familie gehen, dann muss man die Familie halt dementsprechend auch aufklären, informieren, damit die halt auch die Warnsignale sehen, weil du kannst niemanden irgendwie zwingen mit irgendwie ins Ausland zu ziehen, nur weil man irgendwie vor irgendwas Angst hat.

Also, ich würde auch mir ganz klar als Ziel setzen mit meinen Freunden, Bekannten zu reden und dann gemeinsam vielleicht auch eine Lösung zu finden irgendwo, weil natürlich, wenn du zu fünft irgendwie eine Burg kaufst oder ein paar Häuschen kaufst, irgendwo ist es angenehmer als irgendwo allein zu wohnen, weil man schwächt sich ja erstmal, wenn man seine vertraute Umgebung verlässt.

Ich habe dazu auch viele Videos gemacht, ne, und auch ein Buch geschrieben, wie so eine Auswanderungsstrategie aussehen kann, aber jetzt in der Beratung sehe ich jeder Jeck ist anders. Es kommt drauf an, hast du 5000 € oder 5 Millionen, ne? Also da muss man halt überlegen, welches Land ist auch passend, ne? Nicht jeder fühlt sich irgendwie wohl in Spanien oder nicht jeder fühlt sich wohl irgendwie dann in Schweden, in der Schweiz oder in Südamerika.

Deswegen, also mein Rat ist wirklich die direkte Aufklärung, um vielleicht sogar noch vor Ort eine Besserung herbeizuführen, weil umso mehr Menschen wissen, hey, hier läuft einiges schief im "Staat der DMark", ja, dann kann man ja auch was dagegen machen, indem man dementsprechend wählt, oder Druck auf die Politik ausübt.

Art. 914. O executado, independentemente de penhora, depósito ou caução, poderá se opor à execução por meio de embargos.

(...)

§ 2º Na execução por carta, os embargos serão oferecidos no juízo deprecante ou no juízo deprecado, mas a competência para julgá-los é do juízo deprecante, salvo se versarem unicamente sobre vícios ou defeitos da penhora, da avaliação ou da alienação dos bens efetuadas no juízo deprecado.

Mide la concentración de iones de hidrógeno (H+) en una solución.

A minha reflexão sobre os princípios ilustrados na Figura 7 tem no caso do ensino de algumas áreas fundamentais da engenharia (por exemplo física, eletrónica, sistemas embebidos…), uma aplicação que considero um bom exemplo: a conceção e uso de laboratórios remotos (com experiências reais, através de equipamentos controlados à distância, e virtuais, através de simuladores). As e-atividades que este tipo de abordagem permite vão muito além de meros exercícios, materializando estes quatro princípios de forma exemplar. Propiciam a interação, não só entre os participantes, mas também a interação direta e prática com equipamentos e fenómenos reais. Estimulam a autonomia, ao permitir que os participantes conduzam experiências ao seu próprio ritmo, cometendo erros e aprendendo com eles, algo que é fundamental para a "aprendizagem profunda". Promovem a abertura, pelo acesso a equipamentos de laboratório dispendiosos e específicos, que de outra forma estariam inacessíveis à maioria dos participantes. Reconhecem a diversidade de aprendizagem, por permitirem um aprender pelo fazer, tanto a participantes com gosto pela prática, como aos que ficam inibidos na presença dos equipamentos (com medo de estragar pela “falta de jeito”), complementando a aprendizagem teórica. A pertinência deste modelo tem um exemplo paradigmático no que levou ao desenvolvimento dos ambientes (ferramentas, formas de uso, atores…) que tornaram possíveis os temas que abordámos neste curso. O trabalho de Sir Tim Berners-Lee no CERN, que deu origem à World Wide Web e aos primeiros web browsers, foi uma resposta a uma necessidade premente: permitir que milhares de cientistas, geograficamente dispersos, pudessem colaborar, aceder a dados e operar remotamente equipamentos nos projetos do maior laboratório de física de partículas do mundo. O artigo (https://iopscience.iop.org/article/10.1088/1748-0221/3/08/S08003) que apresenta a experiência ATLAS, uma das duas que comprovou em 2013 a existência do bosão de Higgs - a mítica partícula que se procurava sem sucesso há décadas - tem 2927 autores, ilustrando-o bem. A WWW nasceu, portanto, da necessidade de "laboratórios remotos" para uma comunidade científica global. Tendo vivido de perto essa experiência (um dos 2927), e sabendo que há assuntos que só se aprendem realmente fazendo, tenho a convicção, já com algum tempo (e também contacto com o assunto: https://www.physics.rutgers.edu/~eandrei/389/muon/1322-1326.pdf) de que estes laboratórios remotos, permitindo aos estudantes de cursos no formato de e-learning fazer trabalhos experimentais (e, como vimos, fazê-los de forma semelhante ao que se faz em algumas áreas da investigação fundamental), devem ser uma parte integrante destas e-atividades em determinados cursos. É certo que, como foi mencionado numa das sessões síncronas, o investimento inicial e a manutenção são exigentes, condicionando a oferta formativa. Parece-me, no entanto, que é um investimento com retorno seguro.

Paradoxalmente, também existe fenómeno inverso, há estudantes (particularmente de áreas tecnológicas) que dominam ferramentas como Discord, onde o ambiente de colaboração, através da comunicação instantânea e da partilha de conteúdos é sofisticado. Até mesmo plataformas como a Twitch, dedicadas à transmissão e interação em tempo real, influenciam a forma com os estudantes interagem com conteúdos síncronos. Numa resposta pedagógica construtiva, é possível converter a disparidade digital numa oportunidade de aprendizagem colaborativa: estudantes experientes nestas ferramentas podem funcionar como “mentores digitais” em atividades peer-to-peer, deixando ao docente o papel de facilitador e orientador do processo de aprendizagem.

Será primordial ter em conta os diversos modelos e teorias da aprendizagem na conceção das e-atividades

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The authors investigated sleep and circadian rhythm disturbances in Fmr1 KO mice. Initially, they monitored daily home cage behaviors to assess sleep and circadian disruptions. Next, they examined the adaptability of circadian rhythms in response to photic suppression and skeleton photic periods. To explore the underlying mechanisms, they traced retino-suprachiasmatic connectivity. The authors further analyzed the social behaviors of Fmr1 KO mice and tested whether a scheduled feeding strategy could mitigate sleep, circadian, and social behavior deficits. Finally, they demonstrated that scheduled feeding corrected cytokine levels in the plasma of mutant mice. 

Strengths: 

(1) The manuscript addresses an important topic-investigating sleep deficits in an FXS mouse model and proposing a potential therapeutic strategy. 

(2) The study includes a comprehensive experimental design with multiple methodologies, which adds depth to the investigation. 

We thank the reviewer for the positive comments.

Weaknesses: 

(1) The first serious issue in the manuscript is the lack of a clear description of how they performed the experiments and the missing definitions of various parameters in the results.  

We thank the reviewer for pointing out lapses in the editing of the manuscript. We were trying to keep the descriptions of previously published methods brief but must have gone too far, the manuscript has been carefully checked for grammar and readability. Description of the experimental design has been refined and a graphical presentation has been added as Suppl Fig 3. The sleep and circadian parameters have been thoroughly explained in the methods and briefly in the figure legnds.

(2) Although the manuscript has a relatively long Methods section, some essential information is missing. For instance, the definition of sleep bout, as described above, is unclear. Additional missing information includes

Figure 2: "Rhythmic strength (%)" and "Cycle-to-cycle variability (min)." 

Figure 3: "Activity suppression." 

Figure 4: "Rhythmic power (V%)" (is this different from rhythmic strength (%)?) and "Subjective day activity (%)." 

We have provided definitions for the general audience of the terms used in the field of circadian rhythms, such as sleep bout, rhythm power, cycle-to-cycle, masking, and % of activity during the day in the methods and Fig legends. Most of the techniques used in this study, for example, the behavioral measurement of sleep or locomotor activity, are well established and have been used in multiple published works, including our own. We have made sure to include citations for interested readers.

Figure 5: Clear labeling of the SCN's anatomical features and an explanation for quantifying only the ventral part instead of the entire SCN. 

We have added more landmarks (position of the third ventricle and optic chiasm) to Fig 5, and have outlined the shell and core of the SCN in two additional images of the ventral hypothalamus in Suppl fig 4.

We had actually quantified the fluorescence in the whole SCN as well as in the ventral part.This was/is described in the methods as well as reported in the results section and Table 4 “Likewise, a subtle decrease in the intensity of the labelled fibers was found in the whole SCN (Table 4) of the Fmr1 KO mice as compared to WT.“ 

Methods: ” Two methods of analyses were carried out on the images of 5 consecutive sections per animal containing the middle SCN. First, the relative intensity of the Cholera Toxin fluorescent processes was quantified in the whole SCN, both left and right separately, by scanning densitometry using the Fiji image processing package of the NIH ImageJ software (https://imagej.net). A single ROI of fixed size (575.99 μm x 399.9 μm, width x height) was used to measure the relative integrated density (mean gray values x area of the ROI) in all the images. The values from the left and right SCN were averaged per section and 5 sections per animal were averaged to obtain one value per animal………..”

Since the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the SCN and our goal was to identify differences in the input to the SCN, e.g. defects in the retino-SCN connectivity as suggested by some deficits in circadian behaviour; we also looked at intensity of Cholera Toxin in the fibers arriving to the ventral SCN from the retina.

We have added a sentence in the methods about the rationale for measuring the intensity of the cholera toxin labelled fiber in the whole SCN and also just in the ventral part: “Second, the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the SCN, hence, the distribution….”

Figure 6: Inconsistencies in terms like "Sleep frag. (bout #)" and "Sleep bouts (#)." Consistent terminology throughout the manuscript is essential.

We have now clearly explained that sleep bouts are a measure of sleep fragmentation throughout the manuscript and in the fig legends; in addition, we have corrected the figures, reconciled the terminology, which is now consistent throughout the results and methods.

Methods: “Sleep fragmentation was determined by the number of sleep bouts, which were operationally defined as episodes of continuous immobility with a sleep count greater than 3 per minute, persisting for at least 60 secs.”

(3) Figure 1A shows higher mouse activity during ZT13-16. It is unclear why the authors scheduled feeding during ZT15- 21, as this seems to disturb the rhythm. Consistent with this, the body weights of WT and Fmr1 KO mice decreased after scheduled feeding. The authors should explain the rationale for this design clearly.

We have added to the rationale for the feeding schedule. This protocol was initially used by the Panda group to counter metabolic dysfunction (Hatori et al., 2012). We have used it for many years now (see citations below) in various mouse models presenting with circadian disruption to reset the clock and improve sleep. This study represents our first application/intervention in a mouse model of a neurodevelopmental disease.

Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S, Leblanc M, Chaix A, Joens M, Fitzpatrick JA, Ellisman MH, Panda S. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 2012 Jun 6;15(6):848-60. doi: 10.1016/j.cmet.2012.04.019. Epub 2012 May 17. PMID: 22608008; PMCID: PMC3491655.

Chiem E, Zhao K, Dell'Angelica D, Ghiani CA, Paul KN, Colwell CS. Scheduled feeding improves sleep in a mouse model of Huntington's disease. Front Neurosci. 2024 18:1427125. doi: 10.3389/fnins.2024.1427125. PMID: 39161652.

Whittaker DS, Akhmetova L, Carlin D, Romero H, Welsh DK, Colwell CS, Desplats P. Circadian modulation by time-restricted feeding rescues brain pathology and improves memory in mouse models of Alzheimer's disease. Cell Metab. 2023 35(10):1704- 1721.e6. doi: 10.1016/j.cmet.2023.07.014. PMID: 37607543

Brown MR, Sen SK, Mazzone A, Her TK, Xiong Y, Lee JH, Javeed N, Colwell CS, Rakshit K, LeBrasseur NK, Gaspar-Maia A, Ordog T, Matveyenko AV. Time-restricted feeding prevents deleterious metabolic effects of circadian disruption through epigenetic control of β cell function. Sci Adv. 2021 7(51):eabg6856. doi: 10.1126/sciadv.abg6856. PMID: 34910509

Whittaker DS, Loh DH, Wang HB, Tahara Y, Kuljis D, Cutler T, Ghiani CA, Shibata S, Block GD, Colwell CS. Circadian-based Treatment Strategy Effective in the BACHD Mouse Model of Huntington's Disease. J Biol Rhythms. 2018 33(5):535-554. doi: 10.1177/0748730418790401. PMID: 30084274.

Wang HB, Loh DH, Whittaker DS, Cutler T, Howland D, Colwell CS. Time-Restricted Feeding Improves Circadian Dysfunction as well as Motor Symptoms in the Q175 Mouse Model of Huntington's Disease. eNeuro. 2018 Jan 3;5(1):ENEURO.0431-17.2017. doi: 10.1523/ENEURO.0431-17.2017.

Loh DH, Jami SA, Flores RE, Truong D, Ghiani CA, O'Dell TJ, Colwell CS. Misaligned feeding impairs memories. Elife. 2015 4:e09460. doi: 10.7554/eLife.09460.

(4) The interpretation of social behavior results in Figure 6 is questionable. The authors claim that Fmr1 KO mice cannot remember the first stranger in a three-chamber test, writing, "The reduced time in exploring and staying in the novelmouse chamber suggested that the Fmr1 KO mutants were not able to distinguish the second novel mouse from the first now-familiar mouse." However, an alternative explanation is that Fmr1 KO mice do remember the first stranger but prefer to interact with it due to autistic-like tendencies. Data in Table 5 show that Fmr1 KO mice spent more time interacting with the first stranger in the 3-chamber social recognition test, which support this possibility. Similarly, in the five-trial social test, Fmr1 KO mice's preference for familiar mice might explain the reduced interaction with the second stranger.

Thank you for this interesting interpretation of the social behavior experiments. We used the common interpretations for both the three-chamber test and the 5-trial social interaction test, but have now modified the text leaving space for alternative interpretations, have soften the language, and mentioned decreased sociability in the Fmr1 KO mice. “The reduced time spent exploring the novel-mouse chamber suggest that the mutants were, perhaps, unable to distinguish the second novel mouse from the first, now familiar, mouse, along with decreased sociability.”

In Figure 6C (five-trial social test results), only the fifth trial results are shown. Data for trials 1-4 should be provided and compared with the fifth trial. The behavioral features of mice in the 5-trial test can then be shown completely. In addition, the total interaction times for trials 1-4 (154 {plus minus} 15.3 for WT and 150 {plus minus} 20.9 for Fmr1 KO) suggest normal sociability in Fmr1 KO mice (it is different from the results of 3-chamber). Thus, individual data for trials 1-4 are required to draw reliable conclusions.  

We have added a suppl figure showing the individual trial results for both WT and Fmr1 KO mice as requested (Suppl. Fig. 2).  

In Table 6 and Figure 6G-6J, the authors claim that "Sleep duration (Figures 6G, H) and fragmentation (Figures 6I, J) exhibited a moderate-strong correlation with both social recognition and grooming." However, Figure 6I shows a p-value of 0.077, which is not significant. Moreover, Table 6 shows no significant correlation between SNPI of the three-chamber social test and any sleep parameters. These data do not support the authors' conclusions. 

Thanks for pointing out the error with statement about Fig. 6I.

“…. Sleep duration (Fig. 6G, H; Table 6) exhibited a moderate to strong correlation with both social recognition and grooming time, while sleep fragmentation (measured by sleep bouts number) only correlated with the latter (Fig. 6J); the length of sleep bouts (Table 6) showed moderate correlation with both social recognition and repetitive behavior. In addition, a moderate correlation was seen between grooming time and the circadian parameters, rhythmic power and activity onset variability (Table 6). In short, our work suggests that even when tested during their circadian active phase, the Fmr1 KO mice exhibit robust repetitive and social behavioral deficits. Moreover, the shorter and more fragmented the daytime sleep, the more severe the behavioral impairment in the mutants.”

(5) Figure 7 demonstrates the effect of scheduled feeding on circadian activity and sleep behaviors, representing another critical set of results in the manuscript. Notably, the WT+ALF and Fmr1 KO+ALF groups in Figure 7 underwent the same handling as the WT and Fmr1 KO groups in Figures 1 and 2, as no special treatments were applied to these mice. However, the daily patterns observed in Figures 7A, 7B, 7F, and 7G differ substantially from those shown in Figures 2B and 1A, respectively. Additionally, it is unclear why the WT+ALF and Fmr1 KO+ALF groups did not exhibit differences in Figures 7I and 7J, especially considering that Fmr1 KO mice displayed more sleep bouts but shorter bout lengths in Figures 1C and 1D. 

We appreciate the reviewer’s attention to the subtle details of the behavioral measurement of sleep and believe the reviewer to be referring to differences in the behavioral measurements of sleep with data shown in Table 1 and Table 7. The first set of experiments described in this study was carried out between 2016 and 2017 and involves the comparison between WT and Fmr1 KO mice. The WT and mutants were obtained from JAX. In this initial set of experiments (Table 1), the total amount of sleep in 24 hrs was reduced in the KO, albeit not significantly, and these also exhibited sleep bouts of significantly reduced duration. The pandemic forced us to greatly slow down the research and reduce our mouse colonies. Post-pandemic, we used new cohorts of Fmr1 KO ordered again from JAX for the TRF experiment presented in this study. In these cohorts, the KO mice exhibited a significant reduction in total sleep (Table 7) and the sleep bouts were still shorter but not significantly. We have added to our text to explain that the description of the mutants and TRF interventions were carried out at different times (2017 vs 2022). We would like to emphasize that we always run contemporaneously controls and experimental groups to be used for the statistical analyses. We believe that the data are remarkably consistent over these years, even with different students doing the measurements. 

Furthermore, it is not specified whether the results in Figure 7 were collected after two weeks of scheduled feeding (for how many days?) or if they represent the average data from the two-week treatment period.

This is another good point raised by the reviewer. The activity measurements are collected during the 2 weeks (14 days) then the TRF was extended for a 3 more days to allow the behavioral sleep measurements.

We have added a supplementary figure (Supp Fig 3) depicting the different experimental designs.

The rationale behind analyzing "ZT 0-3 activity" in Figure 7D instead of the parameters shown in Figures 2C and 2D is also unclear. 

We have added to our explanation. In prior work, we found that the TRF protocol has a big impact on the beginning of the sleep time, hence, we specifically targeted this 3-hours interval in the analysis.

In Figure 7F, some data points appear to be incorrectly plotted. For instance, the dark blue circle at ZT13 connects to the light blue circle at ZT14 and the dark blue circle at ZT17. This is inconsistent, as the dark blue circle at ZT13 should link to the dark blue circle at ZT14. Similarly, it is perplexing that the dark blue circle at ZT16 connects to both the light blue and dark blue circles at ZT17. Such errors undermine confidence in the data. The authors need to provide a clear explanation of how these data were processed. 

Thank you for bringing this to our attention. The data were plotted correctly, however, those data points completely overlapped with those behind, masking them. We have now offset a bit them for clarity.

Lastly, in the Figure 7 legend, Table 6 is cited; however, this appears to be incorrect. It seems the authors intended to refer to Table 7. 

We have corrected this error, thank you.  

(6) Similar to the issue in Figure 7F, the data for day 12 in Supplemental Figure 2 includes two yellow triangles but lacks a green triangle. It is unclear how the authors constructed this chart, and clarification is needed. 

We have corrected this error. As the reviewer pointed out, we filled the triangle on day 12 with yellow instead of green.  

(7) In Figure 8, a 5-trial test was used to assess the effect of scheduled feeding on social behaviors. It is essential to present the results for all trials (1 to 4). Additionally, it is unclear whether the results for familial mice in Figure 8A correspond to trials 1, 2, 3, or 4. 

The legend for Figure 8 also appears to be incorrect: "The left panels show the time spent in social interactions when the second novel stranger mouse was introduced to the testing mouse in the 5-trial social interaction test. The significant differences were analyzed by two-way ANOVA followed by Holm-Sidak's multiple comparisons test with feeding treatment and genotype as factors." This description does not align with the content of the left panels. Moreover, two-way ANOVA is not the appropriate statistical analysis for Figure 8A. The authors need to provide accurate details about the analysis and revise the figure legend accordingly. 

We apologies for the confusing Figure legend which has been revised: 

“Fig. 8: TRF improved social memory and stereotypic grooming behavior in the Fmr1 KO mice. (A) Social memory was evaluated with the 5-trial social interaction test as described above. The social memory recognition was significantly augmented in the Fmr1 KO by the intervention, suggesting that the treated mutants were able to distinguish the novel mouse from the familiar mouse. The time spent in social interactions with the novel mouse in the 5^th-trial was increased to WT-like levels in the mutants on TRF. Paired t-tests were used to evaluate significant differences in the time spent interacting with the test mouse in the 4^th (familiar mouse) and 5^th (novel mouse) trials.  *P < 0.05 indicates the significant time spent with the novel mouse compared to the familiar mouse. (B) Grooming was assessed in a novel arena in mice of each genotype (WT, Fmr1 KO) under each feeding condition and the resulting data analyzed by two-way ANOVA followed by the Holm-Sidak’s multiple comparisons test with feeding regimen and genotype as factors. *P < 0.05 indicates the significant difference within genotype - between diet regimens , and #P < 0.05 those between genotypes - same feeding regimen. (C) TRF did not alter the overall locomotion in the treated mice. See Table 8.”

To assess social recognition memory, mice underwent a five-trial social interaction paradigm in a neutral open-field arena. Each trial lasted 5 minutes and was separated by a 1-minute inter-trial interval. During trials 1–4, the test mouse was exposed to the same conspecific (Stimulus A) enclosed within a wire cup to permit olfactory and limited tactile interaction. In trial 5, a novel conspecific (Stimulus B) was introduced. Time spent investigating the stimulus B mouse (defined as sniffing or directing the nose toward the enclosure within close proximity) was scored using AnyMaze software. A progressive decrease in investigation time across trials 1–4 reflects habituation, while a significant increase in trial 5 indicates dishabituation and intact social recognition memory. In our data, there was not a lot of habituation in both genotypes, but clear differences can be appreciated between trial 4 with the now familiar mouse and trial 5 with novel mouse. Fig. 8A plots the results from individual animals in Trial 4 with a familiar mouse and in Trial 5 with a novel mouse, we have well specified this in the legends. As such, these data were analyzed with a pair t-test. 

We used Tow-Way ANOVA to analyse the data reported in Panel 8B and as well as the results in Table 8.  This has been clarified in the legend.

(8) The circadian activity and sleep behaviors of Fmr1 KO mice have been reported previously, with some findings consistent with the current manuscript, while others contradict it. Although the authors acknowledge this discrepancy, it seems insufficiently thorough to simply state that the reasons for the conflicts are unknown. Did the studies use the same equipment for behavior recording? Were the same parameters used to define locomotor activity and sleep behaviors? The authors are encouraged to investigate these details further, as doing so may uncover something interesting or significant. 

We agree with the reviewers, and believe that the main differences were likely in the experimental design and possibly interpretation.

(9) Some subtitles in the Results section and the figure legends do not align well with the presented data. For example, in the section titled "Reduced rhythmic strength and nocturnality in the Fmr1 KOs," it is unclear how the authors justify the claim of altered nocturnality in Fmr1 KO mice. How do the authors define changes in nocturnality? Additionally, the tense used in the subtitles and figure legends is incorrect. The authors are encouraged to carefully review all subtitles and figure legends to correct these errors and enhance readability. 

Nocturnality is defined as the % of total activity within a 24-h cycle that occurred in the night, since this can be confusing and we agree that it was not well explained we have removed it from the subtitle/figure legends. 

We have adjusted the subtitles as recommended; however, the tense of the verbs might be a matter of writing style.

Reviewer #2 (Public review): 

Summary: 

In the present study, the authors, using a mouse model of Fragile X syndrome, explore the very interesting hypothesis that restricting food access over a daily schedule will improve sleep patterns and, subsequently, behavioral capacities. By restricting food access from 12h to 6h over the nocturnal period (active period for mice), they show, in these KO mice, an improvement of the sleep pattern accompanied by reduced systemic levels of inflammatory markers and improved behavior. Using a classical mouse model of neurodevelopmental disorder (NDD), these data suggest that eating patterns might improve sleep quality, reduce inflammation and improve cognitive/behavioral capacities in children with NDD. 

Strengths: 

Overall, the paper is very well-written and easy to follow. The rationale of the study is generally well-introduced. The data are globally sound. The provided data support the interpretation overall. 

Thank you for the positive comments.  

Weaknesses:  

(1) The introduction part is quite long in the Abstract, leaving limited space for the data provided by the present study.

We have revised the Abstract to better focus on the most impactful findings as suggested. 

(2) A couple of points are not totally clear for a non-expert reader:  - The Fmr1/Fxr2 double KO mice are not well described. What is the rationale for performing both LD and DD measures? 

We did not use the Fmr1/Fxr2 double KO mice in this study.  

While measurement of day/night differences in activity rhythms are standardly done in a light/dark (LD) cycle, the organisms must be under constant conditions (DD) to measure their endogenous circadian rhythms (free running activity); this is often needed to uncover a compromised clock as entrainment to the LD cycle can mask deficits in the endogenous circadian rhythms.

(3) The data on cytokines and chemokines are interesting. However, the rationale for the selection of these molecules is not given. In addition, these measures have been performed in the systemic blood. Measures in the brain could be very informative. 

The panel that we used had 16 cytokines/chemokines which are reported in Table 9. The experiment included WT and mutants held under 2 different feeding conditions with an n=8 per group. If we are able to obtain more resources, we would like to also carry out a comprehensive investigation of immunomediator levels as well as RNA-seq or Nanostring in selected brain regions associated with ASD aberrant behavioural phenotypes, for instance the prefrontal cortex.

(4) An important question is the potential impact of fasting vs the impact of the food availability restriction. Indeed, fasting has several effects on brain functioning including cognitive functions. 

We did not address this issue in the present study. Briefly, the distinction between caloric restriction (CR) and TRF, in which no calories are restricted, has important mechanistic implications in mouse models. While both interventions can impact metabolism, circadian rhythms, and aging, they operate via overlapping but distinct molecular pathways. These have been the topic of recent reviews and investigations. Importantly, the fast-feed cycle can also act as a circadian entrainer (Zeitgeber)

Ribas-Latre A, Fernández-Veledo S, Vendrell J. Time-restricted eating, the clock ticking behind the scenes. Front Pharmacol. 2024 Aug 8;15:1428601. doi: 10.3389/fphar.2024.1428601. PMID: 39175542; PMCID: PMC11338815.

Wang R, Liao Y, Deng Y, Shuang R. Unraveling the Health Benefits and Mechanisms of Time-Restricted Feeding: Beyond Caloric Restriction. Nutr Rev. 2025 Mar 1;83(3):e1209-e1224. doi: 10.1093/nutrit/nuae074.

(5) How do the authors envision the potential translation of the present study to human patients? How to translate the 12 to 6 hours of food access in mice to children with Fragile X syndrome? 

Time-restricted feeding (TRF) is a type of intermittent fasting that limits food intake to a specific window of time each day (usually 8–12 hours in humans), is being actively studied in adults for benefits on metabolic health, sleep, and circadian rhythms. However, applying TRF to children is not currently recommended as a general intervention, and there are important developmental, medical, and ethical considerations to take into account.  

On the other hand, we believe that the Fmr1 KO mouse is a good preclinical model for FXS because it closely recapitulates key molecular, cellular, and behavioral phenotypes observed in humans with the disorder. A number of the behavioral phenotypes seen in the mouse mirror those seen in patients including increased anxiety-like behavior, sensory hypersensitivity, social interaction deficits and repetitive behaviors so there is strong face validity.  

As we show in this study, Fmr1 KO mice present with disrupted sleep/wake cycles and reduced amplitude of circadian rhythms, consistent with findings in individuals with FXS. This makes the Fmr1 KO an excellent model to test out circadian based interventions such as scheduled feeding.

We believe that pre-clinical research in Fmr1 KO mice bridges the gap between basic discovery and human clinical application. It provides a controlled, cost-effective, and biologically relevant platform for understanding disease mechanisms and testing interventions. These types of experiments need to be done before jumping to humans to ensure that the human trials are scientifically justified and ethically sound.

Reviewer #1 (Recommendations for the authors): 

The authors should: 

(1) Revise the Methods section for clarity and completeness.  

We have re-worked the methods for clarity and completeness. 

(2) Provide consistent and precise definitions for all parameters and terms.  

We believe that we have provided definitions for all terms.  

(3) Clarify the rationale for experimental designs, such as the feeding schedule.  

We have added to the rationale for the feeding schedule.  This feeding schedule has been used in a number of prior studies including our own.  All this work is cited in the manuscript.   

(4) Reanalyze and transparently present data, including individual trial results.  

We have added to the figure showing the individual trail results for the 5-trial tests as requested (Supplementary Fig. 2).  

(5) Conduct appropriate statistical tests and correct figure legends.  

We believe that we have carried out appropriate statistical tests and have carefully rechecked the figure legends.  

(6) Investigate discrepancies with prior studies to enhance the discussion. 

We have added to our discussion of prior work. 

(7) Improve language quality and ensure consistency in terminology and grammar.  

We have edited the manuscript to improve language quality.  

Reviewer #2 (Recommendations for the authors): 

(1) The Abstract should be rewritten to provide more room for the obtained data.  

We have re-written the Abstract to focus on the most impactful findings. 

(2) An additional sentence describing the double KO mice should be added.  

We did not use double KO mice in this study.  

(3) The rationale for studying LD and DD should be provided. 

Measurement of day/night differences are standardly done in a light/dark cycle.  To measure the endogenous circadian rhythms, the organisms must be under constant conditions (Dark/Dark).

(4) The data on cytokines/chemokines should be strengthened by performing a larger panel of measures both in blood and the brain.  

The panel that we used had 16 cytokines/chemokines which we report in Table 9.  This was a large experiment with 2 genotypes being held under 2 feeding conditions with n=8 mice per group. If we are able to obtain more resources, we would like to also carry out RNA-seq in different brain regions.  

(5) The authors should discuss in more detail the potential role of fastening vs restriction of food access.  

We did not address this issue in the present study.  Briefly, the distinction between caloric restriction (CR) and TRF when no calories are restricted has important mechanistic implications in mouse models. While both interventions can impact metabolism, circadian rhythms, and aging, they operate via overlapping but distinct molecular pathways. 

(6) The authors should also provide some insight into their view on the potential translation of their experimental studies.  

We believe that the Fmr1 KO mouse is considered a good preclinical model for FXS because it closely recapitulates key molecular, cellular, and behavioral phenotypes observed in humans with the disorder. A number of the behavioral phenotypes seen in the mouse mirror those seen in patients including increased anxiety-like behavior, sensory hypersensitivity, social interaction deficits and repetitive behaviors so there is strong face validity.   As we  demonstrate in this study, Fmr1 KO mice exibit disrupted sleep/wake cycles and reduced amplitude of circadian rhythms, consistent with findings in individuals with FXS.  This makes the Fmr1 KO an excellent model to test out circadian based interventions such as scheduled feeding.  

Still we are mindful that the translation of therapeutic findings from mouse to human has proven challenging e.g., mGluR5 antagonists failed in clinical trials despite strong preclinical data (Berry-Kravis et al., 2016).  Therefore, we are cautious in overreaching in our translational interpretations. 

Berry-Kravis, E., Des Portes, V., Hagerman, R., Jacquemont, S., Charles, P., Visootsak, J., Brinkman, M., Rerat, K., Koumaras, B., Zhu, L., Barth, G. M., Jaecklin, T., Apostol, G., & von Raison, F. (2016). Mavoglurant in fragile X syndrome: Results of two randomized, double-blind, placebo-controlled trials. Science translational medicine, 8(321), 321ra5. https://doi.org/10.1126/scitranslmed.aab4109).

Author response:

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

Reviewer #1 (Public review):

Summary:

The manuscript proposes that 5mC modifications to DNA, despite being ancient and widespread throughout life, represent a vulnerability, making cells more susceptible to both chemical alkylation and, of more general importance, reactive oxygen species. Sarkies et al take the innovative approach of introducing enzymatic genome-wide cytosine methylation system (DNA methyltransferases, DNMTs) into E. coli, which normally lacks such a system. They provide compelling evidence that the introduction of DNMTs increases the sensitivity of E. coli to chemical alkylation damage. Surprisingly they also show DNMTs increase the sensitivity to reactive oxygen species and propose that the DNMT generated 5mC presents a target for the reactive oxygen species that is especially damaging to cells. Evidence is presented that DNMT activity directly or indirectly produces reactive oxygen species in vivo, which is an important discovery if correct, though the mechanism for this remains obscure.

Strengths:

This work is based on an interesting initial premise, it is well-motivated in the introduction and the manuscript is clearly written. The results themselves are compelling.

We thank the reviewer for their positive response to our study.  We also really appreciate the thoughtful comments raised.  We have addressed the comments raised as detailed below. 

Weaknesses:

I am not currently convinced by the principal interpretations and think that other explanations based on known phenomena could account for key results. Specific points below.

(1) As noted in the manuscript, AlkB repairs alkylation damage by direct reversal (DNA strands are not cut). In the absence of AlkB, repair of alklylation damage/modification is likely through BER or other processes involving strand excision and resulting in single stranded DNA. It has previously been shown that 3mC modification from MMS exposure is highly specific to single stranded DNA (PMID:20663718) occurring at ~20,000 times the rate as double stranded DNA. Consequently, the introduction of DNMTs is expected to introduce many methylation adducts genome-wide that will generate single stranded DNA tracts when repaired in an AlkB deficient background (but not in an AlkB WT background), which are then hyper-susceptible to attack by MMS. Such ssDNA tracts are also vulnerable to generating double strand breaks, especially when they contain DNA polymerase stalling adducts such as 3mC. The generation of ssDNA during repair is similarly expected follow the H2O2 or TET based conversion of 5mC to 5hmC or 5fC neither of which can be directly repaired and depend on single strand excision for their removal. The potential importance of ssDNA generation in the experiments has not been considered.

We thank the reviewer for this interesting and insightful suggestion.  Our interpretation of our findings is that a subset of MMS-induced DNA damage, specifically 3mC, overlaps with the damage introduced by DNMTs and this accounts for increased sensitivity to MMS when DNMTs are expressed.  However, the idea that the introduction of 3mC by DNMT actually makes the DNA more liable to damage by MMS, potentially through increasing the level of ssDNA, is also a potential explanation, which could operate in addition to the mechanism that we propose.

(2) The authors emphasise the non-additivity of the MMS + DNMT + alkB experiment but the interpretation of the result is essentially an additive one: that both MMS and DNMT are introducing similar/same damage and AlkB acts to remove it. The non-additivity noted would seem to be more consistent with the ssDNA model proposed in #1. More generally non-additivity would also be seen if the survival to DNA methylation rate is non-linear over the range of the experiment, for example if there is a threshold effect where some repair process is overwhelmed. The linearity of MMS (and H2O2) exposure to survival could be directly tested with a dilution series of MMS (H2O2).

We thank the reviewer for this point.  As in the response to point #1, the reviewer’s hypothesis of increased potency of MMS, potentially through increased ssDNA, downstream of 3mC induction by DNMT, is a good one.  We have added a dose-response curve for DNMT-expressing cells to MMS to the revised version of the manuscript.  This shows that there is a non-linear response to MMS in the WT background.  Sensitivity is exacerbated by expression of DNMT and alkB mutation individually but there is also a strong non-additive effect that is particularly marked at low MMS concentrations where sensitivity is much higher in the double mutant than predicted from the two single mutants.  This is consistent with induction of DNA damage by DNMT that is repaired by alkB because alkB can be ‘overwhelmed’ even in WT backgrounds as the reviewer suggests.  However, it is also perfectly possible that the effect is due to increased levels of DNA damage induction in DNMT-expressing cells.  Both these results are compatible with our central hypothesis, namely that DNMT expression induces 3mC.  We have included these results along with discussion of them in the revised text in the results section:

In order to investigate the non-additivity between DNMT expression and alkB mutation further, we investigated the effect of MMS over a range of concentrations for the different strains (Supplemental Figure 1A).  We quantified the non-additivity by comparing between the survival of alkB expressing DNMT to the predicted combined effect of either alkB mutation alone or DNMT expression alone(Supplemental Figure 1B).  Significantly reduced survival than expected was observed, most notably at low concentrations of MMS, which could be due to the saturation of the effect at high concentrations of MMS for alkB mutants expressing DNMT, where extremely high levels of sensitivity were observed.  The non-linear shape of the graph observed for WT cells expressing DNMTs further suggests that the ability of AlkB to repair the DNA is overwhelmed at high MMS concentrations even in the WT background.  These results are consistent with the idea that AlkB repairs a form of DNA damage from MMS that is more prevalent when DNMT is expressed.  This could be because DNMT induces 3mC, repaired by AlkB, and further 3mC is induced by MMS leading to much higher 3mC levels in the absence of AlkB activity.  Alternatively, 3mC induction by DNMT may lead to increased levels of ssDNA, particularly in alkB mutants, which could increase the risk of further DNA damage by MMS exposure and heighten sensitivity.  Either of these mechanisms are consistent with induction of 3mC by DNMT, and  indicate that the induction of DNA damage by DNMT expression has a fitness cost for cells when exposed to genotoxic stress in their environment. 

(3) The substantial transcriptional changes induced by DNMT expression (Supplemental Figure 4) are a cause for concern and highlight that the ectopic introduction of methylation into a complex system is potentially more confounded than it may at first seem. Though the expression analysis shows bulk transcription properties, my concern is that the disruptive influence of methylation in a system not evolved with it adds not just consistent transcriptional changes but transcriptional heterogeneity between cells which could influence net survival in a stressed environment. In practice I don't think this can be controlled for, possibly quantified by single-cell RNA-seq but that is beyond the reasonable scope of this paper.

We fully agree with the reviewer and, indeed, we are very interested in what is driving the transcriptional changes that we observed.  Work is currently underway in the lab to investigate this further but, as the reviewer suggests, is beyond the scope of this paper.  Importantly, we have used the transcriptional data to determine that the effect of DNMTs on ROS is unlikely to be due to failure of ROS-induced detoxification mechanisms by investigating the expression of oxyR regulated genes.  Nevertheless we have explicitly mentioned the concern raised by the reviewer in the revised manuscript as follows:

“The substantial transcriptional responses could potentially affect how individual cells respond to genotoxic stress and thus could be contributing to some of the excess sensitivity to MMS and H2O2 in cells expressing DNMTs. However, the induction of oxyR regulated genes such as catalase was unaffected by 5mC (Supplementary Figure 4B).  Thus, the increased sensitivity to H2O2 is unlikely to be caused by failure of detoxification gene induction by DNMT expression.”

(4) Figure 4 represents a striking result. From its current presentation it could be inferred that DNMTs are actively promoting ROS generation from H2O2 and also to a lesser extent in the absence of exogenous H2O2. That would be very surprising and a major finding with far-reaching implications. It would need to be further validated, for example by in vitro reconstitution of the reaction and monitoring ROS production. Rather, I think the authors are proposing that some currently undefined, indirect consequence of DNMT activity promotes ROS generation, especially when exogenous H2O2 is available. It would help if this were clarified.

We thank the reviewer for picking this up.  In the discussion, we raise two possible explanations for why DNMT (even without H2O2) increases the ROS levels.  One idea is direct activity of DNMT, and one is through the product of DNMT activity (5mC) acting as a platform to generate more ROS from endogenous or exogenous sources.  Whilst we attempted to measure ROS from mSSSI activity in vitro, this experiment gave inconsistent results and therefore we cannot distinguish between these two possibilities.  However, we argued that direct activity is less likely, exactly as the reviewer points out.  We have clarified our discussion in the revised version, rewriting the entire section titled

Oxidative stress as a new source of DNA damage induction by DNMT expression to more clearly set out these possibilities. 

Reviewer #2 (Public review):

5-methylcytosine (5mC) is a key epigenetic mark in DNA and plays a crucial role in regulating gene expression in many eukaryotes including humans. The DNA methyltransferases (DNMTs) that establish and maintain 5mC, are conserved in many species across eukaryotes, including animals, plants, and fungi, mainly in a CpG context. Interestingly, 5mC levels and distributions are quite variable across phylogenies with some species even appearing to have no such DNA methylation.

This interesting and well-written paper discusses the continuation of some of the authors' work published several years ago. In that previous paper, the laboratory demonstrated that DNA methylation pathways coevolved with DNA repair mechanisms, specifically with the alkylation repair system. Specifically, they discovered that DNMTs can introduce alkylation damage into DNA, specifically in the form of 3-methylcytosine (3mC). (This appears to be an error in the DNMT enzymatic mechanism where the generation 3mC as opposed to its preferred product 5-methylcytosine (5mC), is caused by the flipped target cytosine binding to the active site pocket of the DNMT in an inverted orientation.) The presence of 3mC is potentially toxic and can cause replication stress, which this paper suggests may explain the loss of DNA methylation in different species. They further showed that the ALKB2 enzyme plays a crucial role in repairing this alkylation damage, further emphasizing the link between DNA methylation and DNA repair.

The co-evolution of DNMTs with DNA repair mechanisms suggests there can be distinct advantages and disadvantages of DNA methylation to different species which might depend on their environmental niche. In environments that expose species to high levels of DNA damage, high levels of 5mC in their genome may be disadvantageous. This present paper sets out to examine the sensitivity of an organism to genotoxic stresses such as alkylation and oxidation agents as the consequence of DNMT activity. Since such a study in eukaryotes would be complicated by DNA methylation controlling gene regulation, these authors cleverly utilize Escherichia coli (E.coli) and incorporate into it the DNMTs from other bacteria that methylate the cytosines of DNA in a CpG context like that observed in eukaryotes; the active sites of these enzymes are very similar to eukaryotic DNMTs and basically utilize the same catalytic mechanism (also this strain of E.coli does not specifically degrade this methylated DNA) .

The experiments in this paper more than adequately show that E. coli expression of these DNMTs (comparing to the same strain without the DNMTS) do indeed show increased sensitivity to alkylating agents and this sensitivity was even greater than expected when a DNA repair mechanism was inactivated. Moreover, they show that this E. coli expressing this DNMT is more sensitive to oxidizing agents such as H2O2 and has exacerbated sensitivity when a DNA repair glycosylase is inactivated. Both propensities suggest that DNMT activity itself may generate additional genotoxic stress. Intrigued that DNMT expression itself might induce sensitivity to oxidative stress, the experimenters used a fluorescent sensor to show that H2O2 induced reactive oxygen species (ROS) are markedly enhanced with DNMT expression. Importantly, they show that DNMT expression alone gave rise to increased ROS amounts and both H2O2 addition and DNMT expression has greater effect that the linear combination of the two separately. They also carefully checked that the increased sensitivity to H2O2 was not potentially caused by some effect on gene expression of detoxification genes by DNMT expression and activity. Finally, by using mass spectroscopy, they show that DNMT expression led to production of the 5mC oxidation derivatives 5-hydroxymethylcytosine (5hmC) and 5-formylcytosine (5fC) in DNA. 5fC is a substrate for base excision repair while 5hmC is not; more 5fC was observed. Introduction of non-bacterial enzymes that produce 5hmC and 5fC into the DNMT expressing bacteria again showed a greater sensitivity than expected. Remarkedly, in their assay with addition of H2O2, bacteria showed no growth with this dual expression of DNMT and these enzymes.

Overall, the authors conduct well thought-out and simple experiments to show that a disadvantageous consequence of DNMT expression leading to 5mC in DNA is increased sensitivity to oxidative stress as well as alkylating agents.

Again, the paper is well-written and organized. The hypotheses are well-examined by simple experiments. The results are interesting and can impact many scientific areas such as our understanding of evolutionary pressures on an organism by environment to impacting our understanding about how environment of a malignant cell in the human body may lead to cancer.

We thank the reviewer for their response to our study, and value the time taken to produce a public review that will aid readers in understanding the key results of our study. 

Reviewer #3 (Public review):

Summary:

Krwawicz et al., present evidence that expression of DNMTs in E. coli results in (1) introduction of alkylation damage that is repaired by AlkB; (2) confers hypersensitivity to alkylating agents such as MMS (and exacerbated by loss of AlkB); (3) confers hypersensitivity to oxidative stress (H2O2 exposure); (4) results in a modest increase in ROS in the absence of exogenous H2O2 exposure; and (5) results in the production of oxidation products of 5mC, namely 5hmC and 5fC, leading to cellular toxicity. The findings reported here have interesting implications for the concept that such genotoxic and potentially mutagenic consequences of DNMT expression (resulting in 5mC) could be selectively disadvantageous for certain organisms. The other aspect of this work which is important for understanding the biological endpoints of genotoxic stress is the notion that DNA damage per se somehow induces elevated levels of ROS.

Strengths:

The manuscript is well-written, and the experiments have been carefully executed providing data that support the authors' proposed model presented in Fig. 7 (Discussion, sources of DNA damage due to DNMT expression).

Weaknesses:

(1) The authors have established an informative system relying on expression of DNMTs to gauge the effects of such expression and subsequent induction of 3mC and 5mC on cell survival and sensitivity to an alkylating agent (MMS) and exogenous oxidative stress (H2O2 exposure). The authors state (p4) that Fig. 2 shows that "Cells expressing either M.SssI or M.MpeI showed increased sensitivity to MMS treatment compared to WT C2523, supporting the conclusion that the expression of DNMTs increased the levels of alkylation damage." This is a confusing statement and requires revision as Fig. 2 does ALL cells shown in Fig. 2 are expressing DNMTs and have been treated with MMS. It is the absence of AlkB and the expression of DNMTs that that causes the MMS sensitivity.

We thank the reviewer for this and agree that this needs to be clarified with regards to the figure presented and will do so in the revised manuscript. The key comparison is between the active and inactive mSSSI which shows increased sensitivity when active methyltransferases are expressed.  We have clarified this in the revised version of the manuscript as follows:

“Cells expressing either M.SssI or M.MpeI showed increased sensitivity to MMS treatment compared to cells expressing inactive M.SssI”

(2) It would be important to know whether the increased sensitivity (toxicity) to DNMT expression and MMS is also accompanied by substantial increases in mutagenicity. The authors should explain in the text why mutation frequencies were not also measured in these experiments.

This is an important point because it is not immediately obvious that increased sensitivity would be associated with increased mutagenicity (if, for example, 3mC was never a cause of innacurate DNA repair even in the absence of AlkB).  We have now added a Rif resistance assay which demonstrates increased mutagenesis in the presence of DNMT, and that this is exacerbated by loss of AlkB. This is now added as supplemental figure 2 and described in the manuscript as follows:

“One potential consequence of DNMT activity in inducing DNA damage might be increased mutagenesis.  To test this we performed a rifampicin resistance mutagenesis assay, in the absence of MMS, to test whether DNMT induced damage was sufficient to lead to mutation rate increase.  Mutation rate was increased by DNMT expression (p=1.6e-12; two way anova; Supplemental Figure 2) and alkB mutation (two way anova) separately (p<1e-16).  Moreover, there was a significant interaction such that combined alkB mutation and DNMT expression led to a further increased mutation rate compared to the expectation from alkB mutation and DNMT expression separately (p = 7.9e-10; Supplemental Figure 2).  Importantly, DNMT induction alone would be expected to lead to increased mutations due to cytosine deamination(Sarkies, 2022a); however, there is a synergistic effect on mutations when this is combined with loss of AlkB function in alkB mutants. This is consistent with 3mC induction by DNMTs which is repaired by AlkB in WT cells but leads to mutations in alkB mutant cells.

(3) Materials and Methods. ROS production monitoring. The "Total Reactive Oxygen Species (ROS) Assay Kit" has not been adequately described. Who is the Vendor? What is the nature of the ROS probes employed in this assay? Which specific ROS correspond to "total ROS"?

The ROS measurement was with a kit from ThermoFisher: https://www.thermofisher.com/order/catalog/product/88-5930-74.  The probe is DCFH-DA.  This is a general ROS sensor that is oxidised by a large number of cellular reactive oxygen species hence we cannot attribute the signal to a single species.  Use of a technique with the potential to more precisely identify the species involved is something we plan to do in future, but is beyond what we can do as part of this study.  We have added a comment as to the specificity of the ROS sensor in the revised version as follows:

“The ROS detection reagent in this system is DCFH-DA, a generalised ROS sensor that is not specific to any particular ROS molecule.”     

(4) The demonstration (Fig. 4) that DNMT expression results in elevated ROS and its further synergistic increase when cells are also exposed to H2O2 is the basis for the authors' discussion of DNA damage-induced increases in cellular ROS. S. cerevisiae does not possess DNMTs/5mC, yet exposure to MMS also results in substantial increases in intracellular ROS (Rowe et al, (2008) Free Rad. Biol. Med. 45:1167-1177. PMC2643028). The authors should be aware of previous studies that have linked DNA damage to intracellular increases in ROS in other organisms and should comment on this in the text.

We thank the reviewer for this point.  We note that the increased ROS that we observed occur in the presence of DNMTs alone and in the presence of H2O2, not in the presence of MMS; however, the point that DNA damage in general can promote increased ROS in some circumstances is well taken.  We have included a comment on this in the revised version as follows:

“We believe this is a plausible mechanism to explain both increased ROS and increased sensitivity to oxidative stress when DNMT is expressed.  However, other explanations are possible, and it is notable that DNA damaging agents such as MMS can lead to ROS generation(Rowe et al., 2008).  A more detailed chemical and kinetic study of the ROS formation in DNMT-expressing cells would be needed to resolve these questions.”

Misschien raakt u na een plaspil veel vocht kwijt.

This op-ed addresses the issue with the exponential increase in publications and how this is leading to a lower quality of peer review which, in turn, is resulting in more bad science being published. It is a well-written article that tackles a seemingly eternal topic. This piece focussed more on the positives and potential actions which is nice to see as this is a topic that can become stuck in the problems. There are places throughout that would benefit from more clarity and at times there appears to be a bias towards publishers, almost placing blame on researchers. Very simple word changes or headings could immediately resolve any doubt here as I don't believe this is the intention of the article at all.

Additionally, this article is very focussed on peer review (a positive) but I think that it would benefit from small additions throughout that zoom out from this and place the discussion in the context of the wider issues - for example you cannot change peer review incentives without changing the entire incentives around "service" activities including teaching, admin etc. This occurs to a degree with the discussion on other outputs, including preprints and data. Moreover, when discussing service type activities, there is data that reveals certain demographics deliberately avoid this work. Adding this element into the article would provide a much stronger argument for change (and do some good in the new current political climate).

Overall, I thought this was a great piece when it was first posted online and does exactly what a good op-ed should - provoke thought and discussion. Below are some specific comments, in reading order. I do not believe that there are any substantial or essential changes required, particularly given that this is an op-ed article.

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Quote: "Academia is undergoing a rapid transformation characterized by exponential growth of scholarly outputs."

Comment: There's an excellent paper providing evidence to this: https://direct.mit.edu/qss/article/5/4/823/124269/The-strain-on-scientific-publishing which would be a very positive addition

Quote: "it’s challenging to keep up with the volume at which research publications are produced"

Comment: Might be nice to add that this was a complaint dating back since almost the beginning of sharing research via print media, just to reinforce that this is a very old point.

Quote: "submissions of poor-quality manuscripts"

Comment: The use of "poor quality" here is unnecessary. Just because a submission is not accepted, it has no reflection on "quality". As such this does seem to needlessly diminish work rejected by one journal

Quote: "Maybe there are too many poor quality journals too - responding to an underlying demand to publish low quality papers."

Comment: This misses the flip side - poor quality journals encourage and actively drive low quality & outright fraudulent submissions due to the publisher dominance in the assessment of research and academics.

Quote: "even after accounting for quality,"

Comment: Quality is mentioned here but has yet to be clearly defined. What is "quality"? - how many articles a journal publishes? The "prestige" of a journal? How many people are citing the articles?

Quote: "Researchers can – and do – respond to the availability by slicing up their work (and their data) into minimally publishable units"

Comment: I fully agree that some researchers do exactly this. However, again, this seems to be blaming researchers for creating this firehose problem. I think this point could be reworded to not place so much blame or be substantiated with evidence that this is a widespread practice - my experience has been very mixed in that I've worked for people who do this almost to the extreme (and have very high self-citations) and also worked for people who focus on the science and making it as high quality and robust as possible. I agree many respond to the explosion of journals and varied quality in a negative manner but the journals, not researchers are the drivers here.

Quote: "least important aspect of the expected contributions of scholars."

Comment: I think it may be worth highlighting here that sometimes specific demographics (white males) actively avoid these kinds of service activities - there's a good study on this providing data in support of this. It adds an extra dimension into the argument for appropriate incentives and the importance & challenges of addressing this.

Quote: "high quality peer review"

Comment: Just another comment on the use of "quality'. This is not defined and I think when discussing these topics it is vital to be clear what one means by "high quality". For example, a high quality peer review that is designed as quality control would be detecting gross defects and fraud, preventing such work from being published (peer review does not reliably achieve this). In contrast, a high quality peer review designed to help authors improve their work and avoid hyperbole would be very detailed and collegial, not requesting large numbers of additional experiments.

Quote: "conferring public trust in the oversight of science"

Comment: I'm not convinced of this. Conveying peer review as a stamp of approval or QC leads to reduced trust when regular examples emerge with peer review failures - just look at Hydroxychloroquine and how peer review was used to justify that during COVID or the MMR/autism issues that are still on-going even after the work was retracted. I think this should be much more carefully worded, removed or expanded on to provide this perspective - this occurs slightly in the following sentence but it is very important to be clear on this point.

Quote: "Researchers hold an incredible amount of market power in scholarly publishing"

Comment: I like the next few paragraphs but, again, this seems to be blaming researchers when they in fact hold no/little power. I agree that researchers *could* use market pressure but this is entirely unrealistic when their careers depend on publishing X papers in X journal. An argument as to why science feels increasingly non-collaborative perhaps. Funders can have immediate and significant changes. Institutions adopting reward structures, such as teaching for example, would have significant impacts on researcher behaviour. Researchers are adapting to the demands the publication system creates - more journals, greater quantity and reduced quality whilst maintaining control over the assessment - eLife being removed from Wos/Scopus is a prime example of publishers (via their parent companies) preventing innovation or even rather basic improvements.

Quote: "With preprint review, authors participate in a system that views peer review not as a gatekeeping hurdle to overcome to reach publication but as a participatory exercise to improve scholarship."

Comment: This is framing that I really like; improving scholarship, not quality control.

Quote: "buy"

Comment: typo

Quote: "adoption of preprint review can shift the inaccurate belief that all preprints lack review"

Comment: Is this the right direction for preprints though? If we force all preprints to be reviewed and only value reviewed-preprints, then we effectively dismantle the benefits of preprints and their potential that we've been working so hard to build. A recent op-ed by Alice Fleerackers et al provided an excellent argument to this effect. More a question than a suggestion for anything to change.

Quote: "between all of those stakeholders to work together without polarization"

Comment: I disagree here - publishers have repeatedly shown that their only real interest is money. Working with them risks undermining all of the effort (financial, careers, reputation, time) that advocates for change put in. The OA movement should also highlight perfectly why this is such a bad route to go down (again). Publishers grip on preprint servers is a great example - those servers are hard to use as a reader, lack APIs and access to data, are not innovative or interacting with independent services. The community should make the rules and then publishers abide by and within them. Currently the publishers make all of the rules and dominate. Indeed, this is possibly the biggest ommision from this article - the total dominance of publishers across the entire ecosystem. You can't talk about change without highlighting that the publishers don't just own journals but the reference managers, the assessment systems, the databases etc. I may be an outlier on this point but for all of the people I interact with (often those at the bottom of the ladder) this is a strong feeling. Again, not a suggestion for anything to change and indeed the point of an op-ed is to stimulate thought and discussion so dissent is positive.

Note that these annotations were made in hypothes.is and are available here, linked in-text for ease - comments are duplicated in this review.

Reviewer #1 (Public review):

Wojcik et al. conducted a working memory (WM) experiment in which participants had to press the right or left button after being presented with a square (upright) or diamond stimulus. The response mapping ('context') depended on a colour cue presented at the start of each trial. This results in an XOR task, requiring participants to integrate colour and shape information. Importantly, multiple colours could map onto the same context, allowing the authors to disentangle the (neural) representations of context from those of colour.

The authors report that participants learn the appropriate context mappings quickly over the course of the experiment. Neural context representation is evident in the WM delay and emerges later in the experiment, unlike colour representation, which is present only during colour presentation and does not evolve over experimental time. There are furthermore results on neural geometry (averaged cross-generalized decoding) and neural dimensionality (averaged decoding after shattering all task dimensions), which are somewhat harder to interpret.

Overall, the findings are likely Important, as they highlight the flexible and future-oriented nature of WM. The strength of support at the moment is incomplete: there are some loose ends on the context/colour generalization, and the evidence for the XOR neural representation is not (yet) well-established.

I have one (major) concern and several suggestions for improvement.

(1a) As the authors also acknowledge in several places, the XOR dimension is strongly correlated with motor responses, in any case toward the end of the task (and by definition for all correct trials). This should be dealt with properly. Right now, e.g. Figures 2g/i, 2h/j, 3e/g, 3f/h are highly similar, respectively, because of this strong collinearity. I would remove the semi-duplicate graphs and/or deal with this explicitly through some partial regression, trial selection, or similar (and report these correlations).

(1b) Most worrisome in this respect is that one of the key results presented is that XOR decoding increases with learning. But also task accuracy increases, meaning that the proportion of correct trials increases with learning, meaning that the XOR and motor regressors become more similar over experimental time. This means that any classifier picking up on motor signals will be better able to do so later on in the task than earlier on. (In other words, the XOR regressor may be a noisy version of the motor regressor early on, and a more precise version of the motor regressor later on.) Therefore, the increase in XOR decoding over experimental time may be (entirely) due to an increase in similarity between the XOR and motor dimensions. The authors should either rule out this explanation, and/or remove/tone down the conclusions regarding the XOR coding increase. (Note that the takeaway regarding colour/context generalization does not depend on this analysis, fortunately.) The absence of a change in motor decoding with learning (as reported on page 11) does not affect this potential confound; in fact it is made more likely with it.

(2) Bayes factors would be valuable in several places, especially with null results (p. 5) or cases with borderline-significant p-values.

(3) The authors' interpretation of the key results implies that the abstract coding learned over the task should be relevant for behaviour. The current results do not show a particularly strong behavioural relevance of coding, to put it mildly. It might be worth exploring whether neural coding expresses itself in reaction times, rather than (in)correct responses, and reflecting on the (lack of) behavioural relevance in the Discussion.

(4) All data and experiment/analysis code should be made available, in public repositories (i.e., not "upon request").

Reviewer #3 (Public review):

In this work, Brown and colleagues report that the photosensor protein LITE-1 of the nematode C. elegans may also be a chemosensor that can be activated by high concentrations of the compound diacetyl. LITE-1 was described as a putative ion channel of the gustatory receptor family, which is mainly constituted by insect odorant receptors. These form tetrameric ion channels that can be activated by odorants. Specificity is achieved by forming heteromeric channels from three copies of the odorant receptor co-receptor (ORCO) and another subunit that resembles ORCO in the pore-forming C-terminus, but brings in a binding site for the respective odorant. LITE-1 has a very similar structure, according to Alphafold3 predictions, and also carries a binding pocket. In LITE-1, this was proposed to be occupied by a light-absorbing molecule that activates the channel when a photon is absorbed. Alternatively, compounds generated by absorption of high-energy photons may be formed in vivo and bound by the LITE-1 binding pocket. Koh et al. now demonstrate that another, non-light-activated compound, diacetyl, at high concentrations, can activate cells expressing LITE-1. Such (chemosensory) cells are also responsible for the avoidance of high concentrations of diacetyl. LITE-1 activation in excitable cells, i.e, muscles, causes strong body contraction and paralysis, and the authors show that this is also the case when diacetyl is presented. The authors further present molecular docking studies showing that diacetyl could occupy the binding pocket of LITE-1. Last, they show that another compound chemically resembling diacetyl, i.e., 2,3-pentanedione, can also induce avoidance in a LITE-1 dependent manner, though not as potently.

The data are intriguing, and the demonstration of LITE-1 being a diacetyl chemosensor is interesting. Yet, there are a few questions arising that the authors should address.

The authors identified mutants lacking diacetyl responses. In their chemotaxis assay (Figures 1A, B), they show that lite-1 mutants do not avoid high concentrations of diacetyl. However, the animals actually showed attraction, as the chemotaxis index was positive. If the lite-1 animals were insensitive, they should be indifferent, and the chemotaxis index should be close to zero. This means, other neurons contribute to the diacetyl response, and the result of these neurons being activated means/remains attraction? If so, the authors need to rule out any effects of these neurons on the effects they attribute to LITE-1 in the other assays.

The effect of diacetyl on muscle cells (Figure 3C) is pretty rapid, i.e., already during 1 minute after application, the animals are almost maximally contracted. How fast is it really? Can the authors provide a time course with more time points during the first minute? This is a relevant question, as the compound would have to either pass the worm cuticle or enter through the gut and diffuse through the body to reach the muscle cells. Can one expect this to occur within (less than) a minute?

In this context, the authors need to rule out that other mechanisms may be at play. E.g., diacetyl may be immediately sensed by ciliated chemosensory neurons that might release a signaling molecule that leads to activation of LITE-1 in muscles, or that sensitizes it somehow, responding to light used for filming animals. The authors should repeat this assay in a lite-1 mutant background. Furthermore, the authors tested unc-13 mutants to rule out indirect effects on the neurons recorded. Likewise, they should eliminate neuropeptide signaling via unc-31 mutants (a recent paper cited by the authors showed involvement of neuropeptide signaling in LITE-1-mediated light avoidance behavior). Last, to demonstrate that effects are not indirect in response to chemosensory neurons, the authors should repeat the contraction or swimming assay in a tax-4 mutant, which largely lacks chemosensation. This also applies to the chemotaxis assay. Animals should exhibit a chemotaxis index to diacetyl of zero, then.

Does diacetyl activate other neurons expressing LITE-1? A number of cells express LITE-1 at high levels, which the authors have not tested (they restricted their analyses to chemosensory neurons). This is important to address because it leaves the possibility that LITE-1 requires a specific partner only present in these chemosensory neurons to detect diacetyl. This partner would have to be present also in muscles, where diacetyl could activate ectopically expressed LITE-1. According to CeNGEN scRNAseq data, cells expressing LITE-1 can be identified. The ADL and ASH neurons actually come up only at the lowest threshold, so some of the other cells showing much higher levels of LITE-1 mRNAs, i.e., AVG, ALM, PLM, ASG, PHA, PHB, AVM, RIF, or some pharyngeal neurons, should be tested. ASG was among the cells the authors recorded from, but this neuron did not show a response.

The authors need to show that diacetyl responses of ADL and/or ASK can be rescued by expressing LITE-1 specifically in these neurons in a lite-1 mutant background.

Molecular docking studies are not described in detail. How was this done? Diacetyl is a very small molecule. How well can docking algorithms assess this at all? Did the authors preselect the binding pocket, or did the algorithm sample the entire molecular surface of the LITE-1 model and end up with the binding pocket? The latter would be very convincing. The authors should provide control docking experiments with other molecules that caused avoidance in their hands (i.e. benzaldehyde, 2,4,5,trimethlythiazole, isoamyl alcohol, nonanone, octanone), but did not activate LITE-1. Also, they should try docking molecules related to diacetyl, and if there are some that do not dock under the same conditions, such molecules should be used in a behavioral experiment. Ideally, they should also not activate LITE-1. Examples could be, e.g., diacetyl monoxime or 2,4-pentanedione.

Last, the authors should provide a PDB file with the docked diacetyl to allow readers to assess the binding for themselves. Since a large number of mutations of LITE-1 have been reported, it may be that amino acids shown to be essential for LITE-1 function are also required for diacetyl binding. If so, this could be backed up with an experiment.

Reviewer #1 (Public review):

Summary:

This study investigates how mice make defensive decisions when exposed to visual threats and how those decisions are influenced by reward value and social hierarchy. Using a naturalistic foraging setup and looming stimuli, the authors show that higher threat leads to faster escape, while lower threat allows mice to weigh reward value. Dominant mice behave more cautiously, showing higher vigilance. The behavioral findings are further supported by a computational model aimed at capturing how different factors shape decisions.

Strengths:

(1) The behavioral paradigm is well-designed and ethologically relevant, capturing instinctive responses in a controlled setting.

(2) The paper addresses an important question: how defensive behaviors are influenced by social and value-based factors.

(3) The classification of behavioral responses using machine learning is a solid methodological choice that improves reproducibility.

Weaknesses:

(1) Key parts of the methods are hard to follow, especially how trials are selected and whether learning across trials is fully controlled for. For example, it is unclear whether animals are in the nest during the looming stimulus presentations. The main text and methods should clarify whether multiple mice are in the nest simultaneously and whether only one mouse is in the arena during looming exposure. From the description, it seems that all mice may be freely exploring during some phases, but only one is allowed in the arena at a time during stimulus presentation. This point is important for understanding the social context and potential interactions, and should be clearly explained in both the main text and methods.

(2) It is often unclear whether the data shown (especially in the main summary figures) come from the first trial or are averages across several exposures. When is the cut-off for trials of each animal? How do we know how many trial presentations were considered, and how learning at different rates between individuals is taken into account when plotting all animals together? This is important because the looming stimulus is learned to be harmless very quickly, so the trial number strongly affects interpretation.

(3) The reward-related effects are difficult to interpret without a clearer separation of learning vs first responses.

(4) The model reproduces observed patterns but adds limited explanatory or predictive power. It does not integrate major findings like social hierarchy. Its impact would be greatly improved if the authors used it to predict outcomes under novel or intermediate conditions.

(5) Some conclusions (e.g., about vigilance increasing with reward) are counterintuitive and need stronger support or alternative explanations. Regarding the interpretation of social differences in area coverage, it's also possible that the observed behavioral differences reflect access to the nesting space. Dominant mice may control the nest, forcing subordinates to remain in the open arena even during or after looming stimuli. In this case, subordinates may be choosing between the threat of the dominant mouse and the external visual threat. The current data do not distinguish between these possibilities, and the authors do not provide evidence to support one interpretation over the other. Including this alternative explanation or providing data that addresses it would strengthen the conclusions.

(6) While potential neural circuits are mentioned in the discussion, an earlier introduction of candidate brain regions and their relevance to threat and value processing would help ground the study in existing systems neuroscience.

(7) Some figures are difficult to interpret without clearer trial/mouse labeling, and a few claims in the text are stronger than what the data fully support. Figure 3H is done for low contrast, but the interesting findings will be to do this experiment with high contrast. Figure 4H - I don't understand this part. If the amount of time in the center after the loom changes for subordinate mice, how does this lead to the conclusion that they spend most of their time in the reward zone?. Figure 3A - The example shown does not seem representative of the claim that high contrast stimuli are more likely to trigger escape. In particular, the 10% sucrose condition appears to show more arena visits under low contrast than high contrast, which seems to contradict that interpretation. Also, the plot currently uses trials on the Y-axis, but it would be more informative to show one line per animal, using only the first trial for each. This would help separate initial threat responses from learning effects and clarify individual variability.

(8) The analysis does not explore individual variability in behavior, which could be an important source of structure in the data. Without this, it is difficult to know whether social hierarchy alone explains behavioral differences or if other stable traits (e.g., anxiety level, prior experiences) also contribute.

(9) The study shows robust looming responses in group-housed animals, which contrasts with other studies that often require single housing to elicit reliable defensive responses. It would be valuable for the authors to discuss why their results differ in this regard and whether housing conditions might interact with social rank or habituation.

Reviewer #3 (Public review):

Summary:

In this paper the authors conduct two experiments an fMRI experiment and intracranial recordings of neurons in two patients P1 and P2. In both experiments, they employ a SSVEP paradigm in which they show images at a fast rate (e.g. 6Hz) and then they show face images at a slower rate (e.g. 1.2Hz), where the rest of the images are a variety of object images. In the first patient, they record from neurons over a region in the mid fusiform gyrus that is face-selective and in the second patient, they record neurons from a region more medially that is not face selective (it responds more strongly to objects than faces). Results find similar selectivity between the electrophysiology data and the fMRI data in that the location which shows higher fMRI to faces also finds face-selective neurons and the location which finds preference to non faces also shows non face preferring neurons.

Strengths:

The data is important in that it shows that there is a relationship between category selectivity measured from electrophysiology data and category-selective from fMRI. The data is unique as it contains a lot of single and multiunit recordings (245 units) from the human fusiform gyrus - which the authors point out - is a humanoid specific gyrus.

Weaknesses:

My major concerns are two-fold: (i) There is a paucity of data; Thus, more information (results and methods) is warranted; and in particular there is no comparison between the fMRI data and the SEEG data.

(ii) One main claim of the paper is that there is evidence for suppressed responses to faces in the non-face selective region. That is, the reduction in activation to faces in the non-face selective region is interpreted as a suppression in the neural response and consequently the reduction in fMRI signal is interpreted as suppression. However, the SSVEP paradigm has no baseline (it alternates between faces and objects) and therefore it cannot distinguish between lower firing rate to faces vs suppression of response to faces.

(1) Additional data: the paper has 2 figures: figure 1 which shows the experimental design and figure 2 which presents data, the latter shows one example neuron raster plot from each patient and group average neural data from each patient. In this reader's opinion this is insufficient data to support the conclusions of the paper. The paper will be more impactful if the researchers would report the data more comprehensively.

(a) There is no direct comparison between the fMRI data and the SEEG data, except for a comparison of the location of the electrodes relative to the statistical parametric map generated from a contrast (Fig 2a,d). It will be helpful to build a model linking between the neural responses to the voxel response in the same location - i.e., estimate from the electrophysiology data the fMRI data (e.g. Logothetis & Wandell, 2004)

(b) More comprehensive analyses of the SSVEP neural data: It will be helpful to show the results of the frequency analyses of the SSVEP data for all neurons to show that there are significant visual responses and significant face responses. It will be also useful to compare and quantify the magnitude of the face responses compared to the visual responses.

(c) The neuron shown in E shows cyclical responses tied to the onset of the stimuli, is this the visual response? If so, why is there an increase in the firing rate of the neuron before the face stimulus is shown in time 0? The neuron's data seems different than the average response across neurons; This raises a concern about interpreting the average response across neurons in panel F which seems different than the single neuron responses

(d) Related to (c) it would be useful to show raster plots of all neurons and quantify if the neural responses within a region are homogeneous or heterogeneous. This would add data relating the single neuron response to the population responses measured from fMRI. See also Nir 2009.

(e) When reporting group average data (e.g., Fig 2C,F) it is necessary to show standard deviation of the response across neurons.

(f) Is it possible to estimate the latency of the neural responses to face and object images from the phase data? If so, this will add important information on the timing of neural responses in the human fusiform gyrus to face and object images.

(g) Related to (e) In total the authors recorded data from 245 units (some single units and some multiunits) and they found that both in the face and nonface selective most of the recoded neurons exhibited face -selectivity, which this reader found confusing: They write " Among all visually responsive neurons, we 87 found a very high proportion of face-selective neurons (p < 0.05) in both activated 88 and deactivated MidFG regions (P1: 98.1%; N = 51/52; P2: 86.6%; N = 110/127)'. Is the face selectivity in P1 an increase in response to faces and P2 a reduction in response to faces or in both it's an increase in response to faces

(1) Additional methods (a) it is unclear if the SSVEP analyses of neural responses were done on the spikes or the raw electrical signal. If the former, how is the SSVEP frequency analysis done on discrete data like action potentials? (b) it is unclear why the onset time was shifted by 33ms; one can measure the phase of the response relative to the cycle onset and use that to estimate the delay between the onset of a stimulus and the onset of the response. Adding phase information will be useful.

(2) Interpretation of suppression:

The SSVEP paradigm alternates between 2 conditions: faces and objects and has no baseline; In other words, responses to faces are measured relative to the baseline response to objects so that any region that contains neurons that have a lower firing rate to faces than objects is bound to show a lower response in the SSVEP signal. Therefore, because the experiment does not have a true baseline (e.g. blank screen, with no visual stimulation) this experimental design cannot distinguish between lower firing rate to faces vs suppression of response to faces. The strongest evidence put forward for suppression is the response of non-visual neurons that was also reduced when patients looked at faces, but since these are non-visual neurons, it is unclear how to interpret the responses to faces.

Comments on revisions:

In the revision, the authors added information and answered several of the main questions. Several points remain unanswered because the authors would like to publish a short format paper here, and suggest that answering these questions is outside the scope of the paper. The authors would like to leave some of the more detailed analyses for a subsequent longer paper.

2:11 Würden Sie das befürworten, dass Deutschland mehr Flüchtlinge aufnimmt?<br /> - Ja.<br /> - Das finden wir gut. Würden Sie auch einen Flüchtling bei sich zu Hause aufnehmen?<br /> Weil wir haben gerade momentan das Problem, dass viele Flüchtlingsheime auch sehr voll sind.<br /> - Es ist nicht die Aufgabe des Einzelnen solche Probleme zu lösen.<br /> - Wer ist dann verantwortlich?<br /> - Der Staat.<br /> - Weil wir bieten ja die Heime, aber die sind eben voll.<br /> Da müssen wir jetzt übergangsweise noch eine Lösung finden. - Es geht prinzipiell darum, dass der Staat dieses Problem lösen muss.

so hart, diese kognitive dissonanz zwischen "ich will das" und "die experten sollen das machen".<br /> klingt wie ein hirntoter steuerzahler, der alles rationalisert was von oben kommt, und der sein steuergeld bei der arbeit sehen will...<br /> aka "wir wollen es schön haben aber nichts dafür opfern"

Un "andamio en membrana" se refiere a una estructura porosa tridimensional, a menudo hecha de biomateriales, que actúa como soporte para el crecimiento celular y la regeneración de tejidos.

§ 9º Qualquer alteração na legislação federal que reduza ou eleve a arrecadação do imposto: (Incluído pela Emenda Constitucional nº 132, de 2023)

I - deverá ser compensada pela elevação ou redução, pelo Senado Federal, das alíquotas de referência de que trata o § 1º, XII, de modo a preservar a arrecadação das esferas federativas, nos termos de lei complementar; (Incluído pela Emenda Constitucional nº 132, de 2023)

II - somente entrará em vigor com o início da produção de efeitos do ajuste das alíquotas de referência de que trata o inciso I deste parágrafo. (Incluído pela Emenda Constitucional nº 132, de 2023)

§ 10. Os Estados, o Distrito Federal e os Municípios poderão optar por vincular suas alíquotas à alíquota de referência de que trata o § 1º, XII. (Incluído pela Emenda Constitucional nº 132, de 2023)

§ 11. Projeto de lei complementar em tramitação no Congresso Nacional que reduza ou aumente a arrecadação do imposto somente será apreciado se acompanhado de estimativa de impacto no valor das alíquotas de referência de que trata o § 1º, XII.

The passage from the East Coast to the West Pacific Ocean was a priority for many explorers to help with trade with the west

CONTRADICCIÓN DE CRITERIOS 338/2022. Los tribunales colegiados realizaron un ejercicio interpretativo para determinar qué requisitos deben ser cumplidos para conceder la suspensión de los actos reclamados con efectos restitutorios y analizaron si fueron colmados en los asuntos sometidos a su consideración, en específico en relación con la posibilidad jurídica de conceder la medida cautelar, ante la eventualidad de dejar sin materia el juicio de amparo en lo principal.

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Referee #2

Evidence, reproducibility and clarity

SUMMARY OF THE PRESENTED FINDINGS

Abstract

1. LCOR (Ligand-dependent corepressor), which suppresses tumor growth by inducing the antigen presentation machinery (APM) of the tumor cells and constrains cellular plasticity.
2. poly β-(amino esters) (pBAE) nanoparticles (NPs).. Our results show optimal endosomal escape, which results in high transfection efficiency in vitro and in vivo
3. the combination of Lcor mRNA-loaded NPs with anti-PDL1 or anti-CTLA4 immunotherapies eradicated most of the tumors in our preclinical TNBC model.

Introduction

a. These structures facilitate endosomal escape due to protonation of tertiary amines at lower pH7.

Results

b. In human models MDAMB-231 and MCF7 cells, the NPs also showed high eGFP mRNA transfection efficiency

c. The efficiency of eGFP mRNA-loaded pBAE-NPs to transfect mRNA into different mouse breast cancer cells (AT3, 4T07, EO771, EMT6, 66cl4, EpRAS, and 4T1) was tested using NPs encapsulating eGFP mRNA,

d. Synthetic Lcor mRNA contained a Cap1, 5' and 3' untranslated regions (UTR) and a standard polyA tail (Fig. S2A), and all uracil were replaced for 5-methoxyuracil (5-moU) to avoid immunogenic reactions27,28. First, we measured and detected high levels of Lcor mRNA by qRT-PCR

e. NPs were stable at 25ºC for 24 h (Fig. S2C). In contrast, under conditions simulating the physiological environment (37ºC), a decrease in FRET signaling was detected ... indicating disassembly of the NPs after 2 h (Fig. S2C).

f. Lcor mRNA NPs, induces the expression of APM genes in AT3 and 4T07 cell lines

g. AT3 cells that constitutively overexpress ovalbumin (OVA). In these cells, OVA is cleaved, generating the SIINFEKL antigen peptide presented in the H-2Kb context. This can be used to measure APM activity using the anti-SIINFEKL antibody via flow cytometry.

h. We also observed a time- and dose-dependent effect regarding APM induction.

i. When tumors reached 0.5 x 0.5 cm2, we treated them intratumorally with pBAE-NPs loaded with 5 ug of synthetic FLuc or eGFP mRNA. We detected BLI at 3 h, meaning that tumor cells had taken up the mRNA-loaded NPs and translated a luciferase active protein within 3 h. In both models, expression peaked around 6 to 10 hours after administration

j. After local administration of 5 μg of Lcor mRNA-loaded NPs, we observed a rapid increase in Lcor mRNA in the tumor tissue, followed by a decrease, reaching baseline levels after 24 h (Fig. 3C). ..To unravel the protein dynamics, we used ... LCOR-HA protein and uniquely detect the ectopic protein using anti-HA by IF. As expected, LCOR-HA protein expression was delayed, peaking 3 h after administration (Fig. 3D). Linked to protein expression, at 3 h and 6 h after administration, we detected an increase in APM genes by RT-qPCR (Fig. 3E and S3D).

k. the combination of Lcor mRNA-loaded NPs with anti-PDL1 therapy not only reduced tumor growth but also led to tumor eradication in 5 out of 7 mice.

l. The combination of Lcor mRNA-loaded NPs with different ICIs showed high efficiency in preclinical models, thus supporting the feasibility of starting clinical studies and thus bringing the treatment closer to patients.

Major points

L. 277: "NPs were stable at 25ºC for 24 h (Fig. S2C). In contrast, under conditions simulating the physiological environment (37ºC), a decrease in FRET signaling was detected ... indicating disassembly of the NPs after 2 h (Fig. S2C)." - The disassembly of the NPs after 2 h is key to the performance of the chosen approach.

L. 296: "The results showed an increased number of cells with higher OVA-SIINFEKL presentation, indicating the enhanced activity of the APM induced by the Lcor mRNA-loaded pBAE-NPs... demonstrate the efficiency of this mRNA nanotechnology to rescue the function of the LCOR TF in inducing tumor cell immunogenicity and thus modulating tumor phenotypes." - There is a key difference between activating antigen-presenting machinary and inducing immunogenicity, i.e. recognition by the immune system and activation of effector cells. There is no indication on how effective endogenous immune responses (e.g. antibody titers, TIL infiltration, cytokine release) are to the administration of Lcor mRNA-loaded NPs.

L. 325: "Based on these results, we estimated an optimal therapeutic regimen of Lcor-mRNA-loaded pBAE-NPs administration in our preclinical experimental models would be every 3 days." - It is highly unclear how the authors came to this conclusion, as it should be based on the time frame of optimal immune responses.

L. 332: "Lcor mRNA-loaded NPs were administered at a dose of 250 μg/kg by intratumoral (i.t.) injection twice a week" - This possibly is the strongest limitation of this study. Intratumor injections of largely unfeasible/unrealistic in clinical setting. Even more, the management of metastatic disease appears out of question.

L. 337: "the results revealed that Lcor mRNA monotherapy was enough to reduce 4T07 tumor 338 growth." - These effects appear rather limited (Fig. 4A,B) and are not statistically significant in Fig. S4B and Fig. S5A.

L. 338: "the combination of Lcor mRNA-loaded NPs with anti-PDL1 therapy not only reduced tumor growth but also led to tumor eradication in 5 out of 7 mice" - Fig. 4A bottom left panel. Three of the tumor growth curves abruptly stop at below 200 mm3. Typically, this is mouse death. This reduces the tumor pool to four xenografts. Among these, we notice two complete responses and two tumor progressions. Two tumor progressions are seen also in the combination Lcor mRNA+ α-PD-L1 group. We are unsure about the statistics of this experiment.

L. 350: "The combination of Lcor mRNA-loaded NPs with different ICIs showed high efficiency in preclinical models, thus supporting the feasibility of starting clinical studies and thus bringing the treatment closer to patients."

• Please see comment on L. 332. It appears unrealistic to consider clinical studies in patients unless a systemic administration of Lcor mRNA-loaded NPs is tackled and corresponding therapeutic efficacy is shown.

Significance

General assessment:strengths and limitations.

The identification of a candidate therapeutic means, by supplying Lcor mRNA for induction of antigen-presenting molecules is of potential interest. As this is not a basic science study, but aims at developing feasible therapeutics, it falls short in this respect, as most likely unfeasible in patients. The combined effect with anti-immune blockade agents is of interest. However, if one assumes that effective immunostimulation was indeed induced by Lcor mRNA, its overall impact on tumor growth is per se weak, if any. Maybe only antigen presentation is induced, but this is in the absence of costimulatory signals? This needs to be investigated.

Advance

This article is based on good papers that were published years ago. The science novelty is limited. As the idea is to develop a novel therapeutic approach, the lack of realistic feasibility severely limits merits.

Audience

Scientists involved in preclinical studies.

Reviewer expertise

This reviewer and his research group have cloned the genes and biochemically characterized novel tumor drivers. He identified their function as stimulators of tumor cell growth and of metastatic spreading, together with roles in cell-cell adhesion, signal transduction and local cancer invasion. This led to the discovery of their prognostic / predictive relevance in human cancer. Two murine models of rare genetic diseases were generated by ablating the corresponding murine genes. He then pioneered the development of software for the identification of fusion oncogenes and of transcription factor-DNA binding sites. This reviewer fostered novel anti-cancer immunotherapies. He generated anti-cancer cytotoxic T lymphocytes, by the use of in vitro engineered antigen presenting cells. Using proprietary discovery platforms, this reviewer developed novel anti-cancer monoclonal antibodies, that selectively target cancer cells. This led to the engineering of humanized antibody-drug conjugates, bispecific anti-CD3/activated Trop-2 antibodies and innovative CAR-T designs. ADCs are now being tested in clinical trials in cancer patients.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Serra-Mir et al investigate the therapeutic potential of delivering the mRNA of LCOR transcription factor via nanoparticles to enhance the efficacy of immune checkpoint inhibitors. The authors show that the mRNA delivery mediated by H and R-nanoparticles was efficient in multiple breast cancer cell lines in vitro. Moreover, using mouse models, they show that LCOR mRNA delivery may improve the efficacy of the treatment with anti-PDL1 or anti-CTLA4 checkpoint inhibitors against tumors. Although this proof-of-concept study has promising aspects, there are significant weaknesses that should be addressed. Details below.

Major points:

1. In vitro delivery of LCOR appears to be effective in both AT3 and 4T07 cell lines when continuously exposed to the mRNA loaded nanoparticles. However, the impact of LCOR on antigen presentation machinery (APM) is rather mixed and not very convincing. The expression pattern and kinetics of several APM genes are inconsistent with LCOR kinetics and at several timepoints the expression in LCOR samples is essentially the same as in mutant LCOR negative controls (Figure 2D). Moreover, the APM reporter assay experiments show that APM in LCOR transduced 4T07 cells is induced rather modestly at best (Figure 2E). The APM effect needs to be demonstrated more rigorously to be convincing.
2. Considering that previous studies by the authors suggest a role for LCOR in regulating stem cell properties in normal and malignant mammary cells (Celia-Terrassa et al Nat Cell Bio 2017; Perez-Nunez et al Nat Can 2022), it is important to address whether transduced LCOR mRNA impacts these properties. Moreover, other autocrine cell functions such as proliferation and apoptosis are also relevant and should be analyzed.
3. The impact of LCOR delivery on immune responses in mouse models could be more rigorous. Analysis of APM genes shows rather modest difference in these gene after LCOR transduction (Figure 3E). Is this sufficient to induce effective anti-tumor immune response? What is the status of T cell activity or exhaustion? Furthermore, LCOR may regulate cytokines and chemokines that are critical for modulation of the immune environment. Did the authors measure any immune-modulating cytokines in the tumor microenvironment, following LCOR expression? Finally, whereas the study focuses on APM and its function, LCOR may directly modulate expression of checkpoint activators on cancer cells. The impact of LCOR transduction on PD-L1, PD-L2 and CTLA-4 expression in cancer cells should be determined.
4. In line with point nr 2, it would be important to analyze the impact of delivered LCOR mRNA on cell functions such as proliferation and apoptosis in the mouse tumors. Even if LCOR delivery sensitizes tumors to checkpoint inhibitors, it cannot be assumed that the impact of LCOR is primarily due to induction of the APM.
5. The experiments analyzing treatment efficacy in the 4T07 model in mice show lack of consistency and a substantial variation between mice that are treated in the same manner. Even the group treated with PBS and Ctr-mRNA contains mice with tumors that regress (Figure 5A). This inconsistency suggests that more mice are required to generate a convincing pattern. Furthermore, the inclusion of a second model would provide a stronger case for a broad applicability of the LCOR treatment with checkpoint inhibitors. Indeed, it is surprising that the authors did not use the AT3 model in vivo considering that mRNA delivery and LCOR expression is substantially more efficient in AT3 compared to 4T07.
6. Following the injection of LCOR nanoparticles to the tumor, the proportion and spatial distribution of LCOR expressing cells should be determined. This is particularly relevant in light of the almost complete elimination of the tumors treated with combination therapy (Figures 4 and 5). Is this striking impact on tumors in spite of mRNA being delivered only to a small portion of cells within the tumor?
7. The in vivo results indicate that expression levels of Fluc mRNA decline rapidly post-treatment, returning to baseline within 24 hours after peaking at 10 hours (Supplementary Figure 3). Although the investigators treat mice every 3rd day with LCOR nanoparticles in their therapeutic experiments, the analysis of durability of immune responses after single injection should be done and can provide important practical insights to guide therapeutic design.

Minor points:

1. The authors mention that LCOR mRNA delivery synergizes with checkpoint inhibitor treatment. However, synergy has a specific meaning when drug interaction is analyzed. This was not really addressed or calculated.
2. There seems to be a mistake in the text (lines 261-263). Based on Figure 1C the mRNA delivery efficiency is higher in AT3 cells compared to 4T07 cells (very difficult to determine anything from Figure 3D, since the cell density is not visible).
3. It is surprising how little expression of luciferase is observed in the 4T07 model (Figure S3), even if almost 60% of cancer cells and 40% of stromal cells are positive (Figure 3A). What could explain this discrepancy?
4. Representative FACS plots from Figure 3 should be shown.
5. There are issues with the figure legends of Figure 3 (from 3C onwards) and Figure S2 (from 2D onwards) that need to be fixed.

Significance

The study is a proof-of-concept investigation addressing whether LCOR mRNA can be delivered by nanoparticles to sensitize tumors to immunotherapy. This approach aims to overcome the limitations and difficulties of targeting transcription factors for therapeutic purposes. However, although the delivery of LCOR mRNA appears to be sufficient, further characterization of the resulting impact needs to be done. This includes both impact on immune responses as well as cell-autonomous impact on cancer cell proliferation and apoptosis.

DOI: 10.1097/TXD.0000000000001832

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-1547, RRID:AB_2716324)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2716324

What is this?

Document de Synthèse : La Nouvelle Hiérarchie des Professions en France

• Ce document de synthèse analyse les évolutions récentes du marché du travail en France, mettant en lumière un changement notable dans la perception et la rémunération des métiers.

Traditionnellement, les carrières intellectuelles et les études supérieures étaient perçues comme les garantes d'une meilleure rémunération et d'un épanouissement professionnel.

Cependant, la pénurie de main-d'œuvre dans certains secteurs manuels et artisanaux a bouleversé cette hiérarchie, offrant des opportunités inattendues en termes de salaires et de qualité de vie.

Thèmes Principaux et Idées Clés :

1. Revalorisation des Métiers Manuels et Artisanaux :

• Changement de Perception : Le reportage souligne un revirement. "Pendant longtemps, les métiers intellectuels promettaient de meilleures carrières, plus rémunératrices et plus épanouissantes.

Tandis que les métiers manuels ont clairement été dénigrés, souvent dès l'école." Aujourd'hui, cette stigmatisation diminue en raison du manque criant de main-d'œuvre.

• Salaires Surprenants : Des professions comme grutier, chauffagiste, plombier, soudeur ou maçon offrent désormais des salaires très attractifs, souvent sans nécessiter de longues études.

Amandine, une ancienne monitrice d'auto-école, a doublé son salaire en devenant grutière, passant de "1 200, 1 300 à peu près" à "2, 900 et quelques euros" nets par mois.

Mickaël, un jeune plombier-chauffagiste, gagne "2432 euros et 99 centimes" nets par mois après seulement un an d'ancienneté, avec des primes pouvant porter son brut à environ "3000 euros".

• Autonomie et Valorisation : Ces métiers offrent une grande autonomie et un savoir-faire valorisant. Mickaël, par exemple, gère ses 13 clients et ses commandes de matériel, jouissant de "l'autonomie d'un artisan et la sécurité d'un emploi salarié".

Clémence, chauffeur poids lourd, trouve que c'est un "métier bizarrement, malgré ce qu'on pourrait imaginer, qui est plutôt valorisant."

2. La Pénurie de Main-d'Œuvre : Un Facteur Clé de Revalorisation :

• Demande Supérieure à l'Offre : La France compte "plus d'un million de postes à pourvoir".

Des secteurs comme le transport (45 000 chauffeurs supplémentaires nécessaires) et la plomberie (manque de main-d'œuvre "excessif") sont particulièrement touchés.

• Pouvoir de Négociation des Salariés : Cette pénurie inverse le rapport de force.

Clémence, la chauffeuse poids lourd, illustre ce point :

"On a plus de pouvoir qu'un mec qui va être dans la pub, où le patron va dire « Je ne suis pas content, tu t'en vas, de toute façon, il y en a 80 derrière »...

Là, c'est l'inverse." Les entreprises sont contraintes de proposer des conditions attractives, allant même jusqu'à "débaucher des gens dans d'autres entreprises".

• Recrutement Simplifié : Pour certains postes, l'envie de travailler et la ponctualité priment sur les diplômes.

David Arslan, chef d'entreprise en ravalement de façade, ne demande "aucun diplôme", seulement "la ponctualité et l'envie de travailler" pour un salaire de "2 000 euros net mensuel".

3. La Reconversion Professionnelle : Une Tendance Croissante :

• Quête de Sens et de Meilleure Qualité de Vie : De nombreux salariés, y compris des cadres, se reconvertissent. "Depuis 2021, 20% des cadres ont entamé une reconversion professionnelle". Clémence, ancienne directrice artistique à Paris, a troqué son "Bac plus 5" et un salaire de "1 600 net" pour un permis poids lourd lui rapportant "entre les 2 500 et 3 000 euros net", et une meilleure qualité de vie.
• Éviter le "Perdre sa Vie à la Gagner" : La question est posée : "Faut-il tout miser sur des études supérieures pour finir dans un bureau stressé, avec des horaires à rallonge ou une charge de travail XXL ? Faudrait-il perdre sa vie à la gagner ?"
• Formations Adaptées : Des initiatives comme l'école Gustave, qui forme gratuitement des plombiers en 15 mois, répondent à ce besoin de reconversion rapide et efficace. L'école garantit un salaire minimum de "2 000 euros net" en sortie et un taux d'embauche de "95% en CDI".

4. Le Revers de la Médaille pour les Professions Intellectuelles :

• Débuts de Carrière Difficiles : Certaines professions intellectuelles, malgré de longues études, offrent des rémunérations de départ modestes. Aurélie, avocate avec "7 ans d'études après le bac", se retrouve avec "à peine plus d'un SMIC" net après avoir payé ses charges, soit "1 500 euros net" pour des journées parfois très longues et improductives (temps d'attente non payé).
• Désillusion et Fort Taux de Démission : Le décalage entre les attentes (prestiges, revenus) et la réalité du métier conduit à la désillusion. "30% des avocats démissionnent au cours des 10 premières années d'exercice."
• Évolution des Salaires : Si les débuts sont difficiles, les carrières intellectuelles peuvent offrir une meilleure progression salariale sur le long terme. Le reportage note qu'après 8 ans, le salaire d'un plombier "va plafonner autour de 2 700 euros", tandis que pour les avocats, "ce sera deux fois plus, 5 400 euros mensuels en moyenne."

5. L'Entrepreneuriat Manuel comme Voie de Succès :

Exemple de David Arslan : L'histoire de David Arslan, patron d'une PME de ravalement de façade réalisant "10 millions d'euros de chiffre d'affaires", est emblématique.

Parti de rien, il a bâti sa réussite sur un savoir-faire manuel, démontrant que "tout est possible" avec "de l'or dans les mains".

Opportunités du Marché : La demande dans des secteurs comme l'isolation et la rénovation (stimulée par la hausse des tarifs de l'énergie) offre des opportunités de croissance exponentielle pour les entreprises du bâtiment.

David Arslan connaît une augmentation de "30%" de demandes et est contraint de refuser des chantiers faute de main-d'œuvre.

Conclusion :

Le marché du travail français est en pleine mutation.

La pénurie de main-d'œuvre dans les métiers manuels et artisanaux a non seulement revalorisé ces professions en termes de salaire et d'attractivité, mais elle a également ouvert la voie à des reconversions massives pour des individus cherchant une meilleure qualité de vie et un épanouissement professionnel.

Tandis que certaines carrières intellectuelles peinent à offrir des débuts de carrière rémunérateurs, les "mains en or" et les entrepreneurs du bâtiment peuvent désormais atteindre des sommets financiers et professionnels insoupçonnés, remettant en question les hiérarchies établies et l'importance des études longues pour le succès.

En esta parte se defiende y se explica la importancia o necesidad de realizar esta investigación, los motivos que justifican que se llevara a cabo y los beneficios que se obtienen al realizarla.

La viabilidad o métodos es la capacidad de llevar a cabo esta investigación, hacer un análisis de los recursos a los que tenemos acceso y la realidad de hasta donde podemos desarrollar nuestra investigación para garantizar su culminación.

Los objetivos son todas aquellas metas que desea alcanzar la investigación a lo largo de su desarrollo, ya sea que se desvíen ligeramente o que se alcancen en su totalidad, siempre deben estar ligadas a la pregunta central de la investigación,

En esta parte se define la problemática de la investigación, la pregunta central de estudio, su enfoque y los recursos con los que se cuenta.

Un enunciado debe contener el contexto que engloba la problemática de la investigación, incluyendo las causas, cuál es el problema y diferentes preguntas para aclarar qué es la problemática.

Es la idea central de una investigación, delimita la investigación y la información que esta contendrá, así mismo el contexto geográfico y temporal.

Objectif : mise en place d'une politique de données

ça ne correspond pas toujours exactement à ce qui est déposé dans NAKALA, car il s'agit souvent de matériels supplémentaires, soit des annexes, soit des contenus multimédia, mais il n'y a pas d'autres endroit pour déposer ces contenus en leur donnant un identifiant pérenne, et ils apportent du contenu supplémentaire aux articles de recherche.

bana da arada oluyor, canım.

la sección anterior es ciertamente lo peor redactado de todo el trabajo. Hay que seguir trabajando para mejorar esa sección

supongo que se esta hablando de la figura de arriba...

Primero, esta super chica la letra no se lee nada. Segundo hacer un caption mas descriptivo de la figura. Tercero, aclarar que significa P+ P-, etc, no esta explicado en el texto

otra figura sin mencionar ... creo que sola hay una figura mencionada en toda la tesis.

tuve que leer tres veces este párrafo para entender de que se trataba. No es mejor empezar diciendo "En lo siguiente discutimos los casos donde el valor del coeficiente de pearson no es consistente con el valor del coeficiente de sperman ..." sin tantas vueltas! Hay que ser más directos en un texto técnico y pensar más en facilitarle las cosas al lector

no se entiende bien todo este párrafo hya que reescribirlo mejor

primero, muy chico los labels. Segundo poner en la figura solo la referencia del panel, (a) en este caso, y en el caption hacer la descripción. Si no queda desprolijo, parece que hay 3 captions. Además le da más lugar a la figura que es lo importante. Yo pondría más ticks en el eje x

muy chicos los items de la leyenda, hay que tratar de que al insertar la figura esta letra se vea parecida a la del texto principal. La forma más fácil de hacer eso es definir el ancho de la figura en 8 inches (~ancho de pagina A4), y fijar la letra en 11

yo marcaría en el mapa, con un borde por ejemplo, a los swing states. Por que tener que volver a la figura de arriba para chekear cuales son es bastante molesto. Mencionar la figura en el texto principal por favor

explicar esto antes de describir la figura

explicar quien hizo la validación manual. No es lo mismo que la haya hecho el autor de la tesis que una persona random. Explicar si los documentos elegidos fueron al azar

yo haria una tabla para comparar mejor la diferencia entre berttopic y rollcall, por que así como esta expresado en estos párrafos queda medio confuso

igual que antes, no estoy seguro de toda esta explicación de bert topic sume algo. No es una técnica que se aumento durante el trabajo, no es una técnica poco conocida que se invento en el grupo

no estoy seguro que toda la explicación que sigue aporte algo interesante. Parece como fuera de contexto del trabajo

Las figuran siempre deben estar referidas en el texto. No se utiliza el ":" para indicar que va a aparecer una figura. Tampoco son admitidas en un texto académico referencias del tipo "la figura de abajo" o "la figura de arriba". Acá hay que poner algo así: "En la Fig 2.4 mostramos una visualización de la secuencia ...". Claro y directo, mencionar la figura y describirla

el lector puede no estar enterado de que es esto, explicar lo que es o citar, no puede salir de la nada

explicar como se decide si hay coherencia y definir coherencia en este contexto. Veo mucha terminología que sale de la nada, hay que clarificar en el texto cuando aparece un concepto nuevo

explicar mas en que consistía el corpus. Una caracterización estadística inicial de los textos hubiese estado bien. A esta altura no pretendo que lo hagas pero se pueden mencionar algunos datos, por ejemplo la cantidad promedio de tokens por párrafo y por texto? son similares la cantidades de tokens en los documentos asociados a Trump y a los otros candidatos

en un texto académico el uso de ":" en este contexto queda un poco desprolijo. Siendo una tesis de ciencias de datos, no debería estar mejor explicitado los diferentes componentes de la base de datos en un diagrama de flujos o una tabla donde de vea claramente que datos manejaste? me parece que quedaría mucho mejor

que es este formato? escribir bien y poner la pagina del grupo si existe

el lenguaje de la tesis es castellano

espero que la necesidad de integrar estas tablas este bien discutido en la sección 4.4

esto es bastante fácil de hacer, espero que haya buena razón por la cual no pudieron avanzar en esa dirección

explicar que significa esto, es bueno o malo?, como se mejoraría? no entendí la idea

prevalencia es la palabra?

como se define la relevancia?

además de todos los comentarios anteriores que aplican a esta figura, estaría bueno que la posición de los temas en el circulo sea siempre en el mismo lugar para todo el compendio de gráficos de esta sección, de esta manera seria más fácil para el lector comparar rápidamente entre estados

"En la figura 4.14 se muestran los resultados para el estado de Arizona correspondiente al periodo Marzo-Julio de 2024. Se puede observar ..."

por favor, definir claramente cual es el objetivo al comienzo de una sección, sin vueltas: voy a hacer [este] análisis por que me interesa ver [esto]. Si no se hace muy difícil la lectura realmente

alguna conjetura del por que?

no entendí, hay una explicación entonces acerca de la inconsistencia en los coeficientes?. Explicarla en forma clara

yo pondría los tópicos en castellano, así es consistente con la discusión en el texto principal

esto es de la tabla de arriba?. De nuevo, creo que ya lo mencione un millón de veces: TODA FIGURA Y/O TABLA DEBE ESTAR MENCIONADA EN EL TEXTO PRINCIPAL Y DISCUTIDA COMO CORRESPONDE.

no cuesta nada mencionar la fecha para ayudar al lector a enfocarse mejor

no se menciona ningún criterio de densidad en esa sección

la explicación de los casos quedaría mas clara y en una tabla de doble entrada

esto lo tenias que a ver mencionado en la ausente discusión de la figura 4.11

no es mejor primero decir esto, que es lo que se hizo. Escrito como esta confunde, hay que decir primero y de forma clara y directa lo que se hizo y como, sin vueltas. Luego después si se quiere mencionar lo que no se pudo hacer

no entiendo este párrafo, esta para anunciar la sección siguiente?, no se entiende y queda descolgado, si se quiere mantenerlo explicitar más el contexto

cuatro no es igual a tres, no se entiende por que seria igual de adecuado. Es la misma base de datos? aclararlo

Sería útil para el lector mencionar esta meta al comienzo de la sección, así uno puede ir interpretando mejor el por que los resultados que van apareciendo

no encontré una mensión a esto en la sección 2.1.1

de nuevo, por que no se discute la figura? te dicen algo esas oscilaciones en el panel b) derecha? falta mucha discusión acá. No se entiende cual es el objetivo de mostrar esto si no se discute

esto es la variable T en ecuacion 3.2.b ? aclarar

toda la discusión de esta carilla parece media obvia, si hay algo que no sea tan obvio o anti intuitivo estaría bueno comentarlo. O si hay diferencias en los discursos de los contrincantes en un estado estaría interesante remarcarlo y tratar de conjeturar el por que

Acá se necesita más discusión. No puede ser que se muestran dos gráficos que ocupan una carilla cada uno y la descripción sea tan exigua

estaría bien captions mas descriptivos. Por otro lado, la letra esta media chica

explicar por que se usaron dos criterios distintos

adonde?

explicar primero para que se hizo el análisis, no se entiende que se pretende con esto

explicar mejor, no se entiende

explicar objetivo de esto

y entonces? no se entiende para donde va esta discusión. explicar claramente que significan estas diferencias señaladas y cuales son las consecuencias para el análisis

explicar como se define la importancia

hay que explicar un poco mas esto

mencionar la tabla en el texto

explicar en que consiste, en promedio cuantas etiquetas hay por documento? por otro lado, no era que se hacia separación de tópicos por párrafo? en esa pagina la separación es por el full texto? explicar bien eso en el texto

escribir esto en el texto principal, las ecuaciones no llevan captions. Por otro lado, explicar bien que hace esa formula por que no se entiende. Un ejemplo sencillo haría más fácil la tarea del lector

y que significa?, explicar por que puede existir una correlación no lineal en tus datos y por que te parece necesario calcular esa métrica

esto es importante en la explicación, no la pondría dentro de un paréntesis

no se entiende que se esta queriendo explicar acá

el detalle de los hyperparametros hubiese sido interesante ponerlo en una tabla, informando también la versión de los algoritmos utilizados

explicar por que se decidió esta opción