RLAIF tận dụng các hê thống AI (thường là các LLM mạnh mẽ hơn hoặc chuyên biệt hơn) để cung cấp các phản hồi đồi với đầu ra của LLM được huấn luyện. 3 Hướng tiếp cận chính của RLAIF: - Chắt lọc (distill) các phản hồi từ AI để huấn luyện reward model. - Prompt LLMs như một hàm reward - Self-rewarding

2 hướng RL chính được sử dụng trong các mô hình LLM hiện nay: - RL truyền thống (RLHF hoặc RLAIF): Các phương pháp này yêu cầu huấn luyện ra 1 reward model. Đặc điểm: quá trình huấn luyện phức tạp và thường không ổn định. Điển hình là thuật toán PPO. Các mô hình như InstructGPT, GPT-4, Claude-3 đều sử dụng phương pháp này

• RL đơn giản hóa (DPO, RPO): các phương pháp này loại bỏ reward model, thay vào đó đề xuất hướng giải quyết ổn định và hiệu quả hơn về mặt tính toán. Các mô hình như Llama 3, Qwen 2 và Nemotron-4 30B đều đi theo hướng này

This beautiful study highlights the power of SRS microscopy for discovering and following up on important observations. If addressed, I have a couple of questions that would provide clarity to the reader.

1. The inset graph showing the normalized pure spectra of proteins and lipids is critical for understanding the protein and lipid channels used throughout. I see where the normalization is explained in the methods, but I didn't find the details about how those curves were obtained. What was used for the pure protein and lipid samples? How many wavenumbers were sampled (just the 5 with the tick marks on the x-axis)?

2. In Figure 1, it would be helpful to add markings to point out the additional structures described in the figure caption for non-experts in C. elegans.

3. In Figure 2, the inset really demonstrates the protein buildup in the pseudocoelomic space. Did you notice that that region on the worm was particularly striking across treatments? Would comparing the quantification in those FOV show a more pronounced effect than the entire worm?

4. The schematic showing the experimental design in Fig 1G. is helpful, but I didn't see the reasoning in the text to explain why the daf-2 mutants weren't exposed to the calorie restriction treatment like for ceh-60 worms.

5. In Figure S1, were the protein or lipid quantifications ever normalized to the area of the worm?

6. What do you make of the decreased protein buildup in ceh-60 CR worms as compared to ceh-60 fed (Fig. 6F). I see that there was no additive effect of CR on the mutants in terms of lifespan, but I found it interesting that it did look significantly lower in this plot.

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Reply to the reviewers

Detailed response to Reviewer comments

We thank the reviewers for their positive and constructive evaluation of the paper. We have addressed in full the concerns raised as detailed below. We apologize for the long time it took us to respond, which was a consequence of local circumstances in the last year.

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

Summary:

The authors analyzed circulating cell-free DNA for COVID-19 using deep sequencing of the methylation and histone modification. The major output was cell-specific quantification. The study involved 120 unvaccinated, hospitalized patients, 19 asymptomatic/mild cases, and 40 controls. Between COVID-19 and controls, they found significant differences in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts. The latter two cell types had significant differences even in the asymptomatic patients. It is unclear if the damage seen is related to COVID-19 specifically, or related to general inflammation or infection.

Strengths of the study include relatively high WGBS/targeted sequencing, along with fragment-level analysis with methods described in their previous work (Loyfer et al. Nature). In addition, they add and ChIP-seq data using their published methods. The work comes from a group with leading expertise in methylation cell-free DNA analysis.

Overall, the work is most comprehensive analysis to date for COVID-19, and the data would be a valuable resource to the research community. We have major and minor comments that do not necessarily require additional experimental work.

We thank the reviewer for these supportive comments.

Major comments:

1. There is a lack of data and the methods are presented in such a way that the results and conclusion can be reproduced and evaluated. Neither the code nor the data to generate the results are available. Both need to be made available during the peer review process.

Missing data: Fragment-level FASTQ, BAM, or PAT files are needed to reproduce the results. Missing Scripts, for example in Github, is standard and reasonable for reproducing the figures shown. Missing targeted assay method details: - The authors should show the data, methods, and details for: "The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed."

Thank you. WGBS data files are currently being uploaded to GEO and are waiting for an accession number.

For the validation of targeted markers, we added a new supplemental table (S11) with data on the methylation status of the loci used in this study in different cells and tissues (i.e. marker specificity), and provided a detailed text and references to the methods used.

The authors did not list the major limitations of the study in the discussion or elsewhere. These should include (or be addressed with experimental or conclusion changes):

1) The small sample size of the asymptomatic/mild group (if the emphasis of the paper, as suggested by the title, is on the asymptomatic/mild group - see the next major point.

Thank you, indeed this is a limitation, we have now addressed this issue in the text. Despite this limitation, findings regarding to this population were statistically significant.

2) The targeted assay is used on the vast majority of samples, including all of the asymptomatic/mild group. However, it is limited to a particular subset of cell types (total defined by all possible cell types in the body). Those cell types were determined based on WGBS data on only 6 COVID-19 cases.

Thank you, indeed this is a limitation. WGBS was done on 6 critically ill patients, to uncover the potential cell types that will be of most interest in the targeted assay. In comparison to the WGBS, the targeted assay has a deeper coverage and therefore greater sensitivity. We have now addressed this issue in the limitations section.

3) The methylation references for the WGBS data were limited to a fraction of all human cell types. For example, this paper was not able to evaluate Schwann cells or peripheral nerves, which was a significant finding for COVID-19 related multisystem inflammatory syndrome (PMID 37279751).

The WGBS atlas (PMID: 36599988) consists of ~40 cell types that we were able to isolate at a high purity. While this is the most complete methylome atlas of human cell types generated to date, it is indeed incomplete. Unfortunately the scarcity of Schwann cells prevented us from determining the methylome of this cell type, and the matter is to be investigated in future studies. Note that the study referred to by the reviewer described the cell-free transcriptome rather than the cfDNA methylome of patients. cfDNA methylation analysis of Schwann cells remains a challenge to be addressed in future studies. This limitation is explained in the revised text.

4) The case and control groups (severe, asymptomatic mild, and control) were collected at different times and circumstances, allowing for potential pre-analytical confounders.

We now addressed this limitation in the text.

5) cfDNA levels can be influenced by several unmeasured factors, including death, replication leading to more turnover, clearance/stability, and movement from tissue into circulation. The methods used cannot distinguish between these possibilities .

Indeed, the mechanism by which cfDNA concentration is increased is not fully understood, but is certainly correlated with pathology. We clarify this in the revised text.

6) (if true) the controls used for the targeted assay were not age/sex matched. The median age for the controls skew younger per Table S1, S2, S3.

We used control samples that were collected before the pandemic, to make sure that they were not infected with COVID-19. Consequently, there are minor demographic differences (e.g. controls tend to be younger than the hospitalized patients, though similar age to the asymptomatic donors).

Note that in previous studies, cfDNA levels and origins did not show differences in sex.

In the WGBS samples, we did age and sex matched the samples.

We explain this issue in the revised text.

7) (optional) It is unclear whether the differences found are attributable to COVID-19, coronavirus infection, viral infections, infections in general, or inflammation in general. The appropriate alternative controls were not addressed in this study. The paper shows some degree of correlation with acute inflammatory markers (CRP, ferritin, neutrophil contribution).

Indeed, elevated cfDNA from specific tissues reflects tissue turnover or death, with no indication of the cause of pathology. We now addressed this limitation in the text.

The title is a bit misleading in that it revolves around the asymptotic patients. However, this is also the group with the lowest representation at n=19. The vast majority of the data is related to the hospitalized patients. While other studies may have looked at hospitalized patients, I agree with the authors that there is merit in deep sequencing and the correlated clinical data.

Thank you. We chose to highlight in the title the most novel and provocative finding of the study.

More details on the patient inclusion criteria are needed. Were the asymptomatic/mild positive by PCR test or a point of care immunoassay? We know the viral load is quite dynamic for these patients. What was the timing of the blood draw?

Likewise, how did you find the hospitalized patients? Was it comprehensive over a period of time? These details help reveal any potential biases in the selection process.

We do not have information on the viral load in patients. All were positive for a PCR test. For the asymptomatic cases we know the time of the test, and this information is now added in Supplemental Table S2.

Hospitalized patients were recruited and consented at the Shaare Zedek Medical Center in a rather comprehensive manner – we recruited all patients that we could during May-June 2020. This is explained in the revised methods section.

Minor comments:

1. The abstract states: "Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage." However, in Fig 5, there seems to be a bimodal distribution for the lung epithelial and cardiomyocytes. Unclear if that is an artifact of the graph.

It is quite interesting that the asymptomatic/mild group seems to have a bimodal distribution in lung epithelial and cardiomyocyte cfDNA. Perhaps this data is not available, and the sample size is small, but could there have been a clinical difference between the two groups (e.g. asymptomatic versus mild, or had symptoms later?). It is unclear how precise the measurements are for the lung epithelial cells.

Thank you for this comment. Since cfDNA levels of the hospitalized patients are increased by orders of magnitude, we have arranged the graphs in logarithmic scale. Consequently, the bimodality that the reviewer mentions reflects only a slight absolute difference of cfDNA levels from lung and cardiomyocytes: +-1 GE/ml, and we assume that this difference does not reflect clinical significance (and is not statistically different from the controls). This is referred to in the revised text.

The authors listed 2 prior studies that looked at cell type or tissue damage during COVID-19. There are 2 other studies that I am aware of: PMID: 33651717 (n=84 with n=18 nonhospitalized) but probably shallow WGBS, and 37279751 (n=205 pediatric patients). Importantly, the latter paper found Schwann cells were significantly elevated, which is missing from the current study's assessment. In addition, citation 14 from the same group already found significantly increased vascular endothelial cfDNA in COVID-19 patients with severe disease versus mild. While some findings are consistent, there are also discrepancies.

As explained above, our DNA methylation atlas does not contain a Schwann cell entry, so we cannot refer to cfDNA from this cell type; the mentioned study used cfRNA to assess this population. This is mentioned in the limitations of the study.

We now cite more comprehensively existing literature of liquid biopsies in Covid-19, and discuss the potential sources of discrepancy. We believe these result from differences in the methylome atlas, from the higher depth of the targeted assay compared with WGBS, and from our assessment of a unique population of asymptomatic patients.

Is Fig 2 necessary? Fig. 5 seems to display the same data but with the asymptomatic group.

Indeed there is some redundancy. Figure 2 shows data on hospitalized patients, and Figure 5 focuses on asymptomatic patients but uses as reference the same controls and severe patients as in Figure 2. We believe that this arrangement helps clarity.

"Elevated lung cfDNA reflects excessive lung cell death" - recommend this statement is tempered as direct evidence is not available in this study. An alternative explanation could be that endothelial cells are damaged, and it is easier for lung cfDNA to enter blood circulation rather than the respiratory system.

Thank you for this comment. We have addressed this possibility in the revised Discussion.

Fig 6: Add unit of measure to heatmap.

Added.

Supplemental Fig 1.: Add label to unit of measure in caption or figure. Average or median beta value over a series of CpGs?

Added. Each row represents a single CpG beta value.

The authors state the targeted assay "allows for a more accurate and sensitive detection of cfDNA from a given source", which should be tempered unless clear evidence is presented for these statements. In addition, it targets only a small subset of all cell types. The highest cell type contribution from MK cells is only represented by 2 markers

We now discuss this in more detail and with caution. Indeed targeted assays may not be more accurate given the use of few markers, but we do believe they are at least theoretically more sensitive given the use of PCR and deep sequencing.

Targeted assay has a few caveats that the authors should mention or fix:

The method is not described in detail.

More details are now provided, including multiplex PCR method and a reference to the script used for interpreting sequence data.

Methods besides WGBS can have biases in methylation representation and a beta correlation between the 12 samples that underwent WGBS and the targeted assay would be reassuring.

We have added a graph (new __Supplementary Figure S3) showing a good correlation of Covid-19 WGBS data and targeted analysis of the same samples.__

The level of precision at the lower end of cellular contribution would be helpful too. The lung epithelial and cardiomyocyte cells were present at the lower end of the spectrum. This can be shown in a titration of the purified cells into plasma, or at least an in silico titration analyzed with only the targeted markers.

Thank you. The targeted methylation assay is capable of detecting ~0.1% contribution of DNA from a given source, or 1-5 genome equivalents from this source. This is true also for our lung and cardiomyocyte markers, as previously shown (PMID 35450968, 29691397).

The authors state "(i) Evidence of frequent cardiomyocyte death in hospitalized patients... it has not been appreciated that cardiac cell death is a feature shared by most hospitalized patients." However, COVID-19 patients have elevated troponin.

Thank you. Evidence for troponin elevation was indeed reported in some, but not most of the hospitalized patients (see PMID: 32652195, 33121710, 32219356, 32211816). Note that troponin is not a definitive evidence of cardiac cell death (e.g. the significance of elevated troponin after a marathon or in patients with kidney disease is not clear). This provides a justification for the use of cfDNA for this purpose, as we have shown previously (PMID: 37290439). This is clarified in the revised Discussion.

The authors state "This signal presumably reflects elevated turnover of red blood cells and increased rate of erythropoiesis". However, could it be also higher nucleated RBCs released into circulation as the authors cited?

Thank you. Both of these possibilities are valid, and are not mutually exclusive. Elevated NRBC was reported in severe COVID-19, and is strongly associated with higher erythropoiesis. This is clarified in the revised Discussion.

Fig 2, 4, 5: The graphs seem to suggest that the authors picked 0.001 GE/mL as not detected. Should they label that point appropriately as "not detected" or "ND"? It is not clear why 0.001 GE/mL was picked, and the analytical sensitivity of the targeted test is not reported.

Right, this was due to the non-zero limit of log graphs. We explain this in the text.

How many mLs of plasma were used?

We have now added to supplemental tables the amount of plasma that was used for each patient.

__Reviewer #1 (Significance (Required))____: __ - General assessment: Strengths - 1) Interesting topic: Non-invasive tabulation using deep methylation sequencing of cell type shedding into circulation of an important disease (COVID-19). 2) Deep sequencing using methylation and histone output is a significant improvement on past studies. Although targeting limits the scope of the cell types, the targeting was based on relatively deep WGBS sequencing on 6 cases and 6 controls.

Limitations - The unique aspects (targeted assay and deep sequencing) are missing data and detailed methodology for reanalysis and reproducibility. See major comment 2.

• Advance: The authors used deep sequencing through brute force (WGBS) and a unique targeted assay to study COVID-19 from a large group (n=120 patients). They found that endothelial and erythroblast lineages are overrepresented based on the presence and severity of the COVID-19 infection. Their findings are significant and go beyond what has been published. The methodologies and data (i.e. the controls) would be a great resource to the community that can be used beyond the scope of COVID-19.

• Audience: This article would be appealing to a broad, translational/clinical audience. The authors have published on methylation deconvolution several times before, but to my knowledge, the broader targeted assay is unique and there is a large dataset with correlated clinical information that may be of broad utility.

• Reviewer expertise: technical expertise with circulating cell-free DNA. translational/clinical expertise.

__Reviewer #2 (Evidence, reproducibility and clarity (Required))____: __ they performed deep WGBS on severe COVID-19 and HC plasma samples, applied the novel UXM algorithm that includes 40 human cell types to identify the tissue origins of cfDNA, and showed increased cfDNA from diverse cell and tissue types in COVID-19 patients than healthy controls. Besides WGBS, they also performed targeted methylation assay to measure cellular turnovers/death and tissue injury from major cell and tissue types involved in COVID-19 pathogenesis and used as a predictor of poor outcome. Finally, they showed that cfChIP-seq can identify heightened immune responses associated with COVID-19 and asymptomatic patients. Previous studies have shown that cfDNA has a great potential to map tissue injuries in COVID-19 and predict patient outcomes (Cheng et al., 2021 & Andargie et al., 2021). The expanded reference methylation atlas and the addition of targeted methylation assay and cfCHIP-seq in this study are very informative and fascinating. Please allow me to congratulate Ben-Ami and colleagues for this wonderful work.

Thank you for this encouraging feedback.

Below are some points that need to be addressed to improve the manuscript: Major 1. Given the heterogeneous nature of COVID-19 clinical manifestation, the limited number of patients (n=6) raises concern about the significance of WGBS analysis. The authors need to provide further details as to why they performed WGBS only from 6 samples out of 120 subjects and what was the selection criteria

Study design was impacted by resource limitations. We were able to perform deep WGBS only on a small number of samples, so have used this as a guide to the general nature of tissue turnover in COVID-19 patients, and later used a narrower, highly sensitive, more affordable and more broadly available targeted assay. This is clarified in the revised text (Discussion, section on limitations of study).

The gene expression analysis with cfCHIp-seq is interesting. Likewise, Differentially Methylated Regions (DMR) can infer gene expression. Is the methylation analysis also showing increased interferon response in COVID-19 patients? This study also showed increased cfDNA from monocytes that is not reflected in blood cell counts. Does cfCHIP-seq identify inflammatory response-related genes in monocytes/macrophages? Hadjadj et al. 2020 (PMID: 32661059: Science) reported impaired interferon response in severe COVID-19 patients. Whereas this study showed heightened interferon response in severe and asymptomatic/mild COVID-19 patients compared to healthy controls, there was no difference between Mild and Severe COVID-19 patients. The author should consider validating their finding with plasma cytokine measurement. cfChip-seq also identifies cfDNA tissues-of-origin (PMID: 33432199). How is the correlation between these three assays (WGBS, targeted methylation assay and cfCHIP-seq) to detect cell death/turnover?

• Thank you for these comments. While cfChip does indeed reflect gene expression patterns in the cells that released cfDNA, cfDNA methylation patterns are indicative of cell identity (i.e. tissue of origin) but not dynamic gene expression (PMID: 30100054). __Unfortunately, current cfChip technology while revealing gene expression patterns in the cells that released cfChromatin, does not inform which cell types have expressed these genes (e.g. monocytes or T cells). Thus we can state that the cells releasing cfDNA expressed interferon stimulated genes, but we cannot say which cells were expressing these genes. __

We were unable to perform additional measurements e.g. cytokines since our blood samples are almost entirely depleted.

With regards to the tissue origins of cfDNA: as shown in the paper, there is a general good agreement between WGBS and the targeted assay. In the revised version we show a good correlation between findings in specific samples that were subject to both WGBS and the targeted assay (Supplemental Figure S3). In our hands the sensitivity and specificity of cfChip-seq for detecting tissue origins of cfDNA are lower than cfDNA methylation, hence we elected to use the cfChip information only for inference of gene expression.

It is unclear whether hospitalized COVID-19 subjects experienced particular organ involvement. It would be interesting to link the tissue-specific cfDNA to different COVID-19 endotypes. For instance, cardiac involvement and cardiomyocyte cfDNA.

Indeed, linking tissue-specific cfDNA to clinical phenotype has been challenging. Elevated lung cfDNA is correlated with disease severity (which is well established to be associated with pulmonary damage). We were unable to link elevated cardiac cfDNA to a clinical cardiac phenotype, also because of the limited cardiac assays that were performed on the hospitalized patients e.g troponin and cardiac eco.

Previous studies reported cfDNA concentration in healthy controls ranges between 3 and 15 ng/mL. This study's median cfDNA level for asymptomatic COVID-19 patients falls within that range. It would be interesting if the authors comment on the methodology differences, including plasma volume, correction for extraction efficiency, and cfDNA assay type.

Indeed, asymptomatic patients had a mild, though highly statistically significant elevation in total cfDNA concentration relative to controls, as shown in Figure 5. Samples of asymptomatic patients and controls were obtained and processed identically using the Qiasymphony liquid handling robot. This is described in the revised methods. Plasma volume collected for each sample is now shown in Supp Tables S1-4.

Were the asymptomatic/Mild case samples collected in the same time frame as Hospitalized patients? It would be interesting if the authors comment on the effect of SARSCOV-2 variants and viral loads on plasma cfDNA level.

Yes, all collected at the same period (May-October 2020). This is stated in the revised methods. Unfortunately we do not have information on specific variants on viral loads.

The author showed cfDNA from total T cells and CD8 cells in particular. The authors should comment on why CD4+ T was not shown instead of T cells (which includes both CD4 and CD8 cells).

Unfortunately our current methylome atlas does not allow for identification of specific methylation markers for CD4+ cells (PMID: 34842142).

Considering the expensive nature of deep sequencing, it would be interesting if the authors comment on applying the UXM algorithm for low and medium- and low-coverage sequenced samples.

The algorithm applies to WGBS samples regardless of depth, obviously with reduced performance in low coverage sequencing. Formal analysis of performance on multiple WGBS samples is ongoing.

Minor 1. The timing of blood sample collection from hospital admission or testing positive for COVID-19 is important to use cfDNA as a predictor of outcome. The authors should explain when the sample was collected for asymptomatic/mild patients. If it's not in the "acute phase" it should also be clarified for comparison with hospitalized COVID-19.

We have now added the time of sampling – typically a week or two after diagnosis (Supplemental Table S2).

Is there a reason the authors included repeated measures of cfDNA within the same subject (N=120, n-142; Figure 1A)? The author should consider statistical correction for repeated measures. This is important to reduce bias.

Thank you, we have now reanalyzed the data including only one sample for each patient. The results are largely the same as the original analysis (for reviewer eyes only).

I believe the authors forgot to include "Code and data availability" declaration. I encourage the authors to make publicly available the WGBS data and deconvolution algorithm for reproducibility purposes.

WGBS data files are currently being uploaded to GEO and are waiting for an accession number.

Figure 1D should show individual data points to see the pattern of tissue-specific cfDNA better, especially as COVID-19 shows heterogeneous clinical presentation. Please consider overlaying the data point on the histogram.

Thank you, we have changed the graph to show each datapoint.

Methods - Page 27, the first sentences from the last paragraph, please include the unit

Thank you, we have changed the paragraph.

after the number "75".... In fact, this paragraph is identical to the previous paper (PMID: 33432199); please consider paraphrasing the section.

Done.

Please clearly define "deteriorated." What WHO score or range is considered as deteriorated?

Deteriorated patients were defined as [maximal WHO score post sample] – [WHO score at sampling day] > 0. This is now clarified in the revised results section.

The authors mix between 40 and 37 reference cell types. Please be consistent.

Thank you. Done.

Page 6, line 3, please replace erythrocyte with erythroblast.

Done.

Page 28, line 10, please replace COVID with COVID-19.

Done.

Figure 5D needs a key for recovered versus deteriorated.

Done (figure 4D).

Figure 5, legend title, please fix the number of healthy controls.... (n=30-45).

Done.

__Reviewer #2 (Significance (Required))____: __ This manuscript used a deep WGBS approach with an expanded human cell-type methylation atlas and novel deconvolution algorithm, targeted methylation assay (which makes the cfDNA test easy to use in a clinical lab setting) and cfChIP-seq on plasma cfDNA based on epigenetic markers to identify specific cellular/organ involved in COVID-19 pathogenesis and identify potential mechanistic insights associated with heightened inflammatory response. Compared to the previous study, the limited sample size raises concerns about the significance of whole-genome bisulfite sequencing data in COVID-19 patients. Additionally, whether the tissue-specific cfDNA tracks specific COVID-19-associated endotypes has yet to be discussed. Taken together, this cfDNA may help to understand COVID-19 pathogenesis and define tissue or organ injuries.

My expertise is in Genomics and Immunology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

In the manuscript entitled "Epigenetic liquid biopsies reveal elevated vascular endothelial cell turnover and erythropoiesis in asymptomatic COVID-19 patients," Ben-Ami and colleagues perform WGBS, targeted methylation assay and cfChIP-seq to measure cellular turnover/death or tissue injuries and infer gene expression profile in COVID-19 patients and healthy controls. First, they performed deep WGBS on severe COVID-19 and HC plasma samples, applied the novel UXM algorithm that includes 40 human cell types to identify the tissue origins of cfDNA, and showed increased cfDNA from diverse cell and tissue types in COVID-19 patients than healthy controls. Besides WGBS, they also performed targeted methylation assay to measure cellular turnovers/death and tissue injury from major cell and tissue types involved in COVID-19 pathogenesis and used as a predictor of poor outcome. Finally, they showed that cfChIP-seq can identify heightened immune responses associated with COVID-19 and asymptomatic patients. Previous studies have shown that cfDNA has a great potential to map tissue injuries in COVID-19 and predict patient outcomes (Cheng et al., 2021 & Andargie et al., 2021). The expanded reference methylation atlas and the addition of targeted methylation assay and cfCHIP-seq in this study are very informative and fascinating. Please allow me to congratulate Ben-Ami and colleagues for this wonderful work.

Below are some points that need to be addressed to improve the manuscript:

Major

1. Given the heterogeneous nature of COVID-19 clinical manifestation, the limited number of patients (n=6) raises concern about the significance of WGBS analysis. The authors need to provide further details as to why they performed WGBS only from 6 samples out of 120 subjects and what was the selection criteria.
2. The gene expression analysis with cfCHIp-seq is interesting. Likewise, Differentially Methylated Regions (DMR) can infer gene expression. Is the methylation analysis also showing increased interferon response in COVID-19 patients? This study also showed increased cfDNA from monocytes that is not reflected in blood cell counts. Does cfCHIP-seq identify inflammatory response-related genes in monocytes/macrophages? Hadjadj et al. 2020 (PMID: 32661059: Science) reported impaired interferon response in severe COVID-19 patients. Whereas this study showed heightened interferon response in severe and asymptomatic/mild COVID-19 patients compared to healthy controls, there was no difference between Mild and Severe COVID-19 patients. The author should consider validating their finding with plasma cytokine measurement. cfChip-seq also identifies cfDNA tissues-of-origin (PMID: 33432199). How is the correlation between these three assays (WGBS, targeted methylation assay and cfCHIP-seq) to detect cell death/turnover?
3. It is unclear whether hospitalized COVID-19 subjects experienced particular organ involvement. It would be interesting to link the tissue-specific cfDNA to different COVID-19 endotypes. For instance, cardiac involvement and cardiomyocyte cfDNA.
4. Previous studies reported cfDNA concentration in healthy controls ranges between 3 and 15 ng/mL. This study's median cfDNA level for asymptomatic COVID-19 patients falls within that range. It would be interesting if the authors comment on the methodology differences, including plasma volume, correction for extraction efficiency, and cfDNA assay type.
5. Were the asymptomatic/Mild case samples collected in the same time frame as Hospitalized patients? It would be interesting if the authors comment on the effect of SARSCOV-2 variants and viral loads on plasma cfDNA level.
6. The author showed cfDNA from total T cells and CD8 cells in particular. The authors should comment on why CD4+ T was not shown instead of T cells (which includes both CD4 and CD8 cells).
7. Considering the expensive nature of deep sequencing, it would be interesting if the authors comment on applying the UXM algorithm for low and medium- and low-coverage sequenced samples.

Minor

1. The timing of blood sample collection from hospital admission or testing positive for COVID-19 is important to use cfDNA as a predictor of outcome. The authors should explain when the sample was collected for asymptomatic/mild patients. If it's not in the "acute phase, " it should also be clarified for comparison with hospitalized COVID-19.
2. Is there a reason the authors included repeated measures of cfDNA within the same subject (N=120, n-142; Figure 1A)? The author should consider statistical correction for repeated measures. This is important to reduce bias.
3. I believe the authors forgot to include "Code and data availability" declaration. I encourage the authors to make publicly available the WGBS data and deconvolution algorithm for reproducibility purposes.
4. Figure 1D should show individual data points to see the pattern of tissue-specific cfDNA better, especially as COVID-19 shows heterogeneous clinical presentation. Please consider overlaying the data point on the histogram.
5. Methods - Page 27, the first sentences from the last paragraph, please include the unit.
6. after the number "75".... In fact, this paragraph is identical to the previous paper (PMID: 33432199); please consider paraphrasing the section.
7. Please clearly define "deteriorated." What WHO score or range is considered as deteriorated?
8. The authors mix between 40 and 37 reference cell types. Please be consistent.
9. Page 6, line 3, please replace erythrocyte with erythroblast.
10. Page 28, line 10, please replace COVID with COVID-19.
11. Figure 5D needs a key for recovered versus deteriorated.
12. Figure 5, legend title, please fix the number of healthy controls.... (n=30-45).

Significance

This manuscript used a deep WGBS approach with an expanded human cell-type methylation atlas and novel deconvolution algorithm, targeted methylation assay (which makes the cfDNA test easy to use in a clinical lab setting) and cfChIP-seq on plasma cfDNA based on epigenetic markers to identify specific cellular/organ involved in COVID-19 pathogenesis and identify potential mechanistic insights associated with heightened inflammatory response. Compared to the previous study, the limited sample size raises concerns about the significance of whole-genome bisulfite sequencing data in COVID-19 patients. Additionally, whether the tissue-specific cfDNA tracks specific COVID-19-associated endotypes has yet to be discussed. Taken together, this cfDNA may help to understand COVID-19 pathogenesis and define tissue or organ injuries.

My expertise is in Genomics and Immunology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary:

The authors analyzed circulating cell-free DNA for COVID-19 using deep sequencing of the methylation and histone modification. The major output was cell-specific quantification. The study involved 120 unvaccinated, hospitalized patients, 19 asymptomatic/mild cases, and 40 controls. Between COVID-19 and controls, they found significant differences in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts. The latter two cell types had significant differences even in the asymptomatic patients. It is unclear if the damage seen is related to COVID-19 specifically, or related to general inflammation or infection.

Strengths of the study include relatively high WGBS/targeted sequencing, along with fragment-level analysis with methods described in their previous work (Loyfer et al. Nature). In addition, they add and ChIP-seq data using their published methods. The work comes from a group with leading expertise in methylation cell-free DNA analysis.

Overall, the work is most comprehensive analysis to date for COVID-19, and the data would be a valuable resource to the research community. We have major and minor comments that do not necessarily require additional experimental work.

Major comments:

1. There is a lack of data and the methods are presented in such a way that the results and conclusion can be reproduced and evaluated. Neither the code nor the data to generate the results are available. Both need to be made available during the peer review process.

Missing data: Fragment-level FASTQ, BAM, or PAT files are needed to reproduce the results. Missing Scripts, for example in Github, is standard and reasonable for reproducing the figures shown. Missing targeted assay method details: - The authors should show the data, methods, and details for: "The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed."

1. The authors did not list the major limitations of the study in the discussion or elsewhere. These should include (or be addressed with experimental or conclusion changes):
   1. The small sample size of the asymptomatic/mild group (if the emphasis of the paper, as suggested by the title, is on the asymptomatic/mild group - see the next major point).
   2. The targeted assay is used on the vast majority of samples, including all of the asymptomatic/mild group. However, it is limited to a particular subset of cell types (total defined by all possible cell types in the body). Those cell types were determined based on WGBS data on only 6 COVID-19 cases.
   3. The methylation references for the WGBS data were limited to a fraction of all human cell types. For example, this paper was not able to evaluate Schwann cells or peripheral nerves, which was a significant finding for COVID-19 related multisystem inflammatory syndrome (PMID 37279751).
   4. The case and control groups (severe, asymptomatic mild, and control) were collected at different times and circumstances, allowing for potential pre-analytical confounders.
   5. cfDNA levels can be influenced by several unmeasured factors, including death, replication leading to more turnover, clearance/stability, and movement from tissue into circulation. The methods used cannot distinguish between these possibilities.
   6. (if true) the controls used for the targeted assay were not age/sex matched. The median age for the controls skew younger per Table S1, S2, S3.
   7. (optional) It is unclear whether the differences found are attributable to COVID-19, coronavirus infection, viral infections, infections in general, or inflammation in general. The appropriate alternative controls were not addressed in this study. The paper shows some degree of correlation with acute inflammatory markers (CRP, ferritin, neutrophil contribution).
2. The title is a bit misleading in that it revolves around the asymptotic patients. However, this is also the group with the lowest representation at n=19. The vast majority of the data is related to the hospitalized patients. While other studies may have looked at hospitalized patients, I agree with the authors that there is merit in deep sequencing and the correlated clinical data.
3. More details on the patient inclusion criteria are needed. Were the asymptomatic/mild positive by PCR test or a point of care immunoassay? We know the viral load is quite dynamic for these patients. What was the timing of the blood draw?

Likewise, how did you find the hospitalized patients? Was it comprehensive over a period of time? These details help reveal any potential biases in the selection process.

Minor comments:

1. The abstract states: "Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage." However, in Fig 5, there seems to be a bimodal distribution for the lung epithelial and cardiomyocytes. Unclear if that is an artifact of the graph.

It is quite interesting that the asymptomatic/mild group seems to have a bimodal distribution in lung epithelial and cardiomyocyte cfDNA. Perhaps this data is not available, and the sample size is small, but could there have been a clinical difference between the two groups (e.g. asymptomatic versus mild, or had symptoms later?). It is unclear how precise the measurements are for the lung epithelial cells. 2. The authors listed 2 prior studies that looked at cell type or tissue damage during COVID-19. There are 2 other studies that I am aware of: PMID: 33651717 (n=84 with n=18 nonhospitalized) but probably shallow WGBS, and 37279751 (n=205 pediatric patients). Importantly, the latter paper found Schwann cells were significantly elevated, which is missing from the current study's assessment. In addition, citation 14 from the same group already found significantly increased vascular endothelial cfDNA in COVID-19 patients with severe disease versus mild. While some findings are consistent, there are also discrepancies. 3. Is Fig 2 necessary? Fig. 5 seems to display the same data but with the asymptomatic group. 4. "Elevated lung cfDNA reflects excessive lung cell death" - recommend this statement is tempered as direct evidence is not available in this study. An alternative explanation could be that endothelial cells are damaged, and it is easier for lung cfDNA to enter blood circulation rather than the respiratory system. 5. Fig 6: Add unit of measure to heatmap. Supplemental Fig 1.: Add label to unit of measure in caption or figure. Average or median beta value over a series of CpGs? 6. The authors state the targeted assay "allows for a more accurate and sensitive detection of cfDNA from a given source", which should be tempered unless clear evidence is presented for these statements. In addition, it targets only a small subset of all cell types. The highest cell type contribution from MK cells is only represented by 2 markers. 7. Targeted assay has a few caveats that the authors should mention or fix: The method is not described in detail. Methods besides WGBS can have biases in methylation representation and a beta correlation between the 12 samples that underwent WGBS and the targeted assay would be reassuring. The level of precision at the lower end of cellular contribution would be helpful too. The lung epithelial and cardiomyocyte cells were present at the lower end of the spectrum. This can be shown in a titration of the purified cells into plasma, or at least an in silico titration analyzed with only the targeted markers. 8. The authors state "(i) Evidence of frequent cardiomyocyte death in hospitalized patients... it has not been appreciated that cardiac cell death is a feature shared by most hospitalized patients." However, COVID-19 patients have elevated troponin.<br /> 9. The authors state "This signal presumably reflects elevated turnover of red blood cells and increased rate of erythropoiesis". However, could it be also higher nucleated RBCs released into circulation as the authors cited? 10. Fig 2, 4, 5: The graphs seem to suggest that the authors picked 0.001 GE/mL as not detected. Should they label that point appropriately as "not detected" or "ND"? It is not clear why 0.001 GE/mL was picked, and the analytical sensitivity of the targeted test is not reported. 11. How many mLs of plasma were used?

Significance

General assessment:

Strengths 1. Interesting topic: Non-invasive tabulation using deep methylation sequencing of cell type shedding into circulation of an important disease (COVID-19). 2. Deep sequencing using methylation and histone output is a significant improvement on past studies. Although targeting limits the scope of the cell types, the targeting was based on relatively deep WGBS sequencing on 6 cases and 6 controls.

Limitations The unique aspects (targeted assay and deep sequencing) are missing data and detailed methodology for reanalysis and reproducibility. See major comment 2.

Advance: The authors used deep sequencing through brute force (WGBS) and a unique targeted assay to study COVID-19 from a large group (n=120 patients). They found that endothelial and erythroblast lineages are overrepresented based on the presence and severity of the COVID-19 infection. Their findings are significant and go beyond what has been published. The methodologies and data (i.e. the controls) would be a great resource to the community that can be used beyond the scope of COVID-19.

Audience: This article would be appealing to a broad, translational/clinical audience. The authors have published on methylation deconvolution several times before, but to my knowledge, the broader targeted assay is unique and there is a large dataset with correlated clinical information that may be of broad utility.

Reviewer expertise: technical expertise with circulating cell-free DNA. translational/clinical expertise.

I find this statement crazy how racial and cultural identity in jazz have traditionally been framed within a simplistic Black-and-white binary, which eliminate the contributions of other racial and ethnic groups, such as European, Indigenous, and Asian musicians, who have played significant roles in its development. I just feel like people should enjoy listening to jazz music and stop racially classify it.

Morton’s insistence on his European heritage and his claim to being white shows the struggle for social mobility with the Creole society. By trying to involve himself with European, "respectable" identities, Morton is contrasting with Bechet, who had a more flexible and uncertain view of race. This shows the struggles of how jazz musicians in that era had to navigate and often balance complex racial and cultural identities

HIs experience with jazz, was not just about skill but was wrapped in moral judgments. His father’s physical punishment and threats demonstrate how deeply disapproved the profession was. Jazz was considered "dirty"and set the standard.

Morton’s racial prejudice wasn’t unusual but was accepted to Creoles. Why was this seen as normal?

Trying to popularize jazz, especially by white people misrepresents its history

What were social problems that contributed to jazz history?

He was already earning a good wage at thirteen. This shows that music could be a profitable career, even for young musicians. But some still looked down on it so, it wasn’t about the money, but rather the social aspects attached.

The early jazz years saw very few professional musicians in New Orleans, only seven Black musicians were known. This shows the challenges faced by those pursuing music as a profession in a society where music wasn’t yet seen as a legitimate career path, especially among the Black community

The music in New Orleans was very alive, there were all of these community events. It wasn’t just about performances but about a culture of music. I found it interesting how musicians were developing their skills mostly in these public settings

It is interesting to see how this dynamic has existed, even as far back as Sidney Bechet’s time. Even while heavily criticizing African Americans as a whole, Bechet had no issue letting musical techniques from Uptown influence his own music. Instances like these in modern times have occurred with other music genres, like rap (another genre with roots from Africa).

How would I interpret this statement in the musical evolution in New Orleans?

Creoles attempts to distinguish themselves from african americans were so strong that they adopted attitudes that were like off of white supremacy. This basically meant the problem between creoles and african americans were like blacks and whites.

The influx of Anglo-Americans led to stricter enforcement of racial classifications, which impacted Creoles' social standing regardless of their cultural identity or background

Code Noir helped form the Creole community in New Orleans by encouraging racial mixing

How might their Creole background have shaped the way they viewed jazz and its cultural roots?

I found it interesting that Jazz is not just a kind of music but also a way for black people to express their culture, identity, and experiences.

What does the author mean by 'genetic' here?

The division between two things marked as completely different.

Voici un sommaire minuté basé sur la transcription de la conférence sur le conflit sévère de séparation :

• 0:04-0:17: Introduction et remerciements par l'animateur de l'événement. Il remercie Michel Rabitail et Vanessa Lete pour l'organisation et l'animation.
• 0:17-0:55: L'animateur rappelle qu'il s'agit de la deuxième conférence sur les conflits sévères de séparation en peu de temps et que ce sujet est devenu une priorité au Québec, notamment parce que c'était le seul motif de signalement à la hausse pendant la pandémie. Il souligne que la plupart des professionnels n'ont pas reçu de formation spécifique sur les approches familiales et systémiques.
• 1:55-2:47 L'animateur explique l'objectif de cette journée scientifique et clinique. Il mentionne les distinctions entre le conflit sévère de séparation, l'aliénation parentale, et la violence conjugale. Il annonce une présentation de projets appliqués et des approches basées sur des données probantes comme la médiation, les approches systémiques et les approches sensibles aux traumatismes.
• 2:54-4:54: Introduction des trois conférencières : Elisabeth Godbout, professeure spécialiste des transitions familiales, des séparations et des conflits sévères de séparation ; Amylie Paquin Boudreau, psychologue clinicienne spécialiste des enjeux de conflits sévères de séparation et de violence familiale ; et Catherine Turbide, professeure spécialiste des conflits sévères de séparation, de violence conjugale post-séparation et des trajectoires de service en protection de la jeunesse.
• 4:54-5:29: L'animateur souligne l'expertise des conférencières et l'importance de cette thématique.
• 5:29-6:03: Elisabeth Godbout prend la parole et remercie l'Institut Universitaire jeunes en difficulté pour l'invitation. Elle souligne que le conflit de séparation est un sujet de prédilection dans leurs travaux.
• 6:53-7:25 : Présentation du plan de la conférence pour les 3 heures à venir..
• 7:25-8:38: Introduction au sujet des conflits sévères de séparation, problématique apparue il y a une trentaine d'années, d'abord identifiée par les professionnels de la justice et des services psychosociaux. Le premier article sur le sujet est celui de Janet Johnston en 1994. La problématique a pris de l'ampleur dans les années 2000, avec une augmentation des écrits scientifiques et des dossiers judiciaires. Il est noté que cette problématique représente un défi pour l'intervention.
• 9:45-10:09: Il est souligné que la distinction entre conflit sévère de séparation, violence conjugale et contrôle coercitif est primordiale.
• 10:09-11:17: Présentation des objectifs de la journée : faire le point sur les connaissances, présenter les services existants au Québec, et aborder les bases d'une évaluation appropriée.
• 11:17-12:09 : Présentation d'un programme et d'une intervention en soulignant l'importance des approches pour comprendre la complexité du problème.
• 12:09-12:42: Définition du conflit sévère de séparation : un terme "parapluie" qui englobe plusieurs problématiques de nature et de sévérité différentes. Il se manifeste par une hostilité importante entre les parents, rendant la communication difficile, voire inexistante.
• 13:54-14:06: Le conflit sévère de séparation inclut des signalements répétés à la protection de la jeunesse, des recours fréquents aux tribunaux, parfois malintentionnés.
• 14:06-15:34: Les conflits sévères de séparation sont des dossiers qui encombrent le système et consomment beaucoup de ressources. Les enfants sont affectés par l'exposition à des conflits répétés et intenses.
• 15:34-17:31: La distinction entre les conflits de séparation et la violence conjugale est complexe : il est nécessaire de distinguer les manifestations de violence liées au conflit de celles liées à un déséquilibre de pouvoir. Il est important de souligner que la violence dans le contexte du conflit peut être sporadique ou situationnelle, alors que dans la violence conjugale il y a un déséquilibre de pouvoir.
• 17:31-18:49: Le conflit sévère de séparation est souvent chronique et enlisée dans le temps, les parents restent en colère comme au premier jour de leur séparation. Une distinction est faite entre les conflits normaux lors d'une séparation et les situations qui perdurent. La durée des conflits dans le temps reste mal connue.
• 18:49-20:27: Les différentes dimensions du conflit sont présentées, notamment la judicialisation importante des litiges. Les conflits judiciaires sont à distinguer des difficultés parentales. Présentation d'une étude avec un échantillon de parents québécois récemment séparés.
• 20:27-21:24: Les analyses ont permis de dégager un sous-groupe de parents vivant des conflits élevés au plan judiciaire et des difficultés parentales. Il s'agit d'une zone "très préoccupante" de 13% des parents.
• 22:00-24:03: Un tiers des parents de ce sous-groupe ont eu une présentation en cour, 30% ont eu recours à une expertise en matière de garde. Les dimensions du conflit sont enchevêtrées. Il y a une persistance dans le temps pour un sous-groupe de ce 13%.
• 24:03-25:08: Les données des dossiers judiciaires montrent qu'une minorité de parents va à procès, la majorité règle les questions entourant leur séparation durant les procédures.
• 25:08-27:45 : Les données de la protection de la jeunesse montrent qu'une minorité de dossiers comportent un litige actif en matière de garde d'enfants. Ces dossiers sont plus souvent réouverts avec une détérioration du fonctionnement des enfants. Au Québec, 15,7 % des évaluations faites par les services de protection de la jeunesse ont été faites en raison de mauvais traitements psychologiques, dont 14,8% comportaient des conflits de séparation. L'exposition à la violence conjugale demeure le motif le plus souvent signalé, suivi des conflits de séparation.
• 27:45-28:46: Présentation d'un panorama des services au Québec pour traiter ou prévenir le conflit sévère de séparation.
• 28:46-30:06: Les services sont divisés en différents types et niveaux, allant de la prévention à l'intervention spécialisée. Les services de première ligne sont peu documentés et il manque d'outils.
• 30:06-31:20: Présentation de la séance d'information de groupe sur la parentalité après la rupture, un service universel mais surtout utilisé par les parents en litige. Ce service n'a pas été évalué..
• 31:20-32:54 : La médiation familiale est un service très utilisé et apprécié. Par contre, son efficacité à prévenir la détérioration des conflits reste à être étudiée.
• 32:54-33:51 : Il existe des services juridiques de prévention et de règlement des différends, offerts en pratique privée. Leur efficacité à prévenir les conflits n'a pas été examinée.
• 33:51-34:53 : Des programmes de groupes plus ciblés sont offerts par des organismes communautaires et par le CUS centre sud de l'île de Montréal. Un silence est fait sur l'action du milieu communautaire qui est jugée très importante, mais non nommée.
• 34:53-36:03: Les services de première ligne sont peu documentés. Il y a un manque d'outils pour les professionnels pour intervenir dans ce type de problématique. Plus on augmente en sévérité, plus les interventions sont contraignantes. On utilise le judiciaire comme levier d'intervention.
• 36:03-37:04 : Les services indiqués pour les conflits chroniques incluent l'expertise, des programmes spécialisés, la supervision des échanges de garde et la coordination parentale. Les services les plus spécialisés sont concentrés dans les grands centres.
• 37:04-38:32: Les services offerts par les intervenants psychosociaux en pratique privée se développent. Un programme du ministère de la Justice à Montréal est en cours d'évaluation. Le coaching parental est aussi une intervention privée.
• 38:32-41:08 : Des programmes sociojudiciaires importants tels que le programme parentalité conflit résolution et le programme d'intervention sociojudiciaire en conflit sévère de séparation sont présentés. Ces programmes ont fait l'objet d'évaluations et ont montré un potentiel intéressant.
• 41:08-42:02: L'offre de services est morcelée, manque d'intégration et ne traite pas le conflit sévère de séparation comme un problème de santé publique. Une réelle prévention des conflits de séparation devrait être le défi des prochaines années.
• 42:02-43:31 : Il faut rehausser les pratiques et les évaluations et s'appuyer sur les programmes dont l'efficacité a été démontrée. Il manque de données sur l'efficacité des services les plus accessibles, tandis qu'il y a plus de données sur les services spécialisés. Les services les plus spécialisés sont souvent offerts par des praticiens en pratique privée.
• 43:31-44:02: Les conférencières souhaitent savoir ce qui amène les participants à suivre cette formation.
• 44:02-44:44 : Les conférencières demandent aux participants de se situer dans l'écosystème des services.
• 44:44-45:05 : Transition vers une séquence sur l'évaluation qui sera présentée par Catherine Turbide.
• 45:05-47:53: Avant l'évaluation, on doit réfléchir aux questions que l'on doit se poser en tant qu'intervenant. Il existe des biais documentés sur ces questions, notamment, la question de savoir si un conflit est toujours un "tango" qui se danse à deux.
• 47:53-49:03: On constate souvent que l'on croit que les parents utilisent les services pour se venger l'un de l'autre. Or, les écrits montrent que c'est beaucoup plus complexe. Une étude a montré que seulement 13 % des allégations sont malicieuses.
• 49:03-51:07: Ce n'est pas notre prémisse de base, mais il faut y être attentif. Il y a plusieurs raisons qui expliquent l'utilisation des services, comme une mauvaise gestion des émotions ou un manque de moyens.
• 51:07-51:19: Amélie Paquin prend la parole et souligne l'importance de prendre le temps de faire une bonne évaluation avant d'intervenir.
• 51:19-52:16 : L'évaluation permet d'élaborer un plan d'intervention ajusté aux besoins des familles, à leurs ressources et aux limites de la pratique. Les situations de conflits sévères de séparation sont variées..
• 52:16-53:00: La plupart des services actuels sont fondés sur le principe de coparentalité, un idéal qui n'est pas toujours réaliste. Par principe de coparentalité on entend : la prise de décisions négociées, la communication fonctionnelle et la coopération.
• 53:00-57:37: La littérature scientifique soulève un problème avec cet idéal de coparentalité. Pour certaines familles, l'intervention familiale n'est pas appropriée, notamment en présence de violence conjugale. Il faut être attentif quand on travaille une rupture de lien parent enfant.
• 57:37-59:10 : Il faut prendre conscience qu'il y a des prérequis pour avoir une coparentalité coopérative. Il y a des parents qui n'ont pas les ressources pour y arriver. Les parents qui sont à l'extrémité du continuum sur le conflit sévère de séparation n'ont pas nécessairement ces ressources.
• 59:10-1:00:47 : Il faut être nuancé dans la présentation de ces prérequis, ce n'est pas un objectif qui est réaliste tout le temps. Il y a un travail de deuil de la relation, un travail de régulation des émotions qui doit être fait avant de vouloir travailler la coparentalité.
• 1:00:47-1:01:24: Présentation d'une figure exclusive, démontrant la réflexion sur l'intégration de l'évaluation et de la séquence d'intervention.
• 1:01:24-1:02:11: Il faut être neutre et objectif dans l'évaluation, et distinguer si l'objectif est de traiter la famille ou de la structurer..
• 1:02:11-1:03:26: L'objectif de structuration vise à mettre des limites, des règles claires et s'assurer de leur respect. L'objectif de traitement vise à apporter des changements profonds dans les interactions familiales. Certains parents vivent un deuil compliqué de la relation, d'autres ont des problèmes psychosociaux complexes ou des antécédents traumatiques.
• 1:03:26-1:05:30: Quand on est dans un objectif de traitement, on a confiance dans les ressources des parents pour faire des changements profonds. Quand on est dans une logique de structuration, on fait face à des parents qui n'ont pas la collaboration ou la motivation pour changer. Il faut être réaliste face à ces familles et partir de leurs ressources.
• 1:05:30-1:05:57: Transition vers la présentation de deux programmes en protection de la jeunesse, par Catherine Turbide.
• 1:05:57-1:06:26 : Les programmes sont déployés en région..
• 1:06:26-1:07:37 : Présentation du projet PCS, initiative du 6 de la Mauricie qui a constaté un besoin de mieux intervenir auprès de ces familles.
• 1:07:37-1:08:16 : Les objectifs du programme sont de connaître, d'évaluer et d'intervenir auprès des familles en conflit de séparation. Il s'agit d'un projet qui vise à donner des outils pour faire l'évaluation du conflit.
• 1:08:16-1:09:39: Le programme comprend une trousse de soutien, des groupes de soutien pour les intervenants et une communauté de pratique.
• 1:09:39-1:10:12: Présentation d'une évaluation du point de vue des intervenants, qui montre que l'utilisation du programme est variable.
• 1:10:12-1:11:59: Les intervenants trouvent que le programme a amélioré leur pratique, mais n'a pas diminué le temps consacré aux dossiers..
• 1:11:59-1:12:31: Il y a eu une amélioration individuelle et collective.
• 1:12:31-1:14:06 : Les capsules de la trousse sont utiles, elles couvrent une vaste gamme de thématiques associées aux conflits de séparation. Elles permettent de mieux comprendre certaines dynamiques..
• 1:14:06-1:15:22 : Le groupe de soutien est la modalité qui fait le plus consensus. Il permet aux intervenants de se sentir soutenus, de concrétiser l'utilisation de la trousse, de démythifier les problématiques, de baisser le stress. Par contre, il n'est pas toujours disponible selon les besoins.
• 1:15:22-1:16:46: La communauté de pratique augmente le sentiment de compétence et permet de bonifier les connaissances, mais peut ajouter une surcharge de travail.
• 1:16:46-1:18:15 : Facteurs qui facilitent l'utilisation des outils : sentiment de spécialisation, cointervention. Les facteurs qui font obstacle : la surcharge de travail, l'instabilité des équipes, le contexte organisationnel.
• 1:18:15-1:19:23: Recommandations : désigner des leaders, favoriser la participation des nouveaux employés, réduire le nombre de dossiers, favoriser la cointervention, prévoir des modalités plus souples..
• 1:19:23-1:21:34: Présentation du programme d'intervention sociojudiciaire en conflit sévère de séparation, qui est un programme de partenariat entre les milieux psychosocial et judiciaire. Il a été mis en place dans 36 établissements et est maintenant déployé dans 10 établissements. Le programme vise à concerter l'intervention de la protection de la jeunesse et le processus judiciaire. Son objectif est d'augmenter le nombre de situations qui se règlent par des mesures consensuelles.
• 1:21:34-1:22:09 : Les familles ciblées sont celles où la sécurité et le développement de l'enfant sont compromis en raison d'un conflit sévère de séparation. Les parents doivent reconnaître la présence du conflit et son impact sur l'enfant.
• 1:22:09-1:22:58: Le programme implique une trajectoire judiciaire adaptée et une intervention psychosociale avec des rencontres hebdomadaires pendant 6 à 9 mois.
• 1:22:58-1:23:54: Présentation d'une évaluation du volet qualitatif du programme, auprès de 30 professionnels.
• 1:23:54-1:24:43: Le programme permet de remettre aux parents le pouvoir de changer leur situation familiale. Il améliore la communication, recentre les parents sur les besoins des enfants et favorise la négociation..
• 1:24:43-1:25:30 : Un enjeu demeure les critères d'admissibilité des familles. Le programme est difficile à mettre en œuvre pour les familles ayant peu de motivation. Un autre défi est le soutien des familles lorsque le programme est terminé.
• 1:25:30-1:26:22: Il y a des difficultés liées à la concertation des interventions cliniques et judiciaires. Une évaluation est en cours pour étudier les effets du programme auprès des familles. Proposition d'une pause de 15 minutes.
• 1:26:22-1:26:41: Amélie Paquin prend le relais pour la présentation des méthodes d'interventions auprès des familles vivant un conflit sévère de séparation..
• 1:26:41-1:27:06 : Survol des groupes éducatifs pour les parents séparés, des interventions individuelles pour les parents et celles pour les enfants. Les interventions sont présentées en fonction de ce que la littérature dit sur qui est orienté vers ses services et qui ne l'est pas.
• 1:27:06-1:28:01: Les groupes éducatifs pour parents séparés sont des interventions de groupes où les parents ont accès à plusieurs rencontres. Il y a beaucoup de travail à faire pour que ces groupes soient réellement efficaces. La forme des groupes est hétérogène. Ils ont surtout une portée préventive ou universelle.
• 1:28:01-1:29:26: Les groupes éducatifs sont souvent offerts à tous les parents séparés, sans tenir compte des problématiques associées au conflit sévère de séparation. Ils ne sont pas toujours adaptés aux enjeux des parents dans cette situation, notamment les enjeux liés à la violence conjugale. Ils peuvent même être problématiques dans certains contextes.
• 1:29:26-1:30:02 : La littérature scientifique propose d'adapter les groupes pour les familles en conflit sévère de séparation. Il y a de nombreuses recherches sur l'efficacité des groupes. La taille des effets est faible, notamment pour les familles avec un haut niveau de conflit. Les effets ne se maintiennent pas dans le temps.
• 1:30:02-1:31:20 : Une réforme des groupes éducatifs est suggérée : se concentrer sur la parentalité plutôt que la coparentalité, accompagner les parents à développer leurs ressources individuelles, leur régulation émotionnelle. D'autres auteurs proposent d'ajouter de l'information sur la violence conjugale et la violence familiale.
• 1:31:20-1:32:32: Il serait intéressant d'intégrer de l'information sur l'influence des traumas relationnels et des contextes d'adversité sur la régulation émotionnelle. Il est important de sortir du focus de la séparation pour comprendre la trajectoire relationnelle des familles.
• 1:32:32-1:33:01: Présentation des interventions individuelles auprès des parents. C'est souvent un préalable nécessaire avant les interventions familiales.
• 1:33:01-1:34:33 : Les séparations peuvent faire émerger des émotions difficiles, des traumas non-intégrés. Il est important de clarifier le mandat lors de ces interventions. Il y a des limites au travail individuel.
• 1:34:33-1:35:51: Il est important de mettre les limites de l'intervention dès le début et de rester neutre face aux informations données sur l'autre parent. Il faut faire attention de ne pas prendre partie dans le conflit tout en amenant les parents à se protéger et à nuancer leurs convictions.
• 1:35:51-1:37:16: Importance de l'approche systémique, de tenir compte des enfants dans la relation. On doit aider le parent à mieux comprendre la position de son enfant. C'est important de garder en tête l'approche sensible au trauma.
• 1:37:16-1:39:07 : Il est primordial de considérer l'impact du contexte d'adversité et des traumas relationnels sur les parents, en plus de la séparation. Il faut être conscient du fonctionnement psychologique des parents, de leurs limites et pronostics d'évolution.
• 1:39:07-1:40:26: Il faut reconnaître que tous les parents n'évolueront pas. Il existe des critères d'inclusion et d'exclusion à l'intervention individuelle. Présentation des interventions individuelles auprès des enfants.
• 1:40:26-1:42:30 : Il faut se questionner sur les bénéfices d'une intervention individuelle pour l'enfant. L'intervention individuelle est appropriée si l'enfant est demandeur ou si le conflit a des impacts importants sur son bien-être. L'intervention individuelle peut aider l'enfant à développer des ressources, à mieux comprendre la situation et à nommer ses besoins et émotions.
• 1:42:30-1:43:00 : L'enfant a besoin d'adultes qui lui permettent de nommer ses émotions et qui puissent valider son expérience..
• 1:43:00-1:43:30 : Il est important de ne pas donner de faux espoirs et d'aider l'enfant à comprendre ses limites, ce qu'il peut en faire..
• 1:43:30-1:44:45: Transition vers la présentation des approches qui peuvent être utiles par Catherine Turbide, notamment l'approche de négociation sur intérêt, ou approche de médiation.
• 1:44:45-1:45:32: Cette approche a été utilisée en protection de la jeunesse autour de 2005, pour amener les intervenants à soutenir les familles dans leur propre démarche de résolution de problèmes.
• 1:45:32-1:47:22: Cette approche a ensuite été déployée dans des contextes variés, notamment en protection de la jeunesse, elle est utile pour comprendre les enjeux du conflit de séparation. Le but est que les participants comprennent les grands principes et sachent dans quoi s'embarquent les parents qui travaillent avec cette approche.
• 1:47:22-1:48:22: L'approche est une structure de résolution de problèmes et de communication coopérative qui implique la présence simultanée des parties. Il faut donc éviter de l'utiliser dans les cas de violence conjugale.
• 1:48:22-1:49:58 : Une distinction doit être faite entre la médiation, un processus volontaire mené par un médiateur impartial, et l'approche de médiation, qui peut être utilisée dans des contextes non-volontaires. L'intervenant peut avoir un mandat et une partialité, mais doit quand même favoriser la résolution de problème de manière collaborative.
• 1:49:58-1:50:37 : L'approche implique une évaluation, l'identification des besoins, le tour de toutes les options, l'évaluation de leur pertinence et l'élaboration d'une entente. La posture de l'intervenant est de soutien et d'accompagnement, et non de prescription de solution.
• 1:50:37-1:51:47: Les principes de l'approche : il faut utiliser un vocabulaire positif, c'est mieux de parler de besoins que de problèmes, chaque personne doit avoir une place pour exprimer ses besoins et être responsabilisée, la confiance et le respect sont au premier plan.
• 1:51:47-1:52:35: Le processus est centré sur les intérêts et non sur les positions. L'objectif est d'amener les parents à se centrer sur leurs besoins. Le processus doit favoriser la communication et la collaboration.
• 1:52:35-1:53:38: Il est préférable que la solution soit choisie par les parties plutôt que d'être imposée. Le processus a pour objectif que les parents soient capables de reproduire les techniques et de les utiliser dans le futur.
• 1:53:38-1:54:37: Les étapes de la négociation : l'accueil, la négociation des intérêts et les résultats. L'accueil est très structuré. Les règles doivent être très explicites.
• 1:54:37-1:56:56 : La négociation des intérêts : décoder les besoins, repérer les besoins communs, prioriser les besoins, identifier les options et en faire un choix. Il faut identifier les besoins de manière précise et respectueuse.
• 1:56:56-1:58:12 : Décoder les besoins est un travail de coconstruction avec les parents. Il faut valider le besoin, et sortir des positions pour aller dans l'intérêt. Il faut donner la possibilité aux personnes d'exprimer leur besoin..
• 1:58:12-2:01:04 : L'outil du tableau est souvent utilisé pour repérer les besoins communs et les prioriser. Il faut faire une liste de solution exhaustive sans se soucier de sa pertinence.
• 2:01:04-2:02:14: Choisir une option qui satisfait toutes les parties, l'entente peut être verbale ou écrite..
• 2:02:14-2:02:31: Transition vers les approches transversales..
• 2:02:31-2:04:29 : L'approche systémique et l'approche sensible aux traumas sont essentielles pour un travail pertinent auprès des familles en conflit sévère de séparation. L'approche systémique : considérer que l'intervention sur un individu peut avoir un impact sur le système. L'intervenant fait partie du système, ses caractéristiques et décisions vont influencer le système.
• 2:04:29-2:06:06: Les professionnels influencent le fonctionnement familial et doivent être conscients de leurs propres caractéristiques, connaissances et biais. Ils doivent être humbles et avoir des réactions conscientes et inconscientes qui peuvent cristalliser le système..
• 2:06:06-2:07:30: Il faut faire attention à l'optimisme excessif et avoir une posture de scepticisme saine. Être attentif aux situations de clivage ou au tout ou rien. Il faut se remettre en question et s'entourer pour réfléchir.
• 2:07:30-2:09:50 : Il faut éviter de tomber dans le piège de l'impuissance, éviter la critique, et ne pas venir reproduire les dynamiques parentales. Les professionnels doivent se considérer comme faisant partie du système plutôt que comme un expert à l'extérieur.
• 2:09:50-2:10:26 : L'approche sensible au trauma est appliquée récemment aux familles en conflit sévère de séparation. Il y a un historique de vie marqué par l'adversité et des traumas émotionnels et relationnels qui sont revivifiés lors de la séparation. La séparation vient déclencher des traumas plus précoces.
• 2:10:26-2:11:54 : Le simple fait de se retrouver dans des procédures judiciaires ou dans une intervention de la DPJ peut être traumatisant. Il faut faire preuve de sensibilité et comprendre le vécu des familles.
• 2:11:54-2:13:25 : Il faut éviter de voir les parents comme étant de mauvaise foi. Il faut voir le conflit comme un signe de souffrance, mettre de l'humanité, et sortir du "qui a raison" pour amener les parents à faire sens de leur vécu..

Author response:

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

Reviewer #1 (Public Review)

Summary:

The authors wanted to use AlphaFold-multimer (AFm) predictions to reduce the challenge of physics-based protein-protein docking.

Strengths:

They found that two features of AFm predictions are very useful. 1) pLLDT is predictive of flexible residues, which they could target for conformational sampling during docking; 2) the interface-pLLDT score is predictive of the quality of AFm predictions, which allows the authors to decide whether to do local or global docking.

Weaknesses:

(1) As admitted by the authors, the AFm predictions for the main dataset are undoubtedly biased because these structures were used for AFm training. Could the authors find a way to assess the extent of this bias?

Indeed, the AFm training included most of the structures in the DB5 benchmark for its training as many structures (either unbound or bound) were deposited before the training cut-off period. One of the challenges of estimating this bias is the availability of new structures - both bound and unbound deposited after the training cut-off. Estimating the extent of training bias is therefore conditional on these factors and difficult. A few studies have attempted to address this bias (Yin et al, 2022, https://doi.org/10.1002/pro.4379).

In our study, we assess this bias by comparing the AFm structures to the bound and unbound forms and calculating their Ca RMSDs and TM-scores (new addition). We now elaborate in the Results:Dataset curation section and we have added a figure comparing the TM-scores in the supplement.

We added a clarifying text and a note about the TM-score calculation in the manuscript as follows:

“Since most of the benchmark targets in DB5.5 were included in AlphaFold training, there would be training bias associated with their predictions (i.e. our measured success rates are an upper bound).”

“We also calculated the TM-scores of the AFm predicted complex structures with respect to the bound and the unbound crystal structures (Supplementary Figure S2). As TM-scores reflect a global comparison between structures and are less sensitive to local structural deviations, no strong conclusions could be derived. This is in agreement with our intuition that since both unbound and bound states of proteins will share a similar fold, and AlphaFold can predict structures with high TM-scores in most cases, gauging the conformational deviations with TM-scores would be inconclusive.”

(2) For the CASP15 targets where this bias is absent, the presentation was very brief. In particular, it would be interesting to see how AFm helped with the docking. The authors may even want to do a direct comparison with docking results without the help of AFm.

Unfortunately since this was a CASP-CAPRI round, the structure of the unbound Antigen or the nanobodies was unavailable. Thus we cannot perform a comparison without using AF2 at all since we need a structure prediction tool to produce the unbound nanobody and the nanobody-antigen complex template structure to dock. This has been clarified in the main text for better understanding for the readers.

“Since the nanobody-antigen complexes were CASP targets, we did not have unbound structures, rather only the sequences of individual chains. Therefore, for each target, we employed the AlphaRED strategy as described in Fig 7.”

Reviewer #1 (Recommendations For The Authors):

For suggestions for major improvements, see comments under weaknesses. One additional suggestion: the authors found that pLLDT is predictive of flexible residues. Can they try to find AFm features that are predictive of the interface site? Such information may guide their docking to a local site.

This is a great idea that we and others have been thinking about considerably. Prior work by Burke et al. (Towards a structurally resolved human protein interaction network) examines AlphaFold’s ability to predict PPIs. For high-confidence predicted models of interacting protein complexes, the authors showed that pDockQ correlated reasonably well with correct protein interactions.

That being said, binding site identification, particularly in a partner-agnostic fashion, i.e. determining binding patches on a given protein, is an area of on-going research . We hope a future study examines AlphaFold3 or ESM3 specifically for this task.

“Further, we tested multiple thresholds to estimate the optimum cut-off for distinguishing near-native structures (defined as an interface-RMSD < 4 Å) from the predictions. Figure 3.B summarizes the performance with a confusion matrix for the chosen interface-pLDDT cutoff of 85. 79 % of the targets are classified accurately with a precision of 75%, thereby validating the utility of interface-pLDDT as a discriminating metric to rank the docking quality of the AFm complex structure predictions. With AlphaFold3 and ESM3 being released, investigating features that could predict flexible residues or interface site would be valuable, as this information may guide local docking.”

Minor:

Page 3, lines 73-77, state how many targets were curated from DB5.5.

We have now clarified this in the manuscript. All 254 targets curated from DB5.5 at the time of this benchmark study.

“For each protein target, we extracted the amino acid sequences from the bound structure and predicted a corresponding three-dimensional complex structure with the ColabFold implementation of the AlphaFold multimer v2.3.0 (released in March 2023) for the 254 benchmark targets from DB5.5.”

In Figure 1, the color used for medium is too difficult to distinguish from the grey color used for rigid.

We thank you for this suggestion. We have updated the color to olive. Further, based on Reviewer 2’s suggestions, we have moved this plot to the Supplementary.

Reviewer #2 (Public Review):

Summary:

In short, this paper uses a previously published method, ReplicaDock, to improve predictions from AlphaFold-multimer. The method generated about 25% more acceptable predictions than AFm, but more important is improving an Antibody-antigen set, where more than 50% of the models become improved.

When looking at the results in more detail, it is clear that for the models where the AFm models are good, the improvement is modest (or not at all). See, for instance, the blue dots in Figure 6. However, in the cases where AFm fails, the improvement is substantial (red dots in Figure 6), but no models reach a very high accuracy (Fnat ~0.5 compared to 0.8 for the good AFm models). So the paper could be summarized by claiming, "We apply ReplicaDock when AFm fails", instead of trying to sell the paper as an utterly novel pipeline. I must also say that I am surprised by the excellent performance of ReplicaDock - it seems to be a significant step ahead of other (not AlphaFold) docking methods, and from reading the original paper, that was unclear. Having a better benchmark of it alone (without AFm) would be very interesting.

We thank the reviewer for highlighting the performance of ReplicaDock. ReplicaDock alone is benchmarked in the original paper (10.1371/journal.pcbi.1010124), with full details on the 2022 version of DB5.5 in the supplement. Indeed ReplicaDock2 achieves the highest reported success rates on flexible docking targets reported in the literature (until this AlphaRED paper!).

Regarding this statement about “the paper could be summarized…” it might be helpful to give more context. ReplicaDock is a replica exchange Monte Carlo sampling approach for protein docking that incorporates flexibility in an induced-fit fashion. However, the choice of which backbone residues to move is solely dependent on contacts made during each docking trajectory. In the last section of the ReplicaDock paper, we introduced “Directed Induced-fit” where we biased the backbone sampling only towards those residues where we knew the backbone is flexible (this information is obtained because for the benchmark set, we had both unbound and bound structures and hence could cherry-pick the specific residues which are mobile). We agree with the reviewers that AlphaRED is essentially a derivative of ReplicaDock, however, the two major claims that we make in this paper are:

(1) AlphaFold pLDDT is an effective predictor of backbone flexibility for practical use in docking.

(2) We can automate the Directed InducedFit approach within ReplicaDock by utilizing this pLDDT information per residue for conformational sampling in protein docking; and in doing so, create a pipeline that would allow us to go from sequence-to-structure-to-complex, specifically capturing conformational changes.

To conclude these claims, we pose the following questions in the Introduction:

“(1) Do the residue-specific estimates from AF/AFm relate to potential metrics demonstrating conformational flexibility?

(2) Can AF/AFm metrics deduce information about docking accuracy?

(3) Can we create a docking pipeline for in-silico complex structure prediction incorporating AFm to convert sequence-to-structure-to-docked complexes?”

This work requires a pipeline, the center of which lies in ReplicaDock as a docking method, but has functionalities that were absent in prior work. The goal is also to develop a one-stop shop without manual intervention (a prerequisite for biasing backbone sampling in ReplicaDock) that could be utilized by structural biologists efficiently.

We clarify this points in the abstract and main text as follows:

Abstract: “In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm \add{to better sample conformational changes.”

Introduction:

“The overarching goal is to create a one-stop, fully-automated pipeline for simple, reproducible, and accurate modeling of protein complexes. We investigate the aforementioned questions and create a protocol to resolve AFm failures and capture binding-induced conformational changes. We first assess the utility of AFm confidence metrics to detect conformational flexibility and binding site confidence.”

These results also highlight several questions I try to describe in the weakness section below. In short, they boil down to the fact that the authors must show how good/bad ReplicaDock is at all targets (not only the ones where AFm fails. In addition, I have several more technical comments.

Strengths:

Impressive increase in performance on AB-AG set (although a small set and no proteins).

We thank the reviewer for their comments.

Weaknesses:

The presentation is a bit hard to follow. The authors mix several measures (Fnat, iRMS, RMSDbound, etc). In addition, it is not always clear what is shown. For instance, in Figure 1, is the RMSD calculated for a single chain or the entire protein? I would suggest that the author replace all these measures with two: TM-score when evaluating the quality of a single chain and DockQ when evaluating the results for docking. This would provide a clearer picture of the performance. This applies to most figures and tables.

We apologize for the lack of clarity owing to different metrics. Irms and fnat are standard performance metrics in the docking field, but we agree that DockQ would be simpler when the detail of the other metrics are not required. We have updated the figures Figure 5 and Figure 8 to also show DockQ comparisons.

Regarding Figure 1, as highlighted in Line 90 of the main-text, “Figure 1 shows the Ca-RMSD of all protein partners of the AFm predicted complex structures with respect to the bound and the unbound.” As suggested by the reviewer in their further comments, we have moved this FIgure to the Supplementary. We have also included TM-score comparison in the Supplementary ( SupFig S2) and included clarifying statements in the main text:

“We also tested TM-scores to measure the structural deviations of the AFm predicted complex structures with respect to the bound and unbound structures (Supplementary Figure S2). However, this metric is not sensitive enough to detect the subtle, local conformational changes upon binding.”

For instance, Figure 9 could be shown as a distribution of DockQ scores.

We have now updated Figure 5 to include DockQ scores in Panel D. Since DockQ is a function of iRMSD, fnat and L-RMSD, it shows cumulative improvement in performance. Some of the nuanced details, such as, the protocol improves i-RMSD considerably but fnat improvement is lacking, and can highlight whether backbone sampling is the challenge or is it sidechain refinement.Therefore, we need to retain the iRMSD and fnat metrics in panel A-C . But We have incorporated this in the main text as follows:

“Finally, to evaluate docking success rates, we calculate DockQ for top predictions from AFm and AlphaRED respectively (Figure 5D). AlphaRED demonstrates a success rate (DockQ>0.23) for 63% of the benchmark targets. Particularly for Ab-Ag complexes, AFm predicted acceptable or better quality docked structures in only 20% of the 67 targets. In contrast, the AlphaRED pipeline succeeds in 43% of the targets, a significant improvement.”

Further, we have reevaluated success rates in Figure 8 (previously Figure 9) and have updated the manuscript to report these updated success rates.

“By utilizing the AlphaRED strategy, we show that failure cases in AFm predicted models are improved for all targets (lower Irms for 97 of 254 failed targets) with CAPRI acceptable-quality or better models generated for 62% of targets overall (Fig 8)”.

The improvements on the models where AFm is good are minimal (if at all), and it is unclear how global docking would perform on these targets, nor exactly why the plDDT<0.85 cutoff was chosen.

We agree with the reviewers that the improvement on the models with good AFm predictions is minimal. We acknowledge this in the text now as follows:

“Most of the improvements in the success rates are for cases where AFm predictions are worse. For targets with good AFm predictions, AlphaRED refinement results in minimal improvements in docking accuracy.”

The choice of pLDDT cutoff = 85 is elaborated in the “Interface-pLDDT correlates with DockQ and discriminates poorly docked structures” section, paragraph 3. Briefly, we tested multiple metrics and the interface pLDDT had the highest AUC, indicating that it is the best metric for this task. For interface-pLDDT we tested multiple thresholds, and the cutoff of 85 resulted in the highest percentage of true-positive and true-negative rates. This is illustrated with the confusion matrix in Figure 3.B with the precision scores. We now clarify this in the text as follows:

“With interface-pLDDT as a discriminating metric, we tested multiple thresholds to estimate the optimum cut-off for distinguishing near-native structures (defined as an interface-RMSD < 4 Å) from the predictions. Figure 3B summarizes the performance with a confusion matrix for the chosen interface-pLDDT cutoff of 85. 79% of the targets are classified accurately with a precision of 75%, thereby validating the utility of interface-pLDDT as a discriminating metric to rank the docking quality of the AFm complex structure predictions.”

To better understand the performance of ReplicaDock, the authors should therefore (i) run global and local docking on all targets and report the results, (ii) report the results if AlphaFold (not multimer) models of the chains were used as input to ReplicaDock (I would assume it is similar). These models can be downloaded from AlphaFoldDB.

The performance of ReplicaDock on DB5.5 is tabulated in our prior work (https://doi.org/10.1371/journal.pcbi.1010124) and we direct the reviewers there for the detailed performance and results. In our opinion, the benchmark suggested by the reviewer would be redundant and not worth the computational expense.

The scope of this paper is to highlight a structure prediction + physics-based modeling pipeline for docking to adapt to the accuracy of up-and-coming structure prediction tools.

Using AlphaFold monomer chains as input and benchmarking on that, albeit interesting scientifically, will not be useful for either the pipeline or biologists who would want a complex structure prediction. We thank the authors for their comments but want to reemphasize that the end goal of this work is to increase the accuracy of complex structure predictions and PPIs obtained from computational tools.

Further, it would be interesting to see if ReplicaDock could be combined with AFsample (or any other model to generate structural diversity) to improve performance further.

We would like to highlight that ReplicaDock is a stand-alone tool for protein docking and here we demonstrate the ability of adapting it with metrics derived from AlphaFold or other structure prediction tools (say ESMFold) such as pLDDT for conformational sampling and improving docking accuracy. We definitely agree that adapting it to use with tools such as AFSample will be interesting but it is out of scope of this work.

The estimates of computing costs for the AFsample are incorrect (check what is presented in their paper). What are the computational costs for RepliaDock global docking?

The authors of the AFSample paper report that “AFsample requires more computational time than AF2, as it generates 240 models, and including the extra recycles, the overall timing is 1000 more costly than the baseline.” We have reported these exact numbers in our manuscript.

The computational costs of ReplicaDock are 8-72 CPU hours on a single node with 24 processors as reported in our prior work.

For AlphaRED, the costs are slightly higher owing to the structure prediction module in the beginning and are up to 100 CPU hrs for our largest (max Nres) target.

It is unclear strictly what sequences were used as input to the modelling. The authors should use full-length UniProt sequences if they were not done.

We report this in the methods section of the manuscript as well as in Figure 5. Full length complex sequences were used for the models that we extracted from DB5.5.

“As illustrated in Fig. 5, given a sequence of a protein complex, we use the ColabFold implementation of AF2-multimer to obtain a predictive template.”

We clarify this in the methods section as:

“For each target in the DB5.5 dataset, we first extracted the corresponding FASTA sequence for the bound complex and then obtained AlphaFold predicted models with the ColabFold v1.5.2 implementation of AlphaFold and AlphaFold-multimer (v.2.3.0).”

The antibody-antigen dataset is small. It could easily be expanded to thousands of proteins. It would be interesting to know the performance of ReplicaDock on a more extensive set of Antibodies and nanobodies.

This work demonstrates the performance on the docking benchmark, i.e. given unbound structure can you predict the bound complexes. With this regard, our analysis has been focussed on targets where both the unbound and bound structures are available so that we could evaluate the ability of AlphaRED on modeling protein flexibility and docking accuracy. For antibody-antigen complexes, there are only 67 structures with both unbound and bound complexes available and they constituted our dataset. Benchmarking AlphaRED on all antibody-antigen targets can give biased results as most Ab-Ag complexes are in AlphaFold training set. Further, our work is more aimed towards predicting conformational flexibility in docking and not rigid-body docked complexes, so benchmarking on existing bound Ab-Ag structures is out of scope for this work.

Using pLDDT on the interface region to identify good/bas models is likely suboptimal. It was acceptable (as a part of the score) for AlphaFold-2.0 (monomer), but AFm behaves differently. Here, AFm provides a direct score to evaluate the quality of the interaction (ipTM or Ranking Confidence). The authors should use these to separate good/bad models (for global/local docking), or at least show that these scores are less good than the one they used.

We thank the reviewers for this suggestion.

Reviewer #2 (Recommendations For The Authors):

Some Figures could be skipped/improved

Fig 1: Use TM-score instead a much better measure (and the figure is not necessary).

Figure 1 compares the bias of AlphaFold towards unbound or bound forms of the proteins. We believe that this figure highlights the slight inherent bias of AlphaFold towards bound structures over unbound.

As the reviewers have suggested we have included a plot comparing the TM-scores for the structures. Further, we have moved this figure to the Supplementary.

Fig 2. Skip B (why compare RMSD with pLDDT?). Add a figure to see how this correlates over all targets not just two.

RMSD and LDDT both represent metrics to evaluate conformational variability between two structures, such as the bound and unbound forms of the same protein structure. On one hand where RMSD measures overall deviation of residues, LDDT allows the estimation of relative domain orientations and concerted proteins. We have elaborated this in Methods as well as in the Results section titled “AlphaFold pLDDT provides a predictive confidence measure for backbone flexibility”.

The data for the benchmark targets is now included in the Supplementary (Supplementary Figures S3-S4).

Fig 3. Color the different chains of a protein differently. Thereby the Receptor/Ligand/Bound labels can be omitted.

We thank the reviewers for this suggestion. However, the color scheme is chosen to highlight (1) the relative orientation of protein partners relative to each other. We have ensured that the alignment is over one partner (Receptor) so that you could see the relative orientation of the other partner (Ligand) in the modeled protein over the bound structure (in one color). (2) The coloring of the receptor and ligand chain is by pLDDT (from red to blue) to highlight that for decoys with incorrectly predicted interfaces, the pLDDT scores of the interface residues are indeed lower and can be a discriminating metric. We elaborate this in the caption of Figure 3 as well as in the section “Interface-pLDDT correlates with DockQ and discriminates poorly docked structures”. Coloring the chains of a protein differently will obfuscate the point that we are aiming to make and will be inconclusive for the readers as they would need to rely only on quantitative metrics (Irms and DockQ) reported but won’t be able to visualize the interface pLDDT of the incorrectly bound structures. We hope that this justifies the choice of our color scheme.

Fig 4. Include RankConf, ipTM, pDockQ, and other measures in the plos (they are likely better). Include DockQ for the top targets. It is difficult to estimate for multi chain complexes.

We thank the reviewer for this suggestion. We have now included the DockQ performances for all targets in Figure 5 (previously Figure 6) as well as re-evaluated our final success rates based on the DockQ calculations in Figure 8 (previously Figure 9).

Fig 5. use a better measure to split (see above).

We have elaborated on the choice of the split for the comments above and the interface pLDDT threshold of 85 is a decision made post observation on the docking benchmark. We do want to highlight that the cut-off is arbitrary and in our online server (ROSIE) as well as in custom scripts, this cut-off can be tuned by the user as required. We would suggest a cut-off of 85 based on our observations but the users are welcome to tune this as per their needs.

Fig 6. Replace lrms/fnat with DockQ.

We have now included DockQ scores in our manuscript.

Fig 7. Color the different chains of a protein differently.

We have colored the protein chains differently. AlphaFold models are in Orange, Bound complexes are in Gray, and predicted proteins from AlphaRED are in Blue-Green indicating the two partners. All models are aligned over the receptor so relative orientations of the ligand protein can be observed.

Fig 8 Color the different chains of a protein differently.

The chains are colored differently. We would like the reviewer to elaborate more on what they would like to observe as we believe our color scheme makes intuitive sense for readers.

Fig 9. Use DockQ instead of CAPRI criteria.

The figure has been updated based on DockQ. To elaborate, the CAPRI criteria is set based on DockQ scores as elaborated in the figure caption.

Reviewer #3 (Public review):

Summary:

The authors are showing evidence that they claim establishes the controversial epigenetic mark, DNA 6mA, as promoting genome stability.

Strengths:

The identification of a poorly understood protein, METTL3, and its subsequent characterization in DDR is of high quality and interesting.

Weaknesses:

(1) The very presence of 6mA (DNA) in mammalian DNA is still highly controversial and numerous studies have been conclusively shown to have reported the presence of 6mA due to technical artifacts and bacterial contamination. Thus, to my knowledge there is no clear evidence for 6mA as an epigenetic mark in mammals, and consequently, no evidence of writers and readers of 6mA. None of this is mentioned in the introduction. Much of the introduction can be reduced, but a paragraph clearly stating the controversy and lack of evidence for 6mA in mammals needs to be added, otherwise, the reader is given an entirely distorted view of the field.

These concerns must also be clearly in the limitations section and even in the results section which fails to nuance the authors' findings.

(2) What is the motivation for using HT-29 cells? Moreover, the materials and methods do not state how the authors controlled for bacterial contamination, which has been the most common cause of erroneous 6mA signals to date. Did the authors routinely check for mycoplasma?

(3) The single cell imaging of 6mA in various cells is nice. The results are confirmed by mass spec as an orthogonal approach. Another orthogonal and quantitative approach to assessing 6mA levels would be PacBio. Similarly, it is unclear why the authors have not performed dot-blots of 6mA for genomic DNA from the given cell lines.

(4) The results of Figure 3 need further investigation and validation. If the results are correct the authors are suggesting that the majority of 6mA in their cell lines is present in the DNA, and not the RNA, which is completely contrary to every other study of 6mA in mammalian cells that I am aware of. This could suggest that the antibody is not, in fact, binding to 6mA, but to unmodified adenine, which would explain why the signal disappears after DNAse treatment. Indeed, binding of 6mA to unmethylated DNA is a commonly known problem with most 6mA antibodies and is well described elsewhere.

(5) Given the lack of orthologous validation of the observed DNA 6mA and the lack of evidence supporting the presence of 6mA in mammalian DNA and consequently any functional role for 6mA in mammalian biology, the manuscript's conclusions need to be toned down significantly, and the inherent difficulty in assessing 6mA accurately in mammals acknowledged throughout.

Author response:

eLife Assessment <br /> This manuscript reports important findings that the methyltransferase METTL3 is involved in the repair of abasic sites and uracil in DNA, mediating resistance to floxuridine-driven cytotoxicity. The presented evidence for the involvement of m6A in DNA is incomplete and requires further validation with orthogonal approaches to conclusively show the presence of 6mA in the DNA and exclude that the source is RNA or bacterial contamination. 

We thank the editors for recognizing the importance of our work and the relevance of METTL3 in DNA repair. However, we wholly disagree with the second sentence in the eLife assessment, and we want to clarify why our evidence for the involvement of 6mA in DNA is complete.  

The identification of 6mA in DNA, upon DNA damage, is based first on immunofluorescence observations using an anti-m6A antibody. In this setting, removal of RNA with RNase treatment fails to reduce the 6mA signal, excluding the possibility that the source of signal is RNA. In contrast, removal of DNA with DNase treatment removes all 6mA signal, strongly suggesting that the species carrying the N6-methyladenosine modification is DNA (Figure 3D, E). Importantly, in Figure 3F, we provide orthogonal, quantitative mass spectrometry data that independently confirm this finding. Mass spectrometry-liquid chromatography of DNA analytes, conclusively shows the presence of 6mA in DNA upon treatment with DNA damaging agents and excludes that the source is RNA, based on exact mass. Reviewer #2 recognized the strengths of this approach to generate solid evidence for 6mA in DNA.

Cells only show the 6mA signal when treated with DNA damaging agents, and the 6mA is absent from untreated cells (Figure 3D, E, F). This provides strong evidence that the 6mA signal is not a result of bacterial contamination in our cell lines. Moreover, our cell lines are routinely tested for mycoplasma contamination. It could be possible that stock solutions of DNA damaging agents may be contaminated, but this would need to be true for all individual drugs and stocks tested. The data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3G, H) provides strong evidence against bacterial contamination in our stocks.  

In summary, we provide conclusive evidence, based on orthogonal methods, that the METTL3-dependent N6-methyladenosine modification is deposited in DNA, not RNA, in response to DNA damage. 

Public Reviews: <br /> Reviewer #1 (Public review): <br /> Summary: 

The authors sought to identify unknown factors involved in the repair of uracil in DNA through a CRISPER knockout screen. 

Typo above: “CRISPER” should be “CRISPR”.

Strengths: 

The screen identified both known and unknown proteins involved in DNA repair resulting from uracil or modified uracil base incorporation into DNA. The conclusion is that the protein activity of METTL3, which converts A nucleotides to 5mA nucleotides, plays a role in the DNA damage/repair response. The importance of METTL3 in DNA repair, and its colocalization with a known DNA repair enzyme, UNG2, is well characterized. 

Typo above: “5mA” should be “6mA”.

Weaknesses: <br /> This reviewer identified no major weaknesses in this study. The manuscript could be improved by tightening the text throughout, and more accurate and consistent word choice around the origin of U and 6mA in DNA. The dUTP nucleotide is misincorporated into DNA, and 6mA is formed by methylation of the A base present in DNA. Using words like 6mA "deposition in DNA" seems to imply it results from incorporation of a methylated dATP nucleotide during DNA synthesis.

The increased presence of 6mA during DNA damage could result from methylation at the A base itself (within DNA) or from incorporation of pre-modified 6mA during DNA synthesis. Our data do not directly discriminate between these two mechanisms, and we will clarify this point in the discussion.

Reviewer #2 (Public review): <br /> Summary: <br /> In this work, the authors performed a CRISPR knockout screen in the presence of floxuridine, a chemotherapeutic agent that incorporates uracil and fluoro-uracil into DNA, and identified unexpected factors, such as the RNA m6A methyltransferase METTL3, as required to overcome floxuridine-driven cytotoxicity in mammalian cells. Interestingly, the observed N6-methyladenosine was embedded in DNA, which has been reported as DNA 6mA in mammalian genomes and is currently confirmed with mass spectrometry in this model. Therefore, this work consolidated the functional role of mammalian genomic DNA 6mA, and supported with solid evidence to uncover the METTL3-6mA-UNG2 axis in response to DNA base damage. <br /> Strengths: <br /> In this work, the authors took an unbiased, genome-wide CRISPR approach to identify novel factors involved in uracil repair with potential clinical interest. 

The authors designed elegant experiments to confirm the METTL3 works through genomic DNA, adding the methylation into DNA (6mA) but not the RNA (m6A), in this base damage repair context. The authors employ different enzymes, such as RNase A, RNase H, DNase, and liquid chromatography coupled to tandem mass spectrometry to validate that METTL3 deposits 6mA in DNA in response to agents that increase genomic uracil. <br /> They also have the Mettl3-KO and the METTL3 inhibition results to support their conclusion. <br /> Weaknesses:<br /> Although this study demonstrates that METTL3-dependent 6mA deposition in DNA is functionally relevant to DNA damage repair in mammalian cells, there are still several concerns and issues that need to be improved to strengthen this research.

First, in the whole paper, the authors never claim or mention the mammalian cell lines contamination testing result, which is the fundamental assay that has to be done for the mammalian cell lines DNA 6mA study.

Our cell lines are routinely tested for bacterial contamination, specifically mycoplasma, and we plan to state this information in a revised version of the manuscript.

Importantly, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on the presence of DNA damage and not caused by contamination in the cell lines (Figure 3D, E, F). While it could be possible that stock solutions of DNA damaging agents may be contaminated, this would need to be the case for all individual drugs and stocks tested that induce 6mA, which seems very unlikely. Finally, the data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3 G, H) provides strong evidence against bacterial contamination in our drug stocks.

Second, in the whole work, the authors have not supplied any genomic sequencing data to support their conclusions. Although the sequencing of DNA 6mA in mammalian models is challenging, recent breakthroughs in sequencing techniques, such as DR-Seq or NT/NAME-seq, have lowered the bar and improved a lot in the 6mA sequencing assay. Therefore, the authors should consider employing the sequencing methods to further confirm the functional role of 6mA in base repair.

While we agree that it could be important to understand the precise genomic location of 6mA in relation to DNA damage, this is outside the scope of the current study. Moreover, this exercise may prove unproductive. If 6mA is enriched in DNA at damage sites or as DNA is replicated, the genomic mapping of 6mA is likely to be stochastic. If stochastic, it would be impossible to obtain the read depth necessary to map 6mA accurately.

Third, the authors used the METTL3 inhibitor and Mettl3-KO to validate the METTL3-6mA-UNG2 functional roles. However, the catalytic mutant and rescue of Mettl3 may be the further experiments to confirm the conclusion. 

We believe this to be an excellent suggestion from Reviewer #2 but we are unable to perform the proposed experiment at this time. We encourage future studies to explore the rescue experiment.

Reviewer #3 (Public review):

Summary:

The authors are showing evidence that they claim establishes the controversial epigenetic mark, DNA 6mA, as promoting genome stability.

Strengths:

The identification of a poorly understood protein, METTL3, and its subsequent characterization in DDR is of high quality and interesting.

Weaknesses:

(1) The very presence of 6mA (DNA) in mammalian DNA is still highly controversial and numerous studies have been conclusively shown to have reported the presence of 6mA due to technical artifacts and bacterial contamination. Thus, to my knowledge there is no clear evidence for 6mA as an epigenetic mark in mammals, and consequently, no evidence of writers and readers of 6mA. None of this is mentioned in the introduction. Much of the introduction can be reduced, but a paragraph clearly stating the controversy and lack of evidence for 6mA in mammals needs to be added, otherwise, the reader is given an entirely distorted view of the field.

These concerns must also be clearly in the limitations section and even in the results section which fails to nuance the authors' findings.

We agree with the reviewer that the presence and potential function of 6mA in mammalian DNA has been debated. Importantly, the debate regarding the presence and quantity of 6mA in DNA has been previously restricted to undamaged, baseline conditions. In complete agreement with this notion, we do not detect appreciable levels of 6mA in untreated cells. We will revise the introduction to introduce the debate about 6mA in DNA. We, however, want to highlight that our study provides for the first time, convincing evidence (based on orthogonal methods) that 6mA is present in DNA in response to a stimulus, DNA damage.

(2) What is the motivation for using HT-29 cells? Moreover, the materials and methods do not state how the authors controlled for bacterial contamination, which has been the most common cause of erroneous 6mA signals to date. Did the authors routinely check for mycoplasma?

HT-29 is a cell line of colorectal origin and chemotherapeutic agents that introduce uracil and uracil derivatives in DNA, as those used in this study, are relevant for the treatment of colorectal cancer. As indicated above, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on DNA damage and not caused by a potential bacterial contamination (Figure 3D, E, F). Additionally, our cell lines are routinely tested for bacterial contamination, specifically mycoplasma.

(3) The single-cell imaging of 6mA in various cells is nice but must be confirmed by orthogonal approaches. PacBio would provide an alternative and quantitative approach to assessing 6mA levels. Similarly, it is unclear why the authors have not performed dot-blots of 6mA for genomic DNA from the given cell lines.

We are confused by this point since an orthogonal approach to detect 6mA, mass spectrometry-liquid chromatography, was employed. This method does not use an antibody and confirms the increase of 6mA in DNA when cells were treated with DNA damaging agents. This data is presented in Figure 3F.

It is sensible to hypothesize that the localization of 6mA is consistent with DNA replication (like uracil deposition). In this event, the genomic mapping of 6mA is likely to be stochastic. This would make quantification with PacBio sequencing difficult because it would be very challenging to achieve the appropriate read depth to call a modified base.

Dot blots rely on an antibody and thus are not truly orthogonal to our immunofluorescence-based measurements. We preferred the mass spectrometry-liquid chromatography approach we took as a true orthogonal approach.

(4) The results of Figure 3 need further investigation and validation. If the results are correct the authors are suggesting that the majority of 6mA in their cell lines is present in the DNA, and not the RNA, which is completely contrary to every other study of 6mA in mammalian cells that I am aware of. This could suggest that the antibody is not, in fact, binding to 6mA, but to unmodified adenine, which would explain why the signal disappears after DNAse treatment. Indeed, binding of 6mA to unmethylated DNA is a commonly known problem with most 6mA antibodies and is well described elsewhere.

Based on this and the following comment, we are convinced that Reviewer #3 has overlooked two critical elements of our study:

First, the immunofluorescence work presented in Figure 3, showing 6mA signal in response to DNA damage, uses cells that were pre-extracted to remove excess cytoplasmic RNA. This method is often used in immunofluorescence experiments of this kind. The pre-extraction method removes most of the cytoplasmic content, and the majority of the cytoplasmic m6A RNA signal. Supplementary Figure 3D shows cells that have not been pre-extracted prior to staining. These images show the cytoplasmic m6A signal is abundant if we do not perform the pre-extraction step.

If the antibody used to label 6mA significantly reacted with unmodified adenine, we would expect a large signal in untreated or untreated and denatured conditions. In contrast, an increase in 6mA is not observed in either case.

Second, the orthogonal approach we employed, mass spectrometry coupled with liquid chromatography, measures 6mA DNA analytes specifically by exact mass. This approach does not depend on an antibody and yields results consistent with those from the immunofluorescence experiments.

(5) Given the lack of orthologous validation of the observed DNA 6mA and the lack of evidence supporting the presence of 6mA in mammalian DNA and consequently any functional role for 6mA in mammalian biology, the manuscript's conclusions need to be toned down significantly, and the inherent difficultly in assessing 6mA accurately in mammals acknowledged throughout.

Typo above: “difficultly” should be “difficulty”.

As discussed in response to prior comments, Figure 3 does provide two independent and orthologous methods that demonstrate 6mA presence in DNA specifically, and not RNA, in response to DNA damage. Complementary and orthogonal datasets are presented using either immunofluorescence microscopy or mass spectrometry-liquid chromatography of extracted DNA. The latter method does not rely on an antibody and can discriminate 6mA DNA versus RNA based on exact mass. We will revise the text to clarify that Figure 3F is a completely orthogonal approach.

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

Response to Reviewers

We thank the three reviewers for their insightful and constructive comments, which have helped improve the manuscript. Our replies to each comment are provided below.

Reviewer #1

Evidence, reproducibility and clarity

The abscission checkpoint, also known as NoCut, is a genome protection mechanism that remains poorly understood. This pathway is conserved from yeast to humans and protects the genome against chromosome bridges, a dangerous missegregation event that can have catastrophic consequences on genome stability. Dam et al now report the role of Srs2, a DNA helicase, as a key factor in the abscission checkpoint. The authors establish Srs2 as bona fide factor in this pathway by showing its involvement in abscission delays when chromatin bridges are induced. Importantly, yeast defective for Srs2 show increased levels of DNA damage when the frequency of chromatin bridges is increased. The authors also provide genetic evidence supporting a model whereby the interaction of SrS2 with PCNA s required for abscission regulation. In the second part of the manuscript, the authors study the human homologue of SRS2, PARI, in abscission regulation. The manuscript provides convincing evidence that PARI is also required for abscission delays in the presence of chromatin bridges. Critically, this role is specific for chromosome missegregation as abscission delays in response to nucleoporin depletion remain intact in PARI-depleted cells. Thus there is a conserved requirement for these DNA helicases in the abscission checkpoint.

* Overall, these are important advances in our understanding of the abscission checkpoint. The data is high quality and convincing in general. However, the impact of PARI depletion on genome stability needs to be further demonstrated to support key claims in the manuscript. Specifically:*

• Disruptions of the abscission checkpoint in human cells result in bi-nucleation or increased levels of DNA damage. In this context, the authors need to show that PARI-depleted cells with increased frequency of chromatin bridges exhibit increased levels of bi-nucleation, DNA damage or both.

We thank the reviewer for its positive assessment of our work. While our data establish that Srs2 inhibits abscission to prevent DNA damage in yeast, we agree with the reviewer that we have not tested the consequences of PARI loss on DNA damage or cytokinesis failure in HeLa cells. We will address this in the revised version of our study.

Significance

The abscission checkpoint, remains poorly understood. There is evidence in the literature that disruptions in this pathway increase susceptibility to cancer. The identification of the Srs2/PARI helicases as key components in this pathway is a considerable step forward in this field.

Reviewer #2

__Evidence, reproducibility and clarity __

The Aurora B-mediated abscission checkpoint ("NoCut" in yeast) prevents tetraploidization or chromatin breakage in the presence of chromatin bridges in cytokinesis and the mechanisms of its activation are a matter of active investigation. In the present study, Dam et al propose that the conserved Srs2/PARI DNA helicase is required for the activation of the abscission checkpoint in response to chromatin bridges generated by DNA replication stress or topoisomerase inhibition. This is a timely and very interesting topic and the potential identification of a novel regulatory protein that activates the abscission checkpoint would be important. However, in my opinion, some Figures are of relatively low quality and need improving, there are apparent discrepancies between data and important control experiments are missing, which preclude the reader from fully evaluating the conclusions of this study. Some direct evidence of the role of Srs2/PARI on DNA bridges is also required. Also, it would be nice to investigate mechanistic details of the potential Srs2/PARI functions in the abscission checkpoint, and how it fits with other recently published signaling pathways that activate the abscission checkpoint in cytokinesis.

Specific comments: 1. The DNA channel (Ht2B-mCherry) in Figure 1A is of very low quality to be able to verify the authors interpretations of when the individual chromatin bridges are resolved (probably broken). For example, in the WT movie, they claim that the bridge is intact in frames 10 min and 14 min (yellow arrow) and that the bridge is resolved at 16 min (asterisk); however, I'm not convinced this is the case, because I can only see a very small portion of the bridge already at the 10 min and 14 min time-points. In my opinion, this bridge could have been broken much earlier, probably at 10 min. Also, WT +HU, is this bridge really intact at 10 min and at 14 min? In Srs2Δ + HU, the bridge appears broken to me much earlier, perhaps at 30 min. There is a distinct possibility that the authors could not calculate the resolution times accurately from these movies (please also see my next comment, #2). The authors could perhaps use a more sensitive bridge marker such as GFP-BAF.

To clarify our approach, chromosome segregation was considered complete only when bridges were no longer detectable, while discontinuous or faint bridges were still classified as unresolved, as stretched DNA may result in weak nucleosome signals. This definition aligns with the bridge resolution times reported in Figure 1B-E. To improve clarity, we have revised the Results section to specify our classification criteria, and added all frames from the time-lapse movies in Figure 1A as a new figure (Supplementary Figure S1).

In Figure 1B, they conclude that Srs2Δ cells treated with HU exhibit increased time from anaphase onset to bridge resolution compared with WT or Srs2Δ cells. This result appears at odds with data from Fig. 2C showing that Srs2Δ+HU finish abscission at similar times to WT or Srs2Δ cells as judged by plasma membrane morphology. (final cut). Given that the final cut of the plasma membrane should cause chromatin bridges to break, if Srs2 is required for an abscission delay in response to HU-induced chromatin bridges, I would expect Srs2Δ + HU cells to exhibit accelerated plasma membrane cut and also faster chromatin bridge resolution compared with controls. This discrepancy could at least in part be caused by the relatively low quality of movies used for the calculations in Fig. 1.

This is a perceptive point. To clarify, we analyzed the timing of chromosome segregation, membrane ingression at the abscission site, and abscission relative to anaphase onset, as shown in the new Supplementary Figure S2. In HU-treated cells (both WT and srs2∆), bridge resolution and membrane ingression occur around the same time (~10 minutes after anaphase onset), with srs2∆ cells exhibiting slightly earlier membrane contraction. This suggests that bridges resolve during cytokinesis (see also our reply to the next comment) but does not distinguish whether they break prematurely or resolve normally. Our key finding is that membrane abscission is delayed in HU-treated cells in an Srs2-dependent manner, raising the question of whether this delay is important to prevent bridge breakage. This hypothesis is tested and supported by Figure 2D, where delaying cytokinesis (via cyk3∆) reveals the protective role of Srs2.

Fig. 2 shows faster abscission times (membrane cut) in Srs2Δ+HU cells compared with WT+HU. The authors interpret this data as evidence for a role of Srs2 in abscission delay in response to HU-induced chromatin bridges (page 7 and elsewhere). However, there is no direct evidence that the cells analyzed in Fig.2 exhibited DNA bridges in cytokinesis. One could argue that HU-induced DNA replication stress caused DNA lesions at the nuclear chromatin, which affected completion of cytokinesis in the absence or presence of Srs2. What proportion of HU-treated cells in cytokinesis exhibit DNA bridges? Judging from Fig. 1D this could be as low as 0-20%. The authors should analyze HU-treated cells that clearly exhibit DNA bridges, either by live-cell imaging or in fixed cells experiments. As it stands and together with my previous comments #1 and 2, I'm not convinced this data fully supports a role for Srs2 in the abscission delay in response to HU-induced DNA bridges.

We appreciate the reviewer's concern. The presence of chromatin bridges in HU-treated cells during cytokinesis (membrane ingression) is documented in the new Supplementary Figure S2, as noted in our response to the previous comment. Additionally, our previous study (Amaral 2016, PMID: 27111841, Figure 1D) demonstrated that under the same HU treatment conditions used here, >90% of wild-type cells exhibit chromatin bridges during cytokinesis. This strongly supports the conclusion that the effects observed in Figure 2 are linked to the presence of DNA bridges.

In Fig. 2D, there is no evidence to support that Mre11 foci are caused by bridge breakage, and not by replication-stress induced DNA lesions at the main nucleus (no DNA bridge is evident, also see comment #3).

The use of the cyk3 mutant in Figure 2D specifically addresses this concern. If Mre11 foci resulted from replication stress-induced lesions in the main nucleus, delaying cytokinesis should have no impact on damage levels. However, we observe that delaying cytokinesis via the cyk3 mutation significantly reduces Mre11 foci, strongly suggesting that these foci arise from chromatin bridge breakage rather than replication stress, and that delaying cytokinesis provides extra time to solve the chromosome segregation problem. This conclusion is further supported by previous studies showing that cyk3∆ delays cytokinesis (Amaral 2016, PMID: 27111841, Figure 2C; Onishi et al. 2013, PMID: 23878277). We have clarified this point in the revised text.

Figure 3: the authors use a top2-4 mutant strain to generate DNA bridges from catenated DNA and investigate the potential role of Srs2 in the abscission delay. However, no DNA bridges are obvious in the cells shown in Fig. 3. What proportion of top2-4 mutant cells in cytokinesis exhibit DNA bridges? Does this explain the striking difference in the percentage of cells that haven't completed abscission after 30-60 min in WT+HU vs Top2-4 cells? Please also see my previous comments above.

The top2-4 mutant is well-characterized, and under the conditions used here, 100% of cells exhibit DNA bridges during cytokinesis (see for example Amaral et al., 2016, Figure 3A). We have clarified this point in the revised text. Notably, previous work has shown that top2-4-induced bridges are thicker and more persistent than those caused by HU-induced replication stress. This difference might contribute to the more severe abscission defect observed in top2-4 cells, though we have not directly tested this.

The authors propose that association of Srs2 with PCNA is required for complete inhibition of abscission in top2-4 mutant cells with chromatin bridges. Assuming a role for Srs2 in abscission timing in cytokinesis with chromatin bridges is fully proven, it is essential that the authors also investigate the localization of Srs2 and PCNA on chromatin bridges, using GFP-tagged proteins or appropriate antibodies in fixed and/or living cells. This would suggest a direct role of these proteins on chromatin bridges and considerably strengthen the authors hypothesis. Alternatively, Srs2 and PCNA may indirectly affect abscission timing through their well-established roles at nuclear chromatin.

The perturbations used in Figure 4 have been previously shown to disrupt Srs2-PCNA and PCNA-chromatin interactions (Armstrong et al., 2012; Ayyagari et al., 1995; Johnson et al., 2016; Kubota et al., 2013), as referenced in our manuscript. Given this well-established evidence, we believe additional imaging experiments would be redundant. Moreover, we do not claim that Srs2 or PCNA must specifically localize to chromatin bridges for NoCut function. Instead, our data demonstrate their genetic requirement for abscission inhibition in the presence of bridges. Whether these proteins localize exclusively on bridges or more broadly on chromatin remains unresolved, a point we explicitly discuss in the manuscript.

In Fig. 4D, the authors show an abscission delay in elg1Δ mutant cells in the presence of dicentric bridges compared with cytokinesis without bridges and interpret this as evidence that artificially retaining PCNA on dicentric chromatin bridges is sufficient to inhibit abscission. It is important that the authors demonstrate that PCNA localizes to dicentric bridges in elg1Δ mutant, but not in ELG1 control, cells, e.g., by immunofluorescence, to support their claim and their proposed model.

As noted in our previous response, the association of PCNA with chromatin throughout the cell cycle and its regulation by Elg1 have been extensively characterized in prior studies. Given this established evidence, additional imaging experiments would be redundant.

We also clarify that we do not claim that PCNA is specifically retained on chromatin bridges in elg1Δ mutants. Rather, our model is based on the overall retention of PCNA on chromatin in elg1Δ cells, as demonstrated in published studies.

Notably, elg1Δ mutants without dicentric bridges retain PCNA on chromatin but do not exhibit delayed abscission. However, only elg1Δ mutants with chromatin bridges inhibit abscission, indicating that PCNA retention alone is not sufficient—it is the presence of a bridge with retained PCNA that is critical. This distinction has been clarified in the revised manuscript.

In Fig. 5, the authors claim that HeLa cells treated with the Top2 inhibitor ICRF193 exhibit delayed midbody resolution compared with controls and that depletion of PARI by siRNA accelerates abscission in ICRF-treated cells. They interpret this as evidence for a role of PARI in the abscission delay in response to ICRF-induced chromatin bridges. However, no bridges are visible at any time-frame in cells in Fig. 5B raising the possibility that the observed time-differences are due to some effect of ICRF in cytokinesis without bridges. I'm also not convinced that in Fig. 5B the midbodies in NT/ICRF/230 min, siPARI/DMSO/110 min and siPARI/ICRF/150 min were resolved as indicated by the authors, as I can definitely see both midbody arms very clearly in these photos. The p-values are also just below the p

We acknowledge that the chromatin bridges in Figure 5B are challenging to visualize and may appear discontinuous. This is not due to poor image quality but likely reflects the low chromatin density of these structures. To clarify this, we now include magnified and contrast-enhanced images to better highlight the bridges, and quantification in Fig. 5C. Additionally, in the revised manuscript, we will provide new images using GFP-BAF, which directly binds DNA, to more clearly demonstrate the presence of chromatin bridges in ICRF-treated cells. These data will confirm that most cytokinetic cells in ICRF-treated conditions exhibit bridges.

Regarding the midbodies shown in Figure 5B, the presence of one or both arms intact does not indicate unresolved abscission but rather that the midbody has been severed, a distinction we explicitly describe in the manuscript.

Concerning the statistical analysis, we note that the p-value threshold of 0.05 is a widely accepted convention for statistical significance, and we have applied it appropriately in our analysis.

Finally, regarding the EM images in Figure 5C, these are single-section images, which do not allow us to determine definitively whether the bridges are physically broken when they appear discontinuous. It is possible that portions of the bridge extend outside the sectioned image. Regardless, we do not claim that these bridges are intact or broken. Rather, our key conclusion is that their presence at the abscission site in ICRF-treated cells is not affected by PARI knockdown, supporting our model.

In Fig. 6, the authors examine actin patches in PARI-depleted and control cells as a marker of abscission. Although a role for PARI in actin patch formation would be very interesting, I'm not sure how it fits with the present story. The actin inside the intercellular canal described by Bai et al (removal of which correlates with abscission) appears very different to the accumulations of actin at the base of the intercellular canal described by Sreigemann et al and by Dandoulaki et al. I can definitely see actin patches (similar to the ones in Steigemann et al) in Fig. 6 NT/ICRF, but I can't see any at the other treatments (I disagree with the arrows). Incidentally, I can see a DNA bridge only in NT/ICRF, but not in the other treatments.

We have revised our description of this figure for greater clarity. In control cells, actin accumulates at the cleavage furrow during anaphase and gradually disperses (clears) as cytokinesis progresses. We do not see patches in untreated cells, and we have updated the y-axis label in Figure 5B from “% of cells with actin patches” to “% of cells with actin clearance” to better reflect our observations.

Actin patches were observed only in ICRF-193-treated cells and were often associated with chromatin bridges. Cells that successfully disassembled these actin patches were classified as having completed actin clearance. Our data indicate that PARI depletion increases the fraction of cells that clear chromatin from the division plane, facilitating actin patch disassembly.

The actin patches observed in our study closely resemble those reported by Steigemann et al., and notably, we used the same cell line as in that study. Regarding Bai et al., they used both phalloidin and actin-GFP. For example, Figure 5C in Bai et al., shows examples of both actin patches near chromatin bridges, which resemble those in our study, and filamentous actin structures within the intercellular canal, which appear distinct.

Finally, a bridge fragment lacking actin patches is visible in PARI knockdown cells treated with ICRF, and we have now highlighted this in the revised figure.

1. Midbody resolutions are clearer in Fig. 7, perhaps with the exception of siPARI/DMSO. However, no DNA bridges are visible, raising again the possibility that the authors investigate effects in cytokinesis without DNA bridges.

See our response to point 8: while bridges are difficult to visualize, our analysis confirms that ICRF treatment induces bridges that persist during cytokinesis.

Can the authors investigate whether the helicase activity of PARI is required for the abscission checkpoint, by depletion-reconstitution experiments with a helicase-mutant protein?

PARI lacks detectable Walker motifs and associated ATPase activity, suggesting PARI lacks helicase activity (Moldovan et al., 2012). Therefore, we have not pursued depletion-reconstitution experiments with a helicase-mutant protein.

The authors should investigate localization of PARI to the midbody/ DNA bridge in cytokinesis with chromatin bridges. Recent reports have proposed that a Top2-MRN-ATM-Chk2 pathway activates the Aurora B-dependent abscission checkpoint in human cells (PMIDs: 37638884, 33355621). The authors should examine localization of Aurora B and some of the above proteins in control and PARI-deficient cells to establish if/how PARI fits in the above pathway.

As noted in our manuscript, we attempted to visualize PARI at midbodies and DNA bridges but were unable to detect any signal. This could be due to either its absence in these regions or its low concentration, making detection challenging.

We agree that investigating the Top2-MRN-ATM-Chk2 pathway in this context is important. We will examine the localization of key pathway components, including Aurora B, in control and PARI-deficient cells, and include the results in the revised manuscript.

1. The authors use ICRF to generate chromatin bridges. If ICRF is continuously present in their assays, one would expect it to inhibit Top2 and impair the abscission checkpoint (PMIDs: 37638884, 33355621). How do the authors reconcile this with their proposed model?

This is an important point. Studies from the Zachos lab have shown that Topoisomerase IIα-DNA covalent complexes (Top2ccs) accumulate near the midbody in cells with chromatin bridges and play a key role in initiating abscission checkpoint signaling by recruiting MRN, ATM, and Aurora B. Supporting this model, ICRF-193 treatment does not alter midbody disassembly timing in HeLa cells, as shown in Petsalaki et al., 2023 (Figure S4D).

However, our results indicate that ICRF-193-treated HeLa cells exhibit delayed midbody severing, suggesting that at least some aspects of abscission checkpoint signaling remain active under these conditions. One possible explanation for this discrepancy is the difference in ICRF-193 concentration: our study uses a low dose (250 nM) versus 10 µM in the Zachos group study. We favor the hypothesis that this lower dose preserves sufficient Top2 activity to support some level of checkpoint signaling while still effectively generating chromatin bridges.

Additional comments:

Page 8: "Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with PCNA". The authors should test this experimentally or provide appropriate references supporting this claim.

We have clarified our statement and provided the reference: Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with non-SUMOylated PCNA (Armstrong et al. 2012).

1. Page 6: "Deletion of SRS2 further increased the fraction of anaphase cells with RPA foci, rising to approximately 30% in the absence of HU..."; however, this rise was not statistically significant as indicated in Fig. 1C.

Thank you for noting this - we have removed this statement.

Fig. 1C, D: SDs are missing. Fig. 1E: please show the p-values.

These data in Figures 1C-D represent percentages from cells pooled from two independent experiments with similar results. P-values were calculated using Dunn’s multiple comparison test. Standard deviations are not applicable in this case. We have included the p-values for Figure 1E.

Fig. 2D: please show SDs and individual values.

These data represent percentages from cells pooled from independent experiments with similar results. P-values were calculated using Fisher’s exact test. Standard deviations and individual values are not applicable in this case.

1. Why do the authors show the spindle pole body in their movies?

We do this to infer the time of anaphase onset; see our response to points 1-3 and Fig. S2.

Fig. 4A: WT and top2-4 cells have the same symbol in the graph.

We have changed the symbols.

Significance

Strengths: potentially novel regulator of the abscission checkpoint. Timely and interesting topic of broad scientific interest.

Limitations: problems with quality of some data and withy the interpretation. Also, more mechanistic evidence is required to significantly advance our knowledge in the field.

Reviewer #3

Evidence, reproducibility and clarity:

Summary: Building on the specific connection between DNA bridges that bear marks of replication stress and the NoCut checkpoint (Amaral 2016, 2017), which prevents completion of cytokinesis, Dam et al. test the helicase Srs2/PARI for a role in this checkpoint pathway. The authors have produced a thorough study investigating the role of this helicase in both yeast and mammalian cells in the presence of DNA bridges. The manuscript includes clear evidence that Srs2 is important to resolve chromatin bridges, remove replication protein A (RPA) from chromatin, and delay cytokinesis under replication stress. Further, the authors show that loss of Srs2 under replication stress increases DNA damage, marked by elevated MRE11 foci in a manner dependent on cytokinesis (i.e., dependent on Cyk3). Srs2 deletion also partially abrogates the abscission delay seen upon topo-II inactivation. They further report that Srs2 must interact with PCNA to delay abscission in S. cerevisiae. While chromatin bridges formed when a dicentric chromosome is present escape detection by the NoCut checkpoint, inactivation of Elg1, which unloads PCNA and associated factors following DNA replication, results in delayed abscission. In HeLa cells, the Srs2 ortholog PARI is shown to similarly help promote abscission delay in the presence of DNA bridges following topoisomerase inhibition, as loss of PARI through siRNA knockdown prevents this abscission delay. Mechanistically, when PARI levels are reduced in HeLa cells, actin patches that function to stabilize the midbody and protect DNA bridges do not form/persist robustly as in cells with intact PARI. Consistent with a specific role in sensing the presence of a DNA bridge, depletion of PARI did not impact abscission checkpoint activity in response to depletion of the NPC component, Nup153. Finally, the authors show that PARI depletion reduced time to abscission to the same extent as treatment with an Aurora B inhibitor, and PARI depletion in conjunction with Aurora B inhibition did not reduce abscission timing further than singular treatments, suggesting that PARI works within the Aurora B-mediated NoCut signaling cascade.

Major comments: The manuscript is well written and, in general, the conclusions are thoroughly supported, but there are a few recommendations for addition or revision.

1. The first of these is for a more thorough introduction of helicases potentially involved in cytokinesis and more clear rationale for why the focus is on Srs2.

We appreciate the reviewer’s suggestion and have expanded the introduction to better contextualize helicases in cytokinesis and clarify our focus on Srs2.

Figure 1 E lacks statistical analysis. In addition, the text referring to 1E leads to confusion because the distinction between "RPA foci during anaphase" and "RPA coated chromatin bridges" is not made clear. The authors should clarify that the data presented in 1E shows quantification of cells with RPA foci during anaphase, not RPA coated chromatin bridges, and use consistent wording between the text and figure/figure legend. Further, how cells with RPA foci were identified, and what is classified as an RPA focus from images should be described in the methods.

We appreciate the reviewer’s feedback. In the revised manuscript, we have included statistical analysis for Figure 1E and clarified the distinction between "RPA foci during anaphase" and "RPA-coated chromatin bridges" to ensure consistency. Additionally, we have updated the Methods section to specify how cells with RPA foci were identified and what criteria were used to classify RPA foci based on the imaging data.

In some cases, it is unclear whether DNA bridge formation is prevented vs aberrantly broken. For example, under Top2 inactivation, does the absence of Srs2 prevent bridge formation or promote their breakage along with premature midbody abscission? Confirming the frequency of chromatin bridge formation would address this and, further, monitoring RPA persistence would validate whether RPA clearance from bridges is consistently correlated with Srs2 activity (an interesting observation from Figure 1 that is not followed up on). Similarly, other conditions that appear to interfere with abscission delay (e.g., disrupting Srs2-PCNA interaction) should be monitored for whether the formation of DNA bridges has been altered.

We agree this is important and will address it in a full revision. We will quantify chromatin bridge formation under Top2 inactivation to determine whether Srs2 mutations affect bridge frequency or stability. Additionally, we will monitor RPA persistence in top2 cells to assess whether RPA clearance correlates with Srs2 activity. While we find it unlikely that bridge formation is prevented by srs2 mutations, as Top2 is essential for decatenation, our experiments will directly test this possibility.

In Figure 4A, the data show that the PIP-box is required for timely abscission. Imaging data from yeast strains with the PIP-box deletion alone should be included, rather than only showing the deletion in combination with the SIM deletion.

We agree with the reviewer’s suggestion, and will include imaging data from yeast strains with the PIP-box deletion alone in the revised manuscript.

While the authors state that PARI and PCNA were not detectable at bridges in mammalian cells, it would be worth examining whether RPA is persistent on DNA bridges in mammalian cells depleted of PARI to understand how closely this pathway resembles the features found in yeast.

Here too, we agree with the reviewer’s suggestion, and will include imaging data from HeLa cells visualizing RPA in the revised manuscript.

In Figure 6, the authors should describe in the methods how cells with actin patches were identified and quantified and explain what criteria must be met to be identified as an actin patch. Actin patches were described as "disassembling more quickly" in PARI-depleted cells, but the images look as if actin patches are not forming properly in these cells. The images are crisp and clear, but a change in wording may be necessary to accurately describe the data.

Thank you for pointing this out. We agree that the wording was confusing (see our reply to reviewer 2, comment 9) and have revised our description of this figure for greater clarity. In control cells, actin accumulates at the cleavage furrow during anaphase and gradually disperses (clears) as cytokinesis progresses. We do not see patches in untreated cells, and we have updated the y-axis label in Figure 5B from “% of cells with actin patches” to “% of cells with actin clearance” to better reflect our observations. Actin patches were observed only in ICRF-193-treated cells and were often associated with chromatin bridges. Cells that successfully disassembled these actin patches were classified as having completed actin clearance. Our data indicate that PARI depletion increases the fraction of cells that clear chromatin from the division plane, facilitating actin patch disassembly.

Minor suggestions to improve the manuscript are:

Include a diagram that shows hallmarks of cell division and what is being tracked in particular assays (e.g., DNA bridge duration vs time to abscission).

Thank you for this suggestion, which we have implemented in Figure S2A.

In the elegant CLEM experiments presented in Figure 5, organelle labels could be added to orient the readers.

We added organelle labels to CLEM images.

The data in supplemental Figure 2 should be moved to Figure 5. The fact that there are similar levels of chromatin bridges is vital information and stresses that the defect lies in detection and response to the bridge as opposed to formation of bridges when PARI is depleted.

We agree, and have moved Figure S2 to Figure 5 (now Figure 5C).

Significance

The link between DNA bridges and NoCut/abscission checkpoint signaling is a fundamental aspect of cell cycle regulation. This manuscript makes a significant contribution to our understanding of this pathway by introducing a novel role for the helicase Srs2/PARI in execution of an abscission delay in the presence of DNA bridges. This is an important contribution as there is sparse information about cellular factors that mediate detection and response to DNA bridges, which is vital to protecting genome integrity. Although, as the authors themselves state, "the molecular mechanisms by which Srs2 and PARI function in NoCut remain unclear," this study, with some revisions, merits publication as it reveals a conserved role for a factor in this important response pathway and provides new insights into why certain DNA bridges (i.e., bridges formed by dicentric chromosomes) are not recognized by the NoCut pathway.

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Referee #2

Evidence, reproducibility and clarity

The Aurora B-mediated abscission checkpoint ("NoCut" in yeast) prevents tetraploidization or chromatin breakage in the presence of chromatin bridges in cytokinesis and the mechanisms of its activation are a matter of active investigation. In the present study, Dam et al propose that the conserved Srs2/PARI DNA helicase is required for the activation of the abscission checkpoint in response to chromatin bridges generated by DNA replication stress or topoisomerase inhibition. This is a timely and very interesting topic and the potential identification of a novel regulatory protein that activates the abscission checkpoint would be important. However, in my opinion, some Figures are of relatively low quality and need improving, there are apparent discrepancies between data and important control experiments are missing, which preclude the reader from fully evaluating the conclusions of this study. Some direct evidence of the role of Srs2/PARI on DNA bridges is also required. Also, it would be nice to investigate mechanistic details of the potential Srs2/PARI functions in the abscission checkpoint, and how it fits with other recently published signaling pathways that activate the abscission checkpoint in cytokinesis.

Specific comments:

1. The DNA channel (Ht2B-mCherry) in Figure 1A is of very low quality to be able to verify the authors interpretations of when the individual chromatin bridges are resolved (probably broken). For example, in the WT movie, they claim that the bridge is intact in frames 10 min and 14 min (yellow arrow) and that the bridge is resolved at 16 min (asterisk); however, I'm not convinced this is the case, because I can only see a very small portion of the bridge already at the 10 min and 14 min time-points. In my opinion, this bridge could have been broken much earlier, probably at 10 min. Also, WT +HU, is this bridge really intact at 10 min and at 14 min? In Srs2Δ + HU, the bridge appears broken to me much earlier, perhaps at 30 min. There is a distinct possibility that the authors could not calculate the resolution times accurately from these movies (please also see my next comment, #2). The authors could perhaps use a more sensitive bridge marker such as GFP-BAF.
2. In Figure 1B, they conclude that Srs2Δ cells treated with HU exhibit increased time from anaphase onset to bridge resolution compared with WT or Srs2Δ cells. This result appears at odds with data from Fig. 2C showing that Srs2Δ+HU finish abscission at similar times to WT or Srs2Δ cells as judged by plasma membrane morphology. (final cut). Given that the final cut of the plasma membrane should cause chromatin bridges to break, if Srs2 is required for an abscission delay in response to HU-induced chromatin bridges, I would expect Srs2Δ + HU cells to exhibit accelerated plasma membrane cut and also faster chromatin bridge resolution compared with controls. This discrepancy could at least in part be caused by the relatively low quality of movies used for the calculations in Fig. 1.
3. Fig. 2 shows faster abscission times (membrane cut) in Srs2Δ+HU cells compared with WT+HU. The authors interpret this data as evidence for a role of Srs2 in abscission delay in response to HU-induced chromatin bridges (page 7 and elsewhere). However, there is no direct evidence that the cells analyzed in Fig.2 exhibited DNA bridges in cytokinesis. One could argue that HU-induced DNA replication stress caused DNA lesions at the nuclear chromatin, which affected completion of cytokinesis in the absence or presence of Srs2. What proportion of HU-treated cells in cytokinesis exhibit DNA bridges? Judging from Fig. 1D this could be as low as 0-20%. The authors should analyze HU-treated cells that clearly exhibit DNA bridges, either by live-cell imaging or in fixed cells experiments. As it stands and together with my previous comments #1 and 2, I'm not convinced this data fully supports a role for Srs2 in the abscission delay in response to HU-induced DNA bridges.
4. In Fig. 2D, there is no evidence to support that Mre11 foci are caused by bridge breakage, and not by replication-stress induced DNA lesions at the main nucleus (no DNA bridge is evident, also see comment #3).
5. Figure 3: the authors use a top2-4 mutant strain to generate DNA bridges from catenated DNA and investigate the potential role of Srs2 in the abscission delay. However, no DNA bridges are obvious in the cells shown in Fig. 3. What proportion of top2-4 mutant cells in cytokinesis exhibit DNA bridges? Does this explain the striking difference in the percentage of cells that haven't completed abscission after 30-60 min in WT+HU vs Top2-4 cells? Please also see my previous comments above.
6. The authors propose that association of Srs2 with PCNA is required for complete inhibition of abscission in top2-4 mutant cells with chromatin bridges. Assuming a role for Srs2 in abscission timing in cytokinesis with chromatin bridges is fully proven, it is essential that the authors also investigate the localization of Srs2 and PCNA on chromatin bridges, using GFP-tagged proteins or appropriate antibodies in fixed and/or living cells. This would suggest a direct role of these proteins on chromatin bridges and considerably strengthen the authors hypothesis. Alternatively, Srs2 and PCNA may indirectly affect abscission timing through their well-established roles at nuclear chromatin.
7. In Fig. 4D, the authors show an abscission delay in elg1Δ mutant cells in the presence of dicentric bridges compared with cytokinesis without bridges and interpret this as evidence that artificially retaining PCNA on dicentric chromatin bridges is sufficient to inhibit abscission. It is important that the authors demonstrate that PCNA localizes to dicentric bridges in elg1Δ mutant, but not in ELG1 control, cells, e.g., by immunofluorescence, to support their claim and their proposed model.
8. In Fig. 5, the authors claim that HeLa cells treated with the Top2 inhibitor ICRF193 exhibit delayed midbody resolution compared with controls and that depletion of PARI by siRNA accelerates abscission in ICRF-treated cells. They interpret this as evidence for a role of PARI in the abscission delay in response to ICRF-induced chromatin bridges. However, no bridges are visible at any time-frame in cells in Fig. 5B raising the possibility that the observed time-differences are due to some effect of ICRF in cytokinesis without bridges. I'm also not convinced that in Fig. 5B the midbodies in NT/ICRF/230 min, siPARI/DMSO/110 min and siPARI/ICRF/150 min were resolved as indicated by the authors, as I can definitely see both midbody arms very clearly in these photos. The p-values are also just below the p<0.05 threshold, which could in part be due to the quality of the movies quantified. Also, in Fig. 5C, the authors show evidence of DNA at the midbody in ICRF-treated cells by CLEM; however, this DNA appears broken before abscission in both cases and could not have been derived from premature abscission.
9. In Fig. 6, the authors examine actin patches in PARI-depleted and control cells as a marker of abscission. Although a role for PARI in actin patch formation would be very interesting, I'm not sure how it fits with the present story. The actin inside the intercellular canal described by Bai et al (removal of which correlates with abscission) appears very different to the accumulations of actin at the base of the intercellular canal described by Sreigemann et al and by Dandoulaki et al. I can definitely see actin patches (similar to the ones in Steigemann et al) in Fig. 6 NT/ICRF, but I can't see any at the other treatments (I disagree with the arrows). Incidentally, I can see a DNA bridge only in NT/ICRF, but not in the other treatments.
10. Midbody resolutions are clearer in Fig. 7, perhaps with the exception of siPARI/DMSO. However, no DNA bridges are visible, raising again the possibility that the authors investigate effects in cytokinesis without DNA bridges.
11. Can the authors investigate whether the helicase activity of PARI is required for the abscission checkpoint, by depletion-reconstitution experiments with a helicase-mutant protein?
12. The authors should investigate localization of PARI to the midbody/ DNA bridge in cytokinesis with chromatin bridges. Recent reports have proposed that a Top2-MRN-ATM-Chk2 pathway activates the Aurora B-dependent abscission checkpoint in human cells (PMIDs: 37638884, 33355621). The authors should examine localization of Aurora B and some of the above proteins in control and PARI-deficient cells to establish if/how PARI fits in the above pathway.
13. The authors use ICRF to generate chromatin bridges. If ICRF is continuously present in their assays, one would expect it to inhibit Top2 and impair the abscission checkpoint (PMIDs: 37638884, 33355621). How do the authors reconcile this with their proposed model?

Additional comments: 14. Page 8: "Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with PCNA". The authors should test this experimentally or provide appropriate references supporting this claim. 15. Page 6: "Deletion of SRS2 further increased the fraction of anaphase cells with RPA foci, rising to approximately 30% in the absence of HU..."; however, this rise was not statistically significant as indicated in Fig. 1C. 16. Fig. 1C, D: SDs are missing. Fig. 1E: please show the p-values. 17. Fig. 2D: please show SDs and individual values. 18. Why do the authors show the spindle pole body in their movies? 19. Fig. 4A: WT and top2-4 cells have the same symbol in the graph.

Significance

Strengths: potentially novel regulator of the abscission checkpoint. Timely and interesting topic of broad scientific interest.

Limitations: problems with quality of some data and withy the interpretation. Also, more mechanistic evidence is required to significantly advance our knowledge in the field.

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Referee #1

Evidence, reproducibility and clarity

Summary

This study showed measurements and integration of time-series multiple omics data of the human kidney PDGFR beta+ cells responding to TGF-beta stimuli. The authors also presented key pathways that were inferred based on estimating activities of TFs and kinases, and confirmed by knockdown experiments whose phenotypes can be observed by means of imaging.

Major concerns

1. The content of Discussion is too thin. Particularly, it is uncommon to see a discussion section with no citations like this manuscript. Cite related studies and compare with the own results so that the authors can argue originality and novelty of this work. I also see some citations in Results. Usually it is opposite: little citations in Results section and many citations in Discussions.
2. Put more emphasis on presenting biological relevances in order for readers to easily recognize them. I guess that Figs. 4C and 4F are examples of such biological findings.
3. Draw the whole picture(s) of the integrated networks, not only subnetworks. If too much complicated, the complexity itself will be important information for readers.
4. On SMAD2:

4a) The responses of p-SMAD2 in Fig. S2 are remarkably different in the two batches. The authors should discuss the reason of these outcomes. Which of the two batches exhibited similar responses to the phosphoproteome data?

4b) What possible reasons do authors think about that SMAD2/3 are not included in the transcriptional regulatory networks presented in Figs. 3 and 4 in spite of their importance in the TGFbeta signaling? Should be argued.

4c) What molecular mechanism can cause the increase in SERPINE1 expression dependent on TGFbeta? The mechanism may involve SMAD2/3 but neither presented nor argued. Should be clarified.

4d) It seems inconsistent that knockdown of the early-activated TFs cause extensive ECM accumulation in the knockdown experiment presented in Fig. 4B. Did the authors see suppression of ECM accumulation by knockdown of SMAD2/3? Should be presented.

Minor concerns

1. Fig. 1D: Numbers in the Venn diagram of 'proteomics technologies' do not match with the numbers in another Venn diagram on the right hand side. Should be corrected or explained.
2. Fig. 2B: 'INFalpha' should be IFNalpha, so is 'INFgamma'.
3. Fig. 2B, Fig. S4C: What does the sign of 'Pathway enrichment score' mean? How is it calculated? Should be explained.
4. Do not fit curves to data that should be drawn in line graphs (e.g. Figs. 3F, 4E, 4G etc.).
5. How did the authors plot the regression curves presented in Fig. 4D? Should be clarified.
6. What is 'PKN'? Maybe 'Prior Knowledge Network', but clearly spelled out when it first appears.
7. Did the PNK-nodes in the networks exhibit quantitative changes in any of the omics data?
8. What do the axes of the heatmaps mean in Fig. S3A? Why are there more categories than total sample numbers? Should be clarified.

Significance

The omics data were well measured under appropriate quality controls. Hence, this study will attract interests from specialists of kidney fibrosis and systems biologists. But there still remains concerns regarding arguments and data presentation of the manuscript.

Joint Public Review:

Summary:

The authors of the study investigated the generalization capabilities of a deep learning brain age model across different age groups within the Singaporean population, encompassing both elderly individuals aged 55 to 88 years and children aged 4 to 11 years. The model, originally trained on a dataset primarily consisting of Caucasian adults, demonstrated a varying degree of adaptability across these age groups. For the elderly, the authors observed that the model could be applied with minimal modifications, whereas for children, significant fine-tuning was necessary to achieve accurate predictions. Through their analysis, the authors established a correlation between changes in the brain age gap and future executive function performance across both demographics. Additionally, they identified distinct neuroanatomical predictors for brain age in each group: lateral ventricles and frontal areas were key in elderly participants, while white matter and posterior brain regions played a crucial role in children. These findings underscore the authors' conclusion that brain age models hold the potential for generalization across diverse populations, further emphasizing the significance of brain age progression as an indicator of cognitive development and aging processes.

Strengths:

(1) The study tackles a crucial research gap by exploring the adaptability of a brain age model across Asian demographics (Chinese, Malay, and Indian Singaporeans), enriching our knowledge of brain aging beyond Western populations.<br /> (2) It uncovers distinct anatomical predictors of brain aging between elderly and younger individuals, highlighting a significant finding in the understanding of age-related changes and ethnic differences.

In summary, this paper underscores the critical need to include diverse ethnicities in model testing and estimation.

Comments on revisions:

The previously mentioned weaknesses were addressed in the revision process. As stated earlier the paper tackles a crucial research gap by exploring the adaptability of a brain-age model across Asian demographics (Chinese, Malay, and Indian Singaporeans), enriching our knowledge of brain aging beyond Western populations.

Author response:

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

Public Reviews:

Reviewer #1 (Public review): 

Hotinger et al. explore the population dynamics of Salmonella enterica serovar Typhimurium in mice using genetically tagged bacteria. In addition to physiological observations, pathology assessments, and CFU measurements, the study emphasizes quantifying host bottleneck sizes that limit Salmonella colonization and dissemination. The authors also investigate the genetic distances between bacterial populations at various infection sites within the host.

Initially, the study confirms that pretreatment with the antibiotic streptomycin before inoculation via orogastric gavage increases the bacterial burden in the gastrointestinal (GI) tract, leading to more severe symptoms and heightened fecal shedding of bacteria. This pretreatment also significantly reduces between-animal variation in bacterial burden and fecal shedding. The authors then calculate founding population sizes across different organs, discovering a severe bottleneck in the intestine, with founding populations reduced by approximately 10^6-fold compared to the inoculum size. Streptomycin pretreatment increases the founding population size and bacterial replication in the GI tract. Moreover, by calculating genetic distances between populations, the authors demonstrate that, in untreated mice, Salmonella populations within the GI tract are genetically dissimilar, suggesting limited exchange between colonization sites. In contrast, streptomycin pretreatment reduces genetic distances, indicating increased exchange.

In extraintestinal organs, the bacterial burden is generally not substantially increased by streptomycin pretreatment, with significant differences observed only in the mesenteric lymph nodes and bile. However, the founding population sizes in these organs are increased. By comparing genetic distances between organs, the authors provide evidence that subpopulations colonizing extraintestinal organs diverge early after infection from those in the GI tract. This hypothesis is further tested by measuring bacterial burden and founding population sizes in the liver and GI tract at 5 and 120 hours post-infection. Additionally, they compare orogastric gavage infection with the less injurious method of infection via drinking, finding similar results for CFUs, founding populations, and genetic distances. These results argue against injuries during gavage as a route of direct infection. 

To bypass bottlenecks associated with the GI tract, the authors compare intravenous (IV) and intraperitoneal (IP) routes of infection. They find approximately a 10-fold increase in bacterial burden and founding population size in immune-rich organs with IV/IP routes compared to orogastric gavage in streptomycin-pretreated animals. This difference is interpreted as a result of "extra steps required to reach systemic organs."

While IP and IV routes yield similar results in immune-rich organs, IP infections lead to higher bacterial burdens in nearby sites, such as the pancreas, adipose tissue, and intraperitoneal wash, as well as somewhat increased founding population sizes. The authors correlate these findings with the presence of white lesions in adipose tissue. Genetic distance comparisons reveal that, apart from the spleen and liver, IP infections lead to genetically distinct populations in infected organs, whereas IV infections generally result in higher genetic similarity. 

Finally, the authors investigate GI tract reseeding, identifying two distinct routes. They observe that the GI tracts of IP/IV-infected mice are colonized either by a clonal or a diversely tagged bacterial population. In clonally reseeded animals, the genetic distance within the GI tract is very low (often zero) compared to the bile population, which is predominantly clonal or pauciclonal. These animals also display pathological signs, such as cloudy/hardened bile and increased bacterial burden, leading the authors to conclude that the GI tract was reseeded by bacteria from the gallbladder bile. In contrast, animals reseeded by more complex bacterial populations show that bile contributes only a minor fraction of the tags. Given the large founding population size in these animals' GI tracts, which is larger than in orogastrically infected animals, the authors suggest a highly permissive second reseeding route, largely independent of bile. They speculate that this route may involve a reversal of known mechanisms that the pathogen uses to escape from the intestine. 

The manuscript presents a substantial body of work that offers a meticulously detailed understanding of the population dynamics of S. Typhimurium in mice. It quantifies the processes shaping the within-host dynamics of this pathogen and provides new insights into its spread, including previously unrecognized dissemination routes. The methodology is appropriate and carefully executed, and the manuscript is well-written, clearly presented, and concise. The authors' conclusions are well-supported by experimental results and thoroughly discussed. This work underscores the power of using highly diverse barcoded pathogens to uncover the within-host population dynamics of infections and will likely inspire further investigations into the molecular mechanisms underlying the bottlenecks and dissemination routes described here.

Major point:

Substantial conclusions in the manuscript rely on genetic distance measurements using the Cavalli-Sforza chord distance. However, it is unclear whether these genetic distance measurements are independent of the founding population size. I would anticipate that in populations with larger founding population sizes, where the relative tag frequencies are closer to those in the inoculum, the genetic distances would appear smaller compared to populations with smaller founding sizes independent of their actual relatedness. This potential dependency could have implications for the interpretation of findings, such as those in Figures 2B and 2D, where antibiotic-pretreated animals consistently exhibit higher founding population sizes and smaller genetic distances compared to untreated animals.

Thank you for raising this important point regarding reliance on cord distances for gauging genetic distance in barcoded populations. The reviewer is correct that samples with more founders will be more similar to the inoculum and thus inherently more similar to other samples that also have more founders. However, creation of libraries containing very large numbers of unique barcodes can often circumvent this issue. In this case, the effect size of chance-based similarity is not large enough to change the interpretation of the data in Figures 2B and 2D. In our case, the library has ~6x10⁴ barcodes, and the founding populations in Figure 2B are ~10³. Randomly resampling to create two populations of 10³ cells from an initial population with 6x10⁴ barcodes is expected to yield largely distinct populations with very little similarity. Thus, the similarity between streptomycin-treated populations in Figure 2D is likely the result of biology rather than chance.  

Reviewer #2 (Public review):

In this paper, Hotinger et. al. propose an improved barcoded library system, called STAMPR, to study Salmonella population dynamics during infection. Using this system, the authors demonstrate significant diversity in the colonization of different Salmonella clones (defined by the presence of different barcodes) not only across different organs (liver, spleen, adipose tissues, pancreas, and gall bladder) but also within different compartments of the same gastrointestinal tissue. Additionally, this system revealed that microbiota competition is the major bottleneck in Salmonella intestinal colonization, which can be mitigated by streptomycin treatment. However, this has been demonstrated previously in numerous publications. They also show that there was minimal sharing between populations found in the intestine and those in the other organs. Upon IV and IP infection to bypass the intestinal bottleneck, they were able to demonstrate, using this library, that Salmonella can renter the intestine through two possible routes. One route is essentially the reverse path used to escape the gut, leading to a diverse intestinal population; while the other, through the bile, typically results in a clonal population. Although the authors showed that the STAMPR pipeline improved the ability to identify founder populations and their diversity within the same animal during infections, some of the conclusions appear speculative and not fully supported.

(1) It's particularly interesting how the authors, using this system, demonstrate the dominant role of the microbiota bottleneck in Salmonella colonization and how it is widened by antibiotic treatment (Figure 1). Additionally, the ability to track Salmonella reseeding of the gut from other organs starting with IV and IP injections of the pathogen provides a new tool to study population dynamics (Figure 5). However, I don't think it is possible to argue that the proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces have different founder populations for reasons other than stochastic variations. All the barcoded Salmonella clones have the same fitness and the fact that some are found or expanded in one region of the gastrointestinal tract rather than another likely results from random chance - such as being forced in a specific region of the gut for physical or spatial reasons-and subsequent expansion, rather than any inherent biological cause. For example, some bacteria may randomly adhere to the mucus, some may swim toward the epithelial layer, while others remain in the lumen; all will proliferate in those respective sites. In this way, different founder populations arise based on random localization during movement through the gastrointestinal tract, which is an observation, but it doesn't significantly contribute to understanding pathogen colonization dynamics or pathogenesis. Therefore, I would suggest placing less emphasis on describing these differences or better discussing this aspect, especially in the context of the gastrointestinal tract.

Thank you for helping us identify this area for further clarification. We agree with the reviewer’s interpretation that seeding of proximal and distal small intestine, Peyer's patches (PPs), cecum, colon, and feces with different founder populations is likely caused by stochastic variations, consistent with separate stochastic bottlenecks to establishing these separate niches. To clarify this point we have modified the text in the results section, “Streptomycin treatment decreases compartmentalization of S. Typhimurium populations within the intestine”.

Change to text:

“Except for the cecum and colon, in untreated animals the S. Typhimurium populations in different regions of the intestine were dissimilar (Avg. GD ranged from 0.369 to 0.729, 2D left); i.e., there is little sharing between populations in the intestine. These data suggest that there are separate bottlenecks in different regions of the intestine that cause stochastic differences in the identity of the founders. Interestingly, when these founders replicate, they do not mix, remaining compartmentalized with little sharing between populations throughout the intestinal tract (i.e., barcodes found in one region are not in other regions, Figure S3). This was surprising as the luminal contents, an environment presumably conducive to bacterial movement, were not removed from these samples.”

In this section we are interested in the underlying biology that occurs after the initial bottleneck to preserve this compartmentalization during outgrowth of the intestinal population. In other words, what prevents these separate populations from merging (e.g., what prevents the bacteria replicating in the proximal small intestine from traveling through the intestine and establishing a niche in the distal small intestine)? While we do not explore the mechanisms of compartmentalization, we observe that it is disrupted by streptomycin pretreatment, suggesting a microbiota-dependent biological cause. 

(2) I do think that STAMPR is useful for studying the dynamics of pathogen spread to organs where Salmonella likely resides intracellularly (Figure 3). The observation that the liver is colonized by an early intestinal population, which continues to proliferate at a steady rate throughout the infection, is very interesting and may be due to the unique nature of the organ compared to the mucosal environment. What is the biological relevance during infection? Do the authors observe the same pattern (Figures 3C and G) when normalizing the population data for the spleen and mesenteric lymph nodes (mLN)? If not, what do the authors think is driving this different distribution?

Thank you for raising this interesting point. These data indicate that the liver is seeded from the intestine early during infection. The timing and source of dissemination have relevance for understanding how host and pathogen variables control the spread of bacteria to systemic sites. For example, our conclusion (early dissemination) indicates that the immune state of a host at the time of exposure to a pathogen, and for a short period thereafter, are what primarily influence the process of dissemination, not the later response to an active infection. 

We observe that the liver and mucosal environments within the intestine have similar colonization behaviors. Both niches are seeded early during infection, followed by steady pathogen proliferation and compartmentalization that apparently inhibits further seeding. This results in the identity of barcodes in the liver population remaining distinct from the intestinal populations, and the intestinal populations remaining distinct from each other.

We observe a similar pattern to the liver in the spleen and MLN (the barcodes in the spleen and MLN are dissimilar to the population in the intestine). To clarify this point, we have modified the text (below) and added this analysis as a supplemental figure (S4).

Change to text:

Genetic distance comparison of liver samples to other sites revealed that, regardless of streptomycin treatment, there was very little sharing of barcodes between the intestine and extraintestinal sites (Avg. GD >0.75, Figure 3C). Furthermore, the MLN and spleen populations also lacked similarity with the intestine (Figure S4). These analyses strongly support the idea that S. Typhimurium disseminates to extraintestinal organs relatively early following inoculation, before it establishes a replicative niche in the intestine.

(3) Figure 6: Could the bile pathology be due to increased general bacterial translocation rather than Salmonella colonization specifically? Did the authors check for the presence of other bacteria (potentially also proliferating) in the bile? Do the authors know whether Salmonella's metabolic activity in the bile could be responsible for gallbladder pathology?

The reviewer raises interesting points for future work. We did not check whether other bacterial species are translocating during S. Typhimurium infection. The relevance of Salmonella’s metabolic activity is also very interesting, and we hope these questions will be answered by future studies.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor points:

(1) P. 9/10 "... the marked delay in shedding after IP and IV relative to orogastric inoculation suggest that the S. Typhimurium population encounters substantial bottleneck(s) on the route(s) from extraintestinal sites back to the intestine.": Can you conclude that from the data? It could also be possible that there is a biological mechanism (other than chance events) that delays the re-entry to the intestine.

We propose that the delay in shedding indicates additional obstacles that bacteria face when re-entering the intestine, and that there are likely biological mechanisms that cause this delay. However, these unknown mechanisms effectively act as additional bottlenecks by causing a stochastic loss of population diversity. 

(2) P. 11 "...both organs would likely contain all 10 barcodes. In contrast, a library with 10,000 barcodes can be used to distinguish between a bottleneck resulting in Ns = 1,000 and Ns = 10,000, since these bottlenecks result in a different number of barcodes in output samples. Furthermore, high diversity libraries reduce the likelihood that two tissue samples share the same barcode(s) due to random chance, enabling more accurate quantification of bacterial dissemination.": I agree with the general analysis, but I find it misleading to talk about the presence of barcodes when the analyses in this manuscript are based on the much more powerful comparison of relative abundance of individual tags instead of their presence or absence.

The reviewer raises an excellent point, and the distinction between relative abundance versus presence/absence is discussed extensively in the original STAMPR manuscript. Although relative abundance is powerful, the primary metric used in this study (Ns) is calculated principally from the number of barcodes, corrected (via simulations) for the probability of observing the same barcode across distinct founders. Although this correction procedure does rely on barcode abundance, the primary driver of founding population quantification is the number of barcodes.

(3) P.14 "the library in LB supplemented with SM was not significantly different than the parent strain" and Figure 2C: How was significance tested? How many times were the growth curves recorded? On my print-out, the red color has different shades for different growth curves.

Significance was tested with a Mann-Whitney and growth curves were performed 5 times. Growth curves are displayed with 50% opacity, and as a result multiple curves directly on top of each other appear darker. The legend to S2 has been modified accordingly.

(4) P.16: close bracket in the equation for FRD calculation.

Done

(5) Figure 2C "Average CFU per founder": I found the wording confusing at first as I thought you divided the average bacterial burden per organ by Ns, instead of averaging the CFU/Ns calculated for each mouse.

The wording has been clarified. 

(6) Figure 3B: It would be helpful to include expected genetic distances in the schematic as it is difficult to infer the genetic distance when only two of three, respectively, different "barcode colors" are used. While I find the explanation in the main text intuitive, a graphical representation would have helped me.

Thank you for the suggestion. Unfortunately, using colors to represent barcodes is imperfect and limits the diversity that can be depicted. We have modified Figure 3B to further clarify. 

(7) Figure 3C: Why do you compare the genetic distance to the liver, when you discuss the genetic distance of the intestinal population? Is it not possible that the intestinal populations are similar to the extraintestinal organs except the liver?

For clarity, we chose to highlight exclusively the liver. However, we observed a similar pattern to the liver in other extraintestinal organs. To clarify the generalizability of this point we have added a supplemental figure with comparisons to MLN and Spleen (Supplemental figure S4) as well as further text.

(8) Figure 3C & S5A: I found "+SM" and "+SM, Drinking" confusing and would have preferred "+SM, Gavage" and "+SM, Drinking" for clarity.

Done, thank you for the suggestion.

(9) Figure 3G&H: I find it worthy of discussion that the bacterial burden increases over time, while the founding population decreases. Does that not indicate that replication only occurs at specific sites leading to the amplification of only a few barcodes and thereby a larger change of the relative barcode abundance compared to the inoculum?

From 5h to 120h the size of the founding population decreases in multiple intestinal sites. This likely indicates that the impact of the initial bottleneck is still ongoing at 5h, although further temporal analysis would be required to define the exact timing of the bottleneck. Notably, the passage time through the mouse intestine is ~5h. Many of the founders observed at 5h could be a population that will never establish a replicative niche, and failing to colonize be shed in the feces, bottlenecking the population between 5h and 120h. To clarify this point we have added the following text:

Section “S. Typhimurium disseminates out of the intestine before establishing an intestinal replicative niche”.

“In contrast to the liver, there were more founders present in samples from the intestine (particularly in the colon) at 5 hours versus 120 hours (Figure 3H). These data likely indicate that many of the founders observed in the intestine at 5 hours are shed in the feces prior to establishing a replicative niche, and demonstrates that the forces restricting the S. Typhimurium population in the intestine act over a period of > 5 hours.”  

(10) Figure S2A: I do not understand this figure. Why are there more than 70.000 tags listed? I was under the impression the barcode library in S. Typhimurium had 55.000 tags while only the plasmid pSM1 had more than 70.000 (but the plasmid should not be relevant here). Why are there distinct lines at approximately 10^-5 and a bit lower? I would have expected continuously distributed barcode frequencies.

During barcode analysis, each library is mapped to the total barcode list in the barcode donor pSM1, which contains ~70,000 barcodes. This enables consistent analysis across different bacterial libraries. The designation “barcode number” refers to the barcode number in pSM1, meaning many of the barcodes in the Salmonella library are at zero reads. This graph type was chosen to show there was no bias toward a particular barcode, however there is significant overlap of the points, making individual barcode frequencies difficult to see. We have changed the x-axis to state “pSM1 Barcode Number” and clarified in the figure legend.

Since the y-axes on these graphs is on a log10 scale, the lines represent barcodes with 1 read, 2 reads, 3 reads, etc. As the number of reads per barcode increases linearly, the space between them decreases on logarithmic axes.

(11) There are a few typos in the figure legends of the supplementary material. For example Figure S2: S. Typhimurium not italicized, ~7x105 no superscript. Fig. S4&5 ", Open circles" is "O" is capitalized.

Typos have been corrected.

Reviewer #1 (Public review):

Summary:

The manuscript investigates lipid scrambling mechanisms across TMEM16 family members using coarse-grained molecular dynamics (MD) simulations. While the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations, several critical issues undermine its novelty, impact, and alignment with experimental observations.

Critical issues:

(1) Lack of Novelty:<br /> The phenomenon of lipid scrambling via an open hydrophilic groove is already well-established in the literature, including through atomistic MD simulations. The authors themselves acknowledge this fact in their introduction and discussion. By employing coarse-grained simulations, the study essentially reiterates previously known findings with limited additional mechanistic insight. The repeated observation of scrambling occurring predominantly via the groove does not offer significant advancement beyond prior work.

(2) Redundancy Across Systems:<br /> The manuscript explores multiple TMEM16 family members in activating and non-activating conformations, but the conclusions remain largely confirmatory. The extensive dataset generated through coarse-grained MD simulations primarily reinforces established mechanistic models rather than uncovering fundamentally new insights. The effort, while statistically robust, feels excessive given the incremental nature of the findings.

(3) Discrepancy with Experimental Observations:<br /> The use of coarse-grained simulations introduces inherent limitations in accurately representing lipid scrambling dynamics at the atomistic level. Experimental studies have highlighted nuances in lipid permeation that are not fully captured by coarse-grained models. This discrepancy raises questions about the biological relevance of the reported scrambling events, especially those occurring outside the canonical groove.

(4) Alternative Scrambling Sites:<br /> The manuscript reports scrambling events at the dimer-dimer interface as a novel mechanism. While this observation is intriguing, it is not explored in sufficient detail to establish its functional significance. Furthermore, the low frequency of these events (relative to groove-mediated scrambling) suggests they may be artifacts of the simulation model rather than biologically meaningful pathways.

Conclusion:

Overall, while the study is technically sound and presents a large dataset of lipid scrambling events across multiple TMEM16 structures, it falls short in terms of novelty and mechanistic advancement. The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript investigates lipid scrambling mechanisms across TMEM16 family members using coarse-grained molecular dynamics (MD) simulations. While the study presents a statistically rigorous analysis of lipid scrambling events across multiple structures and conformations, several critical issues undermine its novelty, impact, and alignment with experimental observations.

Critical issues:

(1) Lack of Novelty:

The phenomenon of lipid scrambling via an open hydrophilic groove is already well-established in the literature, including through atomistic MD simulations. The authors themselves acknowledge this fact in their introduction and discussion. By employing coarse-grained simulations, the study essentially reiterates previously known findings with limited additional mechanistic insight. The repeated observation of scrambling occurring predominantly via the groove does not offer significant advancement beyond prior work.

We agree with the reviewer’s statement regarding the lack of novelty when it comes to our observations of scrambling in the groove of open Ca²⁺-bound TMEM16 structures. However, we feel that the inclusion of closed structures in this study, which attempts to address the yet unanswered question of how scrambling by TMEM16s occurs in the absence of Ca²⁺, offers new observations for the field. In our study we specifically address to what extent the induced membrane deformation, which has been theorized to aid lipids cross the bilayer especially in the absence of Ca²⁺, contributes to the rate of scrambling (see references 36, 59, and 66). There are also several TMEM16F structures solved under activating conditions (bound to Ca²⁺ and in the presence of PIP2) which feature structural rearrangements to TM6 that may be indicative of an open state (PDB 6P48) and had not been tested in simulations. We show that these structures do not scramble and thereby present evidence against an out-of-the-groove scrambling mechanism for these states. Although we find a handful of examples of lipids being scrambled by Ca²⁺-free structures of TMEM16 scramblases, none of our simulations suggest that these events are related to the degree of deformation.

(2) Redundancy Across Systems:

The manuscript explores multiple TMEM16 family members in activating and non-activating conformations, but the conclusions remain largely confirmatory. The extensive dataset generated through coarse-grained MD simulations primarily reinforces established mechanistic models rather than uncovering fundamentally new insights. The effort, while statistically robust, feels excessive given the incremental nature of the findings.

Again, we agree with the reviewer’s statement that our results largely confirm those published by other groups and our own. We think there is however value in comparing the scrambling competence of these TMEM16 structures in a consistent manner in a single study to reduce inconsistencies that may be introduced by different simulation methods, parameters, environmental variables such as lipid composition as used in other published works of single family members. The consistency across our simulations and high number of observed scrambling events have allowed us to confirm that the mechanism of scrambling is shared by multiple family members and relies most obviously on groove dilation.

(3) Discrepancy with Experimental Observations:

The use of coarse-grained simulations introduces inherent limitations in accurately representing lipid scrambling dynamics at the atomistic level. Experimental studies have highlighted nuances in lipid permeation that are not fully captured by coarse-grained models. This discrepancy raises questions about the biological relevance of the reported scrambling events, especially those occurring outside the canonical groove.

We thank the reviewer for bringing up the possible inaccuracies introduced by coarse graining our simulations. This is also a concern for us, and we address this issue extensively in our discussion. As the reviewer pointed out above, our CG simulations have largely confirmed existing evidence in the field which we think speaks well to the transferability of observations from atomistic simulations to the coarse-grained level of detail. We have made both qualitative and quantitative comparisons between atomistic and coarse-grained simulations of nhTMEM16 and TMEM16F (Figure 1, Figure 4-figure supplement 1, Figure 4-figure supplement 5) showing the two methods give similar answers for where lipids interact with the protein, including outside of the canonical groove. We do not dispute the possible discrepancy between our simulations and experiment, but our goal is to share new nuanced ideas for the predicted TMEM16 scrambling mechanism that we hope will be tested by future experimental studies.

(4) Alternative Scrambling Sites:

The manuscript reports scrambling events at the dimer-dimer interface as a novel mechanism. While this observation is intriguing, it is not explored in sufficient detail to establish its functional significance. Furthermore, the low frequency of these events (relative to groove-mediated scrambling) suggests they may be artifacts of the simulation model rather than biologically meaningful pathways.

We agree with the reviewer that our observed number of scrambling events in the dimer interface is too low to present it as strong evidence for it being the alternative mechanism for Ca²⁺-independent scrambling. This will require additional experiments and computational studies which we plan to do in future research. However, we are less certain that these are artifacts of the coarse-grained simulation system as we observed a similar event in an atomistic simulation of TMEM16F.

Conclusion:

Overall, while the study is technically sound and presents a large dataset of lipid scrambling events across multiple TMEM16 structures, it falls short in terms of novelty and mechanistic advancement. The findings are largely confirmatory and do not bridge the gap between coarse-grained simulations and experimental observations. Future efforts should focus on resolving these limitations, possibly through atomistic simulations or experimental validation of the alternative scrambling pathways.

Reviewer #2 (Public review):

Summary:

Stephens et al. present a comprehensive study of TMEM16-members via coarse-grained MD simulations (CGMD). They particularly focus on the scramblase ability of these proteins and aim to characterize the "energetics of scrambling". Through their simulations, the authors interestingly relate protein conformational states to the membrane's thickness and link those to the scrambling ability of TMEM members, measured as the trespassing tendency of lipids across leaflets. They validate their simulation with a direct qualitative comparison with Cryo-EM maps.

Strengths:

The study demonstrates an efficient use of CGMD simulations to explore lipid scrambling across various TMEM16 family members. By leveraging this approach, the authors are able to bypass some of the sampling limitations inherent in all-atom simulations, providing a more comprehensive and high-throughput analysis of lipid scrambling. Their comparison of different protein conformations, including open and closed groove states, presents a detailed exploration of how structural features influence scrambling activity, adding significant value to the field. A key contribution of this study is the finding that groove dilation plays a central role in lipid scrambling. The authors observe that for scrambling-competent TMEM16 structures, there is substantial membrane thinning and groove widening. The open Ca²⁺-bound nhTMEM16 structure (PDB ID 4WIS) was identified as the fastest scrambler in their simulations, with scrambling rates as high as 24.4 {plus minus} 5.2 events per μs. This structure also shows significant membrane thinning (up to 18 Å), which supports the hypothesis that groove dilation lowers the energetic barrier for lipid translocation, facilitating scrambling.

The study also establishes a correlation between structural features and scrambling competence, though analyses often lack statistical robustness and quantitative comparisons. The simulations differentiate between open and closed conformations of TMEM16 structures, with open-groove structures exhibiting increased scrambling activity, while closed-groove structures do not. This finding aligns with previous research suggesting that the structural dynamics of the groove are critical for scrambling. Furthermore, the authors explore how the physical dimensions of the groove qualitatively correlate with observed scrambling rates. For example, TMEM16K induces increased membrane thinning in its open form, suggesting that membrane properties, along with structural features, play a role in modulating scrambling activity.

Another significant finding is the concept of "out-of-the-groove" scrambling, where lipid translocation occurs outside the protein's groove. This observation introduces the possibility of alternate scrambling mechanisms that do not follow the traditional "credit-card model" of groove-mediated lipid scrambling. In their simulations, the authors note that these out-of-the-groove events predominantly occur at the dimer interface between TM3 and TM10, especially in mammalian TMEM16 structures. While these events were not observed in fungal TMEM16s, they may provide insight into Ca²⁺-independent scrambling mechanisms, as they do not require groove opening.

Weaknesses:

A significant challenge of the study is the discrepancy between the scrambling rates observed in CGMD simulations and those reported experimentally. Despite the authors' claim that the rates are in line experimentally, the observed differences can mean large energetic discrepancies in describing scrambling (larger than 1kT barrier in reality). For instance, the authors report scrambling rates of 10.7 events per μs for TMEM16F and 24.4 events per μs for nhTMEM16, which are several orders of magnitude faster than experimental rates. While the authors suggest that this discrepancy could be due to the Martini 3 force field's faster diffusion dynamics, this explanation does not fully account for the large difference in rates. A more thorough discussion on how the choice of force field and simulation parameters influence the results, and how these discrepancies can be reconciled with experimental data, would strengthen the conclusions. Likewise, rate calculations in the study are based on 10 μs simulations, while experimental scrambling rates occur over seconds. This timescale discrepancy limits the study's accuracy, as the simulations may not capture rare or slow scrambling events that are observed experimentally and therefore might underestimate the kinetics of scrambling. It's however important to recognize that it's hard (borderline unachievable) to pinpoint reasonable kinetics for systems like this using the currently available computational power and force field accuracy. The faster diffusion in simulations may lead to overestimated scrambling rates, making the simulation results less comparable to real-world observations. Thus, I would therefore read the findings qualitatively rather than quantitatively. An interesting observation is the asymmetry observed in the scrambling rates of the two monomers. Since MARTINI is known to be limited in correctly sampling protein dynamics, the authors - in order to preserve the fold - have applied a strong (500 kJ mol-1 nm-2) elastic network. However, I am wondering how the ENM applies across the dimer and if any asymmetry can be noticed in the application of restraints for each monomer and at the dimer interface. How can this have potentially biased the asymmetry in the scrambling rates observed between the monomers? Is this artificially obtained from restraining the initial structure, or is the asymmetry somehow gatekeeping the scrambling mechanism to occur majorly across a single monomer? Answering this question would have far-reaching implications to better describe the mechanism of scrambling.

The main aim of our computational survey was to directly compare all relevant published TMEM16 structures in both open and closed states using the Martini 3 CGMD force field. Our standardized simulation and analysis protocol allowed us to quantitatively compare scrambling rates across the TMEM16 family, something that has never been done before. We do acknowledge that direct comparison between simulated versus experimental scrambling rates is complicated and is best to be interpreted qualitatively. In line with other reports (e.g., Li et al, PNAS 2024), lipid scrambling in CGMD is 2-3 orders of magnitude faster than typical experimental findings. In the CG simulation field, these increased dynamics due to the smoother energy landscape are a well known phenomenon. In our view, this is a valuable trade-off for being able to capture statistically robust scrambling dynamics and gain mechanistic understanding in the first place, since these are currently challenging to obtain otherwise. For example, with all-atom MD it would have been near-impossible to conclude that groove openness and high scrambling rates are closely related, simply because one would only measure a handful of scrambling events in (at most) a handful of structures.

Considering the elastic network: the reviewer is correct in that the elastic network restrains the overall structure to the experimental conformation. This is necessary because the Martini 3 force field does not accurately model changes in secondary (and tertiary) structure. In fact, by retaining the structural information from the experimental structures, we argue that the elastic network helped us arrive at the conclusion that groove openness is the major contributing factor in determining a protein’s scrambling rate. This is best exemplified by the asymmetric X-ray structure of TMEM16K (5OC9), in which the groove of one subunit is more dilated than the other. In our simulation, this information was stored in the elastic network, yielding a 4x higher rate in the open groove than in the closed groove, within the same trajectory.

Notably, the manuscript does not explore the impact of membrane composition on scrambling rates. While the authors use a specific lipid composition (DOPC) in their simulations, they acknowledge that membrane composition can influence scrambling activity. However, the study does not explore how different lipids or membrane environments or varying membrane curvature and tension, could alter scrambling behaviour. I appreciate that this might have been beyond the scope of this particular paper and the authors plan to further chase these questions, as this work sets a strong protocol for this study. Contextualizing scrambling in the context of membrane composition is particularly relevant since the authors note that TMEM16K's scrambling rate increases tenfold in thinner membranes, suggesting that lipid-specific or membrane-thickness-dependent effects could play a role.

Considering different membrane compositions: for this study, we chose to keep the membranes as simple as possible. We opted for pure DOPC membranes, because it has (1) negligible intrinsic curvature, (2) forms fluid membranes, and (3) was used previously by others (Li et al, PNAS 2024). As mentioned by the reviewer, we believe our current study defines a good standardized protocol and solid baseline for future efforts looking into the additional effects of membrane composition, tension, and curvature that could all affect TMEM16-mediated lipid scrambling.

Reviewer #3 (Public review):

Strengths:

The strength of this study emerges from a comparative analysis of multiple structural starting points and understanding global/local motions of the protein with respect to lipid movement. Although the protein is well-studied, both experimentally and computationally, the understanding of conformational events in different family members, especially membrane thickness less compared to fungal scramblases offers good insights.

We appreciate the reviewer recognizing the value of the comparative study. In addition to valuable insights from previous experimental and computational work, we hope to put forward a unifying framework that highlights various TMEM16 structural features and membrane properties that underlie scrambling function.

Weaknesses:

The weakness of the work is to fully reconcile with experimental evidence of Ca²⁺-independent scrambling rates observed in prior studies, but this part is also challenging using coarse-grain molecular simulations. Previous reports have identified lipid crossing, packing defects, and other associated events, so it is difficult to place this paper in that context. However, the absence of validation leaves certain claims, like alternative scrambling pathways, speculative.

It is generally difficult to quantitatively compare bulk measurements of scrambling phenomena with simulation results. The advantage of simulations is to directly observe the transient scrambling events at a spatial and temporal resolution that is currently unattainable for experiments. The current experimental evidence for the precise mechanism of Ca²⁺-independent scrambling is still under debate. We therefore hope to leverage the strength of MD and statistical rigor of coarse-grained simulations to generate testable hypotheses for further structural, biochemical, and computational studies.

Author response:

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

Reviewer #1 (Public review):

Summary:

The paper proposes that the placement of criteria for determining whether a stimulus is 'seen' or 'unseen' can significantly impact the validity of neural measures of consciousness. The authors found that conservative criteria, which require stronger evidence to classify a stimulus as 'seen,' tend to inflate effect sizes in neural measures, making conscious processing appear more pronounced than it is. Conversely, liberal criteria, which require less evidence, reduce these effect sizes, potentially underestimating conscious processing. This variability in effect sizes due to criterion placement can lead to misleading conclusions about the nature of conscious and unconscious processing.

Furthermore, the study highlights that the Perceptual Awareness Scale (PAS), a commonly used tool in consciousness research, does not effectively mitigate these criterion-related confounds. This means that even with PAS, the validity of neural measures can still be compromised by how criteria are set. The authors emphasize the need for careful consideration and standardization of criterion placement in experimental designs to ensure that neural measures accurately reflect the underlying cognitive processes. By addressing this issue, the paper aims to improve the reliability and validity of findings in the field of consciousness research.

Strengths:

(1) This research provides a fresh perspective on how criterion placement can significantly impact the validity of neural measures in consciousness research.

(2) The study employs robust simulations and EEG experiments to demonstrate the effects of criterion placement, ensuring that the findings are well-supported by empirical evidence.

(3) By highlighting the limitations of the PAS and the impact of criterion placement, the study offers practical recommendations for improving experimental designs in consciousness research.

Weaknesses:

The primary focused criterion of PAS is a commonly used tool, but there are other measures of consciousness that were not evaluated, which might also be subject to similar or different criterion limitations. A simulation could applied to these metrics to show how generalizable the conclusion of the study is.

We would like to thank reviewer 1 for their positive words and for taking the time to evaluate our manuscript. We agree that it would be important to gauge generalization to other metrics of consciousness. Note however, that the most commonly used alternative methods are postdecision wagering and confidence, both of which are known to behave quite similarly to the PAS (Sandberg, Timmermans , Overgaard & Cleeremans, 2010). Indeed, we have confirmed in other work that confidence is also sensitive to criterion shifts (see https://osf.io/preprints/psyarxiv/xa4fj). Although it has been claimed that confidence-derived aggregate metrics like meta-d’ or metacognitive efficiency may overcome criterion shifts, it would require empirical data rather than simulation to settle whether this is true or not (also see the discussion in https://osf.io/preprints/psyarxiv/xa4fj). Furthermore, out of these metrics, the PAS seems to be the preferred one amongst consciouness researchers (see figure 4 in Francken, Beerendonk, Molenaar, Fahrenfort, Kiverstein, Seth, Gaal S van, 2022; as well as https://osf.io/preprints/psyarxiv/bkxzh). Thus, given the fact that other metrics are either expected to behave in similar ways and/or because it would require more empirical work to determine along which dimension(s) criterion shifts would operate in alternative metrics, we see no clear path to implement the suggested simulations. We anticipate that aiming to do this would require a considerable amount of additional work, figuring out many things which we believe would better suit a future project. We would of course be open to doing this if the reviewer would have more specific suggestions for how to go about the proposed simulations.

Reviewer #2 (Public review):

Summary:

The study investigates the potential influence of the response criterion on neural decoding accuracy in consciousness and unconsciousness, utilizing either simulated data or reanalyzing experimental data with post-hoc sorting data.

Strengths:

When comparing the neural decoding performance of Target versus NonTarget with or without post-hoc sorting based on subject reports, it is evident that response criterion can influence the results. This was observed in simulated data as well as in two experiments that manipulated the subject response criterion to be either more liberal or more conservative. One experiment involved a two-level response (seen vs unseen), while the other included a more detailed four-level response (ranging from 0 for no experience to 3 for a clear experience). The findings consistently indicated that adopting a more conservative response criterion could enhance neural decoding performance, whether in conscious or unconscious states, depending on the sensitivity or overall response threshold.

Weaknesses:

(1) The response criterion plays a crucial role in influencing neural decoding because a subject's report may not always align with the actual stimulus presented. This discrepancy can occur in cases of false alarms, where a subject reports seeing a target that was not actually there, or in cases where a target is present but not reported. Some may argue that only using data from consistent trials (those with correct responses) would not be affected by the response criterion. However, the authors' analysis suggests that a conservative response criterion not only reduces false alarms but also impacts hit rates. It is important for the authors to further investigate how the response criterion affects neural decoding even when considering only correct trials.

We would like to thank reviewer 2 for taking the time to evaluate our manuscript. We appreciate the suggestion to investigate neural decoding on only correct trials. What we in fact did is consider target trials that are 'correct' (hits = seen target present trials) and 'incorrect' (misses = unseen target present trials) separately, see figure 4A and figure 4B. This shows that the response criterion also affects the neural measure of consciousness when only considering correct target present trials. Note however, that one cannot decode 'unseen' (target present) trials if one only aims to decode 'correct' trials, because those are all incorrect by definition. We did not analyze false alarms (these would be the 'seen' trials on the noise distribution of Figure 1A), as there were not enough trials of those, especially in the conservative condition (see Figure 2C and 2D), making comparisons between conservative and liberal impossible. However, the predictions for false alarms are pretty straightforward, and follow directly from the framework in Figure 1.

(2) The author has utilized decoding target vs. nontarget as the neural measures of unconscious and/or conscious processing. However, it is important to note that this is just one of the many neural measures used in the field. There are an increasing number of studies that focus on decoding the conscious content, such as target location or target category. If the author were to include results on decoding target orientation and how it may be influenced by response criterion, the field would greatly benefit from this paper.

We thank the reviewer for the suggestion to decode orientation of the target. In our experiments, the target itself does not have an orientation, but the texture of which it is composed does. We used four orientations, which were balanced out within and across conditions such that presence-absence decoding is never driven by orientation, but rather by texture based figure-ground segregation (for similar logic, see for example Fahrenfort et al, 2007; 2008 etc). There are a couple of things to consider when wanting to apply a decoding analysis on the orientation of these textures:

(1) Our behavioral task was only on the presence or absence of the target, not on the orientation of the textures. This makes it impossible to draw any conclusions about the visibility of the orientation of the textures. Put differently: based on behavior there is no way of identifying seen or unseen orientations, correctly or incorrectly identified orientations etc. For examply, it is easy to envision that an observer detects a target without knowing the orientation that defines it, or vice versa a situation in which an observer does not detect the target while still being aware of the orientation of a texture in the image (either of the figure, or of the background). The fact that we have no behavioral response to the orientation of the textures severely limits the usefulness of a hypothetical decoding effect on these orientations, as such results would be uninterpretable with respect to the relevant dimension in this experiment, which is visibility.

(2) This problem is further excarbated by the fact that the orientation of the background is always orthogonal to the orientation of the target. Therefore, one would not only be decoding the orientation of the texture that constitutes the target itself, but also the texture that constitutes the background. Given that we also have no behavioral metric of how/whether the orientation of the background is perceived, it is similarly unclear how one would interpret any observed effect.

(3) Finally, it is important to note that – even when categorization/content is sometimes used as an auxiliary measure in consciousness research (often as a way to assay objective performance) - consciousness is most commonly conceptualized on the presence-absence dimension. A clear illustration of this is the concept of blindsight. Blindsight is the ability of observers to discriminate stimuli (i.e. identify content) without being able to detect them. Blindsight is often considered the bedrock of the cognitive neuroscience of consciousness as it acts as proof that one can dissociate between unconscious processing (the categorization of a stimulus, i.e. the content) and conscious processing of that stimulus (i.e. the ability to detect it).

Given the above, we do not see how the suggested analysis could contribute to the conclusions that the manuscript already establishes. We hope that – given the above - the reviewer agrees with this assessment.

Reviewer #3 (Public review):

Summary:

Fahrenfort et al. investigate how liberal or conservative criterion placement in a detection task affects the construct validity of neural measures of unconscious cognition and conscious processing. Participants identified instances of "seen" or "unseen" in a detection task, a method known as post hoc sorting. Simulation data convincingly demonstrate that, counterintuitively, a conservative criterion inflates effect sizes of neural measures compared to a liberal criterion. While the impact of criterion shifts on effect size is suggested by signal detection theory, this study is the first to address this explicitly within the consciousness literature. Decoding analysis of data from two EEG experiments further shows that different criteria lead to differential effects on classifier performance in post hoc sorting. The findings underscore the pervasive influence of experimental design and participants report on neural measures of consciousness, revealing that criterion placement poses a critical challenge for researchers.

Strengths and Weaknesses:

One of the strengths of this study is the inclusion of the Perceptual Awareness Scale (PAS), which allows participants to provide more nuanced responses regarding their perceptual experiences. This approach ensures that responses at the lowest awareness level (selection 0) are made only when trials are genuinely unseen. This methodological choice is important as it helps prevent the overestimation of unconscious processing, enhancing the validity of the findings.

A potential area for improvement in this study is the use of single time-points from peak decoding accuracy to generate current source density topography maps. While we recognize that the decoding analysis employed here differs from traditional ERP approaches, the robustness of the findings could be enhanced by exploring current source density over relevant time windows. Event-related peaks, both in terms of timing and amplitude, can sometimes be influenced by noise or variability in trial-averaged EEG data, and a time-window analysis might provide a more comprehensive and stable representation of the underlying neural dynamics.

We thank reviewer 3 for their positive words and for taking the time to evaluate our manuscript. If we understand the reviewer correctly, he/she suggests that the signal-to-noise ratio could be improved by averaging over time windows rather than taking the values at singular peaks in time. Before addressing this suggestion, we would like to point out that we plotted the relevant effects across time in Supplementary Figure S1A and S1B. These show that the observed effects were not somehow limited in time, i.e. only occuring around the peaks, but that they consistenly occured throughout the time course of the trial. In line with this observation one might argue that the results could be improved further by averaging across windows of interest rather than taking the peak moments alone, as the reviewer suggests. Although this might be true, there are many analysis choices that one can make, each of which could have a positive (or negative) effect on the signal to noise ratio. For example, when taking a window of interest, one is faced with a new choice to make, this time regarding the number of consecutive samples to average across (i.e. the size of the window), etc. More generally there is a long list of choices that may affect the precise outcome of analyses, either positively or negatively. Having analyzed the data in one way, the problem with adding new analysis approaches is that there is no objective criterion for deciding which analysis would be ‘best’, other than looking at the outcome of the statistical analyses themselves. Doing this would constitute an explorative double-dipping-like approach to analyzing the results, which – aside from potentially increasing the signal to noise ratio – is likely to also result in an increase of the type I error rate. In the past, when the first author of this manuscript has attempted to minimize the number of statistical tests, he has lowered the number of EEG time points by simply taking the peaks (for example see https://doi.org/10.1073/pnas.1617268114), and that is the approach that was taken here as well. Given the above, we prefer not to further ‘try out’ additional analytical approaches on this dataset, simply to improve the results. We hope the reviewer sympathizes with our position that it is methodologically most sound to stick to the analyses we have already performed and reported, without further exploration.

It is helpful that the authors show the standard error of the mean for the classifier performance over time. A similar indication of a measure of variance in other figures could improve clarity and transparency.

That said, the paper appears solid regarding technical issues overall. The authors also do a commendable job in the discussion by addressing alternative paradigms, such as wagering paradigms, as a possible remedy to the criterion problem (Peters & Lau, 2015; Dienes & Seth, 2010). Their consideration of these alternatives provides a balanced view and strengthens the overall discussion.

We thank the reviewer for this suggestion. Note that we already have a measure of variance in the other figures too, namely showing the connected data points of individual participants. Indvidual data points as a visualization of variance is preferred by many journals (e.g., see https://www.nature.com/documents/cr-gta.pdf), and also shows the spread of relevant differences when paired points are connected. For example, in Figure 2, 3 and 4, the relevant difference is between the liberal and conservative condition. When wanting to show the spread of the differences between these conditions, one option would be to first subtract the two measures in a pairwise fashion (e.g., liberal-conservative), and then plot the spread of those differences using some metric (e.g. standard error/CI of the mean difference). However, this has the disadvantage of no longer separately showing the raw scores on the conditions that are being compared. Showing conditions separately provides clarity to the reader about what is being compared to what. The most common approach to visualizing the variance of the relevant difference in such cases, is to plot the connected individual data points of all participants in the same plot. The uniformity of the slope of these lines in such a visualization provides direct insight into the spread of the relevant difference. Plotting the standard error of the mean on the raw scores of the conditions in these plots would not help, because this would not visualize the spread of the relevant difference (liberal-conservative). We therefore opted in the manuscript to show the mean scores on the conditions that we compare, while also showing the connected raw data points of individual participants in the same plot. One might argue that we should then use that same visualization in figure 3A, but note that this figure is merely intended to identify the peaks, i.e. it does not compare liberal to conservative. Furthermore, plotting the decoding time lines of individual participants would greatly diminish the clarity of this figure. Given our explanation, we hope the reviewer agrees with the approach that we chose, although we are of course open to modifying the figures if the reviewer has a suggestion for doing so while taking into account the points we raise here in our response.

Impact of the Work:

This study effectively demonstrates a phenomenon that has been largely unexplored within the consciousness literature. Subjective measures may not reliably capture the construct they aim to measure due to criterion confounds. Future research on neural measures of consciousness should account for this issue, and no-report measures may be necessary until the criterion problem is resolved.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

The authors could further elaborate on the results of the PAS to provide a clearer insight into the impact of response criteria, which is notably more complex than in other experiments. Specifically, the results demonstrate that conservative response criterion condition displays a considerably higher sensitivity compared to those with a liberal response criterion. It would be interesting to explore whether this shift in sensitivity suggests a correlation between changes in response criteria and conscious experiences, and how the interaction between sensitivity and response criteria can affect the neural measure of consciousness.

We thank the reviewer for this suggestion. Note that the change in sensitivity that we observed is minor compared to the change we observed in response criterion (hedges g criterion in exp 2 = 2.02, compared to hedges g sensitivity/d’ in exp 2 = 0.42). However, we do investigate the effect of sensitivity (disregarding response criterion) on decoding accuracy. To this end we devised Figure 3C (for the full decoding time course see Supplementary Figure S1B). These figures show that the small behavioral sensitivity effects observed in both experiments (hedges g sensitivity in exp 1 = 0.30, exp 2 = 0.42) did not translate into significant decoding differences between conservative and liberal in either experiment. This comes as no surprise given the small corresponding behavioral effects. Note that small sensitivity differences between liberal and conservative conditions are commonplace, plausibly driven by the fact that being liberal also involves being more noisy in one’s response tendencies (i.e. sometimes randomly indicating presence). Further, the reviewer suggests that we might correlate changes in response criteria to changes in conscious experience. The only relevant metric of conscious experience for which we have data in this manuscript is the Perceptual Awareness Scale (PAS), so we assume the reviewer asks for a correlation between experimentally induced changes in response criterion with the equivalent changes in d’. To this end we computed the difference in the PAS-based d’ metric between conservative and liberal, as well as the difference in the PAS-based criterion metric between conservative and liberal, and correlated these across subjects (N=26) using a Spearman rank correlation. The result shows that these metrics do not correlate r(24)=0.04, p=0.85. Note however that small-N correlations like these are only somewhat reliable for large effect sizes. An N of 26 and a mere power of 80% requires an effect size of at least r=0.5 to be detectable, so even if a correlation were to exist we may not have had enough power to detect it. Due to these caveats we opted to not report this null-correlation in the manuscript, but we are of course willing to do so if the reviewer and/or editor disagrees with this assessment.

Reviewer #3 (Public review):

Summary:

Tubert et al. investigate the mechanisms underlying the pause response in striatal cholinergic interneurons (SCINs). The authors demonstrate that optogenetic activation of thalamic axons in the striatum induces burst activity in SCINs, followed by a brief pause in firing. They show that the duration of this pause correlates with the number of elicited action potentials, suggesting a burst-dependent pause mechanism. The authors demonstrated this burst-dependent pause relied on Kv1 channels. The pause is blocked by a SKF81297 and partially by sulpiride and mecamylamine, implicating D1/D5 receptor involvement. The study also shows that the ZD7288 does not reduce the duration of the pause, and that lesioning dopamine neurons abolishes this response, which can be restored by clozapine.

Weaknesses:

While this study presents an interesting mechanism for SCIN pausing after burst activity, there are several major concerns that should be addressed:

(1) Scope of the Mechanism: It is important to clarify that the proposed mechanism may apply specifically to the pause in SCINs following burst activity. The manuscript does not provide clear evidence that this mechanism contributes to the pause response observed in behavioral animals. While the thalamus is crucial for SCIN pauses in behavioral contexts, the exact mechanism remains unclear. Activating thalamic input triggers burst activity in SCINs, leading to a subsequent pause, but this mechanism may not be generalizable across different scenarios. For instance, approximately half of TANs do not exhibit initial excitation but still pause during behavior, suggesting that the burst-dependent pause mechanism is unlikely to explain this phenomenon. Furthermore, in behavioral animals, the duration of the pause seems consistent, whereas the proposed mechanism suggests it depends on the prior burst, which is not aligned with in vivo observations. Additionally, many in vivo recordings show that the pause response is a reduction in firing rate, not complete silence, which the mechanism described here does not explain. Please address these in the manuscript.

(2) Terminology: The use of "pause response" throughout the manuscript is misleading. The pause induced by thalamic input in brain slices is distinct from the pause observed in behavioral animals. Given the lack of a clear link between these two phenomena in the manuscript, it is essential to use more precise terminology throughout, including in the title, bullet points, and body of the manuscript.

(3) Kv1 Blocker Specificity: It is unclear how the authors ruled out the possibility that the Kv1 blocker did not act directly on SCINs. Could there be an indirect effect contributing to the burst-dependent pause? Clarification on this point would strengthen the interpretation of the results.

(4) Role of D1 Receptors: While it is well-established that activating thalamic input to SCINs triggers dopamine release, contributing to SCIN pausing (as shown in Figure 3), it would be helpful to assess the extent to which D1 receptors contribute to this burst-dependent pause. This could be achieved by applying the D1 agonist SKF81297 after blocking nAChRs and D2 receptors.

(5) Clozapine's Mechanism of Action: The restoration of the burst-dependent pause by clozapine following dopamine neuron lesioning is interesting, but clozapine acts on multiple receptors beyond D1 and D5. Although it may be challenging to find a specific D5 antagonist or inverse agonist, it would be more accurate to state that clozapine restores the burst-dependent pause without conclusively attributing this effect to D5 receptors.

Comments on revisions:

The authors have addressed many of my concerns. However, I remain unconvinced that adding an 'ex vivo' experiment fully resolves the fundamental differences between the burst-dependent pause observed in slices - defined by the duration of a single AHP - and the pause response in CHINs observed in vivo, which may involve contributions from more than one prolonged AHP. In vivo, neurons can still fire action potentials during the pause, albeit at a lower frequency. Moreover, in behaving animals, pause duration does not vary with or without initial excitation. The mechanism proposed demonstrates that the pause duration, defined by the length of a single AHP, is positively correlated with preceding burst activity.

To improve clarity, I recommend using the term 'SCIN pause' to describe the ex vivo findings, distinguishing them more explicitly from the 'pause response' observed in behaving animals. This distinction would help contextualize the ex vivo findings as potentially contributing to, but not fully representing, the pause response in vivo.

Again, it would be helpful to present raw data for pause durations rather than relying solely on ratios. This approach would provide the audience with a clearer understanding of the absolute duration of the burst-dependent pause and allow for better comparison to the ~200 ms pause observed in behaving animals.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Tubert C. et al. investigated the role of dopamine D5 receptors (D5R) and their downstream potassium channel, Kv1, in the striatal cholinergic neuron pause response induced by thalamic excitatory input. Using slice electrophysiological analysis combined with pharmacological approaches, the authors tested which receptors and channels contribute to the cholinergic interneuron pause response in both control and dyskinetic mice (in the LDOPA off state). They found that activation of Kv1 was necessary for the pause response, while activation of D5R blocked the pause response in control mice. Furthermore, in the LDOPA off-state of dyskinetic mice, the absence of the pause response was restored by the application of clozapine. The authors claimed that (1) the D5R-Kv1 pathway contributes to the cholinergic interneuron pause response in a phasic dopamine concentration-dependent manner, and (2) clozapine inhibits D5R in the L-DOPA off state, which restores the pause response.

Strengths:

The electrophysiological and pharmacological approaches used in this study are powerful tools for testing channel properties and functions. The authors' group has well-established these methodologies and analysis pipelines. Indeed, the data presented were robust and reliable.

Thank you for your comments.

Weaknesses:

Although the paper has strengths in its methodological approaches, there is a significant gap between the presented data and the authors' claims.

There was no direct demonstration that the D5R-Kv1 pathway is dominant when dopamine levels are high. The term 'high' is ambiguous, and it raises the question of whether the authors believe that dopamine levels do not reach the threshold required to activate D5R under physiological conditions.

We acknowledge that further work is necessary to clarify the role of the D5R in physiological conditions. While we haven’t found effects of the D1/D5 receptor antagonist SCH23390 on the pause response in control animals (Fig. 3), it is still possible that dopamine levels reach the threshold to stimulate D5R when burst firing of dopaminergic neurons contributes to dopamine release. We believe the pause response depends, among other factors, on the relative stimulation levels of SCIN D2 and D5 receptors, which is likely not an all-or-nothing phenomenon. To reduce ambiguity, we have eliminated the labels referring to dopamine levels in Figure 6F.

Furthermore, the data presented in Figure 6 are confusing. If clozapine inhibits active D5R and restores the pause response, the D5R antagonist SCH23390 should have the same effect. The data suggest that clozapine-induced restoration of the pause response might be mediated by other receptors, rather than D5R alone.

Thank you for letting us clarify this issue. Please note that the levels of endogenous dopamine 24 h after the last L-DOPA challenge in severe parkinsonian mice are expected to be very low. In the absence of an agonist, a pure D1/D5 antagonist would not exert an effect, as demonstrated with SCH23390 alone, which did not have an impact on the SCIN response to thalamic stimulation (Fig. 6). While clozapine can also act as a D1/D5 receptor antagonist, its D1/D5 effects in absence of an agonist are attributed to its inverse agonist properties (PMID: 24931197). Notably, SCH23390 prevented the effect of clozapine, allowing us to conclude that ligand-independent D1/D5 receptor-mediated mechanisms are involved in suppressing the pause response in dyskinetic mice. We now made it clearer in the third paragraph of the Discussion.

Reviewer #2 (Public review):

Summary:

This manuscript by Tubert et al presents the role of the D5 receptor in modulating the striatal cholinergic interneuron (CIN) pause response through D5R-cAMP-Kv1 inhibitory signaling. Their model elucidates the on / off switch of CIN pause, likely due to the different DA affinity between D2R and D5R. This machinery may be crucial in modulating synaptic plasticity in cortical-striatal circuits during motor learning and execution. Furthermore, the study bridges their previous finding of CIN hyperexcitability (Paz et al., Movement Disorder 2022) with the loss of pause response in LID mice.

Strengths:

The study had solid findings, and the writing was logically structured and easy to follow. The experiments are well-designed, and they properly combined electrophysiology recording, optogenetics, and pharmacological treatment to dissect/rule out most, if not all, possible mechanisms in their model.

Thank you for your comments.

Weaknesses:

The manuscript is overall satisfying with only some minor concerns that need to be addressed. Manipulation of intracellular cAMP (e.g. using pharmacological analogs or inhibitors) can add additional evidence to strengthen the conclusion.

Thank you for the suggestion. While we acknowledge that we are not providing direct evidence of the role of cAMP, we chose not to conduct these experiments because cAMP levels influence several intrinsic and synaptic currents beyond Kv1, significantly affecting  membrane oscillations and spontaneous firing, as shown in Paz et al. 2021. However, we are modifying the fourth paragraph of the Discussion so there is no misinterpretation about our findings in the current work.

Reviewer #3 (Public review):

Summary:

Tubert et al. investigate the mechanisms underlying the pause response in striatal cholinergic interneurons (SCINs). The authors demonstrate that optogenetic activation of thalamic axons in the striatum induces burst activity in SCINs, followed by a brief pause in firing. They show that the duration of this pause correlates with the number of elicited action potentials, suggesting a burst-dependent pause mechanism. The authors demonstrated this burst-dependent pause relied on Kv1 channels. The pause is blocked by an SKF81297 and partially by sulpiride and mecamylamine, implicating D1/D5 receptor involvement. The study also shows that the ZD7288 does not reduce the duration of the pause and that lesioning dopamine neurons abolishes this response, which can be restored by clozapine.

Weaknesses:

While this study presents an interesting mechanism for SCIN pausing after burst activity, there are several major concerns that should be addressed:

(1) Scope of the Mechanism:

It is important to clarify that the proposed mechanism may apply specifically to the pause in SCINs following burst activity. The manuscript does not provide clear evidence that this mechanism contributes to the pause response observed in behavioral animals. While the thalamus is crucial for SCIN pauses in behavioral contexts, the exact mechanism remains unclear. Activating thalamic input triggers burst activity in SCINs, leading to a subsequent pause, but this mechanism may not be generalizable across different scenarios. For instance, approximately half of TANs do not exhibit initial excitation but still pause during behavior, suggesting that the burst-dependent pause mechanism is unlikely to explain this phenomenon. Furthermore, in behavioral animals, the duration of the pause seems consistent, whereas the proposed mechanism suggests it depends on the prior burst, which is not aligned with in vivo observations. Additionally, many in vivo recordings show that the pause response is a reduction in firing rate, not complete silence, which the mechanism described here does not explain. Please address these in the manuscript.

Thank you for your valuable feedback. While the absence of an initial burst in some TANs in vivo may suggest the involvement of alternative or additional mechanisms, this does not exclude a participation of Kv1 currents. We have seen that subthreshold depolarizations induced by thalamic inputs are sufficient to produce an afterhyperpolarization (AHP) mediated by Kv1 channels (see Tubert et al., 2016, PMID: 27568555). Although such subthreshold depolarizations are not captured in current recordings from behaving animals, intracellular in vivo recordings have demonstrated an intrinsically generated AHP after subthreshold depolarization of SCIN caused by stimulation of excitatory afferents (PMID: 15525771). Additionally, when pause duration is plotted against the number of spikes elicited by thalamic input (Fig. 1G), we found that one elicited spike is followed by an interspike interval 1.4 times longer than the average spontaneous interspike interval. We acknowledge the potential involvement of additional factors, including a decrease of excitatory thalamic input coinciding with the pause, followed by a second volley of thalamic inputs (Fig. 1J-K, after observations by Matsumoto et al., 2001- PMID: 11160526), as well as the timing of elicited spikes relative to ongoing spontaneous firing (Fig. 1D-E). Dopaminergic modulation (Fig. 3) and regional differences among striatal regions (PMID: 24559678) may also contribute to the complexity of these dynamics. 

(2) Terminology:

The use of "pause response" throughout the manuscript is misleading. The pause induced by thalamic input in brain slices is distinct from the pause observed in behavioral animals. Given the lack of a clear link between these two phenomena in the manuscript, it is essential to use more precise terminology throughout, including in the title, bullet points, and body of the manuscript.

While we acknowledge that our study does not include in vivo evidence, we believe ex vivo preparations have been instrumental in elucidating the mechanisms underlying the responses observed in vivo. We also agree with previous ex vivo studies in using consistent terminology. However, we will clarify the ex vivo nature of our work in the abstract and bullet points for greater transparency.

(3) Kv1 Blocker Specificity:

It is unclear how the authors ruled out the possibility that the Kv1 blocker did not act directly on SCINs. Could there be an indirect effect contributing to the burst-dependent pause? Clarification on this point would strengthen the interpretation of the results.

Thank you for letting us clarify this issue. In our previous work (Tubert et al., 2016) we showed that the Kv1.3 and Kv1.1 subunits are selectively expressed in SCIN throughout the striatum. Moreover, gabaergic transmission is blocked in our preparations. We are including a phrase to make it clearer in the manuscript (Results section, subheading “The pause response to thalamic stimulation requires activation of Kv1 channels”).

(4) Role of D1 Receptors:

While it is well-established that activating thalamic input to SCINs triggers dopamine release, contributing to SCIN pausing (as shown in Figure 3), it would be helpful to assess the extent to which D1 receptors contribute to this burst-dependent pause. This could be achieved by applying the D1 agonist SKF81297 after blocking nAChRs and D2 receptors.

Thank you for letting us clarify this point. We show that blocking D2R or nAChR reduces the pause only for strong thalamic stimulation eliciting 4 SCIN spikes (Figure 3G), whereas the D1/D5 agonist SKF81297 is able to reduce the pause induced by weaker stimulation as well (Figure 3C). In addition, the D1/D5 receptor antagonist SCH23390 does not modify the pause response (Figure 3C). This may indicate that nAChR-mediated dopamine release induced by thalamic-induced bursts more efficiently activates D2R compared to D5R. We speculate that, in this context, lack of D5R activation may be necessary to keep normal levels of Kv1.3 currents necessary for SCIN pauses.

(5) Clozapine's Mechanism of Action:

The restoration of the burst-dependent pause by clozapine following dopamine neuron lesioning is interesting, but clozapine acts on multiple receptors beyond D1 and D5.

Although it may be challenging to find a specific D5 antagonist or inverse agonist, it would be more accurate to state that clozapine restores the burst-dependent pause without conclusively attributing this effect to D5 receptors.

Thank you for your insightful observation. We acknowledge the difficulty of targeting dopamine receptors pharmacologically due to the lack of highly selective D1/D5 inverse agonists. We used SCH23390, which is a highly selective D1/D5 receptor antagonist devoid of inverse agonist effects, to block clozapine’s ability to restore SCIN pauses (Figure 6C). This indicates that the restoration of SCIN pauses by clozapine depends on D1/D5 receptors. Furthermore, in a previous study, we demonstrated that clozapine’s effect on restoring SCIN excitability in dyskinetic mice (a phenomenon mediated by Kv1 channels in SCIN; Tubert et al., 2016) was not due to its action on serotonin receptors (Paz, Stahl et al., 2022). While our data do not rule out the potential contribution of other receptors, such as muscarinic acetylcholine receptors, we believe they strongly support the role of D1/D5 receptors. To reflect this, we added a statement discussing the potential contribution of receptors beyond D1/D5 in the last paragraph of the Discussion.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The effect of MgTx was not consistent with the previous study (Tubert, 2016). I expected MgTx to increase the basal firing rate of cholinergic interneurons.

Thank you for highlighting this. In our previous study we used ACSF in the recording pipette, instead of the intracellular solution -higher in potassium- used in the present study. This is likely related to the higher spontaneous firing rates of SCIN observed in the present study, which made the SCIN response stand out. In addition, our previous study analyzed the effect of MgTx on spontaneous firing frequency of SCIN isolated from major circuit regulation by adding CNQX and picrotoxin to the bath, while in this study we needed to preserve the thalamic input and only picrotoxin in the bath was used. Given these differences, the two conditions are not strictly comparable but rather give complementary information.

(2) In the text, the authors claim that "SCINs recorded in the parkinsonian OFF-L-DOPA condition show an increase in membrane excitability that mimics changes acutely induced by SKF81297 in SCINs from control mice." However, the data for SKF81297 do not support this claim.

We modified the text to make it clearer that the cited phrase refers to a previous publication (PMID: 35535012) in which SCIN intrinsic excitability was characterized via analysis of responses to somatic current injection in whole-cell recordings. In the present study Fig. 3D shows SKF81297 effects on interspike intervals during spontaneous activity with a trend towards increased firing, and Fig. 4E a lack of effect on “burst duration” for responses with different numbers of spikes elicited by thalamic afferent stimulation. 

(3) I recommend testing whether other receptors, such as D2R, contribute to the clozapineinduced pause response in the L-DOPA off state.

Thank you for your suggestion. We acknowledge that studying the role of D2R is important. However, our preliminary data suggest that a comprehensive follow up study, which is beyond the scope of this manuscript, is necessary to understand their role. 

Reviewer #2 (Recommendations for the authors):

(1) For Figure 1D-E, I understand that the authors are trying to state that the previous spontaneous spike contributes to a hyperpolarized window that induces a delay in the evoked spikes. However, it is almost impossible to discriminate between spontaneous and evoked spikes in this experiment. Furthermore, considering the tonic firing property, I highly suspect that even a sham control design (no optogenetic light) will give you a similar distribution as in Figure 1E (the longer IN X1, the shorter in X2).

We agree that some spikes following stimulus onset may have occurred independently of the light stimulus, as it is also possible during behavioral tasks. We used the baseline recordings to estimate the effects of a sham stimulus as requested and included the data in Fig. 1E-F. As expected, the sham stimulation data showed a similar inverse relationship with the time elapsed from the preceding spike, but latencies were longer than with the stimulus (except for times close to the average ISI), suggesting that the optical stimulation increased the probability of evoking a spike (Fig. 1F). Remarkably, the pause following this threshold stimulation was significantly longer than the baseline ISI, as reported in the main text (Results section, last sentence of first paragraph).

(2) The authors used optogenetics to induce thalamic inputs to induce the pause after bursts. Considering CINs also receive inputs from different brain regions, e.g. cortex, does this phenomena/pause after bursts also exist following cortical inputs?

We did not study the SCIN response to cortical inputs, but both thalamic and cortical inputs seem to drive SCIN pause-responses as observed by others (PMID: 24553950).  

(3) The effect of the D5R inverse agonism, and the further combined with D5R agonist and antagonist, faithfully reveal/confirm the increase of ligand-independent activity of D5R in LID reported previously. It would be ideal to also directly modulate intracellular cAMP (as in the 2022 paper) to confirm the rescue effects on the CIN pause response.

Please, see our response in the public review.

(4) In healthy conditions, the balance between D2R and D5R signaling (shown in Figure 6F left) switches the pause and no pause modes which potentially contributes to cortical-striatal plasticity. How about in LID off L-DOPA condition? Is it possible to rescue/modulate the pause on/off mode by D2R agonism in LID?

We haven’t tested the effect of D2 agonists yet, but this is scheduled for follow up studies. 

Reviewer #3 (Recommendations for the authors):

(1) The authors use the ratio of pause duration to baseline ISI to describe the pause, which is useful for detecting significant differences. However, it would be beneficial to also report the actual duration of the burst-dependent pause to provide readers with a clearer understanding of the variation in pauses.

In all figures we report the average baseline ISI duration for each experiment / experimental condition, allowing readers to estimate actual pause durations. We added in the main text actual average pause durations corresponding to Fig. 1H, which are representative of those observed along the study.

(2) In Figure 3D, a more detailed comparison would be helpful, as there appears to be a significant difference between the SKF81297 group and others.

We acknowledge that there might be a trend towards reduced ISIs, however, it was statistically non-significant (see legend of figure 3). In addition, the effect of SKF81297 seems unrelated to this trend, as its effect is also seen under the effect of ZD7288, which substantially prolongs the baseline ISI (Fig. 4E-F).

Author response:

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

eLife Assessment  

This manuscript reports important findings that the methyltransferase METTL3 is involved in the repair of abasic sites and uracil in DNA, mediating resistance to floxuridine-driven cytotoxicity. Convincing evidence shows the involvement of m6A in DNA based on single cell imaging and mass spec data. The authors present evidence that the m6A signal does not result from bacterial contamination or RNA, but the text does not make this overly clear.

We thank the editors for recognizing the importance of our work and the relevance of METTL3 and 6mA in DNA repair. We agree the evidence presented can be regarded as convincing, in that it includes validation with orthogonal approaches and excludes the source of 6mA being RNA or bacterial contamination.

To clarify, the identification of 6mA in DNA, upon DNA damage, is based first on immunofluorescence observations using an anti-m6A antibody. In this setting, removal of RNA with RNase treatment fails to reduce the 6mA signal, excluding the possibility that the source of signal is RNA. In contrast, removal of DNA with DNase treatment removes all 6mA signal, strongly suggesting that the species carrying the N6-methyladenosine modification is DNA (Figure 3D, E). Importantly, in Figure 3F, G, we provide orthogonal, quantitative mass spectrometry data that independently confirm this finding. Mass spectrometry-liquid chromatography of DNA analytes, conclusively shows the presence of 6mA in DNA upon treatment with DNA damaging agents and excludes that the source is RNA, based on exact mass. 

Cells only show the 6mA signal when treated with DNA damaging agents, and the 6mA is absent from untreated cells (Figure 3D, E, H, I). This provides strong evidence that the 6mA signal is not a result of bacterial contamination in our cell lines. Furthermore, our cell lines are routinely tested for mycoplasma contamination. It could be possible that stock solutions of DNA damaging agents may be contaminated, but this would need to be true for all individual drugs and stocks tested, which is highly unlikely. Moreover, the data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3H, I) provides strong evidence against bacterial contamination in our stocks.  

In summary, we provide conclusive evidence, based on orthogonal methods, that the METTL3-dependent N6-methyladenosine modification is deposited in DNA, not RNA, in response to DNA damage and have now clarified these points in the results and discussion. 

Public Reviews:  

Reviewer #1 (Public review):  

Summary:  

The authors sought to identify unknown factors involved in the repair of uracil in DNA through a CRISPR knockout screen.  

Strengths:  

The screen identified both known and unknown proteins involved in DNA repair resulting from uracil or modified uracil base incorporation into DNA. The conclusion is that the protein activity of METTL3, which converts A nucleotides to 6mA nucleotides, plays a role in the DNA damage/repair response. The importance of METTL3 in DNA repair, and its colocalization with a known DNA repair enzyme, UNG2, is well characterized.  

Weaknesses:  

This reviewer identified no major weaknesses in this study. The manuscript could be improved by tightening the text throughout, and more accurate and consistent word choice around the origin of U and 6mA in DNA. The dUTP nucleotide is misincorporated into DNA, and 6mA is formed by methylation of the A base present in DNA. Using words like 6mA "deposition in DNA" seems to imply it results from incorporation of a methylated dATP nucleotide during DNA synthesis.  

The increased presence of 6mA during DNA damage could result from methylation at the A base itself (within DNA) or from incorporation of pre-modified 6mA during DNA synthesis. Our data do not directly discriminate between these two mechanisms, and we clarified this point in the discussion.  

Reviewer #2 (Public review):  

Summary:  

In this work, the authors performed a CRISPR knockout screen in the presence of floxuridine, a chemotherapeutic agent that incorporates uracil and fluoro-uracil into DNA, and identified unexpected factors, such as the RNA m6A methyltransferase METTL3, as required to overcome floxuridine-driven cytotoxicity in mammalian cells. Interestingly, the observed N6-methyladenosine was embedded in DNA, which has been reported as DNA 6mA in mammalian genomes and is currently confirmed with mass spectrometry in this model. Therefore, this work consolidated the functional role of mammalian genomic DNA 6mA, and supported with solid evidence to uncover the METTL3-6mA-UNG2 axis in response to DNA base damage.  

Strengths:  

In this work, the authors took an unbiased, genome-wide CRISPR approach to identify novel factors involved in uracil repair with potential clinical interest.  

The authors designed elegant experiments to confirm the METTL3 works through genomic DNA, adding the methylation into DNA (6mA) but not the RNA (m6A), in this base damage repair context. The authors employ different enzymes, such as RNase A, RNase H, DNase, and liquid chromatography coupled to tandem mass spectrometry to validate that METTL3 deposits 6mA in DNA in response to agents that increase genomic uracil.  

They also have the Mettl3-KO and the METTL3 inhibition results to support their conclusion.  

Weaknesses:  

Although this study demonstrates that METTL3-dependent 6mA deposition in DNA is functionally relevant to DNA damage repair in mammalian cells, there are still several concerns and issues that need to be improved to strengthen this research.  

First, in the whole paper, the authors never claim or mention the mammalian cell lines contamination testing result, which is the fundamental assay that has to be done for the mammalian cell lines DNA 6mA study.  

Our cell lines are routinely tested for bacterial contamination, specifically mycoplasma, and we state this information in the revised manuscript. 

Importantly, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on the presence of DNA damage and not caused by contamination in the cell lines (Figure 3D, E, H, I). While it could be possible that stock solutions of DNA damaging agents may be contaminated, this would need to be the case for all individual drugs and stocks tested that induce 6mA, which is very unlikely. Finally, the data showing 6mA signal is not significantly different from untreated cells when a DNA damaging agent is combined with a METTL3 inhibitor (Figure 3 H, I) provides strong evidence against bacterial contamination in our drug stocks.  

Second, in the whole work, the authors have not supplied any genomic sequencing data to support their conclusions. Although the sequencing of DNA 6mA in mammalian models is challenging, recent breakthroughs in sequencing techniques, such as DR-Seq or NT/NAME-seq, have lowered the bar and improved a lot in the 6mA sequencing assay. Therefore, the authors should consider employing the sequencing methods to further confirm the functional role of 6mA in base repair.  

While we agree that it could be important to understand the precise genomic location of 6mA in relation to DNA damage, this is outside the scope of the current study. Moreover, this exercise may prove unproductive. If 6mA is enriched in DNA at damage sites or as DNA is replicated, the genomic mapping of 6mA is likely to be stochastic. If stochastic, it would be impossible to obtain the read depth necessary to map 6mA accurately. 

Third, the authors used the METTL3 inhibitor and Mettl3-KO to validate the METTL36mA-UNG2 functional roles. However, the catalytic mutant and rescue of Mettl3 may be the further experiments to confirm the conclusion.  

We believe this to be an excellent suggestion from Reviewer #2 but we are unable to perform the proposed experiment at this time. We encourage future studies to explore the rescue experiment.  

Reviewer #3 (Public review):  

Summary:  

The authors are showing evidence that they claim establishes the controversial epigenetic mark, DNA 6mA, as promoting genome stability.  

Strengths:  

The identification of a poorly understood protein, METTL3, and its subsequent characterization in DDR is of high quality and interesting.  

Weaknesses:  

(1) The very presence of 6mA (DNA) in mammalian DNA is still highly controversial and numerous studies have been conclusively shown to have reported the presence of 6mA due to technical artifacts and bacterial contamination. Thus, to my knowledge there is no clear evidence for 6mA as an epigenetic mark in mammals, and consequently, no evidence of writers and readers of 6mA. None of this is mentioned in the introduction. Much of the introduction can be reduced, but a paragraph clearly stating the controversy and lack of evidence for 6mA in mammals needs to be added, otherwise, the reader is given an entirely distorted view of the field.  

These concerns must also be clearly in the limitations section and even in the results section which fails to nuance the authors' findings. 

We agree with the reviewer that the presence and potential function of 6mA in mammalian DNA has been debated. Importantly, the debate regarding the presence and quantity of 6mA in DNA has been previously restricted to undamaged, baseline conditions. In complete agreement with this notion, we do not detect appreciable levels of 6mA in untreated cells. We revised the introduction section to present the debate about 6mA in DNA. We, however, want to highlight that our study provides, for the first time, convincing evidence (based on two orthogonal methods) that 6mA is present in DNA in response to a stimulus, DNA damage. We do not claim or provide any data that suggest 6mA is a baseline epigenetic mark.  

(2) What is the motivation for using HT-29 cells? Moreover, the materials and methods do not state how the authors controlled for bacterial contamination, which has been the most common cause of erroneous 6mA signals to date. Did the authors routinely check for mycoplasma? 

HT-29 is a cell line of colorectal origin and chemotherapeutic agents that introduce uracil and uracil derivatives in DNA, as those used in this study, are relevant for the treatment of colorectal cancer. As indicated above, we do not observe 6mA in untreated cells, strongly suggesting that the 6mA signal observed is dependent on DNA damage and not caused by a potential bacterial contamination (Figure 3D, E, H, I). Additionally, our cell lines are routinely tested for bacterial contamination, specifically mycoplasma. 

(3) The single cell imaging of 6mA in various cells is nice. The results are confirmed by mass spec as an orthogonal approach. Another orthogonal and quantitative approach to assessing 6mA levels would be PacBio. Similarly, it is unclear why the authors have not performed dot-blots of 6mA for genomic DNA from the given cell lines.

We are confused by this point since an orthogonal approach to detect 6mA, mass spectrometry-liquid chromatography, was employed. This method does not use an antibody and confirms the increase of 6mA in DNA when cells were treated with DNA damaging agents. This data is presented in Figure 3F, G. 

It is sensible to hypothesize that the localization of 6mA is consistent with DNA replication (like uracil deposition). In this event, the genomic mapping of 6mA is likely to be stochastic. This would make quantification with PacBio sequencing difficult because it would be very challenging to achieve the appropriate read depth to call a modified base. 

Dot blots rely on an antibody and thus are not truly orthogonal to our immunofluorescence-based measurements. We preferred the mass spectrometry-liquid chromatography approach we took as a true orthogonal approach.  

(4) The results of Figure 3 need further investigation and validation. If the results are correct the authors are suggesting that the majority of 6mA in their cell lines is present in the DNA, and not the RNA, which is completely contrary to every other study of 6mA in mammalian cells that I am aware of. This could suggest that the antibody is not, in fact, binding to 6mA, but to unmodified adenine, which would explain why the signal disappears after DNAse treatment. Indeed, binding of 6mA to unmethylated DNA is a commonly known problem with most 6mA antibodies and is well described elsewhere.  

Based on this and the following comment, we are convinced that Reviewer #3 has overlooked two critical elements of our study:

First, the immunofluorescence work presented in Figure 3, showing 6mA signal in response to DNA damage, uses cells that were pre-extracted to remove excess cytoplasmic RNA. This method is often used in immunofluorescence experiments of this kind. The pre-extraction method removes most of the cytoplasmic content, and the majority of the cytoplasmic m6A RNA signal. Supplementary Figure 3D shows cells that have not been pre-extracted prior to staining. These images show the cytoplasmic m6A signal is abundant if we do not perform the pre-extraction step. 

If the antibody used to label 6mA significantly reacted with unmodified adenine, we would expect a large signal in untreated or untreated and denatured conditions. In contrast, an increase in 6mA is not observed in either case.

Second, the orthogonal approach we employed, mass spectrometry coupled with liquid chromatography, measures 6mA DNA analytes specifically by exact mass. This approach does not depend on an antibody and yields results consistent with those from the immunofluorescence experiments. 

(5) Given the lack of orthologous validation of the observed DNA 6mA and the lack of evidence supporting the presence of 6mA in mammalian DNA and consequently any functional role for 6mA in mammalian biology, the manuscript's conclusions need to be toned down significantly, and the inherent difficulty in assessing 6mA accurately in mammals acknowledged throughout.  

As discussed in response to prior comments, Figure 3 does provide two independent and orthologous methods that demonstrate 6mA presence in DNA specifically, and not RNA, in response to DNA damage. Complementary and orthogonal datasets are presented using either immunofluorescence microscopy or mass spectrometry-liquid chromatography of extracted DNA. The latter method does not rely on an antibody and can discriminate 6mA DNA versus RNA based on exact mass. We revised the text to clarify that Figure 3F, G is a completely orthogonal approach. 

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):  

The authors cited most of the related publications; however, the reviewer suggested that three 2015 papers in Cell (Dahua Chen's, Yang Shi's, and Chuan He's) and the 2016 Nature (Andrew Xiao's) article are worth citing here because those are the milestone works reported the genomic DNA 6mA, for the first wave, in eukaryotic and mammalian genomes.  

Furthermore, in Tao P. Wu and Andrew Z. Xiao's 2016 Nature article, the result has already emphasized the genomic DNA 6mA is enriched in the H2A.X sites; therefore, that work indicated the link between DNA damage and repair and 6mA's functional role. The authors may add some comments or discussion on this point.  

Last but not least, the authors may also need to discuss the reported evidence of DNA 6mA's function in mitochondria.  

We thank the reviewer for these suggestions. We revised our introduction and include additional references and discussion points, as suggested by the reviewer. 

Reviewer #3 (Recommendations for the authors):  

Minor points:  

(1) In general, the manuscript is too verbose, and the amount of text can be dramatically reduced/sharpened. The introduction in particular is too long. 

We revised the manuscript and reduced text when appropriate.

(2) Each results section can also be condensed to improve clarity significantly. Indeed the results section reads like a 'Result & Discussion' section, which is then followed by a Discussion. Maybe the discussion section can be shortened to a 'conclusion'.

We revised the results section when appropriate and reworked the discussion.

Importantly, we revised the text related to Figure 3 as it does appear that Reviewer #3 did not appreciate key results present in this figure, specifically the orthogonal, mass spectrometry approach validating the discovery of 6mA DNA species (Figure 3F, G). We added a schematic as Figure 3F to further clarify this point as well. 

(3) The accession number for sequencing data in GEO data should be provided.  

The accession numbers is now provided in the manuscript. GSE282260.

(4) All figures are unnecessarily small and in some cases, supporting figures from the supplementary data should be moved into the main figure to improve clarity. 

The figures are of high image quality and can be enlarged easily. If there are specific figures that the reviewer believes will improve clarity, we would be happy to move them.

webinaire du Learning Planet Institute présente une discussion autour de l’« apprenance », concept central exploré à travers six citations de penseurs variés.

L’objectif est de relier le désengagement à l’apprenance, en examinant les facteurs influençant l’engagement dans l’apprentissage, notamment chez les publics fragilisés.

La discussion explore des thèmes tels que le rôle de la reconnaissance sociale, l’autodétermination (besoins d’autonomie, compétence et appartenance), la prise en compte des enjeux, ressources et contraintes de l’apprenant, et la posture du formateur comme accompagnateur plutôt que simple transmetteur de savoir.

Finalement, un outil numérique, « Learning Coach », est présenté comme une solution pour soutenir l’autonomie et la performance de l’apprenant.

Voici un document de synthèse pour un briefing, basé sur les informations de la transcription du webinaire "Chercheurs d'or #1" , en mettant l'accent sur les points clés :

Titre : Convictions pédagogiques : du désengagement à l'apprenance

Introduction

• Le Learning Planet Institute est un institut de recherche pluridisciplinaire axé sur les pédagogies actives et la recherche participative.
• Le webinaire "Chercheurs d'or" a pour but de créer un espace d'échange entre chercheurs et praticiens afin de partager des savoirs et de discuter des défis rencontrés dans l'accompagnement de l'apprentissage.
• Le séminaire se concentre sur l'engagement dans les processus d'apprentissage, en particulier chez les personnes en situation de vulnérabilité.

Problématique centrale

• Comment passer du désengagement à l'apprenance, en particulier chez les personnes en situation non désirée, ayant des blocages liés à des expériences passées ou un manque de confiance en soi ?
• Le désengagement est défini comme le fait de quitter une formation, tandis que le non-engagement est le fait de ne pas s'engager dans une formation. Les deux sont liés à la motivation et à la capacité de maintenir son intention.
• L'apprenance est définie comme un ensemble de dispositions favorables à l'acte d'apprendre.

Définition et composantes de l'apprenance

• L'apprenance est composée de facteurs :
  • Affectifs (ce que j'aime ou pas apprendre)
  • Cognitifs (l'importance de l'apprentissage tout au long de la vie)
  • Conatifs (l'engagement et la capacité à saisir les opportunités d'apprentissage)
• L'apprenance est un facteur interne qui favorise l'engagement et inhibe le désengagement, mais des facteurs externes (comme la distance, le contexte économique) influencent également l'apprentissage.

Six phrases clés pour une vision pédagogique

1. "On apprend toujours seul, mais jamais sans les autres." : Même en autodidaxie, on se base sur le travail des autres et la dimension sociale est importante. La reconnaissance par les autres est un facteur important pour l'engagement.
2. "On apprend bien que ce qu'on a étudié par soi-même." : L'expérience personnelle et intime du savoir est essentielle pour l'intégrer. La connaissance est plus que le savoir, c'est l'expérience de l'objet étudié.
3. "On apprend par intérêt, par valeur ou par besoin." : La motivation est un levier essentiel. Il y a trois niveaux principaux de motivation : l'intérêt (plaisir), les valeurs (convictions personnelles), et le besoin (nécessité). Il est important d'identifier les motivations profondes de l'apprenant.
4. "On apprend selon ses enjeux, ses ressources et ses contraintes." : L'apprentissage est influencé par la perception de l'environnement. Il est important de prendre en compte les ressources, les contraintes et les enjeux de l'apprenant. Le sentiment d'efficacité personnelle (SEP) est important mais peut être un frein s'il est trop élevé.
5. "On doit pouvoir apprendre aussi vite que le monde change." : La quantité de savoir augmente rapidement, il est donc nécessaire de développer les capacités d'apprentissage des individus.
6. "Renoncer à fabriquer, accompagner la liberté d'apprendre." : Le rôle du formateur est d'accompagner l'apprenant vers sa propre liberté d'apprentissage, et non pas de le formater. Il est important de donner à l'apprenant les moyens de choisir et d'apprendre en toute autonomie.

Outil "Learning Coach"

• Un compagnon d'apprentissage qui vise à développer l'autonomie de l'apprenant et à supporter sa performance d'apprentissage.
• Il offre des modules pour le profil de l'apprenant, un coach virtuel, des activités et une IA pédagogique.
• Il permet de personnaliser l'accompagnement et s'adapte au profil de l'apprenant en utilisant l'IA.

Enjeux et perspectives

• Il est nécessaire d'adapter les formations aux besoins de chacun et de favoriser l'apprentissage collaboratif.
• L'importance de l'autonomie de l'apprenant est centrale dans l'apprentissage tout au long de la vie.
• La technologie peut être un outil pour aider les apprenants à apprendre à apprendre, mais il faut faire attention à ne pas exclure les personnes non numérisées.

Ce document de synthèse devrait vous fournir une base solide pour votre briefing, en mettant en évidence les concepts clés et les enjeux liés à l'apprentissage et à l'apprenance.

Reviewer #2 (Public review):

Summary:

The authors aim to develop a neural mass model characterized by a few collective variables mimicking the dynamics of a network of Hodgkin - Huxley neurons encompassing ion-exchange mechanisms. They describe in detail the derivation of the mean-field model, then they compare experimental results obtained for the hippocampus of a mouse with the neural network simulations and the mean-field results. Furthermore, they report a bifurcation analysis of the developed model and simulation of a small network containing various coupled neural masses, somehow moving towards the simulation of an entire connectome.

Strengths:

The author attempts to develop a mean-field model for a globally coupled network of heterogeneous Hodgkin-Huxley neurons with an explicit ion exchange mechanism between the cell interior and exterior.

Weaknesses:

(1) It seems that the reduction methodology that is employed is not the most suitable one for the single-neuron model they are considering.<br /> (2) The authors' derivation of the neural mass model is based on several assumptions, and not all well justified.<br /> (3) The formulation of the mean-field derivation is unnecessarily complicated. It could be heavily simplified by following previously published approaches to derive biologically realistic neural masses.<br /> (4) The model seems to work only for highly synchronized situations and not for the standard asynchronous evolution usually observed in neural circuits.

General Statements:

The authors honestly declared the many limitations of their approach. It is assumed that the results of the mean-field are somehow inconsistent with the neural network simulations as expected.

The authors suggest employing this model for the simulations on the whole connectome to follow seizure propagation, however, I believe that the Epileptor remains superior in this respect to this model. That indeed includes biophysical parameters but their correspondence with the ones employed in the network dynamics remains elusive, due to the many assumptions required to derive this mean-field model. Furthermore, it is more complicated than the Epileptor, I do not think that the present model will be largely employed by the community.

1

Violates: - 1. The y-axis is different from women and men. - 2. Arguably for women and especially for men, the number of unsolicited emails vs. height is negligible or at least a far smaller difference than the line graph implies. - 3. The demographic (i.e., locale, profession, race) of participant pool is unclear other than the distinction between women and men. - 4. Given #2, the conclusions from the data are exaggerated.

1

Violates: - 1. Awful visual presentation of the data. - 2. The number of units sold looks like it’s less in 2022 than in 2019 and if not, the difference seems negligible. Further, the number of units sold decreased in between 2019 and 2022. - 4. For reasons in #1, the conclusions are exaggerated.

This page needs to be changed for this course but also I don't really love this page so if you find alternative ways to present details of assignments this can be changed. Emhasis on experience.

The module titlles need updated.

Voici un sommaire minuté basé sur le webinaire LISA, avec les points clés et les temps approximatifs où ils sont abordés dans la source vidéo :

Introduction (0:00 - 2:00) * Présentation du dispositif LISA, conçu pour améliorer le bien-être et la santé mentale des élèves. * L'objectif de LISA est de favoriser l'épanouissement des élèves au sein d'un collectif, tout en reconnaissant leur unicité. * Importance d'aborder la santé mentale à l'école, en complément des dispositifs existants comme les PAP. * Présentation des outils LISA : plateforme numérique, formations, ressources élaborées en collaboration interdisciplinaire.

Outils et ressources LISA (2:00 - 4:00) * Description de la plateforme numérique, avec l'outil de repérage et la base de ressources. * Explication du questionnaire "Facettes" pour une observation structurée des élèves. * Génération d'un tableau de bord individuel avec les forces et les besoins de chaque élève. * Présentation de la base de ressources Lzapédia et des formations proposées. * Mention du programme de recherche mené par des spécialistes, avec un comité éthique scientifique.

Le monde digital et ses enjeux (4:00 - 10:00) * Constat de la transformation des modes de communication par les outils digitaux. * Distinction entre les différents services digitaux : jeux vidéo, réseaux sociaux, sites de rencontre, etc.. * Les réseaux sociaux répondent au besoin essentiel de relations sociales, mais introduisent la notion de métrique. * Les services digitaux sont vivants, s'adaptent aux usages et peuvent maintenir l'utilisateur engagé. * Risque de sollicitation permanente, d'univers permanents et de réponses immédiates. * Le scrolling et l'excitation qu'il procure sont décrits comme des facteurs qui peuvent maintenir l'usage.

Usage des écrans chez les jeunes (10:00 - 16:00) * Les adolescents sont majoritairement exposés à la pornographie, aux réseaux sociaux et aux jeux vidéo. * Certains passent plus de 20h par semaine sur ces services, ce qui a suscité des inquiétudes. * La question de l'addiction à Internet a émergé, en particulier pour les jeux vidéo. * Une étude longitudinale montre que les usages importants se maintiennent pour certains jeunes, alors que d'autres diminuent. * L'activation de l'aire striatale ventrale est liée à la sévérité de la conduite addictive à la pornographie. * Les usages de la pornographie restent relativement stables chez les adolescents sur 3 ans.

Caractéristiques des services digitaux et notion d'addiction (16:00 - 23:00) * Les services digitaux ont des caractéristiques communes et spécifiques. * La question centrale est de savoir ce qui est considéré comme "trop de temps". * L'usage peut être utile et s'inscrire dans une passion harmonieuse. * Les critères d'addiction classiques sont appliqués au domaine des écrans : préoccupation, sevrage, tolérance, etc.. * Tous ces critères ne sont pas équivalents pour identifier une conduite addictive. * L'OMS a reconnu le trouble du jeu vidéo comme une addiction. * Les caractéristiques personnelles et les motivations (gratification, compensation) contribuent au maintien de l'usage. * Diverses motivations à jouer aux jeux vidéo sont identifiées : avancement, compétition, relations sociales, évasion. * L'évasion est un facteur qui revient souvent et qui est lié aux conduites addictives.

Réseaux sociaux et bien-être digital (23:00 - 30:00) * Les jeunes sont très connectés en ligne et communiquent souvent via les réseaux sociaux. * Les motivations à utiliser les réseaux sociaux sont sociales, de conformité, de régulation émotionnelle, de connaissance et d'excitation. * Le bien-être digital est devenu central, car le bien-être général passe par le numérique. * Définition du bien-être : expérience et fonctionnement psychologique optimaux. * Importance de trouver un équilibre entre les avantages et inconvénients de la connectivité. * Les difficultés fonctionnelles incluent les conduites addictives, le harcèlement, les difficultés de gestion des conflits.

Régulation et outils (30:00 - 36:00) * Les outils de contrôle du temps d'écran sont disponibles sur les smartphones. * Ces outils sont utilisés mais leur efficacité est jugée modérée. * Il faut optimiser l'usage, en étant conscient des avantages et inconvénients pour que les outils digitaux soient au service du bien-être. * Nécessité de prendre du recul sur l'usage et d'identifier les changements possibles. * La diversité des personnes, des besoins et des ressources est importante à considérer.

Questions et réponses (36:00 - Fin) * Discussion sur la manière de lutter contre la surexposition aux écrans des élèves. * Importance d'échanger avec les jeunes sur leur usage, leurs perceptions et les priorités à gérer. * Les parents doivent être des modèles dans leur utilisation des écrans. * Recommandations sur la gestion du temps de jeu après l'école : il faut considérer les autres intérêts et priorités de l'enfant et mettre en balance le temps passé sur les écrans avec les autres aspects de sa vie. * Il faut s'interesser à ce que l'enfant apprécie dans ses jeux, et envisager d'autres activités. * Comment convaincre les adolescents malgré de bons résultats scolaires : il faut maintenir une discussion ouverte et explorer d'autres activités. * Comment intégrer les compétences numériques à l'école sans créer d'addiction : il faut inclure la question de l'usage harmonieux et la conscience de l'usage. * Les effets de la lumière des écrans : il y a des liens entre les troubles de l'attention et l'usage des écrans, mais il est difficile de dire quelle est la cause et la conséquence. * L'approche de la régulation du temps d'écran varie selon l'âge de l'enfant. Pour les plus jeunes, il faut diversifier les activités et les inviter à participer à d'autres types d'activités, tandis que pour les plus âgés, on peut les impliquer davantage dans la réflexion. * Les activités appréciées hier ne disparaissent pas, mais il faut diversifier et proposer des activités avec des composantes ludiques ou de partage social.

Ce sommaire minuté offre une vue d'ensemble des points abordés lors du webinaire, permettant de retrouver facilement les informations importantes. Les éléments en gras mettent en évidence les points clés et les recommandations.

aandelenfusie?

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

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Reply to the reviewers

Manuscript number: RC-2024-02810

Corresponding author(s): Eric CHEVET

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We would like to thank the reviewers who pointed towards specific points in our manuscript which once addressed will make the work stronger.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

• *

• Reviewer 1 (General comments) raised the possibility that some interactions are post-lysis artifacts as ER lumen proteins are biotinylated. This is indeed true and this was our first reaction when analyzing the data. We and others previously demonstrated that a subset of ER luminal proteins can reflux (PMID: 38865586) out of the ER to the cytosol in both mammalian cells (PMID: 33710763, PMID: 37925033) and yeast (PMID: 32246734, PMID: 31101715) upon ER stress notably in mammalian cells some PDIs (PMID: 33710763) or some chaperones such as BiP (PMID: 37487081). To address whether PDIA4 could possibly be biotinylated by BirA*, we tested if PDIA4 could be found in the cytosolic fraction (using methodologies previously reported) (Fig. 1) (see also section 3).

These experiments show that PDIA4 can be found in the cytosol under ER stress conditions and thereby become a substrate for our fusion IRE1-BirA* protein. Moreover, our interactome study we found other ER-resident proteins, actually also found in other IRE1 proximitome approaches using TurboID (PMID: 38727283) such as HSP90AB1. This information will be added in the revised manuscript as well. To further address this reviewer’s comment, we propose, using the subcellular-fractionation protocol previously used, to assess the presence of other ER luminal protein from our BioID experiment (such as HSP90AB1 or GRP78/BiP) in the cytosol upon basal and ER stress conditions and test the interaction IRE1/PDIA4 using in situ cross-linking followed by a co-immunoprecipitation approach with or without ER stress.

• Reviewer 1 & 2 (Specific points):
• Figure2D: Reviewer 1 cannot appreciate the ER stress-induced expression of XBP1s.
• Reviewer 2 questions the uses of different stressors along the paper.

We agree that these points could be significantly improved. We will address these specific points by transfecting HEK293T cells with BirA* alone or IRE1-BirA and stressing the cells with 3 different ER stressors used in this study (DTT, Tg, Tm) and then evaluate XBP1 mRNA splicing using RT-qPCR and XBP1s expression using Western blotting. IRE1-BirA overexpression will be quantified compared to endogenous IRE1. Regarding Fig 2D the WB in MA2-KO cells with increasing amount of transfected IRE1-BirA will be repeated to show a better image of the XBP1s blot.

• Reviewer 1 (Specific point) suggests that BirA might not be expressed since the protein is not visible on the western blot Fig2E. The cytosolic BirA* (cBirA*) has been expressed and was detected by mass spectrometry. All the mass spectrometry data presented in the manuscript corresponded to those found using IRE1-BirA* of which those found with cBirA* alone were removed. This information was indeed missing and will be added in the revised version as well as the datasets corresponding to cBirA* alone. In addition, we will show the western blot on cBirA transfected cells.

• Reviewer 1 & 3 (Specific points):

• Figure7: Reviewer 1 asks for a IRE1/hnRNPL co-immunoprecipitation.
• Figure7: Reviewer 3 asks to develop the results obtained on hnRNPL. Does the depletion of HNRNPL influence the expression of SEL1L? Does it influence some other aspect of IRE1 stability maybe through a protein-protein interactions?

We will perform IRE1 immunoprecipitation by transfecting HEK293T cells with IRE1-flag and then blot hnRNRPL, SEL1L and SYNV1. We will also test the expression of SEL1L upon hnRNRPL knockdown and test other ERAD proteins clients by western blotting to address whether our result is specific to IRE1. Moreover, to further document the role of hnRNRPL on the biology of IRE1 we will evaluate how the absence of hnRNPL impacts on IRE1 signaling through comparison of RNAseq data from IRE1 deficient cells (or IRE1 RNase inhibitor treated cells) with those obtained from hnRNPL silenced cells. This should allow us to identify gene networks specific of IRE1 (or IRE1 RNase) and common to those impacted by hnRNPL silencing. At last, we will evaluate how the relationship hnRNPL/IRE1 impacts on cells’ ability to cope with chemically induced ER stress. To do so we first propose to compare ER stress-induced cell death in cells invalidated for IRE1 (genetically or pharmacologically) and others silenced for hnRNPL. These results will be confronted to those obtained in vivo in the fly (collaboration Pedro Domingos ongoing).

• Reviewer 2 & 3 (Specific points):
• Reviewer 2 raised the possibility that the large basal interactor might be due to the very long time periods in the BioID process. The reviewer asks if we did perform a time course of biotin treatment.
• Reviewer 3 asks for a timecourse of ER stress (with treatment shorter than 16h) to better catch the dynamic nature of IRE1 PPIs that regulate IRE1 activity.

We agree with these comments. We used a BirA* enzyme to characterize the IRE1 interactome, this enzyme (BirA*) which requires at least 16h to label efficiently proteins at proximity with biotin. To validate (or not) our interactome data, we propose to perform experiments with shorter labelling time, and use an IRE1-TurboID and perform different time course (ranging from 30min to 8h) with or without stress in the presence of biotin. Biotinylated proteins will be purified and we will test the presence of different proteins that have been captured in our first IRE1-BioID analysis using Western blotting with specific antibodies.

• Reviewer 3 (Specific points):
• Reviewer 3 says that other RIDD targets should be tested, notably BLOS1 (Fig5D). Moreover, the reviewer suggests to include a condition with a RNAse inhibitor as positive control.

We will perform transfection in HEK293T cells with the different siRNA candidates as we did in Fig5D. Then we will assess the effect of the different knockdown on RIDD targets by testing BLOS1 and DGAT2, two robust RIDD targets, by RT-qPCR. This experiment will be performed with or without stress, in the presence or not of MKC8866 and in the presence of Actinomycin D in order to block transcription which could lead to confounding effects in terms of gene expression.

• Reviewer 3 (Specific points):
• Reviewer 3 asks to validate the direct interaction between PTPN1 and IRE1 and to further developed the role of IRE1/PTPN1 interaction in the splicing activity of IRE1.

To test the direct interaction between IRE1 and PTPN1, we are planning to use GST-PTPN1 (commercially available) and HIS-IRE1 recombinant proteins produced in the laboratory (either WT or N638D) as previously reported by us (PMID: 20237204). We will then perform successive GST-pulldown in presence of GST-PTPN1 and HIS-IRE1. In addition, we are also planning to measure XBP1 mRNA splicing by RT-qPCR upon PTPN1 knockdown in HEK293Tcells expressing IRE1 WT or IRE1 N638D mutant and treated, or not, with ER stress inducers. In these conditions, the activity of IRE1 and its mutant in terms of RNase activity (XBP1 mRNA splicing and RIDD) will be evaluated.

• The reviewers asked for some precisions that could be answered directly in the manuscript. Here are the modifications of the text.
• Reviewer 1 (specific point) found that Figure 1 is misleading.

The meta-analysis depicted in Figure 1 of the manuscript includes data from many studies aiming at identifying IRE1 interactors using high-throughput methods. However, one must consider that those interactors were studied in different backgrounds: different cell types, technics and treatments. In addition, considering the low abundance of IRE1 and the high number of interactors shown in Figure 1, it should be highly improbable that all those IRE1 interactions occur at the same time. The comment of Figure 1 will be modified to better appreciate the way this network was built alongside its associated bias. We agree that we could use this figure in supplemental material to justify our strategy for in situ proximity labelling.

• Reviewer 1 (specific point) asks how the MS analysis was carried out to avoid false positive. Mass spectrometry data were indeed analyzed by subtracting the hits found in control conditions (cBirA*) from the hits detected with IRE1-BirA*, as hypothesized by the reviewer. The manuscript text will be modified accordingly to better appreciate the curation that was performed and the cBirA* dataset added on the ProteomeXchange database.

• Reviewer 1 (minor points) argues that apoptosis is not a major cluster from the stressed interactome. Here, we highlight that the term “Regulation of apoptotic process” is exclusively enriched in the stressed interactome, therefore referring to terminal UPR that occurs during prolonged stress. Also, this term includes 16 IRE1 interactors (which corresponds to 30% of the stressed interactome and 7% of the global interactome). Altogether, this explains why we considered this term to comment to comment the Gene Ontology. The manuscript will be modified to better illustrate the choice of this term.

• Reviewer 1 (minor points) asks to discuss the possibility of interactions due to IRE1 overexpression and the bias associated with the technic (plus how authors fixed these issues). Bias due to IRE1 overexpression are discussed in the Section “Approach limitations” as follows: “Since we used transient overexpression of IRE1 for our BioID study, there might be an increased basal level of ER stress compared to stable transfection, modifying the basal UPR signaling properties.” This will be modified to discuss a potential increase in the number of IRE1 interactors due to IRE1 overexpression. Regarding the technical approach, our BioID approach does not allow to detect transient interactions, a limitation that will be commented to this section.

• Reviewer 2 (specific points) argues that addition of the bars from Figure S2C should reach 100%. The analysis carried out for Fig S2C uses the COMPARTMENTS plugin on Cytoscape (Binder et al. 2014) and does not aim to add up the percentage to 100%. In detail, this plugin individually calculates a score (from 0 to 5) for a protein in each subcellular compartments listed in the panel, based on manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods. Then for each compartment, we counted the number of proteins with a score higher than 4,75 (= 95% of 5) and calculated the abundance percentage relatively to the total number of proteins of the datasets (for BioID or Ref independently), providing the values displayed in the panel S2C. The fact that each analysis is independent from one another and that one protein may be counted in several compartments makes the addition to 100% irrelevant.

• Reviewer 2 (minor points) specifies that the Adamson dataset used in our analysis is a Perturb-Seq. We thank the reviewer for noticing this imprecision. The manuscript will be revised to be more specific about the nature of the Adamson dataset (e.g. replacing CRISPR screen by CRISPRi screen coupled with Perturb-Seq).

• Reviewer 2 (minor points) asks to rework some figures to enlarge the size of the font and to better separate the panels of some figures. Additionally, he suggests that the manuscript could benefit of a careful English editing. We thank the reviewer for this comment. Figures will be reworked for improved readability (e.g. font size and panel boundaries). Regarding the manuscript, it will be reworked to improve the writing quality and correct the mistakes.

• Reviewer 2 (minor points) pointed on page 10 the sentence “Thus, IRE1 BioID identified new IRE1 interactors and revealed that IRE1 interactions are responsive to stress” while the majority of the interactions occur in basal. We thank the reviewer for this comment and agree that the sentence could be clarified. The fact that 25% of the interactions appear specifically during ER stress treatment despite the stress already induced by IRE1 overexpression suggests that the exogenous stress is still able to modify IRE1 interactions. It therefore indicates that overexpressed IRE1 interacts with a different landscape of proteins upon induced ER stress.

• Reviewer 3 (specific points) asks for some precision about the duration of the stress treatments used for the BioID. We thank the reviewer for noticing some of these inconsistencies in the manuscript. To be precise, the stress treatment (Tg or TM) of the BioID carried out for mass spectrometry is concomitant to the addition of exogenous biotin, which is indeed 16h treatment. While we agree such stress treatment is longer than usual, we highlight that both biotin and ER stress treatment had to be added for the same duration, to allow the detection of ER stress interactors during the slow kinetic of BirA* dependent biotinylation. The results section, figure legends and Materials and Methods will be edited to harmonize the concomitant ER stress/biotin treatment for BioID coupled with mass spectrometry.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

• Reviewer 1 (General comments) raised the possibility that some interactions are post-lysis artifacts as ER lumen proteins are biotinylated. PDIA4 is an ER luminal protein identified by our cytosolic BioID. To test whether this protein could be found in the cytosol, we performed subcellular fractionation and were able to observe PDIA4 in the cytosolic fraction (Fig 1 Revision). This was confirmed by quantifying the relative signal between PDIA4 and Calnexin used as the ER marker. The experiment will be expanded to other ER luminal proteins found in our interactome.

• Reviewer 1 (Specific point) suggests that BirA might not be expressed since the protein is not visible on the western blot Fig2. In addition, Reviewer 1 asks how the MS analysis was carried out to avoid false positive. As mentioned above, BirA has not been detected by western blot so far. However, it was by mass spectrometry, as shown by the table displaying BirA Signal Intensity (Fig 2 Revision). BirA is less expressed in control condition than fused with IRE1, which may explain a low signal exerted by the streptavidin-HRP blot.

• Reviewer 1 (Specific point) asks for an improved visualization of panel 5A, showing a NATIVE-PAGE with higher exposure associated quantification of %oligomerization. Also, reviewer 1 suggests adding a corresponding SDS-PAGE for IRE1. Regarding IRE1 oligomerization, Panel 5A has been reworked according to the reviewer’s comment (Fig 3 Revision). A higher exposed picture of the NATIVE-PAGE is provided and SDS-PAGE in the same conditions is shown. Quantification of % IRE1 oligomerization is also provided to better appreciate this result. Figure 5 of the manuscript will be reworked to implement such modifications.

Figure ____3____ Revision: Rework of panel 5A with IRE1 SDS-PAGE and quantification of IRE1 oligomerization.

• Reviewer 3 (specific point) asks for a quantification of IRE1-BirA overexpression compared to WT. To address this reviewer’s comment, a preliminary result has been obtained using Western blot, regarding the comparison of the expression between overexpressed IRE1-BirA* and WT IRE1. This shows that IRE1-BirA* is expressed between 5 to 8 times more than WT, independently of ER stress induction by DTT (Fig 4 Revision). This will be repeated at least twice independently to consolidate the data.

• Reviewer 3 (Specific point) asks for a comparison of the IRE1 BioID with the Turbo-ID recently published by Ahmed et al. Ahmed et al identified 155 interactors for IRE1α and 137 for IRE1β in the HMC1.2 leukemia cell line. Yet, the entire list of these interactors is neither available in the manuscript nor on the ProteomeXchange database. When comparing our interactors with the hits released in their work (Ahmed et al. 2024), we find 20 (including IRE1) that are shared with our dataset (__Fig 5 Revision, __IRE1 is not indicated on the Venn diagram).

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Figure ____5____ Revision: Venn diagram of IRE1 shared interactors between Le Goupil et al BioID and the available data in Ahmed et al TurboID 2024 (data on ProteomeXchange PXD047343 not yet available).

Considering that the approach Ahmed et al. used relies on another proximity labeling method, that the experiment was carried out in another cell line and that the total number of hits is of the same order of magnitude as that obtained in our analysis, one can be relatively confident about our results. We agree that a full comparison will be more informative (we will provide a full comparison in the revised version by using the proteomeXchange dataset if available, if not, we will contact them directly).

• Reviewer 3 (Specific point) asks whether the IRE1 N683D mutant could exert a different basal activity than the WT IRE1. The IRE1α mutant N683D has been controlled upon reception. Preliminary results measuring the splicing of XBP1 by RT-qPCR in basal conditions showed that the mutant’s basal activity is at a steady-state level through time, comparable to the WT (Fig 6 Revision). Provided that this mutant is expressed at a lower level than IRE1 WT, one might consider that the ability of N683D to exert a higher XBP1 mRNA splicing activity on its own than the WT is neglectable.

* *

• *

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

• *

• Reviewer 2 (Specific point) suggests to develop the results regarding the comparison between IRE1α, IRE1β and PERK interactors. Regarding the IRE1α/PERK comparison, both interactome was performed in HEK293T cells using the BirA* system (PMID: 37366380), minoring the issues regarding methodological bias. Functionally, both sensors aim to alleviate ER stress, and one might hypothesize that these interactors commonly regulate IRE1 and PERK pathways, either to promote or limit the ER stress response. In accordance the GO further suggests that these interactors are closely associated with ER stress regulation. When focusing on structural aspects, IRE1 and PERK both display a kinase domain. Alignment of the sequence of IRE1α and PERK kinase domain only shows a limited conservation (24% identity calculated with Clustal Omega), however, when looking at 3D structures of the respective kinase domain (PDB: 4G31 for PERK and PDB: 4YZ9 for IRE1), we observe common features (e.g. N-lobe, 7 α-helixes in the C-lobe), which might underline similar ways of interactor-dependent regulation.

We agree with this reviewer that the comparison of the different interactomes is of great interest and that this will be part of our investigations in the future. At present time, we provide

below a Venn diagram that integrates data from different datasets (our data on IRE1α and b bioID interactomes in HEK293T cells (https://doi.org/10.1101/2024.10.27.620453), the PERK bioID interactome in HEK293T cells (PMID: 37366380), the IRE1α turboID interactome in HMC1.2 cells (PMID: 38727283) and the IRE1b IP/MS interactome in goblet cell lines (PMID: 38177501)).

Figure 6: Venn diagram of the shared interactors between IRE1a, IRE1b, and PERK from several studies.

This shows that the IRE1α and PERK interactomes, generated using BirA* fusions in HEK293T cells share 43 proteins which may be of course highly interesting to evaluate whether these interactions could occur through IRE1α and/or PERK kinase domains (e.g., PERK and IRE1α interaction with PTPN1). Regarding the IRE1α/IRE1β comparison, the IRE1β interactome was evaluated either using bioID (our data) or using IP-MS in LS174T goblet-like cells (PMID: 38177501) - provided that data from Ahmed et al. not available yet. Hence, we agree here that these differences impose biases that are not optimal to compare the interactomes (for instance AGR2 is not endogenously expressed in HEK293T cells). Overall, we do not plan to extend the experiments on these topics, as this is not directly aligned with the main scope of our study, but are definitely interested in pursuing the relevance of the shared interactomes in future studies. As the manuscript does not provide much explanation of these panels in the results section, we are considering either improving the discussion of existing panels, or deleting them from the manuscript.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, the authors utilize a proximity ligation approach to probe protein-protein interactions involved in regulating the activity and stability of the ER stress sensing protein IRE1. Specifically, they express an IRE1-BirA fusion protein that they use to identify specific protein-protein interactions that influence the relative IRE1 RNAse activities of XBP1 splicing and RIDD. They go on to focus on two hits, PTPN1 and HNRPL, showing that these proteins influence IRE1 RNAse activity and stability, respectively.

Overall, the primary value of this manuscript is the list of potential interactors that is generated through this approach. Limitations are largely discussed in the manuscript. These include the fact that only interactors in the cytosol are accurately profiled owing the construct design and the potential for overexpression artifacts. Apart from those, there are some other issues with the manuscript that should be addressed, which are highlighted in more detail below. Ultimately, this manuscript doesn't provide a lot to move the field forward apart from providing another list of potential IRE1 interactors. The two 'hits' pursued are not sufficiently developed to reveal new insights into IRE1 regulation, as the mechanisms are not well developed and it isn't clear something 'new' has been discovered that directly relates to IRE1. I strongly recommend that the authors advance on of these hits to more deeply understand the mechanistic insights related to their (potential) involvement of IRE1 regulation.

Specific Comments.

1. The authors bring up the potential for overexpression artifacts, but they should define how much overexpression is observed by comparing the relative expression of overexpressed protein to endogenous IRE1 by western blotting.
2. There is some confusion regarding the timing of the BioID experiments, especially as it relates to the addition of ER stress. In the text, it seems that the authors treat with ER stress for 16 h, while the legend suggests 6 h treatments. A 16 h treatment is far too long to interpret potential regulators of IRE1 activity, so this is an important point. Related, the authors should do a timecourse of ER stress to better catch the dynamic nature of IRE1 PPIs that regulate IRE1 activity (but this should be a short timecourse).
3. Along the same lines as above, Ahmed et al recently published another proximity ligase profile for IRE1, as highlighted by the authors. Yet, the authors do not show any comparisons between their list and the list generated by Ahmed et al. This is critical, as it could help generate a more reliable list of IRE1 interactors identified by this approach. In many ways, as alluded to by the authors, the more rapid labeling afforded by TurboID used by Ahmed et al would show a better snapshot of IRE1 interactors, limiting the potential impact of this study, so it is essential to benchmark their approach to the previous manuscript.
4. The authors use CD59 as a putative RIDD target for the studies described in Fig. 5D. Other targets should also be used to convince that these effects can be attributed to RIDD. Notably, the canonical RIDD target BLOS1 should be used. Further, the authors should show that the Tg-dependent reduction in CD59 is sensitive to co-treatment with IRE1 RNAse inhibitors. Without further experiments on this point, these experiments are difficult to interpret as RIDD targets (apart from BLOS1) are well established to not be canonical across cell types.
5. The authors have previously demonstrated that PTPN1 is involved in regulating XBP1 splicing, although the work presented here is suggested to reveal a new importance for direct interactions with IRE1. However, this needs to be further developed. The authors use a bioinformatic approach termed iPIN to suggest interactions, although this appears to be a proprietary software that has not been published. The identify a potential interface for this interaction and then show that some mutations near this potential site of interaction seem to reduce IRE1 stability, while increasing interactions with PTPN1 (overexpressed) and XBP1 splicing. However, there are a number of concerns here. Does the mutation, N638D basally increase the specific activity of splicing, which can be measured using recombinant proteins. Further, the co-IPs are not well controlled, as there is no evidence that PTPN1-mCherry doesn't come down with beads or any other protein. In other words, the potential role for PTPN1 in regulating XBP1 splicing needs to be better developed to convince that this represents an important activity mediated through direct IRE1 interactions.
6. Similarly, the results with HNRNPL need to be further developed. It is well established that IRE1 ERAD is regulated by the activity of SYNV1 (HRD1) and SEL1L. So does genetic depletion of HNRPNL influence expression of these factors (HRD1 is shown but not SEL1L). Does it affect their interaction? Or does it influence some other aspect of IRE1 stability maybe through a protein-protein interactions? Again, more information is needed to determine the potential importance of HNRNPL in IRE1 stabilization.

Significance

Overall, the primary value of this manuscript is the list of potential interactors that is generated through this approach. Limitations are largely discussed in the manuscript. These include the fact that only interactors in the cytosol are accurately profiled owing the construct design and the potential for overexpression artifacts.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by Le Goupil et al. presents the results of a protein proximity screen for the UPR sensor IRE1 using the method BioID. The data include a list of interactors, their comparison with computational analysis of curated databases as well as previously published experimental data such as genome wide siRNA or CRISPRi screens and focused Perturb-Seq data. By focusing on the intersection of these data sets, the authors putatively connect IRE1 to previously unknown cellular activities. The authors also make an effort to validate these data by couple of examples where they identify HNRNPL as an interacting partner and stabilizer of IRE1. Overall, this manuscript makes important contributions towards establishing a framework to understand IRE1 biology more fully; however, significant validation and functional characterization would be required to fully evaluate the robustness/utility of the IRE1 interactome that is presented.

Specific points:

1. What is the reason to use different ER stressors in different experiments, i.e. DTT, TG, or TM?
2. Figure S2C: percentages should add up to 100% for enabling meaningful comparison of the two.
3. Are the number of common interactors between IRE1 and PERK too high for structurally different proteins? Is it because they are embedded in the same membrane and thus there may be some ´non-specific´ interactors? It may also be due to long incubation periods (see below). For proper examination of this, of course, requires BioID experiment in the same cell type under the same conditions. This should be underlined in the text. The same goes for the comparison of IRE1 and IRE1
4. It may be surprising that the great majority of the interactors are at the basal level, without stress. Since IRE1 activity is stress-induced, how are these basal interactors change IRE1 activity upon stress? Could this large basal interactor set be due to the very long time periods in the BioID process (18-24 h)? Or are the majority of the interactors mediating non-canonical IRE1 functions, as suggested in the literature (even some of these are stress activated)? Regarding this, did the authors do a time course to identify the optimal time of biotin treatment, the time point at which a plateau is reached in terms of approximate number of proteins associated?

Minor points:

1. The manuscript will significantly benefit from careful English language editing. There are spelling errors, omission of punctuations, half sentences, and repetitive language.
2. The data from Adamson et al. paper referenced on page 6 is a CRISPRi screen coupled to Perturb-Seq, not a simple CRISPR screen.
3. 50 nM Thapsigargin is referred to as a mild stressor, but it is actually a strong stressor that can even kill some cell types.
4. Figure texts are often too small and hard to follow, e.g. in the Venn-diagrams.
5. Boundries of Figures S2D-E-F are too difficult to discern.
6. Statement on top of page 10: ¨Thus, IRE1 BioID identified new IRE1 interactors and revealed that IRE1 interactions are responsive to stress¨. However, the majority of the interactors ara basal, not responsive to stress.

Significance

Strengths:

Robust experimental approach with a well-established technique that provides in situ interactome data for a central protein in proteostasis.

Weakness:

Lack of further experimental validation of the data. This is, however, a big task, and will take significant additional effort and time.

Advance:

The study makes conceptual and incremental increase in defining the IRE1 interactome and opens the way for further studies.

Audience:

The findings of this study is of interest to basic molecular and cell biologists with an interest in intracellular signaling, as well as those that may be interested in UPR-disease connection, e.g. cancer and neurodegenerative disease.

Reviewer Expertise:

UPR biology in normal and pathological conditions.

Windows ALPC (Advanced Local Procedure Call) provides a message-passing mechanism for communication between processes, optimized for performance within the Windows environment. Windows uses connection ports for clients to request services from server processes. ALPC supports different message-passing methods, depending on the message size, including smaller messages using a message queue and larger ones via section objects, which leverage shared memory. Notably, ALPC is designed to handle local communication on the same machine efficiently, and its internal complexity allows clients to make decisions about message size to avoid unnecessary copying. While ALPC is a powerful IPC mechanism, it is not directly exposed to application developers but is used behind the scenes by the system's standard remote procedure call (RPC) system.

The section on Pipes focuses on a basic but foundational IPC method in UNIX and Windows systems, where data is transmitted between two processes through a unidirectional or bidirectional conduit. The design of pipes involves considerations like whether they allow bidirectional communication, whether a relationship is required between processes (such as parent–child), and whether communication can occur across a network. Named pipes, an extension of regular pipes, allow for more flexible communication, as they can be used by unrelated processes and even across machines, though they still rely on local IPC principles.

In the section discussing POSIX Shared Memory, the shared memory mechanism relies on memory-mapped files and uses the shm_open(), ftruncate(), and mmap() functions to facilitate inter-process communication (IPC). Shared memory allows processes to communicate efficiently by writing to a memory region that can be accessed by multiple processes. The producer-consumer model is used in this example, where one process (the producer) writes to shared memory, and another (the consumer) reads the data. This method is particularly fast compared to other IPC methods because it avoids the overhead of copying data between processes. However, it is important to note that proper synchronization is required to ensure that concurrent access to shared memory does not lead to data corruption or inconsistency.

In Mach's Message Passing, communication between tasks occurs via messages sent to and received from ports. Mach message passing is designed for distributed systems and allows efficient communication, even between systems. Each task is associated with ports that manage communication through unidirectional messages, and port rights control access to these messages. The flexibility of message passing in Mach is evident in its queuing system, where messages are queued in FIFO order and can be managed using different waiting strategies if the queue is full. Additionally, Mach uses virtual memory mapping techniques to avoid the overhead of copying messages, enhancing performance in local communications.

Voici un sommaire minuté du podcast "L'esquive du secteur", avec les idées fortes mises en gras :

• 0:00-0:27 : Introduction avec un extrait de slam de Neïla qui parle de l'avenir et des clichés sociaux.
• 0:27-1:01 : Safia, une des réalisatrices, évoque son amitié avec Neïla et leurs parcours dans les quartiers populaires de Compiègne. Elles se sont connues en 6ème.
• 1:01-1:33 : Elles parlent du club slam qu'elles ont fait au collège et de la dérogation scolaire qu'elles ont été poussées à faire pour éviter le lycée de leur secteur.
• 1:33-2:25 : Réflexions sur les préjugés liés aux écoles de quartiers et sur l'ascenseur social, perçu comme rare dans leur entourage.
• 2:25-2:41 : Safia explique avoir voulu recontacter d'autres amis ayant fait des parcours similaires pour ce podcast.
• 2:41-3:17 : Présentation du podcast "L'esquive du secteur" par Halima et Safia Elkhatabi.
• 3:17-3:40 : Feriel, une amie de Safia et Neïla, raconte comment elle a aussi fait une dérogation en fin de 3ème pour éviter le collège ZEP de leur ville.
• 3:40-4:57 : Elle décrit le collège qu'elle a rejoint, le collège André Malraux, comme un bon collège avec des filières d'excellence, mais où la mixité était un sujet de préoccupation. Elle évoque aussi comment un professeur leur a fait comprendre qu'elles avaient le potentiel pour faire mieux, c'est-à-dire aller dans un lycée hors secteur.
• 4:57-5:47 : Introduction d'Idir, rencontré par Safia lors de leurs études supérieures en sociologie urbaine. Idir raconte son arrivée en France en 1996 et son enfance dans un quartier HLM.
• 5:47-6:53 : Idir parle de la réputation de son collège de secteur et du fait qu'ils n'avaient pas de devoirs, ce qu'il relie au fait que les élèves ne pouvaient pas se faire aider à la maison.
• 6:53-7:35 : Lecture d'un extrait de "Comme nous existons" de Kaoutar Harchi, évoquant le "placement" des enfants à l'école par leurs parents.
• 7:35-8:24 : Safia rend visite à Néné, une amie d'enfance devenue principale adjointe de collège en région parisienne. Elles ont grandi dans le même quartier.
• 8:24-10:28 : Néné raconte l'histoire de ses parents, arrivés du Sénégal, et comment son frère a été inscrit dans une autre école maternelle que celle de leur quartier, ce qui a influencé leur parcours scolaire. Elle décrit un sentiment d'être "à côté" du quartier et du collège de secteur.
• 10:28-11:21 : Slam "Dans mon quartier".
• 11:21-12:09 : Neïla explique que sa mère refusait qu'elle aille au lycée de secteur à cause de la mauvaise expérience de ses frères et sœurs, et qu'elle a été encouragée par certains enseignants à aller dans d'autres lycées. Elle a choisi la classe européenne pour avoir l'anglais renforcé et pour contourner la carte scolaire.
• 12:09-13:36 : Évocation du processus de dérogation et de la sélection des élèves pour les lycées hors secteur. Les élèves devaient avoir un niveau excellent et de très bonnes notes. Neïla remarque qu'à 14 ans, ils étaient déjà en quelque sorte "sociologues", analysant les parcours qui fonctionnaient.
• 13:36-14:26 : Lecture d'un extrait de "Comme nous existons", évoquant les stratégies mises en place par les parents pour que leur enfant intègre le bon établissement scolaire et l'obtention d'une dérogation scolaire.
• 14:26-15:54 : Neïla se souvient du choc culturel ressenti lors des premiers jours au lycée, notamment à cause des prénoms des élèves qu'ils n'avaient pas l'habitude d'entendre. Sentiment d'infériorité et difficulté à se sociabiliser.
• 15:54-16:45 : Elle parle du choc du niveau scolaire et de la découverte des classes préparatoires et des grandes écoles.
• 16:45-18:00 : Mamie, la petite sœur de Néné, raconte comment elle prenait le bus très tôt pour aller au lycée du centre-ville.
• 18:00-19:14 : Elle décrit la composition sociale de sa classe, majoritairement des enfants du centre-ville, et son sentiment d'être une exception en venant du Clo des Roses. Évocation des goûts musicaux différents.
• 19:14-21:16 : Neïla se souvient de sa rencontre avec une fille du conservatoire qui s'étonnait de la voir dans ce lycée, ce qui a accentué son sentiment d'être à part. Elle parle du mépris de classe, notamment lors d'une expérience dans une boutique de vêtements.
• 21:16-22:35 : Elle évoque le mépris de classe de certains professeurs et le fait que la classe européenne était parfois décriée. Certains professeurs, au contraire, l'ont encouragée. Elle a fini par faire Science Po. Elle parle de la distance créée avec ses amis du collège de secteur.
• 22:35-24:01 : Mamie raconte qu'elle s'est fait de nouvelles amies au lycée, venant de la campagne autour de Compiègne. Elle parle de la tristesse et du sentiment de perte de ses amies du collège de secteur.
• 24:01-25:01 : Réflexion sur la différence perçue entre les amis du lycée et ceux du collège de secteur, et sur le fait de se sentir entre deux mondes.
• 25:01-26:34 : Néné parle de la difficulté de certains enseignements au lycée et du sentiment de ne pas être traitée de la même manière que d'autres élèves. Elle évoque son orientation vers la voie techno, alors que d'autres élèves avec les mêmes résultats étaient orientés vers des filières générales.
• 26:34-27:38 : Slam "Je me suis réveillée".
• 27:38-28:21 : Safia remarque que les problèmes qu'ils voyaient adolescents sont toujours les mêmes aujourd'hui et exprime que cela donne espoir.
• 28:21-30:50 : Idir et Safia discutent, en tant qu'adultes, de leur regard sur leurs parcours scolaires et de leur dérogation. Idir questionne la notion de mérite et se demande si leur parcours est reproductible. Il craint que leur réussite serve à valider un système excluant. Il culpabilise d'avoir déjoué le système sans le changer.
• 30:50-32:23 : Ils reconnaissent que les lycées réputés leur ont ouvert des portes, mais que la dérogation est une forme d'exception. Ils soulèvent la question de l'individualisme poussé par le système.
• 32:23-33:16 : Safia évoque une cérémonie organisée par sa ville pour mettre à l'honneur les "talents de quartier" et critique cette initiative.
• 33:16-34:34 : Elle explique pourquoi elle a refusé d'y participer plusieurs fois et se demande pourquoi elle aurait plus sa place que d'autres personnes de son quartier. Elle explique que ce qui l'anime est la transmission.
• 34:34-35:09 : Conclusion avec un retour du slam de Neïla et un appel à se battre pour ce en quoi on croit.

Behandlingsmål kan vara: 1. Kortsiktiga. (ex. att återgå till vanliga sömnvanor) 2. Långsiktiga (ex. att fortsätta med medicin under perioder av symtomfrihet)

De kan även: 3. Täcka stora domäner av funktion (ex. öka nöjdhet med jobb; öka kontakten med community aktivitet; minska social isolering) 4. Vara specifika (minska frekvensen av specifika tvångsbeteenden; identifiera återkommande problematiska relationsteman 5. Vara generella (öka sammanhållning i familjen; öka känslan av intimitet och stabilitet i relationer)

Formatting a business letter with these five main sections.

Voici un sommaire minuté de la transcription, mettant en évidence les idées fortes :

• 0:05-0:27 : Introduction par Benoit Le Corre, le journaliste, qui exprime son questionnement sur le but de filmer Jérémie et présente son approche de raconter la vie des gens. Il mentionne que c'est son deuxième reportage et demande aux spectateurs de s'abonner à sa chaîne.
• 0:39-1:27 : Benoit anticipe que la vidéo ne sera pas facile à regarder et qu'elle n'a pas été facile à tourner. Il décrit un faux départ, expliquant qu'il n'a pas tout de suite sorti la caméra parce qu'il ne le sentait pas. Il souligne que Jérémie n'a pas l'habitude de la caméra, et il décide d'y aller doucement.
• 1:29-2:35 : Jérémie se montre peu confiant quant à ce que Benoit pourra tirer de lui, soulignant le désordre de sa maison et son manque d'argent. Il décrit sa chambre comme une "chambre d'alcc**lo". Il révèle qu'il a consommé deux bouteilles de pastis et plusieurs bières la veille.
• 2:37-3:05 : Jérémie révèle qu'il a lui-même demandé à être filmé. Benoit explique qu'il a rencontré Jérémie grâce à son voisin Jacques, un abstinent qui essaie d'aider Jérémie. Il se demande si faire cette vidéo est une bonne idée, mais il espère que ça pourra l'aider.
• 3:09-3:35 : Jérémie se prépare pour le tournage, en soulignant ses habitudes de coiffure et son choix de vêtements. Il mentionne ne pas aimer sa tête avec des lunettes. Il révèle qu'il a "bouffé l'héritage de son père" décédé en 2011.
• 3:38-4:06 : Jérémie montre à Benoit l'ancienne salle de jeux de sa fille, remplie de Playmobil. Il hésite à les vendre tout de suite, mais envisage de le faire le lendemain.
• 4:16-4:49 : Jérémie avoue que cette vidéo est aussi pour sa fille. Il explique qu'il est plus facile pour lui de parler à Benoit qu'à sa fille. Il exprime la difficulté d'expliquer à sa fille pourquoi il est ainsi.
• 4:52-5:27 : Jérémie se souvient de la dernière fois qu'il a vu sa fille dans un magasin. Il est ému en évoquant comment sa fille tient compte de son budget.
• 5:30-5:51 : Benoit remarque que les journalistes dissimulent parfois des choses, et que les gestes sont importants. Il dit qu'il retourne voir Jérémie le lendemain avec un peu d'appréhension.
• 6:02-6:30 : Jérémie explique qu'il est toujours fatigué le matin tant qu'il n'a pas bu. Il est interrogé sur ce qu'il ferait avec 150 000 euros, et il mentionne partir en voyage avec sa fille.
• 6:33-6:56 : Jérémie explique qu'il va chez sa mère. Il révèle qu'il n'a pas encore bu ce matin-là. Il explique qu'il a un décollement de la rétine qui affecte sa vision.
• 6:59-7:17 : Jérémie révèle qu'il a été chauffeur. Il explique qu'il ne veut pas être filmé pour le moment avec sa mère.
• 8:09-8:36 : La mère de Jérémie exprime son inquiétude face à l'addiction de son fils. Elle se sent coupable et pense que Jérémie a un chagrin. Jérémie demande à Benoit s'il veut boire un verre.
• 8:40-8:53 : Jérémie admet qu'il n'a pas fait grand chose pour arrêter de boire parce qu'il ne le faisait pas pour lui. Il mentionne avoir tenté trois cures, mais sans succès.
• 9:04-9:12 : Jérémie exprime son souhait d'aller skier avec sa mère et sa fille s'il gagnait 150 000 euros.
• 9:13-9:48 : La mère de Jérémie avoue qu'elle a eu peur de lui quand il était sous l'influence de l'alcool. Jérémie révèle qu'il ne se rendait pas compte de ce qu'il lui a fait subir.
• 9:50-10:14 : Jérémie va à son épicerie habituelle. Il commande un pastis, comme d'habitude. Benoit remarque la familiarité entre Jérémie et le commerçant.
• 10:31-10:39 : Le commerçant exprime sa tristesse de voir Jérémie détruire sa vie à chaque fois qu'il passe en caisse.
• 10:47-11:17 : Jérémie et Benoit partagent un repas. Jérémie dit qu'il a été touché par les propos de sa mère, et il a réalisé qu'elle a eu peur de lui.
• 11:17-11:29 : Benoit explique qu'il s'est mis en retrait le premier soir car il était difficile de communiquer avec Jérémie.
• 11:29-11:54 : Une chanson sur l'amour est jouée, et il y a une réflexion sur le thème de l'amour.
• 12:24-12:39 : Benoit exprime son optimisme quant au journalisme et à la possibilité de changement pour Jérémie et sa famille. Il espère mener une interview formelle avec Jérémie.
• 13:14-13:41 : Benoit propose de faire une interview, mais cela s'avère compliqué. Jérémie révèle qu'il n'a pas dormi de la nuit et qu'il a bu avec un ami. Benoit explique qu'il va reprendre des questions qu'il a déjà posées.
• 13:41-14:27 : Jérémie admet qu'il est alcoolique et qu'il a commencé à boire à l'âge de 11-12 ans. Il explique qu'il accepte d'être filmé pour sa mère, qu'il a vue triste et dont il admire l'amour inconditionnel. Il avoue avoir été difficile avec ses beaux-pères.
• 14:31-15:03 : Jérémie reconnait que son corps lui dit stop. Il est confronté à l'idée que sa mort pourrait attrister ses proches. Il comprend qu'il risque de laisser des gens tristes s'il meurt.
• 15:05-15:54 : Jérémie exprime son désir d'être là pour sa fille, qu'il pense rendre triste à cause de son problème d'alcool. Il imagine le futur dans cinq ans où il serait "beau gosse" et irait chercher sa fille à l'école.
• 15:57-16:22 : Benoit arrête le tournage car il sent qu'il a envie de sauver Jérémie, ce qui n'est pas son rôle. Il comprend que sa présence n'aide pas Jérémie et que le changement ne peut venir que de lui-même. Il conclut que l'avenir de Jérémie est flou, et qu'il ne faut pas le juger.
• 16:35-17:07 : Benoit mentionne qu'il a lui-même mis dix ans à se remettre de la mort de son père. Il propose à Jérémie d'arrêter le tournage s'il en a besoin. Jérémie accepte de continuer, disant que ces 15 minutes sont comme deux verres.

Author response:

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

eLife Assessment

This important work proposes a neural network model of interactions between the prefrontal cortex and basal ganglia to implement adaptive resource allocation in working memory, where the gating strategies for storage are adjusted by reinforcement learning. Numerical simulations provide convincing evidence for the superiority of the model in improving effective capacity, optimizing resource management, and reducing error rates, as well as solid evidence for its human-like performance. The paper could be strengthened further by a more thorough comparison of model predictions with human behavior and by improved clarity in presentation. This work will be of broad interest to computational and cognitive neuroscientists, and may also interest machine-learning researchers who seek to develop brain-inspired machine-learning algorithms for memory.

We thank the reviewers for their thorough and constructive comments, which have helped us clarify, augment and solidify our work. Regarding the suggestion to include a “more thorough comparison with with human behavior”, we believe this comment reflects one of the reviewer’s suggestion to compare with sequential order effects. We now include a new section with simulations showing that the network exhibits clear recency effects in accordance with the literature, and where such recency effects are known to be related to WM interference and not due to passive decay. Overall our work makes substantial contact with human behavioral patterns that have been documented in the human literature (and which as far as we know have not been jointly captured by any one model), such as the shape of the error distributions, including probability of recall and variable precision;  attraction to recently presented items,  sensitivity to reinforcement history, set-size dependent chunking, recency effects,  dopamine manipulation effects, as well of a range of human data linking capacity limitations to frontostriatal function. It also provides a theoretical proposal for the well established phenomenon of capacity limitations in humans, suggesting that they arise due to difficulty in WM management.

Below we address each reviewer individually, responding to each comment and providing the relevant location in the paper that the changes and additions were made. Reviewer responses are included in blue/bold for clarity.  

Public Reviews:

Reviewer 1:

Thank you for your comments. We appreciate your statements of the strengths of this paper and your suggestions to improve this paper.

First, the method section appears somewhat challenging to follow. To enhance clarity, it might be beneficial to include a figure illustrating the overall model architecture. This visual aid could provide readers with a clearer understanding of the overall network model.

Additionally, the structure depicted in Figure 2 could be potentially confusing. Notably, the absence of an arrow pointing from the thalamus to the PFC and the apparent presence of two separate pathways, one from sensory input to the PFC and another from sensory input to the BG and then to the thalamus, may lead to confusion. While I recognize that Figure 2 aims to explain network gating, there is room for improvement in presenting the content accurately.

As suggested, we added a figure (new figure 2) illustrating the overall model architecture before expanding it to show the chunking circuitry. This figure also shows the projections from thalamus to PFC (we preserve the previous figure 2, now figure 3, as an example sequence of network gating decisions, in more abstract form to help facilitate a functional understanding of the sequence of events without too much clutter). We also made several other general clarifications to the methods sections to make it more transparent and easier to follow, as per your suggestions.   

Still, for the method part, it would enhance clarity to explicitly differentiate between predesigned (fixed) components and trainable components. Specifically, does the supplementary material state that synaptic connection weights in striatal units (Go&NoGo) are trained using XCAL, while other components, such as those in the PFC and lateral inhibition, are not trained (I found some sentences in 'Limitations and Future Directions')?

We have now explicitly specified learned and fixed components. We have further explained the role of XCAL and how striatal Go/NoGo weights are trained. We have also added clarification on how gating policies are learned via eligibility traces and synaptic tags.

I'm not sure about the training process shown in Figure 8. It appears that the training may not have been completed, given that the blue line representing the chunk stripe is still ascending at the endpoint. The weights depicted in panel d) seem to correspond with those shown in panels b) and c), no? Then, how is the optimization process determined to be finished? Alternatively, could it be stated that these weight differences approach a certain value asymptotically? It would be better to clarify the convergence criteria of the optimization process.

The training process has been clarified and we specify (in the last paragraph of the Base PBWM Model) how we determine when training is complete. We also can confirm that the network behavior has stabilized in learning even if the Go/NoGo weights continue to grow over time for the chunked layer (due to imperfect performance and reinforcement of the chunk gating strategy).

Reviewer 2:

Thank you for your comments. We appreciate your notes on the strengths of the paper and your suggestions to help improve the paper.

The model employs a spiking neural network, which is relatively complex. Additionally, while this paper validates the effectiveness of chunking strategies used by the brain to enhance working memory efficiency through computational simulations, further comparison with related phenomena observed in cognitive neuroscience experiments on limited working memory capacity, such as the recency effect, is necessary to verify its generalizability.

Thank you for proposing we add in more connections with human WM. Based on your specific recommendation, we have included the section “Network recapitulates human sequential effects in working memory.” where we discuss recency effects in human working memory and how our model recapitulates this effect. We have also made the connections to human data and human work more explicit throughout the manuscript (Figure 4c). As noted in response to the assessment, we believe our model does make contact with a wide variety of cognitive neuroscience data in human WM, such as the shape of the error distributions,  including probability of recall and variable precision;  attraction to recently presented items,  sensitivity to

reinforcement history, set-size dependent chunking, recency effects, and dopamine manipulation effects, as well of a range of human data linking capacity limitations to frontostriatal function. It also provides a theoretical proposal for the well established phenomenon of capacity limitations in humans, suggesting that they arise due to difficulty in WM management.

Recommendations For The Authors:

Reviewer 1:

I appreciate the authors' clear discussion of the limitations of this work in the section "Limitations and Future Directions". The development of a comprehensive model framework to overcome these constraints should require a separate paper, though, I am curious if the authors have attempted any experiments, such as using two identically designed chunking layers, that could partially support the assumptions presented in the paper.

Expanding the number of chunking layers is a great future direction. We felt that it was most effective for this paper to begin with a minimal set up with proof of concept. We hypothesize that, given our results, a reinforcement learning algorithm would be able to learn to select the best level of abstraction (degree of chunking) in more continuous form, but would require more experience across a range of tasks to do so.

I'm not sure whether it's appropriate that "Frontostriatal Chunking Gating..." precedes "Dopamine Balance is...", maybe it would be better to reverse the order thus avoiding the need to mention the role of dopamine before delving into the details. Additionally, including a summary at the end of the Introduction, outlining how the paper is organized, could provide readers with a clear roadmap of the forthcoming content.

We appreciate this suggestion. After careful thought, we wanted to preserve the order because we felt it was important to make the direct connection between set size and stripe usage following the discussion on performance based on increasing stripes.  

The authors could improve the overall polish of the paper. The equations in the Method section are somewhat confusing: Eq. (2) appears incorrect, as it lacks a weight w_i and n should presumably be in the denominator. For Eq. (3), the comma should be replaced with ']'... It would be advisable to cross-reference these equations with the original O'Reilly and Frank paper for consistency.

Thank you for pointing out the errors in the method equations- those equations were indeed rendering incorrectly. We have fixed this problem.  

Additionally, there are frequent instances of missing figure and reference citations (many '?'s), and it would be beneficial to maintain consistent citation formatting throughout the paper: sometimes citations are presented as "key/query coding (Traylor, Merullo, Frank, and Pavlick, 2024; see also Swan and Wyble, 2014)", while other times they are written as "function (O'Reilly & Frank, 2006)"...

Lastly, there is an empty '3.1' section in the supplementary material that should be addressed.

The citation issues were fixed. The supplementary information was cleaned and the missing section was removed. Thank you for mentioning these errors.  

Reviewer 2:

Thank you for the following recommendations and suggestions. We respond to each individual point based on the numbering system used in your review.  

(1) This paper utilizes the experimental paradigm of visual working memory, in which different visual stimuli are sequentially loaded into the working memory system, and the accuracy of memory for these stimuli is calculated.

The authors could further plot the memory accuracy curve as the number of items (N) increases, under both chunking and non-chunking strategies. This would allow for the examination of whether memory accuracy suddenly declines at a specific value of N (denoted as Nc), thereby determining the limited capacity of working memory within this experimental framework, which is about 4 different items or chunks. Additionally, it could be investigated whether the value of Nc is larger when the chunking strategy is applied.

We have included an additional plot (Probability of Recall) as a supplemental figure to Figure 5 to explore the probability of recall as a function of set size for both chunking and no chunking models.  This plot shows that the chunking model increases probability of recall when set size exceeds allocated capacity (but that nevertheless both models show decreases in recall with set size, consistent with the literature).

(2) The primacy effect or recency effect observed in the experiments and traditional working memory models, including the slot model and the limited resource model, should be examined to see if it also appears in this model.

The literature on human working memory shows a prevalent recency effect (but not a primacy effect, which is thought to be due to episodic memory, and which is not included in our model). We have added a section showing that our model demonstrates clear recency effects.

(3) The construction of the model and the single neuron dynamics involved need further refinement and optimization:

Model Description: The details of the model construction in the paper need to be further elaborated to help other researchers better understand and apply the model in reproducing or extending research. Specifically:

a) The construction details of different modules in the model (such as Input signal, BG, striatum, superficial PFC, deep PFC) and the projection relationships between different modules. Adding a diagram to illustrate the network construction would be beneficial.

To aid in the understanding of the model construction and model components, we have included an additional figure (Figure 1: Base Model) that explains the key layers and components of the model.  We have also altered the overall model figures to show more clearly that the inputs project to both PFC and striatum, to highlight that information is temporarily represented in superficial PFC layers even before striatal gating, which is needed for storage after the input decays.

We have expanded the methods and equations and we also provide a link to the model github for purposes of reproducibility and sharing.  

A base model figure was added to specify key connections.  

a) The numbers of excitatory and inhibitory neurons within different modules and the connections between neurons.

We added clarification on the type of connections between layers (specifying which are fixed and learned). We have also added the size of layers in a new appendix section “Layer Sizes and Inner Mechanics”

b) The dynamics of neurons in different modules need to be elaborated, including the description of the dynamic equations of variables (such as x) involved in single neuron equations.

Single neuron dynamics are explained in equations 1-4. Equations 5-6 explain how activation travels between layers. The specific inhibitory dynamics in the chunking layer are elaborated in Figure 4. PBWM Model and Chunking Layer Details. The Appendix section “Neural model  implementational details” states the key equations, neural information and connectivity. Since there is a large corpus of background information underlying these models, we have linked the Emergent github and specifically the Computational Cognitive Neuroscience textbook which has a detailed description of all equations. For the sake of paper length and understability, we chose the most relevant equations that distinguish our model.  

c) The selection of parameters in the model, especially those that significantly affect the model's performance.

The appendix section hyperparameter search details some of the key parameters and why those values were chosen.  

d) The model employs a sequential working memory paradigm, the forms of external stimuli involved in the encoding and recalling phases (including their mathematical expressions, durations, strengths, and other parameters) need to be elaborated further.

We appreciate this comment. We have expanded the Appendix section “Continuous Stimuli” to include the details of stimuli presentation (including durations etc).  

(4) The figures in the paper need optimization. For example, the size of the schematic diagram in Figure 2 needs to be enlarged, while the size of text such as "present stimulus 1, 2, recall stimulus 1" needs to be reduced. Additionally, the citation of figures in the main text needs to be standardized. For example, Figure 1b, Figure 1c, etc., are not cited in the main text.

The task sequence figure (original Figure 2) has been modified and following your suggestions, text sizes have been modified.  

(5) Section 3.1 in the appendix is missing.

Supplemental section 3.1 is removed.

what bacteriophages are and the definition of it

Bacteriophages are viruses that can kill bacteria, and people first found out about them in the early 1900s. Even though they tried using phages to treat infections, it was hard to use them and antibiotics became more common, but now, because some bacteria don't respond to antibiotics, phages are being looked at again as a way to fight these "superbugs."

gnosys

Author response:

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

Reviewer #1 (Public Review):

MacDonald et al., investigated the consequence of double knockout of substance P and CGRPα on pain behaviors using a newly created mouse model. The investigators used two methods to confirm knockout of these neuropeptides: traditional immunolabeling and a neat in vitro assay where sensory neurons from either wildtype or double knock are co-cultured with substance P "sniffer cells", HEK cells stably expressing NKR1 (a substance P receptor), GCaMP6s and Gα15. It should be noted that functional assays confirming CGRPα knockout were not performed. Subsequently, the authors assayed double knockout mice (DKO) and wildtype (WT) mice in numerous behavioral assays using different pain models, including acute pain and itch stimuli, intraplanar injection of Complete Freund's Adjuvant, prostaglandin E2, capsaicin, AITC, oxaliplatin, as well as the spared nerve injury model. Surprisingly, the authors found that pain behaviors did not differ between DKO and WT mice in any of the behavioral assays or pain paradigms. Importantly, female and male mice were included in all analyses. These data are important and significant, as both substance P and CGRPα have been implicated in pain signaling, though the magnitude of the effect of a single knockout of either gene has been variable and/or small between studies.

The conclusions of the study are largely supported by the data; however, additional experimental controls and analyses would strengthen the authors claims.

We thank the reviewer for their insightful comments and have answered them below.

(1) The authors note that single knockout models of either substance P or CGRPα have produced variable effects on pain behaviors that are study-dependent. Therefore, it would have strengthened the study if the authors included these single knockout strains in a side-by-side analysis (in at least some of the behavioral assays), as has been done in prior studies in the field when using double- or triple-knockout mouse models (for example, see PMID: 33771873). If in the authors hands, single knockouts of either peptide also show no significant differences in pain behaviors, then the finding that double knockouts also do not show significant differences would be less surprising.

In our study, we found no phenotypic differences between WT and DKO mice, suggesting Substance P and CGRPα are largely dispensable for pain behavior. We agree that if we had we observed significant changes in behavior, it would have been interesting to examine the effects of knocking out each gene individually to determine which peptide is responsible for the phenotype. However, given the double deletion had no effect, we can predict that loss of each alone would have no or minor effects. In line with this, a more recent study that comprehensively phenotyped the Calca KO mouse found no deficits in a range of danger related behaviors (PMID: 34376756). Overall, as we are reporting negative data about the Double KO, we do not believe extensive studies of the single KOs is necessary to support the findings of our paper.

(2) It is unclear why the authors only show functional validation of substance P knockout using "sniffer" cells, but not CGRPα. Inclusion of this experiment would have added an additional layer of rigor to the study.

Imaging of CGRPα release is more challenging using the ‘sniffer’ approach because functional CGRP receptors require the expression of two genes: Calcrl (or Calcr) along with Ramp1. We now have succeeded in generating a new stable cell line expressing Calcrl and Ramp1, along with GCaMPs and human Galpha15 and include new data in the revised Figure 1F-H and Figure Supplement 1B. These cells respond robustly to CGRPalpha, but not to SP. In contrast, the existing SP cell line responds to SP but not CGRPalpha. Capsaicin evokes a strong response in these cells in co-culture with DRGs. This response is dramatically reduced in the DKO. This data therefore confirms our mice have a loss of CGRPalpha signaling as indicated by IHC.

(3) The authors should be a bit more reserved in the claims made in the manuscript. The main claim of the study is that "CGRPα and substance P are not required for pain transmission." However, the authors also note that neuropeptides can have opposing effects that may produce a net effect of no change. In my view, the data presented show that double knockout of substance P and CGRPα do not affect somatic pain behaviors, but do not preclude a role for either of these molecules in pain signaling more generally. Indeed, the authors also note that these neuropeptides could be involved in nociceptor crosstalk with the immune or vascular systems to promote headache. The authors only assayed pain responses to glabrous skin stimulation. How the DKO mice would behave in orofacial pain assays, migraine assays, visceral pain assays, or bone/joint pain assays, for example, was not tested. I do not suggest the authors include these experiments, only that they address the limitations/weaknesses of their study more thoroughly.

The reviewer makes an important point that we agree with. Our study assesses acute and chronic pain in peptide DKO mice lacking Substance P and CGRPα. Most of our data focuses on the hindpaw as pain in the paw is the gold-standard approach for phenotyping pain targets and numerous well-validated chronic pain models have been developed for this body site.  However, to extend the conclusions to other tissues, we did also look at visceral pain and GI distress using acetic acid and LiCl models (Figure 2J and Figure 2 supplement). We agree with the reviewer that given the utility of CGRP monoclonal antibodies, migraine experiments would be interesting for future studies using these mice, a point we highlight in the discussion. Bone/joint pain is also clearly important from a translational perspective, but outside the scope of the current study.

(4) A more minor but important point, the authors do not describe the nature of the WT animals used. Are the littermates or a separately maintained colony of WT animals? The WT strain background should be included in the methods section.

The WT strain are C57/BL6j from Jackson Lab. This has been added to the methods.

Reviewer #2 (Public Review):

Summary:

The paper aimed to examine the effect of co-ablating Substance P and CGRPα peptides on pain using Tac1 and Calca double knockout (DKO) mice. The authors observed no significant changes in acute, inflammatory, and neuropathic pain. These results suggest that Substance P and CGRPα peptides do not play a major role in mediating pain in mice. Moreover, they reveal that the lack of behavioral phenotype cannot be explained by the redundancy between the two peptides, which are often co-expressed in the same neuron

Strengths:

The paper uses a straightforward approach to address a significant question in the field. The authors confirm the absence of Substance P and CGRPα peptides at the levels of DRG, spinal cord, and midbrain. Subsequently, they employ a comprehensive battery of behavioral tests to examine pain phenotypes, including acute, inflammatory, and neuropathic pain. Additionally, they evaluate neurogenic inflammation by measuring edema and extravasation, revealing no changes in DKO mice. The data are compelling, and the study's conclusions are well-supported by the results. The manuscript is succinct and well-presented.

We thank the reviewer for their enthusiasm for the importance of our work.

Reviewer #3 (Public Review):

In this study, the authors were assessing the role of double global knockout of substance P and CGPRα on the transmission of acute and chronic pain. The authors first generated the double knockout (DKO) mice and validated their animal model. This is then followed by a series of acute and chronic pain assessments to evaluate if the global DKO of these neuropeptides are important in modulating acute and chronic pain behaviors. Authors found that these DKO mice Substance P and CGRPα are not required for the transmission of acute and chronic pain although both neuropeptides are strongly implicated in chronic pain. This study does provide more insight into the role of these neuropeptides on chronic pain processing, however, more work still needs to be done. (see the comments below).

We thank the reviewer for their detailed and constructive feedback, and below outline the steps we have taken to answer their concerns.

(1) In assessing the double KO (result #1), why are different regions of the brains shown for substance P and CGRPα (for example, midbrain for substance P and amygdala for CGRPα)? Since the authors mentioned that these peptides co-expressed in the brain (as in the introduction), shouldn't the same brain regions be shown for both IHC? It would be ideal if the authors could show both regions (midbrain and amygdala) in addition to the DRG and spinal cord for both peptides in their findings.<br /> In addition, since this is double KO, the authors should show more representative IHC-stained brain regions (spanning from the anterior to posterior).

We could not co-stain both SP and CGRP in the same sections as the DKO mouse has endogenous GFP and RFP fluorescence, limiting us to one channel (far red). Specifically, we use a Calca KO that is a Cre:GRP knock-in/knockout (Chen et al 2018, PMID30344042) and Tac1 KO is a tagRFP knock-in/knockout (Wu et al 2018 PMID29485996). This is why we show different brain sections.

(2) It is also unclear as to why the authors only assessed the loss of substance P signaling in the double KO mice. Shouldn't the same be done for CGRPα signaling? Either the authors assess this, or the authors have to provide clear explanations as to why only substance P signaling was assessed.

As noted in our response to Reviewer 1, imaging of CGRP release is more challenging using the ‘sniffer’ approach because functional CGRP receptors require the expression of two genes: Calcrl (or Calcr) along with Ramp1. We have now generated this cell line and performed the experiment (see revised Figure 1 and Figure 1 Supplement).

(3) Has these animal's naturalistic behavior been assessed after the double KO (food intake, sleep, locomotion for example)? I think this is important as changes to these naturalistic behaviors can affect pain processes or outcomes.

We agree that assessment of naturalistic behavior including food intake, sleep and locomotion would be interesting to look at in DKO mice. However, our study is focused on acute and chronic pain behavior of these animals, and therefore a comprehensive phenotypic assessment of naturalistic home-cage behavior is outside the scope of our study.

(4) Figure 2H: The authors acknowledge that there is a trend to decrease with capsaicin-evoked coping-like responses. However, a close look at the graph suggests that the lack of significance could be driven by 1 mouse. Have the authors run an outlier test? Alternatively, the authors should consider adding more n to these experiments to verify their conclusions.

We were reluctant to add more animals searching for significance. Instead, we investigated the potential phenotype further by looking at cfos staining in the cord and found no differences (Figure 2, supplement 1). This result suggests loss of the two peptides does not grossly disrupt capsaicin evoked pain signal transmission between the nociceptor and post-synaptic dorsal neurons in the spinal cord.

(5) Similarly, the values for WT in the evoked cFos activity (Figure 2- Suppl Figure 1) are pretty variable. Considering that the n number is low (n = 5), authors should consider adding more n.<br /> Also, since the n number is low in this experiment (eg. 5 vs 4), does this pass the normality test to run a parametric unpaired t-test? Either the authors increase their n numbers or run the appropriate statistical test.

As described in the statistical tables, the Shapiro-Wilk test indicates these data do pass the normality test. Therefore, we retain the use of the unpaired t test, which demonstrates no significant difference between the groups.

(6) In most of the results, authors ran a parametric test despite the low n number. Authors have to ensure that they are carrying out the appropriate statistical test for their dataset and n number.

We now provide a table of the statistical results, which provides detailed information about all statistical tests performed in this study. For experiments where we make a single comparison between the two distributions (WT vs DKO), we have run a Shapiro-Wilk test. Where the data from both groups pass the normality test, we retain the use of the unpaired t test. Where the Shapiro-Wilk test indicates data from either group are unlikely to be normally distributed, we now use a Mann-Whitney U test to compare the groups, as this non-parametric test makes no assumptions about the underlying distribution.

Many experiments involved two factors (genotype, and e.g. temperature, drug, time-point). These data were analyzed in the original submission using 2-WAY ANOVA or Repeated Measures 2-WAY ANOVA, followed by post-hoc Sidak’s tests to compute p values adjusted for multiple comparisons. Because there is no widely agreed non-parametric alternative to 2-WAY ANOVA for analyzing data with two factors and that enables us to account for multiple comparisons, we used 2-WAY ANOVA as is typically used in the field for these kinds of experiments. We reasoned sticking with the 2-WAY ANOVA was the best course of action based on information provided by the statistical software used for this study - https://www.graphpad.com/support/faq/with-two-way-anova-why-doesnt-prism-offer-a-nonparametric-alternative-test-for-normality-test-for-homogeneity-of-variances-test-for-outliers/

We note that regardless of the test, our conclusion that there are no major changes in acute or chronic pain behaviors are clear and strongly supported.

(7) Along the same line of comment with the previous, authors should increase the n number for DKO for staining (Figure 4) as n number is only 3 and there is variability in the cFos quantification in the ipsilateral side.

We believe this is not necessary as the finding is clear that there is no difference.

(8) Authors should provide references for statement made in Line 319-321 as authors mentioned that there are accumulating evidence indicating that secretion of these neuropeptides from nociceptor peripheral terminals modulates immune cells and the vasculature in diverse tissues.

We now provide several references to primary papers and reviews supporting this statement.

(9) Authors state that the sample size used was similar to those from previous studies, but no references were provided. Also, even though the sample sizes used were similar, I believe that the right statistic test should be used to analyze the data.

We have now cited several classic studies phenotyping mouse KOs in pain in the methods that used similar sample sizes. As detailed above, we have taken the reviewer’s feedback on board and performed normality testing to ensure the correct statistical test is used for each experiment.

(10) In the discussion, the authors noted that knocking out of a gene remains the strongest test of whether the molecule is essential for a biological phenomenon. At the same time, it was acknowledged that Substance P infusion into the spinal cord elicits pain, but it is analgesic in the brain. The authors might want to expand more on this discussion, including how we can selectively assess the role of these neuropeptides in areas of interest. For example, knocking out both Substance P and CGRPα in selected areas instead of the global KO since there are reported compensatory effects.

This is highlighted in the closing paragraph: “Emerging approaches to image and manipulate these molecules (Girven et al., 2022; Kim et al., 2023), as well as advances in quantitating pain behaviors (Bohic et al., 2023; MacDonald and Chesler, 2023), may ultimately reveal the fundamental roles of neuropeptides in generating our experience of pain.” The Kim preprint (now published, and so the citation has been updated in the text) describes a method of inactivating neuropeptide transmission in select brain regions in a cell-type specific manner.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I do not have any major comments. My minor comments are as follows:

(1) What was the control group for all behavioral studies? Was it WT from an independent colony or one of the littermates was used for generating controls?

We used C57/Bl6 mice from Jax. This is now mentioned in methods.

(2) In Fig. 2H, it seems that the effect will become significant if several mice are added.

We are reluctant to add mice searching for significance. Sample sizes were determined before we collected the data blind.

(3) There is no figure 3, but two figures 4.

Thank you. This has been corrected.

(4) Multiple typos in the legend for figure 4 (lines 234-254). Line 242 (& n=8 (3M, 3F)), line 243 (swelling and plasma), line 252 ((n=8 for) & n=6 for DKO (4M, 4F)).

Thank you. This has been corrected.

(5) In Figure 4 (lines 273-285), the contralateral side is mentioned in B but no images are shown.

Thank you. We removed the mention.

(6) Although ligand knockouts cannot be compared directly with receptor inhibition, the readers could benefit from discussing studies of receptor ablation and/or pharmacological inhibition.

We do discuss the classic studies of receptor KO, and the clinical data on receptor blockers here –

“However, selective antagonists of the Substance P receptor NKR1 failed to relieve chronic pain in human clinical trials (Hill, 2000). Although CGRP monoclonal antibodies and receptor blockers have proven effective for subsets of migraine patients, their usefulness for other types of pain in humans is unclear (De Matteis et al., 2020; Jin et al., 2018). In line with this, knockout mice deficient in Substance P, CGRPα or their receptors have been reported to display some pain deficits, but the analgesic effects are neither large nor consistent between studies (Cao et al., 1998; De Felipe et al., 1998; Guo et al., 2012; Salmon et al., 2001, 1999; Zimmer et al., 1998).” 

Reviewer #3 (Recommendations For The Authors):

Minor comments:

(1) Figure 1E: What does chambers mean? Additionally, are the 12 chambers equally from the male and female samples (6 from male and 6 from female)?

We have changed this to well. Each replicate is an individual well from 8 well chamber slide. In all these experiments, the wells are approximately evenly distributed by mouse, because from each mouse we cultured around 8 wells’ worth of DRGs.

(2) Figure 1D: What does low and high mean in the Hargreaves test?

These refer to a low and high active intensity of the radiant heat stimulus. Number is now described in the methods. 40 and 55 in the intensity units used by the instrument.

(3) Figure 2-Suppl Figure 1: Authors should provide a bigger image of the image so that it is clearer to the readers.

We think the image is of a reasonable size and comparable to the images used elsewhere in the paper.

(4) Authors should consider labeling their supplementary figures in running numbers or combining supplementary figures together to avoid confusion. For example, Figure 2-Supplementary Figure 1 and Figure 2- Supplementary Figure 2 can be combined as just Supplementary Figure 2.

We agree with the reviewer this would be clearer, but we have followed eLife’s convention for labelling and numbering supplements.

(5) Figure 3 is mislabeled as Figure 4.

Thank you. We have corrected this.

(6) Only female mice were used in the CFA experiment, which does not go in line with the rest of the results which consist of both sexes.

We have repeated the experiment with additional male mice. To be consistent with the von frey data, these were followed for 7 days, and so the figure now shows a 7 day time course.

(7) Typo in line 243. The word "and" is subscript.

Thank you. We have corrected this.

(8) There is a typo in the legend for Figure 4 where E is labeled I, G is labeled as F, and J is labeled as J.

Thank you. We have corrected this.

(9) Authors should specify what "several weeks" means (Line 263).

It means three weeks. We tested to 21 days. We will replace with three.

(10) Authors should specify what "one day" means (Line 267). For example, how many days after the intraplantar oxaliplatin treatment? Also, authors should justify why that specific time point was selected or have a reference for it.

This means one day after - 24 hours. Please see PMID: 33693512. Two references are provided in them methods.

(11) Figure 4 legend: authors should again be specific on what "prolonged" entails (Line 277).

We have replaced prolonged with 30 minutes brushing. Specifically, 3 x 10 min stim period, with 1 min rest between stim. It is in the methods.

(12) In the methods section, authors state that both male and female mice were used for all experiments. However, only female mice were used in the CFA experiment (see minor comment #6). Authors should verify and correct this.

This is correct. We only used female mice for one of the groups. We have since repeated with males, now included in the data.

(13) Authors should be more specific in the methods section on how long the habituation per day, how many days and what were the mice habituation to (experimenter, room, chamber, etc)?

As noted in the methods, mice are habituated for at least an hour to the chambers, and thus implicitly to the room. We do not perform explicit habituation to the investigator such as repeated handling.

(14) Authors need to provide more information on the semi-automated procedure they are referring to in Line 397. Also, authors should also provide the criteria for cFos quantification (eg. Intensity, etc). If this has been published before, they should provide the reference.

We have added this. We used the ‘Find maxima’ and ‘Analyze particles’ functions in FIJI, followed by a manual curation step.

(15) How much acetone was applied and how was it applied to the paw? (Line 495)

We used the same applicator (1ml syringe with a well at the top) to generate a droplet of acetone that was used for all mice. This has been added to methods.

(16) Authors should specify the amount of capsaicin injected in μl (Line 500).

20 ul. We have added this.

(17) Authors should explain or reference why they are analyzing the 15 min interval between 5 and 20 minutes for injection (Line507-508).

Acetic acid behaviour lasts around 30 mins in our hands. We chose the 15 minute interval because it reduces burdensome hand scoring time by 50% versus doing the whole 30 mins. We reasoned that in the first 5 mins post injection the animal behaviour may be contaminated by stress related to handling, injection and return to chamber. Thus, 5 and 20 minutes provided a sensible time-frame for scoring the behavior when it is at its peak.

(18) Authors have to provide more information/explanation on how they decide on the conditioned taste aversion protocol. Like why they do 30 mins exposure to a single water-containing bottle followed 90 mins exposure to both bottles. If this has been published before, they should provide the reference.

We read dozens of different published protocols in the literature, and piloted one that was something of an amalgam of some of them with various adaptations of convenience. Because it worked on our first attempt, we stuck to it. The advantage of the CTA assay is it is incredibly robust to changes in the specificities of the paradigm, evincing the clear survival value of learning to avoid tastes that make you sick.

(19) Authors again should provide more detail in their methods section.

a. Specify the time frame that they are assessing here (Line 533).

This can be seen in the Figure. 0 to 120 mins. We have added it to the methods.

b. How long were the mice allowed to recover post-SNI before mechanical allodynia was assessed (Line 545)?

This is apparent in the figures. 2 days to 21 days. We have added it to the methods.

c. How much of the oxaliplatin was injected into the mice?

40 ug / 40 ul (see PMID:33693512)

Editors note: Reviewers agreed that addressing the concerns about power, outliers, and statistics, as well as functional validation of CGRPα would raise the strength of evidence to compelling, and inclusion of comparison to single KO would raise it to exceptional.

Should you choose to revise your manuscript, please check to ensure full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

Voici un sommaire minuté des idées fortes de la transcription, avec les éléments clés en gras:

• 0:09-0:29 : Introduction du live par Catherine Jubot, psychologue de l'éducation nationale et directrice du CIO de Bourgoin, et présentation du thème : les ressources et l'accompagnement pour l'orientation post-lycée.
• 0:35-1:30 : Présentation d'Estelle de Moisson, psychologue de l'éducation nationale au CIO de Grenoble. L'accent est mis sur le fait que l'orientation est un processus qui prend du temps et qu'il est normal d'avoir des hésitations ou de changer d'avis. Il est important de ne pas rester seul et de demander de l'aide.
• 1:30-2:01 : Information sur l'enregistrement du live et possibilité de poser des questions via le chat. Présentation du rôle des CIO, lieux d'accueil pour accompagner la réflexion sur l'orientation.
• 2:01-3:23 : Les CIO offrent un accueil personnalisé, des entretiens, des ateliers, et de la documentation (ONISEP, CIDJ). Ils accueillent tous les publics (jeunes scolarisés, adultes, décrocheurs). Les psychologues de l'éducation nationale (anciennement conseillers d'orientation psychologues) sont rattachés aux CIO et interviennent dans les établissements scolaires.
• 3:23-4:01 : Rôle des psychologues de l'éducation nationale : écoute bienveillante, accompagnement sur les parcours, les passerelles, les voies de formation et les secteurs professionnels.
• 4:01-5:00 : La période de Parcoursup est un moment stressant. Les psychologues accompagnent les jeunes dans la connaissance de soi (points forts, centres d'intérêt, personnalité) et dans l'identification de secteurs professionnels potentiels.
• 5:00-6:10 : Importance de s'informer sur les formations (BTS, BUT, licences, classes préparatoires, etc.). Se faire accompagner pour comprendre le jargon de Parcoursup et les différents types d'études (courtes, longues, professionnalisantes, théoriques).
• 6:10-7:17 : Les psychologues aident à s'informer sur les métiers et à utiliser des questionnaires d'intérêt, qui ne sont pas des "boules de cristal" mais des outils de discussion. Il ne faut pas hésiter à les consulter, même sans question précise.
• 7:17-8:15 : Les psychologues sont disponibles dans les établissements scolaires (sur rendez-vous) et dans les CIO (avec et sans rendez-vous, y compris pendant les vacances scolaires).
• 8:15-9:04 : Les sites des CIO proposent des informations sur les journées portes ouvertes et les ateliers. Certains CIO proposent des ateliers spécifiques, par exemple sur Parcoursup ou les études à l'international.
• 9:04-9:35 : Des ateliers peuvent aussi être proposés pour les parents, car l'orientation est une source de stress pour eux aussi.
• 9:35-10:15 : L'orientation est une question qui peut se poser tout au long de la vie, y compris à l'âge adulte. La réflexion sur l'orientation est un processus continu.
• 10:15-11:09 : L'orientation n'est pas un enjeu majeur unique, il est normal de se tromper et de se réorienter. Il existe des passerelles entre les formations.
• 11:09-12:03 : Il est possible de se réorienter, y compris en cours d'année universitaire (semestre 2). Le dossier Parcoursup est conservé d'une année sur l'autre. Se tromper une fois permet d'être plus motivé la deuxième année.
• 12:03-13:15 : Importance d'aller à la pêche aux informations, en particulier les journées portes ouvertes (JPO). Anticiper l'orientation réduit le stress. Il faut se familiariser avec les lieux, discuter avec les étudiants, et les professeurs.
• 13:15-14:46 : Plus on s'informe, plus le choix sera pertinent. On est là pour aider même après les JPO. Ne pas rester seul et se rappeler que se tromper n'est pas grave, car on apprend de ces expériences.
• 14:46-15:22 : Les changements font partie de la vie et permettent de se construire. Se réorienter est possible et ne doit pas être vu comme un échec.
• 15:22-16:03 : Ressources en ligne pour préparer l'orientation : plateforme Avenir, site de l'ONISEP (vidéos sur ONISEP TV), ressources régionales (côté formation).
• 16:03-17:50 : Parcoursup est une mine d'or d'informations, avec des cartes de visite pour chaque formation, les critères d'analyse des candidatures, et les formations similaires. On peut y accéder même sans candidater, et contacter les responsables pédagogiques et les étudiants ambassadeurs.
• 17:50-18:25 : Il est possible de créer un compte Parcoursup dès la seconde et de mettre des formations en favori.
• 18:25-19:33 : Importance de se renseigner pour sortir du flou, des clichés et des idées reçues. Les conseils sont utiles mais il faut vérifier par soi-même. La décision finale revient à l'élève.
• 19:33-20:10 : Ne pas prendre pour argent comptant tous les conseils. L'orientation est un choix personnel. Il faut explorer et ne pas s'autocensurer.
• 20:10-20:17 : Transition vers les questions du public.
• 20:17-20:55 : Réponses aux questions :
  • Possibilité de prendre rendez-vous pendant les heures de cours, avec un justificatif
  • Possibilité de venir avec ses parents
• 20:55-22:20 : Réponses aux questions:
  • Les questionnaires en ligne existent mais ne sont pas des "boules de cristal". Ils sont plus utiles comme base de discussion avec un professionnel.
  • Les questionnaires sont une photo de soi à un instant T.
  • L'important est de travailler la connaissance de soi avec un professionnel
• 22:20-23:11 : Les jeunes ont rarement "envie de tout faire". L'orientation est une question d'élimination et de découverte des 180 secteurs professionnels.
• 23:11-24:20 : Les filières générales ne sont pas prioritaires en BTS. Il existe des quotas. Il est important de vérifier sur Parcoursup les profils des élèves recrutés. L'apprentissage peut être une solution pour les élèves de filière générale.
• 24:20-25:07 : Il ne faut pas s'autocensurer et il faut expliquer sa motivation pour intégrer un BTS.
• 25:07-26:35 : Déroulement d'un rendez-vous avec un psychologue d'orientation. L'entretien s'adapte à la demande de chacun, avec un temps de consultation plus long dans les CIO. Il n'y a pas de bonnes ou de mauvaises questions ou de mauvais projets, le meilleur projet est celui qui vous convient.
• 26:35-27:35 : Passage d'une seconde professionnelle à une seconde générale et technologique : possible en fin d'année si l'avis d'orientation est favorable.
• 27:35-28:18 : Après un bac pro, il est possible d'accéder aux mêmes études qu'un bac général ou technologique avec un bon dossier scolaire.
• 28:18-30:22 : La pression sur le choix des spécialités est à relativiser. Certaines formations très scientifiques nécessitent des spécialités scientifiques, mais il existe des remises à niveau à l'université.
• 30:22-31:14 : Il faut enlever la pression sur le choix des spécialités. Vérifier les informations sur parcoursup et les doublettes de spécialités les plus prises.
• 31:14-31:38 : Il n'est pas possible de refaire le même bac pour améliorer ses résultats.
• 31:38-32:05 : Conclusion du live : ne restez pas seul, contactez les CIO ou les psychologues scolaires..

Ce sommaire met en évidence les principaux points abordés dans le live, en soulignant l'importance de l'accompagnement, de l'information, et du droit à l'erreur dans le processus d'orientation.

Reviewer #2 (Public review):

Summary:

Wolbachia are maternally transmitted bacteria that can manipulate host reproduction in various ways. Some Wolbachia induce male killing (MK), where the sons of infected mothers are killed during development. Several MK-associated genes have been identified in Homona magnanima, including Hm-oscar and wmk-1-4, but the mechanistic links between these Wolbachia genes and MK in the native host are still unclear.

In this manuscript, Arai et al. show that Hm-oscar is the gene responsible for Wolbachia-induced MK in Homona magnanima. They provide evidence that Hm-Oscar functions through interactions with the sex determination system. They also found that Hm-Oscar disrupts sex determination in male embryos by inducing female-type dsx splicing and impairing dosage compensation. Additionally, Hm-Oscar suppresses the function of Masc. The manuscript is well-written and presents intriguing findings. The results support their conclusions regarding the diversity and commonality of MK mechanisms, contributing to our understanding of the mechanisms and evolutionary aspects of Wolbachia-induced MK.

Comments on revisions:

The authors have already addressed the reviewer's concerns.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Insects and their relatives are commonly infected with microbes that are transmitted from mothers to their offspring. A number of these microbes have independently evolved the ability to kill the sons of infected females very early in their development; this male killing strategy has evolved because males are transmission dead-ends for the microbe. A major question in the field has been to identify the genes that cause male killing and to understand how they work. This has been especially challenging because most male-killing microbes cannot be genetically manipulated. This study focuses on a male-killing bacterium called Wolbachia. Different Wolbachia strains kill male embryos in beetles, flies, moths, and other arthropods. This is remarkable because how sex is determined differs widely in these hosts. Two Wolbachia genes have been previously implicated in male-killing by Wolbachia: oscar (in moth male-killing) and wmk (in fly male-killing). The genomes of some male-killing Wolbachia contain both of these genes, so it is a challenge to disentangle the two.

This paper provides strong evidence that oscar is responsible for male-killing in moths. Here, the authors study a strain of Wolbachia that kills males in a pest of tea, Homona magnanima. Overexpressing oscar, but not wmk, kills male moth embryos. This is because oscar interferes with masculinizer, the master gene that controls sex determination in moths and butterflies. Interfering with the masculinizer gene in this way leads the (male) embryo down a path of female development, which causes problems in regulating the expression of genes that are found on the sex chromosomes.

We would like to thank you for evaluating our manuscript.

Strengths:

The authors use a broad number of approaches to implicate oscar, and to dissect its mechanism of male lethality. These approaches include:

(1) Overexpressing oscar (and wmk) by injecting RNA into moth eggs.

(2) Determining the sex of embryos by staining female sex chromosomes.

(3) Determining the consequences of oscar expression by assaying sex-specific splice variants of doublesex, a key sex determination gene, and by quantifying gene expression and dosage of sex chromosomes, using RNASeq.

(4) Expressing oscar along with masculinizer from various moth and butterfly species, in a silkmoth cell line.

This extends recently published studies implicating oscar in male-killing by Wolbachia in Ostrinia corn borer moths, although the Homona and Ostrinia oscar proteins are quite divergent. Combined with other studies, there is now broad support for oscar as the male-killing gene in moths and butterflies (i.e. order Lepidoptera). So an outstanding question is to understand the role of wmk. Is it the master male-killing gene in insects other than Lepidoptera and if so, how does it operate?

Thank you for your comments. Wolbachia strains often carry wmk genes, but as observed in this study, the homologs in Homona showed no apparent MK ability. These showed strong male lethality in D. melanogaster, but it is still unclear whether the genes are the master male-killing gene in Diptera. It is also possible that the genes show toxicities in other lepidopteran insects as well as in other insect taxa. Further functional validation assays in different insects are warranted to clarify whether wmk shows toxicity in different insect taxa. We have also discussed the functions of wmk in the Discussion section (lines 301-306).

Weaknesses:

I found the transfection assays of oscar and masculinizer in the silkworm cell line (Figure 4) to be difficult to follow. There are also places in the text where more explanation would be helpful for non-experts (see recommendations).

Thank you for your suggestion. We have thoroughly revised the manuscript to address all the questions, comments and suggestions you raised in “recommendations”. In particular, we have revised the section on the transfection assays of Oscar and Masc in Bm-N4 cells (result section “Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes” starts on line 214 and Fig. 4; materials and methods section ”Transfection assays and quantification of BmIMP^M”, starts on line 483). We have also provided more detailed explanations for non-experts in some contexts (in response to your recommendation). We believe that the resulting revisions have significantly improved the quality and comprehensiveness of our manuscript.

Reviewer #2 (Public review):

Summary:

Wolbachia are maternally transmitted bacteria that can manipulate host reproduction in various ways. Some Wolbachia induce male killing (MK), where the sons of infected mothers are killed during development. Several MK-associated genes have been identified in Homona magnanima, including Hm-oscar and wmk-1-4, but the mechanistic links between these Wolbachia genes and MK in the native host are still unclear.

In this manuscript, Arai et al. show that Hm-oscar is the gene responsible for Wolbachia-induced MK in Homona magnanima. They provide evidence that Hm-Oscar functions through interactions with the sex determination system. They also found that Hm-Oscar disrupts sex determination in male embryos by inducing female-type dsx splicing and impairing dosage compensation. Additionally, Hm-Oscar suppresses the function of Masc. The manuscript is well-written and presents intriguing findings. The results support their conclusions regarding the diversity and commonality of MK mechanisms, contributing to our understanding of the mechanisms and evolutionary aspects of Wolbachia-induced MK.

We would like to thank you for evaluating our manuscript.

Strengths/weaknesses:

(1) The authors found that transient overexpression of Hm-oscar, but not wmk-1-4, in Wolbachia-free H. magnanima embryos induces female-biased sex ratios. These results are striking and mirror the phenotype of the wHm-t infected line (WT12). However, Table 1 lists the "male ratio," while the text presents the "female ratio" with standard deviation. For consistency, the calculation term should be uniform, and the "ratio" should be listed for each replicate.

We have revised the first results section (Hm-oscar induces female-biased sex ratios, starting from line 147) accordingly to maintain the consistency in the calculation term. In the revised manuscript, the 'male ratio' is now consistently used, in alignment with Fig. 1. In addition, we have included all sex ratio information (number of males and females) in the supplementary data file for transparency and clarity.

(2) The error bars in Figure 3 are quite large, and the figure lacks statistical significance labels. The authors should perform statistical analysis to demonstrate that Hm-oscar-overexpressed male embryos have higher levels of Z-linked gene expression.

The large error bar on each chromosome (Fig.3a-d) likely reflect the overall variation in expression levels across different transcripts. Accordingly, we have included statistical data for Figure 3 based on the Steel-Dwass test for expression levels. However, displaying statistical significance directly on the whisker plots would make the figure too cluttered due to the numerous combinations. Instead, we have provided all the statistical data in the supplementary data file. To further support the claim that Z-linked genes are more highly expressed in wHm-t-infected/Hb-Oscar-injected embryos, we have included the expression data for a Z-linked gene tpi, along with its statistical data in the revised manuscript (Fig. 3e, lines 210-212).

(3) The authors demonstrated that Hm-Oscar suppresses the masculinizing functions of lepidopteran Masc in BmN-4 cells derived from the female ovaries of Bombyx mori. They should clarify why this cell line was chosen and its biological relevance. Additionally, they should explain the rationale for evaluating the expression levels of the male-specific BmIMP variant and whether it is equivalent to dsx.

Thank you for your suggestion. We selected BmN-4 cell line because previous studies have established it as a reliable model for investigating the functions of lepidopteran masc genes and the interactions between masc and Oscar genes (Katsuma et al., 2019; 2022). In addition, BmIMP^M is a male-specific regulator of the male-type dsx, making it an ideal target for assessing the 'maleness' induced by transfection of the masc gene in female-derived BmN-4 cells (Suzuki et al., 2010; Katsuma et al., 2015). We have included more detailed background information in the revised manuscript and have thoroughly revised this section (Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes, starting at line 214) and Figure 4 for better clarity.

(4) Although the authors show that Hm-oscar is involved in Wolbachia-induced MK in Homona magnanima and interacts with the sex determination system in lepidopteran insects, the precise molecular mechanism of Hm-oscar-induced MK remains unclear. Further studies are needed to elucidate how Hm-oscar regulates Homona magnanima genes to induce MK, though this may be beyond the scope of the current manuscript.

Based on our findings and previous studies in Homona, Ostrinia and Bombyx (Arai et al., 2023a; Katsuma et al., 2023; Kiuchi et al., 2014), we hypothesize that the molecular mechanisms underlying _w_Hm-induced MK are likely linked to impaired dosage compensation caused by the inhibition of Masc function by the Hm-Oscar protein. While the precise mechanisms remain unclear, unbalanced Z-linked gene expression due to the impaired dosage compensation (i.e., 2-fold higher Z-linked gene expression compared to normal males) is known to be lethal for lepidopteran males (Kiuchi et al., 2014; Fukui et al., 2015; Visser et al., 2021). We have outlined this hypothesis in the Discussion section (lines 245-254).

Reviewer #3 (Public review):

Summary:

Overall, this is a clearly written manuscript with nice hypothesis testing in a non-model organism that addresses the mechanism of Wolbachia-mediated male killing. The authors aim to determine how five previously identified male-killing genes (encoded in the prophage region of the wHm Wolbachia strain) impact the native host, Homona magnanima moths. This work builds on the authors' previous studies in which:

(1) They tested the impact of these same wHm genes via heterologous expression in Drosophila melanogaster.

(2) They examined the activity of other male-killing genes (e.g., from the wFur Wolbachia strain in its native host: Ostrinia furnacalis moths).

Advances here include identifying which wHm gene most strongly recapitulates the male-killing phenotype in the native host (rather than in Drosophila), and the finding that the Hm-Oscar protein has the potential for male-killing in a diverse set of lepidopterans, as inferred by the cell-culture assays.

Strengths:

Strengths of the manuscript include the reverse genetics approaches to dissect the impact of specific male-killing loci, and the use of a "masculinization" assay in Lepidopteran cell lines to determine the impact of interactions between specific masc and oscar homologs.

We would like to thank you for evaluating our manuscript.

Weaknesses:

My major comments are related to the lack of statistics for several experiments (and the data normalization process), and opportunities to make the manuscript more broadly accessible.

Thank you for your suggestions. We have thoroughly revised the manuscript to provide clearer explanations for non-experts. In addition, we have included more detailed statistical data for Figure 3 and Figure 4 based on the Steel-Dwass tests. For Figure 3a-d, displaying statistical significance directly on the whisker plots would make the figure too cluttered due to the numerous combinations. Therefore, we have provided all the statistical data in the supplementary data file. To further support the claim that Z-linked genes are more highly expressed in w_Hm-t-infected/Hm-Oscar-injected embryos, we have included the expression data for a Z-linked gene _tpi, along with its statistical data in the revised manuscript (Fig.3e, lines 210-212). Regarding Figure 4, we have revised the Figure based on the reviewer’s suggestions, and provided more detailed information on how the expression data were analyzed (Transfection assays and quantification of BmIMP^M, lines 495-520). We have also included more detailed background information on the assay system (Hm-oscar suppresses the masculinizing functions of lepidopteran masc genes, lines 215-237). Although we did not observe statistical significance based on the Steel-Dwass test, likely due to limited replicates, the observed changes in the IMP gene expression remain clearly evident.

The manuscript I think would be much improved by providing more details regarding some of the genes and cross-lineage comparisons. I know some of this is reported in previous publications, but some summary and/or additional analysis would make this current manuscript much more approachable for a broader audience, and help guide readers to specific important findings. For example, a graphic and/or more detail on how the wmk/oscar homologs (within and across Wolbachia strains) differ (e.g., domains, percent divergence, etc) would be helpful for contextualizing some of the results. I recognize the authors discuss this in parts (e.g., lines 223-227), but it does require some bouncing between sections to follow. Similarly, the experiments presented in Figure 4 indicate that Hm-oscar has broad spectrum activity: how similar are the masc proteins from these various lepidopterans? Are they highly conserved? Rapidly evolving? Do the patterns of masc protein evolution provide any hints at how Oscar might be interacting with masc?

Thank you for your valuable suggestion. To address this, we have included a visualization of the structural differences between the Oscar and wmk homologs in Figure 1a of the revised manuscript. In addition, we have included more detailed information for these genes and revised the introduction (lines 110-114; 124-137) and discussion (lines 255-266) to provide a clearer and more comprehensive overview. We have also described the similarity of the Masc proteins and Oscar proteins that we used, which is now reflected in the revised Figure 4b and 4d. More detailed information on these proteins is available in the supplementary data. Notably, Masc proteins exhibit high sequence variability with conserved domains (Figure 4d). Previous study identified the N-terminal region of Masc as crucial for the Oscar function (Katsuma et al., 2022). The wide spectrum of the actions of Hm-Oscar likely stems from these conserved structures of Masc, but the effects might have undergone evolutionary tuning through interactions with the native host as discussed in lines 293-294.

It is clear from Figure 1 that the combinations of wmk homologs do not cause male killing on their own. Did the authors test if any of the wmk homologs impact the MK phenotype of oscar? It looks like a previous study tested this in wFur (noted in lines 250-252), but given that the authors also highlight the differences between the wFur-oscar and Hm-oscar proteins, this may be worth testing in this system. Related to this, what is the explanation for why there would be 4 copies of wmk in Hm?

Thank you for your valuable suggestion. Unfortunately, we have not yet tested the effects of co-expression of wmk and Oscar. Due to a technical issue, the mixing of multiple constructs results in a reduced amount of mRNA (i.e. mixing wmk-3 and Hm-Oscar at the same concentration results in a 2-fold lower concentration in mRNA for both genes compared to mono-injected groups). In addition, we have previously tested injecting mRNA at the twofold higher concentration (i.e. 2 ug/ul mRNA), which resulted in very low hatchability regardless of the genes. Katsuma et al (2022) tested the effect of wmk on the sex determination system, but did not test the effect of co-injection/transfection of wmk and Oscar. Considering the results of this and previous studies (Katsuma et al., 2022; Arai et al., 2023), it is likely that the targets of the wmk and oscar genes are different (as discussed in lines 267-289). Co-injection of wmk and oscar may not produce additive effects. Nevertheless, we would like to test the results in future studies using the Drosophila system as well.

As you point out, it is an interesting point that the moth-derived MK Wolbachia w_Hm-t encodes four _wmk genes, although they have no apparent effect on host survival. The exact functional relevance of these wmk homologs remains unclear. However, they may play a role in Wolbachia biology as transcriptional regulators, given that they encode HTH domains. Wolbachia generally encode several wmk homologs in their genome, regardless of whether they induce MK. This suggests that the functions of the wmk genes may be 'suppressed' in certain Wolbachia-host systems. The wmk and Hm-oscar genes are located within a prophage region, and some wmk genes are tandemly arrayed (as described in Arai et al., 2023). These wmk homologs may have increased in number by horizontal phage transfer, and the region containing wmk and adjacent sequences may act as a genomic island for virulence. So far, the function of wmk homologs has only been tested in D. melanogaster and H. magnanima, and further studies in other Wolbachia-host systems are highly warranted to test whether wmk exerts MK effects in other insect models. These points have been briefly discussed in the revised manuscript (lines 301-306; 318-320).

Why are some of the broods male-biased (2/3) rather than ~50:50? (Lines 170-175, Figure 2a). For example, there is a strong male bias in un-hatched oscar-injected and naturally infected embryos, whereas the control uninfected embryos have normal 50:50 sex ratios. It is difficult to interpret the rate of male-killing given that the sex ratios of different sets of zygotes are quite variable.

The observed male-biased sex ratios in unhatched embryos are due to the occurrence of MK during embryogenesis. In the unhatched groups, the skew towards males reflects that fact that the male embryos were targeted and killed by Wolbachia/Oscar, leading to a surplus of unhatched male embryos. Conversely, hatched individuals show a higher proportion of females because many of the males were eliminated during embryogenesis. Thus, the unhatched embryos are more male-biased, while the hatched individuals are more female-biased in the Hm-oscar/_w_Hm-t treated groups. We have revised the relevant section (Males are killed mainly at the embryonic stage, lines 179-186) and provided more detailed information to clarify this explanation.

Figure 2b - it appears there are both male and female bands in the HmOsc male lane. I think this makes sense (likely a partial phenotype due to the nature of the overexpression approach), but this is worth highlighting, especially in the context of trying to understand how much of the MK phenotype might be recapitulated through these methods. Related, there is no negative control for this PCR.

Thank you for your suggestion. As you noted, a faint dsx-M band is visible in the Hm-oscar treated group in Figure 2b. This is consistent with previous findings by Arai et al. (2023), which reported that male embryos with low-density w_Hm-t showed double bands of _dsx-M and dsx-F, similar to what we observed in this study. This information has been included in the revised manuscript in lines 196-198, as follows:

“Notably, male embryos expressing Hm-oscar also exhibited weak male-type dsx splicing in addition to the female-type splicing, resembling the previously observed pattern in male embryos infected with low-titer _w_Hm-t (Arai et al., 2023a).”

Also, we appreciate your comment regarding the missing of negative control. The figure has now been revised as we realised that the negative control lane had been lost during the preparation of the figure. We also included the relevant molecular marker information in both the figure legends and Figure 2b.

It appears the RNA-seq analysis (Figure 3) is based on a single biological replicate for each condition. And, there are no statistical comparisons that support the conclusions of a shift in dosage compensation. Finally, it is unclear what exactly is new data here: the authors note "The expression data of the wHm-t-infected and non-infected groups were also calculated based on the transcriptome data included in Arai et al. (2023a)" - So, are the data in Figure 3c and 3d a re-print of previous data? The level of dosage compensation inferred by visually comparing the control conditions in 3b and 3d does not appear consistent. With only one biological replicate library per condition, what looks like a re-print of previous data, and no statistical comparisons, this is a weakly supported conclusion.

Thank you for your suggestion. In this study, we generated the RNA-seq data for the Hm-oscar/GFP-injected groups, but did not sequence the w_Hm-t-infected/NSR lines. Instead, the previously generated RNA-seq data of _w_Hm-t-infected/NSR (Arai et al., 2023) were re-analyzed (rather than simply reprinted) to evaluate whether the expression patterns of _Hm-oscar-injected and w_Hm-t-infected groups are similar. We have revised the Results section (_Hm-oscar impairs dosage compensation in male embryos, lines 200-212), the Materials and methods section (Quantification of Z chromosome-linked genes, lines 454-456), and the figure legends to provide more precise information about this analysis.

Although we did not perform replicates for the RNA-seq comparisons, it is important to note that each RNA-seq sample contains 50-60 male/female individuals. We believe the results are still robust and clearly indicative of the trends we observe. This was further supported by the quantification of Hmtpi gene expression, which we have visualized in Figure 3e (and lines 210-212). As you noted, the expression patterns in Figure 3b (GFP injected) and Figure 3d (NSR) are not completely identical. This discrepancy may be due to the differences between injection treatments and natural infections. Nevertheless, both treatments are consistent in showing that gene expressions on the Z chromosome (Chr01 and Chr15) are not upregulated.

We have also added more detailed statistical data for Figure 3 based on the Steel-Dwass tests. For Figure 3a-d, however, showing the statistical significance directly on the whisker plots would create excessive clutter due to the numerous combinations of chromosomes. Instead, we have provided the full statistical data in the supplementary data file. Furthermore, to support/strengthen our conclusion that Z-linked genes are highly expressed in w_Hm-t-infected/_Hm-Oscar-injected embryos, we have included expression data for the Z-linked gene tpi, along with statistical data, in the revised manuscript (Fig. 3e, lines 210-212).

In Figure 4: There are no statistics to support the conclusions presented here. Additionally, the data have gone through a normalization process, but it is difficult to follow exactly how this was done. The control conditions appear to always be normalized to 100 ("The expression levels of BmImpM in the Masc and Hm-Oscar/Oscar co-transfected cells were normalized by setting each Masc-transfected cell as 100"). I see two problems with this approach:

(1) This has eliminated all of the natural variation in BmImpM expression, which is likely not always identical across cells/replicates.

(2) How then was the percentage of BmImpM calculated for each of the experimental conditions? Was each replicate sample arbitrarily paired with a control sample? This can lead to very different outcomes depending on which samples are paired with each other. The most appropriate way to calculate the change between experimental and control would be to take the difference between every single sample (6 total, 3 control, 3 experimental) and the mean of the control group. The mean of the control can then be set at 100 as the authors like, but this also maintains the variability in the dataset and then eliminates the issue of arbitrary pairings. This approach would also then facilitate statistical comparisons which is currently missing.

Thank you for your suggestion. As you pointed out in (1), the previous analysis did indeed eliminate the natural variation in BmIMP-M expression. In the revised manuscript and Figure 4, we have reanalyzed the data following your suggestion and have described the variation across replicates.

For (2), the data shown in the previous manuscript were normalized to 100 for each Masc-treated group. In doing so, each replicate sample was arbitrarily paired with a control sample from the same cell lot to account for variations that might occur due to differences in cell lots. However, following your recommendation, we have revised the figure to set the average of the Hm-masc treated group to 100, rather than using arbitrary pairings. More detailed normalization procedures have been provided in the section 'Transfection assays and quantification of BmIMP' (lines 483-520). Additionally, we have provided more detailed background information on the assay system in lines 218-223. Although we did not observe statistical significance based on the Steel-Dwass test, likely due to the limited number of replicates, the differences in IMP gene expression between the Masc-treated and Masc&Hm-oscar-treated groups remain evident.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Line 38: change to: 'Wolbachia are maternally transmitted'.

Revised accordingly (line 38).

Line 69: remove 'seemingly'.

Revised accordingly (line 69).

Paragraph starting line 123: I don't think this is so clear to a reader who is not familiar with the work and system. It would be helpful to more clearly explain that candidate male-killing genes from Wolbachia that infect Homona were inserted into Drosophila melanogaster, and that their expression was then induced, with interesting patterns (and that it can be a bit difficult to interpret the transgenic expression of genes from a moth male-killer that are inserted into a fly). Also, the sentence about the combined action of cifA and cifB in Drosophila cytoplasmic incompatibility is also confusing to a non-expert. I would suggest removing it.

Thank you for your suggestion. We have revised the paragraph (lines 124-139) to provide clearer background information, making it easier for non-experts to follow. We have also removed the sentence regarding the combined effect of cifA and cifB to improve the flow and overall clarity.

Line 170: what is the explanation for the male-biased sex ratio instead of 50-50?

The male-biased sex ratio occurs because MK happens during embryogenesis. Unhatched embryos include males that were killed by Wolbachia/Oscar, resulting in a higher proportion of unhatched male embryos. Conversely, the hatched individuals display a female bias, as most of the males were eliminated during embryogenesis. Thus, the unhatched embryos are more male-biased, while the hatched individuals are more female-biased in the Hm-oscar/_w_Hm-t treated groups. We have revised the section “Males are killed mainly at the embryonic stage” (lines 170-186) to include more detailed information explaining this phenomenon.

Line 190: please explain what are the Z chromosomes in Bombyx and Homona and Lepidoptera in general (chromosomes 1 and 15?), as this is not so clear for a non-expert.

Thank you for your suggestion. I have revised the section (lines 200-212) to include more precise background information about the chromosome constitutions in lines 202-204 as follows:

“Unlike other lepidopteran species, Tortricidae, including H. magnanima, generally possess a large Z chromosome that is homologous to B. mori chromosomes 1 (Z) and 15 (autosome).”

Line 222: please explain oscar diversity and classification in more detail, as this is not so clear for a non-expert.

Thank you for your suggestion. We have revised the sentences to provide clearer background information on the diversity of oscar genes (lines 255-264).

Figure 4: I found this difficult to follow. Why are there 2 rows (HmOscar and Oscar)? Does oscar here refer to oscar from Ostrinia? I am also a bit confused about the baseline control of Masc in these cell lines. If I understand Lepidoptera sex determination, then these cell lines are expressing high levels of female-specific piRNAs that suppress Masc. How specific are these piRNAs (i.e. do Bombyx piRNAs suppress Mascs from other Lepidoptera)? How much extra Masc will override endogenous piRNA? Information is lost by setting Masc expression to 100% in each separate comparison.

Yes, the Oscar indicates the w_Fur-encoded _oscar (Oscar from Ostrinia) that was tested to compare function with the Homona-derived Hm-oscar gene. In addition, following the reviewer's suggestions, we have revised the figure and included more detailed information on how we adjusted the expressions in the M&M section.

A previous study (Shoji et al., 2017, RNA 23:86–97) demonstrated that the Fem piRNA (29 bp) in Bombyx mori requires a 17 bp complementary sequence from its 5' region for its function. However, in species other than B. mori, no significant homology (i.e., over 17 bp matches) was found between the B. mori Fem piRNA and the masc genes analyzed in this study. Therefore, it is likely that the Fem piRNA expressed in BmN-4 cells is unable to suppress the masculinizing function driven by masc genes in other lepidopteran species. In addition, we did not quantify the levels of piRNA in this system, but the expression levels of masc are probably too high to be suppressed.

Figure 4 legend: spelling of Spodoptera.

Revised accordingly.

Reviewer #2 (Recommendations for the authors):

In Figure 2, what is the dsx splicing type for the hatched male in the Hm-oscar-injected group and the wHm-t infected line? Dsx-F or dsx-M?

Thank you for your suggestion. Unfortunately, we have not tested splicing in the hatched male neonates (1st instar larvae), partly due to difficulties in obtaining sufficient material for RNA extraction. Based on the previous publication in the Ostrinia system, where Oscar-bearing w_Sca induces MK, the hatched males (ZZ) exhibit female type _dsx as observed in the male embryos (Herran et al., 2022). The hatched Homona males may show double bands for dsx-M and dsx-F as observed in this study.

The size of the markers (in kilobase pairs) should be indicated in Figure 2.

We have accordingly included the marker information in the revised Figure 2b and the figure legends.

In Figure 3, could the authors identify which genes exhibit higher expression levels in the Hm-oscar-injected group and the wHm-t infected line? Could they provide hints for the possible mechanism of male-killing?

In the RNA-seq data shown in Figure 3a-d, we observed that both the Hm-oscar-injected and w_Hm-infected groups generally exhibited upregulated expression of Z-linked genes. Rather than the upregulation or downregulation of a specific gene, we consider that global upregulation of Z-linked genes, caused by improper dosage compensation, is lethal for males. The Z chromosome contains various genes involved in key biological processes such as endocrine function and detoxification, and disruption of these processes may contribute to male lethality. Additionally, in this revised manuscript, we have provided more detailed information on the expression level of the Z-linked gene _tpi. We have also discussed the potential mechanisms of MK in the Discussion section (lines 245-254).

The format of the references should be consistent. Gene and species names should be italicized.

We have accordingly formatted.

Reviewer #3 (Recommendations for the authors):

The authors use the term "upstream" (e.g., Oscar suppressed the function of masculinizer, the upstream male sex determinant...), which was sometimes confusing. In many cases, it reads as though the masculinizer was upstream of oscar, but what I think the authors are trying to convey is that masculinizer is a primary sex-determining factor.

Thank you for your suggestion. We have accordingly revised the term.

Line 101: which insect is wFur from?

It is from Ostrinia furnacalis - line 104 has been revised.

Figure 1: it would be helpful to indicate the statistical results on the figure.

Accordingly, we have added statistical data (binominal test) for Figure 1. The data for the Steel-Dwass test have been included in the supplementary data.

Figure 2b: please label the ladder on the gel.

Thank you for your suggestion. We have accordingly labeled the DNA ladder on the gel.

Reviewer #1 (Public review):

The paper by Auer et. makes several contributions:

(1) The study developed a novel approach to map the microstructural organization of the human amygdala by applying radiomics and dimensionality reduction techniques to high-resolution histological data from the BigBrain dataset.

(2) The method identified two main axes of microstructural variation in the amygdala, which could be translated to in vivo 7 Tesla MRI data in individual subjects.

(3) Functional connectivity analysis using resting-state fMRI suggests that microstructurally defined amygdala subregions had distinct patterns of functional connectivity to cortical networks, particularly the limbic, frontoparietal, and default mode networks.

(4) Meta-analytic decoding was used to suggest that the superior amygdala subregion's connectivity is associated with autobiographical memory, while the inferior subregion was linked to emotional face processing.

(5) Overall, the data-driven, multimodal approach provides an account of amygdala microstructure and possibly function that can be applied at the individual subject level, potentially advancing research on amygdala organization.

Author response:

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

Reviewer #1:

The paper by Auer et. makes several contributions: (1) The study developed a novel approach to map the microstructural organization of the human amygdala by applying radiomics and dimensionality reduction techniques to high-resolution histological data from the BigBrain dataset. (2) The method identified two main axes of microstructural variation in the amygdala, which could be translated to in vivo 7 Tesla MRI data in individual subjects. (3) Functional connectivity analysis using resting-state fMRI suggests that microstructurally defined amygdala subregions had distinct patterns of functional connectivity to cortical networks, particularly the limbic, frontoparietal, and default mode networks. (4) Meta-analytic decoding was used to suggest that the superior amygdala subregion's connectivity is associated with autobiographical memory, while the inferior subregion was linked to emotional face processing. (5) Overall, the data-driven, multimodal approach provides an account of amygdala microstructure and possibly function that can be applied at the individual subject level, potentially advancing research on amygdala organization.

We thank the Reviewer for the positive comments and insightful evaluation of the work.

(1.1) Although these are meritorious contributions there are some concerns that I will summarize below. The paper makes little-to-no contact with the monkey literature regarding the anatomy of amygdala subregions, their functionality, and their patterns of anatomical connectivity. This is surprising because such literature on non-human primates is a very important starting point for understanding the human amygdala. I recommend taking a careful look at the work by Helen Barbas, among others. There are too many papers to cite but a notable example is: Ghashghaei, H. T., Hilgetag, C. C., & Barbas, H. (2007). Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. Neuroimage, 34(3), 905-923. The work of Amaral is also highly relevant.

As suggested, we included the important work of Amaral et al. as well as Ghashghaei et al. highlighting its contribution to mapping the intricate anatomy and function of the amygdala in non-human primates. We comment on this in the Introduction of the manuscript. Please see P.3.

“Early research on the amygdala in non-human primates has been instrumental in understanding its intricate structure, function and patterns of anatomical connectivity (Amaral and Price 1984; Ghashghaei et al. 2007). This foundational study highlights the amygdala’s different subdivisions, most notably the basomedial nucleus (BM), basolateral nucleus (BL), and central nucleus (Ce) (Amaral et al. 1992). Furthermore, this work describes a dense network between these subdivisions and the prefrontal cortex, most strongly found in the posterior orbitofrontal and anterior cingulate areas.”

(1.2) Furthermore, the authors subscribe to a model with LB, CM, and SF sectors. How does the SF sector relate to monkey anatomy?

The overall organization of these subregions is largely conserved between humans and monkeys, reflecting their evolutionary relationship. While the basic subregional organization is conserved, there are still some important structural and functional differences between human and monkey amygdalae. For example, the SF subregion, often described in humans includes parts of the cortical nuclei (VCo), anterior amygdaloid area (AAA), amygdalohippocampal transition area (AHi), amygdalopiriform transition area (APir) as well as the lateral olfactory tract (LOT). This remark was added in the Discussion, on P.12:

“However, this region has been previously described as consisting of three main subdivisions: LB, CM, and SF, each composed of smaller subnuclei with distinct connectivity patterns and functions (Amunts et al. 2005; Ball et al. 2007; Bzdok et al. 2013; de Olmos and Heimer 1999). These subregions are largely conserved between humans and monkeys, reflecting their evolutionary relationship. However, there are still some considerable differences such as in the SF subregion, where its description in monkeys additionally contains the lateral olfactory tract (LOT) (De Olmos 1990).”

(1.3) The authors use meta-analytical decoding via NeuroSynth. If the authors like those results of course they should keep them but the quality of coordinate reporting in the literature is insufficient to conclude much in the context of amygdala subregion function in my opinion. I believe the results reported are at most "somewhat suggestive".

We agree with the Reviewer that use of data from NeuroSynth poses unique challenges, particularly relating to investigations of a small structure such as the amygdala. However, to clarify, these analyses decode the cortex-wide functional connectivity patterns of amygdala subregions and not activations within subregions defined by our microanatomical analyses. Additionally, comments from Reviewer 2 suggested expanding the NeuroSynth decoding to the contralateral hemisphere. As such, we decided to keep this analysis in the main manuscript but rephrase the interpretation of these findings in the Discussion to emphasize their exploratory nature on P.13:

“Functional decoding of subregional functional connectivity patterns indicated possible dissociations in cognitive (e.g., memory) and affective (e.g., emotional face processing) functions of the amygdala, echoing previous accounts of this region’s involvement in associative processing of emotional stimuli. Notably, these findings link the functional connectivity profile of a subregion partially co-localizing with LB to emotional face processing. The LB subregion has been previously linked to associative processing related to the integration of sensory information (Bzdok et al. 2013; Ghods-Sharifi, St Onge, and Floresco 2009; Pessoa 2010; Winstanley et al. 2004; Boyer 2008), which is consistent with the association with visual emotional information processing identified in the present work.”

(1.4) Another significant concern has to do with the results in Figure 3. The red and yellow clusters identified are quite distinct but the differences in functional connectivity are very modest. Figure 3C reveals very similar functional connectivity with the networks investigated. This is very surprising, and the authors should include a careful comparison with related findings in the literature. Overall, there is limited comparison between the observed results and those obtained via other methods. On a more pessimistic note, the results of Figure 3 seem to question the validity of the general approach.

We agree with the Reviewer that we can indeed observe considerable overlap between functional connectivity profiles of amygdala subregions. The amygdala is a relatively small structure, leading to likely interconnectivity between its subregions (Bzdok et al. 2013) in addition to considering BOLD signal autocorrelation within this region. In addition, functional signals in the amygdala are affected by relatively lower signal-to-noise ratio (SNR), a limitation extending to temporobasal and mesiotemporal regions. Despite these challenges, our technique remained sensitive to detect subtle differences in connectivity patterns even in this small group of subjects in this restricted subcortical territory.

In the revised manuscript, we further highlight these caveats in the Discussion (P.13):

“Although these findings are promising, we also observe considerable overlap between functional connectivity networks of both our defined subregions. Indeed, the amygdala is a relatively small structure, leading to likely interconnectivity between its subregions and locally high signal autocorrelation. Functional connectivity and microstructure in the amygdala are certainly related, however previous work suggests they do not perfectly overlap (Bzdok et al. 2013). In addition, this region is affected by relatively low signal-to-noise ratio (SNR), as is observed in broader temporobasal and mesiotemporal territories.”

(1.5) Some statements in the Discussion feel unwarranted. For example, "significant dissociation in functional connectivity to prefrontal structures that support self-referential, reward-related, and socio-affective processes." This feels way beyond what can be stated based on the analyses performed.

We agree that this interpretation may reach beyond the analyses performed and reported findings. We have adjusted this portion of the text accordingly in our Discussion on functional connectivity findings (P.13):

“Qualitatively, we found that the subregion defined by the highest 25% of U1 values mainly overlapped with what is commonly defined as the superficial and centromedial subregions, whereas the lowest 25% U1 values subregion overlapped mostly with the laterobasal division. Interestingly, CM and SF characterized subregions showed significantly stronger functional connectivity to prefrontal structures. This finding aligns with previous work demonstrating unique affiliations between the CM subregion and anterior cingulate and frontal cortices (Kapp, Supple, and Whalen 1994; Barbour et al. 2010), as well as between the SF subregion and the orbitofrontal cortex (Goossens et al. 2009; Caparelli et al. 2017; Pessoa 2010; Klein-Flügge et al. 2022).”

Additionally, we have also edited our Discussion to ensure that our interpretations are grounded in the analyses conducted, while framing the findings as potential avenues for future work. Please see P.13.

“Functional decoding of functional connectivity results indicated possible dissociations in cognitive (e.g., memory) and affective (e.g., emotional face processing) functions of the amygdala, echoing previous accounts of this region’s functional specialization and subregional segregation of associative processing of emotional stimuli.”

Recommendations for the authors:

(1.6) Figure 1 has panels A-I but only A-D are discussed in the caption. The orientation of the slices is not indicated which makes it very hard to follow for most readers.

The subpanels are now referred to in the revised Results. We also added a notation on the orientation of the slices and described them accordingly in our Figure 1 description. (P.5-6):

“(A) The amygdala was segmented from the 100-micron resolution BigBrain dataset using an existing subcortical parcellation (Xiao et al. 2019). Slice orientation is consistent across all panels in this figure.”

(1.7) Some figure references in the text seem to be incorrect; please check that the text refers to the correct figure number and panel.

We thank the Reviewer for pointing this out. We thoroughly revised the correspondence between figure panel labels and their referencing in the text.

Reviewer #2:

This study bridges a micro- to macroscale understanding of the organization of the amygdala. First, using a data-driven approach, the authors identify structural clusters in the human amygdala from high-resolution post-mortem histological data. Next, multimodal imaging data to identify structural subunits of the amygdala and the functional networks in which they are involved. This approach is exciting because it permits the identification of both structural amygdalar subunits, and their functional implications, in individual subjects. There are, however, some differences in the macro and microscale levels of organization that should be addressed.

Strengths:

The use of data-driven parcellation on a structure that is important for human emotion and cognition, and the combination of this with high-resolution individual imaging-based parcellation, is a powerful and exciting approach, addressing both the need for a template-level understanding of organization as well as a parcellation that is valid for individuals. The functional decoding of rsfMRI permits valuable insight into the functional role of structural subunits. Overall, the combination of micro to macro, structure, and function, and general organization to individual relevance is an impressive holistic approach to brain mapping.

We thank the Reviewer for their constructive and helpful feedback on our work.

Weaknesses:

(2.1) UMAP 1, as calculated from the histological data, appears to correlate well across individuals, and decently with the MRI data, although the medial-lateral coordinate axis is an outlier. UMAP 2, on the other hand, does not appear to correlate well with imaging data or across individuals. This does pose a problem with the claim that this paper bridges micro- and macroscale parcellations. One might certainly expect, however, that different levels of organization might parcellate differently, but the authors should address this in the discussion and offer ways forward.

Data driven methods hold several advantages for the quantitative extraction of signal from the underlying data in an observer-independent manner. However, these techniques are also sensitive to potential idiosyncrasies in the data. In the present work, our main analyses rely on the processing of a histological dataset (BigBrain) providing a unique opportunity for high-resolution analysis of amygdala histology and in vivo translation of findings leveraging ultra-high field MRI (n=10). However, both datasets are limited by their small sample size (n=1 for BigBrain and n=10 for MICA-PNI). As a result, we speculate that signal variations captured by U2 may be sensitive to artifacts or subject-specific sources of variance. Moving forward, this hypothesis could be assessed in future work via the analysis of larger histological and neuroimaging datasets to better track recurring features picked up by U2 or the association of these unique topographies with behavioural markers.

As suggested, we included a section in our Discussion highlighting this shortcoming and the importance for larger datasets moving forward. Please see P.11-12.

“However, it is important to note that both datasets analyzed in this work are limited by their small sample size (n=1 for BigBrain and n=10 for MICA-PNI). We speculate that the signal variations captured by U2 may be sensitive to artifacts or subject-specific sources of variance, potentially explaining why it was not consistent between subjects and modalities. Moving forward, this hypothesis could be assessed in future work via the analysis of larger histological and neuroimaging datasets to better track recurring features picked up by U2 or the association of these unique topographies with behavioural markers.”

(2.1) It would be interesting to see functional decoding for the right amygdala. This could be included in the supplementary material. A discussion of differences in the results in the two hemispheres could be illuminating.

In accordance with the Reviewer’s suggestion, we added Supplementary figure S2 exploring the decoding of connectivity profiles of the right amygdala stratified by its cytoarchitectural embedding with UMAP.

Upon analysis, dissociation in functional connectivity patterns over the right amygdala were less evident, leading to overall similar functional decoding across the two clusters. We refer to this Supplementary Figure in our Discussion on P.13.

“For the right amygdala, dissociation in functional connectivity patterns were more subtle, leading to overall similar functional decoding across the two clusters. (Figure S2)”

(2.3) The authors acknowledge that this mapping matches some but not all subunits that have been previously described in the amygdala. It would be helpful to neuroanatomists if the authors could discuss these differences in more detail in the discussion, to identify how this mapping differs and what the implications of this are.

In our work, we focus on mapping the three well characterized amygdala subregions, specifically the superficial (SF), centromedial (CM) and laterobasal (LB) subdivisions. Qualitative histological accounts have indeed delineated multiple subunits within these subregions which we now describe in the revised manuscript. Due to the lower resolution of in vivo MRI data used in this work relative to post mortem histology, we focused our analyses on larger subregions that could be more reliably mapped to native quantitative T1 spaces of each participant. We now overview this issue in the Discussion. Please see P.12.

“Although qualitative histological accounts have indeed delineated multiple subunits within these general regions, the present work focuses on three subdivisions (Amunts et al. 2005) to account for resolution disparities when translating our findings to in vivo MRI data. The LB subdivision includes the basomedial nucleus (Bm), basolateral nucleus (BL), lateral nucleus (LA) and paralaminar nucleus (PL). Moving medially, the CM subdivision includes the central (Ce) and medial nuclei (Me), while the SF subdivision includes the anterior amygdaloid area (AAA), amygdalohippocampal transition area (AHi), amygdalopiriform transition area (APir), and ventral cortical nucleus (VCo) (Heimer et al. 1999). However, disagreement on the precise attribution of nuclei to broader subdivisions motivated our investigations of probabilistic subunits of the amygdala (Kedo et al. 2018). The development of new tools to segment amygdala subnuclei in vivo offers opens opportunities for future work to further validate our framework at the precision of these nuclei within subjects (Saygin et al. 2017).”

(2.4) The acronym UMAP is not explained. A brief explanation and description would be useful to the reader.

We moved the expanded acronym from the Methods to the first instance of the term UMAP in our paper, found in the Introduction. As suggested, we also added a sentence describing the technique. Please see P.6.

“We then applied Uniform Manifold Approximation and Projection (UMAP), a non-linear dimensionality reduction technique that preserves the local and global structure of high-dimensional data by projecting it into a lower-dimensional space (Becht et al. 2018), to the resulting 20-feature matrix to derive a 2-dimensional embedding of amygdala cytoarchitecture (Figure 1D).”

威廉·布莱克的版画《牛顿》（Newton）是他最著名的作品之一，创作于1795年。这幅作品不仅是布莱克艺术风格的典型代表，也深刻反映了他对科学、理性和启蒙思想的批判态度。以下是关于这幅版画的详细解释：

1. 画面内容

• 牛顿的形象：画面中，牛顿被描绘成一个裸体的男性形象，坐在一块岩石上，身体弯曲，专注于手中的卷轴和几何图形。他的姿态显得僵硬且不自然，象征着理性和科学的局限性。
• 几何图形：牛顿手中的卷轴和周围的几何图形代表了他对数学和物理学的专注。这些图形象征着理性和科学的方法论，布莱克通过这种方式强调了牛顿对抽象知识的追求。
• 背景：背景是一个黑暗的、洞穴般的环境，象征着理性和科学所忽视的精神和想象力世界。岩石和水流的细节增加了画面的深度和复杂性。

2. 象征意义

• 对理性的批判：布莱克通过牛顿的形象表达了对启蒙时代理性主义的批判。他认为，过度依赖理性和科学会忽视人类的精神和想象力，导致心灵的贫瘠。
• 科学与灵性的对立：牛顿专注于几何图形，忽视了周围的世界，象征着科学和灵性之间的对立。布莱克认为，真正的知识应该结合理性和想象力，而不是仅仅依赖其中之一。
• 人体的象征：牛顿的裸体形象象征着人类的原始状态和纯粹性，但他的僵硬姿态和专注的表情则暗示了理性和科学对人类自然状态的扭曲。

3. 艺术风格

• 线条与形式：布莱克在《牛顿》中使用了简洁而有力的线条，突出了人物的轮廓和姿态。这种风格增强了画面的象征意义和表现力。
• 明暗对比：画面中的明暗对比强烈，突出了牛顿的形象和几何图形，同时营造出一种神秘和压抑的氛围。
• 细节与纹理：布莱克在岩石和水流的细节处理上非常精细，增加了画面的深度和质感，使观者能够感受到画面的复杂性和多义性。

4. 历史与文化背景

• 启蒙时代：18世纪的启蒙时代强调理性、科学和进步，但布莱克对此持批判态度。他认为，启蒙思想忽视了人类的精神和情感，导致社会的机械化和心灵的贫瘠。
• 浪漫主义：布莱克是浪漫主义运动的重要人物，强调想象力、情感和灵性的重要性。《牛顿》反映了浪漫主义对启蒙理性的批判，呼吁回归人类的精神和创造力。

5. 影响与遗产

• 艺术影响：布莱克的《牛顿》对后来的艺术家和思想家产生了深远影响，尤其是在探讨科学、理性和灵性关系方面。
• 文化象征：这幅作品成为批判科学理性和呼吁精神解放的文化象征，被广泛引用和讨论。

总结

威廉·布莱克的《牛顿》通过简洁而有力的艺术语言，深刻表达了对科学和理性的批判。牛顿的形象和周围的几何图形象征着理性和科学的局限性，而黑暗的背景则暗示了被忽视的精神和想象力世界。这幅作品不仅是布莱克艺术风格的典型代表，也反映了他对启蒙思想的深刻反思和对人类精神解放的呼吁。

Reviewer #1 (Public review):

Nio and colleagues address an important question about how the cerebellum and ventral tegmental area (VTA) contribute to the extinction learning of conditioned fear associations. This work tackles a critical gap in the existing literature and provides new insights into this question in humans through the use of high-field neuroimaging with robust methodology. The presented results are novel and will broadly interest both the extinction learning and cerebellar research communities. As such, this is a very timely and impactful manuscript. However, there are several points that could be addressed during the review process to strengthen the claims and enhance their value for readers and the broader scientific community.

Points to Address:

(1) Reward Interpretation and Skin Conductance Responses (SCR):<br /> A central premise of the manuscript is that 'unexpected omissions of expected aversive events' are rewarding, which plays a critical role in extinction learning. The authors also suggest that the cerebellum is involved in reward processing. However, it is unclear how this conclusion can be directly drawn from their task, which does not explicitly model 'reward.' Instead, the interpretation relies on SCR, which seems more indicative of association or prediction rather than reward per se. Is SCR a valid metric of reward experienced during the extinction of feared associations? Or could these findings reflect processes tied more closely to predictive learning? Please, discuss.

(2) Reinforcement Agent and SCR Modeling:<br /> The modeling approach with the deep reinforcement agent treats SCR as a personalized expectation of shock for a given trial. However, this interpretation seems misaligned with participants' actual experience - they are aware of the shock but exhibit evolving responses to it over time. Why is this operationalization useful or valid? It would benefit the manuscript to provide a clearer justification for this approach.

(3) Clarity and Visualization of Results:<br /> The results section is challenging to follow, and the visualization and quantification of findings could be significantly improved. Terms like 'trending' appear frequently - what does this mean, and is it worth reporting? Adding clear statistical quantifications alongside additional visualizations (e.g., bar or violin plots of group means within specific subregions within the cerebellum, or grouped mean activity in VTA and DCN) would enhance clarity and allow readers to better assess the distribution and systematicity of effects. Furthermore, the figures are overly complex and difficult to read due to the heavy use of abbreviations. Consider splitting figures by either phase of the experiment or regions, and move some details to the supplemental material for improved readability.

(4) Theoretical Context for Paradigm Phases:<br /> The manuscript benefits from the comprehensive experimental paradigm, which includes multiple phases (acquisition, extinction, recall, reacquisition, re-extinction). This design has great potential for providing a more holistic view of conditioned fear learning and extinction. However, the manuscript lacks clarity on what insights can be drawn from these distinct phases. What theoretical framework underpins the different stages, and how should the results be interpreted in this context? At present, the findings seem like a display of similar patterns across phases without sufficient interpretation. Providing a stronger theoretical rationale and reorganizing the results by experimental phase could significantly improve readability and impact.

(5) Cerebellum-VTA Connectivity Analysis:<br /> The authors argue that the cerebellum modulates VTA activity, yet they perform the PPI analysis in the reverse direction. Why does this make sense? In their DCM analysis, they found a bidirectional relationship (both cerebellum - VTA and VTA-cerebellum), yet the discussion focused on connectivity from the cerebellum to VTA. A more careful interpretation of the connectivity findings would be useful - especially the strong claims in the discussion on the cerebellum providing the reward signal to the VTA should be tempered.

Author response:

Reviewer 1:

(1) Reward Interpretation and Skin Conductance Responses (SCR):

The reviewer raises a valid point, as the model from which we derive prediction errors describes predictive learning—specifically, the occurrence of shock—without incorporating additional reward learning effects. SCRs are used to fit the model’s hyperparameters but do not directly measure reward; rather, they serve as a marker of arousal.

In our paradigm, SCRs are measured during CS presentation and primarily reflect predictive learning, as they are closely linked to contingency awareness. The association between estimated prediction errors during unexpected US omissions and reward remains reliant on existing literature.

In the revised manuscript, we will further elaborate on these points to clarify the distinction between predictive learning and direct reward processing, while contextualizing our findings within the broader literature on reward signaling and fear extinction.

(2) Reinforcement Agent and SCR Modeling:

Notably, we do not use SCR as a personalized expectation measure due to its limited reliability at the individual level; instead, the model's hyperparameters are fitted on the entire SCR dataset, yielding per-trial prediction and prediction error estimates for each CS sequence rather than for individual participants.

(3) Clarity and Visualization of Results:

We recognize that the presentation of our results can be improved and will take steps to enhance figure clarity, also ensuring that trend-level results are clearly distinguished.

(4) Theoretical Context for Paradigm Phases:

Regarding the differences across experimental phases, we recognize the theoretical significance of these distinctions. However, our primary focus is on identifying commonalities in unexpected US omission responses across phases rather than emphasizing phase-specific differences. Nevertheless, we will provide a brief clarification on phase differences to enhance the manuscript’s interpretability.

(5) Cerebellum-VTA Connectivity Analysis:

Furthermore, we acknowledge that our conclusion regarding the modulation of the dopaminergic system by the cerebellum should be framed more cautiously. We will temper our claims to better reflect the bidirectional and potentially indirect nature of cerebellum-VTA interactions. Additionally, we plan to include PPI results using a cerebellar seed showing the VTA, potentially in the supplementary material.

Reviewer 2:

(1) Success of extinction learning based on Self-reports and SCRs?

The reviewer points to a problem, which is inherent to extinction learning: The initial fear association is not erased, but merely inhibited, and is prone to return. Although the recall phase follows the extinction phase, we did not expect a complete inhibition of the conditioned response; instead, spontaneous recovery is expected. In fact, the spontaneous recovery observed in the recall phase provided us with an additional opportunity to investigate unexpected US omissions, which was our primary focus.

(2) Concerns on reliability of event-based contrasts using three events:

Regarding concerns about the reliability of analyses based on three events, we believe that the consistency of our parametric modulation analysis— which incorporates all events— combined with the three-event analysis results, provides further support for the observed patterns. We are currently discussing ways of additional analysis for further verification of the reliability of using three events.

(3) Deviations from preregistration:

Finally, we will carefully review all deviations from our preregistration to ensure transparency. Any methodological or analytical changes will be explicitly addressed in the revised manuscript.

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Referee #2

Evidence, reproducibility and clarity

Peroxisomes are single membrane organelles conserved in all eukaryotes and play important roles in various metabolic reactions, such as beta oxidation of fatty acids. In general, proteins localized in the peroxisomal matrix encode either a C-terminal PTS1 signal or an N-terminal PTS2 signal, and Pex5 acts as a cargo receptor in the PTS1 pathway and Pex7 in the PTS2 pathway, respectively. Previous studies have suggested that some matrix proteins (e.g., Eci1) are transported into the peroxisomal matrix in the PTS1-independent manner, but the mechanism is still unclear. In the present study, Peer et al. determined the Cryo-EM structure of the Pex5-Eci1 complex, which revealed a new interaction site that is distinct from the recognition site of the canonical PTS1 signal, providing important insight into the PTS1-independent, but the Pex5-dependent matrix protein transport. This study by Peer et al. will be of interest to a broad readership in basic cell biology other than peroxisomes.

The reviewer feels that the manuscript needs to be revised in the following points.

Major comments

1. The authors showed that Pex5 binds to Eci1 in a PTS1 signal-independent manner from pull-down experiments in Figure 2, but this result is qualitative. If the authors add quantitative data on the interaction between Pex5 and Eci1 from isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR), this would make this paper more convincing. This could be done in 2 months.
2. It is not clear to what extent the new interaction sites between Pex5 and Eci1 is important for transport to peroxisomes, as revealed in this study. I suggest, for example, expressing Eci1 with a mutation at a site involved in interaction with Pex5 in yeast and analyzing its effect on peroxisomal localization as additional experiments anew. I believes that this could be done in about 2 months.

Minor comments

1. The results of yeast cell imaging in Figures 1, 2 and S1 are all qualitative and not quantitative. Furthermore, there are no descriptions of the experimental reproducibility of the data. I suggest that these points need to be improved.
2. I feel that information of sample preparation for cryo-EM analysis of the Pex5-Eci1 complex is not enough since it is only described in the methods. I suggest the authors to add the results of gel-filtration chromatography and CBB-stained SDS-PAGE in the manuscript.
3. The authors discuss the interaction interface between Pex5 and Eci1 in Figures 4 and 5, but the manuscript presented at this stage is difficult at least for me to understand the interaction between them. I recommend the authors to add new figure(s) to show more detailed interaction. Also, I suggest that cryo-EM density map around the interaction region between Pex5 and Eci1 should be presented more detail.

Significance

My expertise is in yeast cell biology and structural biology. From this perspective, I think that the strengths of this study are, first, that Pex5-dependent peroxisomal transport of Eci1 in yeast cells occurs independently of PTS1 signal and its paralog Dci1, and that the cryo-EM structure of the Pex5-Eci1 complex reveals a new interaction site other than PTS1 between Pex5 and Eci1. This work is of broad interest not only to peroxisomes, but also to many cell biologists specializing in organelles, and ultimately to structural biologists. On the other hand, the authors' cryo-EM data suggest that 2-3 molecules of Pex5 bind to the Eci1 hexamer. However, it is unclear how the binding of multiple Pex5 molecules to the Eci1 hexamer affects their transport to peroxisomes, and further analysis is needed to elucidate the transport mechanism in more detail.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Proteins are imported into peroxisomes by mobile receptors such as PEX5. PEX5 recognizes cargo proteins in the cytosol by their peroxisome targeting signal (PTS) and then shuttles them across the peroxisomal membrane into the matrix. While most peroxisomal proteins contain well-characterized signals that bind to PEX5 either directly (PTS1) or through PEX7 (PTS2), some proteins interact with PEX5 independently of these canonical signals. The molecular basis of these unconventional interactions has been poorly understood.

The manuscript by Peer et al. deals with one such protein called Eci1 in yeast. Eci1 has a PTS1 signal at its C terminus and a putative PTS2 signal at its N terminus, yet the authors show that neither of these signals is required for import of Eci1 into peroxisomes. They also show that import of Eci1 cannot be entirely explained by piggy-backing on its paralog Dci1. Regardless, import of Eci1 depends entirely on PEX5, indicating that Eci1 can bind to PEX5 unconventionally. To identify this additional interface, the authors solve the cryo-EM structure of PEX5 bound to Eci1 (which is a hexamer). Surprisingly, the structure reveals that PEX5 binds to only one of the six Eci1 subunits, and that two distinct interfaces are apparent. One reflects the canonical interaction between the PTS1 signal of Eci1 and the receptor's cognate PTS1-binding TPR domain. The other interface is novel and of potential interest. It involves a region of Eci1 that engages a segment of PEX5 upstream of the TPR domain. This segment has not been previously implicated in binding protein cargo.

Major issues:

1. The major issue with the paper is that the novel interface between Eci1 and PEX5 has not been demonstrated to be important for import into peroxisomes. Specifically, mutagenesis of both sides of the interface is required to demonstrate that this interaction mediates import of Eci1 lacking the canonical PTS1 signal (and also in the absence of the paralog Dci1). Such data are indisputably a precondition for publication of this paper. Pull-down experiments should also be performed to demonstrate that the interface is sufficient for interacting with PEX5 in the absence of the PTS1 signal on Eci1.
2. The paper hinges on the demonstration of a residual interaction between PEX5 and Eci1 lacking its PTS1 signal. However, the pull-down experiment in Figure 3 that allegedly shows this result lacks a critical control for non-specific binding of Eci1 to the nickel beads alone. Also, this experiment does not show a direct interaction between PEX5 and Eci1, since the two proteins are co-expressed in bacteria and then pulled down using an engineered His-tag in PEX5. This experiment should be repeated using PEX5 and Eci1 purified separately and then mixed in vitro. Please show a coomassie-stained SDS-PAGE gel to assess protein purity in addition to the immunoblot, and please show the pull-down in a more conventional way comparing the input and the bound fraction (it is unclear what is meant by soluble and elution fractions).
3. The presentation of the structure in Figure 4 should be improved. An overview of the complex should be shown first, and then each interface should be pointed out in a different view (and accordingly labeled). It is distracting and not necessary to show all six subunits of Eci1 in different colors. The non-conventional interface should be shown more clearly, with key amino acids numbered and labeled, and the configurations of their side chains highlighted. Please also highlight the salt bridges and hydrogen bonds at this interface that are mentioned in the text but never illustrated.
4. The data in Figs. S2 and S3 raise doubts about the reported resolution of PEX5 in the cryo-EM structure. Please provide examples of the density map and the fit to the model.
5. Please provide data for the purification of the complex between PEX5 and Eci1, including a gel-filtration chromatogram and an SDS-PAGE gel of the purified sample used for cryo-EM.
6. OPTIONAL: The observation that the non-conventional interface between PEX5 with Eci1 corresponds to the site of CoA binding is interesting. This interaction might keep the enzyme inactive while in the cytosol and bound to PEX5, until it would be correctly delivered into peroxisomes and released from the receptor. Alternatively, it could also reflect regulation of Eci1 import by CoA. This idea could easily be tested by pull-down experiments performed with or without CoA, or perhaps by an in vitro Eci1 activity assay in the presence or absence of PEX5. The significance of the paper would be considerably improved if this interaction reflected a mechanism to regulate Eci1 activity or import.

Minor issues:

1. The manuscript has many grammatical mistakes which should be addressed. The absence of line numbers precludes us from indicating specific issues.
2. In general, when referring to a single subunit from the Eci1 hexamer, please use the terms subunit or protomer, and avoid the use of the term monomer which is misleading.
3. In Fig. 1C, it is unclear whether the experiment was performed in the absence or presence of PEX11. Since the paper hinges on the demonstration of an unconventional interaction between Eci1 and PEX5, perhaps this experiment should be performed in pex11 knockout cells (to enlarge peroxisomes as in Fig. 1B) to show that the residual peroxisomal localization indeed corresponds to the matrix.
4. In Fig. 6, it would help to show each structure individually and then the overlay.
5. Fig. S4 should include a scale bar and box size.
6. Why are phosphorylation sites indicated in Fig. S6?
7. In Fig. S8, please show the structures of Eci1 bound to PEX5 and to CoA individually, and then the overlay. The figure is very diffucult to understand otherwise.
8. In Fig. S9, please label the homologous interface residues on Eci1 and Dci1 in individual views, and then show the overlay.

Significance

The main finding of the paper is a noncanonical interaction between Eci1 and the peroxisomal import receptor PEX5. This interaction could solve a longstanding mystery about how Eci1 can be targeted to peroxisomes in the absence of its canonical peroxisome targeting signal. Because the authors have not demonstrated that this interaction is sufficient for import of Eci1 in vivo, this key conclusion of the paper remains unconfirmed. If this omission were corrected, the paper would add another example to the growing list of proteins that are imported into peroxisomes by binding unconventionally to PEX5.

The authors employ an interesting strategy to confirm that Eci1 is correctly imported into the peroxisomal matrix in vivo (and not just recruited to the cytosolic surface of the peroxisomal membrane). This strategy involves enlarging peroxisomes (which normally are diffraction limited) by knocking out a factor required for peroxisome division, allowing the matrix to be resolved from the limiting membrane by light microscopy. Failure to adequately demonstrate import into the matrix had plagued many earlier studies on protein targeting to peroxisomes. The strategy employed in this paper could therefore be useful to other researchers.

In its current form, the manuscript would be of some interest to the peroxisomal community and perhaps also to researchers studying protein targeting to membrane-bounded organelles. However, if the authors could show that the novel interface between PEX5 and Eci1 functions in part to regulate Eci1 enzymatic activity (or conversely, Eci1 import by CoA), then the paper would be of much broader interest to the fields of metabolism and metabolic regulation.

Reviewer #1 (Public review):

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

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

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

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

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

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

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

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

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

(9) Why were only male mice used?

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

eLife Assessment

The authors show that: 1) following brief peripheral optogenetic stimulation of forepaw proprioceptors in mice, sensory-evoked responses in primary motor cortex (M1) are delayed relative to primary somatosensory cortex (S1); 2) the responses in both cortical areas follow a triphasic pattern of activation-suppression-activation; 3) directly activating cortical parvalbumin-positive (PV) inhibitory interneurons mimicked both the suppression and rebound components of the sensory-evoked response; and 4) partially suppressing activity in S1 reduces the sensory-evoked response in M1. The conclusions are convincing and build on prior work on cortical circuits related to the mouse forelimb from this group (Yamawaki et al., 2021, eLife, doi:10.7554/eLife.66836). More rigorously determining whether the peripheral stimulation approach used evokes movements would strengthen the conclusions. It is also possible that these effects would differ for peripheral mechanoreceptor stimulation. Overall, this in vivo work assessing sensory responses in forepaw-related cortical circuits represents a valuable comparison to previously published work.

Reviewer #3 (Public review):

Summary:

This is a solid study of stimulus-evoked neural activity dynamics in the feedforward pathway from mouse hand/forelimb mechanoreceptor afferents to S1 and M1 cortex. The conclusions are generally well supported, and match expectations from previous studies of hand/forelimb circuits by this same group (Yamawaki et al., 2021), from the well-studied whisker tactile pathway to whisker S1 and M1, and from the corresponding pathway in primates. The study uses the novel approach of optogenetic stimulation of PV afferents in the periphery, which provides an impulse-like volley of peripheral spikes, which is useful for studying feedforward circuit dynamics. These are primarily proprioceptors, so results could differ for specific mechanoreceptor populations, but this is a reasonable tool to probe basic circuit activation. Mice are awake but not engaged in a somatosensory task, which is sufficient for the study goals.

The main results are:<br /> (1) brief peripheral activation drives brief sensory-evoked responses at ~ 15 ms latency in S1 and ~25 ms latency in M1, which is consistent with classical fast propagation on the subcortical pathway to S1, followed by slow propagation on the polysynaptic, non-myelinated pathway from S1 to M1;<br /> (2) each peripheral impulse evokes a triphasic activation-suppression-rebound response in both S1 and M1;<br /> (3) PV interneurons carry the major component of spike modulation for each of these phases;<br /> (4) activation of PV neurons in each area (M1 or S1) drives suppression and rebound both in the local area and in the other downstream area;<br /> (5) peripheral-evoked neural activity in M1 is at least partially dependent on transmission through S1.

All conclusions are well-supported and reasonably interpreted. There are no major new findings that were not expected from standard models of somatosensory pathways or from prior work in the whisker system.

Strengths:

This is a well-conducted and analyzed study in which the findings are clearly presented. This will provide important baseline knowledge from which studies of more complex sensorimotor processing can build.

Weaknesses:

A few minor issues should be addressed to improve clarity of presentation and interpretation:

(1) It is critical for interpretation that the stimulus does not evoke a motor response, which could induce reafference-based activity that could drive, or mask, some of the triphasic response. Figure S1 shows that no motor response is evoked for one example session, but this would be stronger if results were analyzed over several mice.

(2) The recordings combine single and multi-units, which is fine for measures of response modulation, but not for absolute evoked firing rate, which is only interpretable for single units. For example, evoked firing rate in S1 could be higher than M1, if spike sorting were more difficult in S1, resulting in a higher fraction of multi-units relative to M1. Because of this, if reporting of absolute firing rates is an essential component of the paper, Figs 3D and 4E should be recalculated just for single units.

(3) In Figure 5B, the average light-evoked firing rate of PV neurons seems to come up before time 0, unlike the single-trial rasters above it. Presumably, this reflects binning for firing rate calculation. This should be corrected to avoid confusion.

(4) In Figure 6A bottom, please clarify what legends "W. suppression" and "W. rebound" mean.

Voici un résumé minuté avec les idées fortes en gras, basé sur la transcription fournie :

• [0:00-0:55] Introduction et présentation d'Oscar : La narratrice ironise sur le bonheur d'être mère, présentant Oscar, son fils de 2 ans et 8 mois, comme étant tout sauf calme et gentil. La version courte de son quotidien est idyllique, mais la version longue inclut des hurlements, des portes claquées et des morsures.

• [0:55-1:39] Les colères d'Oscar et le désarroi de la mère : Oscar fait des colères soudaines et intenses, frappant, tapant des pieds et devenant rouge. La mère se sent perdue face à ces réactions. L'histoire d'Oscar commence bien avant sa naissance, avec la rencontre de ses parents.

• [1:39-2:50] Rencontre des parents et désaccord initial sur les enfants : La narratrice raconte sa rencontre avec le père d'Oscar, Alex. Au début, Alex ne voulait pas d'enfants, ayant une mauvaise image des enfants colériques dans les supermarchés. Il ne voulait pas non plus discipliner un enfant.

• [2:50-3:31] L'arrivée du premier enfant et l'acceptation du second : La narratrice finit par convaincre Alex d'avoir un enfant. Leur premier fils, Emile, est calme et patient. Alex accepte plus facilement l'idée d'un deuxième enfant.

• [3:31-4:45] La naissance d'Oscar et ses premières colères : Oscar naît par césarienne et sa première colère survient avant même son premier anniversaire. Sa nounou, Mary, décrit une crise de pleurs intense et inexplicable.

• [4:45-5:34] Un journal des colères et des descriptions à la crèche : La mère commence à tenir un journal des colères d'Oscar. Les colères se produisent de plus en plus souvent et de plus en plus fort. Le personnel de la crèche décrit les colères d'Oscar comme étant impressionnantes et sonores.

• [5:34-6:42] Les difficultés du père face aux colères : Le père d'Oscar se sent démuni face à ses colères matinales. Partir en laissant son enfant dans cet état est horrible pour lui.

• [6:42-7:40] Le comportement d'Oscar à la crèche : À la crèche, Oscar joue avec ses amis, écoute des histoires, chante joyeusement et fait des concessions. Il reçoit même une médaille de bonne conduite de sa référente.

• [7:40-8:32] Les colères à la maison : À la maison, c'est la "castagne" pour chaque activité : bain, repas, sommeil. Les parents essaient différentes méthodes, mais sont en désaccord sur la marche à suivre.

• [8:32-9:26] Le désespoir des parents et les explications de l'entourage : La narratrice craque, surtout quand Alex dit que c'est ce qu'il craignait. L'entourage donne des explications variées (place du deuxième enfant, signe astrologique, genre de l'enfant, âge), auxquelles la mère s'accroche temporairement.

• [9:26-10:12] Témoignage d'une amie : Une amie, Elise, raconte les colères de son propre enfant, Louis, à partir de 2 ans, qui sont devenues ingérables vers 3 ans et demi-4 ans.

• [10:12-11:16] La violence révélée par les enfants : Elise révèle que son fils l'a poussée dans ses retranchements et lui a fait découvrir un aspect monstrueux de sa personnalité. Elle se voit déformée par la colère dans le miroir. Ces accès de colère peuvent monter puis disparaître en un instant.

• [11:16-12:07] La culpabilité : Les mères se sentent coupables de ne pas savoir gérer les colères de leurs enfants et d'être elles-mêmes débordées par leurs émotions. Le père d'Oscar a découvert un pan inconnu de la personnalité de sa femme, marqué par le stress et la colère.

• [12:07-12:53] Stratégies d'évitement et peur : La mère cède souvent pour éviter les colères. Les réactions de son enfant lui font peur, et les siennes aussi. Une autre amie, Rachel, raconte qu'elle anticipait les colères de sa fille pour les éviter.

• [12:53-14:01] Les colères en public et le jugement des autres : Les colères dans la rue sont particulièrement difficiles à gérer. La mère décrit des épisodes de colères dans le métro et dans une aire de jeux. Elle se sent jugée par les autres.

• [14:01-15:34] Un témoignage d'Elise : Elise raconte une scène de colère de son fils au supermarché, où elle a été publiquement critiquée par d'autres clients. Elle se sent une mauvaise mère.

• [15:34-16:48] Besoin d'aide et recours aux livres : Les parents se rendent compte qu'ils ont besoin d'aide. Ils cherchent des conseils dans des livres sur l'éducation bienveillante et la parentalité positive.

• [16:48-17:41] Conseils de Charlotte Ducharme : Une experte, Charlotte Ducharme, donne des conseils sur YouTube : changer ses croyances sur la colère, faire preuve d'empathie et aider l'enfant à revenir au calme.

• [17:41-18:59] Application des conseils et retour à la réalité : La mère essaie d'appliquer les conseils pour aider son enfant à contrôler ses émotions. Un livre pour enfant est utilisé comme support. Cependant les crises reprennent.

• [18:59-19:33] Le manque de cohérence des parents : Les parents ont du mal à rester cohérents dans leur approche éducative et alternent entre bienveillance et autoritarisme. Ils n'arrivent pas à tenir la distance.

• [19:33-21:21] Recours à la psychothérapie : La famille décide de consulter un pédopsychiatre. La première séance est chaotique, Oscar ayant mis le cabinet sens dessus dessous. La psy explique que c'est une manière pour lui de se défouler.

• [21:21-22:23] Suite des colères et consultations : Les colères d'Oscar continuent, avec des épisodes au centre de loisirs et pendant le bain. La mère consulte la psychologue de la crèche.

• [22:23-23:31] Séance avec la psychologue de la crèche : Les parents décrivent les colères, leur propre incompréhension et leurs disputes.

• [23:31-25:22] Explications de la psychologue : La psy explique qu'Oscar n'arrive pas à exprimer ce qu'il ressent autrement que par la colère. Il cherche l'attention et une relation exclusive avec ses parents. Son comportement est une manière d'être avec eux, même si cette manière n'est pas satisfaisante.

• [25:22-26:37] Pistes de solutions : Il est important que les parents fassent front ensemble, qu'ils définissent des règles, qu'ils trouvent du temps pour eux sans les enfants et qu'ils identifient ce sur quoi ils peuvent lâcher prise. La situation va finir par s'améliorer.

• [26:37-27:39] Un changement inattendu : Après avoir appliqué les conseils de la psychologue, un changement étrange se produit : les colères d'Oscar disparaissent d'un coup. Il apprend même à dire "oui".

• [27:39-28:54] L'explication du changement : La mère réalise qu'Oscar était devenu le centre de son attention à cause de ses colères. En étant moins le seul à hurler, Oscar se détache de l'image de "vilain canard" que sa mère avait de lui.

• [28:54-29:19] Ne pas enfermer les enfants dans un rôle : Il ne faut pas enfermer les enfants dans un rôle car ils finissent par y ressembler. Il est important de se souvenir qu'Oscar est aussi un enfant affectueux, joueur et plein d'énergie.

Voici un document de synthèse pour un briefing, basé sur la conférence de Sonia Lavadinho et les échanges qui ont suivi, en mettant en évidence les points clés :

Titre : La Ville Relationnelle : Un Nouveau Paradigme pour la Santé et le Bien-être Urbain

Introduction

• Sonia Lavadinho, anthropologue urbaine et fondatrice du cabinet Bfluid, a présenté une vision novatrice de la ville, en mettant l'accent sur la nécessité de repenser nos environnements urbains pour favoriser la santé physique et mentale.
• La ville relationnelle propose de remplacer le paradigme de la ville fonctionnelle, héritée du 20e siècle, par un modèle plus adapté aux défis du 21e siècle.
• L'objectif est de créer des espaces qui encouragent le mouvement, les interactions sociales et le lien avec le vivant.

Problématiques et Constats

• Déconnexion du corps biologique : Nous passons trop de temps assis, devant des écrans, et en voiture, ce qui nous déconnecte de nos corps et de nos besoins biologiques.
• Manque de mouvement : Les enfants passent moins de temps à l'extérieur que les prisonniers, et le contact avec la nature est de plus en plus rare.
• Solitude et manque d'empathie : Ces problèmes sont directement liés au manque de contact avec son propre corps et avec les autres.
• Zoning intergénérationnel : Les différentes générations sont séparées, ce qui entrave la mixité sociale et le développement d'une ville pour tous.
• Sédentarité : Le temps passé en voiture contribue à la sédentarité, et les enfants sont de plus en plus dépendants de la voiture pour leurs déplacements.
• Isolement et solitude: Ces phénomènes touchent toutes les catégories d'âge et augmentent les risques de maladies.

Concepts Clés

• Espaces "mielleux" : Des espaces publics où les gens ont envie de rester plus longtemps et de revenir plus souvent, favorisant les interactions sociales et le bien-être.
• La ville du dehors : Le contact avec l'eau, le vivant, et la nature est essentiel pour le bien-être.
• Ville amie de toutes les générations : Une ville qui permet de se connecter avec les personnes d'âges différents.
• Biodiversité des publics : La diversité des âges, des genres et des origines dans les espaces publics est un signe de santé et de vitalité.
• Ville comestible et communale: Une ville qui permet aux habitants de renouer avec leur alimentation et les circuits courts.
• Ville du temps libre : Une ville qui valorise le temps libre et notamment la nuit comme source d'activité et de bien-être.

Les Sept Figures de la Ville Relationnelle

• La ville du dehors.
• La ville amie de toutes les générations.
• La ville du faire et du tiers solidaire.
• La ville de la surprise.
• La ville comestible et communale.
• La ville du temps libre.

Cinq Leviers d'Action pour l'Existant

1. Armature des rues : Transformer les grandes rues et avenues en "ruisseaux de fraîcheur" avec plus de végétation.
2. Parcs et leur "deuxième peau" : Agir sur les rues qui entourent les parcs pour augmenter leur accessibilité et leur attractivité.
3. Bandes ludiques : Intégrer le jeu dans l'espace public pour que les enfants puissent jouer librement.
4. Carrefours vivants : Transformer les carrefours, souvent sources de stress, en lieux de vie en augmentant la convivialité et en y intégrant une diversité d'usages.
5. Parkings prévenants : Améliorer les parkings pour que le premier pas hors de la voiture soit agréable.

Stratégies et Méthodes

• Double rue : Activer des boucles itinérantes pour les piétons afin d'animer un quartier et de favoriser le commerce.
• Balades croquées : Impliquer les citoyens en leur prêtant les crayons des dessinateurs pour qu'ils expriment leurs idées.
• Atelier "on se marie" : Un espace de discussion pour que tous soient d'accord avec le projet avant sa validation.
• Monitoring et évaluation : Suivre les indicateurs de qualité de vie dans les espaces publics pour mesurer l'impact des projets.

Recommandations

• Agir à grande échelle : Ne pas travailler rue par rue ou école par école, mais avoir une approche systémique.
• Travailler avec les "ninzagers" : Inclure les personnes qui bloquent les projets (architectes, urbanistes, etc.) dès le début, en les mettant sur le terrain avec les citoyens.
• Rapprocher le "soft" et le "hard" de la ville : Faire collaborer les acteurs de la santé, de la culture et du social avec ceux de l'urbanisme.
• Prioriser l'espace public : Aménager l'espace public avant de construire les bâtiments pour que les habitants puissent en profiter dès leur arrivée.
• La nuit comme levier : Investir le premier et le quatrième temps de la nuit pour amener plus de sécurité et de convivialité.
• Mettre l'accent sur la biodiversité des publics : Amener une variété de personnes dans la rue pour créer une surveillance naturelle et réduire la violence.

Conclusion

• La ville relationnelle est un concept essentiel pour améliorer la santé et le bien-être des populations urbaines.
• Les cinq leviers d'action proposés par Sonia Lavadinho offrent des pistes concrètes pour transformer nos villes.
• Il est crucial d'impliquer les citoyens dans les projets et de suivre leur impact.
• La ville de demain sera relationnelle ou ne sera pas.

Voici un résumé minuté de la transcription de la conférence de Sonia Lavadinho, avec les idées fortes mises en évidence en gras :

• 0:00-0:21 : Introduction de la 2e journée des JASP, avec un accueil des participants.
• 0:31-1:50 : Remerciements et présentation de l'équipe organisatrice des Journées annuelles de santé publique.
• 1:50-2:46 : Reconnaissance du territoire autochtone ancestral sur lequel se déroule l'événement, et mention des 11 nations autochtones du Québec.
• 2:46-3:49 : Présentation des JASP comme événement de formation accrédité, et importance du réseautage, soulignant la collaboration au sein du réseau de santé publique.
• 3:55-4:32 : Annonce d'une plénière avec le Docteur Luc Boileau et Pierre Gerlier, axée sur les transformations en cours.
• 4:32-5:33 : Informations sur la traduction simultanée par IA, et sur des activités en marge comme une conférence sur les points de services locaux.
• 5:33-6:11 : Présentation d'un 5 à 7 organisé par une équipe de santé au travail, avec un jeu coopératif basé sur le thème de la pandémie.
• 6:11-7:06 : Remerciements à l'équipe de l'Institut, aux responsables d'activités, et mention des partenaires du consortium pour la formation continue en santé publique.
• 7:06-7:53 : Annonce du concours de la relève en 3 minutes, des présentations par affiche, et mention des gagnants dans les catégories nouvelles connaissances et pratiques inspirantes.
• 7:53-9:24 : Annonce d'une mention d'honneur catégorie nouvelle connaissance et pratique inspirante et du prix étudiant dans la même catégorie.
• 9:24-11:29 : Introduction de Sonia Lavadinho et de sa conférence sur la ville relationnelle. Ses objectifs sont d'ouvrir une réflexion sur les innovations urbanistiques favorables à la santé et d'illustrer les processus et résultats de ces innovations. Il est aussi précisé que Sonia Lavadinho est une anthropologue urbaine et géographe, fondatrice du cabinet Bfluid.
• 11:29-12:23 : Sonia Lavadinho commence sa conférence en insistant sur le lien entre l'environnement urbain et la santé, soulignant que nos environnements urbains peuvent nous rendre malades ou plus sains.
• 12:23-13:01 : Elle partage des données sur l'augmentation du taux d'obésité chez les migrants aux États-Unis, mettant en évidence l'impact du mode de vie urbain sur l'alimentation.
• 13:01-14:16 : Elle souligne que le manque de mouvement et la déconnexion de nos corps biologiques sont des problèmes majeurs, citant une étude anglaise sur le temps passé à l'extérieur par les enfants. Elle ajoute que le contact avec le vivant est aussi essentiel.
• 14:16-15:27 : Elle aborde la question de la solitude et du manque d'empathie, liés au manque de contact avec son propre corps et avec les autres, et met en avant une approche d'espaces "mielleux".
• 15:27-16:15 : Elle présente son livre "La ville relationnelle", qui insiste sur sept figures de la ville relationnelle.
• 16:15-17:31 : Elle souligne l'importance de la ville du dehors, de la ville amie de toutes les générations et le problème du zoning intergénérationnel.
• 17:31-19:04 : Elle parle du phénomène inédit de la cohabitation de toutes les générations sur terre, soulignant la nécessité d'une ville pour tous.
• 19:04-20:26 : Elle aborde des concepts comme la ville du faire, du tiers solidaire, de la surprise, la ville comestible, et la ville du temps libre, soulignant l'importance de la nuit pour lutter contre l'obésité.
• 20:26-21:22 : Elle met l'accent sur les cycles de vie, en particulier les enfants dès le ventre de la mère, et l'importance de l'accompagnement en cas de situation handicapante et de vieillissement.
• 21:22-22:20 : Elle propose trois manières de faire, notamment la balade nocturne, les dynamiques de proximité et le premier pas actif hors de chez soi en privilégiant le lien avec le vivant.
• 22:20-23:47 : Elle prend comme exemple un quartier de Bordeaux, pour montrer le lien entre la capacité à produire la ville du dehors et les espaces verts, la présence de l'eau, de l'animalité, et les rencontres informelles. Elle souligne l'importance de la biodiversité des publics et de liens de voisinage.
• 23:47-24:47 : Elle introduit cinq leviers pour agir dans l'existant, en vue de créer une ville relationnelle, active et en bonne santé, et mentionne le plan d'urbanisme favorable à la santé de la ville de Paris.
• 24:47-26:16 : Elle détaille le premier levier : l'armature des rues, en mentionnant l'exemple de Lyon et de Barcelone qui a lancé une refonte totale de ses grandes avenues.
• 26:16-27:57 : Elle illustre avec l'exemple de Buenos Aires, avec une carte mentale des problèmes de marchabilité, et une stratégie de relier les parcs aux gares et les gares entre elles. Elle attire l'attention sur le fait qu'il est possible d'avoir un impact sur la moitié de la population en travaillant seulement 3% des rues.
• 27:57-29:43 : Elle parle du second levier, qui est de travailler sur les parcs déjà existants et sur leur deuxième peau (les rues qui les entourent), pour répondre à des questions de vulnérabilité, de fragilité, d'isolement et de fraîcheur.
• 29:43-30:55 : Elle présente le troisième levier : les bandes ludiques, qui consistent à mettre le jeu à portée des enfants sans qu'ils aient à demander la permission. Elle mentionne l'exemple du Superkilen à Copenhague, qui relie trois quartiers populaires.
• 30:55-32:28 : Elle évoque l'opération "rue aux écoles" à Paris, pour amener plus de végétalisation et de jeux, et pour que les rues deviennent utilisables par tous. Elle souligne la nécessité d'agir massivement et pas école par école.
• 32:28-33:10 : Elle met en avant les bénéfices rétroactifs de ces actions pour les ainés, les actifs, et pour lutter contre le zoning intergénérationnel et la solitude.
• 33:10-34:47 : Elle parle du quatrième levier : les carrefours, en mentionnant un exemple à Mendrizio, puis à Bâle, où l'approche est d'apaiser les carrefours, car ils sont source de stress. Elle parle de l'impact cumulé du stress lié à la traversée des carrefours.
• 34:47-36:21 : Elle explique comment transformer les carrefours en lieux de vie, en libérant l'espace, en mettant des zones de rencontre, et en créant une situation d'attention mutuelle. Elle souligne qu'agir sur un carrefour a un impact exponentiel car il est à la croisée de plusieurs quartiers.
• 36:21-37:41 : Elle introduit le cinquième levier : les parkings prévenants, en s'inspirant de ce qui a été fait à Montréal pendant la pandémie, pour faire en sorte que le premier pas hors de la voiture soit agréable.
• 37:41-38:59 : Elle donne des exemples de parkings à Hambourg et à Copenhague qui incitent les automobilistes à quitter leur voiture et à être plus actif. Elle ajoute que le temps passé en voiture contribue à la sédentarité.
• 38:59-40:44 : Elle aborde la question de l'isolement et de la solitude, qui touchent toutes les catégories d'âge et qui augmentent les risques de maladies cardiaques ou d'AVC.
• 40:44-42:08 : Elle mentionne l'exemple de Bilbao qui a travaillé sur les coupures entre les quartiers en agissant notamment sur les ponts, et en créant des espaces confortables pour les piétons.
• 42:08-42:54 : Elle souligne l'importance de faire l'espace public avant les immeubles pour que les habitants puissent profiter de l'espace dès le premier jour.
• 42:54-43:57 : Elle présente les indicateurs mis en place pour la ville de Paris, comme la biodiversité, les sociabilités, les ralentissements, et la latéralisation, afin de déterminer si un espace public est réellement relationnel. Elle termine en disant que la ville de demain sera relationnelle ou ne sera pas.
• 43:57-44:33 : Elle pose la question aux participants sur ce qu'ils veulent emporter comme type d'actions et sur ce qu'ils ont retenu de sa conférence.
• 44:33-45:25 : Une participante de la Direction de santé publique prend la parole et pose deux questions, l'une concernant la gentrification et l'autre la préservation du patrimoine.
• 45:25-46:58 : Sonia Lavadinho explique que pour résoudre le problème de la gentrification, elle utilise un système de double rue, ce qui permet d'activer plusieurs rues à la fois et donc de mieux contrôler les prix.
• 46:58-48:33 : Concernant le patrimoine, elle admet que c'est un problème car il y a souvent des conflits avec les architectes des bâtiments de France. Elle recommande d'associer les acteurs du patrimoine dès le départ du projet.
• 48:33-50:05 : Elle mentionne les "ninzagers" (les personnes qui bloquent les projets) et recommande de les faire participer dès le début en les mettant sur le terrain avec les citoyens. Elle souligne la nécessité de rapprocher ceux qui font le "soft" de la ville (santé, culture, petite enfance, etc.) de ceux qui font le "hard" de la ville (voirie, espaces verts, etc.).
• 50:05-50:58 : Une participante de l'Office de participation publique de Longueuil questionne sur l'implication des citoyens dans les projets.
• 50:58-52:30 : Sonia Lavadinho explique comment elle implique les citoyens en allant sur le terrain, en filmant ce que les gens font et ne font pas, et en identifiant les comportements latents. Elle parle aussi du taux d'évaporation piéton, comme indicateur.
• 52:30-54:14 : Elle détaille la méthode des "balades croquées", où les dessinateurs prêtent leurs crayons aux gens pour qu'ils donnent leurs idées. Elle explique aussi l'atelier "on se marie" pour que les gens soient d'accord avec le projet.
• 54:14-55:40 : Elle parle de l'importance du monitoring et de l'évaluation des projets, avec des exemples de villes comme Barcelone et Zurich. Elle souligne le rôle du monde de la santé dans la mise en place d'indicateurs simples.
• 55:40-56:13 : Un participant questionne sur l'impact de l'insécurité et de l'itinérance sur la fréquentation des espaces publics.
• 56:13-58:29 : Sonia Lavadinho répond qu'en général, c'est l'inverse, car les espaces verts et la présence d'enfants augmentent la sécurité. Elle insiste sur l'importance d'amener une grande biodiversité de publics dans la rue pour créer un effet de surveillance.
• 58:29-1:00:05 : Elle ajoute que la ville de la nuit est aussi un levier, surtout en agissant sur le premier temps de la nuit (18h-21h) et sur le quatrième temps (5h-7h30). Elle recommande de travailler de façon contextuelle et non pas de manière défensive.
• 1:00:05-1:00:31 : Fin de la conférence et remerciements.

Résumé de la vidéo [00:00:03][^1^][1] - [00:24:01][^2^][2]:

Cette vidéo aborde les troubles de santé mentale chez les adolescents et comment les aider. Elle présente des témoignages et des conseils pratiques pour soutenir les jeunes en détresse.

Temps forts: + [00:00:03][^3^][3] Introduction * Avertissement sur les contenus sensibles * Importance de contacter les numéros d'urgence en cas de crise * Présentation des histoires de Julie, Pierre et Nathalie + [00:01:54][^4^][4] Définition de l'adolescence * Période entre 12 et 18 ans * Transition physique, psychique et sociale * Un jeune sur sept développe un trouble psychique + [00:03:07][^5^][5] Témoignages de secouristes * Rencontre avec Sarah et Christelle * Soutien apporté à un adolescent nommé Julien * Importance de la vigilance et de la communication + [00:06:02][^6^][6] Intervention de Christelle * Application de la méthode AÉRER * Discussion avec Julien sur ses scarifications et pensées suicidaires * Importance de prévenir les parents et de retirer les objets dangereux + [00:19:17][^7^][7] Formation PSSM * Apport de la formation pour les secouristes * Importance de la confiance et de la légitimité * Soutien mutuel entre les secouristes formés

Résumé de la vidéo [00:24:17][^1^][1] - [00:42:48][^2^][2]:

Cette vidéo aborde comment aider un adolescent en difficulté, en mettant en avant l'importance de la formation en santé mentale pour les enseignants et les professionnels travaillant avec les jeunes.

Temps forts: + [00:24:17][^3^][3] Importance de la formation * Formation en santé mentale pour les enseignants * Aide à détecter les signes de détresse * Encourage la collaboration entre collègues + [00:27:00][^4^][4] Témoignage de Léa * Léa partage son expérience personnelle * Soutien reçu de ses professeurs et de l'infirmière scolaire * Importance de l'écoute et de la confidentialité + [00:31:54][^5^][5] Intervention du Dr. Olivier Canceil * Troubles spécifiques chez les adolescents * Impact de la crise Covid sur la santé mentale * Influence des réseaux sociaux et des addictions + [00:36:02][^6^][6] Aider les adolescents * Difficulté de gagner la confiance des jeunes * Importance de l'écoute et de la validation des émotions * Conséquences de la non-intervention + [00:38:34][^7^][7] Module Jeunes de PSSM * Formation pour les adultes interagissant avec les jeunes * Adaptation du module australien * Importance de la pair-aidance et de la déstigmatisation

synthèse détaillé basé sur les sources que vous avez fournies.

Document de Synthèse : Automutilations Non Suicidaires

Introduction

Ce document de synthèse examine les principaux thèmes et informations clés concernant les automutilations non suicidaires (NSSI), tirés de l'épisode de podcast "Apprendre à aider" de Premiers Secours en Santé Mentale (PSSM) France.

Il explore la nature des NSSI, leurs causes, les approches d'aide et de soutien, et l'importance de la formation en secourisme en santé mentale.

1. Définition et Types d'Automutilations Non Suicidaires

Définition: Les NSSI sont des blessures physiques intentionnelles infligées à soi-même sans l'intention de mourir. Elles sont une forme de comportement autoagressif.

"L'automutilation se caractérise par des blessures physiques directes plus ou moins sévères qu'une personne s'inflige à elle-même avec ou sans intention suicidaire."

Types fréquents :

• Scarifications (incisions superficielles de la peau)
• Égratignures
• Coups intentionnels contre une surface dure
• Coups de poing et claques
• Morsures
• Brûlures

« il existe différents types d'automutilation la plus fréquente c'est se couper avec un objet quelconque aigu une lame de rasoir ou tout autre objet tranchant mais il y a tout un ensemble de automutilation possi comme se frapper se mordre se tirer les cheveux se gratter des plai se tirer des petx donc toute stratégie qui permet de d'infliger volontairement une blessure au corps »

2. Prévalence et Facteurs de Risque

Âge d'apparition: Les NSSI apparaissent le plus souvent à l'adolescence (autour de 13-14 ans), avec un pic autour de 18 ans, et peuvent persister à l'âge adulte.

"globalement les automatisations apparaissent autour de 13 ou 14 ans avec un pic autour de 18 ans mais on peut dire que globalement les automutilations sont observées dans la période entre 13 et 20 25 ans 28 ans"

• Prévalence chez les jeunes : On estime que 12 à 35% des jeunes sont concernés par des comportements auto-punitifs comme les scarifications.

• Augmentation chez les filles et jeunes femmes : Un rapport de 2024 indique une augmentation notable des actes d'automutilation chez les filles et les jeunes femmes.

• Troubles associés : Les personnes qui s'automutilent ont cinq fois plus de risque d'être concernées par un trouble dépressif.

"les personnes qui pratiquent l'automutilation ont cinq fois plus de risque d'être concerné par un trouble dépressif"

• Facteurs de risque: Faible estime de soi, difficultés à réguler les émotions, sentiment de désespoir, difficultés à trouver des solutions, image corporelle négative et culpabilité.

3. Raisons et Fonctions des Automutilations

• Régulation émotionnelle: Les NSSI sont souvent utilisées pour soulager une souffrance émotionnelle intense et des ressources limitées pour y faire face. Elles peuvent être perçues comme un exutoire.

• Prévention du suicide: Dans certains cas, l'automutilation peut prévenir un acte suicidaire en évacuant l'idée de suicide.

"parfois chez les mêmes sujets la il peut y avoir aussi des sujets suicidaires mais dans un certain nombre de situations les automutilisations protègent permettent d'évacuer l'idée de suicide"

• Sentiment d'être vivant: La douleur peut ramener une personne à la conscience de son corps, se sentir vivant et présent (en particulier chez les personnes avec des sentiments de dissociation).

• Communication: Les NSSI peuvent être une manière d'exprimer sa détresse et d'attirer l'attention, souvent de manière non intentionnelle.

• Évitement: Elles peuvent être utilisées pour éviter des situations stressantes ou difficiles.

4. Le Lien entre Automutilation et Suicide

• Pas une intention suicidaire: Il est important de souligner que les NSSI ne sont pas motivées par l'intention de se donner la mort, bien qu'il puisse y avoir une continuité entre les pensées suicidaires et les passages à l'acte pour certaines personnes.

• Facteurs de risques communs : Les facteurs de risque pour l'automutilation et le suicide sont très proches (faible estime de soi, difficultés à réguler ses émotions, etc.).

• "les raisons enfin les facteurs de risque présents dans les autromutilations sont quasiment les mêmes que ceux qui sont présents dans les comportements suicidaires les suicidaires"

• Différenciation nécessaire: Malgré les similitudes, la majorité des adolescents qui s'automutilent ne tenteront jamais de se suicider.

5. Approches d'Aide et d'Intervention

• Posture du secouriste:

Réguler ses propres émotions pour éviter la sur-implication ou la banalisation. "la première chose je dirais c'est réguler sa propre expérience de secouriste c'est-à-dire garder un niveau émotionnel qui permette de ne pas être ni dans la surinchè nous ni dans la banalisation"

• Faire preuve de curiosité et d'empathie pour comprendre l'expérience de l'adolescent sans jugement.

• Écoute active: Explorer avec la personne la fonction des automutilations, les émotions qui précèdent l'acte, les déclencheurs, le contexte, etc.

• Stratégies d'atténuation

La règle des 15 minutes : aider l'adolescent à passer les périodes de crise émotionnelle intense qui peuvent durer 10-15 minutes.

• Techniques de respiration, utilisation du froid (glace sur les bras, eau sur le visage), rappels d'images positives, etc.

• Recourir à des activités alternatives : écouter de la musique, faire de l'exercice physique intense, caresser un animal, dessiner, etc.

• Importance de la verbalisation: Encourager la personne à exprimer ses émotions et ses pensées pour pouvoir les analyser et les changer.

• "on travaille beaucoup sur la verbalisation avec Sarah qui élabore très très bien et et qui en plus a besoin d'élaborer mettre des mots sortir les mots et les déposer en l'occurrence à moi puisque c'est moi qui étais là lui fait énormément de bien et lui permet de bah de relativiser un petit peu"

• Orientation vers des professionnels: Diriger la personne vers des médecins, psychologues, ou d'autres professionnels de santé mentale pour un suivi plus approfondi.

• "Le rôle du secouriste c'est bien de toute façon de renvoyer ses adolescents vers des professionnels de santé"

6. Témoignages et Études de Cas

Le témoignage de Laurence: Laurence Roux-Fouillet, sophrologue et secouriste en santé mentale, a accompagné une jeune fille qui se scarifiait. Elle a mis l'accent sur l'écoute active, la mise en confiance, l'identification des alternatives à l'automutilation et l'orientation vers des professionnels.

Le témoignage croisé de Bérangère et Sarah :

Bérangère, éducatrice et secouriste en santé mentale, a aidé Sarah, une jeune femme souffrant de TOC et d'automutilations.

La prise en charge immédiate de Bérangère, son approche calme et empathique et l'utilisation d'outils d'auto-aide (mur d'outils d'auto-aide avec des billets cognitifs, etc.) ont permis de stabiliser Sarah pendant un moment de crise.

Importance des kits d'auto-aide: Permettent de donner des alternatives lorsque la personne se sent dépassée.

7. Rôle de la Formation PSSM

• Apprentissage des gestes et des bons mots: La formation PSSM fournit un cadre et des outils pour aborder et aider les personnes en souffrance psychique, notamment celles qui s'automutilent.

• Approche structurée et rassurante: La formation PSSM est très structurée, ce qui rassure les secouristes et les aide à ne pas faire d'erreurs.

• "moi je la conseillerais parce que c'est extrêmement structuré donc c'est très rassurant"

• Développement du vocabulaire précis et non stigmatisant: Permet de gagner du temps dans la prise en charge des situations et de se positionner dans un rôle qui aide et qui rassure.

• Accès à des ressources précieuses: Le site de PSSM France propose des ressources, des adresses et des contacts qui sont précieux pour l'orientation et le soutien des personnes en souffrance.

• "le site de pssm France quand on est secouriste regorge d'expérience d'adresse moi je trouve par exemple le rissaut associatif est extrêmement fourni très dense"

• Renforcement du rôle du secouriste : Renforce et soutient les pratiques existantes en offrant un cadre structuré et des compétences plus précises.

Conclusion

Les automutilations non suicidaires sont des comportements complexes qui nécessitent une approche empathique, attentive et bienveillante.

Les formations en secourisme en santé mentale, comme celles proposées par PSSM France, jouent un rôle essentiel dans la préparation des citoyens et des professionnels pour intervenir de manière efficace et appropriée auprès des personnes en souffrance.

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Author response:

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

Reviewer #1 (Public Review):

In the present work the authors explore the molecular driving events involved in the establishment of constitutive heterochromatin during embryo development. The experiments have been carried out in a very accurate manner and clearly fulfill the proposed hypotheses.

Regarding the methodology, the use of: i) an efficient system for conversion of ESCs to 2C-like cells by Dux overexpression; ii) a global approach through IPOTD that reveals the chromatome at each stage of development and iii) the STORM technology that allows visualization of DNA decompaction at high resolution, helps to provide clear and comprehensive answers to the conclusion raised.

The contribution of the present work to the field is very important as it provides valuable information on chromatin-bound proteins at key stages of embryonic development that may help to understand other relevant processes beyond heterochromatin maintenance.

The study could be improved through a more mechanistic approach that focuses on how SMARCAD1 and TOPBP1 cooperate and how they functionally connect with H3K9me3, HP1b and heterochromatin regulation during embryonic development. For example, addressing why topoisomerase activity is required or whether it connects (or not) to SWI/SNF function and the latter to heterochromatin establishment, are questions that would help to understand more deeply how SMARCAD1 and TOPBP1 operate in embryonic development.

We would like to thank the reviewer for the positive evaluation of our work and the methodology we employed. We greatly appreciated the reviewer’s recognition of our study to “provide valuable information on chromatin-bound proteins at key stages of embryonic development that may help to understand other relevant processes beyond heterochromatin maintenance”. While we acknowledge the value of including mechanistic studies, such an addition would require a substantial amount of experimental work that exceeds our current resources.

Reviewer #1 (Recommendations For The Authors):

In my opinion, the authors could improve the study by deciphering -to a certain extent- the possible mechanism by which SMARCAD1 and TOPBP1 are cooperating in their system to establish H3K9me3 and consequently heterochromatin; and whether it is different (or not) from that already reported in yeast (ref 27). In fact, is it only SMARCAD1 that participates in this process or the whole SWI/SNF complex? Could the lack of SMARCAD1 compromise the proper assembly of the SWI/SNF complex? In this regard, a model describing the main findings of the study and the discussion of the possible mechanisms involved -based on the current bibliography- would be appreciated. This, although speculative, would illustrate the range of possibilities that could be operating in the maintenance of heterochromatin during embryonic development. In conclusion, it would be great if the authors could link -mechanistically- the dots connecting SMARCD1, TOPBP1, H3K9me3/HP1/heterochromatin.

As suggested by the reviewer and to enrich the discussion, we have included some additional sentences and references in the revised discussion section.

As a minor point, In Figure 3A, left panel it appears that the protein precipitating with H3K9me3 reacts with TOPBP1 but its molecular weight does not exactly match to the TOPBP1 band found in the input. The authors should clarify this point and it is also recommended that IPs and inputs are run in the same gel. Please replace Figure 3A right panel.

Following the reviewer’s suggestion and to improve the reading flow, we have restructured the order of the figures and removed the original Figure 3A. The revised Figure 3A-C panel illustrates the SMARCAD1 association with H3K9me3 in ESCs and 2C- cells, while capturing the reduced SMARCAD1-H3K9me3 association in 2C⁺ cells.

Reviewer #2 (Public Review):

The manuscript by Sebastian-Perez describes determinants of heterochromatin domain formation (chromocenters) at the 2-cell stage of mouse embryonic development. They implement an inducible system for transition from ESC to 2C-like cells (referred to as 2C⁺) together with proteomic approaches to identify temporal changes in associated proteins. The conversion of ESCs to 2C⁺ is accompanied by dissolution of chromocenter domains marked by HP1b and H3K9me3, which reform upon transition back to the 2C-like state. The innovation in this study is the incorporation of proteomic analysis to identify chromatin-associated proteins, which revealed SMARCAD1 and TOPBP1 as key regulators of chromocenter formation.

In the model system used, doxycycline induction of DUX leads to activation of EGFP reporter regulated by the MERVL-LTR in 2C⁺ cells that can be sorted for further analysis. A doxycycline-inducible luciferase cell line is used as a control and does not activate the MERVL-LTR GFP reporter. The authors do see groups of proteins anticipated for each developmental stage that suggest the overall strategy is effective.

The major strengths of the paper involve the proteomic screen and initial validation. From there, however, the focus on TOPBP1 and SMARCAD1 is not well justified. In addition, how data is presented in the results section does not follow a logical flow. Overall, my suggestion is that these structural issues need to be resolved before engaging in comprehensive review of the submission. This may be best achieved by separating the proteomic/morphological analyses from the characterization of TOPBP1 and SMARCAD1.

We appreciate the reviewer’s positive evaluation of our inducible system to trigger the transition from ESCs to 2C-like cells, and the strength of the chromatin proteomics we conducted. In response to the reviewer’s suggestion, we have reorganized the order of the figures, particularly Figure 1 and Figure 2, and revised the text to improve readability and flow.

Reviewer #2 (Recommendations For The Authors):

There are some very interesting components to the study but, as noted, the narrative requires changes and the rationale for focusing on TOPBP1 and SMARCAD1 is not strong at present. Specific comments are noted below

(1) Inclusion of authentic 2C cells for comparative chromocenter analysis (or at least a more fulsome discussion of how the system has been benchmarked in previous studies).

We have included more detail in the revised methods section, in the “Cell lines and culture conditions” paragraph. We have added: “The Dux overexpression system was benchmarked according to previously reported features. Dux overexpression resulted in the loss of DAPI-dense chromocenters and the loss of the pluripotency transcription factor OCT4 (fig. S1E) (6, 7), upregulation of specific genes of the 2-cell transcriptional program such as endogenous Dux, MERVL, and major satellites (MajSat) (fig. S1F) (6, 7, 11, 26, 58), and accumulation in the G2/M cell cycle phase (fig. S1G), with a reduced S phase consistent in several clonal lines (fig. S1H) (15).”

(2) In Figure 1A, the text indicates a loss of chromocenters, but it may be better described as decompaction because the DAPI/H3K9me3 staining shows diffuse/expanded structures (this is in fact how it is described in relation to Figure 2).

We have changed the text accordingly, now describing it as “decompaction”.

(3) Table S1 has 6 separate tabs but these are not specified in the text. It would be useful to separate the 397 proteins unique to Luc and 2C- cells since they form much of the basis for the remaining analysis. This approach also assumes it is the absence of a protein in the 2C⁺ that accounts for the lack of chromocenters (noting there are 510 proteins unique to the 2C⁺ state that are not discussed).

We have referenced the supplementary table as Table S1 in the text for simplicity. It includes: Table S1A - List of Protein Groups identified by mass spectrometry in -EdU, Luc, 2C- and 2C⁺ cells; Table S1B - Input data for SAINT analysis; Table S1C - SAINT results of the comparison 2C- vs Luc and 2C⁺ vs Luc; Table S1D - SAINT results of the comparison Luc vs 2C- and 2C⁺ vs 2C-; Table S1E - SAINT results of the comparison Luc vs 2C⁺ and 2C- vs 2C⁺; and Table S1F - Total number of PSM per protein in the different cells and conditions tested.

(4) Since there is no change in H3K9me3 levels, loss of SUV420H2 from 2C⁺ chromatin (figure 1G) coupled with potential changes in H4K20me3 could contribute the morphological differences. SUV420H2 is known to regulate chromocenter clustering in a way the requires H4K20me3 but this is not addressed or cited (PUBMED: 23599346).

As suggested by the reviewer, we have added additional sentences and references in the revised manuscript.

(5) In Figure 1C, there does appear to be overlap between the 2C⁺ and 2C- populations (while the Luc population is distinct) even though they are morphologically distinct when imaged in Figure 2A. The 2C- cells are thought to be an intermediate, low Dux expressing population.

Chromatome profiling through genome capture provides a snapshot of the chromatin-bound proteome in the analyzed samples (shown in revised Fig. 2B). As indicated by the reviewer and previously reported in the literature, 2C- cells are an intermediate population before reaching 2C⁺ cells. For this study, we have focused on H3K9me3 morphological changes. Even though 2C- and 2C⁺ cells are distinct with respect to H3K9me3 morphology (shown in revised Fig. 1B), analysis of the chromatome data from hundreds of chromatin-bound proteins revealed some overlap between these two populations. However, replicates from the same population tend to cluster together, for example, 2C⁺ rep1 and 2C⁺ rep3, and 2C- rep1 and 2C- rep2. Collectively, these data suggest that a defined subset of coordinated changes in the chromatome likely triggers the transition from 2C- to 2C⁺ cells. Further experimental investigation of the chromatome dataset during the 2C-like transition would be interesting, however, we believe it is beyond the scope of this study.

(6) Data with SUV39H1 and 2 is difficult to accommodate; what about other H3K9 methyltransferases or proteins such as TRIM28 (KAP1) and SETDB1 (this comes up in the discussion but is not assessed in the results section).

We agree that investigating the role of TRIM28 (KAP1) and SETDB1 in this experimental setting could be of interest, however, we believe that these experiments go beyond the scope of the presented study.

(7) Rationale for choosing TOPBP1 needs to be improved. How do TOPBP1 levels relate to TOPI/TOP2A/TOP2B levels across the 3 cell populations? By what criteria does topoisomerase inhibitor treatment increase 2C⁺ like cells? Moreover, to what extent will inhibiting topoisomerases lead to global heterochromatin and cell cycle changes regardless of cell type.

Following the reviewer’s suggestion, we have included some additional references throughout the text to strengthen our rationale for selecting TOPBP1, given its well-established critical role in DNA replication and repair. Additionally, we have revised the results and discussion sections to include new sentences that propose a potential mechanism by which topoisomerase inhibitors may indirectly recruit TOPBP1 to facilitate DNA repair, ultimately leading to an increase in 2C⁺ cells.

(8) Likewise, the decision to look at SMARCAD1 based solely on its interaction with TOPBP1 seems somewhat arbitrary and it did not seem to come up as of interest in the iPOTD analysis. Moreover, they were not able to validate the interaction with their own analyses.

We have revised the text to clarify the connection further.

(9) The flow of results is confusing. The first section concludes with a focus on TOPBP1 and SMARCAD1, then progresses to morphological characterization of heterochromatin regions in the next two sections before returning to TOPBP1 and SMARCAD1. It seems like it would make more sense to describe the model system and morphological characterization at the beginning of the results section and then transition to the proteomic analysis and characterization of TOPBP1 and SMARCAD1 (with the expectation that the rationale be improved).

As suggested by the reviewer, we have reordered the figures, particularly Figure 1 and Figure 2, and rephased the text to improve the overall reading flow.

(10) There has been considerable work done on characterizing chromatin structure, epigenetic changes, and morphology during early embryonic development. It is therefore difficult to see what validating some of these changes in the inducible model is adding much in the way of new knowledge. It may, but this is not articulated in the current text.

As detailed before, we have rephrased the text to improve the overall reading flow, which we hope has improved the understanding of the impact of our results.

(11) It is difficult to disentangle broader effects of both TOPBP1 and SMARCAD1 from those described here; they may induce phenotypes, but these may not be unique to this model system.

We agree with the reviewer, but to address this point would require additional experiments which would go beyond the scope of the presented study.

(12) One of the issues with this assay is global chromatin recovery; it is not focused on heterochromatin compartments. The statement "We identified a total of 2396 proteins, suggesting an efficient pull-down of chromatin-associated factors (fig. S2D and Table S1)" does not demonstrate efficiency. Additional functional annotation would be required to establish this claim, including what fraction are known chromatin-associated proteins (with a focus on the heterochromatin compartment).

We have changed the text accordingly. The resulting statement reads as: “We identified a total of 2396 proteins, suggesting an effective pull-down of putative chromatin-associated factors (fig. S2D and Table S1)”.

Reviewer #3 (Public Review):

The manuscript entitled "SMARCAD1 and TOPBP1 contribute to heterochromatin maintenance at the transition from the 2C-like to the pluripotent state" by Sebastian-Perez et al. adopted the iPOTD method to compare the chromatin-bound proteome in ESCs and 2C-like cells generated by Dux overexpression. The authors identified 397 chromatin-bound proteins enriched only in ESC and 2C- cells, among which they further investigated TOPBP1 due to its potential role in controlling chromocenter reorganization. SMARCD1, a known interacting protein of TOPBP1, was also investigated in parallel. The authors observed increased size and decreased number of H3K9me3-heterochromatin foci in Dux-induced 2C⁺ cells. Interestingly, depletion of TOPBP1 or SMARCD1 also led to increased size and decreased number of H3K9me3 foci. However, depletion of these proteins did not affect entry into or exit from the 2C-like state. Nevertheless, the authors showed that both TOPBP1 and SMARCD1 are required for early embryonic development.

Although this manuscript provides new insights into the features of 2C-like cells regarding H3K9me3-heterochromatin reorganization, it remains largely descriptive at this stage. It does not provide new insights into the following important aspects: 1) how SMARCD1 associates with H3K9me3 and contributes to heterochromatin maintenance, 2) how TOPBP1 regulates the expression of SMARCD1 and facilitates its localization in heterochromatin foci, 3) whether the remodelling of chromocenter is causally related to the mutual transitions between ESCs and 2C-like cells. Furthermore, some results are over-interpreted. Additional experiments and analyses are needed to increase the strength of mechanistic insights and to support all claims in the manuscript.

We would like to thank the reviewer for their positive and thorough evaluation of our manuscript. We have revised the text and hope that the overall flow is now clearer. Moreover, while we acknowledge the value of including mechanistic studies, such an addition would require a substantial amount of experimental work that exceeds our current resources. 

Reviewer #3 (Recommendations For The Authors):

Major points:

(1) Fig.2: the DNA decompaction of the chromatin fibers shown in 2C⁺ cells may be more related to a relaxed 3D chromatin conformation (Zhu, NAR 2021; Olbrich, Nat Commun 2021) than chromatin accessibility. The authors should discuss this point.

As suggested by the reviewer, we have included some additional sentences and references in the revised manuscript to address this concern.

(2) Chemical inhibition of topoisomerases resulted in an increase in the percentage of 2C⁺ cells. Does depletion of TOPBP1 also resulted in increased percentage of 2C⁺ cells? Please include this result in Fig. 3E. Additionally, it should be noted that DDR and p53 have been reported to activate Dux (Stashpaz, eLife 2020; Grow, Nat Genet 2021), and thus, may contribute to the increased percentage of 2C⁺ cells observed upon topoisomerase inhibition. This point should be discussed in the manuscript.

To address this concern, we have included some additional sentences and references in the revised manuscript.

(3) Fig 3A: the TOPBP1 band in the IP sample is questionable, and therefore the conclusion that TOPBP1 is associated with H3K9me3 is difficult to draw from Fig 3A. Additionally, the authors mentioned that association of TOPBP1 and SMARCAD1 is undetected in ESCs, likely due to the suboptimal efficiency of available antibodies. As these are key conclusions in this study, the authors are suggested to try other commercially available TOPBP1 antibodies (e.g., Abcam #ab-105109, used by ElInati, PNAS 2017) or knock-in tags to perform the co-IP experiment.

Following the reviewer’s suggestion and to improve the reading flow, we have restructured the order of figures and removed the original Figure 3A. The revised Figure 3A-C panel illustrates the SMARCAD1 association with H3K9me3 in ESCs and 2C- cells, while capturing the reduced SMARCAD1-H3K9me3 association in 2C⁺ cells.

(4) Fig. 3C-D, Fig. S3D: the authors claimed reduction of both SMARCAD1 expression and its co-localization with H3K9me3 foci in 2C⁺ cells, but did not perform mechanistic studies. It is important to know if TOPBP1 expression also decreases in 2C⁺ cells. Additionally, it is unclear if the reduced co-localization of SMARCAD1 with H3K9me3 foci results from its altered nuclear localization or simply from reduced expression level? In either case, please provide some mechanistic insights.

While we acknowledge the value of including mechanistic studies, such an addition would require a substantial amount of experimental work that exceeds our current resources. 

(5) Fig. 3K, Fig. S4D-E: does SMARCAD1 expression decrease upon TOPBP1 depletion? Statistical analysis of SMARCAD1 intensity in Fig. S4E is needed, and a Western blot analysis is strongly suggested. Additionally, it is unclear if the reduced co-localization of SMARCAD1 with H3K9me3 foci results from its altered nuclear localization or simply from reduced expression level? In Fig. 3K, TOPBP1-depleted cells appear to show decreased size and increased number of H3K9me3 foci, which is inconsistent with Fig. S4B-C. The authors should clarify this discrepancy. Furthermore, statistics should be performed to determine whether Smarcad1/Topbp1 knockdown could further increase the size and decrease the number of H3K9me3 foci in 2C⁺ cells. This would provide additional evidence for the involvement of these proteins in heterochromatin maintenance.

We did not observe Smarcad1 downregulation after Topbp1 knockdown (shown in fig. S4A). In Figs. S4B and S4C, we observed that the number of H3K9me3 foci decreased, and their area became larger after knocking down either Smarcad1 or Topbp1, compared to scramble controls. These results align with the reviewer’s comment. Additionally, it should be noted that these findings were derived from the quantification of tens of cells and hundreds of foci, as indicated in the figure legend. This resulted in statistical significance after applying the test indicated in the figure legend.

(6) Fig. 3J is suggested to be moved to Fig. 4. Additionally, performing immunostaining of SMARCAD1, TOPBP1, and H3K9me3 during pre-implantation development would provide valuable information on their protein-level dynamics, interactions, and functions in early embryos. This would further strengthen the conclusions drawn in the manuscript.

We agree that performing these additional experiments would provide additional valuable information, however this would require a substantial amount of experimental work that exceeds our current resources.

(7) Fig. 4 and Fig. S5: the authors observed reduced H3K9me3 signal in the Smarcad1 MO embryos at the 8-cell stage, but claim that they failed to examine Topbp1 MO embryos at the 8-cell stage due to their developmental arrest at the 4-cell stage. However, based on Fig. 4A, not all Topbp1 MO embryos were arrested at the 4-cell stage, and it is still possible to examine the H3K9me3 signal in 8-cell Topbp1 MO embryos, which is critical for demonstrating its function in early embryos. Also, how to interpret the increased HP1b signal in Topbp1 MO embryos?

For Topbp1 silencing, we observed an even more severe phenotype compared to Smarcad1 MO. All the Topbp1 MO-injected embryos (100 %) arrested at the 4-cell stage and did not develop further (shown in Fig. 4A and 4B). Therefore, the severity of the Topbp1 morpholino phenotype posed a technical challenge in evaluating the H3K9me3 signal in 8-cell Topbp1 MO embryos, as none of the injected embryos developed beyond the 4-cell stage.

We believe the increased HP1b signal in Topbp1 MO embryos could indicate potential alterations in chromatin organization and heterochromatin stability. Specifically, we observed remodeling of heterochromatin in both 2-cell and 4-cell Topbp1 MO arrested embryos compared to controls, as evidenced by the spreading and increased HP1b signal (shown in fig. S5F-S5I). Further investigations could enhance our understanding of the underlying defects in Topbp1 knockdown embryos, extending beyond heterochromatin-related errors.

Minor points:

(1) Page 4, the third row from the bottom: please revise the sentence.

We have reviewed the text and it now reads correctly in the revised manuscript.

(2) Fig. 1C: The authors claimed "Luc replicates clustered separately from 2C⁺ and 2C- conditions", however, Luc rep3 is apparently clustered with 2C conditions.

(3) The GFP signal in Fig. S1E is confusing.

(4) Please include ESC in Fig. 2D-E. Also label the colors in Fig. 2E.

As indicated in the figure legend of the revised Fig. 1F: “Cells with a GFP intensity score > 0.2 are colored in green. Black dots indicate 2C- cells and green dots indicate 2C⁺ cells.”

(5) Fig. 2G: Transposition of the heatmap (show genes in rows) is suggested to improve readability.

(6) Page 7, the third row from the bottom: incorrect citation of Fig. 1K.

Thank you for spotting this incorrect citation. We have corrected it in the revised manuscript.

(7) Page 8, row 15, Fig. S3D should be cited to support the decreased expression of SMARCAD1 in 2C⁺ cells.

We have cited the corresponding supplementary figure S3D in the mentioned sentence.

(8) Fig. 2H: what is the difference between "2C-" and "ESC-like"?

We named 2C- to those cells not expressing the GFP reporter in the transition from ESCs to 2C⁺ cells. We named ESC-like cells to those cells that do not express the GFP reporter during exit, meaning from sorted and purified 2C⁺ to a GFP negative state.

(9) Fig. S4A-C: compared with shTopbp1#2, shTopbp1#1 appears to be slightly more effective in knockdown, but less dramatic changes in the size/number of H3K9me3 foci.

(10) Fig. 4: please show the effectiveness of Topbp1 MO by Immunostaining of TOPBP1.

(11) Fig. 4C: please label the developmental stage as in Fig. 4E and 4G.

We have added a “8-cell” label in the Figure 4C, as suggested by the reviewer.

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this study, Zhao and colleagues investigate inflammasome activation by E. tarda infections. They show that E. tarda induces the activation of the NLRC4 inflammasome as well as the non-canonical pathway in human THP1 macrophages. Further dissecting NLRC4 activation, they find that T3SS translocon components eseB, eseC and eseD are necessary for NLRC4 activation and that delivery of purified eseB is sufficient to trigger NAIP-dependent NLRC4 activation. Sequence analysis reveals that eseB shares homology within the C-terminus with T3SS needle and rod proteins, leading the authors to test if this region is necessary for inflammasome activation. They show that the eseB CT is required and that it mediates interaction with NAIP. Finally, they that homologs of eseB in other bacteria also share the same sequence and that they can activate NLRC4 in a HEK293T cell overexpression system.

Strengths:

This is a very nice study that convincingly shows that eseB and its homologs can be recognized by the human NAIP/NLRC4 inflammasome. The experiments are well designed, controlled and described, and the papers is convincing as a whole.

Weaknesses:

The authors need to discuss their study in the context of previous papers that have shown an important role for E. tarda flagellin in inflammasome activation and test whether flagellin and/or E. tarda T3SSs needle or rod can activate NLRC4.

The authors show that eseB and its homologs can activate NLRC4, but there are also other translocon proteins that are very different such as YopB or PopB. and share little homology with eseB. It would be nice to include a section comparing the different type 3 secretion systems. are there 2 different families of T3SSs, those that feature translocon components that are recognized by NAIP-NLRC4 and those that cannot be recognized?

(1) The authors need to discuss their study in the context of previous papers that have shown an important role for E. tarda flagellin in inflammasome activation and test whether flagellin and/or E. tarda T3SSs needle or rod can activate NLRC4.

According to the reviewer’s suggestion, we added the relevant discussion (lines 326-334) and carried out additional experiments to examine whether E. tarda flagellin, needle, and rod could activate NLRC4. The relevant results are shown in Figure S3, Figure S5, and lines 226-230 and 269-274.

(2) The authors show that eseB and its homologs can activate NLRC4, but there are also other translocon proteins that are very different such as YopB or PopB. and share little homology with eseB. It would be nice to include a section comparing the different type 3 secretion systems. are there 2 different families of T3SSs, those that feature translocon components that are recognized by NAIP-NLRC4 and those that cannot be recognized?

According to the reviewer’s suggestion, additional experiments were performed to examine the NLRC4-activating potentials of 14 translocator proteins that share low sequence identities with EseB. The relevant results and discussion are shown in Figure S8 and lines 289-301; 364-372, and 377-379.

Reviewer #2 (Public Review):

Summary:

This work by Zhao et al. demonstrates the role of the Edwardsiella tarda type 3 secretion system translocon in activating human macrophage inflammation and pyroptosis. The authors show the requirement of both the bacterial translocon proteins and particular host inflammasome components for E. tarda-induced pyroptosis. In addition, the authors show that the C-terminal region of the translocon protein, EseB, is both necessary and sufficient to induce pyroptosis when present in the cytoplasm. The most terminal region of EseB was determined to be highly conserved among other T3SS-encoding pathogenic bacteria and a subset of these exhibited functionally similar effects on inflammasome activation. Overall, the data support the conclusions and interpretations and provide interesting insights into interactions between bacterial T3SS components and the host immune system.

Strengths:

The authors use established and reliable molecular biology and bacterial genetics strategies to characterize the roles of the bacterial T3SS translocon and host inflammasome pathways to E. tarda-induced pyroptosis in human macrophages. These observations are naturally expanded upon by demonstrating the specific regions of EseB that are required for inflammasome activation and the conservation of this sequence among other pathogenic bacteria.

Weaknesses:

The functional assessment of EseB homologues is limited to inflammasome activation at the protein level but does not include the effects on cell viability as shown for E. tarda EseB. Confirmation that EseB homologues have similar effects on cell death would strengthen this portion of the manuscript.

According to the reviewer’s suggestion, the effects of representative EseB homologs on cell death were examined in the revised manuscripts (Figure 5D, Figure S7 and line 289).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I only have a few suggestions on how to improve the study:

Activation of caspase-4 requires entry into the host cytosol. Can this be observed with E. tarda and is it T3SS dependent? The fact that deleting the translocon components abrogates all GSDMD activation (see Fig. 2D) suggests that also Casp4 activation requires an active T3SS. It would be useful for the reader to include some more information on the cellular biology of E. tarda.

In our study, we found that E. tarda could enter THP-1 cells (Figure S1), and host cell entry was not affected by deletion of eseB-D (Δ_eseB-D_) in the T3SS system (Figure 2B, C). Additional experiments showed that Δ_eseB-D_ abolished the ability of E. tarda to activate Casp4 (Figure S2), implying that Casp4 activation required an active T3SS. Relevant changes in the revised manuscript: lines 223 and 224, 341-342.

The data presented by the authors suggest that escB is sensed by NLRC4 when overexpressed, they do however not prove that during an infection escB is the main factor that drives NLRC4 activation, since deficiency in escB also abrogated translocation of other potential activators of NLRC4, e.g. flagellin and T3SS needle and rod subunits. I would thus find it essential to properly test if E. tarda flagellin can activate NLRC4 by comparing a WT and flagellin deficient strain, and/or by transfecting or expressing E.t. flagellin in these cells, as well as testing whether E.t. rod and needle subunits act as NLRC4 activators. This is important as previous studies suggested that flagellin is the main activator of cytotoxicity during E. tarda infection.

Previous studies have shown that flagellin is required for E. tarda-induced macrophage death in fish [1] but not in mice [2]. In the revised manuscript, we performed additional experiments to examine whether E. tarda flagellin, needle, and rod could activate NLRC4. The relevant results are shown in Figure S3, Figure S5, and lines 226-230 and 269-274, and 326-334.

References

(1) Xie HX, Lu JF, Rolhion N, Holden DW, Nie P, Zhou Y, et al. Edwardsiella tarda-induced cytotoxicity depends on its type III secretion system and flagellin. Infect Immun. 2014;82(8):3436-45. doi: 10.1128/IAI.01065-13.

(2) Chen H, Yang D, Han F, Tan J, Zhang L, Xiao J, et al. The bacterial T6SS effector EvpP prevents NLRP3 inflammasome activation by inhibiting the Ca²⁺-dependent MAPK-JNK pathway. Cell Host Microbe. 2017;21(1):47-58. doi: 10.1016/j.chom.2016.12.004.

Figure 5/S4, please list the names of the eseB homologs. It is cumbersome to have to access GenBank with the accession number to be able to understand what proteins the authors define as homologs of eseB.

The names were added to the revised Table S2, Figure 5 and Figure S6 (the original Figure S4).

The authors mention that other translocon proteins, such as YopB/D and PopB/D, were suggested to cause inflammasome activation. How do these compare to eseB and its homologs? Do they share the CT motif?

Additional experiments were performed to compare the inflammasome activation abilities of EseB and other translocator proteins including YopD and PopD. The relevant results and discussion are shown in Figure S8 and lines 289-301, 364-372, and 377-379.

It would be nice to show that there are potentially two groups of translocon proteins, one group sharing homology to needle subunits within the CT region and another that is different. A quick look at the sequence of these proteins suggests that they are quite different and much larger than eseB.

In our study, additional experiments with more translocator proteins indicated that the possession of EseB T6R-like terminal residues does not necessarily guarantee the protein to activate the NLRC4 inflammasome. Relevant results and discussion are shown in lines 289-301, 364-372, and 377-379.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, Satouh et al. report giant organelle complexes in oocytes and early embryos. Although these structures have often been observed in oocytes and early embryos, their exact nature has not been characterized. The authors named these structures "endosomal-lysosomal organelles form assembly structures (ELYSAs)". ELYSAs contain organelles such as endosomes, lysosomes, and probably autophagic structures. ELYSAs are initially formed in the perinuclear region and then migrate to the periphery in an actin-dependent manner. When ELYSAs are disassembled after the 2-cell stage, the V-ATPase V1 subunit is recruited to make lysosomes more acidic and active. The ELYSAs are most likely the same as the "endolysosomal vesicular assemblies (ELVAs)", reported by Elvan Böke's group earlier this year (Zaffagnini et al. doi.org/10.1016/j.cell.2024.01.031). However, it is clear that Satouh et al. identified and characterized these structures independently. These two studies could be complementary. Although the nature of the present study is generally descriptive, this paper provides valuable information about these giant structures. The data are mostly convincing, and only some minor modifications are needed for clarification and further explanation to fully understand the results.

Reviewer #2 (Public Review):

Satouh et al report the presence of spherical structures composed of endosomes, lysosomes, and autophagosomes within immature mouse oocytes. These endolysosomal compartments have been named as Endosomal-LYSosomal organellar Assembly (ELYSA). ELYSAs increase in size as the oocytes undergo maturation. ELYSAs are distributed throughout the oocyte cytoplasm of GV stage immature oocytes but these structures become mostly cortical in the mature oocytes. Interestingly, they tend to avoid the region which contains metaphase II spindle and chromosomes. They show that the endolysosomal compartments in oocytes are less acidic and therefore non-degradative but their pH decreases and becomes degradative as the ELYSAs begin to disassemble in the embryos post-fertilization. This manuscript shows that lysosomal switching does not happen during oocyte development, and the formation of ELYSAs prevents lysosomes from being activated. Structures similar to these ELYSAs have been previously described in mouse oocytes (Zaffagnini et al, 2024) and these vesicular assemblies are important for sequestering protein aggregates in the oocytes but facilitate proteolysis after fertilization. The current manuscript, however, provides further details of endolysosomal disassembly post-fertilization. Specifically, the V1-subunit of V-ATPase targeting the ELYSAs increases the acidity of lysosomal compartments in the embryos. This is a well-conducted study and their model is supported by experimental evidence and data analyses.

Reviewer #3 (Public Review):

Fertilization converts a cell defined as an egg to a cell defined as an embryo. An essential component of this switch in cell fate is the degradation (autophagy) of cellular elements that serve a function in the development of the egg but could impede the development of the embryo. Here, the authors have focused on the behavior during the egg-to-embryo transition of endosomes and lysosomes, which are cytoplasmic structures that mediate autophagy. By carefully mapping and tracking the intracellular location of well-established marker proteins, the authors show that in oocytes endosomes and lysosomes aggregate into giant structures that they term Endosomal LYSosomal organellar Assembl[ies] (ELYSA). Both the size distribution of the ELYSAs and their position within the cell change during oocyte meiotic maturation and after fertilization. Notably, during maturation, there is a net actin-dependent movement towards the periphery of the oocyte. By the late 2-cell stage, the ELYSAs are beginning to disintegrate. At this stage, the endo-lysosomes become acidified, likely reflecting the activation of their function to degrade cellular components.

This is a carefully performed and quantified study. The fluorescent images obtained using well-known markers, using both antibodies and tagged proteins, support the interpretations, and the quantification method is sophisticated and clearly explained. Notably, this type of quantification of confocal z-stack images is rarely performed and so represents a real strength of the study. It provides sound support for the conclusions regarding changes in the size and position of the ELYSAs. Another strength is the use of multiple markers, including those that indicate the activity state of the endo-lysosomes. Altogether, the manuscript provides convincing evidence for the existence of ELYSAs and also for regulated changes in their location and properties during oocyte maturation and the first few embryonic cell cycles following fertilization.

At present, precisely how the changes in the location and properties of the ELYSAs affect the function of the endo-lysosomal system is not known. While the authors' proposal that they are stored in an inactive state is plausible, it remains speculative. Nonetheless, this study lays the foundation for future work to address this question.

Minor point: l. 299. If I am not mistaken, there is a typo. It should read that the inhibitors of actin polymerization prevent redistribution from the cytoplasm to the cortex during maturation.

Minor point: A few statements in the Introduction would benefit from clarification. These are noted in the comments to the authors.

We sincerely appreciate the editorial board of eLife and the reviewers for their helpful and constructive comments on our manuscript. We are pleased that the reviewers acknowledged that we identified and characterized this assembly structure independently. In the revised manuscript, we have carefully considered the reviewers’ comments and conducted additional analysis to address each of them.

Regarding the typographical errors, we revised the description to fit with our findings and the reviewers’ comments. We also found that the primer sequence was correct, and we carefully checked the accuracy of the entire manuscript.

We hope that the revised version will now be deemed suitable for publication in eLife.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Q. 1) The authors state in the Abstract that ELYSAs contain autophagosome-like membranes in the outer layer. However, this seems to be just speculation based on the LC3 staining results and is not directly shown. Are there autophagosome-like double membrane structures in ELYSAs?

We appreciate this comment. We also agree with this concern; however, it was difficult to assert that they are autophagosomes based on the observation of the electron micrographs. For this reason, we rephrased it to be "Most ELYSAs are also positive for an autophagy regulator, LC3.” (lines 33). In addition, we revised the notation to LC3-positive structures in the Result and Discussion section (line 165-169, 286).

Q. 2) The data in Figure 2A, showing a decrease in the number of LAMP1 structures, seems to contradict the data in Figure 1B, showing an apparent increase in LAMP1 structures. Please explain this discrepancy. If the authors did not count structures just below the plasma membrane, please explain the rationale for this.

We really appreciate the valuable comment. Regarding the number of LAMP1-positive structures, it is not suitable for comparison with Figure 1B, etc., as pointed out by the reviewer, since the distribution of the LAMP1 signal differs from plane to plane. To avoid any potential confusion, we added new images of the Z-projection of the immunostained images that can better reflect the number of positive structures in the whole oocyte/embryo in Figure 2.

In addition, as the reviewer pointed out, there is a technical difficulty in measuring the LAMP1-positive signal on the plasma membrane or just below it. We explained how and why we had to delete plasma membrane signals in our response #21.

Q. 3) The actin dependence is not observed in Figure 5C. What is the difference between Figure 5C and 5E? Please explain further.

We apologize for the lack of clarity; Figures 5C and 5E show the average number of LAMP1-positive structures (5C) and the percentage of the sum of granule volumes in LAMP1 positive structure (5E), respectively, after classifying the LAMP1 positive granules by their diameters.

We removed Figure 5E for the sake of conciseness since we already mentioned a similar fact in Figure 5C. To clarify the corresponding explanations, we moved figures that were not classified by diameter to Supplementary Figure 8 to improve readability. Moreover, we have rewritten the main text on lines 200–211.

Q. 4) While the actin inhibitors reduce the number of peripheral LAMP1 structures (Figure 5F), they do not affect their number in the central region (Figure 5G). How can the authors conclude that actin inhibitors inhibit the migration of LAMP1 structures?

We appreciate the comment. As pointed out, the number of large LAMP1-positive structures in the medial region did not change. Therefore, we have avoided the description that ELYSAs migrate from the middle region to the cell periphery and have unified the description of whether large structures in the periphery occur. Please refer to the subsection title (line 188), the following descriptions (lines 189–199), the related description in the Results (lines 200–211), and the title and the legend of Figure 5.

Q. 5) The authors show that the V1A subunit associates with the surface of LAMP1 structures as punctate structures (Figure 6B). What are these V1A-positive structures? Is V1A recruited to some specific domains of ELYSAs, or are V1A-positive active lysosomes recruited to ELYSAs? Please provide an interpretation of these data. The phrase "The V1-subunit of V-ATPase is targeted to these structures" (line 262) is not appropriate because it is indistinguishable whether only the V1 subunits are recruited or active lysosomes containing the V1 subunit are recruited.

Thank you for the valuable comment. Indeed, our analysis, including the analysis of Fig. 8 described on line 262, did not clarify whether free V1A-mCherry molecules accessed the ELYSA periphery or whether lysosomes with V1A-mCherry molecules newly merged into the ELYSA. Therefore, we added this interpretation to lines 232–234 of the Results and revised the Discussion as "The number of membrane structures positive for V1A-mCherry increase upon ELYSA disassembly, indicating further acidification of the endosomal/lysosomal compartment" (lines 292–294).

Q. 6) Why did the authors use LysoSensor as a marker for ELYSA instead of LAMP1 in Figure 8 and 9? Some reasons should be given.

There is a clear technical reason for this: when LAMP1-EGFP was expressed in a zygote, it was largely migrated to the plasma membrane before and after the 2-cell stage, making it difficult to capture the change of ELYSAs. To circumvent this difficulty, we used Lysosensor to visualize ELYSAs instead of LAMP1-EGFP. This explanation was added to lines 258–260.

Q. 7) In Figure 9A, it is not clear whether the activity of LysoSensor-positive structures is lower at this stage compared to other stages. It may be shown in Figure S7, but the data are not clearly visible. A direct comparison would be ideal.

A new analysis similar to that shown in Fig. 9 for early 2-cells and 4-cells was performed and added to Figure S7. To support direct comparison, the ranges of axes were set to be similar.

As a result, the quantified MagicRed signal on the isolated LysoSensor-positive punctate structure in MII oocyte was nearly the same as that in early 2-cells and 4-cells. In early 2-cells, LysoSensor gave a signal at the cellular boundary, where MagicRed staining was not observed, confirming that MagicRed activity is higher in the interior than in the cell periphery in post-fertilization embryos. We have included an additional description in the main text (lines 280–282).

Q. 8) In the phrase "pregnant mare serum gonadotropin or an anti-inhibin antibody" (line 382), is "or" correct?

When inducing superovulatory stimulation, an anti-inhibin antibody (distributed as CARD HyperOva) can be used as a substitute for PMSG (after additional stimulation with hCG), which results in the production of eggs of similar quality to those of PMSG. This was used in most experiments. To amend the lack of clarity, a reference (Takeo and Nakagata Plos One, 2015) was added to the description of HyperOva (line 417).

Q. 9) In almost all graphs, please indicate what the X-axis is indicating (not just "number") so that readers can understand what number is being represented without reading the legends.

We revised the axis titles in all figures.

Q. 10) Since grayscale images provide better contrast than color images, it is recommended that single-color images be shown in grayscale.

We replaced all single-color images with grayscale images.

Reviewer #2 (Recommendations For The Authors):

Specific comments:

Q. 11) Figure 1 and S1- Both Rab5 and Rab7 co-localize with LAMP1. However, there seems to be a lot of LAMP1-free Rab5 dots as compared to the Lamp1-free Rab7. As a result, LAMP1 and Rab7 are co-localized more frequently than LAMP1 and Rab5 (video1). Could it be that early endosomes (Rab5+) are yet to be incorporated into ELYSAs? If so, a brief discussion of this phenomenon would be nice.

Thank you very much for the comment. We agree with the reviewer’s interpretation. In accordance with this suggestion, we clearly stated in the main text: “Although small punctate structures that are RAB5-positive but LAMP1-negative also spread over the cytosol, most giant structures were positive for RAB5 and LAMP1 (Video 1)” (lines 91–93). In the Discussion section, a brief statement was included: “Considering the large number of RAB5-positive and LAMP1-negative punctate structures in MII oocytes, these layers may also reflect the assembly mechanism of the ELYSA” (lines 318–320).

Q. 12) Video 3 (and Figure 6) clearly shows the dynamics of LAMP1-labelled vesicles during maturation, which is impressive. In contrast to the live cell imaging after LAMP1 mRNA injection, Figure 1 used anti-LAMP1 Ab to detect endogenous levels of LAMP1. It appears that mRNA microinjection causes LAMP1 overexpression causing more (but smaller) vesicles to form. It should be easy to quantify and compare the vesicles in Figure 1 and 6

We appreciate the comment. As mentioned, injections of EGFP-LAMP1 mRNA are useful for the visualization of LAMP1 dynamics during the maturation phase from GV to MII by live cell imaging, which is not feasible with immunostaining. However, the fluorescence emitted by EGFP-LAMP1 is only a few tenths of that of antibody staining, and because of the technical difficulty of microinjection into GV oocytes, the signal-to-noise ratio sufficient for imaging was merely one in ten oocytes. In addition, live cell imaging of oocytes in Figure 6 had to be carried out with very low excitation light exposure to reduce the toxicity. It was also performed with a low magnification lens and a longer step size in the z-axis. For these reasons, in examining the point raised, we performed an additional 3D object analysis, in the same way as in Figure 2, on the data of IVM oocytes injected with EGFP-LAMP1 mRNA using the same lens as in Figure 1 and with a longer exposure time than in live imaging. The results were compared with the MII data of Figures 1 and 2.

As a result, as shown in the new Figure S8, more objects with a diameter of 0.2–0.4 µm were found than in the immunostaining data, which fits the reviewer’s point. In addition, the counts were lower for the 0.6–1.0 µm diameter, but there was no significant difference in the number of larger LAMP1 positive structures corresponding to the ELYSA size. We consider that this was appropriate for the original purpose of characterizing the ELYSA formation process. A description of these points has been added to lines 221–225.

Q. 13) In Figure 4A and B- Seems like not all LAMP1-positive structures were LC3-positive. Is there any size or location within the oocyte that determines LC3 positivity?

We appreciate the valuable comment. To answer this comment, we proceeded with a new 3D object-based co-localization analysis on Lamp1 and LC3, determined the number, volume, and distribution within the oocyte, and incorporated the results as Supplementary Figure 6. To examine the positivity, we further analyzed the percentage of double-positive structures of all the LAMP1-positive structures. The results showed that their average diameter significantly shifted from 2.36 µm (GV) to 3.78 µm (MII). Moreover, it was clearly indicated that LAMP1-positive structures smaller than 2 µm in diameter are rarely positive for LC3. In terms of location, measuring the distance of the double positive structures from the oocyte center (the cellular geometric center) indicated that they tend to be observed at the periphery of both stages of oocytes (more than 80% in > 30 µm in the MII oocyte). Of note, no clear tendency of double positivity was observed. A description of these points has been added to lines 174–186.

Q. 14) In discussion, line 256- Small ELYSAs are formed in GV oocytes. Since you haven't checked the smaller-sized, growing oocytes, I suggest rephrasing this sentence as 'are present' rather than 'are formed'.

We agree with the reviewer’s suggestion and changed it to "present" (line 287).

Q. 15) Line 188- ELISA should instead be ELYSA

Thank you for pointing this out. We have found a few more typographical errors, and all of them have been corrected (lines 213 and 321).

Reviewer #3 (Recommendations For The Authors):

Q. 16) Line 42: What do you mean by 'zygotic gene expression following the degradation of the cellular components of each maternal and paternal gamete'? ZGA requires this degradation? Please provide supporting references from the literature.

We apologize for the confusing wording. We meant to say that both ZGA and degradation of parental components are required. To avoid misunderstanding, we have revised “zygotic gene expression as well as the degradation of the cellular components of each maternal and paternal gamete” and inserted a new reference (line 44).

Q. 17) 50: MII means metaphase II, not meiosis II.

We corrected the clerical mistake (line 50).

Q. 18) 51: Define LC3.

We added the definition of LC3 (line 51-52).

Q. 19) 60: 'lysosomal activity in oocytes is upregulated by sperm-derived factors as the oocytes grow and mature'. As written, the sentence implies that oocytes grow and mature after fertilization. This may be true for maturation, but I would be surprised to learn that there is growth of the oocyte after fertilization.

We appreciate this valuable comment.

The C. elegans lives mainly as a hermaphrodite, which contains a couple of U-shaped gonad arms including the ovary, spermatheca and uterus in the body. Oocytes grow in the ovary and maturate upon receiving major sperm proteins secreted from sperms and ovulated to the spermatheca for fertilization. In 2017, Kenyon’s group reported that major sperm proteins act as sperm-secreted hormones to upregulates the lysosomal activity in oocytes during oocyte growth and maturation. We have revised our manuscript to avoid misunderstanding, to ' lysosomal activity in oocytes is upregulated by major sperm proteins secreted from sperms as the oocytes grow and mature '. (L. 61-66).

Q. 20) 94 and Figure 1B: While it is clear that many LAMP1 foci at the late 2-cell stage do not also contain RAB5, it seems that the majority of RAB5 loci also stain for LAMP1. This may be a minor point in the context of the paper but could be clarified.

We could not easily agree with the suggestion because of the possibility that the images might give different impressions on each plane. Therefore, as a way to verify this point, we attempted to quantify the co-localization by reconstructing the 3D puncta information based on the two types of antibody staining data. Unfortunately, as shown in Fig. 1AB, Rab5 had a high cytoplasmic background, and although we were able to extract peaks, we could not reliably recalibrate the three-dimensional punctate structure (please refer to the new Supplementary Fig. 6). Therefore, co-localization on each other's punctate structure (LAMP1/RAB5 vs. RAB5/LAMP1) could not be verified. The validation using specific planes also showed large differences between planes, with overlapping punctate structures counted separately in adjacent planes, making reliable quantification difficult. This is an issue that will be addressed in the future.

On the other hand, the newly added Z-projection figure (Fig. 1AB) shows that RAB5-positive and LAMP1-negative punctate structures tend to accumulate along the LAMP1-positive punctate structures larger than 1 µm at the late 2-cell stage in all observed embryos; we added this statement on lines 99–101.

Q. 21) 100-102 and Figure 2A: Does the decrease in the total number of LAMP1 foci refer just to cytoplasmic or also to membrane foci? If the former, what was the reason for not including the membrane in the analysis?

We appreciate the critical question. The LAMP1 signal on the plasma membrane interfered with the measurement of the signals just below the plasma membrane. The biological cause of this increased signal on the plasma membrane, as shown in Fig. 2E, seemed to be caused by the migration of the LAMP1 signals post-fertilization, which was also reported in a previous paper by Zaffagnini et al. (2024), published in Cell.

In our analysis, oocytes are giant cells, and confocal imaging has a technical limitation in obtaining the same fluorescent intensity along the z-axis. However, 3D-object analysis requires thresholding based on absolute values. As a result of this situation, the presence of the plasma membrane signal caused punctate structures located close to the membrane to be captured and recognized as a single, very large LAMP1-positive structure, resulting in the loss of the punctate structure that should be measured.

To avoid this issue, we have used several programs to correct the fluorescence difference along the z-axis; nonetheless, these attempts were unsuccessful. Therefore, as described in the Materials and Methods section, we applied only background subtraction at each z-position and then manually removed the plasma membrane signal (which was thin and continuous at the edges). Furthermore, when the plasma membrane and punctate structure signals overlapped, we paid attention not to remove the signals but to separate them. Thus, we believe that the decrease in the number and volume of LAMP1-positive structures after fertilization is still a phenomenon associated with the shift of LAMP1 to the plasma membrane.

Q. 22) Figure 2B, F, G: As the x-axis does not represent a continuous variable, adjacent data points should not be connected by a line. The histogram representations in A, C, and E are much easier to understand. I suggest presenting all data in this format.

We revised the line graphs to bar graphs. Besides, to make the significance among populations clearer, the significances are now expressed using alphabetical indicators.

Q. 23) Figure 2B, C: It seems that the values for the different stages are expressed relative to the value at MII. Why not use the GV value at the base-line? This would follow the developmental trajectory of the oocyte/embryo more directly and would not (I believe) change the conclusions.

We appreciated the comment. We meant to express that ELYSA develops most in the MII phase and that it decreases after fertilization, so considering the reviewer’s suggestion, we expressed GV-MII changes based on GV and changes after fertilization based on the MII phase (Fig. 2C, D).

Reviewer #1 (Public review):

Summary:

This paper is an elegant, mostly observational work, detailing observations that polysome accumulation appears to drive nucleoid splitting and segregation. Overall I think this is an insightful work with solid observations.

Strengths:

The strengths of this paper are the careful and rigorous observational work that leads to their hypothesis. They find the accumulation of polysomes correlates with nucleoid splitting, and that the nucleoid segregation occurring right after splitting correlates with polysome segregation. These correlations are also backed up by other observations:

(1) Faster polysome accumulation and DNA segregation at faster growth rates.<br /> (2) Polysome distribution negatively correlating with DNA positioning near asymmetric nucleoids.<br /> (3) Polysomes form in regions inaccessible to similarly sized particles.

These above points are observational, I have no comments on these observations leading to their hypothesis.

Weaknesses:

It is hard to state weaknesses in any of the observational findings, and furthermore, their two tests of causality, while not being completely definitive, are likely the best one could do to examine this interesting phenomenon.

Points to consider / address:

Notably, demonstrating causality here is very difficult (given the coupling between transcription, growth, and many other processes) but an important part of the paper. They do two experiments toward demonstrating causality that help bolster - but not prove - their hypothesis. These experiments have minor caveats, my first two points.

(1) First, "Blocking transcription (with rifampicin) should instantly reduce the rate of polysome production to zero, causing an immediate arrest of nucleoid segregation". Here they show that adding rifampicin does indeed lead to polysome loss and an immediate halting of segregation - data that does fit their model. This is not definitive proof of causation, as rifampicin also (a) stops cell growth, and (b) stops the translation of secreted proteins. Neither of these two possibilities is ruled out fully.

1a) As rifampicin also stops all translation, it also stops translational insertion of membrane proteins, which in many old models has been put forward as a possible driver of nucleoid segregation, and perhaps independent of growth. This should at last be mentioned in the discussion, or if there are past experiments that rule this out it would be great to note them.

1b) They address at great length in the discussion the possibility that growth may play a role in nucleoid segregation. However, this is testable - by stopping surface growth with antibiotics. Cells should still accumulate polysomes for some time, it would be easy to see if nucleoids are still segregated, and to what extent, thereby possibly decoupling growth and polysome production. If successful, this or similar experiments would further validate their model.

(2) In the second experiment, they express excess TagBFP2 to delocalize polysomes from midcell. Here they again see the anticorrelation of the nucleoid and the polysomes, and in some cells, it appears similar to normal (polysomes separating the nucleoid) whereas in others the nucleoid has not separated. The one concern about this data - and the differences between the "separated" and "non-separated" nuclei - is that the over-expression of TagBFP2 has a huge impact on growth, which may also have an indirect effect on DNA replication and termination in some of these cells. Could the authors demonstrate these cells contain 2 fully replicated DNA molecules that are able to segregate?

(3) What is not clearly stated and is needed in this paper is to explain how polysomes do (or could) "exert force" in this system to segregate the nucleoid: what a "compaction force" is by definition, and what mechanisms causes this to arise (what causes the "force") as the "compaction force" arises from new polysomes being added into the gaps between them caused by thermal motions.

They state, "polysomes exert an effective force", and they note their model requires "steric effects (repulsion) between DNA and polysomes" for the polysomes to segregate, which makes sense. But this makes it unclear to the reader what is giving the force. As written, it is unclear if (a) these repulsions alone are making the force, or (b) is it the accumulation of new polysomes in the center by adding more "repulsive" material, the force causes the nucleoids to move. If polysomes are concentrated more between nucleoids, and the polysome concentration does not increase, the DNA will not be driven apart (as in the first case) However, in the second case (which seems to be their model), the addition of new material (new polysomes) into a sterically crowded space is not exerting force - it is filling in the gaps between the molecules in that region, space that needs to arise somehow (like via Brownian motion). In other words, if the polysome region is crowded with polysomes, space must be made between these polysomes for new polysomes to be inserted, and this space must be made by thermal (or ATP-driven) fluctuations of the molecules. Thus, if polysome accumulation drives the DNA segregation, it is not "exerting force", but rather the addition of new polysomes is iteratively rectifying gaps being made by Brownian motion.

The authors use polysome accumulation and phase separation to describe what is driving nucleoid segregation. Both terms are accurate, but it might help the less physically inclined reader to have one term, or have what each of these means explicitly defined at the start. I say this most especially in terms of "phase separation", as the currently huge momentum toward liquid-liquid interactions in biology causes the phrase "phase separation" to often evoke a number of wider (and less defined) phenomena and ideas that may not apply here. Thus, a simple clear definition at the start might help some readers.

(4) Line 478. "Altogether, these results support the notion that ectopic polysome accumulation drives nucleoid dynamics". Is this right? Should it not read "results support the notion that ectopic polysome accumulation inhibits/redirects nucleoid dynamics"?

(5) It would be helpful to clarify what happens as the RplA-GFP signal decreases at midcell in Figure 1- is the signal then increasing in the less "dense" parts of the cell? That is, (a) are the polysomes at midcell redistributing throughout the cell? (b) is the total concentration of polysomes in the entire cell increasing over time?

(6) Line 154. "Cell constriction contributed to the apparent depletion of ribosomal signal from the mid-cell region at the end of the cell division cycle (Figure 1B-C and Movie S1)" - It would be helpful if when cell constriction began and ended was indicated in Figures 1B and C.

(7) In Figure 7 they demonstrate that radial confinement is needed for longitudinal nucleoid segregation. It should be noted (and cited) that past experiments of Bacillus l-forms in microfluidic channels showed a clear requirement role for rod shape (and a given width) in the positing and the spacing of the nucleoids.<br /> Wu et al, Nature Communications, 2020 . "Geometric principles underlying the proliferation of a model cell system" https://dx.doi.org/10.1038/s41467-020-17988-7

(8) "The correlated variability in polysome and nucleoid patterning across cells suggests that the size of the polysome-depleted spaces helps determine where the chromosomal DNA is most concentrated along the cell length. This patterning is likely reinforced through the displacement of the polysomes away from the DNA dense region"

It should be noted this likely functions not just in one direction (polysomes dictating DNA location), but also in the reverse - as the footprint of compacted DNA should also exclude (and thus affect) the location of polysomes

(9) Line 159. Rifampicin is a transcription inhibitor that causes polysome depletion over time. This indicates that all ribosomal enrichments consist of polysomes and therefore will be referred to as polysome accumulations hereafter". Here and throughout this paper they use the term polysome, but cells also have monosomes (and 2 somes, etc). Rifampicin stops the assembly of all of these, and thus the loss of localization could occur from both. Thus, is it accurate to state that all transcription events occur in polysomes? Or are they grouping all of the n-somes into one group?

Reviewer #3 (Public review):

Summary:

Papagiannakis et al. present a detailed study exploring the relationship between DNA/polysome phase separation and nucleoid segregation in Escherichia coli. Using a combination of experiments and modelling, the authors aim to link physical principles with biological processes to better understand nucleoid organisation and segregation during cell growth.

Strengths:

The authors have conducted a large number of experiments under different growth conditions and physiological perturbations (using antibiotics) to analyse the biophysical factors underlying the spatial organisation of nucleoids within growing E. coli cells. A simple model of ribosome-nucleoid segregation has been developed to explain the observations.

Weaknesses:

While the study addresses an important topic, several aspects of the modelling, assumptions, and claims warrant further consideration.

Major Concerns:

Oversimplification of Modelling Assumptions:

The model simplifies nucleoid organisation by focusing on the axial (long-axis) dimension of the cell while neglecting the radial dimension (cell width). While this approach simplifies the model, it fails to explain key experimental observations, such as:

(1) Inconsistencies with Experimental Evidence:

The simplified model presented in this study predicts that translation-inhibiting drugs like chloramphenicol would maintain separated nucleoids due to increased polysome fractions. However, experimental evidence shows the opposite-separated nucleoids condense into a single lobe post-treatment (Bakshi et al 2014), indicating limitations in the model's assumptions/predictions. For the nucleoids to coalesce into a single lobe, polysomes must cross the nucleoid zones via the radial shells around the nucleoid lobes.

(2) The peripheral localisation of nucleoids observed after A22 treatment in this study and others (e.g., Japaridze et al., 2020; Wu et al., 2019), which conflicts with the model's assumptions and predictions. The assumption of radial confinement would predict nucleoids to fill up the volume or ribosomes to go near the cell wall, not the nucleoid, as seen in the data.

(3) The radial compaction of the nucleoid upon rifampicin or chloramphenicol treatment, as reported by Bakshi et al. (2014) and Spahn et al. (2023), also contradicts the model's predictions. This is not expected if the nucleoid is already radially confined.

(4) Radial Distribution of Nucleoid and Ribosomal Shell:

The study does not account for well-documented features such as the membrane attachment of chromosomes and the ribosomal shell surrounding the nucleoid, observed in super-resolution studies (Bakshi et al., 2012; Sanamrad et al., 2014). These features are critical for understanding nucleoid dynamics, particularly under conditions of transcription-translation coupling or drug-induced detachment. Work by Yongren et al. (2014) has also shown that the radial organisation of the nucleoid is highly sensitive to growth and the multifork nature of DNA replication in bacteria.

The omission of organisation in the radial dimension and the entropic effects it entails, such as ribosome localisation near the membrane and nucleoid centralisation in expanded cells, undermines the model's explanatory power and predictive ability. Some observations have been previously explained by the membrane attachment of nucleoids (a hypothesis proposed by Rabinovitch et al., 2003, and supported by experiments from Bakshi et al., 2014, and recent super-resolution measurements by Spahn et al.).

Ignoring the radial dimension and membrane attachment of nucleoid (which might coordinate cell growth with nucleoid expansion and segregation) presents a simplistic but potentially misleading picture of the underlying factors.

This reviewer suggests that the authors consider an alternative mechanism, supported by strong experimental evidence, as a potential explanation for the observed phenomena:<br /> Nucleoids may transiently attach to the cell membrane, possibly through transertion, allowing for coordinated increases in nucleoid volume and length alongside cell growth and DNA replication. Polysomes likely occupy cellular spaces devoid of the nucleoid, contributing to nucleoid compaction due to mutual exclusion effects. After the nucleoids separate following ter separation, axial expansion of the cell membrane could lead to their spatial separation.

Incorporating this perspective into the discussion or future iterations of the model may provide a more comprehensive framework that aligns with the experimental observations in this study and previous work.

Simplification of Ribosome States:<br /> Combining monomeric and translating ribosomes into a single 'polysome' category may overlook spatial variations in these states, particularly during ribosome accumulation at the mid-cell. Without validating uniform mRNA distribution or conducting experimental controls such as FRAP or single-molecule measurements to estimate the proportions of ribosome states based on diffusion, this assumption remains speculative.

Reviewer #1 (Public review):

Summary:

In this study, Wang and colleagues generate single-cell transcriptome and chromatin accessibility data from testicular tissues of two OA and three NOA cases. The authors analyze this dataset to identify novel cellular populations, marker genes, and inter-population interactions that may contribute to proper spermatogenesis. Then they propose a role of specific Sertoli cell subtypes and their interactions via Notch signaling in germ cell development. However, I remain skeptical of their central argument (also highlighted in the title) that stage-specific interactions between Sertoli and germ cells are a key component in NOA development, as my initial concerns regarding potential data misrepresentation, lack of statistical testing, and the rationale behind some of the analyses have not been sufficiently addressed.

(1) As noted in my previous comments, the analysis of Sertoli cell subtypes is potentially misleading and lacks proper statistical support. The authors claim a significant loss of Sertoli subpopulations in NOA cases, and provide the absolute number of cells in Figure 6B. However, this observation could easily be driven by the total number of cells captured during the experiment and the anatomical location of the specimens. There is no statistical basis to make the claim that this loss is significant. Furthermore, the same analysis should be performed on scATAC-seq cells and presented alongside.

(2) As pointed out in my initial concerns, some parts of the analyses require additional explanation to clarify their logical flow. For example, the logic of using between-sample correlations to assess colocalization of Sertoli and germ cells is lost on me. How can this be used to infer the important role of specific Sertoli cell populations in spermatogenesis, other than the fact that some of the genes are more co-expressed in the sub-populations? And how is this related to the claim that these cell populations are actually co-localized in the tissue? The authors then dedicate nearly a page describing the pathways enriched in Sertoli and germ cells, but the relevance is unclear, and the argument that these subtypes are functionally related is not convincing enough.

(3) The statement regarding Notch signaling as a critical component in Sertoli and germ cell interaction is not supported by actual evidence. The inference based on CellphoneDB and an epigenome snapshot that shows not much difference are insufficient to justify this claim.

(4) The manuscript is overly wordy and descriptive, making it difficult to read and understand the points. The main text needs to be more concise and on point, with unnecessary details removed to sharpen the key points. Non-essential results (e.g. Figure S10 and S11) unrelated to the main argument should be removed.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript is dedicated heavily to cell type mapping and identification of sub-type markers in the human testis but does not present enough results from cross-investigation between NOA cases versus control. Their findings are mostly based on transcriptome and the authors do not make enough use of the scATAC-seq data in their analyses as they put forward in the title. Overall, the authors should do more to include the differential profile of NOA cases at the molecular level - specific gene expression, chromatin accessibility, TF binding, pathway, and signaling that are perturbed in NOA patients that may be associated with azoospermia.

Strengths:

(1) The establishment of single-cell data (both RNA and ATAC) from the human testicular tissues is noteworthy.

(2) The manuscript includes extensive mapping of sub-cell populations with some claimed as novel, and reports marker gene expression.

(3) The authors present inter-cellular cross-talks in human testicular tissues that may be important in adequate sperm cell differentiation.

Weaknesses:

(1) A low sample size (2 OA and 3 NOA cases). There are no control samples from healthy individuals.

Thank you for your comments. We recognize that the small sample size in this study somewhat limits its generalizability. However, in transcriptomic research, limited sample sizes are a common issue due to the complexities involved in acquiring samples, particularly in studies about the reproductive system. Healthy testicular tissue samples are difficult to obtain, and studies (doi: 10.18632/aging.203675) have used obstructive azoospermia as a control group in which spermatogenesis and development are normal.

(2) Their argument about interactions between germ and Sertoli cells is not based on statistical testing.

Thank you for your comments. Due to limited funding, we have not yet fully and deeply conducted validation experiments, but we plan to carry out related experiments in the later stage. We hope that the publication of this study will help to obtain more financial support to further investigate the interactions between germ cells and Sertoli cells.

(3) Rationale/logic of the study. This study, in its present form, seems to be more about the role of sub-Sertoli population interactions in sperm cell development and does not provide enough insights about NOA.

Thank you for your comments. In Figure 6, we conducted an in-depth analysis and comparison of the differences between the Sertoli cell subtypes and the germ cell subtypes involved in spermatogenesis in the OA and NOA groups. The results revealed that in the NOA group, especially in the NOA3 group, which has a lower sperm count compared to NOA2 and NOA1, there is a significant loss of Sertoli cell subtypes including SC3, SC4, SC5, SC6, and SC8. The NOA1 group, with a sperm count close to that of the OA group, also had a Sertoli cell profile similar to the OA group. The NOA2 group, with a sperm count between that of NOA1 and NOA3, also exhibited an intermediate profile of Sertoli cell subtypes. Therefore, we suggest that change in Sertoli cell subtypes is a key factor affecting sperm count, rather than just the total number of Sertoli cells. We believe that through these analyses, we can provide in-depth insights into NOA, and we hope that the publication of this study will help obtain more funding support to further validate and expand on these findings.

(4) The authors do not make full use of the scATAC-seq data.

Thank you for your comments.We have added analysis of the scATAC-seq data and shown in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

Shimin Wang et al. investigated the role of Sertoli cells in mediating spermatogenesis disorders in non-obstructive azoospermia (NOA) through stage-specific communications. The authors utilized scRNA-seq and scATAC-seq to analyze the molecular and epigenetic profiles of germ cells and Sertoli cells at different stages of spermatogenesis.

Strengths:

By understanding the gene expression patterns and chromatin accessibility changes in Sertoli cells, the authors sought to uncover key regulatory mechanisms underlying male infertility and identify potential targets for therapeutic interventions. They emphasized that the absence of the SC3 subtype would be a major factor contributing to NOA.

Weaknesses:

Although the authors used cutting-edge techniques to support their arguments, it is difficult to find conceptual and scientific advances compared to Zeng S et al.'s paper (Zeng S, Chen L, Liu X, Tang H, Wu H, and Liu C (2023) Single-cell multi-omics analysis reveals dysfunctional Wnt signaling of spermatogonia in non-obstructive azoospermia. Front. Endocrinol. 14:1138386.). Overall, the authors need to improve their manuscript to demonstrate the novelty of their findings in a more logical way.

Thank you for your detailed review of our work. We greatly appreciate your feedback and have made revisions to our manuscript accordingly.

Regarding the novelty of our research, we believe our study offers conceptual and scientific advances in several ways:

We have systematically revealed the stage-specific roles of Sertoli cell subtypes in different stages of spermatogenesis, particularly emphasizing the crucial role of the SC3 subtype in non-obstructive azoospermia (NOA). Additionally, we identified that other Sertoli cell subtypes (SC1, SC2, SC3...SC8, etc.) also collaborate in a stage-specific manner with different subpopulations of spermatogenic cells (SSC0, SSC1/SSC2/Diffed, Pa...SPT3). These findings provide new insights into the understanding of spermatogenesis disorders.

Compared to the study by Zeng S et al., our research not only focuses on the functional alterations in Sertoli cells but also comprehensively analyzes the interaction patterns between Sertoli cells and spermatogenic cells using scRNA-seq and scATAC-seq technologies. We uncovered several novel regulatory networks that could serve as potential targets for the diagnosis and treatment of NOA.

We sincerely appreciate your constructive comments and will continue to explore this area further, aiming to make a more significant contribution to the understanding of NOA mechanisms.

Reviewer #3 (Public Review):

Summary:

This study profiled the single-cell transcriptome of human spermatogenesis and provided many potential molecular markers for developing testicular puncture-specific marker kits for NOA patients.

Strengths:

Perform single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on testicular tissues from two OA patients and three NOA patients.

Weaknesses:

Most results are analytical and lack specific experiments to support these analytical results and hypotheses.

Thank you for your thorough review of our work. We highly value your feedback and have made revisions to our manuscript accordingly. Indeed, we have conducted immunofluorescence (IF) experiments to validate the data obtained from single-cell sequencing and have expanded the sample size to enhance the reliability of our results. To better present these validation experiments, we have reorganized and renamed the sample information, making it easier for you to understand which samples were used in the specific experiments. Following the publication of this paper, we plan to secure additional funding to deepen our research, particularly in the area of experimental validation. We sincerely appreciate your support and insightful suggestions, which have greatly helped guide our future research directions.

Reviewer #1 (Recommendations For The Authors):

(1) The authors should include results from cross-investigation comparing NOA/OA patients versus controls.

Thank you for your comments. In this study, OA was the control group. Healthy testicular tissue samples are difficult to obtain, and studies (doi: 10.18632/aging.203675) have used OA as a control group in which spermatogenesis and development are normal.

(2) In Table S1, the authors should also include the metric for scATAC-seq, and do more to show the findings the authors obtained in RNA is replicated with chromatin accessibility.

Thank you for your comments. We have added Table S2, which includes the metric for scATAC-seq.

(3) A single sample from each OA and NOA group may not be enough to confirm colocalization. The authors should include results from all available samples and use quantitative measures.

Thank you for your comments. I apologize that the sample size in this study was less than three and we could not conduct quantitative analysis. We will increase the sample size and conduct corresponding experiments in subsequent research.

(4) The Methods section does not include enough description to follow how the analyses were carried out, and is missing information on some of the key procedures such as velocity and cell cycle analyses.

Thank you for your comments. The method about velocity and cell cycle analyses was added in the revised manuscript. The description is as follows:

“Velocity analysis

RNA velocity analysis was conducted using scVelo's (version 0.2.1) generalized dynamical model. The spliced and unspliced mRNA was quantified by Velocity (version 0.17.17).”

“Cell cycle analysis

To quantify the cell cycle phases for individual cell, we employed the CellCycleScoring function from the Seurat package. This function computes cell cycle scores using established marker genes for cell cycle phases as described in a previous study by Nestorowa et al. (2016). Cells showing a strong expression of G2/M-phase or S-phase markers were designated as G2/M-phase or S-phase cells, respectively. Cells that did not exhibit significant expression of markers from either category were classified as G1-phase cells.”

(5) For the purpose of transparency, the authors should upload codes used for analyses so that each figure can be reproduced. All raw and processed data should be made publicly available.

Thank you for your comments. We have deposited scRNA-seq and scATAC-seq data in NCBI. ScRNA-seq data have been deposited in the NCBI Gene Expression Omnibus with the accession number GSE202647, and scATAC-seq data have been deposited in the NCBI database with the accession number PRJNA1177103.

Reviewer #2 (Recommendations For The Authors):

The detailed points the authors need to improve are attached below.

The results presented in the study have several weaknesses:

In Figure 1A, it's required to show HE staining results of all patients who underwent single-cell analysis were provided.

Thank you very much for your valuable suggestions. In Figure 1, we present the HE staining results paired with the single-cell data, covering all patients involved in the single-cell analysis.

- Saying "identification of novel potential molecular markers for distinct cell types" seems unsupported by the data.

Thank you for your comments. I'm sorry for the inaccuracy of my description. We have revised this sentence. The description is as follows: These findings indicate that the scRNA-seq data from this study can serve for cellular classification.

- The methods suggest an integrated analysis of scRNA-seq and scATAC-seq, but from the figures, it seems like separate analyses were performed. It's necessary to have data showing the integrated analysis.

Thank you for your comments. We have added an integrated analysis of scRNA-seq and scATAC-seq. The results were shown in Figure S2.

Figure 2 does not seem to well cover the diversity of germ cell subtypes. The main content appears to be about the differentiation process, and it seems more focused on SSCs (stem cell types), but the intended message is not clearly conveyed.

Thank you for your comments. Figure S1 revealed the diversity of germ cell subtypes. The second part of the results described the integrated findings from Figures 2 and S1.

- In Figure 2B, pseudotime could be shown, and I wonder if the pseudotime in this analysis shows a similar pattern as in Figure 2D.

Thank you for your comments. Figure 2B revealed the pseudotime analysis of 12 germ cell subpopulation. Figure 2D revealed RNA velocity of 12 germ cell subpopulation. The two methods are both used for cell trajectory analysis. The pseudotime in Figure 2B showed a similar pattern as in Figure 2D.

- While staining occurs within one tissue, saying they are co-expressed seems inaccurate as the staining locations are clearly distinct. For example, the staining patterns of A2M and DDX4 (a classical marker) are quite different, so it's hard to claim A2M as a new potential marker just because it's expressed. Also, TSSK6 was separately described as having a similar expression pattern to DDX4, but from the IF results, it doesn't seem similar.

Thank you for your comments. We have revised the Figure.

- It was described that A2M (expressed in SSC0-1), and ASB9 (expressed in SSC2) have open promoter sites in SSC0, SSC2, and Diffing_SPG, but it doesn't seem like they are only open in the promoters of those cell types. For example, there doesn't seem to be a peak in Diffing for either gene. The promoter region of the tracks is not very clear, so overall figure modification seems necessary.

Thank you for your comments. We have revised the Figure.

- The ATAC signal scale for each genomic region should be included, and clear markings for the TSS location and direction of the genes are needed.

Thank you for your comments. We have revised the figure and shown in the revised manuscript.

Figure 3A mostly shows the SSC2 in the G2/M phase, so it seems questionable to call SSC0/1 quiescent. Also, I wonder if the expression of EOMES and GFRA1 is well distinguished in the SSC subtypes as expected.

Thank you for your comments. We will validate in subsequent experiments whether the expression of EOMES and GFRA1 is clearly distinguished in the SSC subtypes.

- In Figure 3C, it would be good to have labels indicating what the x and y axes represent. The figure seems complex, and the description does not seem to fully support it.

Thank you for your comments. We have added labels indicating what the x and y axes represent in the Figure 3C. The x and y axes represent spliced and unspliced mRNA ratios, respectively.

- While TFs are the central focus, it's disappointing that scATAC-seq was not used.

Thank you for your comments. TFs analysis using scATAC-seq will be carried out in the future.

Figure 4: It would be good to have a more detailed discussion of the differences between subtypes, such as through GO analysis. The track images need modification like marking the peaks of interest and focusing more on the promoter region, similar to the previous figures.

Thank you for your comments. GO analysis results were put in Figure S5. The description is as follows:

As shown in Figure S5, SC1 were mainly involved in cell differentiation, cell adhesion and cell communication; SC2 were involved in cell migration, and cell adhesion; SC3 were involved in spermatogenesis, and meiotic cell cycle; SC4 were involved in meiotic cell cycle, and positive regulation of stem cell proliferation; SC5 were involved in cell cycle, and cell division; SC6 were involved in obsolete oxidation−reduction process, and glutathione derivative biosynthetic process; SC7 were involved in viral transcription and translational initiation; SC8 were involved in spermatogenesis and sperm capacitation.

In Figure 5, it would be good to have criteria for the novel Sertoli cell subtype presented. CCDC62 is presented as a representative marker for the SC8 cluster, but from Figure 4C, it seems to be quite expressed in the SC3 cluster as well. Therefore, in Figure 5E's protein-level check, it's unclear if this truly represents a novel SC8 subtype.

Thank you for your comments. CCDC62 expression was higher in SC8 cluster than in SC3. Since some molecular markers were not commercially available in the market, CCDC62 was selected as SC8 marker for immunofluorescence verification. Immunofluorescence results showed that CCDC62 is a novel SC8 marker.

- It might have been more meaningful to use SOX9 as a control and show that markers in the same subtype are expressed in the same location.

Thank you for your comments. To determine PRAP1, BST2, and CCDC62 as new markers for the SC subtype, we co-stained them with SOX9 (a well-known SC marker).

- Figures 4 and 5 could potentially be combined into one figure.

Thank you for your comments. Since combining Figures 4 and 5 into a single image would cause the image to be unclear, two images are used to show it.

In Figure 6, it would be good to support the results with more NOA patient data.

Thank you for your comments. Patient clinical and laboratory characteristics has been presented in Table 1.

- Rather than claiming the importance of SC3 based on 3 single-cell patient data, it would be better to validate using public data with SC3 signature genes (e.g., showing the correlation between germ cell and SC3 ratios).

Thank you for your comments. I'm sorry I didn't find public data with SC3 signature genes. In the future, we will verify the importance of SC3 through in vivo and in vitro experiments.

- 462: It seems to be referring to Figure 6G, not 6D.

Thank you for your comments. We have revised it. The description is as follows: As shown in Figure 6G, State 1 SC3/4/5 were tended to associated with PreLep, SSC0/1/2, and Diffing and Diffed-SPG sperm cells (R > 0.72).

In Figure 7, the spermatogenesis process is basically well-known, so it would be better to emphasize what novel content is being conveyed here. Additionally, emphasizing the importance of SC3 in the overall process based on GO results leaves room for a better approach.

Thank you for your valuable suggestions. Regarding Figure 7, we recognize that the spermatogenesis process is well-known, and we will focus on highlighting the novel content, particularly the role and significance of the SC3 subtype in spermatogenesis disorders. As for the importance of SC3 in the overall process based on GO results, we have validated this in Figure 8 through co-staining experiments between Sertoli cells and spermatogenic cells in OA and NOA groups. The results demonstrate a significant correlation between the number of SC3-positive cells and SPT3 spermatogenic cells, particularly in the NOA5-P8 group, where both SC3 and SPT3 cell counts are notably lower than in the NOA4-P7 group. This further supports the critical role of SC3 in the spermatogenesis process. Your suggestions have prompted us to refine our data presentation and more clearly emphasize the novel aspects of our research. We will continue to strive to ensure that every part of our research contributes meaningfully to the academic community. Thank you again for your guidance.

In Figure 8, only the contents of the IF-stained proteins are listed, which seems slightly insufficient to constitute a subsection on its own. It might have been better to conclude by emphasizing some subtypes.

Thank you for your comments. We have combined this part of the results with other results into one section. The description is as follows:

“Co-localization of subpopulations of Sertoli cells and germ cells

To determine the interaction between Sertoli cells and spermatogenesis, we applied Cell-PhoneDB to infer cellular interactions according to ligand-receptor signalling database. As shown in Figure 6G, compared with other cell types, germ cells were mainly interacted with Sertoli cells. We futher performed Spearman correlation analysis to determine the relationship between Sertoli cells and germ cells. As shown in Figure 6H, State 1 SC3/4/5 were tended to be associated with PreLep, SSC0/1/2, and Diffing and Diffed-SPG sperm cells (R > 0.72). Interestingly, SC3 was significantly positively correlated with all sperm subpopulations (R > 0.5), suggesting an important role for SC3 in spermatogenesis and that SC3 is involved in the entire process of spermatogenesis. Subsequently, to understand whether the functions of germ cells and Sertoli cells correspond to each other, GO term enrichment analysis of germ cells and sertoli cells was carried out (Figure S3, S4). We found that the functions could be divided into 8 categories, namely, material energy metabolism, cell cycle activity, the final stage of sperm cell formation, chemical reaction, signal communication, cell adhesion and migration, stem cells and sex differentiation activity, and stress reaction. These different events were labeled with different colors in order to quickly capture the important events occurring in the cells at each stage. As shown in Figure S3, we discovered that SSC0/1/2 was involved in SRP-dependent cotranslational protein targeting to membrane, and cytoplasmic translation; Diffing SPG was involved in cell division and cell cycle; Diffied SPG was involved in cell cycle and RNA splicing; Pre-Leptotene was involved in cell cycle and meiotic cell cycle; Leptotene_Zygotene was involved in cell cycle and meiotic cell cycle; Pachytene was involved in cilium assembly and spermatogenesis; Diplotene was involved in spermatogenesis and cilium assembly; SPT1 was involved in cilium assembly and flagellated sperm motility; SPT2 was involved in spermatid development and flagellated sperm motility; SPT3 was involved in spermatid development and spermatogenesis. As shown in Figure S4, SC1 were mainly involved in cell differentiation, cell adhesion and cell communication; SC2 were involved in cell migration, and cell adhesion; SC3 were involved in spermatogenesis, and meiotic cell cycle; SC4 were involved in meiotic cell cycle, and positive regulation of stem cell proliferation; SC5 were involved in cell cycle, and cell division; SC6 were involved in obsolete oxidation−reduction process, and glutathione derivative biosynthetic process; SC7 were involved in viral transcription and translational initiation; SC8 were involved in spermatogenesis and sperm capacitation. The above analysis indicated that the functions of 8 Sertoli cell subtypes and 12 germ cell subtypes were closely related.

To further verify that Sertoli cell subtypes have "stage specificity" for each stage of sperm development, we firstly performed HE staining using testicular tissues from OA3-P6, NOA4-P7 and NOA5-P8 samples. The results showed that the OA3-P6 group showed some sperm, with reduced spermatogenesis, thickened basement membranes, and a high number of sertoli cells without spermatogenic cells. The NOA4-P7 group had no sperm initially, but a few malformed sperm were observed after sampling, leading to the removal of affected seminiferous tubules. The NOA5-P8 group showed no sperm in situ (Figure 7A). Immunofluorescence staining in Figure 7B was performed using these tissues for validation. ASB9 (SSC2) was primarily expressed in a wreath-like pattern around the basement membrane of testicular tissue, particularly in the OA group, while ASB9 was barely detectable in the NOA group. SOX2 (SC2) was scattered around SSC2 (ASB9), with nuclear staining, while TF (SC1) expression was not prominent. In NOA patients, SPATS1 (SC3) expression was significantly reduced. C9orf57 (Pa) showed nuclear expression in testicular tissues, primarily extending along the basement membrane toward the spermatogenic center, and was positioned closer to the center than DDX4, suggesting its involvement in germ cell development or differentiation. BEND4, identified as a marker fo SC5, showed a developmental trajectory from the basement membrane toward the spermatogenic center. ST3GAL4 was expressed in the nucleus, forming a circular pattern around the basement membrane, similar to A2M (SSC1), though A2M was more concentrated around the outer edge of the basement membrane, creating a more distinct wreath-like arrangement. In cases of impaired spermatogenesis, this arrangement becomes disorganized and loses its original structure. SMCP (SC6) was concentrated in the midpiece region of the bright blue sperm cell tail. In the OA group, SSC1 (A2M) was sparsely arranged in a rosette pattern around the basement membrane, but in the NOA group, it appeared more scattered. SSC2 (ASB9) expression was not prominent. BST2 (SC7) was a transmembrane protein primarily localized on the cell membrane. In the OA group, A2M (SSC1) was distinctly arranged in a wreath-like pattern around the basement membrane, with expression levels significantly higher than ASB9 (SSC2). TSSK6 (SPT3) was primarily expressed in OA3-P6, while CCDC62 (SC8) was more abundantly expressed in NOA4-P7, with ASB9 (SCC2) showing minimal expression. Taken together, germ cells of a particular stage tended to co-localize with Sertoli cells of the corresponding stages. Germ cells and sertoli cells at each differentiation stage were functionally heterogeneous and stage-specific (Figure 8). This suggests that each stage of sperm development requires the assistance of sertoli cells to complete the corresponding stage of sperm development.”

Reviewer #3 (Recommendations For The Authors):

The authors revealed 11 germ cell subtypes and 8 Sertoli cell subtypes through single-cell analysis of two OA patients and three NOA patients. And found that the Sertoli cell SC3 subtype (marked by SPATS1) plays an important role in spermatogenesis. It also suggests that Notch1/2/3 signaling and integrins are involved in germ cell-Stotoli cell interactions. This is an interesting and useful article that at least gives us a comprehensive understanding of human spermatogenesis. It provides a powerful tool for further research on NOA. However, there are still some issues and questions that need to be addressed.<br /> (1) How to collect testicular tissue, please explain in detail. Extract which part of testicular tissue. It's better to make a schematic diagram.

Thank you for your comments. The process is as follows: Testicular tissues were obtained from two OA patients (OA1-P1 and OA2-P2) and three NOA patients (NOA1-P3, NOA2-P4, NOA3-P5) using micro-dissection of testicular sperm extraction separately.

(2) Whether the tissues of these patients are extracted simultaneously or separately, separated into single cells, and stored, and then single cell analysis is performed simultaneously. Please be specific.

Thank you for your comments. The testicular tissues of these patients were extracted separately, then separated into single cells, and single cell analysis was performed simultaneously.

(3) When performing single-cell analysis, cells from two OA patients were analyzed individually or combined. The same problem occurred in the cells of three NOA patients.

Thank you for your comments. Cells from two OA patients and three NOA patients were analyzed individually.

(4) Can you specifically point out the histological differences between OA and NOA in Figure 1A? This makes it easier for readers to understand the structure change between OA and NOA. Please also label representative supporting cells.

Thank you for your comments. We have revised the description and it was shown in the revised manuscript.

(5) The authors demonstrate that "We speculate that this lack of differentiation may be due to the intense morphological changes occurring in the sperm cells during this period, resulting in relatively minor differences in gene expression." Please provide some verification of this hypothesis? For example, use immunofluorescence staining to observe morphological changes in sperm cells.

Thank you for your comments. Due to limited funds, we will verified this hypothesis in future studies.

(6) The authors demonstrate that " As shown in Figure 5E, we discovered that PRAP1, BST2, and CCDC62 were co-expressed with SOX9 in testes tissues." The staining in Figure 5D is unclear, and it is difficult to explain that SOX9 is co-expressed with PRAP1 BST2 CCDC62 based on the current staining results. The staining patterns of SOX9 (green) and SOX9 (red) are also different. (SOX9 (red) appears as dots, while the background for SOX9 (green) is too dark to tell whether its staining is also in the form of dots.) In summary, increasing the clarity of the staining makes it more convincing. Alternatively, use high magnification to display these results.

Thank you for your comments. I have redyed and updated this part of the immunofluorescence staining results. Please refer to the files named Figure 1, Figure 2, Figure 5, and Figure 8.

(7) In Figure 8, the author emphasized the co-localization of Sertoli cells and Germ cells at corresponding stages and did a lot of staining, but it was difficult to distinguish the specific locations of co-localization, which was similar to Figure 5E. If possible, please mark specific colocalizations with arrows or use high magnification to display these results, in order to facilitate readers to better understand.

Thank you for your comments. We have re-stained and updated this part of the data. Please refer to the immunofluorescence staining data in the updated Figure 8.

(8) The authors emphasize that macrophages may play an important role in spermatogenesis. Therefore, adding relevant macrophage staining to observe the differences in macrophage expression between NOA and OA should better support this idea.

Thank you for your comments. Macrophage-related experiments will be further explored in the future.

(9) Notch1/2/3 signaling and integrin were discovered to be involved in germ cell-Sertoli cell interaction. However there are currently no concrete experiments to support this hypothesis. At least simple verification experiments are needed.

Thank you for your comments. Due to limited funding, studies will be carried out in the future.

(10) Data availability statements should not be limited to the corresponding author, especially for big data analysis. This is crucial to the credibility of this data (Have the scRNA-seq and scATAC-seq in this study been deposited in GEO or other databases, and when will they be released to the public?) The data for such big data analysis needs to be saved in GEO or other databases in advance so that more research can use it.

Thank you for your comments. We have deposited scRNA-seq and scATAC-seq data in NCBI. “ScRNA-seq data have been deposited in the NCBI Gene Expression Omnibus with the accession number GSE202647, and scATAC-seq data have been deposited in the NCBI database with the accession number PRJNA1177103.”

Author response:

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

Reviewer #1 (Public review):

This study by Wu et al. provides valuable computational insights into PROTAC-related protein complexes, focusing on linker roles, protein-protein interaction stability, and lysine residue accessibility. The findings are significant for PROTAC development in cancer treatment, particularly breast and prostate cancers.

The authors' claims about the role of PROTAC linkers and protein-protein interaction stability are generally supported by their computational data. However, the conclusions regarding lysine accessibility could be strengthened with more in-depth analysis. The use of the term "protein functional dynamics" is not fully justified by the presented work, which focuses primarily on structural dynamics rather than functional aspects.

Strengths:

(1) Comprehensive computational analysis of PROTAC-related protein complexes.

(2) Focus on critical aspects: linker role, protein-protein interaction stability, and lysine accessibility.

Weaknesses:

(1) Limited examination of lysine accessibility despite its stated importance.

(2) Use of RMSD as the primary metric for conformational assessment, which may overlook important local structural changes.

Reviewer #1 (Recommendations for the authors):

(1) The authors' claims about the role of PROTAC linkers and protein-protein interaction stability are generally supported by their computational data. However, the conclusions regarding lysine accessibility could be strengthened with more in-depth analysis. Expand the analysis of lysine accessibility, potentially correlating it with other structural features such as linker length.

We thank the reviewers for the suggestions! We performed time dependent correlation analysis to correlate the dihedral angles of the PROTACs and the Lys-Gly distance (Figures 6 and S17). We included detailed explanation on page 16:

“To further examine the correlation between PROTAC rotation and the Lys-Gly interaction, we performed a time-dependent correlation analysis. This analysis showed that PROTAC rotation translates motion over time, leading to the Lys-Gly interaction, with a correlation peak around 60-85 ns, marking the time of the interaction (Figure 6 and Figure S17). In addition, the pseudo dihedral angles also showed a high correlation (0.85 in the case of dBET1) with Lys-Gly distance. This indicated that degradation complex undergoes structural rearrangement and drives the Lys-Gly interaction.”

(2) The use of the term "protein functional dynamics" is not fully justified by the presented work, which focuses primarily on structural dynamics rather than functional aspects. Consider changing "protein functional dynamics" to "protein dynamics" to more accurately reflect the scope of the study.

Thanks to the reviewer for the suggestion to use the more accurate terminology! We agreed with the reviewer that if we keep “protein functional dynamics” in the title, we should focus on how the “overall protein dynamic” links to the “function” – The function is directly related to PROTAC-induced structural dynamics which is commonly seen in “protein-structural-function” relationship, but it is not our main focus. Therefore, we changed the title to replace “functional” by “structural”.  

(3) Incorporate more local and specific characterization methods in addition to RMSD for a more comprehensive conformational assessment.

We thank the reviewer for the suggestion. We performed time dependent correlation analysis to understand how the rotation of PROTACs can translate to the Lys-Gly interaction. In addition, we performed dihedral entropies analysis for each dihedral angle in the linker of the PROTACs to better examine the flexibility of each PROTAC.

We included detailed explanation at page 18: “Our dihedral entropies analysis showed that dBET57 has ~0.3 kcal/mol lower entropies than the other three linkers, suggesting dBET57 is less flexible than other PROTACs (Figure S18).”

Reviewer #2 (Public review):

Summary:

The manuscript reports the computational study of the dynamics of PROTAC-induced degradation complexes. The research investigates how different linkers within PROTACs affect the formation and stability of ternary complexes between the target protein BRD4BD1 and Cereblon E3 ligase, and the degradation machinery. Using computational modeling, docking, and molecular dynamics simulations, the study demonstrates that although all PROTACs form ternary complexes, the linkers significantly influence the dynamics and efficacy of protein degradation. The findings highlight that the flexibility and positioning of Lys residues are crucial for successful ubiquitination. The results also discussed the correlated motions between the PROTAC linker and the complex.

Strengths:

The field of PROTAC discovery and design, characterized by its limited research, distinguishes itself from traditional binary ligand-protein interactions by forming a ternary complex involving two proteins. The current understanding of how the structure of PROTAC influences its degradation efficacy remains insufficient. This study investigated the atomic-level dynamics of the degradation complex, offering potentially valuable insights for future research into PROTAC degradability.

Reviewer #2 (Recommendations for the authors):

(1) Regarding the modeling of the ternary complex, the BRD4 structure (3MXF) is from humans, whereas the CRBN structure in 4CI3 is derived from Gallus gallus. Is there a specific reason for not using structures from the same species, especially considering that human CRBN structures are available in the Protein Data Bank (e.g., 8OIZ, 4TZ4)?

We appreciate the reviewer’s insightful comment regarding the choice of crystal structures of BRD4 and CRBN structures from two species. Our initial selection of 4CI3 for CRBN structure was based on its high resolution and publication in Nature journal. Furthermore, the Gallus gallus CRBN structure shares high degree of sequence and structural similarity with Homo sapiens CRBN, especially in the ligand binding region. At the time of our study, we were aware of 4TZ4 as Homo sapiens CRBN, however, we did not use this structure since no publication or detailed experimental was associated with it. Additionally, PDB 8OIZ, was not publicly available yet for other researchers to use at the time.

(2) Based on the crystal structure (PDB ID: 6BNB) discussed in Reference 6, the ternary complex of dBET57 exhibits a conformation distinct from other PROTACs, with CRBN adopting an "open" conformation. Using the same CRBN structure for dBET57 as for other PROTACs might result in inaccurate docking outcomes.

Thank you for the reviewer’s comment! As noted by the authors in Reference 6, the observed open conformation of CRBN in the dBET57 ternary complex may result from the high salt crystallization conditions, which could drive structural rearrangement, and crystal contacts that may induce this conformation. The authors also mentioned that this open conformation could, in part, reflect CRBN’s intrinsic plasticity. However, they acknowledged that further studies are needed to determine whether this conformational flexibility is a characteristic feature of CRBN that enables it to accommodate a variety of substrates. Despite these observations, we believe that the compatibility of the observed BRD4^BD1 binding conformation with both open and closed CRBN states suggests that these conformational changes are all possible. Therefore, we believe using the same initial CRBN structure for dBET57 as for other PROTACs can still reasonably reveal the dynamic nature of the ternary complex and would not significantly affect the accuracy of our docking outcomes either.

(3) Figure 2 displays only a single frame from the simulations, which might not provide a comprehensive representation. Could a contact frequency heatmap of PROTAC with the proteins be included to offer a more detailed view?

We thank the reviewer for the suggestion! We performed the contact map analysis to observe the average distance between PROTACs and BRD4^BD1 over 400ns of MD simulation (new Figure S4 added).

We included detailed explanation at page 8 and 9: “The residues contact map throughout the 400ns MD simulation also showed different pattern of protein-protein interactions, indicating that the linkers were able to adopt different conformations (Figure S4).”

(4) The conclusions in Figure 3 and S11 are based on a single 400 ns trajectory. The reproducibility of these results is therefore uncertain.

We thank the reviewer for the suggestion! We added one more random seed MD simulation for each PROTAC to ensure the reproducibility of the results. The Result is shown in Figure S21 and the details for each MD run are updated in Table 1.

(5) Figure 4 indicates significant differences between the first and last 100 ns of the simulations. Does this suggest that the simulations have not converged? If so, how can the statistical analysis presented in this paper be considered reliable?

We thank the reviewers for the question. The simulation was initiated with a 10-15A gap between BRD4 and Ub to monitor the movement of degradation machinery and Lys-Gly interaction. The significant changes in pseudo dihedral in Figure 4 shows that the large-scale movement of the degradation complex can initiate the Lys-Gly binding. It does not relate to unstable sampling because the system remains very stable when BRD4 comes close to Ub.

(6) In Figure 5, the dihedral angle of dBET57_#9MD1 is marked on a peptide bond. Shouldn't this angle have a high energy barrier for rotation?

We thank the reviewers for catching the error! Indeed, it was an error that the dihedral angles were marked on the peptide bond. We reworked the figure and double checked our dihedral correlation analysis. The updated correlate dihedral angle selection and the correlation coefficient is shown in Figure 5.

(7) Given that crystal structures for dBET 70, 23, and 57 are available, why is there a need to model the complex using protein-protein docking?

We thank the reviewer for the feedback. Only dBET23 has the ternary complex available in a crystal structure, which has the PROTAC and both proteins, while dBET1, dBET57 and dBET70 are not completed as ternary complexes. Although dBET70 has a crystal structure, its PROTAC’s conformation is not resolved, and thus we decided to still perform protein-protein docking with dBET70. 

We includeed the explanation at page 8: “Only dBET23 crystal structure is available with the PROTAC and both proteins, while the experimentally determined ternary complexes of dBET1, dBET57 and dBET70 are not available. “

(8) On page 9, it is mentioned that "only one of the 12 PDB files had CRBN bound to DDB1 (PDB ID 4TZ4)." However, there are numerous structures of the DDB1-CRBN complex available, including those used for docking like 4CI3, as well as 4CI1, 4CI2, 8OIZ, etc.

We thank the reviewers for the comment! We acknowledged the existence of several DDB1-CRBN complex crystal structures, such as PDB IDs 4CI1, 4CI2, 4CI3, and the more recent 8OIZ. For our study, we chose to use 4TZ4 to maintain consistency in complex construction and to align with the methodology established in a previously published JBC paper (https://doi.org/10.1016/j.jbc.2022.101653), which successfully utilized the same structure for a similar construct. At the time our study was conducted, the 8OIZ structure had not yet been released. We appreciate your suggestion and will consider incorporating alternative structures in future studies to further investigate our findings.

(9) Table 2 is first referenced on page 8, while Table 1 is mentioned first on page 10. The numbering of these tables should be reversed to reflect their order of appearance in the text.

We thank the reviewer for catching the error! We switched the order of Table 1 and Table 2.

Reviewer #3 (Public review):

The authors offer an interesting computational study on the dynamics of PROTAC-driven protein degradation. They employed a combination of protein-protein docking, structural alignment, atomistic MD simulations, and post-analysis to model a series of CRBN-dBET-BRD4 ternary complexes, as well as the entire degradation machinery complex. These degraders, with different linker properties, were all capable of forming stable ternary complexes but had been shown experimentally to exhibit different degradation capabilities. While in the initial models of the degradation machinery complex, no surface Lys residue(s) of BRD4 were exposed sufficiently for the crucial ubiquitination step, MD simulations illustrated protein functional dynamics of the entire complex and local side-chain arrangements to bring Lys residue(s) to the catalytic pocket of E2/Ub for reactions. Using these simulations, the authors were able to present a hypothesis as to how linker property affects degradation potency. They were able to roughly correlate the distance of Lys residues to the catalytic pocket of E2/Ub with observed DC50/5h values. This is an interesting and timely study that presents interesting tools that could be used to guide future PROTAC design or optimization.

Reviewer #3 (Recommendations for the authors):

(1) My most important comment refers to the MM/PBSA analysis, the results of which are shown in Figure S9: binding affinities of -40 to -50 kcal/mol are unrealistic. This would correspond to a dissociation constant of 10^-37 M. This analysis needs to be removed or corrected.

We thank the reviewer for the comment! MM/PBSA analysis indeed cannot give realistic binding free energy. It does not include the configurational entropy loss which should be a large positive value. In addition, while the implicit PBSA solvent model computes solvation free energy, the absolute values may not be very accurate. However, because this is a commonly used energy calculation, and some readers may like to see quantitative values to ensure that the systems have stable intermolecular attractions, we kept the analysis in SI. We edited the figure legend, moved the Figure S10 in SI page 19, and added sentences to clearly state that the calculations did not include configuration entropy loss “Note that the energy calculations focus on non-bonded intermolecular interactions and solvation free energy calculations using MM/PBSA, where the configuration entropy loss during protein binding was not explicitly included. “.

(2) I think that the analysis of what in the different dBETx makes them cause different degradation potency is underdeveloped. The dihedral angle analysis (Figure 4B) did not explain the observed behavior in my opinion. Please add additional, clearer analysis as to what structural differences in the dBETx make them sample very different conformations.

We thank the reviewer for the suggestions! Based on the suggestion, we further performed dihedral entropy analysis for each dihedral angle in the linker part of the PROTAC to examine the flexibility of each PROTAC. Because each PROTAC has a different linker, we now clearly label them in a new Figure S18 in SI page 27. Low dihedral entropies indicate a more rigid structure and thus less flexibility to make a PROTAC more difficult to rearrange and facilitate the protein structural dynamic necessary for ubiquitination.

We added detailed explanation on page 18: “Our dihedral entropy analysis showed that dBET57 has ~0.3 kcal/mol lower configuration entropies than the other dBETs with three different linkers, suggesting that dBET57 is less flexible than the other PROTACs (Figure S18).”

(3) "The movement of the degradation machinery correlated with rotations of specific dihedrals of the linker region in dBETs (Figure 5).": this is not sufficiently clear from the figure. Definitely not in a quantitative way.

We thank the reviewers for the suggestions! To further understand the correlation between PROTACs dihedral angles and the movement of degradation machinery, we performed time dependent correlation analysis to correlate the dihedral angles of the PROTACs and the Lys-Gly distance (Figures 6 and S17).

We included detailed explanation on page 16:

“To further examine the correlation between PROTAC rotation and the Lys-Gly interaction, we performed a time-dependent correlation analysis. This analysis showed that PROTAC rotation translates motion over time, leading to the Lys-Gly interaction, with a correlation peak around 60-85 ns, marking the time of the interaction (Figure 6 and Figure S17). In addition, the pseudo dihedral angles also showed a high correlation (0.85 in the case of dBET1) with Lys-Gly distance. This indicated that degradation complex undergoes structural rearrangement and drives the Lys-Gly interaction.

(4) Cartoons are needed at multiple stages throughout the paper to enhance the clarity of what the modeled complexes looked like (e.g. which subunits they contained).

We thank the reviewers for the suggestions. We added and remade several Figures with cartoons to better represent the stages. We also used higher resolution and included clearer labels for each protein system.

(5) The difference between CRL4A E3 ligase and CRBN E3 ligase is not clear to the non-expert reader.

Thanks for the reviewer’s comment! To clarify the terms "CRL4A E3 ligase" and "CRBN E3 ligase", which refer to different levels of description for the protein complexes, we added a couple of sentences in the Figure 1 legend. As a result, the non-expert readers can clearly know the differences.

As illustrated in Figure 1,

• CRL4A E3 ligase refers to the full E3 ligase complex, which includes all protein components such as CRBN, DDB1, CUL4A, and RBX1.

• CRBN E3 ligase, on the other hand, is a more colloquial term typically used to describe just the CRBN protein, often in isolation from the full CRL4A complex.

(6) Figure 1, legend: unclear why it's E3 in A and E2 in B.

We thank the reviewer for the question! E3 ligase in Figure 1A refers to CRBN E3 ligase, where researchers also simply term it CRBN. We have added a sentence to specify that CRBN E3 ligase is also termed CRBN for simplicity. In Figure 1B, E2 was unclear in the sentences. The full name of E2 should be E2 ubiquitin-conjugating enzyme. Because the name is a bit long, researchers also call it E2 enzyme. We have corrected it and used E2 enzyme to make it clearer. 

(7) "Although the protein-protein binding affinities were similar, other degraders such as dBET1 and dBET57 had a DC50/5h of about 500 nM". It's unclear what experimental data supports the assertion that the protein-protein binding affinities are similar.

We thank reviewer for the question. Indeed, the statement is unclear.

We corrected the sentence in page 6: “Although utilizing the exact same warheads, other degraders such as dBET1 and dBET57 had a DC_50/5h of about 500 nM.”

(8) Was the construction of the degradation machinery complex guided by experimental data (maybe cryo-EM or tomography)? If not, what is the accuracy of the starting complex for MD? This may impact the reliability of the obtained results.

Thank you for your insightful comments! Yes, the construction of the degradation machinery complex was guided by available high-resolution crystal structures, which was selected to maintain consistency and align with the methodology established in a previously published JBC paper (https://doi.org/10.1016/j.jbc.2022.101653).

We acknowledged that static crystal structures represent only a single snapshot of the system and may not capture the full conformational flexibility of the complex. To address this limitation, we performed MD simulations using multiple starting structures. This approach allowed us to explore a broader conformational landscape and reduced the dependence on any single starting configuration, thereby enhancing the reliability of the results.

We hope this clarifies the robustness of our methodology and the steps taken to ensure accuracy in our simulations.

(9) "With quantitative data, we revealed the mechanism underlying dBETx-induced degradation machinery": I think this may be too strong of an assertion. The authors may have developed a mechanistic hypothesis that can be tested experimentally in the future.

We thank the reviewer for the suggestion. This is indeed a strong assertion and needs to be modified. We edited the sentence in page 7: “With quantitative data, we revealed the importance of the structural dynamics of dBETx-induced motions, which arrange positions of surface lysine residues of BRD4^BD1 and the entire degradation machinery.”

(10) Figure S2: are the RMSDs calculated over all residues? Or just the BRD4 residues? Given that the structures are aligned with respect to CRBN, the reported RMSD numbers might be artificially low since there are many more CRBN residues than there are BRD4 residues. Also, why weren't the crystal structures used for dBET 23 and 70 for the modeling? Wouldn't you want to use the most accurate possible structures? Simulations were run for 23. Why not for 70?

We thank the reviewer for the suggestion. We added a sentence to more clearly explain the RMSD calculations in Figure S2: “The structural superposition is performed based on the backbone of CRBN and RMSD calculation is conducted based on the backbone of BRD4^BD1.”

Although dBET70 has crystal structure, its PROTAC structure is not resolved, and thus we decided to still perform protein-protein docking with dBET70.  dBET1 and dBET57 do not have a crystal structure for the ternary complexes.

We included the explanation at page 8: “Only dBET23 crystal structure is available with the PROTACs and both proteins, while the experimentally determined ternary complexes of dBET1, PROTACs of dBET57 and dBET70 are not available. “

a. And there are no crystal structures available for 1 and 57? If so, please clearly say that. Otherwise please report the RMSD.

We thank the reviewer for the suggestion. We included the explanation at page 8: “Only dBET23 crystal structure is available with the PROTACs and both proteins, while the experimentally determined ternary complexes of dBET1, PROTACs of dBET57 and dBET70 are not available.”

(11) Table 2 is referenced before Table 1.

We thank the reviewer for catching the error! We switched the order for Table 1 and Table 2.

(12) Figure S3 is not referenced in the main paper.

We thank the reviewer for catching the error! We now referred Figure S3 on page7.

(13) Minor comments on grammar and sentence structure:

a. It should be "binding of a ternary complex"

b. "Our shows the importance": word missing.

c. "...providing insights into potential orientations for ubiquitination. observe whether the preferred conformations are pre-organized for ubiquitination." Word or words missing.

We thank reviewer for catching the errors! We corrected grammatical errors and unclear sentences throughout the entire paper and revised the sentences to make them easily understandable for non-expert readers.

Author response:

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

eLife assessment

This work describes a convincingly validated non-invasive tool for in vivo metabolic phenotyping of aggressive brain tumors in mice brains. The analysis provides a valuable technique that tackles the unmet need for patient stratification and hence for early assessment of therapeutic efficacy. However, wider clinical applicability of the findings can be attained by expanding the work to include more diverse tumor models.

We thank the Editors for their comments. This concern was also raised by Reviewer 1 in the Public Review, where we address in more detail – please refer to comment PR-R1.C1. In brief, we agree that a more clinically relevant model should provide more translatable results to patients, and acknowledge this better in the revised manuscript: page 18 (lines 14-17), “While patient-derived xenografts and de novo models would be more suited to recapitulate human GBM heterogeneity and infiltration features, and genetic manipulation of glycolysis and mitochondrial oxidation pathways potentially relevant to ascertain DGE-DMI sensitivity for their quantification, (…)”. However, we also believe that the potential of DGE-DMI for application to different glioblastoma models or patients is demonstrated clearly enough with the two immunocompetent models we chose, extensively reported in the literature as reliable models of glioblastoma.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work introduces a new imaging tool for profiling tumor microenvironments through glucose conversion kinetics. Using GL261 and CT2A intracranial mouse models, the authors demonstrated that tumor lactate turnover mimicked the glioblastoma phenotype, and differences in peritumoral glutamate-glutamine recycling correlated with tumor invasion capacity, aligning with histopathological characterization. This paper presents a novel method to image and quantify glucose metabolites, reducing background noise and improving the predictability of multiple tumor features. It is, therefore, a valuable tool for studying glioblastoma in mouse models and enhances the understanding of the metabolic heterogeneity of glioblastoma.

Strengths:

By combining novel spectroscopic imaging modalities and recent advances in noise attenuation, Simões et al. improve upon their previously published Dynamic Glucose-Enhanced deuterium metabolic imaging (DGE-DMI) method to resolve spatiotemporal glucose flux rates in two commonly used syngeneic GBM mouse models, CT2A and GL261. This method can be standardized and further enhanced by using tensor PCA for spectral denoising, which improves kinetic modeling performance. It enables the glioblastoma mouse model to be assessed and quantified with higher accuracy using imaging methods.

The study also demonstrated the potential of DGE-DMI by providing spectroscopic imaging of glucose metabolic fluxes in both the tumor and tumor border regions. By comparing these results with histopathological characterization, the authors showed that DGE-DMI could be a powerful tool for analyzing multiple aspects of mouse glioblastoma, such as cell density and proliferation, peritumoral infiltration, and distant migration.

Weaknesses:

(1) Although the paper provides clear evidence that DGE-DMI is a potentially powerful tool for the mouse glioblastoma model, it fails to use this new method to discover novel features of tumors. The data presented mainly confirm tumor features that have been previously reported. While this demonstrates that DGE-DMI is a reliable imaging tool in such circumstances, it also diminishes the novelty of the study.

PR-R1.C1 – We thank the Reviewer for the detailed analysis and reply below to each point. PR-R1.C1.1 - novelty: We thank the Reviewer for the comments and understand their perspective. While we acknowledge that our paper is more methodologically oriented, we also believe that significant methodological advances are critical for new discoveries. This was our main motivation and is demonstrated in the present work, showing the ability to map in vivo metabolic fluxes in mouse glioma, a “hot topic” and very desirable in the cancer field. 

PR-R1.C1.2 – additional tumor features: To strengthen the biological relevance of this methodologic novelty, we have now included immune cell infiltration among the tumor features assessed, besides perfusion, histopathology, cellularity and cell proliferation. For this, we performed iba-1 immunostaining for microglia/ macrophages, now included in Fig. 2-B. These new results demonstrate significantly higher microglia/macrophage infiltration in CT2A tumors compared to GL261, particularly at the tumor border. This is very consistent with the respective tumor phenotypes, namely differences in cell density and cellularity between the 2 cohorts and across pooled cohorts, as we now report: page 9 (lines 10-18), “Such phenotype differences were reflected in the regional infiltration of microglia/macrophages: significantly higher at the CT2A peritumoral rim (PT-Rim) compared to GL261, and slightly higher in the tumor region as well (Fig 2B). Further quantitative regional analysis of Tumor-to-PT-Rim ROI ratios revealed: (i) 47% lower cell density (p=0.004) and 32% higher cell proliferation (p=0.026) in GL261 compared to CT2A (Fig 2C, Table S3); and (ii) strong negative correlations in pooled cohorts between microglia/macrophage infiltration and cellularity (R=-0.91, p=<0.001) or cell density (R=-0.77, p=0.016), suggesting more circumscribed tumor growth with higher peripheral/peritumoral infiltration of immune cells.”; and page 16 (lines 13-19), “GL261 tumors were examined earlier after induction than CT2A (17±0 vs. 30±5 days, p = 0.032), displaying similar volumes (57±6 vs. 60±14, p = 0.813) but increased vascular permeability (8.5±1.1 vs 4.3±0.5 10³/min: +98%, p=0.001),  more disrupted stromal-vascular phenotypes and infiltrative growth (5/5 vs 0/5), consistent with significantly lower tumor cell density (4.9±0.2 vs. 8.2±0.3 10^-3 cells/µm²: -40%, p<0.001) and lower peritumoral rim infiltration of microglia/macrophages (2.1±0.7 vs. 10.0±2.3 %: -77%, p=0.008)”.

PR-R1.C1.3 – new tumor features and DGE-DMI: Importantly, such regional differences in cellularity/cell density and immune cell infiltration between the two cohorts were remarkably mirrored by the lactate turnover maps (Fig 3-C), as we now report in the manuscript: page 12 (lines 6-15), “GL261 tumors accumulated significantly less lactate in the core (1.60±0.25 vs 2.91±0.33 mM: -45%, p=0.013) and peritumor margin regions (0.94±0.09 vs 1.46±0.17 mM: 36%, p=0.025) than CT2A – Fig 3 A-B, Table S1. Consistently, tumor lactate accumulation correlated with tumor cellularity in pooled cohorts (R=0.74, p=0.014). Then, lower tumor lactate levels were associated with higher lactate elimination rate, k_lac (0.11±0.1 vs 0.06±0.01 mM/min: +94%, p=0.006) – Fig 3B – which in turn correlated inversely with peritumoral rim infiltration of microglia/macrophages in pooled cohorts (R=-0.73, p=0.027) – Fig 3-C. Further analysis of Tumor/P-Margin metabolic ratios (Table S3) revealed: (i) +38% glucose (p=0.002) and -17% lactate (p=0.038) concentrations, and +55% higher lactate consumption rate (p=0.040) in the GL261 cohort; and (ii) lactate ratios across those regions reflected the respective cell density ratios in pooled cohorts (R=0.77, p=0.010) – Fig 3-C”. This is a novel, relevant feature compared to our previous work, as highlighted in our discussion: page 17 (lines 1-8), “Tumor vs peritumor border analyses further suggest that lactate metabolism reflects regional histologic differences: