Directive coaching: I can see how this way of coaching can support teachers who need to master a skill. It is nerve racking to do this type of coaching, however i can see possiblities based on what jim knight is sharing. I need to go deeper to understand better.

This is what I enjoy, as it provides entry points with teachers needs. and I can work with experience teachers through a collaborative and consulting.! We navigate this depending on students goals and the Impact Cycle by Jim Knight.

This just prompt me to think about our new IM curriculum, that I know asking level 3 or 4 DOK questions is essential for students, yet if we do not model how to respond or provide students with time to grapple with learning, then students will not have the ability to think and being able to engage in the conversation of the class, the metacognitive skills are essential part of students leanring.

is there any link that refers to the program?

Carol Cohn fala sobre a necessidade de controle e previsibilidade no discurso de defesa. O tom de Shelby não é apenas de crítica técnica; é de angústia (distressed).

Evidência no Texto: Ele chama o evento de "extremely dangerous situation" e diz estar "very concerned and distressed" (muito preocupado e angustiado).

Análise: A "domesticação" do perigo falhou. Em 1995, o perigo era domesticado pela piada ("exciting watch"). Em 1998, a realidade da proliferação rompeu a bolha da linguagem tecnoestratégica segura. O "cenário sombrio" (previsto no memorando de 95) se concretizou porque a inteligência estava ocupada demais subestimando o vigiado.

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Reply to the reviewers

Revision Plan

Manuscript number: RC-2025-03208

Corresponding author(s): Jared Nordman

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1. General Statements [optional]

All three reviewers of our manuscript were very positive about our work. The reviewers noted that our work represents a necessary advance that is timely, addresses important issues in the chromatin field, and will of broad interest to this community. Given the nature of our work and the positive reviews, we feel that this manuscript would best be suited for the Journal of Cell Biology.

2. Description of the planned revisions

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

Summary:

The authors investigate the function of the H3 chaperone NASP, which is known to bind directly to H3 and prevent degradation of soluble H3. What is unclear is where NASP functions in the cell (nucleus or cytoplasm), how NASP protects H3 from degradation (direct or indirect), and if NASP affects H3 dynamics (nuclear import or export). They use the powerful model system of Drosophila embryos because the soluble H3 pool is high due to maternal deposition and they make use of photoconvertable Dendra-tagged proteins, since these are maternally deposited and can be used to measure nuclear import/export rates.

Using these systems and tools, they conclude that NASP affects nuclear import, but only indirectly, because embryos from NASP mutant mothers start out with 50% of the maternally deposited H3. Because of the depleted H3 and reduced import rates, NASP deficient embryos also have reduced nucleoplasmic and chromatin-associated H3. Using a new Dendra-tagged NASP allele, the authors show that NASP and H3 have different nuclear import rates, indicating that NASP is not a chaperone that shuttles H3 into the nucleus. They test H3 levels in embryos that have no nuclei and conclude that NASP functions in the cytoplasm, and through protein aggregation assays they conclude that NASP prevents H3 aggregation.

Major comments:

The text was easy to read and logical. The data are well presented, methods are complete, and statistics are robust. The conclusions are largely reasonable. However, I am having trouble connecting the conclusions in text to the data presented in Figure 4.

First, I'm confused why the conclusion from Figure 4A is that NASP functions in the cytoplasm of the egg. Couldn't NASP be required in the ovary (in, say, nurse cell nuclei) to stimulate H3 expression and deposition into the egg? The results in 4A would look the same if the mothers deposit 50% of the normal H3 into the egg. Why is NASP functioning specifically in the cytoplasm when it is also so clearly imported into the nucleus? Maybe NASP functions wherever it is, and by preventing nuclear import, you force it to function in the cytoplasm. I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

The concern raised by the reviewer regarding NASP function during oogenesis has been addressed in a previous work published from our lab. Unfortunately, we did not do a good job conveying this work in the original version of this manuscript. We demonstrated that total H3 levels are unaffected when comparing WT and NASP mutant stage 14 egg chambers. This means that the amount of H3 deposited into the eggs does not change in the absence of NASP. To address the reviewer's comment, we will change the text to make the link to our previous work clear.

Second, an alternate conclusion from Figure 4D/E is that mothers are depositing less H3 protein into the egg, but the same total amount is being aggregated. This amount of aggregated protein remains constant in activated eggs, but additional H3 translation leads to more total H3? The authors mention that additional translation can compensate for reduced histone pools (line 416).

Similar to our response above, the total amount of H3 in wild type and NASP mutant stage 14 egg chambers is the same. Therefore, mothers are depositing equal amounts of H3 into the egg. We will make the necessary changes in the text to make this point clear.

As the function of NASP in the cytoplasm (when it clearly imports into the nucleus) and role in H3 aggregation are major conclusions of the work, the authors need to present alternative conclusions in the text or complete additional experiments to support the claims. Again, I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

A common issue raised by all three reviewers was to more convincingly demonstrate that assay that we have used to isolate protein aggregates does, in fact, isolate protein aggregates. To verify this, we will be performing the aggregate isolation assay using controls that are known to induce more protein aggregation. We will perform the aggregation assay with egg chambers or extracts that are exposed to heat shock or the aggregation-inducing chemicals Canavanine and Azetidine-2-carboxylic acid. The chemical treatment was a welcome suggestion from reviewer #3. These experiments will significantly strengthen any claims based on the outcome of the aggregation assay.

We will also make changes to the text and include other interpretations of our work as the reviewer has suggested.

Data presentation:

Overall, I suggest moving some of the supplemental figures to the main text, adding representative movie stills to show where the quantitative data originated, and moving the H3.3 data to the supplement. Not because it's not interesting, but because H3.3 and H3.2 are behaving the same.

Where possible, we will make changes to the figure display to improve the logic and flow of the manuscript

Fig 1:

It would strengthen the figure to include representative still images that led to the quantitative data, mostly so readers understand how the data were collected.

We will add representative stills to Figure 1 to help readers understand how the data is collected. We will also a representative H3-Dendra movie similar to the NASP supplemental movie.

The inclusion of a "simulated 50% H3" in panel C is confusing. Why?

We used a 50% reduction in H3 levels because that is reduction in H3 we measure in embryos laid by NASP-mutant mothers in our previous work. A reduction in H3 levels alone would be predicted to change the nuclear import rate of H3. Thus, having a quantitative model of H3 import kinetics was key in our understanding of NASP function in vivo. We will revise the text to make this clear.

I would also consider normalizing the data between A and B (and C and D) by dividing NASP/WT. This could be included in the supplement (OPTIONAL)

We can normalize the values and include the data in a supplemental figure.

Fig S1:

The data simulation S1G should be moved to the main text, since it is the primary reason the authors reject the hypothesis that NASP influences H3 import rates.

This is a good point. We will move S1G into the Figure 1.

Fig 2:

Once again, I think it would help to include a few representative images of the photoconverted Dendra2 in the main text.

We will add representative images of the photoconversion in Figure 2.

I struggled with A/B, I think due to not knowing how the data were normalized. When I realized that the WT and NASP data are not normalized to each other, but that the NASP values are likely starting less than the WT values, it made way more sense. I suggest switching the order of data presentation so that C-F are presented first to establish that there is less chromatin-bound H3 in the first place, and then present A/B to show no change in nuclear export of the H3 that is present, allowing the conclusion of both less soluble AND chromatin-bound H3.

The order of the presentation of the data was to test if NASP was acting as a nuclear receptor. Since Figure 1 compares the nuclear import, we wanted to address the nuclear export and provide a comprehensive analysis of the role of NASP in H3 nuclear dynamics before advancing on to other consequences of NASP depletion. We can add the graphs with the un-normalized values in the Supplemental Figure to show the actual difference in total intensity values.

Fig S2:

If M1-M3 indicate males, why are the ovaries also derived from males? I think this is just confusing labeling.

We will change the labelling.

Supplemental Movie S1:

Beautiful. Would help to add a time stamp (OPTIONAL).

Thank you! We will add the time stamp to the movie

Fig 3:

Panel C is the same as Fig S1A (not Fig 1A, as is said in the legend), though I appreciate the authors pointing it out in the legend. Also see line 276.

We appreciate the reviewer for pointing this out. We will make the change in the text to correct this.

Panel D is a little confusing, because presumably the "% decrease in import rate" cannot be positive (Y axis). This could be displayed as a scatter (not bar) as in Panels B/C (right) where the top of the Y axis is set to 0.

We understand the reviewer's concern that the decrease value cannot be positive. We can adjust the y-axis so that it caps off at 0.

Fig S3:

A: What do the different panels represent? I originally thought developmental time, but now I think just different representative images? Are these age-matched from time at egg lay?

The different panels show representative images. We can clarify that in the figure legend.

C: What does "embryos" mean? Same question for Fig 4A.

In this figure, embryos mean the exact number of embryos used to form the lysate for the western blot. We will clarify this in the figure legend.

Fig 4:

A: What does "embryos" mean? Number of embryos? Age in hours?

In this figure, embryos mean the exact number of embryos used to form the lysate for the western blot. We will clarify this in the figure legend.

C: Not sure the workflow figure panel is necessary, as I can't tell what each step does. This is better explained in methods. However I appreciated the short explanation in the text (lines 314-5).

The workflow panel helps to identify the samples labelled as input and aggregate for the western blot analysis. Since our input in the western blots does not refer to the total protein lysate, we feel it is helpful to point out exactly what stage at the protocol we are utilizing the sample for our analysis.

Minor comments:

The authors should describe the nature of the NASP alleles in the main text and present evidence of robust NASP depletion, potentially both in ovaries and in embryos. The antibody works well for westerns (Fig S2B). This is sort of demonstrated later in Figure 4A, but only in NAAP x twine activated eggs.

We appreciate the reviewer's comments about the NASP mutant allele. In our previous publication, we characterized the NASP mutant fly line and its effect on both stage 14 egg chambers and the embryos. We will emphasize the reference to our previous work in the text.

Lines 163, 251, 339: minor typos

Line 184: It would help to clarify- I'm assuming cytoplasmic concentration (or overall) rather than nuclear concentration. If nuclear, I'd expect the opposite relationship. This occurs again when discussing NASP (line 267). I suspect it's also not absolute concentration, but relative concentration difference between cytoplasm and nucleus. It would help clarify if the authors were more precise.

We appreciate the reviewer's point and will add the clarification in the text.

Line 189: Given that the "established integrative model" helps to reject the hypothesis that NASP is involved in H3 import, I think it's important to describe the model a little more, even though it's previously published.

We will add few sentences giving a brief description of the model to the text.

Line 203: "The measured rate of H3.2 export from the nucleus is negligible" clarify this is in WT situations and not a conclusion from this study.

We will add the clarification of this statement in the text.

Line 211: How can the authors be so sure that the decrease in WT is due to "the loss of non-chromatin bound nucleoplasmic H3.2-Dendra2?"

From the live imaging experiments, the H3.2-Dendra2 intensity in the nucleus reduces dramatically upon nuclear envelope breakdown with the only H3.2-Dendra2 intensity remaining being the chromatin bound H3.2. Excess H3.2 is imported into the nucleus and not all of it is incorporated into the chromatin. This is a unique feature of the embryo system that has been observed previously. We mention that the intensity reduction is due to the loss of non-chromatin bound nucleoplasmic H3.2.

Line 217: In the conclusion, the authors indicate that NASP indirectly affects soluble supply of H3 in the nucleoplasm. I do believe they've shown that the import rate effect is indirect, but I don't know why they conclude that the effect of NASP on the soluble nucleoplasmic H3 supply is indirect. Similarly, the conclusion is indirect on line 239. Yet, the authors have not shown it's not direct, just assumed since NASP results in 50% decrease to deposited maternal histones.

We appreciate the feedback on the conclusions of Figure 2 from the reviewer. Our conclusions are primarily based on the effect of H3 levels in the absence of NASP in the early embryos. To establish direct causal effects, it would be important to recover the phenotypes by complementation experiments and providing molecular interactions to cause the effects. In this study we have not established those specific details to make conclusions of direct effects. We will change the text to make this more clear.

Line 292: What is the nature of the NASP "mutant?" Is it a null? Similarly, what kind of "mutant" is the twine allele? Line 295.

We will include descriptions of the NASP and twine mutants in the text.

Line 316: Why did the authors use stage 14 egg chambers here when they previously used embryos? This becomes more clear later shortly, when the authors examine activated eggs, but it's confusing in text.

The reason to use stage 14 egg chambers was to establish NASP function during oogenesis. We will modify the text to emphasize the reason behind using stage 14 egg chambers.

Lines 343-348: It's unclear if the authors are drawing extended conclusions here or if they are drawing from prior literature (if so, citations would be required). For example, why during oogenesis/embryogenesis are aggregation and degradation developmentally separated?

This conclusion is based primarily based on the findings from this study (Figure 4) and out previous published work. We will modify the text for more clarity.

Lines 386-7: I do not understand why the authors conclude that H3 aggregation and degradation are "developmentally uncoupled" and why, in the absence of NASP, "H3 aggregation precedes degradation."

This is based data in Figure 4 combined with our previous working showing that the total level of H3 in not changed in NASP-mutant stage 14 egg chambers. Aggregates seem to be more persistent in the stage 14 egg chambers (oogenesis) and they get cleared out upon egg activation (entry into embryogenesis). This provides evidence for aggregation occurring prior to degradation and these two events occurring in different developmental stages. We will change the text to make this more clear.

Line 395: Why suddenly propose that NASP also functions in the nucleus to prevent aggregation, when earlier the authors suggest it functions only in the cytoplasm?

We will make the necessary edits to ensure that the results don't suggest a role of NASP exclusive to the cytoplasm. Our findings highlight a cytoplasmic function of NASP, however, we do not want to rule out that this same function couldn't occur in the nucleus.

Lines 409-413: The authors claim that histone deficiency likely does not cause the embryonic arrest seen in embryos from NASP mutant mothers. This is because H3 is reduced by 50% yet some embryos arrest long before they've depleted this supply. However, the authors also showed that H3 import rates are affected in these embryos due to lower H3 concentration. Since the early embryo cycles are so rapid, reduced H3 import rates could lead to early arrest, even though available H3 remains in the cytoplasm.

We thank the reviewer for their suggestion. This conclusion is based on the findings from the previous study from our lab which showed that the majority of the embryos laid by NASP mutant females get arrested in the very early nuclear cycles (Reviewer #1 (Significance (Required)):

The significance of the work is conceptual, as NASP is known to function in H3 availability but the precise mechanism is elusive. This work represents a necessary advance, especially to show that NASP does not affect H3 import rates, nor does it chaperone H3 into the nucleus. However, the authors acknowledge that many questions remain. Foremost, why is NASP imported into the nucleus and what is its role there?

I believe this work will be of interest to those who focus on early animal development, but NASP may also represent a tool, as the authors conclude in their discussion, to reduce histone levels during development and examine nucleosome positioning. This may be of interest to those who work on chromatin accessibility and zygotic genome activation.

I am a genetics expert who works in Drosophila embryogenesis. I do not have the expertise to evaluate the aggregate methods presented in Figure 4.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary:

This manuscript focuses on the role of the histone chaperone NASP in Drosophila. NASP is a chaperone specific to histone H3 that is conserved in mammals. Many aspects of the molecular mechanisms by which NASP selectively binds histone H3 have been revealed through biochemical studies. However, key aspects of NASP's in vivo roles remain unclear, including where in the cell NASP functions, and how it prevents H3 degradation. Through live imaging in the early Drosophila embryo, which possesses large amounts of soluble H3 protein, Das et al determine that NASP does not control nuclear import or export of H3.2 or H3.3. Instead, they find through differential centrifugation analysis that NASP functions in the cytoplasm to prevent H3 aggregation and hence its subsequent degradation.

Major Comments:

The protein aggregation assays raise several questions. From a technical standpoint, it would be helpful to have a positive control to demonstrate that the assay is effective at detecting protein aggregates. Ie. a genotype that exhibits increased protein aggregation; this could be for a protein besides H3. A common issue raised by all three reviewers was to more convincingly demonstrate that assay that we have used to isolate protein aggregates does, in fact, isolate protein aggregates. To verify this, we will be performing the aggregate isolation assay using controls that are known to induce more protein aggregation. We will perform the aggregation assay with egg chambers or extracts that are exposed to heat shock or the aggregation-inducing chemicals Canavanine and Azetidine-2-carboxylic acid. The chemical treatment was a welcome suggestion from reviewer #3. These experiments will significantly strengthen any claims based on the outcome of the aggregation assay.

If NASP is not required to prevent H3 degradation in egg chambers, then why are H3 levels much lower in NASP input lanes relative to wild-type egg chambers in Fig 4D? We appreciate the reviewer's inputs regarding the reduced H3 levels in the NASP mutant egg chambers. We observe this reduction in H3 levels in the input because of the altered solubility of H3 which leads to the loss of H3 protein at different steps of the aggregate isolation assay. We will add a supplement figure showing H3 levels at different steps of the aggregate isolation assay. We do want to stress, however, that the total levels of H3 in stage 14 egg chambers does not change between WT and the NASP mutant.

A corollary to this is that the increased fraction of H3 in aggregates in NASP mutants seems to be entirely due to the reduction in total H3 levels rather than an increase in aggregated H3. If NASP's role is to prevent aggregation in the cytoplasm, and degradation has not yet begun in egg chambers, then why are aggregated H3 levels not increased in NASP mutants relative to wild-type egg chambers? If the same number of egg chambers were used, shouldn't the total amount of histone be the same in the absence of degradation?

In previously published work, we demonstrated that total H3 levels are unaffected when comparing WT and NASPmutant stage 14 egg chambers. This means that the amount of H3 deposited into the eggs does not change in the absence of NASP. To address the reviewer's comment, we will change the text to make the link to our previous work clear. As stated above, we will add a supplement figure showing H3 levels at different steps of the aggregate isolation assay.

The live imaging studies are well designed, executed, and quantified. They use an established genotype (H3.2-Dendra2) in wild-type and NASP maternal mutants to demonstrate that NASP is not directly involved in nuclear import of H3.2. Decreased import is likely due to reduced H3.2 levels in NASP mutants rather than reduced import rates per se. The same methodology was used to determine that loss of NASP did not affect H3.2 nuclear export. These findings eliminate H3.2 nuclear import/export regulation as possible roles for NASP, which had been previously proposed.

Thank you.

Live imaging also conclusively demonstrates that the levels of H3.2 in the nucleoplasm and in mitotic chromatin are significantly lower in NASP mutants than wild-type nuclei. Despite these lower histone levels, the nuclear cycle duration is only modestly lengthened. The live imagining of NASP-Dendra2 nuclear import conclusively demonstrate that NASP and H3.2 are unlikely to be imported into the nucleus as one complex.

Thank you.

Minor Comments:

Additional details on how the NASP-Dendra2 CRISPR allele was generated should be provided. In addition, additional details on how it was determined that this allele is functional should be provided (e.g. quantitative assays for fertility/embryo viability of NASP-Dendra2 females) We will make these additions to the text.

If statistical tests are used to determine significance, the type of test used should be reported in the figure legends throughout.

We will make the addition of the statistical tests to the figure legends.

The western blot shown in Figure 4A looks more like a 4-fold reduction in H3 levels in NASP mutants relative to wild-type embryos, rather than the quantified 2-fold reduction. Perhaps a more representative blot can be shown.

We have additional blots in the supplemental figure S3C. The quantification was performed after normalization to the total protein levels and we can highlight that in the figure legend.

Reviewer #2 (Significance (Required)):

As a fly chromatin biologist with colleagues that utilize mammalian experimental systems, I feel this manuscript will be of broad interest to the chromatin research community. Packaging of the genome into chromatin affects nearly every DNA-templated process, making the mechanisms by which histone proteins are expressed, chaperoned, and deposited into chromatin of high importance to the field. The study has multiple strengths, including high-quality quantitative imaging, use of a terrific experimental system (storage and deposition of soluble histones in early fly embryos). The study also answers outstanding questions in the field, specifically that NASP does not control nuclear import/export of histone H3. Instead, the authors propose that NASP functions to prevent protein aggregation. If this could be conclusively demonstrated, it would be valuable to the field. However, the protein aggregation studies need improvement. Technical demonstration that their differential centrifugation assay accurately detects aggregated proteins is needed. Further, NASP mutants do not exhibit increased H3 protein aggregation in the data presented. Instead, the increased fraction of aggregated H3 in NASP mutants seems to be due to a reduction in the overall levels of H3 protein, which is contrary to the model presented in this paper.

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

This manuscript by Das et al. entitled "NASP functions in the cytoplasm to prevent histone H3 aggregation during early embryogenesis", explores the role of the histone chaperone NASP in regulating histone H3 dynamics during early Drosophila embryogenesis. Using primarily live imaging approaches, the authors found that NASP is not directly involved in the import or export of H3. Moreover, the authors claimed that NASP prevents H3 aggregation rather than protects against degradation.

Major Comments:

Figure 1A-B: The plotted data appear to have substantial dispersion. Could the authors include individual data points or provide representative images to help the reader assess variability?

We chose to show unnormalized data in Figure 1 so readers could better compare the actual import values of H3 in the presence and absence of NASP. We felt it was a better representation of the true biological difference although raw data is more dispersive. We did also include normalized data in the supplement. Regardless, we will add representative stills to Figure 1 and include a H3-Dendra2 movie in the supplement to show the representative data.

Given that the authors conclude that the reduced nuclear import is due to lowered H3 levels in NASP-deficient embryos, would overexpression of H3 rescue this phenotype? This would directly test whether H3 levels, rather than import machinery per se, drive the effect.

We thank the reviewer for their valuable suggestion. We and others have tried to overexpress histones in the Drosophila early embryo without success. There must be an undefined feedback mechanism preventing histone overexpression in the germline. In fact, a recent paper has been deposited on bioRxiv (https://doi.org/10.1101/2024.12.23.630206) that suggest H4 protein could provide a feedback mechanism to prevent histone overexpression. While we would love to do this experiment, it is not technically feasible at this time.

Figure 2A-B: The authors present the Relative Intensity of H3-Dendra2, but this metric obscures absolute differences between Control and NASP knockout embryos. Please include Total Intensity plots to show the actual reduction in H3 levels.

We will add the total H3-Dendra2 intensity plots to the supplemental figure for the export curves.

Additionally, Western blot analysis of nucleoplasmic H3 from wild-type vs. NASP-deficient embryos would provide essential biochemical confirmation of H3 level reductions.

We will measure nuclear H3 levels by western from 0-2 hr embryos laid by WT and NASP mutant flies.

Figure 4: To support the conclusion that NASP prevents H3 aggregation, I recommend performing aggregation assays by adding compounds that induce unfolding (amino acid analogues that induce unfolding, like canavanine or Azetidine-2-carboxylic acid) or using aggregation-prone H3 mutants.

This is a very helpful suggestion! It is difficult to get chemicals into Drosophila eggs, but we will treat extracts directly with these chemicals. Additionally, we will use heat shocked eggs and extracts as an additional control.

Inclusion of CMA and proteasome inhibition experiments could also clarify whether degradation pathways are secondarily involved or compensatory in the absence of NASP.

The degradation pathway for H3 in the absence of NASP is unknown and a major focus of our future work is to define this pathway. Drosophila does not have a CMA pathway and therefore, we don't know how H3 aggregates are being sensed.

Minor Comments:

(1) The Introduction would benefit from mentioning the two NASP isoforms that exist in mammals (sNASP and tNASP), as this evolutionary context may inform interpretation of the Drosophila results.

We will make the edits in the text to include that Drosophila NASP is the sole homolog of sNASP and that tNASP ortholog is not found in Drosophila.

(2) Could the authors comment on the status of histone H4 in their experimental system? Given the observed cytoplasmic pool of H3, is it likely to exist as a monomer? If this H3 pool is monomeric, does that suggest an early failure in H3-H4 dimerization, and could this contribute to its aggregation propensity?

In our previous work we noted that NASP binds more preferentially to H3 and the levels of H3 we much more reduced upon NASP depletion than H4. We pointed out in this publication that our data was consistent with H3 stores being monomeric in the Drosophila embryo. We don't' have a H4-Dendra2 line to test. In the future, however, this is something we are very keen to look at.

Reviewer #3 (Significance (Required)):

This work addresses a timely and important question in the field of chromatin biology and developmental epigenetics. The focus on histone homeostasis during embryogenesis and the cytoplasmic role of NASP adds a novel perspective. The live imaging experiments are a clear strength, providing valuable spatiotemporal insights. However, I believe that the manuscript would benefit significantly from additional biochemical validation to support and clarify some of the mechanistic claims.

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

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Referee #3

Evidence, reproducibility and clarity

This manuscript by Das et al. entitled "NASP functions in the cytoplasm to prevent histone H3 aggregation during early embryogenesis", explores the role of the histone chaperone NASP in regulating histone H3 dynamics during early Drosophila embryogenesis. Using primarily live imaging approaches, the authors found that NASP is not directly involved in the import or export of H3. Moreover, the authors claimed that NASP prevents H3 aggregation rather than protects against degradation.

Major Comments:

Figure 1A-B: The plotted data appear to have substantial dispersion. Could the authors include individual data points or provide representative images to help the reader assess variability? Given that the authors conclude that the reduced nuclear import is due to lowered H3 levels in NASP-deficient embryos, would overexpression of H3 rescue this phenotype? This would directly test whether H3 levels, rather than import machinery per se, drive the effect.

Figure 2A-B: The authors present the Relative Intensity of H3-Dendra2, but this metric obscures absolute differences between Control and NASP knockout embryos. Please include Total Intensity plots to show the actual reduction in H3 levels. Additionally, Western blot analysis of nucleoplasmic H3 from wild-type vs. NASP-deficient embryos would provide essential biochemical confirmation of H3 level reductions.

Figure 4: To support the conclusion that NASP prevents H3 aggregation, I recommend performing aggregation assays by adding compounds that induce unfolding (amino acid analogues that induce unfolding, like canavanine or Azetidine-2-carboxylic acid) or using aggregation-prone H3 mutants. Inclusion of CMA and proteasome inhibition experiments could also clarify whether degradation pathways are secondarily involved or compensatory in the absence of NASP.

Minor Comments:

(1) The Introduction would benefit from mentioning the two NASP isoforms that exist in mammals (sNASP and tNASP), as this evolutionary context may inform interpretation of the Drosophila results.

(2) Could the authors comment on the status of histone H4 in their experimental system? Given the observed cytoplasmic pool of H3, is it likely to exist as a monomer? If this H3 pool is monomeric, does that suggest an early failure in H3-H4 dimerization, and could this contribute to its aggregation propensity?

Significance

This work addresses a timely and important question in the field of chromatin biology and developmental epigenetics. The focus on histone homeostasis during embryogenesis and the cytoplasmic role of NASP adds a novel perspective. The live imaging experiments are a clear strength, providing valuable spatiotemporal insights. However, I believe that the manuscript would benefit significantly from additional biochemical validation to support and clarify some of the mechanistic claims.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary:

This manuscript focuses on the role of the histone chaperone NASP in Drosophila. NASP is a chaperone specific to histone H3 that is conserved in mammals. Many aspects of the molecular mechanisms by which NASP selectively binds histone H3 have been revealed through biochemical studies. However, key aspects of NASP's in vivo roles remain unclear, including where in the cell NASP functions, and how it prevents H3 degradation. Through live imaging in the early Drosophila embryo, which possesses large amounts of soluble H3 protein, Das et al determine that NASP does not control nuclear import or export of H3.2 or H3.3. Instead, they find through differential centrifugation analysis that NASP functions in the cytoplasm to prevent H3 aggregation and hence its subsequent degradation.

Major Comments:

1. The protein aggregation assays raise several questions.

a. From a technical standpoint, it would be helpful to have a positive control to demonstrate that the assay is effective at detecting protein aggregates. Ie. a genotype that exhibits increased protein aggregation; this could be for a protein besides H3.

b. If NASP is not required to prevent H3 degradation in egg chambers, then why are H3 levels much lower in NASP input lanes relative to wild-type egg chambers in Fig 4D?

c. A corollary to this is that the increased fraction of H3 in aggregates in NASP mutants seems to be entirely due to the reduction in total H3 levels rather than an increase in aggregated H3. If NASP's role is to prevent aggregation in the cytoplasm, and degradation has not yet begun in egg chambers, then why are aggregated H3 levels not increased in NASP mutants relative to wild-type egg chambers? If the same number of egg chambers were used, shouldn't the total amount of histone be the same in the absence of degradation? 2. The live imaging studies are well designed, executed, and quantified. They use an established genotype (H3.2-Dendra2) in wild-type and NASP maternal mutants to demonstrate that NASP is not directly involved in nuclear import of H3.2. Decreased import is likely due to reduced H3.2 levels in NASP mutants rather than reduced import rates per se. The same methodology was used to determine that loss of NASP did not affect H3.2 nuclear export. These findings eliminate H3.2 nuclear import/export regulation as possible roles for NASP, which had been previously proposed. 3. Live imaging also conclusively demonstrates that the levels of H3.2 in the nucleoplasm and in mitotic chromatin are significantly lower in NASP mutants than wild-type nuclei. Despite these lower histone levels, the nuclear cycle duration is only modestly lengthened. 4. The live imagining of NASP-Dendra2 nuclear import conclusively demonstrate that NASP and H3.2 are unlikely to be imported into the nucleus as one complex.

Minor Comments:

1. Additional details on how the NASP-Dendra2 CRISPR allele was generated should be provided. In addition, additional details on how it was determined that this allele is functional should be provided (e.g. quantitative assays for fertility/embryo viability of NASP-Dendra2 females)
2. If statistical tests are used to determine significance, the type of test used should be reported in the figure legends throughout.
3. The western blot shown in Figure 4A looks more like a 4-fold reduction in H3 levels in NASP mutants relative to wild-type embryos, rather than the quantified 2-fold reduction. Perhaps a more representative blot can be shown.

Significance

As a fly chromatin biologist with colleagues that utilize mammalian experimental systems, I feel this manuscript will be of broad interest to the chromatin research community. Packaging of the genome into chromatin affects nearly every DNA-templated process, making the mechanisms by which histone proteins are expressed, chaperoned, and deposited into chromatin of high importance to the field. The study has multiple strengths, including high-quality quantitative imaging, use of a terrific experimental system (storage and deposition of soluble histones in early fly embryos). The study also answers outstanding questions in the field, specifically that NASP does not control nuclear import/export of histone H3. Instead, the authors propose that NASP functions to prevent protein aggregation. If this could be conclusively demonstrated, it would be valuable to the field. However, the protein aggregation studies need improvement. Technical demonstration that their differential centrifugation assay accurately detects aggregated proteins is needed. Further, NASP mutants do not exhibit increased H3 protein aggregation in the data presented. Instead, the increased fraction of aggregated H3 in NASP mutants seems to be due to a reduction in the overall levels of H3 protein, which is contrary to the model presented in this paper.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The authors investigate the function of the H3 chaperone NASP, which is known to bind directly to H3 and prevent degradation of soluble H3. What is unclear is where NASP functions in the cell (nucleus or cytoplasm), how NASP protects H3 from degradation (direct or indirect), and if NASP affects H3 dynamics (nuclear import or export). They use the powerful model system of Drosophila embryos because the soluble H3 pool is high due to maternal deposition and they make use of photoconvertable Dendra-tagged proteins, since these are maternally deposited and can be used to measure nuclear import/export rates.

Using these systems and tools, they conclude that NASP affects nuclear import, but only indirectly, because embryos from NASP mutant mothers start out with 50% of the maternally deposited H3. Because of the depleted H3 and reduced import rates, NASP deficient embryos also have reduced nucleoplasmic and chromatin-associated H3. Using a new Dendra-tagged NASP allele, the authors show that NASP and H3 have different nuclear import rates, indicating that NASP is not a chaperone that shuttles H3 into the nucleus. They test H3 levels in embryos that have no nuclei and conclude that NASP functions in the cytoplasm, and through protein aggregation assays they conclude that NASP prevents H3 aggregation.

Major comments:

The text was easy to read and logical. The data are well presented, methods are complete, and statistics are robust. The conclusions are largely reasonable. However, I am having trouble connecting the conclusions in text to the data presented in Figure 4.

First, I'm confused why the conclusion from Figure 4A is that NASP functions in the cytoplasm of the egg. Couldn't NASP be required in the ovary (in, say, nurse cell nuclei) to stimulate H3 expression and deposition into the egg? The results in 4A would look the same if the mothers deposit 50% of the normal H3 into the egg. Why is NASP functioning specifically in the cytoplasm when it is also so clearly imported into the nucleus? Maybe NASP functions wherever it is, and by preventing nuclear import, you force it to function in the cytoplasm. I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

Second, an alternate conclusion from Figure 4D/E is that mothers are depositing less H3 protein into the egg, but the same total amount is being aggregated. This amount of aggregated protein remains constant in activated eggs, but additional H3 translation leads to more total H3? The authors mention that additional translation can compensate for reduced histone pools (line 416).

As the function of NASP in the cytoplasm (when it clearly imports into the nucleus) and role in H3 aggregation are major conclusions of the work, the authors need to present alternative conclusions in the text or complete additional experiments to support the claims. Again, I do not have additional suggestions for experiments, but I think the authors need to be very clear about the different interpretations of these data and to discuss WHY they believe their conclusion is strongest.

Data presentation:

Overall, I suggest moving some of the supplemental figures to the main text, adding representative movie stills to show where the quantitative data originated, and moving the H3.3 data to the supplement. Not because it's not interesting, but because H3.3 and H3.2 are behaving the same.

Fig 1:

It would strengthen the figure to include representative still images that led to the quantitative data, mostly so readers understand how the data were collected. The inclusion of a "simulated 50% H3" in panel C is confusing. Why? I would also consider normalizing the data between A and B (and C and D) by dividing NASP/WT. This could be included in the supplement (OPTIONAL)

Fig S1:

The data simulation S1G should be moved to the main text, since it is the primary reason the authors reject the hypothesis that NASP influences H3 import rates.

Fig 2:

Once again, I think it would help to include a few representative images of the photoconverted Dendra2 in the main text. I struggled with A/B, I think due to not knowing how the data were normalized. When I realized that the WT and NASP data are not normalized to each other, but that the NASP values are likely starting less than the WT values, it made way more sense. I suggest switching the order of data presentation so that C-F are presented first to establish that there is less chromatin-bound H3 in the first place, and then present A/B to show no change in nuclear export of the H3 that is present, allowing the conclusion of both less soluble AND chromatin-bound H3.

Fig S2:

If M1-M3 indicate males, why are the ovaries also derived from males? I think this is just confusing labeling. Supplemental Movie S1: Beautiful. Would help to add a time stamp (OPTIONAL).

Fig 3:

Panel C is the same as Fig S1A (not Fig 1A, as is said in the legend), though I appreciate the authors pointing it out in the legend. Also see line 276. Panel D is a little confusing, because presumably the "% decrease in import rate" cannot be positive (Y axis). This could be displayed as a scatter (not bar) as in Panels B/C (right) where the top of the Y axis is set to 0.

Fig S3:

A: What do the different panels represent? I originally thought developmental time, but now I think just different representative images? Are these age-matched from time at egg lay? C: What does "embryos" mean? Same question for Fig 4A. Fig 4: A: What does "embryos" mean? Number of embryos? Age in hours? C: Not sure the workflow figure panel is necessary, as I can't tell what each step does. This is better explained in methods. However I appreciated the short explanation in the text (lines 314-5).

Minor comments:

The authors should describe the nature of the NASP alleles in the main text and present evidence of robust NASP depletion, potentially both in ovaries and in embryos. The antibody works well for westerns (Fig S2B). This is sort of demonstrated later in Figure 4A, but only in NAAP x twine activated eggs.

Lines 163, 251, 339: minor typos Line 184: It would help to clarify- I'm assuming cytoplasmic concentration (or overall) rather than nuclear concentration. If nuclear, I'd expect the opposite relationship. This occurs again when discussing NASP (line 267). I suspect it's also not absolute concentration, but relative concentration difference between cytoplasm and nucleus. It would help clarify if the authors were more precise. Line 189: Given that the "established integrative model" helps to reject the hypothesis that NASP is involved in H3 import, I think it's important to describe the model a little more, even though it's previously published. Line 203: "The measured rate of H3.2 export from the nucleus is negligible" clarify this is in WT situations and not a conclusion from this study. Line 201: How can the authors be so sure that the decrease in WT is due to "the loss of non-chromatin bound nucleoplasmid H3.2-Dendra2?" Line 217: In the conclusion, the authors indicate that NASP indirectly affects soluble supply of H3 in the nucleoplasm. I do believe they've shown that the import rate effect is indirect, but I don't know why they conclude that the effect of NASP on the soluble nucleoplasmic H3 supply is indirect. Similarly, the conclusion is indirect on line 239. Yet, the authors have not shown it's not direct, just assumed since NASP results in 50% decrease to deposited maternal histones. Line 292: What is the nature of the NASP "mutant?" Is it a null? Similarly, what kind of "mutant" is the twine allele? Line 295. Line 316: Why did the authors use stage 14 egg chambers here when they previously used embryos? This becomes more clear later shortly, when the authors examine activated eggs, but it's confusing in text. Lines 343-348: It's unclear if the authors are drawing extended conclusions here or if they are drawing from prior literature (if so, citations would be required). For example, why during oogenesis/embryogenesis are aggregation and degradation developmentally separated? Lines 386-7: I do not understand why the authors conclude that H3 aggregation and degradation are "developmentally uncoupled" and why, in the absence of NASP, "H3 aggregation precedes degradation." Line 395: Why suddenly propose that NASP also functions in the nucleus to prevent aggregation, when earlier the authors suggest it functions only in the cytoplasm? Lines 409-413: The authors claim that histone deficiency likely does not cause the embryonic arrest seen in embryos from NASP mutant mothers. This is because H3 is reduced by 50% yet some embryos arrest long before they've depleted this supply. However, the authors also showed that H3 import rates are affected in these embryos due to lower H3 concentration. Since the early embryo cycles are so rapid, reduced H3 import rates could lead to early arrest, even though available H3 remains in the cytoplasm.

Significance

The significance of the work is conceptual, as NASP is known to function in H3 availability but the precise mechanism is elusive. This work represents a necessary advance, especially to show that NASP does not affect H3 import rates, nor does it chaperone H3 into the nucleus. However, the authors acknowledge that many questions remain. Foremost, why is NASP imported into the nucleus and what is its role there?

I believe this work will be of interest to those who focus on early animal development, but NASP may also represent a tool, as the authors conclude in their discussion, to reduce histone levels during development and examine nucleosome positioning. This may be of interest to those who work on chromatin accessibility and zygotic genome activation.

I am a genetics expert who works in Drosophila embryogenesis. I do not have the expertise to evaluate the aggregate methods presented in Figure 4.

this

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intéressant

Reviewer #1 (Public review):

Summary

The manuscript by Ma et al. provides robust and novel evidence that the noctuid moth Spodoptera frugiperda (Fall Armyworm) possesses a complex compass mechanism for seasonal migration that integrates visual horizon cues with Earth's magnetic field (likely its horizontal component). This is an important and timely study: apart from the Bogong moth, no other nocturnal Lepidoptera has yet been shown to rely on such a dual-compass system. The research therefore expands our understanding of magnetic orientation in insects with both theoretical (evolution and sensory biology) and applied (agricultural pest management, a new model of magnetoreception) significance.

The study uses state-of-the-art methods and presents convincing behavioural evidence for a multimodal compass. It also establishes the Fall Armyworm as a tractable new insect model for exploring the sensory mechanisms of magnetoreception, given the experimental challenges of working with migratory birds. Overall, the experiments are well-designed, the analyses are appropriate, and the conclusions are generally well supported by the data.

Strengths

(1) Novelty and significance: First strong demonstration of a magnetic-visual compass in a globally relevant migratory moth species, extending previous findings from the Bogong moth and opening new research avenues in comparative magnetoreception.

(2) Methodological robustness: Use of validated and sophisticated behavioural paradigms and magnetic manipulations consistent with best practices in the field. The use of 5-minute bins to study the dynamic nature of the magnetic compass which is anchored to a visual cue but updated with a latency of several minutes, is an important finding and a new methodological aspect in insect orientation studies.

(3) Clarity of experimental logic: The cue-conflict and visual cue manipulations are conceptually sound and capable of addressing clear mechanistic questions.

(4) Ecological and applied relevance: Results have implications for understanding migration in an invasive agricultural pest with an expanding global range.

(5) Potential model system: Provides a new, experimentally accessible species for dissecting the sensory and neural bases of magnetic orientation.

Weaknesses

While the study is strong overall, several recommendations should be addressed to improve clarity, contextualisation, and reproducibility:

(1) Structure and presentation of results

Requires reordering the visual-cue experiments to move from simpler (no cues) to more complex (cue-conflict) conditions, improving narrative logic and accessibility for non-specialists.

(2) Ecological interpretation

(a) The authors should discuss how their highly simplified, static cue setup translates to natural migratory conditions where landmarks are dynamic, transient or absent.

(b) Further consideration is required regarding how the compass might function when landmarks shift position, are obscured, or are replaced by celestial cues. Also, more consolidated (one section) and concrete suggestions for future experiments are needed, with transient, multiple, or more naturalistic visual cues to address this.

(3) Methodological details and reproducibility

(a) It would be better to move critical information (e.g., electromagnetic noise measurements) from the supplementary material into the main Methods.

(b) Specifying luminance levels and spectral composition at the moth's eye is required for all visual treatments.

(c) Details are needed on the sex ratio/reproductive status of tested moths, and a map of the experimental site and migratory routes (spring vs. fall) should be included.

(d) Expanding on activity-level analyses is required, replacing "fatigue" with "reduced flight activity," and clarifying if such analyses were performed.

(4) Figures and data presentation

(a) The font sizes on circular plots should be increased; compass labels (magnetic North), sample sizes, and p-values should be included.

(b) More clarity is required on what "no visual cue" conditions entail, and schematics or photos should be provided.

(c) The figure legends should be adjusted for readability and consistency (e.g., replace "magnetic South" with magnetic North, and for box plots better to use asterisks for significance, report confidence intervals).

(5) Conceptual framing and discussion

(a) Generalisations across species should be toned down, given the small number of systems tested by overlapping author groups.

(b) It requires highlighting that, unlike some vertebrates, moths require both magnetic and visual cues for orientation.

(c) It should be emphasised that this study addresses direction finding rather than full navigation.

(d) Future Directions should be integrated and consolidated into one coherent subsection proposing realistic next steps (e.g., more complex visual environments, temporal adaptation to cue-field relationships).

(e) The limitations should be better discussed, due to the artificiality of the visual cue earlier in the Discussion.

(6) Technical and open-science points

• Appropriate circular statistics should be used instead of t-tests for angular data shown in the supplementary material.

• Details should be provided on light intensities, power supplies, and improvements to the apparatus.

• The derivation of individual r-values should be clarified.

• Share R code openly (e.g., GitHub).

• Some highly relevant - yet missing - recent and relevant citations should be added, and some less relevant ones removed.

Reviewer #1 (Public review):

Tamao et al. aimed to quantify the diversity and mutation rate of the influenza (PR8 strain) in order to establish a high-resolution method for studying intra-host viral evolution . To achieve this, the authors combined RNA sequencing with single-molecule unique molecular identifiers (UMIs) to minimize errors introduced during technical processing. They proposed an in vitro infection model with a single viral particle to represent biological genetic diversity, alongside a control model using in vitro transcribed RNA for two viral genes, PB2 and HA.

Through this approach, the authors demonstrated that UMIs reduced technical errors by approximately tenfold. By analyzing four viral populations and comparing them to in vitro transcribed RNA controls, they estimated that ~98.1% of observed mutations originated from viral replication rather than technical artifacts. Their results further showed that most mutations were synonymous and introduced randomly. However, the distribution of mutations suggested selective pressures that favored certain variants. Additionally, comparison with closely related influenza strain (A/Alaska/1935) revealed two positively selected mutations, though these were absent in the strain responsible for the most recent pandemic (CA01).

Overall, the study is well-designed, and the interpretations are strongly supported by the data.

The authors have addressed all the comments from the previous round of reviews. No further concerns.

Reviewer #2 (Public review):

Summary:

This manuscript presents a technically oriented application of UMI-based long-read sequencing to study intra-host diversity in influenza virus populations. The authors aim to minimize sequencing artifacts and improve the detection of rare variants, proposing that this approach may inform predictive models of viral evolution. While the methodology appears robust and successfully reduces sequencing error rates, key experimental and analytical details are missing, and the biological insight is modest. The study includes only four samples, with no independent biological replicates or controls, which limits the generalizability of the findings. Claims related to rare variant detection and evolutionary selection are not fully supported by the data presented.

Strengths:

The study addresses an important technical challenge in viral genomics by implementing a UMI-based long-read sequencing approach to reduce amplification and sequencing errors. The methodological focus is well presented, and the work contributes to improving the resolution of low-frequency variant detection in complex viral populations.

Weaknesses:

The application of UMI-based error correction to viral population sequencing has been established in previous studies (e.g., in HIV), and this manuscript does not introduce a substantial methodological or conceptual advance beyond its use in the context of influenza.

The study lacks independent biological replicates or additional viral systems that would strengthen the generalizability of the conclusions. Potential sources of technical error are not explored or explicitly controlled. Key methodological details are missing, including the number of PCR cycles, the input number of molecules, and UMI family size distributions. These are essential to support the claimed sensitivity of the method.

The assertion that variants at {greater than or equal to}0.1% frequency can be reliably detected is based on total read count rather than the number of unique input molecules. Without information on UMI diversity and family sizes, the detection limit cannot be reliably assessed.

Although genetic variation is described, the functional relevance of observed mutations in HA and NA is not addressed or discussed in the context of known antigenic or evolutionary features of influenza. The manuscript is largely focused on technical performance, with limited exploration of the biological implications or mechanistic insights into influenza virus evolution.

The experimental scale is small, with only four viral populations derived from single particles analyzed. This limited sample size restricts the ability to draw broader conclusions about quasispecies dynamics or evolutionary pressures.

Comments on revisions:

The revised manuscript provides additional methodological detail and clearer presentation, which improves transparency. However, the main limitations persist: the study remains small in scale, lacks independent validation, and relies on theoretical rather than empirical support for its claimed detection sensitivity. As a result, the work represents a modest technical advance rather than a substantive contribution to understanding influenza virus evolution.

Author response:

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

Reviewer #1 (Public review):

(1) The methods section is overly brief. Even if techniques are cited, more experimental details should be included. For example, since the study focuses heavily on methodology, details such as the number of PCR cycles in RT-PCR or the rationale for choosing HA and PB2 as representative in vitro transcripts should be provided.

We thank the reviewer for this important suggestion. We have now expanded the Methods section to include the number of PCR cycles used in RT-PCR (line 407) and have explained the rationale for choosing HA and PB2 as representative transcripts (line 388).

(2) Information on library preparation and sequencing metrics should be included. For example, the total number of reads, any filtering steps, and quality score distributions/cutoff for the analyzed reads.

We agree and have added detailed information on library preparation, filtering criteria, quality score thresholds, and sequencing statistics for each sample (line 422, Figure S2).

(3) In the Results section (line 115, "Quantification of error rate caused by RT"), the mutation rate attributed to viral replication is calculated. However, in line 138, it is unclear whether the reported value reflects PB2, HA, or both, and whether the comparison is based on the error rate of the same viral RNA or the mean of multiple values (as shown in Figure 3A). Please clarify whether this number applies universally to all influenza RNAs or provide the observed range.

We appreciate this point. We have clarified in the Results (line 140) that the reported value corresponds to PB2.

(4) Since the T7 polymerase introduced errors are only applied to the in vitro transcription control, how were these accounted for when comparing mutation rates between transcribed RNA and cell-culture-derived virus?

We agree that errors introduced by T7 RNA polymerase are present only in the in vitro–transcribed RNA control. However, even when taking this into account, the error rate detected in the in vitro transcripts remained substantially lower than that observed in the viral RNA extracted from replicated virus (line 140, Fig.3a). Thus, the difference cannot be explained by T7-derived errors, and our conclusion regarding the elevated mutation rate in cell-culture–derived viral populations remains valid.

(5) Figure 2 shows that a UMI group size of 4 has an error rate of zero, but this group size is not mentioned in the text. Please clarify.

We have revised the Results (line 98) to describe the UMI group size of 4.

Reviewer #2 (Public review):

(1) The application of UMI-based error correction to viral population sequencing has been established in previous studies (e.g., HIV), and this manuscript does not introduce a substantial methodological or conceptual advance beyond its use in the context of influenza.

We appreciate the reviewer’s comment and agree that UMI-based error correction has been applied previously to viral population sequencing, including HIV. However, to our knowledge, relatively few studies have quantitatively evaluated both the performance of this method and the resulting within-quasi-species mutation distributions in detail. In our manuscript, we not only validate the accuracy of UMIbased error correction in the context of influenza virus sequencing, but also quantitatively characterize the features of intra-quasi-species distributions, which provides new insights into the mutational landscape and evolutionary dynamics specific to influenza. We therefore believe that our work goes beyond a simple application of an established method.

(2) The study lacks independent biological replicates or additional viral systems that would strengthen the generalizability of the conclusions.

We agree with the reviewer that the lack of independent biological replicates and additional viral systems limits the generalizability of our findings. In this study, we intentionally focused on single-particle–derived populations of influenza virus to establish a proof-of-principle for our sequencing and analytical framework. While this design provided a clear demonstration of the method’s ability to capture mutation distributions at the single-particle level, we acknowledge that additional biological replicates and testing across diverse viral systems would be necessary to confirm the broader applicability of our observations. Importantly, even within this limited framework, our analysis enabled us to draw conclusions at the level of individual viral populations and to suggest the possibility of comparing their mutation distributions with known evolvability. This highlights the potential of our approach to bridge observations from single particles with broader patterns of viral evolution. In future work, we plan to expand the number of populations analyzed and include additional viral systems, which will allow us to more rigorously assess reproducibility and to establish systematic links between mutation accumulation at the single-particle level and evolutionary dynamics across viruses.

(3) Potential sources of technical error are not explored or explicitly controlled. Key methodological details are missing, including the number of PCR cycles, the input number of molecules, and UMI family size distributions.

We thank the reviewer for this important suggestion. We have now expanded the Methods section to include the number of PCR cycles used in RT-PCR (line 407). In addition, we have added information on the estimated number of input molecules. Regarding the UMI family size distributions, we have added the data as Figure S2 and referred to it in the revised manuscript.

Finally, with respect to potential sources of technical error, we note that this point is already addressed in the manuscript by direct comparison with in vitro transcribed RNA controls, which encompass errors introduced throughout the entire experimental process. This comparison demonstrates that the error-correction strategy employed here effectively reduces the impact of PCR or sequencing artifacts.

(4) The assertion that variants at ≥0.1% frequency can be reliably detected is based on total read count rather than the number of unique input molecules. Without information on UMI diversity and family sizes, the detection limit cannot be reliably assessed.

We thank the reviewer for raising this important issue. We agree that our original description was misleading, as the reliable detection limit should not be defined solely by total read count. In the revised version, we have added information on UMI distribution and family sizes (Figure S2), and we now state the detection limit in terms of consensus reads. Specifically, we define that variants can be reliably detected when ≥10,000 consensus reads are obtained with a group size of ≥3 (line 173). 

(5)  Although genetic variation is described, the functional relevance of observed mutations in HA and NA is not addressed or discussed.

We appreciate the reviewer’s suggestion. In our study, we did not apply drug or immune selection pressure; therefore, we did not expect to detect mutations that are already known to cause major antigenic changes in HA or NA, and we think it is difficult to discuss such functional implications in this context. However, as noted in discussion, we did identify drug resistance–associated mutations. This observation suggests that the quasi-species pool may provide functional variation, including resistance, even in the absence of explicit selective pressure. We have clarified this point in the text to better address the reviewer’s concern (line 330).

(6) The experimental scale is small, with only four viral populations derived from single particles analyzed. This limited sample size restricts the ability to draw broader conclusions.

We thank the reviewer for pointing out the limitation of analyzing only four viral populations derived from single particles. We fully acknowledge that the small sample size restricts the generalizability of our conclusions. Nevertheless, we would like to emphasize that even within this limited dataset, our results consistently revealed a slight but reproducible deviation of the mutation distribution from the Poisson expectation, as well as a weak correlation with inter-strain conservation. These recurring patterns highlight the robustness of our observations despite the sample size.

In future work, we plan to expand the number of viral populations analyzed and to monitor mutation distributions during serial passage under defined selective pressures. We believe that such expanded analyses will enable us to more reliably assess how mutations accumulate and to develop predictive frameworks for viral evolution.

Reviewer #1 (Recommendations for the authors):

(1)  Please mention Figure 1 and S2 in the text.

Done. We now explicitly reference Figures 1 and S2 (renamed to S1 according to appearance order) in the appropriate sections (lines 74, 124).

(2)  In Figure 4A, please specify which graph corresponds to PB2 and which to PB2-like sequences.

Corrected. Figure 4A legend now specify PB2 vs. PB2-like sequences.

(3)  Consider reducing redundancy in lines 74, 149, 170, 214, and 215.

We thank the reviewer for this stylistic suggestion. We have revised the text to reduce redundancy in these lines.

Reviewer #2 (Recommendations for the authors):

(1)  The manuscript states that "with 10,000 sequencing reads per gene ...variants at ≥0.1% frequency can be reliably detected." However, this interpretation conflates raw read counts with independent input molecules.

We have revised this statement throughout the text to clarify that sensitivity depends on the number of unique UMIs rather than raw read counts (line 173). To support this, we calculated the probability of detecting a true variant present at a frequency of 0.1% within a population. When sequencing ≥10,000 unique molecules, such a variant would be observed at least twice with a probability of approximately 99.95%. In contrast, the error rate of in vitro–transcribed RNA, reflecting errors introduced during the experimental process, was estimated to be on the order of 10⁻⁶ (line 140, Fig. 3a). Under this condition, the probability that the same artificial error would arise independently at the same position in two out of 10,000 molecules is <0.5%. Therefore, variants present at ≥0.1% can be reliably distinguished from technical artifacts and are confidently detected under our sequencing conditions.

(2) To support the claimed sensitivity, please provide for each gene and population: (a) UMI family size distributions, (b) number of PCR cycles and input molecule counts, and (c) recalculation of the detection limit based on unique molecules.

If possible, I encourage experimental validation of sensitivity claims, such as spike-in controls at known variant frequencies, dilution series, or technical replicates to demonstrate reproducibility at the 0.1% detection level.

We have added (a) histograms of UMI family size distributions for each gene and population (Figure S2), (b) detailed method RT-PCR protocol and estimated input counts (line 407), and (c) recalculated detection limits (line 173).

We appreciate the reviewer’s suggestion and fully recognize the value of spike-in experiments. However, given the observed mutation rate of T7-derived RNA and the sufficient sequencing depth in our dataset, it is evident that variants above the 0.1% threshold can be robustly detected without additional spike-in controls.

Je trouve qu'il y a une erreur. Dans la formation sur HTML5 et CSS3 on nous apprend que notre code doit toujours avoir la structure suivante : header (composée de "nav"), main (composée de plusieurs "section") et de footer. Or, ici, on nous montre un "header" et un "main", et la réponse n'est que "main". Etrange … Il fallait écrire "quelles balises" afin qu'on puisse choisir les deux correspondantes.

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this text helps highlight the versatility of Hip-hop in the classroom, either analyzing the deep political statements that some rappers make in there lyrics to reading and connecting them with poetry. it also helps promote cultural knowledge and academic engagement, for example earlier in the text Sanchez mentions students being labeled as troubled or behind when it wasn't them that was failing the academic institutions it was the institutions that was failing them, even though the students may have been paying attention what ever they ay have been learning wasn't very academically stimulating

When Roland recalls “the knights who to the Dark Tower’s search addressed,” he gestures toward a centuries-old literary tradition. The name Roland first appears in the eleventh-century La Chanson de Roland, a French chanson de geste celebrating the knight’s heroism at Roncevaux Pass under Charlemagne. In 1595, George Peele revived the name in The Old Wives’ Tale. Then, Robert Jamieson recorded a folk version of the tale and placed it within Arthurian legend, making Roland the son of Arthur and Guinevere. Joseph Jacobs’s English Fairy Tales, pictured below, adopted Jaimeson’s version and introduced the “Dark Tower” as the dwelling of the King of Elfland, where Roland must save his sister. Where earlier Rolands fought or rescued, Browning’s hero merely endures, stripped of glory or divine purpose. With this history in mind, this scene helps capture part of why “Childe Roland” continues to haunt later writers. Its hero perseveres not because he hopes to succeed, but because turning back would mean erasing the meaning of every struggle that came before.

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既存の string.Templateとは違うものであることを、簡単に言っておくのはどうでしょうか？ https://docs.python.org/ja/3.14/tutorial/stdlib2.html#templating

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Reply to the reviewers

Review report for 'Sterols regulate ciliary membrane dynamics and hedgehog signaling in health and disease', Lamazière et al.

Reviewer #1

In this manuscript, Lamazière et al. address an important understudied aspect of primary cilium biology, namely the sterol composition in the ciliary membrane. It is known that sterols especially play an important role in signal transduction between PTCH1 and SMO, two upstream components of the Hedgehog pathway, at the primary cilium. Moreover, several syndromes linked to cholesterol biosynthesis defects present clinical phenotypes indicative of altered Hh signal transduction. To understand the link between ciliary membrane sterol composition and Hh signal transduction in health and disease, the authors developed a method to isolate primary cilia from MDCK cells and coupled this to quantitative metabolomics. The results were validated using biophysical methods and cellular Hh signaling assays. While this is an interesting study, it is not clear from the presented data how general the findings are: can cilia be isolated from different mammalian cell types using this protocol? Is the sterol composition of MDCK cells expected to the be the same in fibroblasts or other cell types? Without this information, it is difficult to judge whether the conclusions reached in fibroblasts are indeed directly related to the sterol composition detected in MDCK cells. Below is a detailed breakdown of suggested textual changes and experimental validations to strengthen the conclusions of the manuscript.

We would like to thank the reviewer for their helpful comments

Major comments:

• It appears that the comparison has been made between ciliary membranes and the rest of the cell's membranes, which includes many other membranes besides the plasma membrane. This significantly weakens the conclusions on the sterol content specific to the cilium, as it may in fact be highly similar to the rest of the plasma membrane. It is for example known that lathosterol is biosynthesized in the ER, and therefore the non-presence in the cilium may reflect a high abundance in the ER but not necessarily in the plasma membrane.

The reviewer is correct that we compared the sterol composition of the primary ciliary membrane to the average of the remaining cellular membranes. We agree that this broader reference fraction contains multiple intracellular membranes, including ER- and Golgi-derived compartments, and therefore does not isolate the plasma membrane specifically. We would like to emphasize that our study did not aim to compare the cilium directly to the plasma membrane, nor did we claim that the comparison was in any way related to the plasma membrane. It is also worth noting that previous studies in other ciliated organisms have reported a higher cholesterol content in cilia compared to the plasma membrane, suggesting that the two membranes may not be compositionally identical despite their continuity. However, we concur that determining the sterol composition of the MDCK plasma membrane would provide valuable context and enable a comparison with the membrane continuous with the ciliary membrane. Hence, we are willing to try isolating plasma membrane in the same cellular contexts.

• While the protocol to isolate primary cilium from MDCK cells is a valuable addition to the methods available, it would be good to at least include a discussion on its general applicability. Have the authors tried to use this protocol on fibroblasts for example?

Thank you for the reviewer's positive comment on the value of the ciliary isolation protocol. Indeed, we have attempted to apply the same approach to other ciliated cell types, namely IMCD3 and MEF cells. In the case of IMCD3 cells, we were able to isolate primary cilia using the same general strategy; however, we are still refining the preparation, as the overall yield is lower than in MDCK cells and the amount of material obtained is currently insufficient for comprehensive biochemical analyses. With MEF (fibroblast) cells, the procedure proved even more challenging, as the yield of isolated cilia was extremely low. This difficulty is likely due to the shorter length of fibroblast cilia and to their positioning beneath the cell body, which probably makes them more resistant to detachment. Overall, these observations suggest that while the protocol can be adapted to other cell types, its efficiency depends on cellular architecture. We have added a discussion of these aspects in the revised manuscript to clarify the method's current scope and limitations (lines 492-502).

• Some of the conclusions in the introduction (lines 75-80) seem to be incorrectly phrased based on the data: in basal conditions, ciliary membranes are already enriched in cholesterol and desmosterol, and the treatment lowers this in all membranes.

We agree, this was modified in the revised manuscript (lines 75-80).

• There seems to be little effect of simvastatin on overall cholesterol levels. Can the authors comment on this result? How would the membrane fluidity be altered when mimicking simvastatin-induced composition? Since the effect on Hh signaling appears to be the biggest (Figure 5B) under simvastatin treatment, it would be interesting to compare this against that found for AY9944 treatment. Also, the authors conclude that the effects of simvastatin treatment on ciliary membrane sterol composition are the mildest, however, one could argue that they are the strongest as there is a complete lack of desmosterol.

We thank the reviewer for these insightful comments. Regarding the modest overall effect of simvastatin on cholesterol levels, we would like to note that MDCK cells are an immortalized epithelial cell line with high metabolic plasticity. Such cancer-like cell types are known to exhibit enhanced de novo lipogenesis, particularly under culture conditions with ample glucose availability. This compensatory lipid biosynthesis can partially counterbalance pharmacological inhibition of the cholesterol biosynthetic pathway. Because simvastatin acts upstream in the pathway (at HMG-CoA reductase), its inhibition primarily reduces early intermediates rather than fully depleting end-product cholesterol, explaining the relatively mild changes observed in total cholesterol content.

Concerning desmosterol, we agree with the reviewer that its complete loss under simvastatin treatment is a striking finding that deserves further discussion. Interestingly, our data show that simvastatin treatment produces the strongest inhibition of pathway activation (as measured by SMO activation), but the weakest effect on signal transduction downstream of constitutively active SMOM2. This dichotomy suggests that the absence of desmosterol may preferentially affect the activation step of Hedgehog signaling at the ciliary membrane, without equally impacting downstream propagation. We have expanded the Result section to highlight this potential role of desmosterol in the activation phase of Hedgehog signaling and to contrast it with the effects observed under AY9944 treatment (lines 463-469).

It is not clear to me why the authors have chosen to use SAG to activate the Hh pathway, as this is a downstream mode of activation and bypasses PTCH1 (and therefore a potentially sterol-mediated interaction between the two proteins). It would be very informative to compare the effect of sterol modulation on the ability of ShhN vs SAG to activate the pathway.

Our study aims to demonstrate that the sterol composition of the ciliary membrane plays an essential role in the proper functioning of the Hedgehog (Hh) signaling pathway, comparable in importance to that of oxysterols and free cholesterol. Because ShhN itself is covalently modified by cholesterol, and Smoothened (SMO) can be directly activated by both oxysterols and cholesterol, we reasoned that using a non-native SMO agonist such as SAG would allow us to specifically assess defects arising from alterations in membrane-bound sterols. In this way, pathway activation by SAG provides a more direct readout of the functional contribution of ciliary membrane sterols to SMO activity, independent of potential confounding effects related to ShhN processing, secretion, or PTCH1-mediated regulation.

• The conclusions about the effect of tamoxifen on SMO trafficking in MEFs should be validated in human patient cells before being able to conclude that there is a potential off-target effect (line 438). Also, if that is the case, the experiment of tamoxifen treatment of EBP KO cells should give an additional effect on SMO trafficking. Also, could the CDPX2 phenotypes in patients be the result of different cell types being affected than the fibroblast used in this study?

We agree that carrying the proposed experiment would be a good way to assess a potential off-target effect. However, such validation is beyond the scope of the present study, as this comment on off-target effect was aimed primarily to propose a mechanistic hypothesis to explain the differences observed in Hedgehog pathway activation between patient-derived fibroblasts and tamoxifen-treated MEFs. We leaned towards this hypothesis because drug treatments are known for their overall variable specificity, but we agree other hypotheses are possible, and among them the difference in cell type, as both are fibroblasts but from different origin. We rephrased this passage in the revised manuscript (lines 447-448 ).

Regarding the reviewer's third point, we fully agree that the CDPX2 phenotype in patients is unlikely to arise solely from fibroblast dysfunction. Nevertheless, fibroblasts are the only patient-derived cells currently available to us, and they provide a useful model for assessing ciliary signaling. It is reasonable to expect that similar defects could occur in other, more physiologically relevant cell types.

• For the experiments with the SMO-M2 mutant, it would be useful to show the extent of pathway activation by the mutant compared to SAG or ShhN treatment of non-transfected cells. Moreover, it will be necessary to exclude any direct effects of the compound treatment on the ability of this mutant to traffic to the primary cilium, which can easily be done using fluorescence microscopy as the mutant is tagged with mCherry.

The SmoM2 mutant is indeed a well-characterized constitutively active form of Smoothened that has been extensively studied by us and others. It is well established that this mutant correctly localizes to the primary cilium and robustly activates the Hedgehog pathway in MEFs (see Eguether et al., Dev. Cell, 2014 or Eguether et al, mol.biol.cell, 2018). In our study, we have already included supporting evidence for pathway activation in Supplementary Figure S1b, showing Gli1 expression levels in untreated MEFs transfected with SmoM2, which illustrates the extent of its activation compared to ligand-induced conditions.

In line with the reviewer's recommendation, we will additionally include microscopy data showing SmoM2 localization in MEFs treated with the different sterol modulators. These data should confirm that the observed effects are not due to altered ciliary trafficking of the mutant protein but instead reflect changes in downstream signaling or membrane composition.

Minor comments:

Line 74: 'in patients', should be rephrased to 'patient-derived cells'

This was modified in the revised manuscript

Figure 2A: What do the '+/-' indicate? They seem to be erroneously placed.

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Figure 2B: no label present for which bar represents cilia/other membranes

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Figure 2C: this representation is slightly deceptive, since the difference between cells and cilia for lanosterol is not significantly different as shown in figure 2A.

This representation has been removed in the revised figures.

Figure 3A: it would be useful to also show where 8-DHC is in the biosynthetic pathway.

This has been modified in the revised figures.

Line 373: the title should be rephrased as it infers that DHCR7 was blocked in model membranes, which is not the case.

This has been modified in the revised manuscript.

Lines 377-384: this paragraph seems to be a mix of methods and some explanation, but should be rephrased for clarity.

We believe the technical information within this paragraph are useful for the understanding of the reader. We would rather leave as is unless recommended by other reviewers or editorial staff.

Line 403: 'which could explain the resulting defects in Hedgehog signaling': how and what defects? At this point in the study no defects in Hh signaling have been shown.

This has been modified in the revised manuscript.

Figure 4D: 'd' is missing

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Line 408: SAG treatment resulted in slightly shorter cilia: this is not the case for just SAG treated cilia, but only for the combination of SAG + AY9944. However, in that condition there appears to be a subpopulation of very short cilia, are those real?

This is correct, this is not the case for untreated cilia, but the short population is real, not only in AY9944 but also in Tamoxifen and Simvastatin. Again, the relevance and significance of minor cilia length change is unclear and we are not trying to draw any other conclusion from this than saying that the ciliary compartment is modified.

Figure 5b: it would be good to add that all conditions contained SAG.

This has been modified in the revised figures.

Figure 5D: Since it is shown in Fig 5C that there are no positive cilia -SAG, there is no point to have empty graphs in Fig 5D on the left side, nor can any statistics be done. Similarly for 5K.

We think this is still worth having in the figure. As the reviewer noted in one of his next comment, there are cases where Smoothened or Patched can be abnormally distributed (see also Eguether et al, mol biol cell, 2018). This shows that we checked all conditions for presence or absence of Smo and that there is no signal to be found. We would rather leave it as is unless asked otherwise by editorial staff.

Figure 5E: it is not clearly indicated what is visualized in the inserts, sometimes it's a box, sometimes a line and they seem randomly integrated into the images.

We apologize for the oversight - the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Figure 5H: is this the intensity in just SMO positive cilia? If yes, this should be indicated, and the line at '0' for WT-SAG should be removed. I am also surprised there is then ns found for WT vs SLO, since in WT there are no positive cilia, but in SLO there are a few, so it appears to be more of a black-white situation. Perhaps it would be useful to split the data from different experiments to see if it consistently the case that there is a low percentage of SMO positive cilia in SLO cells.

Yes, as in the rest of figure 5, the fluorescence intensity of Smo is only taken into account in SMO positive cells. This is now indicated in figure legend (lines 890, 898, 903 ). As for Smo positive, this is a good suggestion. We checked and for cilia in non-activated SLO patients, there are 8 positive cilia over a total of 240 counted cilia, mainly from one of the experiments. We could remove the data or leave as is given that the result is not significant.

Fig S1: panels are inverted compared to mentioning in the text.

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Methods-pharmacological treatments: there appear to be large differences in concentrations chosen to treat MDCK versus MEF cells - can the authors comment on these choices and show that the enzymes are indeed inhibited at the indicated concentrations?

We thank the reviewer for this important comment. The concentrations of the pharmacological treatments were optimized separately for MDCK and MEF cells based on cell-type-specific tolerance. For each compound, we used the highest concentration that produced no detectable cytotoxicity or morphological changes. These conditions ensured that the treatments were effective (as seen by changes in sterol composition in MDCK cilia and Hh pathway phenotypes in treated MEFs) and compatible with cell viability and ciliation. Although we did not directly assay enzymatic inhibition in each case, the selected concentrations are consistent with those previously reported to inhibit the targeted enzymes in similar cellular contexts.

Compound

Typical Concentration Range in Mammalian Cell Culture

Typical Exposure Duration

Example Cell Types

Representative Peer-Reviewed References

AY9944 (DHCR7 inhibitor)

1-10 µM widely used; 1 µM for minimal on-target effects; 2.5-10 µM for robust sterol shifts

24-72 h; some sterol studies up to several days

HEK293, fibroblasts, neuronal cells, macrophages

Kim et al., J Biol Chem, 2001 - used 1 µM in dose-response experiments.; Haas et al., Hum Mol Genet, 2007 - 1 µM in cell-based assays.; Recent macrophage sterol study - 2.5-10 µM to induce 7-DHC accumulation.

Simvastatin (HMG-CoA reductase inhibitor)

0.1-10 µM common; 1-10 µM most widely used for robust pathway inhibition

24-72 h

Diverse mammalian lines, including liver, fibroblasts, epithelial cells

Bytautaite et al., Cells (2020) - discusses common in-vitro ranges (1-10 µM).; Mullen et al., 2011 - used 10 µM simvastatin, noting it is a standard in-vitro concentration.

Tamoxifen (modulator of sterol metabolism)

1-20 µM; 1-5 µM for mild/longer treatments; 10-20 µM in cancer/cilia signaling studies

24-72 h (longer treatments often at 1-5 µM)

MDCK, MEFs, MCF-7, diverse epithelial lines

Schlottmann et al., Cells (2022) - used 5-25 µM in sterol-related cell studies.; MCF-7 literature - 0.1-1 µM for estrogenic signaling, higher (5-10 µM) for metabolic/sterol pathway effects.; Additional cancer cell work indicating similar ranges.

This information has been clarified in the revised Methods section (lines 222-224).

(optional): it would be interesting to include a gamma-tubulin staining on the cilium prep to see if there is indeed a presence of the basal body as suggested by the proteomics data.

Thank you, we will try this.

There are many spelling mistakes and inconsistencies throughout the manuscript and its figures (mix of French and English for example) so careful proofreading would be warranted. Moreover, there are many mentionings of 'Hedgehog defects' or 'Hedgehog-linked', where in fact it is a defect in or link to the Hedgehog pathway, not the protein itself. This should be corrected.

We thank the reviewer for noting these issues. We apologize for the inconsistencies observed in the initial submission, as mentioned previously, some of the figures inadvertently included earlier versions, which may have contributed to the errors identified. All figures have now been carefully revised and updated in the resubmitted manuscript.

Regarding the text, we are surprised to hear about the spelling inconsistencies, as the manuscript was professionally proofread prior to submission (documentation can be provided upon request). Nevertheless, we have conducted an additional round of thorough proofreading to ensure consistency throughout the text and figures.

Finally, we have corrected all instances of "Hedgehog defects" or "Hedgehog-linked" to the more accurate phrasing "Hedgehog pathway defect" or "Hedgehog pathway-linked," as suggested by the reviewer throughout the manuscript.

Reviewer #1 (Significance (Required)):

The study of ciliary membrane composition is highly relevant to understand signal transduction in health and disease. As such, the topic of this manuscript is significant and timely. However, as indicated above, there are limitations to this study, most notably the comparison of ciliary membrane versus all cellular membranes (rather than the plasma membrane), which weakens the conclusions that can be drawn. Moreover, cell-type dependency should be more thoroughly addressed. There certainly is a methodological advance in the form of cilia isolation from MDCK cells, however, it is unclear how broadly applicable this is to other mammalian cell types.

We would like to thank the reviewer for their helpful comments and we appreciate the reviewer's recognition of the relevance and timeliness of studying ciliary membrane composition in the context of signaling regulation. We fully acknowledge that our comparison was made between the primary ciliary membrane and the total cellular membrane fraction, which encompasses multiple intracellular membranes. Our intent, however, was to obtain a global overview of how the ciliary membrane differs from the average membrane environment within the cell, thereby highlighting features that are unique to the cilium as a signaling organelle. This approach provides valuable baseline information that complements, rather than replaces, future targeted comparisons with the plasma membrane. As mentioned in this reply, we aim at carrying out these experiments before publication. Regarding cell-type dependency, we concur that ciliary lipid composition may vary between cell types, reflecting differences in their functional specialization. Our method was intentionally established in MDCK cells, which are epithelial and highly ciliated, to ensure sufficient yield and reproducibility. We have initiated trials with other mammalian cell types, including IMCD3 and MEF cells, and while yields remain limited, preliminary results indicate that the approach is adaptable with further optimization. Thus, our current work establishes a robust and reproducible proof of concept in a mammalian model, providing the first detailed sterol fingerprint of a mammalian primary cilium.

We believe this constitutes a significant methodological and conceptual advance, as it opens the way for systematic exploration of ciliary lipid composition across diverse mammalian systems and pathological contexts.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Overview Accumulating evidence suggests that sterols play critical roles in signal transduction within the primary cilium, perhaps most notably in the Hedgehog cascade. However, the precise sterol composition of the primary cilium, and how it may change under distinct biological conditions, remains unknown, in part because of the lack of reproducible, widely accepted procedures to purify primary cilia from mammalian cultured cells. In the present study, the authors have designed a method to isolate the cilium from the MDCK cells efficiently and then utilized this procedure in conjunction with mass spectrometry to systematically analyze the sterol composition of the ciliary membrane, which they then compare to the sterol composition of the cell body. By analyzing this sterol profiling. the authors claim that the cilium has a distinct sterol composition from the cell body, including higher levels of cholesterol and desmosterol but lower levels of 8-DHC and & Lathosterol. This manuscript further demonstrates that alteration of sterol composition within cilia modulates Hedgehog signaling. These results strengthen the link between dysregulated Hedgehog signaling and defects in cholesterol biosynthesis pathways, as observed in SLOS and CDPX2.

While the ability to isolate primary cilia from cultured MDCK cells represents an important technical achievement, the central claim of the manuscript - that cilia have a different sterol composition from the cell body - is not adequately supported by the data, and more rigorous comparisons between the ciliary membrane and key organellar membranes (such as plasma membrane) are required to make this claim. Moreover, although the authors have repeatedly mention that the ciliary sterol composition is "tightly regulated" there is no evidence provided to support such claim. At best, the data suggest that the cilium and cell body may differ in sterol composition (though even that remains uncertain), but no underlying regulatory mechanisms are demonstrated. In addition, much of the 2nd half of the paper represents a rehash of experiments with sterol biosynthesis inhibitors that have already been published in the literature, making the conceptual advance modest at best. Lastly, the link between CDPX2 and defective Hedgehog signaling is tenuous.

We would like to thank the reviewer for their helpful comments

Major comments

Figure 1. C) Although the isolation of cilium from the MDCK cells using dibucaine treatment seems to be very efficient, the quality control of their fractionation procedure to monitor the isolation is limited to a single western blot of the purified cilia vs. cell body samples, with no representative data shown from the sucrose gradient fractionation steps. Given that prior studies (including those from the Marshall lab cited in this manuscript) found that 1) sucrose gradient fractionation was essential to obtain relatively pure ciliary fractions, and 2) the ciliary fractions appear to spread over many sucrose concentrations in those prior studies , the authors should have included the comparison of the fractionation profile from the sucrose gradient while isolating the primary cilium. This additional information would have further clarified and supported the efficiency of their proposed method.

We thank the reviewer for their insightful comments regarding the quality control of our ciliary fractionation. We would like to clarify several important methodological aspects that distinguish our approach from those used in the studies cited (including those from the Marshall lab). In the cited work, the authors used a continuous sucrose gradient ranging from 30 % to 45 %, which allowed visualization of the distribution of ciliary proteins across the gradient. In contrast, we employed a discontinuous sucrose gradient (25 % / 50 %) optimized for higher recovery and reproducibility in our hands. In our preparation, the primary cilia consistently localize at the interface between the 25 % and 50 % layers. We systematically collect five 1 mL fractions from this interface and use fractions 1-3 for downstream analyses, as fractions 4-5 are typically already depleted of ciliary material. This targeted collection ensures good enrichment and low contamination, while avoiding unnecessary dilution of the limited ciliary sample. We also note that the prior studies the reviewer refers to were optimized for proteomic analyses, and therefore used actin as a marker of contamination from the cell body. In our case, the downstream application is lipidomic profiling, for which such protein-based contamination markers are not directly informative, since no reliable lipid marker exists to differentiate between organelle membranes. For this reason, we limited the protein-level validation to a semi-quantitative assessment of ciliary enrichment using ARL13B Western blotting, which robustly reports the presence and enrichment of ciliary membranes. Finally, to complement this targeted validation, we performed proteomic analysis followed by Gene Ontology (GO) Enrichment Analysis using the PANTHER database. This analysis evaluates the overrepresentation of proteins associated with ciliary structures and functions relative to the background frequency in the Canis lupus familiaris proteome. The resulting enrichment profile confirms that the isolated material is highly enriched in ciliary components and somewhat depleted of non-ciliary contaminants, thereby serving as an unbiased and global assessment of sample specificity and purity. We believe that, together, these methodological choices provide a rigorous and quantitative validation of our fractionation efficiency and support the robustness of the cilia isolation protocol used in this study.

1. D) The authors presented proteomic data for the peptides analyzed from the isolated cilia in the form of GO term analysis; however, they did not provide examples of different proteins enriched within their fractionation procedure, aside from Arl13b shown in the blot. Including a summary table with representative proteins identified in the isolated ciliary fraction, along with the relative abundance or percentage distribution of these proteins, would make the data more informative.

We thank the reviewer for this valuable suggestion. As mentioned in the manuscript, our proteomic dataset includes numerous hallmark components of the cilium, such as 18 IFT proteins, 4 BBS proteins, and several Hedgehog pathway components (including SuFu and Arl13b), as well as axonemal (Tubulin, Kinesin, Dynein) and centrosomal proteins (Centrin, CEPs, γ-Tubulin, and associated factors). This composition demonstrates that the isolated fraction is highly enriched in bona fide ciliary components while retaining a small proportion of basal body proteins, which is expected given their physical continuity. Importantly, our dataset shows a 70% overlap with the ciliary proteome published by Ishikawa et al. and a 41% overlap with the CysCilia consortium's list of potential ciliary proteins, which supports both the specificity and reliability of our isolation procedure. Regarding the suggestion to present relative protein abundances, we would like to clarify that defining "relative to what" is challenging in this context. The stoichiometry of ciliary proteins is largely unknown, and relative abundance normalized to total protein content can be misleading, as ciliary structural and signaling components differ greatly in copy number and membrane association. For this reason, we chose to highlight in the text proteins such as BBS and IFTs, which are known to be of low abundance within the cilium; their detection supports the depth and specificity of our proteomic coverage. In addition, we performed an unbiased Gene Ontology (GO) Enrichment Analysis using the PANTHER database, which provides a systematic and quantitative overview of the biological processes and cellular components overrepresented in our dataset relative to the canine proteome. This analysis with regard to purity wa already discussed in the submitted manuscript discussion. To further address the reviewer's comment, we will include as a supplemental table in the revised manuscript, a summary table listing representative ciliary proteins identified in our fraction, including those overlapping with the CysCilia (Gold ans potential lists), CiliaCarta and Ishikawa/Marshall proteomes. This addition should make the dataset more transparent and informative while preserving scientific rigor.

Figure 2.

The authors represented the comparison of sterol content within the cilia versus whole cell (as cell membranes). Since different organelles have a very diverse degree of cholesterol contents within them, for instance plasma membrane itself is around 50 mol% cholesterol levels while organelles like ER have barely any cholesterol. Thus, comparing these two samples and claiming a 2.5-fold increase in cholesterol levels is misleading. A more appropriate comparison would be between isolated primary cilia and isolated plasma membranes (procedures to isolate plasma membranes have been described previously, e.g., Naito et al., eLife 2019; Das et al, PNAS 2013. The absence of such controls makes it difficult to fully validate the reported magnitude of sterols enrichment in cilia relative to the cell surface.

As already discussed above for reviewer 1, we would like to emphasize that our study did not aim to compare the cilium directly to the plasma membrane, nor did we claim that the comparison was in any way related to the plasma membrane. Our intent, was to obtain a global overview of how the ciliary membrane differs from the average membrane environment within the cell, thereby highlighting features that are unique to the cilium as a signaling organelle. This approach provides valuable baseline information that complements, rather than replaces, future targeted comparisons with the plasma membrane. However, we concur that determining the sterol composition of the MDCK plasma membrane would provide valuable context and enable a comparison with the membrane continuous with the ciliary membrane. Hence, we are willing to try isolating plasma membrane in the same cellular contexts, and we thank the reviewer for the proposed literature.

Also, because dibucaine was used here to isolate MDCK cilia, a control experiment to exclude possible effects of the dibucaine treatment on sterol biosynthesis would be helpful.

Thank you for this comment, we will verify this point by quantifying by GC-MS the sterol content of whole MDCK cells with and without 15 minutes-dibucaine treatments.

Figure 3.

Tamoxifen is a potent drug for nuclear hormone receptor activity and thus can independently influence various cellular processes. As several experiments in the later sections of the manuscript rely on tamoxifen treatment of cells, it is important that the authors include appropriate controls for tamoxifen treatment, to confirm that the observed effects do not stem from effects on nuclear hormone receptor activity. This would ensure that the observed effects can be confidently attributed to the experimental manipulation rather than to the intrinsic effects of tamoxifen.

The reviewer is right, tamoxifen, like many drugs, has pleiotropic effects in different cell processes. Aware of this possible issue, we turned to a genetic model creating a CRISPR-CAS9 mediated knock down of EBP, the enzyme targeted by tamoxifen. We showed in figure 5 that the results between tamoxifen treated cells and CRIPSR EBP cells were in accordance with one another, showing that, for hedgehog signaling, the effect of tamoxifen recapitulates the effect of the enzyme KO.

Figure4. The authors present the results of spectroscopy studies to analyze generalized polarization (GP) of liposomes in vitro , but only processed data are shown, and the raw spectra are not provided. The authors need to present representative spectra to enable the readers to interact the raw data from the experiments.

This has been added to new supplemental figure 1 and corresponding figure legend (lines 898-904)

Figure5. B) The experiment shown Gli1 mRNA levels following treatment with inhibitors of cholesterol biosynthesis, but similar findings have already been reported previously (e.g., Cooper et al, Nature Genetics 2003; Blassberg et al, Hum Mol Genet 2016), and the present results do not provide a significant conceptual advance over those earlier studies.

We thank the reviewer for this comment and for highlighting the importance of earlier studies on Hedgehog (Hh) signaling and cholesterol metabolism. While we fully agree that confirming and extending established findings has intrinsic scientific value, we respectfully disagree with the assertion that our work does not provide conceptual novelty.

The seminal work by Cooper et al. (Nature Genetics, 2003) indeed laid the foundation for linking sterol metabolism to Hedgehog signaling, and we cite it as such. However, that study was conducted in chick embryos, a model that is relatively distant from mammalian systems and human pathophysiology. Moreover, their approach relied heavily on cyclodextrin-mediated cholesterol depletion, which is non-specific and extracts multiple sterols from membranes (discussed in this article lines 512-516). In contrast, our study employs pharmacological inhibitors targeting specific enzymes in the sterol biosynthetic pathway, thereby allowing us to modulate distinct steps and intermediates in a controlled and mechanistically informative manner. We also extend these analyses to patient-derived fibroblasts and CRISPR-engineered cells, providing direct human and genetic validation of the observed effects. Importantly, we complement these cellular studies with biochemical characterization of isolated ciliary membranes from MDCK cells, enabling a direct assessment of how specific sterol alterations affect ciliary composition and Hh pathway function - an angle not addressed in prior work.

Regarding Blassberg et al. (Hum. Mol. Genet., 2016), we agree that part of our findings recapitulates their observations on SMO-related signaling defects, which we view as an important confirmation of reproducibility. However, their study primarily sought to distinguish whether Hh pathway impairment in SLOS results from 7-DHC accumulation or cholesterol depletion, concluding that cholesterol deficiency was the main cause. Our results expand on this by demonstrating that perturbations extend beyond these two sterols, and that additional intermediates in the biosynthetic pathway also impact ciliary membrane composition and signaling competence. Furthermore, our experiments using the constitutively active SmoM2 mutant show that Hh signaling defects are not restricted to SMO activation per se, revealing a broader disruption of the signaling machinery within the cilium.

Finally, neither of the above studies examined CDPX2 patient-derived cells or the consequences of EBP enzyme deficiency on Hh signaling. Our finding that this pathway is altered in this genetic context represents, to our knowledge, a novel link between CDPX2 and Hedgehog pathway dysfunction.

Taken together, our work builds upon and extends previous findings by integrating cell-type-specific, biochemical, and patient-based analyses to provide a more comprehensive and mechanistically detailed view of how sterol composition of the ciliary membrane regulates Hedgehog signaling.

In addition, the authors analyze the effect of these inhibitors on SAG stimulation, but the experiment lacks the control for Gli mRNA levels in the absence of SAG treatment. Without this control, it is impossible to know where the baseline in the experiment is and how large the effects in question really are.

Below, we provide the data expressed using the ΔΔCt method (NT + SAG normalized to NT - SAG), which more clearly illustrates the magnitude of the effect in question. As similar qPCR-based Hedgehog pathway activation assays in MEFs have been published previously (see Eguether et al., Dev. Cell 2014; Eguether et al., Mol. Biol. Cell 2018), our goal here was not to re-establish the assay itself but to highlight the comparative effects across experimental conditions. In addition, one of the datasets was obtained using a new batch of SAG, which exhibited stronger pathway activation across all conditions (visible as higher overall expression levels). To ensure valid statistical comparisons across experiments and to focus on relative rather than absolute activation, we therefore chose to present the data as fold change values, which provides a more robust and statistically consistent measure for cross-condition analysis.

J-K) The data represented in these panels for SAG treatment as fraction of Smo and its fluorescence intensity for the same sample appears to be inconsistent between the two graphs. Under SAG treatment for EBP mutants shows higher Smo fluorescence intensity while Smo positive cilia seems to be less than the wild type control cells. If the number of Smo+ cilia (quantified by eye) differs between conditions, shouldn't the quantification of Smo intensity within cilia show a similar difference?

We thank the reviewer for this careful observation. The apparent discrepancy arises because the two panels quantify different parameters. In panel (j), we counted the percentage of cilia positive for SMO (i.e., cilia in which SMO was detected above background). In contrast, panel (k) reports the fluorescence intensity of SMO, but this measurement was performed only within the SMO-positive cilia identified in panel (j). This distinction has now been explicitly clarified in the figure legend, as also suggested by Reviewer 1.

Taken together, these two analyses indicate that although fewer cilia display detectable SMO accumulation in the EBP mutant cells, the amount of SMO present within those cilia that do recruit it is comparable to wild-type levels (as reflected by the non-significant difference in fluorescence intensity). This interpretation helps explain the partial functional preservation of Hedgehog signaling in this condition and contrasts with cases such as AY9944 treatment, where both the number of SMO-positive cilia and the SMO intensity are reduced.

1. I) The rationale for using SmoM2 in the analysis of cholesterol metabolism-related diseases such as SLOS and CDPX2 is unclear. The SmoM2 variant is primarily associated with cancer rather than cholesterol biosynthesis defects and its relevance either of these disorders is not immediately apparent.

We thank the reviewer for this pertinent observation. We fully agree that SmoM2 was originally identified as an oncogenic mutation and is not directly associated with cholesterol biosynthesis disorders. However, our rationale for using this mutant was mechanistic rather than pathological. SmoM2 is a constitutively active form of SMO that triggers pathway activation independently of upstream components such as PTCH1 or ligand-mediated regulation.

By using SmoM2, we aimed to determine whether the signaling defects observed under conditions that alter sterol metabolism (e.g., treatment with AY9944 or tamoxifen) occur upstream or downstream of SMO activation. The results demonstrate that, even when SMO is constitutively active, the Hedgehog pathway remains impaired under AY9944 treatment-and to a lesser extent with tamoxifen-indicating that these sterol perturbations disrupt the pathway beyond the level of SMO activation itself. In contrast, cells treated with simvastatin maintain normal pathway responsiveness, reinforcing the specificity of this effect.

This experiment is therefore central to our study, as it reveals that sterol imbalance can hinder Hedgehog signaling even in the presence of an active SMO, providing new insight into how membrane composition influences downstream signaling competence.

Minor corrections

1. Line 385 seems to be a bit confusing which mentions cilia were treated with AY9944 - do the authors mean that cells were been treated with the drugs before isolation of cilia, or were the purified cilia actually treated with the drugs?

Thank you, this has been modified in the revised manuscript

The authors should add proper label in Figure 2 panel b for the bars representing the cilia and cell membranes.

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Panels in Figure S1 should be re-arranged according to the figure legend and figure reference in line 450.

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Legend for the Figure S1b should be corrected as data sets in graph represents 7 points while technical replicates in legend shows 6 experimental values.

Thank you, this has been modified in the revised manuscript

The labels for drug in Figure 3 and 5 should be corrected from tamoxifene to tamoxifen and simvastatine to simvastatin.

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

Reviewer #2 (Significance (Required)):

In the present study, the authors have designed a method to isolate the cilium from the MDCK cells efficiently and then utilized this procedure in conjunction with mass spectrometry to systematically analyze the sterol composition of the ciliary membrane, which they then compare to the sterol composition of the cell body. By analyzing this sterol profiling. the authors claim that the cilium has a distinct sterol composition from the cell body, including higher levels of cholesterol and desmosterol but lower levels of 8-DHC and & Lathosterol. This manuscript further demonstrates that alteration of sterol composition within cilia modulates Hedgehog signaling. These results strengthen the link between dysregulated Hedgehog signaling and defects in cholesterol biosynthesis pathways, as observed in SLOS and CDPX2.

While the ability to isolate primary cilia from cultured MDCK cells represents an important technical achievement, the central claim of the manuscript - that cilia have a different sterol composition from the cell body - is not adequately supported by the data, and more rigorous comparisons between the ciliary membrane and key organellar membranes (such as plasma membrane) are required to make this claim. Moreover, although the authors have repeatedly mention that the ciliary sterol composition is "tightly regulated" there is no evidence provided to support such claim. At best, the data suggest that the cilium and cell body may differ in sterol composition (though even that remains uncertain), but no underlying regulatory mechanisms are demonstrated. In addition, much of the 2nd half of the paper represents a rehash of experiments with sterol biosynthesis inhibitors that have already been published in the literature, making the conceptual advance modest at best. Lastly, the link between CDPX2 and defective Hedgehog signaling is tenuous.

We thank the reviewer for this detailed summary and for acknowledging the technical advance represented by our method for isolating primary cilia from MDCK cells. However, we respectfully disagree with several aspects of the reviewer's assessment of our work.

As we elaborated in our responses to earlier comments, particularly regarding Figure 5, we disagree with the characterization of part of our study as a "rehash", a somewhat derogatory word, of previously published experiments. Our approach differs from earlier studies by relying on specific pharmacological modulation of defined enzymes in the sterol biosynthesis pathway, rather than using non-specific agents such as cyclodextrins, and by linking these manipulations to direct biochemical measurements of ciliary sterol composition. This strategy allows, for the first time, a targeted and physiologically relevant examination of how specific sterol perturbations affect Hedgehog signaling.

Regarding our statement that ciliary sterol composition is "tightly regulated," we acknowledge that we have not yet explored the underlying molecular mechanisms of this regulation. Nevertheless, the experimental evidence supporting this statement lies in the variation of ciliary sterol composition across multiple treatments that strongly perturb cellular sterols. Despite broad cellular changes, the ciliary sterol profile remains very resilient for some parameters, an observation that, in our view, strongly supports the idea of a selective or regulated process maintaining ciliary sterol identity. This conclusion does not depend on comparison with other membrane compartments.

We also respectfully disagree that the observed differences between cilia and the cell body (which doesn't equal to plasma membrane) are "uncertain." The consistent enrichment in cholesterol and desmosterol, combined with the relative depletion in 8-DHC and lathosterol, were detected across independent replicates using robust lipidomic profiling and are statistically supported. These findings are, to our knowledge, the first quantitative demonstration of a sterol fingerprint specific to a mammalian cilium.

Finally, while we agree that the mechanistic link between CDPX2 and defective Hedgehog signaling warrants further exploration, the data we present, combining pharmacological inhibition (tamoxifen), CRISPR-mediated EBP knockout, and SMOM2 activation assays, all consistently indicate a functional impairment of the Hedgehog pathway under EBP deficiency. This is further reinforced by clinical reports describing Hedgehog-related phenotypes in CDPX2 patients. We therefore believe that our work provides a solid experimental and conceptual basis for connecting EBP dysfunction to Hedgehog signaling defects.

In summary, our study introduces a validated and reproducible method for mammalian cilia isolation, provides the first detailed sterol composition profile of primary cilia, and establishes a functional link between ciliary sterol imbalance and Hedgehog pathway modulation. We believe these findings represent a meaningful conceptual advance and a valuable resource for the field

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

Lamaziere et al. describe an improved protocol for isolating primary cilia from MDCK cells for downstream lipidomics analysis. Using this protocol, they characterize sterol profile of MDCK cilia membrane under standard growth conditions and following pharmacological perturbations that are meant to mimic SLOS and CDPX2 disorders in humans. The authors then assess the impact of the same pharmacological manipulations on Shh pathway activity and validate their findings from these experiments using orthogonal genetic approaches. Major and minor concerns that require attention prior to publication are outlined below.

We would like to thank the reviewer for their comments

Major 1.Since the extent of contamination of the cilia preps with non-cilia membranes is unclear, and variability between replicates is not reported, it makes interpretation of changes in cilia membrane sterol composition in response to pharmacological manipulations somewhat difficult to interpret. Discussing reproducibility of cilia sterol composition between replicates (and including corresponding data) could alleviate these concerns to some extent.

We thank the reviewer for this comment. We would like to clarify that variability between replicates is indeed reported throughout the manuscript. In Figures 2 and 3, all data are presented as mean {plus minus} SEM, as indicated in the figure legends. Specifically, the data in Figure 2 are derived from six independent experiments, reflecting the central dataset used for comparative analyses, while the data in Figure 3 are based on three independent experiments.

We also note that the overall variability between replicates is low, further supporting the reproducibility of our ciliary sterol composition measurements. This consistency across independent biological replicates provides confidence that the differences observed between cilia and the cell body are robust and not due to stochastic contamination or technical variation.

2.An abundant non-ciliary membrane protein (rather than GAPDH) may be a more appropriate loading control in Fig. 1C.

This is a valuable comment and we will find a non-ciliary membrane protein to complement this experiment.

3.Fig. 2b - which bar corresponds to cells and which one to cilia? What do numbers inside bars represent? Please label accordingly.

We apologize for the oversight, the figures initially submitted with the manuscript inadvertently included some earlier versions, which explains several of the discrepancies noted by the reviewers. This issue has been corrected in the revised submission, and all figures have now been updated to reflect the finalized data.

4.Fig. 3b-d, right panels - please define what numbers inside bars represent

Thank you, this was done in the revised manuscript. The numbers are reports of absolute quantification.

5.The font in Figs 2, 3, and 4 is very small and difficult to read. Please make the font and/or panels bigger to improve readability.

We did our best to enlarge font despite space limitations, but we are willing to work with editorial staff to improve readability as suggested.

6.It would help to have a diagram of the key steps in the cholesterol synthesis pathway for reference early in the paper rather than in figure 3.

We thank the reviewer for his comment, but we don't understand why this would be helpful as we only use sterol modulators involving the pathway's enzyme in fig3. We are open to discussion with editorial staff about moving it up to fig2. If they feel this is needed

7.The authors need to discuss why/how global inhibition of enzymes (e.g. via AY9944 treatment) in a cell could cause reduction in cholesterol levels only in the cilium and not in other cell membranes (see also point 1). Yet, tamoxifen treatment lowers cholesterol across the board.

We thank the reviewer for these insightful comments. Regarding the modest overall effect of simvastatin on cholesterol levels, we would like to note that MDCK cells are an immortalized epithelial cell line with high metabolic plasticity. Such cancer-like cell types are known to exhibit enhanced de novo lipogenesis, particularly under culture conditions with ample glucose availability. This compensatory lipid biosynthesis can partially counterbalance pharmacological inhibition of the cholesterol biosynthetic pathway. Because simvastatin acts upstream in the pathway (at HMG-CoA reductase), its inhibition primarily reduces early intermediates rather than fully depleting end-product cholesterol, explaining the relatively mild changes observed in total cholesterol content. . This has been added in a new paragraph in the revised manuscript (lines 371-378).

8.Fig. 5c, g, and j - statistical analyses are missing and need to be added in support of conclusions drawn in the text of the manuscript.

Thank you, this has been done in the revised manuscript

9.The decrease in the fraction of Smo+ cilia observed in EBP KO cells is mild (panel j, no statistics), and there is possibly a clone-specific effect here as well (statistical analysis is needed to determine if EBP139 is indeed different from WT and whether EBP139 and 141 are different from each other). Similarly, Smo fluorescence intensity after SAG treatment (panel k) is the same in WT and EBP KO cells, while there is a marked difference in intraciliary Smo intensity after tamoxifen treatment. The author's conclusion "...we were able to show that results with human cells aligned with our tamoxifen experiments" (line 436) should be modified to more accurately reflect the presented data. Ditto conclusions on lines 440-442, 530-531. In fact, it is the lack of Hh phenotypes in CDPX2 patients that is consistent with the EBP KO data presented in the paper.

We thank the reviewer for this detailed comment. We have now performed the requested statistical analyses and incorporated them into the revised manuscript.

The new analyses confirm that both EBP139 and EBP141 CRISPR KO clones show a statistically significant reduction in the fraction of Smo⁺ cilia compared to WT cells. They also reveal that the two clones differ significantly from each other, consistent with the expected clonal variability inherent to independently derived CRISPR lines.

Despite this variability, several lines of evidence support our conclusion that the EBP KO phenotypes align with the effects observed after tamoxifen treatment:

1- Directionally consistent reduction in Smo⁺ cilia:

Although the magnitude of the decrease differs between clones, both clones display a significant reduction compared to WT, paralleling the reduction observed in tamoxifen-treated cells. This directional consistency is the key point for comparing pharmacological and genetic perturbations.

2-Converging evidence from SmoM2 experiments:

Tamoxifen treatment also reduces pathway output in the context of SmoM2 overexpression. This supports the interpretation that both EBP inhibition (tamoxifen) and EBP loss (CRISPR KO) impair Hedgehog signaling at the level of ciliary function, albeit more mildly than AY9944/SLOS-like perturbations.

3-Interpretation of Smo intensity (panel k):

As clarified in the revised text, the fluorescence intensities in panel K correspond only to cilia that are Smo-positive. The absence of a difference in intensity therefore does not contradict the observed reduction in the number of Smo⁺ cilia. Rather, it explains why the phenotype is milder than that observed for SLOS/AY9944: when Smo is able to enter the cilium, its enrichment level is comparable to WT.

4- Clinical relevance for CDPX2:

While Hedgehog-related phenotypes in CDPX2 patients may be milder or under-reported, several documented features, such as polydactyly (10% of cases), as well as syndactyly and clubfoot, are classically associated with ciliary/Hedgehog signaling defects. This clinical pattern is consistent with the milder yet detectable defects we observe in EBP KO cells.

Minor •Line 310: 'intraflagellar' rather than 'intraciliary' transport particle B is a more conventional term

We agree that intraflagellar is more conventional than intraciliary, but in this case, this is how the GO term is labeled in the database. In our opinion, it should stay as is.

• Fig. 2c - typos in the color key, is grey meant to be "cells" and blue "cilia"? Individual panels are not referenced in the text

This panel has been removed thanks to comment from reviewer 1 and 3 finding it misleading.

• Lines 357-358: "Notably, AY9944 treatment led to a greater reduction in cholesterol content as well as a greater increase in 7-DHC and 8-DHC in cilia than in the other cell membranes" - the authors need to support this statement with appropriate statistical analysis

We respectfully believe there may be a misunderstanding in the reviewer's concern. In all cases, our comparisons are made between treated vs. untreated conditions within each compartment (cell bulk vs. ciliary membrane), and the statistical significance of these differences is already reported as determined by a Mann-Whitney test. In every case, the changes observed are greater in cilia than in the cell body. The statement in the manuscript simply summarizes this quantitative observation. However, if the reviewer feels that an additional statistical test directly comparing the magnitude of the two compartment-specific changes would strengthen the claim, we are willing to include this analysis. Alternatively, if preferred, we can remove the sentence entirely, as the comparison is already clearly visible in Figure 3b.

• Line 473 - unclear what is meant by "olfactory cilia are mainly sensory and not primary". Primary cilia are sensory.

We agree, primary cilia are sensory, but still different from cilia belonging to sensory epithelia like retina photoreceptors or olfactory cilia. Nevertheless, this statement was modified in revised manuscript

• Line 551: 'data not shown'. Please include the data that you would like to discuss or remove discussion of these data from the manuscript.

The data is not shown because there is nothing to show, as we discussed in that sentence, use of cholesterol probe resulted in the disappearance of primary cilia altogether. We are willing to work with editorial staff to find a better way of expressing this idea.

Reviewer #3 (Significance (Required)):

Overall, the manuscript expands our knowledge of cilia membrane composition and reports an interesting link between SLOS and Shh signaling defects, which could at least in part explain SLOS patients' symptoms. The findings reported in the manuscript could be of interest to a broad audience of cell biologists and geneticists.

We would like to thank the reviewer for his recognition of the importance of this work

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Referee #1

Evidence, reproducibility and clarity

Review report for 'Sterols regulate ciliary membrane dynamics and hedgehog signaling in health and disease', Lamazière et al.

In this manuscript, Lamazière et al. address an important understudied aspect of primary cilium biology, namely the sterol composition in the ciliary membrane. It is known that sterols especially play an important role in signal transduction between PTCH1 and SMO, two upstream components of the Hedgehog pathway, at the primary cilium. Moreover, several syndromes linked to cholesterol biosynthesis defects present clinical phenotypes indicative of altered Hh signal transduction. To understand the link between ciliary membrane sterol composition and Hh signal transduction in health and disease, the authors developed a method to isolate primary cilia from MDCK cells and coupled this to quantitative metabolomics. The results were validated using biophysical methods and cellular Hh signaling assays. While this is an interesting study, it is not clear from the presented data how general the findings are: can cilia be isolated from different mammalian cell types using this protocol? Is the sterol composition of MDCK cells expected to the be the same in fibroblasts or other cell types? Without this information, it is difficult to judge whether the conclusions reached in fibroblasts are indeed directly related to the sterol composition detected in MDCK cells. Below is a detailed breakdown of suggested textual changes and experimental validations to strengthen the conclusions of the manuscript.

Major comments:

• It appears that the comparison has been made between ciliary membranes and the rest of the cell's membranes, which includes many other membranes besides the plasma membrane. This significantly weakens the conclusions on the sterol content specific to the cilium, as it may in fact be highly similar to the rest of the plasma membrane. It is for example known that lathosterol is biosynthesized in the ER, and therefore the non-presence in the cilium may reflect a high abundance in the ER but not necessarily in the plasma membrane.
• While the protocol to isolate primary cilium from MDCK cells is a valuable addition to the methods available, it would be good to at least include a discussion on its general applicability. Have the authors tried to use this protocol on fibroblasts for example?
• Some of the conclusions in the introduction (lines 75-80) seem to be incorrectly phrased based on the data: in basal conditions, ciliary membranes are already enriched in cholesterol and desmosterol, and the treatment lowers this in all membranes.
• There seems to be little effect of simvastatin on overall cholesterol levels. Can the authors comment on this result? How would the membrane fluidity be altered when mimicking simvastatin-induced composition? Since the effect on Hh signaling appears to be the biggest (Figure 5B) under simvastatin treatment, it would be interesting to compare this against that found for AY9944 treatment. Also, the authors conclude that the effects of simvastatin treatment on ciliary membrane sterol composition are the mildest, however, one could argue that they are the strongest as there is a complete lack of desmosterol.
• It is not clear to me why the authors have chosen to use SAG to activate the Hh pathway, as this is a downstream mode of activation and bypasses PTCH1 (and therefore a potentially sterol-mediated interaction between the two proteins). It would be very informative to compare the effect of sterol modulation on the ability of ShhN vs SAG to activate the pathway.
• The conclusions about the effect of tamoxifen on SMO trafficking in MEFs should be validated in human patient cells before being able to conclude that there is a potential off-target effect (line 438). Also, if that is the case, the experiment of tamoxifen treatment of EBP KO cells should give an additional effect on SMO trafficking. Also, could the CDPX2 phenotypes in patients be the result of different cell types being affected than the fibroblast used in this study?
• For the experiments with the SMO-M2 mutant, it would be useful to show the extent of pathway activation by the mutant compared to SAG or ShhN treatment of non-transfected cells. Moreover, it will be necessary to exclude any direct effects of the compound treatment on the ability of this mutant to traffic to the primary cilium, which can easily be done using fluorescence microscopy as the mutant is tagged with mCherry.

Minor comments:

Line 74: 'in patients', should be rephrased to 'patient-derived cells'

Figure 2A: What do the '+/-' indicate? They seem to be erroneously placed.

Figure 2B: no label present for which bar represents cilia/other membranes

Figure 2C: this representation is slightly deceptive, since the difference between cells and cilia for lanosterol is not significantly different as shown in figure 2A.

Figure 3A: it would be useful to also show where 8-DHC is in the biosynthetic pathway.

Line 373: the title should be rephrased as it infers that DHCR7 was blocked in model membranes, which is not the case.

Lines 377-384: this paragraph seems to be a mix of methods and some explanation, but should be rephrased for clarity.

Line 403: 'which could explain the resulting defects in Hedgehog signaling': how and what defects? At this point in the study no defects in Hh signaling have been shown.

Figure 4D: 'd' is missing

Line 408: SAG treatment resulted in slightly shorter cilia: this is not the case for just SAG treated cilia, but only for the combination of SAG + AY9944. However, in that condition there appears to be a subpopulation of very short cilia, are those real?

Figure 5b: it would be good to add that all conditions contained SAG.

Figure 5D: Since it is shown in Fig 5C that there are no positive cilia -SAG, there is no point to have empty graphs in Fig 5D on the left side, nor can any statistics be done. Similarly for 5K.

Figure 5E: it is not clearly indicated what is visualized in the inserts, sometimes it's a box, sometimes a line and they seem randomly integrated into the images.

Figure 5H: is this the intensity in just SMO positive cilia? If yes, this should be indicated, and the line at '0' for WT-SAG should be removed. I am also surprised there is then ns found for WT vs SLO, since in WT there are no positive cilia, but in SLO there are a few, so it appears to be more of a black-white situation. Perhaps it would be useful to split the data from different experiments to see if it consistently the case that there is a low percentage of SMO positive cilia in SLO cells. Fig S1: panels are inverted compared to mentioning in the text.

Methods-pharmacological treatments: there appear to be large differences in concentrations chosen to treat MDCK versus MEF cells - can the authors comment on these choices and show that the enzymes are indeed inhibited at the indicated concentrations?

(optional): it would be interesting to include a gamma-tubulin staining on the cilium prep to see if there is indeed a presence of the basal body as suggested by the proteomics data.

There are many spelling mistakes and inconsistencies throughout the manuscript and its figures (mix of French and English for example) so careful proofreading would be warranted. Moreover, there are many mentionings of 'Hedgehog defects' or 'Hedgehog-linked', where in fact it is a defect in or link to the Hedgehog pathway, not the protein itself. This should be corrected.

Significance

The study of ciliary membrane composition is highly relevant to understand signal transduction in health and disease. As such, the topic of this manuscript is significant and timely. However, as indicated above, there are limitations to this study, most notably the comparison of ciliary membrane versus all cellular membranes (rather than the plasma membrane), which weakens the conclusions that can be drawn. Moreover, cell-type dependency should be more thoroughly addressed. There certainly is a methodological advance in the form of cilia isolation from MDCK cells, however, it is unclear how broadly applicable this is to other mammalian cell types.

Objetivos

Penghadiran cerita ini begitu singkat, tetapi tidak sedikit bagian-bagian yang mengena dalam cerita ini. Cerita ini juga membawa interpretasi yang cukup luas bagi pembacanya. Dan dari sudut pandang saya, seperti apapun hal yang terjadi ia akan tetap ada dan selalu bergema. Apalagi ini soal kisah persahabatan empat orang yang punya hobi dan kegembiraan yang sama. Secara keseluruhan cerita ini cukup menjadi misteri juga, dan yang pasti banyak pesan moral yang ingin disampaikan penulis.

Sebuah cerita fiksi yang menjadi gambaran nyata tentang apa yang terjadi sekarang. Manusia digiring untuk lupa terhadap fitrahnya, sehingga ia hidup namun hanya dalan ilusi. Kekosongan dan kehampaan selalu mengisi hatinya, tak menemukan jawaban atas arti hidupnya. Memang cerita yang luar biasa dihadirkan oleh penulis, dan lagi-lagi membuka ruang kesadaran bagi orang-orang yang mungkin berada pada siklus ini. Terima kasih dan apresiasi untuk penulis, tiap tulisan yang dibuat bukan hanya menjadi bahan bacaan saja bagi pembaca, tetapi ruang untuk merenung dan mengartikan seluruh maksud penulis dalam tulisannya. Intinya cerita ini keren, semua orang bisa membacanya dan menjadikannya sebagai pelajaran berharga dalam hidup.

¿Es una comunidad orgánica un símil de una [[cibernética]] positiva y abierta? ¿Es posible pensarlo desde ahí?

TFM

Interesante posibilidad para cruzar con el #TFM y la cuestión del #encantamiento

[[Lewis Mumford]] y [[Waldo Frank]] sobre el conflicto de la visión mecánica u orgánica.

Sobre [[mecanicismo]] y [[organicismo]].

"le dernier chapitre" de cette partie/de ce cours

Manque un "s" à la fin.

"vous" à la place de "nous"

DOI: 10.1371/journal.pgen.1009558

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What is this?

DOI: 10.1371/journal.pgen.1009558

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What is this?

DOI: 10.1371/journal.pgen.1009558

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

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What is this?

DOI: 10.1093/g3journal/jkab172

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What is this?

DOI: 10.2139/ssrn.5740988

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What is this?

DOI: 10.21228/m8br77

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What is this?

DOI: 10.1016/j.stem.2025.10.009

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What is this?

DOI: 10.1016/j.isci.2025.114116

Resource: None

Curator: @scibot

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What is this?

Dossier d'Information : La Quête de la Parentalité Idéale

Synthèse

Ce document synthétise une discussion radiophonique sur la notion de "bon parent", explorant les pressions, les doutes et les stratégies qui définissent la parentalité contemporaine.

Il ressort que l'idéal du parent parfait est une source de stress et de culpabilité, largement alimentée par la compétition sociale et un afflux de connaissances scientifiques qui peuvent être à la fois une aide et un fardeau.

Les intervenants s'accordent sur le fait que la parentalité est un exercice d'équilibriste constant, oscillant entre de grands succès et des échecs patents.

Les thèmes centraux incluent le conflit entre le désir de façonner un "enfant idéal" et la nécessité d'accepter l'enfant réel, la difficulté de se défaire de ses propres projections et traumatismes, et la charge mentale disproportionnée qui pèse souvent sur les mères.

La discussion met en lumière le concept de "parent suffisamment bon" de Donald Winnicott, qui valorise non pas la perfection, mais la capacité à répondre aux besoins de l'enfant tout en introduisant une frustration gérable, essentielle à son développement.

Finalement, la parentalité est présentée comme une expérience partagée, où l'échange, la reconnaissance de sa propre faillibilité et la capacité à "réparer" ses erreurs sont plus importants que la poursuite d'un idéal inaccessible.

--------------------------------------------------------------------------------

1. Introduction au Débat

La question "Qu'est-ce qu'un bon parent ?" a fait l'objet d'une émission sur France Inter, réunissant des chroniqueurs, auteurs et parents pour partager leurs expériences et réflexions.

La discussion, présentée comme une conversation de "praticiens" plutôt que de spécialistes, a exploré les multiples facettes de la parentalité moderne.

Intervenants Principaux :

Nom

Rôle et Affiliation

Nombre d'enfants

Gwenaëlle Boulet

Rédactrice en chef (Popie, Pomme d'Api), autrice de la BD "Ma vie de parent"

Trois

Julien Bisson

Directeur des rédactions (Le 1 hebdo), chroniqueur "Ma vie de parent"

Un

Marie Pernaud

Chroniqueuse (La maison des maternels), animatrice du podcast "Very Important Parents"

Quatre

Sonia de Viller

Journaliste et parente intervenant au cours du débat

Deux (au moins)

Le débat a également été enrichi par les témoignages d'auditeurs, offrant des perspectives vécues sur les défis abordés.

2. L'Auto-Évaluation Parentale : Entre Exigence et Réalité

La discussion s'ouvre sur un exercice d'auto-notation, demandant aux invités de s'évaluer sur une échelle de 1 (parent exécrable) à 10 (parent parfait).

Les réponses révèlent immédiatement la complexité et la variabilité de la perception de soi en tant que parent.

• Gwenaëlle Boulet se donne un 8/10, justifiant cette note élevée par le fait que ses enfants n'ont pas été maltraités et vont globalement bien, tout en admettant leur laisser "suffisamment de quoi aller chez le psy plus tard".

• Julien Bisson souligne la fluctuation de sa performance : il s'évalue à 9/10 la veille au soir après un jeu de société, mais à 2/10 le matin même après avoir "hurlé sur son fils". Sa moyenne se situe donc autour de 5,5/10.

• Marie Pernaud abonde dans ce sens, affirmant que la qualité de sa parentalité varie selon les moments de la journée, notant que "le matin, c'est compliqué quand même".

• Florence, une auditrice de Haute-Savoie, se donne une moyenne de 7,5/10, reconnaissant que sa performance dépend des "circonstances de la vie".

Cette variabilité démontre que la parentalité n'est pas une compétence statique, mais un effort constant et situationnel.

3. Le Conflit Central : Accepter l'Enfant Réel contre Projeter un Idéal

Un thème majeur émerge rapidement : la tension entre l'enfant que les parents désirent et l'enfant qu'ils ont réellement.

• Florence, l'auditrice, définit le bon parent comme celui qui, dès la naissance, considère son enfant "comme un être à part entière" et non "comme sa possession".

L'objectif est de l'aider à se réaliser "selon ce qu'il est lui et non pas ce que je voulais moi, ce qui soit".

• Gwenaëlle Boulet confesse que c'est le "combat de sa vie".

Elle illustre cette lutte avec son désir que ses enfants aiment la littérature, un désir qui s'est heurté à leur indifférence et s'est avéré "contreproductif à souhait".

Elle trouve "hyper dur" d'accepter que son enfant puise "dans d'autres sources que les tiennes pour grandir".

• Julien Bisson conclut que pour s'approcher du "parent idéal", il faut d'abord "éviter de vouloir un enfant idéal".

Cet enfant idéal est celui sur lequel on projette ses propres attentes psychologiques et d'accomplissement.

• Marie Pernaud résume : être un bon parent, "c'est vraiment faire le deuil de l'enfant qu'on aurait voulu avoir".

Face à un conflit, la question à se poser est : "quel est l'enfant qu'on a en fait et comment on doit réagir par rapport à l'enfant qu'on a".

Sonia de Viller ajoute une nuance importante : on n'est pas le même parent pour chaque enfant.

"Je suis pas la même mère avec mon fils aîné et mon cadet et d'ailleurs il me le reproche".

Marie Pernaud confirme que chaque enfant révèle des facettes différentes, positives comme négatives, chez le parent.

4. Les Pressions Modernes et leurs Conséquences

La discussion met en évidence que la parentalité contemporaine est soumise à une série de pressions externes et internes qui complexifient la tâche.

4.1. Le Poids des Connaissances Scientifiques

L'accès à une masse d'informations sur le développement de l'enfant est perçu comme une arme à double tranchant.

• Gwenaëlle Boulet utilise l'analogie de l'effet Dunning-Kruger :

1. La "montagne de la stupidité" : Fin 19e/début 20e, les exigences se limitaient à s'assurer que l'enfant ne meure pas.   

2. La "vallée de l'humilité" : L'arrivée de la psychanalyse et des neurosciences a fait chuter la confiance des parents, écrasés par les connaissances sur ce qu'il "faut surtout pas faire".   

3. Le "plateau de la consolidation" : L'objectif est de remonter en faisant correspondre sa confiance et ses compétences, en utilisant ces connaissances tout en se faisant confiance.

• Julien Bisson qualifie les sciences de l'éducation de "bénédiction et malédiction".

Une bénédiction pour les savoirs apportés, une malédiction car elles "ont creusé énormément la distance entre le parent qu'on a l'impression d'être et le parent qu'on pense devoir être", créant un "mal-être parental énorme".

4.2. La Compétition Sociale et l'Isolement

La société moderne impose une dynamique de comparaison et d'individualisme qui affecte directement les parents.

• La Compétition Parentale : Gwenaëlle Boulet décrit une "compète" ressentie dès la maternité (choisir la "super maternité") et qui se poursuit avec la scolarité (l'âge d'apprentissage de la lecture).

• L'Isolement : Julien Bisson lie cette compétition à une société avec "plus d'individualisme, plus d'isolement", ce qui renforce le sentiment d'être "seul" et "désarmé".

• Témoignage de Charlotte : Une auditrice d'Aix-en-Provence exprime sa difficulté à "créer une communauté de parents".

Elle se sent comme une "extraterrestre" lorsqu'elle propose des initiatives collectives ou parle de l'éducation au "vivre ensemble".

4.3. La Charge Mentale et la Santé des Parents

La recherche de la perfection parentale a un coût direct sur le bien-être des parents.

• Marie Pernaud alerte sur le risque d'épuisement face aux "injonctions". Les parents reçoivent une multitude d'informations et pensent devoir "absolument tout faire".

Elle rappelle le propos d'une Danoise : tant qu'il n'y a ni maltraitance et qu'il y a de l'amour, il ne peut y avoir de mauvaise éducation.

• Julien Bisson cite des chiffres issus d'un numéro du 1 hebdo sur la santé mentale des parents :

◦ Le mal-être parental touche 1 parent sur 5 (20%).  

◦ Le burnout parental affecte 6 à 8 % des parents.  

◦ Les femmes sont plus touchées, non par fragilité, mais parce qu'elles "portent encore aujourd'hui une charge parentale beaucoup plus importante que les hommes".

5. Vers une Parentalité "Suffisamment Bonne"

Face à l'idéal inaccessible, la discussion propose une approche plus réaliste et bienveillante, inspirée du concept du psychanalyste Donald Winnicott.

5.1. Le Concept du Parent "Suffisamment Bon"

• Définition : Un parent suffisamment bon répond aux besoins de l'enfant sans être parfait et sans "faire trop".

• Évolution :

1. Nourrisson : Le parent répond immédiatement et exactement aux besoins du bébé (faim, réconfort).   

2. Enfant : Le parent instaure progressivement "de la frustration gérable".

Il apprend à l'enfant à différer ses désirs, ce qui l'aide à grandir et à "vivre en société".

• Risque de l'anticipation : Anticiper systématiquement les besoins de l'enfant peut freiner son autonomie et son développement émotionnel.

5.2. L'Importance de l'Imperfection et de la Réparation

L'erreur n'est pas seulement inévitable, elle est une composante de la relation.

• Reconnaître ses erreurs : Gwenaëlle Boulet insiste sur l'importance de pouvoir revenir vers son enfant et dire :

"Je suis désolé, je me suis emballée [...] j'avais pas envie de réagir comme ça". Cela permet de "réparer beaucoup de choses".

• Déculpabiliser l'enfant : Julien Bisson ajoute que cela aide l'enfant à comprendre que ce n'est "pas toujours de sa faute", car son objectif principal est de satisfaire ses parents.

5.3. Les Outils Pratiques et le Partage d'Expérience

• Le "Faux Choix" : Gwenaëlle Boulet partage une technique concrète : au lieu de demander "Tu veux prendre ta douche ?", poser la question "Tu veux prendre ta douche maintenant ou dans 5 minutes ?".

Cela offre à l'enfant un "terrain d'expérimentation du choix" tout en atteignant l'objectif du parent.

• L'Influence Partagée : Julien Bisson utilise la métaphore du "buffet" : le parent offre un buffet, mais ne contrôle pas ce que l'enfant va choisir.

De plus, il n'est "pas le seul à le nourrir" (grands-parents, amis, etc.). Il ne faut pas surestimer sa propre influence.

• Le Duo Parental : L'ajustement entre les deux parents, avec leurs bagages respectifs, est un défi mais aussi ce qui "sauve", permettant de prendre de la distance.

6. Témoignages et Citations Clés

Intervenant/Source

Citation ou Idée Clé

Fiva (auditeur)

"Le parent parfait existe mais il n'a pas encore d'enfant."

Cécile Dancy (auditeur)

"Être un bon parent, c'est déjà être capable de travailler ses propres failles pour ne pas les faire peser sur nos enfants."

Peter Ustinov (cité)

"Les parents sont les os sur lesquelles les enfants se font les dents."

Russell Show (cité)

"Si nous accordons à nos enfants notre confiance, si nous les laissons suivre leur propre voix (...) nous allégerons notre vie tout en leur donnant les moyens de s'épanouir."

Ivan (auditeur)

Témoigne avec une grande émotion de sa souffrance en tant que père de deux adolescents.

Il reconnaît avoir projeté des attentes élevées sur son fils aîné, en réaction à sa propre relation difficile avec son père, ce qui a mené à une "cassure".

Il exprime son désarroi face à une situation complexe, concluant : "un bon parent, je ne sais pas ce que c'est [...] c'est simplement essayer de faire du mieux que je peux".

Le témoignage d'Ivan illustre de manière poignante le poids du passé, le risque de la surprotection et le sentiment de désarroi que peuvent ressentir les parents, même avec la volonté de bien faire.

Sa démarche de s'interroger, selon les intervenants, est déjà la preuve qu'il est "probablement un bon parent".

DOI: 10.1016/j.isci.2025.113831

Resource: (Molecular Probes Cat# A-21206, RRID:AB_2535792)

Curator: @scibot

SciCrunch record: RRID:AB_2535792

What is this?

DOI: 10.1016/j.celrep.2025.116576

Resource: (Abcam Cat# ab150129, RRID:AB_2687506)

Curator: @scibot

SciCrunch record: RRID:AB_2687506

What is this?

get rid of this output

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, participants completed two different tasks. A perceptual choice task in which they compared the sizes of pairs of items and a value-different task in which they identified the higher value option among pairs of items with the two tasks involving the same stimuli. Based on previous fMRI research, the authors sought to determine whether the superior frontal sulcus (SFS) is involved in both perceptual and value-based decisions or just one or the other. Initial fMRI analyses were devised to isolate brain regions that were activated for both types of choices and also regions that were unique to each. Transcranial magnetic stimulation was applied to the SFS in between fMRI sessions and it was found to lead to a significant decrease in accuracy and RT on the perceptual choice task but only a decrease in RT on the value-different task. Hierarchical drift-diffusion modelling of the data indicated that the TMS had led to a lowering of decision boundaries in the perceptual task and a lower of non-decision times on the value-based task. Additional analyses show that SFS covaries with model-derived estimates of cumulative evidence and that this relationship is weakened by TMS.

Strengths:

The paper has many strengths including the rigorous multi-pronged approach of causal manipulation, fMRI and computational modelling which offers a fresh perspective on the neural drivers of decision making. Some additional strengths include the careful paradigm design which ensured that the two types of tasks were matched for their perceptual content while orthogonalizing trial-to-trial variations in choice difficulty. The paper also lays out a number of specific hypotheses at the outset regarding the behavioural outcomes that are tied to decision model parameters and are well justified.

Weaknesses:

(1.1) Unless I have missed it, the SFS does not actually appear in the list of brain areas significantly activated by the perceptual and value tasks in Supplementary Tables 1 and 2. Its presence or absence from the list of significant activations is not mentioned by the authors when outlining these results in the main text. What are we to make of the fact that it is not showing significant activation in these initial analyses?

You are right that the left SFS does not appear in our initial task-level contrasts. Those first analyses were deliberately agnostic to evidence accumulation (i.e., average BOLD by task, irrespective of trial-by-trial evidence). Consistent with prior work, SFS emerges only when we model the parametric variation in accumulated perceptual evidence.

Accordingly, we ran a second-level GLM that included trial-wise accumulated evidence (aE) as a parametric modulator. In that analysis, the left SFS shows significant aE-related activity specifically during perceptual decisions, but not during value-based decisions (SVC in a 10-mm sphere around x = −24, y = 24, z = 36).

To avoid confusion, we now:

(i) explicitly separate and label the two analysis levels in the Results; (ii) state up front that SFS is not expected to appear in the task-average contrast; and (iii) add a short pointer that SFS appears once aE is included as a parametric modulator. We also edited Methods to spell out precisely how aE is constructed and entered into GLM2. This should make the logic of the two-stage analysis clearer and aligns the manuscript with the literature where SFS typically emerges only in parametric evidence models.

(1.2) The value difference task also requires identification of the stimuli, and therefore perceptual decision-making. In light of this, the initial fMRI analyses do not seem terribly informative for the present purposes as areas that are activated for both types of tasks could conceivably be specifically supporting perceptual decision-making only. I would have thought brain areas that are playing a particular role in evidence accumulation would be best identified based on whether their BOLD response scaled with evidence strength in each condition which would make it more likely that areas particular to each type of choice can be identified. The rationale for the authors' approach could be better justified.

We agree that both tasks require early sensory identification of the items, but the decision-relevant evidence differs by design (size difference vs. value difference), and our modelling is targeted at the evidence integration stage rather than initial identification.

To address your concern empirically, we: (i) added session-wise plots of mean RTs showing a general speed-up across the experiment (now in the Supplement); (ii) fit a hierarchical DDM to jointly explain accuracy and RT. The DDM dissociates decision time (evidence integration) from non-decision time (encoding/response execution).

After cTBS, perceptual decisions show a selective reduction of the decision boundary (lower accuracy, faster RTs; no drift-rate change), whereas value-based decisions show no change to boundary/drift but a decrease in non-decision time, consistent with faster sensorimotor processing or task familiarity. Thus, the TMS effect in SFS is specific to the criterion for perceptual evidence accumulation, while the RT speed-up in the value task reflects decision-irrelevant processes. We now state this explicitly in the Results and add the RT-by-run figure for transparency.

(1.2.1) The value difference task also requires identification of the stimuli, and therefore perceptual decision-making. In light of this, the initial fMRI analyses do not seem terribly informative for the present purposes as areas that are activated for both types of tasks could conceivably be specifically supporting perceptual decision-making only.

Thank you for prompting this clarification.

The key point is what changes with cTBS. If SFS supported generic identification, we would expect parallel cTBS effects on drift rate (or boundary) in both tasks. Instead, we find: (a) boundary decreases selectively in perceptual decisions (consistent with SFS setting the amount of perceptual evidence required), and (b) non-decision time decreases selectively in the value task (consistent with speed-ups in encoding/response stages). Moreover, trial-by-trial SFS BOLD predicts perceptual accuracy (controlling for evidence), and neural-DDM model comparison shows SFS activity modulates boundary, not drift, during perceptual choices.

Together, these converging behavioral, computational, and neural results argue that SFS specifically supports the criterion for perceptual evidence accumulation rather than generic visual identification.

(1.2.2) I would have thought brain areas that are playing a particular role in evidence accumulation would be best identified based on whether their BOLD response scaled with evidence strength in each condition which would make it more likely that areas particular to each type of choice can be identified. The rationale for the authors' approach could be better justified.

We now more explicitly justify the two-level fMRI approach. The task-average contrast addresses which networks are generally more engaged by each domain (e.g., posterior parietal for PDM; vmPFC/PCC for VDM), given identical stimuli and motor outputs. This complements, but does not substitute for, the parametric evidence analysis, which is where one expects accumulation-related regions such as SFS to emerge. We added text clarifying that the first analysis establishes domain-specific recruitment at the task level, whereas the second isolates evidence-dependent signals (aE) and reveals that left SFS tracks accumulated evidence only for perceptual choices. We also added explicit references to the literature using similar two-step logic and noted that SFS typically appears only in parametric evidence models.

(1.3) TMS led to reductions in RT in the value-difference as well as the perceptual choice task. DDM modelling indicated that in the case of the value task, the effect was attributable to reduced non-decision time which the authors attribute to task learning. The reasoning here is a little unclear.

(1.3.1) Comment: If task learning is the cause, then why are similar non-decision time effects not observed in the perceptual choice task?

Great point. The DDM addresses exactly this: RT comprises decision time (DT) plus non-decision time (nDT). With cTBS, PDM shows reduced DT (via a lower boundary) but stable nDT; VDM shows reduced nDT with no change to boundary/drift. Hence, the superficially similar RT speed-ups in both tasks are explained by different latent processes: decision-relevant in PDM (lower criterion → faster decisions, lower accuracy) and decision-irrelevant in VDM (faster encoding/response). We added explicit language and a supplemental figure showing RT across runs, and we clarified in the text that only the PDM speed-up reflects a change to evidence integration.

(1.3.2) Given that the value-task actually requires perceptual decision-making, is it not possible that SFS disruption impacted the speed with which the items could be identified, hence delaying the onset of the value-comparison choice?

We agree there is a brief perceptual encoding phase at the start of both tasks. If cTBS impaired visual identification per se, we would expect longer nDT in both tasks or a decrease in drift rate. Instead, nDT decreases in the value task and is unchanged in the perceptual task; drift is unchanged in both. Thus, cTBS over SFS does not slow identification; rather, it lowers the criterion for perceptual accumulation (PDM) and, separately, we observe faster non-decision components in VDM (likely familiarity or motor preparation). We added a clarifying sentence noting that item identification was easy and highly overlearned (static, large food pictures), and we cite that nDT is the appropriate locus for identification effects in the DDM framework; our data do not show the pattern expected of impaired identification.

(1.4) The sample size is relatively small. The authors state that 20 subjects is 'in the acceptable range' but it is not clear what is meant by this.

We have clarified what we mean and provided citations. The sample (n = 20) matches or exceeds many prior causal TMS/fMRI studies targeting perceptual decision circuitry (e.g., Philiastides et al., 2011; Rahnev et al., 2016; Jackson et al., 2021; van der Plas et al., 2021; Murd et al., 2021). Importantly, we (i) use within-subject, pre/post cTBS differences-in-differences with matched tasks; (ii) estimate hierarchical models that borrow strength across participants; and (iii) converge across behavior, latent parameters, regional BOLD, and connectivity. We now replace the vague phrase with a concrete statement and references, and we report precision (HDIs/SEs) for all main effects.

Reviewer #2 (Public Review):

Summary:

The authors set out to test whether a TMS-induced reduction in excitability of the left Superior Frontal Sulcus influenced evidence integration in perceptual and value-based decisions. They directly compared behaviour - including fits to a computational decision process model - and fMRI pre and post-TMS in one of each type of decision-making task. Their goal was to test domain-specific theories of the prefrontal cortex by examining whether the proposed role of the SFS in evidence integration was selective for perceptual but not value-based evidence.

Strengths:

The paper presents multiple credible sources of evidence for the role of the left SFS in perceptual decision-making, finding similar mechanisms to prior literature and a nuanced discussion of where they diverge from prior findings. The value-based and perceptual decision-making tasks were carefully matched in terms of stimulus display and motor response, making their comparison credible.

Weaknesses:

(2.1) More information on the task and details of the behavioural modelling would be helpful for interpreting the results.

Thank you for this request for clarity. In the revision we explicitly state, up front, how the two tasks differ and how the modelling maps onto those differences.

(1) Task separability and “evidence.” We now define task-relevant evidence as size difference (SD) for perceptual decisions (PDM) and value difference (VD) for value-based decisions (VDM). Stimuli and motor mappings are identical across tasks; only the evidence to be integrated changes.

(2) Behavioural separability that mirrors task design. As reported, mixed-effects regressions show PDM accuracy increases with SD (β=0.560, p<0.001) but not VD (β=0.023, p=0.178), and PDM RTs shorten with SD (β=−0.057, p<0.001) but not VD (β=0.002, p=0.281). Conversely, VDM accuracy increases with VD (β=0.249, p<0.001) but not SD (β=0.005, p=0.826), and VDM RTs shorten with VD (β=−0.016, p=0.011) but not SD (β=−0.003, p=0.419).

(3 How the HDDM reflects this. The hierarchical DDM fits the joint accuracy–RT distributions with task-specific evidence (SD or VD) as the predictor of drift. The model separates decision time from non-decision time (nDT), which is essential for interpreting the different RT patterns across tasks without assuming differences in the accumulation process when accuracy is unchanged.

These clarifications are integrated in the Methods (Experimental paradigm; HDDM) and in Results (“Behaviour: validity of task-relevant pre-requisites” and “Modelling: faster RTs during value-based decisions is related to non-decision-related sensorimotor processes”).

(2.2) The evidence for a choice and 'accuracy' of that choice in both tasks was determined by a rating task that was done in advance of the main testing blocks (twice for each stimulus). For the perceptual decisions, this involved asking participants to quantify a size metric for the stimuli, but the veracity of these ratings was not reported, nor was the consistency of the value-based ones. It is my understanding that the size ratings were used to define the amount of perceptual evidence in a trial, rather than the true size differences, and without seeing more data the reliability of this approach is unclear. More concerning was the effect of 'evidence level' on behaviour in the value-based task (Figure 3a). While the 'proportion correct' increases monotonically with the evidence level for the perceptual decisions, for the value-based task it increases from the lowest evidence level and then appears to plateau at just above 80%. This difference in behaviour between the two tasks brings into question the validity of the DDM which is used to fit the data, which assumes that the drift rate increases linearly in proportion to the level of evidence.

We thank the reviewer for raising these concerns, and we address each of them point by point:

2.2.1. Comment: It is my understanding that the size ratings were used to define the amount of perceptual evidence in a trial, rather than the true size differences, and without seeing more data the reliability of this approach is unclear.

That is correct—we used participants’ area/size ratings to construct perceptual evidence (SD).

To validate this choice, we compared those ratings against an objective image-based size measure (proportion of non-black pixels within the bounding box). As shown in Author response image 3, perceptual size ratings are highly correlated with objective size across participants (Pearson r values predominantly ≈0.8 or higher; all p<0.001). Importantly, value ratings do not correlate with objective size (Author response image 2), confirming that the two rating scales capture distinct constructs. These checks support using participants’ size ratings as the participant-specific ground truth for defining SD in the PDM trials.

Author response image 1.

Objective size and value ratings are unrelated. Scatterplots show, for each participant, the correlation between objective image size (x-axis; proportion of non-black pixels within the item box) and value-based ratings (y-axis; 0–100 scale). Each dot is one food item (ratings averaged over the two value-rating repetitions). Across participants, value ratings do not track objective size, confirming that value and size are distinct constructs.

Author response image 2.

Perceptual size ratings closely track objective size. Scatterplots show, for each participant, the correlation between objective image size (x-axis) and perceptual area/size ratings (y-axis; 0–100 scale). Each dot is one food item (ratings averaged over the two perceptual ratings). Perceptual ratings are strongly correlated with objective size for nearly all participants (see main text), validating the use of these ratings to construct size-difference evidence (SD).

(2.2.2) More concerning was the effect of 'evidence level' on behaviour in the value-based task (Figure 3a). While the 'proportion correct' increases monotonically with the evidence level for the perceptual decisions, for the value-based task it increases from the lowest evidence level and then appears to plateau at just above 80%. This difference in behaviour between the two tasks brings into question the validity of the DDM which is used to fit the data, which assumes that the drift rate increases linearly in proportion to the level of evidence.

We agree that accuracy appears to asymptote in VDM, but the DDM fits indicate that the drift rate still increases monotonically with evidence in both tasks. In Supplementary figure 11, drift (δ) rises across the four evidence levels for PDM and for VDM (panels showing all data and pre/post-TMS). The apparent plateau in proportion correct during VDM reflects higher choice variability at stronger preference differences, not a failure of the drift–evidence mapping. Crucially, the model captures both the accuracy patterns and the RT distributions (see posterior predictive checks in Supplementary figures 11-16), indicating that a monotonic evidence–drift relation is sufficient to account for the data in each task.

Author response image 3.

HDDM parameters by evidence level. Group-level posterior means (± posterior SD) for drift (δ), boundary (α), and non-decision time (τ) across the four evidence levels, shown (a) collapsed across TMS sessions, (b) for PDM (blue) pre- vs post-TMS (light vs dark), and (c) for VDM (orange) pre- vs post-TMS. Crucially, drift increases monotonically with evidence in both tasks, while TMS selectively lowers α in PDM and reduces τ in VDM (see Supplementary Tables for numerical estimates).

(2.3) The paper provides very little information on the model fits (no parameter estimates, goodness of fit values or simulated behavioural predictions). The paper finds that TMS reduced the decision bound for perceptual decisions but only affected non-decision time for value-based decisions. It would aid the interpretation of this finding if the relative reliability of the fits for the two tasks was presented.

We appreciate the suggestion and have made the quantitative fit information explicit:

(1) Parameter estimates. Group-level means/SDs for drift (δ), boundary (α), and nDT (τ) are reported for PDM and VDM overall, by evidence level, pre- vs post-TMS, and per subject (see Supplementary Tables 8-11).

(2) Goodness of fit and predictive adequacy. DIC values accompany each fit in the tables. Posterior predictive checks demonstrate close correspondence between simulated and observed accuracy and RT distributions overall, by evidence level, and across subjects (Supplementary Figures 11-16).

Together, these materials document that the HDDM provides reliable fits in both tasks and accurately recovers the qualitative and quantitative patterns that underlie our inferences (reduced α for PDM only; selective τ reduction in VDM).

(2.4) Behaviourally, the perceptual task produced decreased response times and accuracy post-TMS, consistent with a reduced bound and consistent with some prior literature. Based on the results of the computational modelling, the authors conclude that RT differences in the value-based task are due to task-related learning, while those in the perceptual task are 'decision relevant'. It is not fully clear why there would be such significantly greater task-related learning in the value-based task relative to the perceptual one. And if such learning is occurring, could it potentially also tend to increase the consistency of choices, thereby counteracting any possible TMS-induced reduction of consistency?

Thank you for pointing out the need for a clearer framing. We have removed the speculative label “task-related learning” and now describe the pattern strictly in terms of the HDDM decomposition and neural results already reported:

(1) VDM: Post-TMS RTs are faster while accuracy is unchanged. The HDDM attributes this to a selective reduction in non-decision time (τ), with no change in decision-relevant parameters (α, δ) for VDM (see Supplementary Figure 11 and Supplementary Tables). Consistent with this, left SFS BOLD is not reduced for VDM, and trialwise SFS activity does not predict VDM accuracy—both observations argue against a change in VDM decision formation within left SFS.

(2) PDM: Post-TMS accuracy decreases and RTs shorten, which the HDDM captures as a lower decision boundary (α) with no change in drift (δ). Here, left SFS BOLD scales with accumulated evidence and decreases post-TMS, and trialwise SFS activity predicts PDM accuracy, all consistent with a decision-relevant effect in PDM.

Regarding the possibility that faster VDM RTs should increase choice consistency: empirically, consistency did not change in VDM, and the HDDM finds no decision-parameter shifts there. Thus, there is no hidden counteracting increase in VDM accuracy that could mask a TMS effect—the absence of a VDM accuracy change is itself informative and aligns with the modelling and fMRI.

Reviewer #3 (Public Review):

Summary:

Garcia et al., investigated whether the human left superior frontal sulcus (SFS) is involved in integrating evidence for decisions across either perceptual and/or value-based decision-making. Specifically, they had 20 participants perform two decision-making tasks (with matched stimuli and motor responses) in an fMRI scanner both before and after they received continuous theta burst transcranial magnetic stimulation (TMS) of the left SFS. The stimulation thought to decrease neural activity in the targeted region, led to reduced accuracy on the perceptual decision task only. The pattern of results across both model-free and model-based (Drift diffusion model) behavioural and fMRI analyses suggests that the left SLS plays a critical role in perceptual decisions only, with no equivalent effects found for value-based decisions. The DDM-based analyses revealed that the role of the left SLS in perceptual evidence accumulation is likely to be one of decision boundary setting. Hence the authors conclude that the left SFS plays a domain-specific causal role in the accumulation of evidence for perceptual decisions. These results are likely to add importance to the literature regarding the neural correlates of decision-making.

Strengths:

The use of TMS strengthens the evidence for the left SFS playing a causal role in the evidence accumulation process. By combining TMS with fMRI and advanced computational modelling of behaviour, the authors go beyond previous correlational studies in the field and provide converging behavioural, computational, and neural evidence of the specific role that the left SFS may play.

Sophisticated and rigorous analysis approaches are used throughout.

Weaknesses:

(3.1) Though the stimuli and motor responses were equalised between the perception and value-based decision tasks, reaction times (according to Figure 1) and potential difficulty (Figure 2) were not matched. Hence, differences in task difficulty might represent an alternative explanation for the effects being specific to the perception task rather than domain-specificity per se.

We agree that RTs cannot be matched a priori, and we did not intend them to be. Instead, we equated the inputs to the decision process and verified that each task relied exclusively on its task-relevant evidence. As reported in Results—Behaviour: validity of task-relevant pre-requisites (Fig. 1b–c), accuracy and RTs vary monotonically with the appropriate evidence regressor (SD for PDM; VD for VDM), with no effect of the task-irrelevant regressor. This separability check addresses differences in baseline RTs by showing that, for both tasks, behaviour tracks evidence as designed.

To rule out a generic difficulty account of the TMS effect, we relied on the within-subject differences-in-differences (DID) framework described in Methods (Differences-in-differences). The key Task × TMS interaction compares the pre→post change in PDM with the pre→post change in VDM while controlling for trialwise evidence and RT covariates. Any time-on-task or unspecific difficulty drift shared by both tasks is subtracted out by this contrast. Using this specification, TMS selectively reduced accuracy for PDM but not VDM (Fig. 3a; Supplementary Fig. 2a,c; Supplementary Tables 5–7).

Finally, the hierarchical DDM (already in the paper) dissociates latent mechanisms. The post-TMS boundary reduction appears only in PDM, whereas VDM shows a change in non-decision time without a decision-relevant parameter change (Fig. 3c; Supplementary Figs. 4–5). If unmatched difficulty were the sole driver, we would expect parallel effects across tasks, which we do not observe.

(3.2) No within- or between-participants sham/control TMS condition was employed. This would have strengthened the inference that the apparent TMS effects on behavioural and neural measures can truly be attributed to the left SFS stimulation and not to non-specific peripheral stimulation and/or time-on-task effects.

We agree that a sham/control condition would further strengthen causal attribution and note this as a limitation. In mitigation, our design incorporates several safeguards already reported in the manuscript:

· Within-subject pre/post with alternating task blocks and DID modelling (Methods) to difference out non-specific time-on-task effects.

· Task specificity across levels of analysis: behaviour (PDM accuracy reduction only), computational (boundary reduction only in PDM; no drift change), BOLD (reduced left-SFS accumulated-evidence signal for PDM but not VDM; Fig. 4a–c), and functional coupling (SFS–occipital PPI increase during PDM only; Fig. 5).

· Matched stimuli and motor outputs across tasks, so any peripheral sensations or general arousal effects should have influenced both tasks similarly; they did not.

Together, these converging task-selective effects reduce the likelihood that the results reflect non-specific stimulation or time-on-task. We will add an explicit statement in the Limitations noting the absence of sham/control and outlining it as a priority for future work.

(3.3) No a priori power analysis is presented.

We appreciate this point. Our sample size (n = 20) matched prior causal TMS and combined TMS–fMRI studies using similar paradigms and analyses (e.g., Philiastides et al., 2011; Rahnev et al., 2016; Jackson et al., 2021; van der Plas et al., 2021; Murd et al., 2021), and was chosen a priori on that basis and the practical constraints of cTBS + fMRI. The within-subject DID approach and hierarchical modelling further improve efficiency by leveraging all trials.

To address the reviewer’s request for transparency, we will (i) state this rationale in Methods—Participants, and (ii) ensure that all primary effects are reported with 95% CIs or posterior probabilities (already provided for the HDDM as pmcmcp_{\mathrm{mcmc}}pmcmc). We also note that the design was sensitive enough to detect RT changes in both tasks and a selective accuracy change in PDM, arguing against a blanket lack of power as an explanation for null VDM accuracy effects. We will nevertheless flag the absence of a formal prospective power analysis in the Limitations.

Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

Some important elements of the methods are missing. How was the site for targeting the SFS with TMS identified? The methods described how M1 was located but not SFS.

Thank you for catching this omission. In the revised Methods we explicitly describe how the left SFS target was localized. Briefly, we used each participant’s T1-weighted anatomical scan and frameless neuronavigation to place a 10-mm sphere at the a priori MNI coordinates (x = −24, y = 24, z = 36) derived from prior work (Heekeren et al., 2004; Philiastides et al., 2011). This sphere was transformed to native space for each participant. The coil was positioned tangentially with the handle pointing posterior-lateral, and coil placement was continuously monitored with neuronavigation throughout stimulation. (All of these procedures mirror what we already report for M1 and are now stated for SFS as well.)

Where to revise the manuscript:

Methods → Stimulation protocol. After the first sentence naming cTBS, insert:<br /> “The left SFS target was localized on each participant’s T1-weighted anatomical image using frameless neuronavigation. A 10-mm radius sphere was centered at the a priori MNI coordinates x = −24, y = 24, z = 36 (Heekeren et al., 2004; Philiastides et al., 2011), then transformed to native space. The MR-compatible figure-of-eight coil was positioned tangentially over the target with the handle oriented posterior-laterally, and its position was tracked and maintained with neuronavigation during stimulation.”

It is not clear how participants were instructed that they should perform the value-difference task. Were they told that they should choose based on their original item value ratings or was it left up to them?

We agree the instruction should be explicit. Participants were told_: “In value-based blocks, choose the item you would prefer to eat at the end of the experiment.”_ They were informed that one VDM trial would be randomly selected for actual consumption, ensuring incentive-compatibility. We did not ask them to recall or follow their earlier ratings; those ratings were used only to construct evidence (value difference) and to define choice consistency offline.

Where to revise the manuscript:

Methods → Experimental paradigm.

Add a sentence to the VDM instruction paragraph:

“In value-based (LIKE) blocks, participants were instructed to choose the item they would prefer to consume at the end of the experiment; one VDM trial was randomly selected and implemented, making choices incentive-compatible. Prior ratings were used solely to construct value-difference evidence and to score choice consistency; participants were not asked to recall or match their earlier ratings.”

Line 86 Introduction, some previous studies were conducted on animals. Why it is problematic that the studies were conducted in animals is not stated. I assume the authors mean that we do not know if their findings will translate to the human brain? I think in fairness to those working with animals it might be worth an extra sentence to briefly expand on this point.

We appreciate this and will clarify that animal work is invaluable for circuit-level causality, but species differences and putative non-homologous areas (e.g., human SFS vs. rodent FOF) limit direct translation. Our point is not that animal studies are problematic, but that establishing causal roles in humans remains necessary.

Revision:

Introduction (paragraph discussing prior animal work). Replace the current sentence beginning “However, prior studies were largely correlational”

“Animal studies provide critical causal insights, yet direct translation to humans can be limited by species-specific anatomy and potential non-homologies (e.g., human SFS vs. frontal orienting fields in rodents). Therefore, establishing causal contributions in the human brain remains essential.”

Line 100-101: "or whether its involvement is peripheral and merely functionally supporting a larger system" - it is not clear what you mean by 'supporting a larger system'

We meant that observed SFS activity might reflect upstream/downstream support processes (e.g., attentional control or working-memory maintenance) rather than the computation of evidence accumulation itself. We have rephrased to avoid ambiguity.

Revision:

Introduction. Replace the phrase with:

“or whether its observed activity reflects upstream or downstream support processes (e.g., attention or working-memory maintenance) rather than the accumulation computation per se.”

The authors do have to make certain assumptions about the BOLD patterns that would be expected of an evidence accumulation region. These assumptions are reasonable and have been adopted in several previous neuroimaging studies. Nevertheless, it should be acknowledged that alternative possibilities exist and this is an inevitable limitation of using fMRI to study decision making. For example, if it turns out that participants collapse their boundaries as time elapses, then the assumption that trials with weaker evidence should have larger BOLD responses may not hold - the effect of more prolonged activity could be cancelled out by the lower boundaries. Again, I think this is just a limitation that could be acknowledged in the Discussion, my opinion is that this is the best effort yet to identify choice-relevant regions with fMRI and the authors deserve much credit for their rigorous approach.

Agreed. We already ground our BOLD regressors in the DDM literature, but acknowledge that alternative mechanisms (e.g., time-dependent boundaries) can alter expected BOLD–evidence relations. We now add a short limitation paragraph stating this explicitly.

Revision:

Discussion (limitations paragraph). Add:

“Our fMRI inferences rest on model-based assumptions linking accumulated evidence to BOLD amplitude. Alternative mechanisms—such as time-dependent (collapsing) boundaries—could attenuate the prediction that weaker-evidence trials yield longer accumulation and larger BOLD signals. While our behavioural and neural results converge under the DDM framework, we acknowledge this as a general limitation of model-based fMRI.”

Reviewer #2 (Recommendations For The Authors):

Minor points

I suggest the proportion of missed trials should be reported.

Thank you for the suggestion. In our preprocessing we excluded trials with no response within the task’s response window and any trials failing a priori validity checks. Because non-response trials contain neither a choice nor an RT, they are not entered into the DDM fits or the fMRI GLMs and, by design, carry no weight in the reported results. To keep the focus on the data that informed all analyses, we now (i) state the trial-inclusion criteria explicitly and (ii) report the number of analysed (valid) trials per task and run. This conveys the effective sample size contributing to each condition without altering the analysis set.

Revision:

Methods → (at the end of “Experimental paradigm”): “Analyses were conducted on valid trials only, defined as trials with a registered response within the task’s response window and passing pre-specified validity checks; trials without a response were excluded and not analysed.”

Results → “Behaviour: validity of task-relevant pre-requisites” (add one sentence at the end of the first paragraph): “All behavioural and fMRI analyses were performed on valid trials only (see Methods for inclusion criteria).”

Figure 4 c is very confusing. Is the legend or caption backwards?

Thanks for flagging. We corrected the Figure 4c caption to match the colouring and contrasts used in the panel (perceptual = blue/green overlays; value-based = orange/red; ‘post–pre’ contrasts explicitly labeled). No data or analyses were changed, just the wording to remove ambiguity.

Revision:

Figure 4 caption (panel c sentence). Replace with:

“(c) Post–pre contrasts for the trialwise accumulated-evidence regressor show reduced left-SFS BOLD during perceptual decisions (green overlay), with a significantly stronger reduction for perceptual vs value-based decisions (blue overlay). No reduction is observed for value-based decisions.”

Even if not statistically significant it may be of interest to add the results for Value-based decision making on SFS in Supplementary Table 3.

Done. We now include the SFS small-volume results for VDM (trialwise accumulated-evidence regressor) alongside the PDM values in the same table, with exact peak, cluster size, and statistics.

Revision:

Supplementary Table 3 (title):

“Regions encoding trialwise accumulated evidence (parametric modulation) during perceptual and value-based decisions, including SFS SVC results for both tasks.”

Model comparisons: please explain how model complexity is accounted for.

We clarify that model evidence was compared using the Deviance Information Criterion (DIC), which penalizes model fit by an effective number of parameters (pD). Lower DIC indicates better out-of-sample predictive performance after accounting for model complexity.

Revision:

Methods → Hierarchical Bayesian neural-DDM (last paragraph). Add:

“Model comparison used the Deviance Information Criterion (DIC = D̄ + pD), where pD is the effective number of parameters; thus DIC penalizes model complexity. Lower DIC denotes better predictive accuracy after accounting for complexity.”

Reviewer #3 (Recommendations For The Authors):

The following issues would benefit from clarification in the manuscript:

- It is stated that "Our sample size is well within acceptable range, similar to that of previous TMS studies." The sample size being similar to previous studies does not mean it is within an acceptable range. Whether the sample size is acceptable or not depends on the expected effect size. It is perfectly possible that the previous studies cited were all underpowered. What implications might the lack of an a priori power analysis have for the interpretation of the results?

We agree and have revised our wording. We did not conduct an a priori power analysis. Instead, we relied on a within-participant design that typically yields higher sensitivity in TMS–fMRI settings and on convergence across behavioural, computational, and neural measures. We now acknowledge that the absence of formal power calculations limits claims about small effects (particularly for null findings in VDM), and we frame those null results cautiously.

Revision:

Discussion (limitations). Add:

“The within-participant design enhances statistical sensitivity, yet the absence of an a priori power analysis constrains our ability to rule out small effects, particularly for null results in VDM.”

- I was confused when trying to match the results described in the 'Behaviour: validity of task-relevant pre-requisites' section on page 6 to what is presented in Figure 1. Specifically, Figure 1C is cited 4 times but I believe two of these should be citing Figure 1B?

Thank you—this was a citation mix-up. The two places that referenced “Fig. 1C” but described accuracy should in fact point to Fig. 1B. We corrected both citations.

Revision:

Results → Behaviour: validity… Change the two incorrect “Fig. 1C” references (when describing accuracy) to “Fig. 1B”.

- Also, where is the 'SD' coefficient of -0.254 (p-value = 0.123) coming from in line 211? I can't match this to the figure.

This was a typographical error in an earlier draft. The correct coefficients are those shown in the figure and reported elsewhere in the text (evidence-specific effects: for PDM RTs, SD β = −0.057, p < 0.001; for VDM RTs, VD β = −0.016, p = 0.011; non-relevant evidence terms are n.s.). We removed the erroneous value.

Revision:

Results → Behaviour: validity… (sentence with −0.254). Delete the incorrect value and retain the evidence-specific coefficients consistent with Fig. 1B–C.

- It is reported that reaction times were significantly faster for the perceptual relative to the value-based decision task. Was overall accuracy also significantly different between the two tasks? It appears from Figure 3 that it might be, But I couldn't find this reported in the text.

To avoid conflating task with evidence composition, we did not emphasize between-task accuracy averages. Our primary tests examine evidence-specific effects and TMS-induced changes within task. For completeness, we now report descriptive mean accuracies by task and point readers to the figure panels that display accuracy as a function of evidence (which is the meaningful comparison in our matched-evidence design). We refrain from additional hypothesis testing here to keep the analyses aligned with our preregistered focus.

Revision:

Results → Behaviour: validity… Add:

“For completeness, group-mean accuracies by task are provided descriptively in Fig. 3a; inferential tests in the manuscript focus on evidence-specific effects and TMS-induced changes within task.”

I don't know what this part is doing, I assume you want to returns a matrix of p-values, but I don't think you need p-values in a matrix table. Cause I can tell from your plot, If p < .05, it show the correlation, but If p > .05, it won't show anything.

If your goal is to evaluate the relationships between all variables, it may be more informative to display the full correlation matrix without masking non-significant values. In that case, you can remove the significance filter so the plot shows every correlation.

Entah mengapa saya seperti yakin bahwa penulis mengambil cerita dari kisah nyata yang pernah terjadi, karena dari pengalaman dan kesaksian langsung yang pernah saya lihat dan dengar bahwa cerita seperti ini benar adanya. Namun, disisi lain penulis mencoba untuk meramunya menjadinya sebuah cerita kompleks yang penuh dengan misteri dan tanda tanya. Dari beberapa alur cerita yang menjadi clue-clue singkat saya juga menangkap ada isu tersendiri dari yang ingin penulis angkat. Mengapa saya begitu yakin jika cerita ini terinspirasi dari kisah nyata, diawal saya sudah menjelaskan tentang keyakinan itu lewat kisah didaerah saya yang bisa saya lihat dan dengar sendiri secara langsung. Kisah ini hampir sama dengan cerita yang dibangun penulis, yang membedakannya adalah tokohnya adalah seorang perempuan paruh baya yang saya tahu kisahnya ia mulai lupa ingatan akibat umurnya. Ia sehari-harinya dikurung didalam kamar, konon katanya karena sikapnya yang aneh membuat ia dikurung. Pihak keluarganya takut jika ia membahayakan orang lain atau dirinya sendiri jika dibiarkan bebas. Seiring waktu berjalan yang saya dengar ia sudah diberi kelonggaran untuk keluar, keluarganya merasa kasihan mengurungnya terus. Setelah itu, perempuan baruh baya itu sering keluar rumah entah kemana, setelah pulang diceritakan ia sedang bertemu dengan seseoranng, tapi cara ia menceritakannya aneh. Selang berapa lama tidak lagi ada yang tau ada dimana perempuan itu, termasuk keluarganya sendiri. Kesaksian terakhir menurut keluarganya ia pergi dari rumah seperti biasa tetapi untuk yang kali ini ia tidak pulang lagi, ia hilang begitu saja tanpa jejak dan kabar, pihak keluarga juga sudah mengikhlaskan, konon ceritanya ia di bawa oleh orang buniyan, kami mengetahuinya sebagai makhluk yang hidup berdampingan dengan kita tetapi berbeda alam. Itulah kisah yang mebuat saya yakin bahwa cerita ini diangkat berdasarkan kisah nyata, dan yang menjadi dasar dari judulnya "Lelaki Istimewa" adalah karena ia sosok lelaki yang bisa berinteraksi dan seolah-olah bisa melihat dan merasakan keberadaan makhluk dari alam yang berbeda. Dari kisah yang saya ceritakan itu juga sudah banyak dialami orang dikampung kami, dan rata-rata orang yang mengalami memang orang yang istimewa, yaitu yang mengalami keterbelakangan mental, lalu kehilangan akal, dan lain sebagainya. Orang-orang seperti itu pasti tidak punya teman, kurang diperhatikan, dan tidak punya orang-orang yang bisa mengerti dirinya. Dari sini saya juga bisa mengambil sudut pandang lain bahwa orang-orang yanng dianggap istimewa ini bisa menciptakan dunianya sendiri, ketika semua orang yang nyata disampingnya tidak ada lagi yang benar-benar memahaminya, ditengah keistimewaannya ia bisa menciptakan dunia imajinernya sendiri, membuat seolah-olah ia punya teman, sahabat yang bisa mengerti dirinya, tahu apa yang dia mau, dan benar-benar bisa memahaminya. Setelah sekian lama akhirnya ia merasa dunianya yang nyata terlalu membosankan untuknya, tak ada yang bisa ia dapatkan lagi didunia nyata, semua sudah terlalu klasik dan biasa saja, dengan dunia imajiner yang telah ia buat, ia percaya disitu lah tempat terbaiknya, dan akhir dari semuanya ia benar-benar memilih lenyap dan hanyut dalam dunianya sediri yang membuat ia dianggap ada, dan dihargai. Itulah mengapa diakhir cerita ia benar-benar menghilang untuk selamanya. Kembali sedikit ke cerita saya tentang perempuan paruh baya itu, jasadnya tak pernah ditemukan, namun semua orang percaya ia sudah bahagia dengan dunianya sekarang. Mungkin itulah sisi kemanusiaan yang ingin dibangun penulis, lewat realita nyata penulis ingin orang-orang sadar bahwa tak hanya orang normal saja yang ingin dimengerti dan dipahami, bahkan dianggap ada keberadaannya, mereka yang memiliki keterbelakangan pun sama. Tetapi, kita sering lupa dan tidak sadar akan hal itu, dicerita inilah penulis ingin kita sadar, penulis ingin kita bisa lebih memanusiakan manusia, jangan sampai orang-orang seperti mereka menciptakan dunianya sendiri dan pergi untuk selamanya karena sudah terlalu nyaman dengan dunianya dibanding dengan dunia nyata yang mereka anggap penuh carut marut. Maka dari itu ayo kita sadar didunia ini tidak ada yang sendiri, kita semua punya teman dan orang-orang yang selalu ada untuk kita dan juga kita bisa menjadi orang yang selalu ada untuk mereka.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

The manuscript by Yin and colleagues addresses a long-standing question in the field of cortical morphogenesis, regarding factors that determine differential cortical folding across species and individuals with cortical malformations. The authors present work based on a computational model of cortical folding evaluated alongside a physical model that makes use of gel swelling to investigate the role of a two-layer model for cortical morphogenesis. The study assesses these models against empirically derived cortical surfaces based on MRI data from ferret, macaque monkey, and human brains.

The manuscript is clearly written and presented, and the experimental work (physical gel modeling as well as numerical simulations) and analyses (subsequent morphometric evaluations) are conducted at the highest methodological standards. It constitutes an exemplary use of interdisciplinary approaches for addressing the question of cortical morphogenesis by bringing together well-tuned computational modeling with physical gel models. In addition, the comparative approaches used in this paper establish a foundation for broad-ranging future lines of work that investigate the impact of perturbations or abnormalities during cortical development.

The cross-species approach taken in this study is a major strength of the work. However, correspondence across the two methodologies did not appear to be equally consistent in predicting brain folding across all three species. The results presented in Figures 4 (and Figures S3 and S4) show broad correspondence in shape index and major sulci landmarks across all three species. Nevertheless, the results presented for the human brain lack the same degree of clear correspondence for the gel model results as observed in the macaque and ferret. While this study clearly establishes a strong foundation for comparative cortical anatomy across species and the impact of perturbations on individual morphogenesis, further work that fine-tunes physical modeling of complex morphologies, such as that of the human cortex, may help to further understand the factors that determine cortical functionalization and pathologies.

We thank the reviewer for positive opinions and helpful comments. Yes, the physical gel model of the human brain has a lower similarity index with the real brain. There are several reasons.

First, the highly convoluted human cortex has a few major folds (primary sulci) and a very large number of minor folds associated with secondary or tertiary sulci (on scales of order comparable to the cortical thickness), relative to the ferret and macaque cerebral cortex. In our gel model, the exact shapes, positions, and orientations of these minor folds are stochastic, which makes it hard to have a very high similarity index of the gel models when compared with the brain of a single individual.

Second, in real human brains, these minor folds evolve dynamically with age and show differences among individuals. In experiments with the gel brain, multiscale folds form and eventually disappear as the swelling progresses through the thickness. Our physical model results are snapshots during this dynamical process, which makes it hard to have a concrete one-to-one correspondence between the instantaneous shapes of the swelling gel and the growing human brain.

Third, the growth of the brain cortex is inhomogeneous in space and varying with time, whereas, in the gel model, swelling is relatively homogeneous.

We agree that further systematic work, based on our proposed methods, with more fine-tuned gel geometries and properties, might provide a deeper understanding of the relations between brain geometry, and growth-induced folds and their functionalization and pathologies. Further analysis of cortical pathologies using computational and physical gel models can be found in our companion paper (Choi et al., 2025), also published in eLife:

G. P. T. Choi, C. Liu, S. Yin, G. Séjourné, R. S. Smith, C. A. Walsh, L. Mahadevan, Biophysical basis for brain folding and misfolding patterns in ferrets and humans. eLife, 14, RP107141, 2025. doi:10.7554/eLife.107141

Reviewer# 2 (Public review):

This manuscript explores the mechanisms underlying cerebral cortical folding using a combination of physical modelling, computational simulations, and geometric morphometrics. The authors extend their prior work on human brain development (Tallinen et al., 2014; 2016) to a comparative framework involving three mammalian species: ferrets (Carnivora), macaques (Old World monkeys), and humans (Hominoidea). By integrating swelling gel experiments with mathematical differential growth models, they simulate sulcification instability and recapitulate key features of brain folding across species. The authors make commendable use of publicly available datasets to construct 3D models of fetal and neonatal brain surfaces: fetal macaque (ref. [26]), newborn ferret (ref. [11]), and fetal human (ref. [22]).

Using a combination of physical models and numerical simulations, the authors compare the resulting folding morphologies to real brain surfaces using morphometric analysis. Their results show qualitative and quantitative concordance with observed cortical folding patterns, supporting the view that differential tangential growth of the cortex relative to the subcortical substrate is sufficient to account for much of the diversity in cortical folding. This is a very important point in our field, and can be used in the teaching of medical students.

Brain folding remains a topic of ongoing debate. While some regard it as a critical specialization linked to higher cognitive function, others consider it an epiphenomenon of expansion and constrained geometry. This divergence was evident in discussions during the Strungmann Forum on cortical development (Silver¨ et al., 2019). Though folding abnormalities are reliable indicators of disrupted neurodevelopmental processes (e.g., neurogenesis, migration), their relationship to functional architecture remains unclear. Recent evidence suggests that the absolute number of neurons varies significantly with position-sulcus versus gyrus-with potential implications for local processing capacity (e.g., https://doi.org/10.1002/cne.25626). The field is thus in need of comparative, mechanistic studies like the present one.

This paper offers an elegant and timely contribution by combining gel-based morphogenesis, numerical modelling, and morphometric analysis to examine cortical folding across species. The experimental design - constructing two-layer PDMS models from 3D MRI data and immersing them in organic solvents to induce differential swelling - is well-established in prior literature. The authors further complement this with a continuum mechanics model simulating folding as a result of differential growth, as well as a comparative analysis of surface morphologies derived from in vivo, in vitro, and in silico brains.

We thank the reviewer for the very positive comments.

I offer a few suggestions here for clarification and further exploration:

Major Comments

(1) Choice of Developmental Stages and Initial Conditions

The authors should provide a clearer justification for the specific developmental stages chosen (e.g., G85 for macaque, GW23 for human). How sensitive are the resulting folding patterns to the initial surface geometry of the gel models? Given that folding is a nonlinear process, early geometric perturbations may propagate into divergent morphologies. Exploring this sensitivity-either through simulations or reference to prior work-would enhance the robustness of the findings.

The initial geometry is one of the important factors that decides the final folding pattern. The smooth brain in the early developmental stage shows a broad consistency across individuals, and we expect the main folds to form similarly across species and individuals.

Generally, we choose the initial geometry when the brain cortex is still relatively smooth. For the human, this corresponds approximately to GW23, as the major folds such as the Rolandic fissure (central sulcus), arise during this developmental stage. For the macaque brain, we chose developmental stage G85, primarily because of the availability of the dataset corresponding to this time, which also corresponds to the least folded.

We expect that large-scale folding patterns are strongly sensitive to the initial geometry but fine-scale features are not. Since our goal is to explain the large-scale features, we expect sensitivity to the initial shape.

Below are some references of other researchers that are consistent with this idea. Figure 4 from Wang et al. shows some images of simulations obtained by perturbing the geometry of a sphere to an ellipsoid. We see that the growth-induced folds mostly maintain their width (wavelength), but change their orientations.

Reference:

Wang, X., Lefévre, J., Bohi, A., Harrach, M.A., Dinomais, M. and Rousseau, F., 2021. The influence of biophysical parameters in a biomechanical model of cortical folding patterns. Scientific Reports, 11(1), p.7686.

Related results from the same group show that slight perturbations of brain geometry, cause these folds also tend to change their orientations but not width/wavelength (Bohi et al., 2019).

Reference:

Bohi, A., Wang, X., Harrach, M., Dinomais, M., Rousseau, F. and Lefévre, J., 2019, July. Global perturbation of initial geometry in a biomechanical model of cortical morphogenesis. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 442-445). IEEE.

Finally, a systematic discussion of the role of perturbations on the initial geometries and physical properties can be seen in our work on understanding a different system, gut morphogenesis (Gill et al., 2024).

We have added the discussion about geometric sensitivity in the section Methods-Numerical Simulations:

“Small perturbations on initial geometry would affect minor folds, but the main features of major folds, such as orientations, width, and depth, are expected to be conserved across individuals [49, 50]. For simplicity, we do not perturb the fetal brain geometry obtained from datasets.”

(2) Parameter Space and Breakdown Points

The numerical model assumes homogeneous growth profiles and simplifies several aspects of cortical mechanics. Parameters such as cortical thickness, modulus ratios, and growth ratios are described in Table II. It would be informative to discuss the range of parameter values for which the model remains valid, and under what conditions the physical and computational models diverge. This would help delineate the boundaries of the current modelling framework and indicate directions for refinement.

Exploring the valid parameter space is a key problem. We have tested a series of growth parameters and will state them explicitly in our revision. In the current version, we chose the ones that yield a relatively high similarity index to the animal brains. More generally, folding patterns are largely regulated by geometry as well as physical parameters, such as cortical thickness, modulus ratios, growth ratios, and inhomogeneity. In our previous work on a different system, gut morphogenesis, where similar folding patterns are seen, we have explored these features (Gill et al., 2024).

Reference:

Gill, H.K., Yin, S., Nerurkar, N.L., Lawlor, J.C., Lee, C., Huycke, T.R., Mahadevan, L. and Tabin, C.J., 2024. Hox gene activity directs physical forces to differentially shape chick small and large intestinal epithelia. Developmental Cell, 59(21), pp.2834-2849.

(3) Neglected Regional Features: The Occipital Pole of the Macaque

One conspicuous omission is the lack of attention to the occipital pole of the macaque, which is known to remain smooth even at later gestational stages and has an unusually high neuronal density (2.5× higher than adjacent cortex). This feature is not reproduced in the gel or numerical models, nor is it discussed. Acknowledging this discrepancy-and speculating on possible developmental or mechanical explanationswould add depth to the comparative analysis. The authors may wish to include this as a limitation or a target for future work.

Yes, we have added that the omission of the Occipital Pole of the macaque is one of our paper’s limitations. Our main aim in this paper is to explore the formation of large-scale folds, so the smooth region is not discussed. But future work could include this to make the model more complete.

The main text has been modified in Methods, Numerical simulations:

“To focus on fold formation, we did not discuss the relatively smooth region, such as the Occipital Pole of the macaque.”

and also in the caption of Figure 4: “... The occipital pole region of macaque brains remains smooth in real and simulated brains.”

(4) Spatio-Temporal Growth Rates and Available Human Data

The authors note that accurate, species-specific spatio-temporal growth data are lacking, limiting the ability to model inhomogeneous cortical expansion. While this may be true for ferret and macaque, there are high-quality datasets available for human fetal development, now extended through ultrasound imaging (e.g., https://doi.org/10.1038/s41586-023-06630-3). Incorporating or at least referencing such data could improve the fidelity of the human model and expand the applicability of the approach to clinical or pathological scenarios.

We thank the reviewer for pointing out the very useful datasets that exist for the exploration of inhomogeneous growth driven folding patterns. We have referred to this paper to provide suggestions for further work in exploring the role of growth inhomogeneities.

We have referred to this high-quality dataset in our main text, Discussion:

“...the effect of inhomogeneous growth needs to be further investigated by incorporating regional growth of the gray and white matter not only in human brains [29, 31] based on public datasets [45], but also in other species.”

A few works have tried to incorporate inhomogeneous growth in simulating human brain folding by separating the central sulcus area into several lobes (e.g., lobe parcellation method, Wang, PhD Thesis, 2021). Since our goal in this paper is to explain the large-scale features of folding in a minimal setting, we have kept our model simple and show that it is still capable of capturing the main features of folding in a range of mammalian brains.

Reference:

Xiaoyu Wang. Modélisation et caractérisation du plissement cortical. Signal and Image Processing. Ecole nationale superieure Mines-Télécom Atlantique, 2021. English. 〈NNT : 2021IMTA0248〉.

(5) Future Applications: The Inverse Problem and Fossil Brains

The authors suggest that their morphometric framework could be extended to solve the inverse growth problem-reconstructing fetal geometries from adult brains. This speculative but intriguing direction has implications for evolutionary neuroscience, particularly the interpretation of fossil endocasts. Although beyond the scope of this paper, I encourage the authors to elaborate briefly on how such a framework might be practically implemented and validated.

For the inverse problem, we could use the following strategies:

a. Perform systematic simulations using different geometries and physical parameters to obtain the variation in morphologies as a function of parameters.

b. Using either supervised training or unsupervised training (physics-informed neural networks, PINNs) to learn these characteristic morphologies and classify their dependence on the parameters using neural networks. These can then be trained to determine the possible range of geometrical and physical parameters that yield buckled patterns seen in the systematic simulations.

c. Reconstruct the 3D surface from fossil endocasts. Using the well-trained neural network, it should be possible to predict the initial shape of the smooth brain cortex, growth profile, and stiffness ratio of the gray and white matter.

As an example in this direction, supervised neural networks have been used recently to solve the forward problem to predict the buckling pattern of a growing two-layer system (Chavoshnejad et al., 2023). The inverse problem can then be solved using machine-learning methods when the training datasets are the folded shape, which are then used to predict the initial geometry and physical properties.

Reference:

Chavoshnejad, P., Chen, L., Yu, X., Hou, J., Filla, N., Zhu, D., Liu, T., Li, G., Razavi, M.J. and Wang, X., 2023. An integrated finite element method and machine learning algorithm for brain morphology prediction. Cerebral Cortex, 33(15), pp.9354-9366.

Conclusion

This is a well-executed and creative study that integrates diverse methodologies to address a longstanding question in developmental neurobiology. While a few aspects-such as regional folding peculiarities, sensitivity to initial conditions, and available human data-could be further elaborated, they do not detract from the overall quality and novelty of the work. I enthusiastically support this paper and believe that it will be of broad interest to the neuroscience, biomechanics, and developmental biology communities.

Note: The paper mentions a companion paper [reference 11] that explores the cellular and anatomical changes in the ferret cortex. I did not have access to this manuscript, but judging from the title, this paper might further strengthen the conclusions.

The companion paper (Choi et al., 2025) has also been submitted to eLife and can be found here:

G. P. T. Choi, C. Liu, S. Yin, G. Séjourné, R. S. Smith, C. A. Walsh, L. Mahadevan, Biophysical basis for brain folding and misfolding patterns in ferrets and humans. eLife, 14, RP107141, 2025. doi:10.7554/eLife.107141

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

This study was conducted and presented to the highest methodological standards. It is clearly written, and the results are thoroughly presented in the main manuscript and supplementary materials. Nevertheless, I would present the following minor points and comments for consideration by the authors prior to finalizing their work:

We thank the reviewer for positive opinions and helpful comments.

(1) Where did the MRI-based cortical surface data come from? Specifically, it would be helpful to include more information regarding whether the surfaces were reconstructed based on individual- or group-level data. It appears the surfaces were group-level, and, if so, accounting for individual-level cortical folding may be a fruitful direction for future work.

The surface data come from public database, which are stated in the Methods Section. “We used a publicly available database for all our 3d reconstructions: fetal macaque brain surfaces are obtained from Liu et al. (2020); newborn ferret brain surfaces are obtained from Choi et al. (2025); and fetal human brain surfaces are obtained from Tallinen et al. (2016).”

These surfaces are reconstructed based on group-level data. Specifically, the macaque atlas images are constructed for brains at gestational ages of 85 days (G85, N \=18_, 9 females), 110 days (G110, _N \=10_, 7 females) and 135 days (G135, _N \=16_,_ 7 females). And yes, future work may focus on individual-level cortical folding, and we expect that more specific results could be found.

(2) One methodological approach for assessing consistency of cortical folding within species might be an evaluation of cross-hemispheric symmetry. I would find this particularly interesting with respect to the gel models, as it could complement the quantification of variation with respect to the computationally derived and real surfaces.

Yes, the cross-hemispheric symmetry comparison can be done by our morphometric analysis method. We have added the results of ferret brain’s left-right symmetry for gel models, simulations, and real surfaces in the supplementary material. A typical conformal mapping figure and the similarity index table are shown here.

(3) Was there a specific reason to reorder the histogram plots in Figure 4c to macaque, ferret, human rather than to maintain the order presented in Figure 4a/b of ferret, macaque, human? I appreciate that this is a minor concern, and all subplots are indeed properly titled, but consistent order may improve clarity.

We have reordered the histogram plots to make all the figure orders consistent.

Reviewer #2 (Recommendations for the authors):

(1) Please consider revising the caption of Figure 1 (or equivalent figures) to explicitly state whether features such as the macaque occipital flatness were reproduced or not.

We thank the reviewer for pointing out the macaque occipital flatness.

Author response table 1.

Left-right similarity index evaluated by comparing the shape index of ferret brains, calculated with vector P-NORM p\=2,

Author response image 1.

Left-right similarity index of ferret brains

Occipital Pole of the macaque remains relatively smooth in both real brains and computational models. But our main aim in this paper is to explore the large-scale folds formation, so the smooth region is not discussed in depth. But future work could include this to make the model more complete.

(2) Some figures could benefit from clearer labelling to distinguish between in vivo, in vitro, and in silico results.

We have supplemented some texts in panels to make the labelling clearer.

(3) The manuscript would benefit from a short paragraph in the Discussion reflecting on how future incorporation of regional heterogeneities might improve model fidelity.

We have added a sentence in the Discussion Section about improving the model fidelity by considering regional heterogeneities.

“Future more accurate models incorporating spatio-temporal inhomogeneous growth profiles and mechanical properties, such as varying stiffness, would make the folding pattern closer to the real cortical folding. This relies on more in vivo measurements of the brain’s physical properties and cortical expansion.”

(4) Suggestions for improved or additional experiments, data, or analyses.

(5) Clarify and justify the selection of developmental stages: The authors should explain why particular gestational stages (e.g., G85 for macaque, GW23 for human) were chosen as starting points for the physical and computational models. A discussion of how sensitive the folding patterns are to the initial geometry would help assess the robustness of the model. If feasible, a brief sensitivity analysis-varying initial age or surface geometry-would strengthen the conclusions.

The initial geometry is one of the important factors that decides the final folding pattern. The smooth brain in the early developmental stage shows a broad consistency across individuals, and we expect the main folds to form similarly across species and individuals.

Generally, we choose the initial geometry when the brain cortex is still relatively smooth. For the human, this corresponds approximately to GW23, as the major folds such as the Rolandic fissure (central sulcus), arise during this developmental stage. For the macaque brain, we chose developmental stage G85, primarily because of the availability of the dataset corresponding to this time, which also corresponds to the least folded.

We expect that large-scale folding patterns are strongly sensitive to the initial geometry but fine-scale features are not. Since our goal is to explain the large-scale features, we expect sensitivity to the initial shape.

We have added the discussion about geometric sensitivity in the section Methods-Numerical Simulations: “Small perturbations on initial geometry would affect minor folds, but the main features of major folds, such as orientations, width, and depth, are expected to be conserved across individuals [49, 50]. For simplicity, we do not perturb the fetal brain geometry obtained from datasets.”

(6) Explore parameter boundaries more explicitly: The paper would benefit from a clearer account of the ranges of mechanical and geometric parameters (e.g., growth ratios, cortical thickness) for which the model holds. Are there specific conditions under which the physical and numerical models diverge? Identifying breakdown points would help readers understand the model’s limitations and applicability.

Exploring the valid parameter space is a key problem. We have tested a series of growth parameters and will state them explicitly in our revision. In the current version, we chose the ones that yield a relatively high similarity index to the animal brains. More generally, folding patterns are largely regulated by geometry as well as physical parameters, such as cortical thickness, modulus ratios, and growth ratios and inhomogeneity. In our previous work on a different system, gut morphogenesis, where similar folding patterns are seen, we have explored these features (Gill et al., 2024).

(7) Address species-specific cortical peculiarities: A striking omission is the flat occipital pole of the macaque, which is not reproduced in the physical or computational models. Given its known anatomical and cellular distinctiveness, this discrepancy warrants discussion. Even if not explored experimentally, the authors could speculate on what developmental or mechanical conditions would be needed to reproduce such regional smoothness.

Please refer to our answer to the public reviewer 2, question (3). From our results, the formation of smooth Occipital Pole might indicate that the spatio-temporal growth rate of gray and white matter are consistent in this region, such that there’s no much differential growth.

(8) Consider integration of available human growth data: While the authors note the lack of spatiotemporal growth data across species, such datasets exist for human fetal brain development, including those from MRI and ultrasound studies (e.g., Nature 2023). Incorporating these into the human model-or at least discussing their implications-would enhance biological relevance.

Yes, some datasets for fetal human brains have provided very comprehensive measurements on brain shapes at many developmental stages. This can surely be implemented in our current model by calculating the spatio-temporal growth rate from regional cortical shapes at different stages.

(9) Recommendations for improving the writing and presentation:

a) The manuscript is generally well-written, but certain sections would benefit from more explicit linksbetween the biological phenomena and the modeling framework. For instance, the Introduction and Discussion could more clearly articulate how mechanical principles interface with genetic or cellular processes, especially in the context of evolution and developmental variation.

We have briefly discussed the gene-regulated cellular process and the induced changes of mechanical properties and growth rules in SI, table S1. In the main text, to be clearer, we have added a sentence:

“Many malformations are related to gene-regulated abnormal cellular processes and mechanical properties, which are discussed in SI”

b) The Discussion could better acknowledge limitations and future directions, including regional dif-ferences in folding, inter-individual variability, and the model’s assumptions of homogeneous material properties and growth.

In the discussion section, we have pointed out four main limitations and open directions based on our current model, including the discussion on spatiotemporal growth and property. To be more complete, we have supplemented other limitations on the regional differences in folding and the interindividual variability. In the main text, we added the following sentence:

“In addition to the homogeneity assumption, we have not investigated the inter-individual variability and regional differences in folding. More accurate and specific work is expected to focus on these directions.”

c) The authors briefly mention the potential for addressing the inverse growth problem. Expanding this idea in a short paragraph - perhaps with hypothetical applications to fossil brain reconstructions-would broaden the paper’s appeal to evolutionary neuroscientists.

We have stated general steps in the response to public reviewer 2, question (5).

(10) Minor corrections to the text and figures:

a) Figures:

Label figures more clearly to distinguish between in vivo, in vitro, and in silico brain representations.– Ensure that the occipital pole of the macaque is visible or annotated, especially if it lacks the expected smoothness.

Add scale bars where missing for clarity in morphometric comparisons.

We thank the reviewer for suggestions to improve the readability of our manuscript.

The in vivo (real), in vitro (gel), and in silico (simulated) results are both distinguished by their labels and different color scheme: gray-white for real brain, pink-white for gel model, and blue-white for simulations, respectively.

The occipital pole of the macaque brain remains relatively smooth in our computational model but notin our physical gel model. We have clarified this in the main text: “To focus on fold formation, we did not discuss the relatively smooth region, such as the Occipital Pole of the macaque.”

All the brain models are rescaled to the same size, where the distance between the anterior-most pointof the frontal lobe and the posterior-most point of the occipital lobe is two units.

b) Text:

Consider revising figure captions to explicitly mention whether specific regional features (e.g., flatoccipital pole) were observed or absent in models.

In Table II (and relevant text), ensure parameter definitions are consistent and explained clearly for across-disciplinary audience.

Add citations to recent human fetal growth imaging work (e.g., ultrasound-based studies) to support claims about available data.

We have added some descriptions of the characters of the folding pattern in the caption of Figure 4,including major folds and smooth regions.

“Three or four major folds of each brain model are highlighted and served as landmarks. The occipital pole region of macaque brains remains smooth in real and simulated brains.”

We have clarified the definition of growth ratio gMsub>g</sub>/g_w and stiffness ratio µ_g/µ_w between gray matter and white matter, and the normalized cortical thickness h/L in Table 2.

We have referred to a high-quality dataset of fetal brain imaging work, the ultrasound-imaging method(Namburete et al. 2023), in our main text, Discussion:

“...the effect of inhomogeneous growth needs to be further investigated by incorporating regional growth of the gray and white matter not only in human brains [29, 31] based on public datasets [45], but also in other species.”

Author response:

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

Reviewer #1 (Public Review): 

Strengths: 

The work uses a simple and straightforward approach to address the question at hand: is dynein a processive motor in cells? Using a combination of TIRF and spinning disc confocal microscopy, the authors provide a clear and unambiguous answer to this question. 

Thank you for the recognition of the strength of our work

Weaknesses: 

My only significant concern (which is quite minor) is that the authors focus their analysis on dynein movement in cells treated with docetaxol, which could potentially affect the observed behavior. However, this is likely necessary, as without it, motility would not have been observed due to the 'messiness' of dynein localization in a typical cell (e.g., plus end-tracking in addition to cargo transport).

You are exactly correct that this treatment was required to provided us a clear view of motile dynein and p50 puncta. One concern about the treatment that we had noted in our original submission was that the docetaxel derivative SiR tubulin could increase microtubule detyrosination, which has been implicated in affecting the initiation of dynein-dynactin motility but not motility rates (doi: 10.15252/embj.201593071). In response to a comment from reviewer 2 we investigated whether there was a significant increase in alpha-tubulin detyrosination in our treatment conditions and found that there was not. We have removed the discussion of this possibility from the revised version. Please also see response to comments raised by reviewer 2. 

Reviewer 1 (Recommendations for the authors):

Major points: 

(1) The authors measured kinesin-1-GFP intensities in a different cell line (drosophila S2 cells) than what was used for the DHC and p50 measurements (HeLa cells). It is unclear if this provides a fair comparison given the cells provide different environments for the GFP. Although the differences may in fact be trivial, without somehow showing this is indeed a fair comparison, it should at least be noted as a caveat when interpreting relative intensity differences. Alternatively, the authors could compare DHC and p50 intensities to those measured from HeLa cells treated with taxol. 

Thank you for this suggestion. We conducted new rounds of imaging with the DHCEGFP and p50-EGFP clones in conjunction with HeLa cells transiently expressing the human kinesin-1-EGFP and now present the datasets from the new experiments. Importantly, our new data was entirely consistent with the prior analyses as there was not a significant difference between the kinesin-1-EGFP dimer intensities and the DHC-EGFP puncta intensities and there was a statistically significant difference in the intensity of p50 puncta, which were approximately half the intensity of the kinesin-1 and DHC. We have moved the old data comparing the intensities in S2 cells expressing kinesin-1-EGFP to Figure 3 - figure supplement 2 A-D and the new HeLa cell data is now shown in Figure 3 D-G.

(2) Given the low number of observations (41-100 puncta), I think a scatter plot showing all data points would offer readers a more transparent means of viewing the single-molecule data presented in Figures 3A, B, C, and G. I also didn't see 'n' values for plots shown in Figure 3. 

The box and whisker plots have now been replaced with scatter plots showing all data points. The accompanying ‘n’ values have been included in the figure 3 legend as well as the histograms in figures 1 and 2 that are represented in the comparative scatter plots.  

(3) Given the authors have produced a body of work that challenges conclusions from another pre-print (Tirumala et al., 2022 bioRxiv) - specifically, that dynein is not processive in cells - I think it would be useful to include a short discussion about how their work challenges theirs. For example, one significant difference between the two experimental systems that may account for the different observations could simply be that the authors of the Tirumala study used a mouse DHC (in HeLa cells), which may not have the ability to assemble into active and processive dynein-dynactin-adaptor complexes. 

Thank you for pointing this out! At the time we submitted our manuscript we were conflicted about citing a pre-print that had not been peer reviewed simply to point out the discrepancy. If we had done so at that time we would have proposed the exact potential technical issue that you have proposed here. However, at the time we felt it would be better for these issues to be addressed through the review process. Needless to say, we agree with your interpretation and now that the work is published (Tirumala et al. JCB, 2024) it is entirely appropriate to add a discussion on Tirumala et al. where contradictory observations were reported. 

The following statement has been added to the manuscript: 

“In contrast, a separate study (Tirumala et al., 2024) reported that dynein is not highly processive, typically exhibiting runs of very short duration (~0.6 s) in HeLa cells. A notable technical difference that may account for this discrepancy is that our study visualizes endogenously tagged human DHC, whereas Tirumala et al. characterized over-expressed mouse DHC in HeLa cells. Over-expression of the DHC may result in an imbalance of the subunits that comprise the active motor complex, leading to inactive, or less active complexes. Similarly, mouse DHC may not have the ability to efficiently assemble into active and processive dynein-dynactin-adaptor complexes to the same extent as human DHC.”

Minor points: 

(1) "Specifically, the adaptor BICD2 recruited a single dynein to dynactin while BICDR1 and HOOK3 supported assembly of a "double dynein" complex." It would be more accurate to say that dynein-dynactin complexes assembled with Bicd2 "tend to favor single dynein, and the Bicdr1 and Hook3 tend to favor two dyneins" since even Bicd2 can support assembly of 2 dynein-1 dynactin complexes (see Urnavicius et al, Nature 2018). 

Thank you, the manuscript has been edited to reflect this point. 

(2) "Human HeLa cells were engineered using CRISPR/Cas9 to insert a cassette encoding FKBP and EGFP tags in the frame at the 3' end of the dynein heavy chain (DYNC1H1) gene (SF1)." It is unclear to what "SF1" is referring. 

SF1 is supplementary figure 1, which we have now clarified as being Figure 1 – figure supplement 1A.

(3) "The SiR-Tubulin-treated cells were subjected to two-color TIRFM to determine if the DHC puncta exhibited motility and; indeed, puncta were observed streaming along MTs..." This sentence is strangely punctuated (the ";" is likely a typo?). 

Thank you for pointing this out, the typo has been corrected and the sentence now reads:

“The SiR-Tubulin-treated cells were subjected to two-color TIRFM and DHC-EGFP puncta were clearly observed streaming on Sir-Tubulin labeled MTs, which was especially evident on MTs that were pinned between the nucleus and the plasma membrane (Video 3)”

(4) I am unfamiliar with the "MK" acronym shown above the molecular weight ladders in Figure 3H and I. Did the authors mean to use "MW" for molecular weight? 

We intended this to mean MW and the typo has been corrected.

(5) "This suggests that the cargos, which we presume motile dynein-dynactin puncta are bound to, any kinesins..." This sentence is confusing as written. Did the authors mean "and kinesins"? 

Agreed. We have changed this sentence to now read: 

“The velocity and low switching frequency of motile puncta suggest that any kinesin motors associated with cargos being transported by the dynein-dynactin visualized here are inactive and/or cannot effectively bind the MT lattice during dynein-dynactin-mediated transport in interphase HeLa cells.”

Reviewer 2 (Recommendations for the authors):

(1) I am confused as to why the authors introduced an FKBP tag to the DHC and no explanation is given. Is it possible this tag induces artificial dimerization of the DHC? 

FKBP was tagged to DHC for potential knock sideways experiments. Since the current cell line does not express the FKBP counterpart FRB, having FKBP alone in the cell line would not lead to artificial dimerization of DHC.

(2) The authors use a high concentration of SiR-tubulin (1uM) before washing it out. However, they observe strong effects on MT dynamics. The manufacturer states that concentrations below 100nM don't affect MT dynamics, so I am wondering why the authors are using such a high amount that leads to cellular phenotypes. 

We would like to note that in our hands even 100 nM SiR-tubulin impacted MT dynamics if it was incubated for enough time to get a bright signal for imaging, which makes sense since drugs like docetaxel and taxol become enriched in cells over time. Thus, it was a trade-off between the extent/brightness of labeling and the effects on MT dynamics. We opted for shorter incubation with a higher concentration of Sir-Tubulin to achieve rapid MT labeling and efficient suppression of plus-end MT polymerization. This approach proved useful for our needs since the loss of the tip-tacking pool of DHC provided a clearer view of the motile population of MT-associated DHC.

(3) The individual channels should be labeled in the supplemental movies. 

They have now been labelled.

(4) I would like to see example images and kymographs of the GFP-Kinesin-1 control used for fluorescent intensity analysis. Further, the authors use the mean of the intensity distribution, but I wonder why they don't fit the distribution to a Gaussian instead, as that seems more common in the field to me. Do the data fit well to a Gaussian distribution? 

Example images and kymographs of the kinesin-1-EGFP control HeLa cells used for the updated fluorescent intensity analysis have been now added to the manuscript in Figure 3 - figure supplement 1. The kinesin-1-EGFP transiently expressed in HeLa cells exhibited a slower mean velocity and run length than the endogenously tagged HeLa dynein-dynactin. Regarding the distribution, we applied 6 normality tests to the new datasets acquired with DHC and p50 in comparison to human kinesin-EGFP in HeLa cells. While we are confident concluding that the data for p50 was normally distributed (p > 0.05 in 6/6), it was more difficult to reach conclusions about the normality of the datasets for kinesin-1 (p > 0.05 in 4/6) and DHC (p > 0.5 in 1/6). We have decided to report the data as scatter plots (per the suggestion in major point 1 by reviewer 1) in the new Figure 3G since it could be misleading to fit a non-normal distribution with a single Gaussian. We note that the likely non-normal distribution of the DHC data (since it “passed” only 1/6 normality tests) could reflect the presence of other populations (e.g. 1 DHC-EGFP in a motile puncta), but we could also not confidently conclude this since attempting to fit the data with a double Gaussian did not pass statistical muster. Indeed, as stated in the text, on lines 197-198 we do not exclude that the range of DHC intensities measured here may include sub-populations of complexes containing a single dynein dimer with one DHC-EGFP molecule.   

Ultimately, we feel the safest conclusion is that there was not a statically significant difference between the DHC and kinesin-1 dimers (p = 0.32) but there was a statistically significant difference between both the DHC and kinesin-1 dimers compared to the p50 (p values < 0.001), which was ~50% the intensity of DHC and kinesin-1. Altogether this leads us to the fairly conservative conclusion that DHC puncta contain at least one dimer while the p50 puncta likely contain a single p50-EGFP molecule. 

(5) The authors suggest the microtubules in the cells treated with SiR-tubulin may be more detyrosinated due to the treatment. Why don't they measure this using well-characterized antibodies that distinguish tyrosinated/detyrosinated microtubules in cells treated or not with SiR-tubulin? 

At your suggestion, we carried out the experiment and found that under our labeling conditions there was not a notable difference in microtubule detyrosination between DMSO- and SiR-Tubulin-treated cells. Thus, we have removed this caveat from the revised manuscript.

(6) "While we were unable to assess the relative expression levels of tagged versus untagged DHC for technical reasons." Please describe the technical reasons for the inability to measure DHC expression levels for the reader.

We made several attempts to quantify the relative amounts of untagged and tagged protein by Western blotting. The high molecular weight of DHC (~500kDa) makes it difficult to resolve it on a conventional mini gel. We attempted running a gradient mini gel (4%-15%), and doing a western blot; however, we were still unable to detect DHC. To troubleshoot, the experiments were repeated with different dilutions of a commercially available antibody and varying concentrations of cell lysate; however, we were unable to obtain a satisfactory result. 

We hold the view that even if it had it worked it would have been difficult to detect a relatively small difference between the untagged (MW = 500kDa) and tagged DHC (MW = 527kDa) by western blot. We have added language to this effect in the revised manuscript. 

Reviewer #3 (Public Review):

(1). CRISPR-edited HeLa clones: 

(i) The authors indicate that both the DHC-EGFP and p50-EGFP lines are heterozygous and that the level of DHC-EGFP was not measured due to technical difficulties. However, quantification of the relative amounts of untagged and tagged DHC needs to be performed - either using Western blot, immunofluorescence or qPCR comparing the parent cell line and the cell lines used in this work. 

See response to reviewer 2 above. 

(ii) The localization of DHC predominantly at the plus tips (Fig. 1A) is at odds with other work where endogenous or close-to-endogenous levels of DHC were visualized in HeLa cells and other non-polarized cells like HEK293, A-431 and U-251MG (e.g.: OpenCell (https://opencell.czbiohub.org/target/CID001880), Human Protein Atlas  ), https://www.biorxiv.org/content/10.1101/2021.04.05.438428v3). The authors should perform immunofluorescence of DHC in the parental cells and DHC-EGFP cells to confirm there are no expression artifacts in the latter. Additionally, a comparison of the colocalization of DHC with EB1 in the parental and DHC-EGFP and p50-EGFP lines would be good to confirm MT plus-tip localisation of DHC in both lines. 

The microtubule (MT) plus-tip localization of DHC was already observed in the 1990s, as evidenced by publications such as (PMID:10212138) and (PMID:12119357), which were further confirmed by Kobayashi and Murayama  in 2009 (PMID:19915671). We hold the view that further investigation into this localization is not worthwhile since the tip-tracking behavior of DHC-dynactin has been long-established in the field.

(iii) It would also be useful to see entire fields of view of cells expressing DHC-EGFP and p50EGFP (e.g. in Spinning Disk microscopy) to understand if there is heterogeneity in expression. Similarly, it would be useful to report the relative levels of expression of EGFP (by measuring the total intensity of EGFP fluorescence per cell) in those cells employed for the analysis in the manuscript. 

Representative images of fields have been added as Figure 1 - figure supplement 1B and Figure 2 – figure supplement 1 in the revised manuscript. We did not see drastic cell-tocell variation of expression within the clonal cell lines.

(iv) Given that the authors suspect there is differential gene regulation in their CRISPR-edited lines, it cannot be concluded that the DHC-EGFP and p50-EGFP punctae tracked are functional and not piggybacking on untagged proteins. The authors could use the FKBP part of the FKBPEGFP tag to perform knock-sideways of the DHC and p50 to the plasma membrane and confirm abrogation of dynein activity by visualizing known dynein targets such as the Golgi (Golgi should disperse following recruitment of EGFP-tagged DHC-EGFP or p50-EGFP to the PM), or EGF (movement towards the cell center should cease). 

Despite trying different concentrations and extensive troubleshooting, we were not able to replicate the reported observations of Ciliobrevin D or Dynarrestin during mitosis. We would like to emphasize that the velocity (1.2 μm/s) of dynein-dynactin complexes that we measured in HeLa cells was comparable to those measured in iNeurons by Fellows et al. (PMID: 38407313) and for unopposed dynein under in vitro conditions. 

(2) TIFRM and analysis: 

(i) What was the rationale for using TIRFM given its limitation of visualization at/near the plasma membrane? Are the authors confident they are in TIRF mode and not HILO, which would fit with the representative images shown in the manuscript? 

To avoid overcrowding, it was important to image the MT tracks that that were pinned between the nucleus and the plasma membrane. It is unclear to us why the reviewer feels that true TIRFM could not be used to visualize the movement of dynein-dynactin on this population of MTs since the plasma membrane is ~ 3-5 nm and a MT is ~25-27 nm all of which would fall well within the 100-200 nm excitable range of the evanescent wave produced by TIRF. While we feel TIRF can effectively visualize dynein-dynactin motility in cells, we have mentioned the possibility that some imaging may be HILO microscopy in the materials and methods.

(ii) At what depth are the authors imaging DHC-EGFP and p50-EGFP? 

The imaging depth of traditional TIRFM is limited to around 100-200 nm. In adherent interphase HeLa cells the nucleus is in very close proximity (nanometer not micron scale) to the plasma membrane with some cytoskeletal filaments (actin) and microtubules positioned between the plasma membrane and the nuclear membrane. The fact that we were often visualizing MTs positioned between the nucleus and the membrane makes us confident that we were imaging at a depth (100 - 200nm) consistent with TIRFM. 

(iii) The authors rely on manual inspection of tracks before analyzing them in kymographs - this is not rigorous and is prone to bias. They should instead track the molecules using single particle tracking tools (eg. TrackMate/uTrack), and use these traces to then quantify the displacement, velocity, and run-time. 

Although automated single particle tracking tools offer several benefits, including reduced human effort, and scalability for large datasets, they often rely on specialized training datasets and do not generalize well to every dataset. The authors contend that under complex cellular environments human intervention is often necessary to achieve a reliable dataset. Considering the nature of our data we felt it was necessary to manually process the time-lapses. 

(iv) It is unclear how the tracks that were eventually used in the quantification were chosen. Are they representative of the kind of movements seen? Kymographs of dynein movement along an entire MT/cell needs to be shown and all punctae that appear on MTs need to be tracked, and their movement quantified. 

Considering the densely populated environment of a cell, it will be nearly impossible to quantity all the datasets. We selected tracks for quantification, focusing on areas where MTs were pinned between the nucleus and plasma membrane where we could track the movement of a single dynein molecule and where the surroundings were relatively less crowded. 

(v) What is the directionality of the moving punctae? 

In our experience, cells rarely organized their MTs in the textbook radial MT array meaning that one could not confidently conclude that “inward” movements were minus-end directed. Microtubule polarity was also not able to be determined for the MTs positioned between the plasma membrane and the nucleus on which many of the puncta we quantified were moving. It was clear that motile puncta moving on the same MT moved in the same direction with the exception of rare and brief directional switching events. What was more common than directional switching on the same MT were motile puncta exhibiting changes in direction at sharp (sometimes perpendicular) angles indicative of MT track switching, which is a well-characterized behavior of dynein-dynactin (See DOI: 10.1529/biophysj.107.120014).

(vi) Since all the quantification was performed on SiR tubulin-treated cells, it is unclear if the behavior of dynein observed here reflects the behavior of dynein in untreated cells. Analysis of untreated cells is required. 

It was important to quantify SiR tubulin-treated cells because SiR-Tubulin is a docetaxel derivative, and its addition suppressed plus-end MT polymerization resulting in a significant reduction in the DHC tip-tracking population and a clearer view of the motile population of MT-associated DHC puncta. Otherwise, it was challenging to reliably identify motile puncta given the abundance of DHC tip-tracking populations in untreated cells.  

(3) Estimation of stoichiometry of DHC and p50 

Given that the punctae of DHC-EGFP and p50 seemingly bleach on MT before the end of the movie, the authors should use photobleaching to estimate the number of molecules in their punctae, either by simple counting the number of bleaching steps or by measuring single-step sizes and estimating the number of molecules from the intensity of punctae in the first frame. 

Comparing the fluorescence intensity of a known molecule (in our case a kinesin-1EGFP dimer) to calculate the numbers of an unknown protein molecule (in our case Dynein or p50) is a widely accepted technique in the field. For example, refer to PMID: 29899040. To accurately estimate the stoichiometry of DHC and p50 and address the concerns raised by other reviewers, we expressed the human kinesin-EGFP in HeLa cells and analyzed the datasets from new experiments. We did not observe any significant differences between our old and new datasets.

(4) Discussion of prior literature 

Recent work visualizing the behavior of dyneins in HeLa cells (DOI:  10.1101/2021.04.05.438428), which shows results that do not align with observations in this manuscript, has not been discussed. These contradictory findings need to be discussed, and a more objective assessment of the literature in general needs to be undertaken.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Overall, it's a well-performed study, however, causality between Plscr1 and Ifnlr1 expression needs to be more firmly established. This is because two recent studies of PLSCR1 KO cells infected with different viruses found no major differences in gene expression levels compared with their WT controls (Xu et al. Nature, 2023; LePen et al. PLoS Biol, 2024). There were also defects in the expression of other cytokines (type I and II IFNs plus TNF-alpha) so a clear explanation of why Ifnlr1 was chosen should also be given.

We appreciate the reviewer’s reference to the two recently published research on PLSCR1’s role in SARS-CoV-2 infections. We have also discussed those studies in the Introduction and Discussion sections of this manuscript. Here, we would like to clarify ourselves for the rationale of investigating Ifn-λr1 signaling.

The reviewer mentioned “defects in the expression of other cytokines (type I and II IFNs plus TNF-alpha)” and requested a clearer explanation of why Ifnlr1 was chosen for study. In our investigation of IAV infection, we observed no defects in the expression of type I and II IFNs or TNF-α in Plscr1^-/- mice; rather, these cytokines were expressed at even higher levels compared to WT controls (Figures 2D and 3A). This indicates that the type I and II IFN and TNF-α signaling pathways remain intact and are not negatively affected by the loss of Plscr1. Notably, Ifn-λr1 expression is the only one among all IFNs and their receptors that is significantly impaired in Plscr1^-/- mice (Figure 3A), justifying our focused investigation of this receptor. To further clarify this point, we have expanded the explanation under the section titled “Plscr1 Binds to Ifn-λr1 Promoter and Activates Ifn-λr1 Transcription in IAV Infection” within the Results. The reviewer noted that previously published studies “found no major differences in gene expression levels compared with their WT controls”, but neither study examined Ifn-λr1 expression.

(1) The authors propose that Plscr1 restricts IAV infection by regulating the type III IFN signaling pathway. While the data show a positive correlation between Ifnlr1 and Plscr1 levels in both mouse and cell culture models, additional evidence is needed to establish causality between the impaired type III IFN pathway, and the increased susceptibility observed in Plscr1-KO mice. To strengthen this conclusion, the following experiments could be undertaken: (i) Measure IAV titers in WT, Plscr1-KO, Ifnlr1-KO, and Plscr1/ Ifnlr1-double KO cells. If the antiviral activity of Plscr1 is highly dependent on Ifnlr1, there should be no further increase in IAV titers in double KO cells compared to single KO cells; (ii) over-express Plscr1 in Ifnlr1-KO cells to determine if it still inhibits IAV infection. If Plscr1's main action is to upregulate Ifnlr1, then it should not be able to rescue susceptibility since Ifnlr1 cannot be expressed in the KO background. If Plscr1 over-expression rescues viral susceptibility, then there are Ifnlr1-independent mechanisms involved. These experiments should help clarify the relative contribution of the type III IFN pathway to Plscr1-mediated antiviral immunity.

We agree with the reviewer that additional evidence is necessary to establish causality between the impaired type III IFN pathway and the increased susceptibility observed in Plscr1-KO mice. As requested by the reviewer, and one step further, we have measured IAV titers in Wt, Plscr1^-/-, Ifn-λr1^-/-, and Plscr1^-/-Ifn-λr1^-/- mouse lungs, which provided us with more comprehensive information at the tissue and organismal level compared to cell culture models. Our results are detailed under “The Anti-Influenza Activity of Plscr1 Is Highly Dependent on Ifn-λr1” within “Results” section and in Supplemental Figure 5. Importantly, there was no further increase in weight loss (Supplemental Figure 5B), total BAL cell counts (Supplemental Figure 5C), neutrophil percentages (Supplemental Figure 5D), and IAV titers (Supplemental Figure 5E) in Plscr1^-/-Ifn-λr1^-/- mouse lungs compared to Ifn-λr1^-/- mouse lungs. These findings indicate that the antiviral activity of Plscr1 is largely dependent on Ifn-λr1.

We agree that overexpression of Plscr1 on an Ifn-λr1^-/- background would provide additional evidence to support our conclusion from the Plscr1^-/-Ifn-λr1^-/- mice. In future studies, we plan to specifically overexpress Plscr1 in ciliated epithelial cells on the Ifn-λr1^-/- background by breeding Plscr1^floxStopFoxj1-Cre⁺Ifn-λr1^-/- mice. In addition, ciliated epithelial cells isolated from Ifn-λr1^-/- murine airways could be transduced with a Plscr1 construct for overexpression. We hypothesize that overexpression of Plscr1 in ciliated epithelial cells will not rescue susceptibility in Ifn-λr1^-/- mice or cells, since our Plscr1^-/-Ifn-λr1^-/- mouse model suggest that Ifn-λr1-independent anti-influenza functions of Plscr1 are likely minor compared to its role in upregulating Ifn-λr1. These future plans have been added to the “Discussion” section, and we look forward to presenting our results in a forthcoming publication.

(3) In Figure 4, the authors demonstrate the interaction between Plscr1 and Ifnlr1. They suggest that this interaction modulates IFN-λ signaling. However, Figures 5C-E show that the 5CA mutant, which lacks surface localization and the ability to bind Ifnlr1, exhibits similar anti-flu activity to WT Plscr1. Does this mean the interaction between Plscr1 and Ifnlr1 is dispensable for Plscr1-mediated antiviral function? Can the authors compare the activation of IFN-λ signaling pathway in Plscr1-KO cells expressing empty vector, WT Plscr1, and 5CA mutant? This could be done by measuring downstream ISG expression or using an ISRE-luciferase reporter assay upon IFN-λ treatment.

We agree with the reviewer that downstream activation of the IFN-λ signaling pathway is a critical component of the proposed regulatory role of PLSCR1. As suggested, we attempted to perform an ISRE-luciferase reporter assay following IFN-λ treatment in PLSCR1 rescue cell lines by transfecting the cells with hGAPDH-rLuc (Addgene #82479) and pGL4.45 [luc2P/ISRE/Hygro] (Promega #E4041).

Despite extensive efforts over several months, we were unable to achieve expression of pGL4.45 [luc2P/ISRE/Hygro] in PLSCR1 rescue cells using either Lipofectamine 3000 or electroporation, as no firefly luciferase activity was detected at baseline or following IFN-λ treatment. In contrast, hGAPDH-rLuc was robustly expressed in these cells.

The pGL4.45 [luc2P/ISRE/Hygro] plasmid was obtained directly from Promega as a purified product, and its sequence was confirmed via whole plasmid sequencing. Additionally, both hGAPDH-rLuc and pGL4.45 [luc2P/ISRE/Hygro] were successfully expressed in 293T cells, indicating that neither the plasmids nor the transfection protocols are inherently faulty.

We suspect that prior modifications to the PLSCR1 rescue cells—such as CRISPR-mediated knockout and lentiviral transduction—may interfere with successful transfection of pGL4.45 [luc2P/ISRE/Hygro] through an as-yet-unknown mechanism. Although these results are disappointing, we will continue troubleshooting and plan to communicate in a separate manuscript once the luciferase assay is successfully established.

Reviewer #1 (Recommendations):

(1) In the introduction, the linkage between the paragraph discussing type III IFN and PLSCR1 needs to be better established. The mention of PLSCR1 being an ISG at the outset may help connect these two paragraphs and make the text appear more logical.

We apologize for the lack of linkage and logic between type 3 IFN and PLSCR1. We have introduced PLSCR1 as an ISG at the beginning of its paragraph as recommended. 

(2) The statement that, “Intriguingly, PLSCR1 is also an antiviral ISG, as its expression can be highly induced by type 1 and 2 interferons in various viral infections[15, 16]. However, whether its expression can be similarly induced by type 3 interferon has not been studied yet.” is incorrect. Xu et al. tested the role of PLSCR1 in type III IFN-induced control of SARS-CoV-2 (ref. 24). This needs to be revised.

We apologize for the incorrect information in the introduction and have revised the paragraph with the proper citation.

(3) In Figure 3B, can the authors provide a comprehensive heatmap that includes all ISGs above the threshold, rather than only a subset? This would offer a more complete overview of the changes in type I, II, and III IFN pathways in Plscr1-KO mice.

As suggested by the reviewer, we have provided a comprehensive heatmap that includes all ISGs above the threshold in Figure 3C (previously Figure 3B). We identified a total of 1,113 ISGs in our dataset with a fold change ≥2. Enlarged heatmaps with gene names are provided in Supplemental Figure 1. Among those ISGs, 584 are regulated exclusively by type 1 IFNs, and 488 are regulated by both type 1 and type 2 interferons. Unfortunately, the Interferome database does not include information on type 3 IFN-inducible genes in mice[1]. Although many ISGs were robustly upregulated in Plscr1^-/- infected lungs, consistent with inflammation data, a large subset of ISGs failed to be transcribed when Ifn-λr1 function was impaired, especially at 7 dpi. We suspect that those non-transcribed ISGs in Plscr1^-/- mice may be specifically regulated by type 3 IFN and represent interesting targets for future research. These results have been added to “Plscr1 Binds to Ifn-λr1 Promoter and Activates Ifn-λr1 Transcription in IAV Infection” within “Results” section.

(4) In Figure 3C, 5B and 7H, immunoblots should also be included to measure changes of Ifnlr1/IFNLR1 protein level.

As requested by the reviewer, we have provided western blots measuring Ifn-λr1/IFN-λR1 protein level in Figure 5B and 7I. The protein expressions were consistent with the PCR results.

(5) In Figure 3H, the amount of RPL30 is also low in the anti-PLSCR1-treated and IgG samples, making it difficult to estimate if ChIP binding is genuinely impacted.

RPL30 Exon 3 serves as a negative control in the ChIP experiment and is not expected to bind either the anti-PLSCR1-treated or the IgG control samples. Anti-Histone H3 treatment is a positive control, with the treated sample expected to show binding to RPL30 Exon 3. We hope this clarification has addressed any further potential confusion from the reviewer.

(6) In Figure 4A, can the authors show a larger slice of the gel with molecular weight markers for both Plscr1 and Ifnlr1. In the coIP, the binding may be indirect through intermediate partners. Proximity ligation assay is a more direct assay for interaction and can be stated as such.

As suggested by the reviewer, we have included whole gel images of Figure 4A with molecular weight markers for both Plscr1 and Ifnlr1 in Supplemental Figure 3. We appreciate the reviewer’s affirmation of proximity ligation assay and have stated it as a more direct assay for interaction under “Plscr1 Interacts with Ifn-λr1 on Pulmonary Epithelial Cell Membrane in IAV Infection” in “Results” section.

(7) In Figure 5A, how is the expression of PLSCR1 WT and mutants driven by an EF-1α promoter can be further upregulated by IAV infection? Can the authors also use immunoblots to examine the protein level of PLSCR1?

We apologize for the confusion and appreciate the reviewer’s careful observation. We were initially surprised by this finding as well, but upon further investigation, we found out that the human PLSCR1 primers used in our qRT-PCR assay can still detect the transcription from the undisturbed portion of the endogenous PLSCR1 mRNA, even in PLSCR1^-/- cells. In the original Figure 5A, data for vector-transduced PLSCR1^-/- were not included because PCR was not performed on those samples at the time. After conducting PCR for vector-transduced PLSCR1^-/- cells, we detected transcription of PLSCR1, which confirms that the signaling originates from endogenous DNA, but not from the EF-1α promoter-driven PLSCR1 plasmid. Please see Author response image 1 below.

Author response image 1.

The forward human PLSCR1 primer we used matches 15-34 nt of Wt PLSCR1, and the reverse primer matches 224-244 nt of Wt PLSCR1. CRISPR-Cas9 KO of PLSCR1 was mediated by sgRNAs in A549 cells and was performed by Xu et al[2]. sgRNA #1 matches 227-246 nt, sgRNA #2 matches 209-228 nt, and sgRNA #3 matches 689-708 nt of Wt PLSCR1. The sgRNAs likely introduced a short deletion or insertion that does not affect transcription. However, those endogenous mRNA transcripts cannot be translated to functional and detectable PLSCR1 proteins, as validated by our western blot (below), as well as western blots performed by Xu et al[2]. Therefore, our primers could amplify endogenous PLSCR1 transcripts upregulated by IAV infection, if 15-244 nt was not disturbed by CRISPR-Cas9 KO. By western blot, we confirmed that only endogenous PLSCR1 expression is upregulated by IAV infection, and exogenous protein expression of PLSCR1 plasmids driven by an EF-1α promoter are not upregulated by IAV infection.

Author response image 2.

To avoid confusion, we have removed the original Figure 5A from the manuscript.

(8) In Figure 5C, the loss of anti-flu activity with the H262Y mutant is modest, suggesting the loss of ifnlr1 transcription is only partly responsible for the susceptibility of Plscr1 KO cells. The anti-flu activity being independent of scramblase activity resembles the earlier discovery of SARS-CoV-2 (Xu et al., 2024). This could be stated in the results since it is an important point that scramblase activity is dispensable for several major human viruses and shifts the emphasis regarding mechanism. It has been appropriately noted in the discussion.

We appreciated the comments and have acknowledged the consistency of our results with those of Xu et al. under “Both Cell Surface and Nuclear PLSCR1 Regulates IFN-λ Signaling and Limits IAV Infection Independent of Its Enzymatic Activity” in the “Results” section.

Reviewer #2 (Recommendations):

(1) The statement that type I interferons are expressed by “almost all cells” is inaccurate (line 61). Type I IFN production is also context-dependent and often restricted to specific cell types upon infection or stimulation.

We apologize for the inaccurate description of the expression pattern of type 1 IFNs and have corrected the restricted cellular sources of type 1 IFNs in the “Introduction”.

(2) The antiviral response is assessed solely through flu M gene expression. Incorporating infectious virus titers (e.g., TCID50 or plaque assay) would provide a more robust and direct measure of antiviral activity.

As requested by the reviewer, we have performed plaque assays on all experiments where flu M gene expression levels were measured (Figure 1G, 5E and 7F, and Supplemental Figure 6E). The plaque assay results are consistent with the flu M gene expressions.

(3) While mRNA expression of interferons is measured, protein levels (e.g., through ELISA) should also be quantified to establish the functional relevance of IFN expression changes.

As requested by the reviewer, we have quantified the protein level of IFN-λ in mouse BAL with ELISA (Figure 2E). The ELISA results are consistent with the mRNA expressions of IFN-λ.

(4) It is unclear whether reduced IFNLR1 expression translates to defective downstream signaling or antiviral responses after IFN-λ treatment in PLSCR1-deficient cells. This is particularly pertinent given the increase in IFN-λ ligand in vivo, which might compensate for receptor downregulation.

We agree with the reviewer that downstream activation of the IFN-λ signaling pathway is a critical aspect of PLSCR1’s proposed regulatory role. To investigate this, we attempted an ISRE-luciferase reporter assay to assess downstream signaling following IFN-λ treatment in PLSCR1 rescue cells. Unfortunately, the experiment encountered unforeseen technical issues. For additional context, please refer to our response to Reviewer #1’s public review #3.

(5) Detailed gating strategies for immune cell subsets are absent and should be included for clarity and reproducibility.

We would like to clarify that the immune cell subsets in BAL fluids were counted manually following cytospin preparation and Diff-Quik staining (Figure 2B and 7H, and Supplemental Figures 2C, 5D, and 8D), rather than by flow cytometry. We hope this resolves the reviewer’s confusion.

(6) The study does not definitively establish that reduced IFN-λ signaling causes the observed in vivo phenotype. Increased morbidity and mortality in PLSCR1-deficient mice could also stem from elevated TNF-α levels and lung damage, as proinflammatory cytokines and/or enhanced lung damage are known contributors to influenza morbidity and mortality. This point warrants detailed discussions.

We agreed with the reviewer that this study does not guarantee a definitive causality between reduced IFN-λ signaling and increased morbidity of Plscr1^-/- mice and more experiments are needed to reach the conclusion. We have acknowledged this limitation of our study in the “Discussion”, as requested by the reviewer. We hope to fully eliminate the confounding elements and definitively establish the proposed causality in future studies.

Reviewer #3 (Public review):

Summary:

Yang et al. have investigated the role of PLSCR1, an antiviral interferon-stimulated gene (ISG), in host protection against IAV infection. Although some antiviral effects of PLSCR1 have been described, its full activity remains incompletely understood.

This study now shows that Plscr1 expression is induced by IAV infection in the respiratory epithelium, and Plscr1 acts to increase Ifn-λr1 expression and enhance IFN-λ signaling possibly through protein-protein interactions on the cell membrane.

Strengths:

The study sheds light on the way Ifnlr1 expression is regulated, an area of research where little is known. The study is extensive and well-performed with relevant genetically modified mouse models and tools.

Weaknesses:

There are some issues that need to be clarified/corrected in the results and figures as presented.

Also, the study does not provide much information about the role of PLSCR1 in the regulation of Ifn-λr1 expression and function in immune cells. This would have been a plus.

We would like to thank the reviewer for the positive feedback and insightful comment regarding the roles of PLSCR1 and IFN-λR1 in immune cells. It is important to note that IFN-λR1 expression is highly restricted in immune cells and is primarily limited to neutrophils and dendritic cells[3]. While dendritic cells were not the focus of this study, we did examine all immune cell subsets in our single cell RNA seq data and performed infection experiments in Plscr1^floxStop/LysM-Cre⁺ mice. We have not observed any significant findings in these populations. On the other hand, we do have some interesting preliminary data suggesting a role for PLSCR1 in regulating Ifn-λr1 expression and function in neutrophils. These findings are discussed in detail in our response to reviewer #3’s recommendation #12.

Reviewer #3 (Recommendations):

(1) In Figure 1B, the Plscr1 label should be moved to the y-axis so that readers don't confuse it with the Plscr1-/- mice used in the other figure panels. The fact that WT mice were used should be added in the figure legend.

We apologize for the confusion in the figures. We have moved Plscr1 label to the y-axis in Figure 1B and have mentioned Wt mice were used in the figure legend.

(2) In Figure 1C and D, the type of dose leading to the presented data should be added to help the reader. Also, shouldn't statistics be added?

We appreciate the suggestion and have added doses to Figure 1C and 1D. We are confused about the request of adding statistics by the reviewer, as two-way ANOVA tests were used to compare weight losses, and the significance was labeled on the figures.

(3) In Figures 1, F, and G, it is not indicated whether sublethal or lethal dose was used for the IAV infection. This should be very clear in the figure and figure legend.

We apologize for the confusion of infection doses used in the figures. We have added doses to Figure 1F, 1G and 1H.

(4) In Figure 1, the CTCF abbreviation should be explained in the Figure legend.

We have explained CTCF in the figure legend as requested.

(5) In Figure 2B, this is percentages of what?

Figure 2B shows the percentages of each immune cell type within total BAL cells.

(6) In Figures 3A and B, transcriptomes for each condition are from how many mice? Also, what do heatmaps show? Fold induction, differences, etc, and from what? What is compared with what? In addition, is there a discordance between the RNAseq data of Figure 3A and the qPCR data of Fig. 3C in terms of Ifnlr1 expression?

In Figure 3A and 3C (previously 3B), RNA from the whole lungs of 9 mice per PBS-treated group and 4 mice per IAV-infected group were pooled for transcriptomic analysis. Figure 3A represents a heatmap of differential gene expression, while Figure 3C (previously 3B) represents fold changes in gene expression relative to uninfected controls. In both heatmaps, gene expression values are color-coded from row minimum (blue) to row maximum (red), enabling comparison across groups within each gene (row). The major comparison of interest in these heatmaps is between Wt infected mice versus Plscr1^-/- infected mice. We have added this information to the figure legend.

We also acknowledge the reviewer’s observation regarding the discordance between the RNA seq data of Figure 3A and the qPCR data of Figure 3B (previously 3C) for Ifnlr1 expression. To address this, we have repeated the qRT-PCR experiment with additional samples at 7 dpi. In the updated results, Wt mice consistently show significantly higher Ifn-λr1 expression than Plscr1^-/- infected mice at both 3 dpi and 7 dpi, consistent with the RNA seq data. However, a time-dependent discrepancy between the RNA-seq and qRT-PCR datasets remains: Ifn-λr1 expression continues to increase at 7 dpi in the RNA-seq data (Figure 3A), whereas it declines in the qRT-PCR results (Figure 3B). The reason for this discrepancy remains unclear and has been addressed in the Discussion section.

(7) In Figure 3D, have the authors checked whether the Ifnlr1 antibody they use is indeed specific for Ifnlr1? Have they used any blocking peptide for the anti-mouse Ifn-λr1 polyclonal antibody they are using? Also, in Figure 3E, the marker used for staining should be indicated in the pictures of the lung section.

Unfortunately, a blocking peptide is not available for the anti-mouse Ifn-λr1 polyclonal antibody used in our study. To assess antibody specificity, we have performed immunofluorescence staining of Ifn-λr1 on lung tissues from Ifn-λr1^-/- mice using the same antibody. No signal was detected (Supplemental Figure 5A), supporting the specificity of the antibody for Ifn-λr1.

As requested by the reviewer, we have added the marker (Ifn-λr1) to the pictures of the lung section in Figure 3E.

(8) In Figure 5, it's better to move each graph's label that stands to the top (e.g. PLSCR1, IFN-λR1 etc) to the y-axis label so that it doesn't get confused with the mouse -/- label.

We apologize for the confusion and have moved the top label to the y-axis in Figure 5.

(9) In Figure 6A, it is claimed that the 'two-dimensional UMAP demonstrated that these main lung cell populations (epithelial, endothelial, mesenchymal, and immune) were dynamic over the course of infection.'. This is not clear by the data. The percentage of cells per cluster should be calculated.

As requested by the reviewer, the proportion (Supplemental Figure 6A) and cell count (Supplemental Figure 6B) of each cluster have been calculated and included in “PLSCR1 Expression Is Upregulated in the Ciliated Airway Epithelial Compartment of Mice following Flu Infection” under “Results” section. Together with the two-dimensional UMAP (Figure 6A), these data demonstrate that the main lung cell populations (epithelial, endothelial, mesenchymal, and immune) were dynamic over the course of infection. Following infection, many populations emerged, particularly within the immune cell clusters. At the same time, some clusters were initially depleted and later restored, such as microvascular endothelial cells (cluster 2). Other populations, such as interferon-responsive fibroblasts (cluster 20), showed a dramatic yet transient expansion during acute infection and disappeared after infection resolved.

(10) In Figure 6 B and C, the legend should indicate that these are Violin plots. Also, if AT2 cells don't express Plscr1, does that indicate that in these cells Plscr1 is not needed for IFN-λR1 expression?

As requested, we have indicated in the legend of Figure 6B and 6C that these are violin plots. Plscr1 is expressed at low levels in AT2 cells. However, it is unclear whether Plscr1 is needed for Ifn-λr1 expression in AT2 cells, and it would be interesting to investigate further.

(11) In lines 302-304, it is stated that 'Among the various epithelial populations, ciliated epithelial cells not only had 303 the highest aggregated expression of Plscr1, but also were the only epithelial cell 304 population in which significantly more Plscr1 was induced in response to IAV infection.'. Which data/ figure support this statement?

Figure 6B shows that among the various epithelial populations, ciliated epithelial cells had the highest aggregated expression of Plscr1. To better illustrate this statement, we have rearranged the order of cell clusters from highest to lowest Plscr1 expression, and added red dots to indicate the mean expression levels for each cluster in Figure 6B.

Ciliated epithelial cells also had the most significant increase in Plscr1 expression (p < 2.22e-16 and p = 6.7e-05) in early IAV infection at 3 dpi (Figure 6C and Supplemental Figure 7A-7K). In comparison, AT1 cells were the only other epithelial cluster to show Plscr1 upregulation at 3dpi, but to a much less extent (p = 0.033, Supplemental Figure 7J). Supplemental Figure 7 was added to better support the statement and the explanation was added to “PLSCR1 Expression Is Upregulated in the Ciliated Airway Epithelial Compartment of Mice following Flu Infection” under “Results” section.

(12) As earlier, if Plscr1 is not expressed in neutrophils (Figure 6F), does that mean IFN-λR1 expression does not require Plscr1 in these cells?

Although Plscr1 is expressed at lower levels in neutrophils compared to epithelial cells, it is still detectable. In fact, our preliminary data suggest that IFN-λR1 expression in neutrophils is dependent on Plscr1. We have isolated neutrophils from peripheral blood and BAL of IAV-infected Wt and Plscr1^-/- mice using a mouse neutrophil enrichment kit. Quantitative PCR results showed that Plscr1^-/- neutrophils exhibit significantly lower expression of Ifn-λr1, alongside elevated levels of Il-1β, Il-6 and Tnf-α in IAV infection (see figures below). These findings suggest that Plscr1 may play an anti-inflammatory role in neutrophils by upregulating Ifn-λr1. These data were not included in the current manuscript because they are beyond the scope of current study, but we hope to address the role of PLSCR1 in regulating IFN-λR1 expression and function in neutrophils in a future study.

Author response image 3.

(13) The Figure 7A legend is not well stated. Something like ' Schematic representation of the experimental design of...' should be included. Also, Figure 7J is not referenced in the text.

We apologize for the unclear Figure 7A legend and have changed it to “Schematic representation of the experimental design of ciliated epithelial cell conditional Plscr1 KI mice.” Figure 8 (previously Figure 7J) has now been referenced in the text.

(14) In the Methods, more specific information in some parts should be provided. For example, the clones of the antibodies used should be included.

Apart from the 10x technology, the kits used and the type of the Illumina sequencing should be provided. Information on how the QC was performed (threshold for reads/cell, detected genes/per cells, and % of mitochondrial genes etc) should be added.

We apologize for the missing information in the “Methods”. We have now provided the clones of the antibodies used, the kit used to generate single-cell transcriptomic libraries, the type of the Illumina sequencing, and the QC performance data.

References

(1) Rusinova, I., et al., Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res, 2013. 41(Database issue): p. D1040-6.

(2) Xu, D., et al., PLSCR1 is a cell-autonomous defence factor against SARS-CoV-2 infection. Nature, 2023. 619(7971): p. 819-827.

(3) Donnelly, R.P., et al., The expanded family of class II cytokines that share the IL-10 receptor-2 (IL-10R2) chain. J Leukoc Biol, 2004. 76(2): p. 314-21.

Author response:

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

Reviewer #1 (Public Review):

Here the authors discuss mechanisms of ligand binding and conformational changes in GlnBP (a small E Coli periplasmic binding protein, which binds and carries L-glutamine to the inner membrane ATP-binding cassette (ABC) transporter). The authors have distinguished records in this area and have published seminal works. They include experimentalists and computational scientists. Accordingly, they provide comprehensive, high-quality, experimental and computational work. They observe that apo- and holo- GlnBP does not generate detectable exchange between open and (semi-) closed conformations on timescales between 100 ns and 10 ms. Especially, the ligand binding and conformational changes in GlnBP that they observe are highly correlated. Their analysis of the results indicates a dominant induced-fit mechanism, where the ligand binds GlnBP prior to conformational rearrangements. They then suggest that an approach resembling the one they undertook can be applied to other protein systems where the coupling mechanism of conformational changes and ligand binding. They argue that the intuitive model where ligand binding triggers a functionally relevant conformational change was challenged by structural experiments and MD simulations revealing the existence of unliganded closed or semi-closed states and their dynamic exchange with open unbound conformations, discuss alternative mechanisms that were proposed, their merits and difficulties, concluding that the findings were controversial, which, they suggest is due to insufficient availability of experimental evidence to distinguish them. As to further specific conclusions they draw from their results, they determine that a conformational selection mechanism is incompatible with their results, but induced fit is. They thus propose induced fit as the dominant pathway for GlnBP, further supported by the notion that the open conformation is much more likely to bind substrate than the closed one based on steric arguments. Considering the landscape of substrate-free states, in my view, the closed state is likely to be the most stable and, thus most highly populated. As the authors note and I agree that state can be sterically infeasible for a deep-pocketed substrate. As indeed they also underscore, there is likely to be a range of open states. If the populations of certain states are extremely low, they may not be detected by the experimental (or computational) methods. The free energy landscape of the protein can populate all possible states, with the populations determined by their relative energies. In principle, the protein can visit all states. Whether a particular state is observed depends on the time the protein spends in that state. The frequencies, or propensities, of the visits can determine the protein function. As to a specific order of events, in my view, there isn't any. It is a matter of probabilities which depend on the populations (energies) of the states. The open conformation that is likely to bind is the most favorable, permitting substrate access, followed by minor, induced fit conformational changes. However, a key factor is the ligand concentration. Ligand binding requires overcoming barriers to sustain the equilibrium of the unliganded ensemble, thus time. If the population of the state is low, and ligand concentration is high (often the case in in vitro experiments, and high drug dosage scenarios) binding is likely to take place across a range of available states. This is however a personal interpretation of the data. The paper here, which clearly embodies massive careful, and high-quality work, is extensive, making use of a range of experimental approaches, including isothermal titration calorimetry, single-molecule Förster resonance energy transfer, and surface-plasmon resonance spectroscopy. The problem the authors undertake is of fundamental importance.

Reviewer #2 (Public Review):

The manuscript by Han et al and Cordes is a tour-de-force effort to distinguish between induced fit and conformational selection in glutamine binding protein (GlnBP). 

We thank the referee for the recognition of the work and effort that has gone into this manuscript. 

It is important to say that I don't agree that a decision needs to be made between these two limiting possibilities in the sense that whether a minor population can be observed depends on the experiment and the energy difference between the states. That said, the authors make an important distinction which is that it is not sufficient to observe both states in the ligand-free solution because it is likely that the ligand will not bind to the already closed state. The ligand binds to the open state and the question then is whether the ligand sufficiently changes the energy of the open state to effectively cause it to close. The authors point out that this question requires both a kinetic and a thermodynamic answer. Their "method" combines isothermal titration calorimetry, single-molecule FRET including key results from multi-parameter photon-by-photon hidden Markov modelling (mpH2MM), and SPR. The authors present this "method" of combination of experiments as an approach to definitively differentiate between induced fit and conformational selection. I applaud the rigor with which they perform all of the experiments and agree that others who want to understand the exact mechanism of protein conformational changes connected to ligand binding need to do such a multitude of different experiments to fully characterize the process. However, the situation of GlnBP is somewhat unique in the high affinity of the Gln (slow offrate) as compared to many small molecule binding situations such as enzyme-substrate complexes. It is therefore not surprising that the kinetics result in an induced fit situation. 

For us these comments are an essential part of the conceptual aspects of our work and the resulting research. From a descriptive viewpoint, it is essential for us (and we tried to further highlight and stress this in the updated version of our paper) that IF and CS are two kinetic mechanisms of ligand binding. They imply – if active in a biomolecular system – a temporal order and timescale separation of ligand binding and conformational changes. Since we found many conflicting results for the binding mechanism of GlnBP, but also other SPBs, we decided to assess the situation in GlnBP. 

In the case of the E-S complexes I am familiar with, the dissociation is much more rapid because the substrate binding affinity is in the micromolar range and therefore the re-equilibration of the apo state is much faster. In this case, the rate of closing and opening doesn't change much whether ligand is present or not. Here, of course, once the ligand is bound the re-equilibration is slow. Therefore, I am not sure if the conclusions based on this single protein are transferrable to most other protein-small molecule systems. 

We do not argue that our results and interpretations are valid for most other protein-ligand systems may those be enzymes or simple ligand binders. Yet, based on the conservation of ABC-related SBPs and the fact that quite a few of them show sub-µM Kds, we render it likely to find many analogous situations as for GlnBP also based on our previous results e.g., from de Boer et al., eLife (2019).

I am also not sure if they are transferrable to protein-protein systems where both molecules the ligand and the receptor are expected to have multiscale dynamics that change upon binding.

As we argue above the two mechanisms IF/CS imply a clear temporal order and separation of timescales for ligand binding and conformational changes. These mechanisms are simple and extreme cases that we tested before more complex kinetic schemes are inferred for the description of ligand binding and conformational changes (which might not be necessary). 

Strengths:

The authors provide beautiful ITC data and smFRET data to explore the conformational changes that occur upon Gln binding. Figure 3D and Figure 4 (mpH2MM data) provide the really critical data. The multi-parameter photon-by-photon hidden Markov modelling (mpH2MM) data. In the presence of glutamine concentrations near the Kd, two FRET-active sub-populations are identified that appear to interconvert on timescales slower than 10 ms. They then do a whole bunch of control experiments to look for faster dynamics (Figure 5). They also do TIRF smFRET to try to compare their results to those of previous publications. Here, they find several artifacts are occurring including inactivation of ~50% of the proteins. They also perform SPR experiments to measure the association rate of Gln and obtain expectedly rapid association rates on the order of 10^{^}8 M-1s-1.

Thank you.  

Weaknesses:

Looking at the traces presented in the supplementary figures, one can see that several of the traces have more than one molecule present. The authors should make sure that they use only traces with a single photobleaching event for each fluorophore. One can see steps in some of the green traces that indicate two green fluorophors (likely from 2 different molecules) in the traces. This is one of the frequent problems with TIRF smFRET with proteins, that only some of the spots represent single molecules and the rest need to be filtered out of the analysis.

We have inspected all TIRF data provided with the manuscript and assume that the referee refers to data shown in current Appendix Figure 4/5. We agree that those traces in which no photo bleaching occurs could potentially be questioned, yet they would not change our interpretations and thus decided to leave the figure as is.

The NMR experiments that the authors cite are not in disagreement with the work presented here. NMR is capable of detecting "invisible states" that occur in 1-5% of the population. SmFRET is not capable of detecting these very minor states. I am quite sure that if NMR spectroscopists could add very high concentrations of Gln they would also see a conversion to the closed population.

We agree with the referee that NMR is capable of detecting invisible states that occur in 1-5% of the population (see e.g., the paper cited in our manuscript by Tang, C et al., Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR. Nature 2007, 449, 1078). Yet, we see a strong disagreement between our work and papers on GlnBP, where a combination of NMR, FRET and MD was used (Feng, Y. et al., Conformational Dynamics of apo‐GlnBP Revealed by Experimental and Computational Analysis. Angewandte Chemie 2016, 55, 13990; Zhang, L. et al., Ligand-bound glutamine binding protein assumes multiple metastable binding sites with different binding affinities. Communications biology 2020, 3, 1). These inconsistencies were also noted by others in the field (Kooshapur, H. et al., NMR Analysis of Apo Glutamine‐Binding Protein Exposes Challenges in the Study of Interdomain Dynamics. Angewandte Chemie 2019, 58, 16899) and we reemphasize that this latest NMR publication comes to similar conclusions as we present in our manuscript.   

Reviewer #1 (Recommendations For The Authors):

The paper embodies massive careful and high-quality work, and is extensive, making use of a range of experimental approaches, including isothermal titration calorimetry, single-molecule Förster resonance energy transfer, and surface-plasmon resonance spectroscopy. Considering this extensiveness, I do not see what more the authors can do.

We very much appreciate the assessment and positive comments of the referee, but still tried to incorporate simulation data to support our interpretations.

Reviewer #2 (Recommendations For The Authors):

(1) Looking at the traces presented in the supplementary figures, one can see that several of the traces have more than one molecule present. The authors should make sure that they use only traces with a single photobleaching event for each fluorophore. One can see steps in some of the green traces that indicate two green fluorophors (likely from 2 different molecules) in the traces. This is one of the frequent problems with TIRF smFRET with proteins, that only some of the spots represent single molecules and the rest need to be filtered out of the analysis.

See response above for iteration of TIRF data selection and analysis.

(2) The NMR experiments that the authors cite are not in disagreement with the work presented here. NMR is capable of detecting "invisible states" that occur in 1-5% of the population. SmFRET is not capable of detecting these very minor states. I am quite sure that if NMR spectroscopists could add very high concentrations of Gln they would also see a conversion to the closed population.

See response above.

Minor point:

(1) It is difficult to see what is going on between apo and holo in Figure 1B. Could the authors make Figure 1a, 1b apo, and 1b holo in the same orientation (by aligning D2 or D1 to each other in all figures) so one can see which helices are in the same place and which have moved?

We respectfully disagree and decided to keep this figure as it is

Author response:

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

Reviewer #1 (Public review):

Summary:

This study focuses on the bacterial metabolite TMA, generated from dietary choline. These authors and others have previously generated foundational knowledge about the TMA metabolite TMAO, and its role in metabolic disease. This study extends those findings to test whether TMAO's precursor, TMA, and its receptor TAAR5 are also involved and necessary for some of these metabolic phenotypes. They find that mice lacking the host TMA receptor (Taar5-/-) have altered circadian rhythms in gene expression, metabolic hormones, gut microbiome composition, and olfactory and innate behavior. In parallel, mice lacking bacterial TMA production or host TMA oxidation have altered circadian rhythms.

Strengths:

These authors use state-of-the-art bacterial and murine genetics to dissect the roles of TMA, TMAO, and their receptor in various metabolic outcomes (primarily measuring plasma and tissue cytokine/gene expression). They also follow a unique and unexpected behavioral/olfactory phenotype. Statistics are impeccable.

Weaknesses:

Enthusiasm for the manuscript is dampened by some ambiguous writing and the presentation of ideas in the introduction, both of which could easily be improved upon revision.

We apologize for the abbreviated and ambiguous writing style in our original submission. Given Reviewer 2 also suggested reorganizing and rewriting certain parts, we have spent time to remove ambiguity by adding additional points of clarification and adding more historical context to justify studying TMA-TAAR5 signaling in regulating host circadian rhythms. We have also reorganized the presentation of data aligned with this.

Reviewer #2 (Public review):

Summary:

In the manuscript by Mahen et al., entitled "Gut Microbe-Derived Trimethylamine Shapes Circadian Rhythms Through the Host Receptor TAAR5," the authors investigate the interplay between a host G protein-coupled receptor (TAAR5), the gut microbiota-derived metabolite trimethylamine (TMA), and the host circadian system. Using a combination of genetically engineered mouse and bacterial models, the study demonstrates a link between microbial signaling and circadian regulation, particularly through effects observed in the olfactory system. Overall, this manuscript presents a novel and valuable contribution to our understanding of hostmicrobe interactions and circadian biology. However, several sections would benefit from improved clarity, organization, and mechanistic depth to fully support the authors' conclusions.

Strengths:

(1) The manuscript addresses an important and timely topic in host-microbe communication and circadian biology.

(2) The studies employ multiple complementary models, e.g., Taar5 knockout mice, microbial mutants, which enhance the depth of the investigation.

(3) The integration of behavioral, hormonal, microbial, and transcript-level data provides a multifaceted view of the observed phenotype.

(4) The identification of olfactory-linked circadian changes in the context of gut microbes adds a novel perspective to the field.

Weaknesses:

While the manuscript presents compelling data, several weaknesses limit the clarity and strength of the conclusions.

(1) The presentation of hormonal, cytokine, behavioral, and microbiome data would benefit from clearer organization, more detailed descriptions, and functional grouping to aid interpretation.

We appreciate this comment and have reorganized the data to improve functional grouping and readability. We have also added additional detail to descriptions of the data in the revised figure legends and results.

(2) Some transitions-particularly from behavioral to microbiome data-are abrupt and would benefit from better contextual framing.

We agree with this comment, and have added additional language to provide smoother transitions. This in many cases brings in historical context of why we focused on both behavioral and microbiome alterations in this body of work.

(3) The microbial rhythmicity analyses lack detail on methods and visualization, and the sequencing metadata (e.g., sample type, sex, method) are not clearly stated.

We apologize for this, and have now added more detail in our methods, figures, and figure legends to ensure the reader can easily understand sample type, sex, and the methods used. 

(4) Several figures are difficult to interpret due to dense layouts or vague legends, and key metabolites and gene expression comparisons are either underexplained or not consistently assessed across models.

Aligned with the last comment we now added more detail in our methods, figures, and figure legends to provide clear information. We have now provided additional data showing the same key metabolites, hormones, and gene expression alterations in each model if the same endpoints were measured.

(5) Finally, while the authors suggest a causal role for TAAR5 and its ligand in circadian regulation, the current data remain correlative; mechanistic experiments or stronger disclaimers are needed to support these claims.

We agree with this comment, and as a result have removed any language causally linking TMA and TAAR5 together in circadian regulation. Instead, we only state finding in each model and refrain from overinterpreting.

Reviewer #3 (Public review):

Summary:

Deletion of the TMA-sensor TAAR5 results in circadian alterations in gene expression, particularly in the olfactory bulb, plasma hormones, and neurobehaviors.

Strengths:

Genetic background was rigorously controlled.

Comprehensive characterization.

Weaknesses:

The weaknesses identified by this reviewer are minor.

Overall, the studies are very nicely done. However, despite careful experimentation, I note that even the controls vary considerably in their gene expression, etc, across time (eg, compare control graphs for Cry 1 in IB, 4B). It makes me wonder how inherently noisy these measurements are. While I think that the overall point that the Taar5 KO shows circadian changes is robust, future studies to dissect which changes are reproducible over the noise would be helpful.

We thank the reviewer for this insightful comment. We completely agree that there are clear differences in the circadian data in experiments from Taar5^-/- mice and those from gnotobiotic mice where we have genetically deleted CutC. Although the data from Taar5^-/- mice show nice robust circadian rhythms, the data from mice where microbial CutC is altered have inherently more “noise”. We attribute some of this to the fact that the Taar5^-/- mouse experiment have a fully intact and diverse gut microbiome . Whereas, the gnotobiotic study with CutC manipulation includes only a 6 member microbiome community that does not represent the normal microbiome diversity in the gut. This defined synthetic community was used as a rigorous reductionist approach, but likely affected the normal interactions between a complex intact gut microbiome and host circadian rhythms. We have added some additional discussion to indicate this in the limitations section of the manuscript.

Impact:

These data add to the growing literature pointing to a role for the TMA/TMAO pathway in olfaction and neurobehavioral.

Reviewer #1 (Recommendations for the authors):

I suggest a revision of the writing and organization. The potential impact of the study after reading the introduction is unclear. One example, in the intro, " TMAO levels are associated with many human diseases including diverse forms of CVD5-12, obesity13,14, type 2 diabetes15,16, chronic kidney disease (CKD)17,18, neurodegenerative conditions including Parkinson's and Alzheimer's disease19,20, and several cancers21,22" It would be helpful to explain how the previous literature has distinguished that the driver of these phenotypes is TMA/TMAO and not increased choline intake. Basically, for a TMA/O novice reader, a more detailed intro would be helpful.

We appreciate this insightful comment and have now provided a more expansive historical context for the reader regarding the effects of choline consumption (which impacts many things, including choline, acetylcholine, phosphatidylcholine, TMA, TMAO, etc) versus the primary effects of TMA and TMAO.

There were also many uses of vague language (regulation/impact/etc). Directionality would be super helpful.

We thank the reviewer for this recommendation and have improved language as suggested to show directionality of our findings. The terms regulation, impact, shape etc. are used only when we describe multiple variable changing at the same time over the time course of a 24-hour circadian period (some increased and some decreased).

Reviewer #2 (Recommendations for the authors):

In the manuscript by Mahen et al., entitled "Gut Microbe-Derived Trimethylamine Shapes Circadian Rhythms Through the Host Receptor TAAR5," the authors investigate the interplay between a host G protein-coupled receptor (TAAR5), the gut microbiota-derived metabolite trimethylamine (TMA), and the host circadian system. Using a combination of genetically engineered mouse and bacterial models, the study demonstrates a link between microbial signaling and circadian regulation, particularly through effects observed in the olfactory system. Overall, this manuscript presents a novel and valuable contribution to our understanding of hostmicrobe interactions and circadian biology. However, several sections would benefit from improved clarity, organization, and mechanistic depth to fully support the authors' conclusions. Below are specific major and minor suggestions intended to enhance the presentation and interpretation of the data.

Major suggestions:

(1) Consider adding a schematic/model figure as Panel A early in the manuscript to help readers understand the experimental conditions and major comparisons being made.

We thank the reviewer for this recommendation and have added a graphical abstract figure to help the reader understand the major comparisons being made. 

(2) Could the authors present body weight and food intake characteristics in Taar5 KO vs. WT animals?

We have added body weight data as requested in Figure 1, Figure supplement 1. Although we have not stressed these mice with a high fat diet for these behavioral studies, under chow-fed conditions studied here we did not find any significant differences in body weight. Given no difference in body weight, we did not collect data on food consumption and have mentioned this as a limitation in the discussion.  

(3) Several figures, especially Figures 3 and 4, and Supplemental Figures, would benefit from more structured organization and expanded legends. Grouping related data into thematic panels (e.g., satiety vs. appetite hormones, behavioral domains) may help improve readability.

We appreciate the reviewer’s thoughtful comments and agree that reorganization would improve clarity. We have reorganized figures to improve clarity and have expanded the figure legends to provide more detail on experimental methods. 

(4) Clarify and expand the description of hormonal and cytokine changes. For instance, the phrase "altered rhythmic levels" is vague - do the authors mean dampened, phase-shifted, enhanced, etc., relative to WT controls?

Given a similar suggestion was made by Reviewer 1, we have provided more precise language focused on directionality and which specific endpoints we are referring to. For anything looking at circadian rhythms, the revised manuscript includes specific indications when we are discussing mesor, amplitude, and acrophase alterations. The terms regulation, impact, shape etc. are used only when we describe multiple complex variables changing at the same time over the time course of a 24-hour circadian period (some increased and some decreased).

(5) Consider grouping hormones and cytokines functionally (e.g., satiety vs. appetite-stimulating, pro- vs. antiinflammatory) to better interpret how these changes relate to the KO phenotype.

We thank the reviewer for this recommendation, and have re-organized figure panels to reflect this.

(6) Please provide a more detailed description of the behavioral results, particularly those in Supplemental Figure 2.

We have both expanded the methods description in the revised figure legends, but have also added a more detailed description of the behavioral results.

(7) As with hormonal data, behavioral outcomes would be easier to follow if organized thematically (e.g., locomotor activity, anxiety-like behavior, circadian-related behavior), especially for readers less familiar with behavioral assays.

We appreciate this reviewer’s comment and agree that we can better group our data to show how each test is associated with the type of behavior it assesses. As a result we have reorganized the behavioral data into broad categories such as olfactory-related, innate, cognitive, depressive/anxiety-like, or social behaviors. We have also new data in each of these behavioral categories to provide a more comprehensive understanding of behavioral alterations seen in Taar5^-/- mice.

(8) The following statement needs clarification: "Also, it is important to note that many behavioral phenotypes examined, including tests not shown, were unaltered in Taar5-/- mice (Figures S2G, S2H, and S2I)." Consider rephrasing to explicitly state the intended message: are the authors emphasizing a lack of behavioral phenotype, or highlighting specific unaltered aspects?

We apologize for this confusing statement, and have changed the verbiage to improve readability. To expand the comprehensive nature of this study, we also now include the tests that were “not shown” in the original submission to provide a more comprehensive understanding of behavioral alterations seen in Taar5^-/- mice. These new data are included as 6 different figure supplements to main Figure 2.

(9) The transition from behavior to microbiome data feels abrupt. Can the authors better explain whether the behavioral changes are thought to result from gut microbial function, independent of TMA-Taar5 signaling?

We apologize for the poor transitions in our writing style. We have spent time to explain the previous findings linking the TMA pathway to circadian reorganization of the gut microbiome (mostly coming from our original paper Schugar R, et al. 2022, eLife) and how this correlates with behavioral phenotypes. Although at this point it is difficult to know whether the microbiome changes are driving behavioral changes, or vice versa it could be central TAAR5 signaling is altering oscillations in gut microbiome, we present our findings here as a framework for follow up studies to more precisely get at these questions. It is important to note that our experiment using defined community gnotobiotic mice with or without the capacity to produce TMA (i.e. CutC-null community) shows that clearly microbial TMA production can impact host circadian rhythms in the olfactory bulb. Additional experiments beyond the scope of this work will be required to test which phenotypes originate from TMA-TAAR5 signaling versus more broad effects of the restructured gut microbiome.

(10) For Figure 3A, please expand the microbiome results with more granularity:

(a) Indicate in the Results section whether the sequencing method was 16S amplicon or metagenomic.

Sequencing was done using 16S rRNA amplicon sequencing using methods published by our group (PMID: 36417437, PMID: 35448550).

(b) State whether samples were from males, females, or a mix. 

We have indicated that all mice from Figure 1 were male mice in the revised figure legend.

(c) Clarify whether beta diversity is based on phylogenetic or non-phylogenetic metrics. Consider using both  types if not already done.

Beta diversity was analyzed using the Bray-Curtis dissimilarity index as the metric. Details have been included in the methods section.

(d) Make lines partially transparent in the Beta-diversity plot so that individual points are visible.

We have now updated the Beta-diversity plot with individual points visualized.

(e) Clarify what percentage of variation in the Beta-diversity plot is explained by CCA1, and whether this low percentage suggests minimal community-level differences.

We have updated the Beta-diversity plot to include the R² and p-values associated with these data.

(f) Confirm if the y-axis on the Beta-diversity plot should be labeled CCA2 rather than "CCAA 1".

We appreciate this comments, given it identified a typographical error in the plot. The revised figure now include the proper label of CCA2 instead of CCAA 1.

(11) For Figure 3B:

(a) Provide a description of the taxonomy plot in the results.

We have added a description of the taxonomy plot in the revised results section.

(b) Add phylum-level labels and enlarge the legend to improve the readability of genus-level data.

We agree this is a good suggestion so have enlarged the legend for the genus-level data and have also added phylum-level plots as well in the revised manuscript in Figure 3, figure supplement 1.

(12) Rhythmicity of the microbiome is central to the manuscript. The current approach of comparing relative abundance at discrete time points is limiting.

We thank the reviewer for this comment. We agree with this statement that discrete timepoint are not enough to describe circadian rhythmicity. In addition to comparing genotypes at discrete time points, we also used a rigorous cosinor analysis to plot the data over a 24-hour time period, and those differences are shown in the figure itself as well as Table 1. 

(a) Please describe how rhythmicity was determined, e.g., what data or statistical method supports the statement: "Taar5-/- mice showed loss of the normal rhythmicity for Dubosiella and Odoribacter genera yet gained in amplitude of rhythmicity for Bacteroides genera (Figure 3 and S3)."

We appreciate this reviewer comment. Rhythmicity was determined using a cosinor analysis by use of an R program. Cosinor analysis is a statistical method used to model and analyze rhythmic patterns in time-series data, typically assuming a sinusoidal (cosine) shape. It estimates key parameters like mesor (mean level), amplitude (height of oscillation), and acrophase (timing of the peak), making it especially useful in fields like chronobiology and circadian rhythm research. We have used this in previous research to describe circadian rhythms. We do plan to improve language considering directionality of these circadian changes. 

(b) Supplemental Figure S3 needs reorganization to highlight key findings. It's not currently clear how taxa are arranged or what trends are being shown.

The data in Figure S3 show the entire 24-hour time course of the cecal taxa that were significantly altered for at least one time point between Taar5^+/+ and Taar5^-/- mice. Given we showed time pointspecific alterations in the Main Figure 3, we thought these more expansive plots would be important to show to depict how the circadian rhythms were altered.

(c) Supplemental Table 1, which includes 16S features, should be referenced and discussed in the microbiome section.

We have now referenced and discussed Supplemental Table 1 which includes all cosinor statistics for microbiome and other data presented in circadian time point studies.

(13) Did the authors quantify the 16S rRNA gene via RT-PCR to determine if this was similar between KO and WT over the 24-hour period?

We did not quantify 16S rRNA gene via RT-PCR, but do not think adding this will change our overall interpretations.

(14) Reorganize Figure 4 to align with the order of results discussed-starting with TMA and TMAO, followed by related metabolites like choline, L-carnitine, and gamma-butyrobetaine.

We thank the reviewer for this comment. We have chosen this organization because it is ordered from substrates (choline, L-carnitine, and betaine) to the microbe-associated products (TMA then TMAO). We will improve the writing associated with this figure to clearly explain this organization.

(a) Although the changes in the latter metabolites are more modest, they may still have physiological relevance. Could the authors comment on their significance?

We appreciate this reviewer comment and agree. We have expanded the results and discussion to address this.

(15) The authors note similarities in circadian gene expression between Taar5 KO mice and Clostridium sporogenes WT vs. ΔcutC mice, but the gene patterns are not consistent.

(a) Can the authors clarify what conclusions can reasonably be drawn from this comparison?

We hesitate to make definitive conclusions in the manuscript on why the gene patterns are not consistent, because it would be speculation. However, one major factor likely driving differences is the status of the diversity of the gut microbiome in the different studies. For instance, in the studies using Taar5^+/+ and Taar5^-/- mice there is a very diverse microbiome in these conventionally housed mice. In contrast, by design the experiment using Clostridium sporogenes WT vs. ΔcutC communities is a reductionist approach that allows us to genetically define TMA production. In these gnotobiotic mice, the simplified community has very limited diversity and this likely alters the host circadian rhythms in gene expression quite dramatically. Although it is impossible to directly compare the results between these experiments given the difference microbiome diversity, there are clearly alterations in host gene expression when we manipulate TMA production (i.e. ΔcutC community) or TMA sensing (i.e. Taar5^-/-). 

(16) Were circadian and metabolic genes (e.g., Arntl, Cry1, Per2, Pemt, Pdk4) also analyzed in brown adipose tissue of Taar5 KO mice, and how do these results compare to the Clostridium models?

We thank the reviewer for this comment. Unfortunately, we did not collect brown adipose tissue in our original Taar5 study. We plan on doing this in future follow up studies studying cold-induced thermogenesis that are beyond the scope of this manuscript. However, we have decided to include data from our two timepoint Taar5 study which looks at ZT2 (9am) and ZT14 (9pm). There are clear differences in circadian genes between these timepoints. 

(17) To allow a more direct comparison, please ensure the same cytokines (e.g., IL-1β, IL-2, TNF-α, IFN-γ, IL6, IL-33) are reported for both the Taar5 KO and microbial models.

We thank the reviewer for this comment and now include data from the same cytokines for each study.

(18) What was the defined microbial community used to colonize germ-free mice with C. sporogenes strains? Did this community exhibit oscillatory behavior?

To define TMA levels using a genetically-tractable model of a defined microbial community, we leveraged access to the community originally described by our collaborator Dr. Federico Rey (University of Wisconsin – Madison) (PMID: 25784704). We chose this community because it provide some functional metabolic diversity and is well known to allow for sufficient versus deficient TMA production. We are thankful for the reviewer comments about oscillatory behavior of this defined community, and to be responsive have performed sequencing to detect the species over time. These data are now included in the revised manuscript and show that there are clear differences in the oscillatory behavior of the defined community members. These data provide additional support that bacterial TMA production not only alters host circadian rhythms, but also the rhythmic behavior of gut bacteria themselves which has never been described before.

(19) Can the authors explain the rationale for measuring additional metabolites such as tryptophan, indole acetic acid, phenylacetic acid, and phenylacetylglycine? How are these linked to CutC gene function or Taar5 signaling?

We appreciate that this could be confusing, but have included other gut microbial metabolites to be as comprehensive as possible. This is important to include because we have found in other gnotobiotic studies where we have genetically altered metabolite production, if we alter one gut microbe-derived metabolite there can be unexpected alterations in other distinct classes of microbe-derived metabolites (PMID: 37352836). This is likely due to the fact that complex microbe-microbe and microbehost interactions work together to define systemic levels of circulating metabolites, influencing both the production and turnover of distinct and unrelated metabolites.

(20) The authors make several strong claims suggesting that loss of Taar5 or disruption of its ligand directly alters the circadian gene network. However, the current data are correlative. The authors should clarify that these findings demonstrate associations rather than direct causal effects, unless additional mechanistic evidence is provided. Approaches such as studies conducted in constant darkness, measurements of wheelrunning behavior, or analyses that control for potential confounding factors, e.g., inflammation or metabolic disruption, would help establish whether the observed changes in clock gene expression are primary or secondary effects. The authors are encouraged to either soften these causal claims or acknowledge this limitation explicitly in the discussion.

We thank the reviewer for this comment. We agree and have softened our language about direct effects of TMA via TAAR5 because we agree the data presented here are correlative only. 

Minor suggestions:

(1) Avoid repetitive phrases such as "it is important to note..." for improved flow. Rephrasing these instances will enhance readability.

We thank the reviewer for this suggestion and have deleted such repetitive phrases.  

(2) For Figure 2, remove interpretations above he graphs and use simple, descriptive panel labels, similar to those in Supplemental Figure 2.

We have removed these interpretations as suggested, but have retained descriptive panel labels to help the reader understand what type of data are being presented.

Reviewer #3 (Recommendations for the authors):

Minor:

In Figure 1D, UCP1 does not appear to be significantly changed.

We thank the reviewer for this comment and agree that UCP1 gene expression is not significantly altered . However, given the key role that UCP1 plays in white adipose tissue beiging, which is suppressed by the TMAO pathway, we think it is critical to show that this effect appears unaffected by perturbed TMA-TAAR5 signaling.

It would be helpful, in the discussion, to summarize any consistent changes across Taar5 KO, CutC deletion, and FMO3 deletion.

We have added this to the discussion, but as discussed above we hesitate to make strong interpretations about consistency between the models because the microbiome diversity is so different between the studies, and we did not measure all endpoints in both models.

For the Cosinor analysis, it may be helpful to remove the p-values that are >0.05 from the figures.

We have now removed any non-significant p-values that are associated with our figures. 

For Figure 2, Supplement 1E, what are the two bars for each genotype?

We appreciate the reviewer pointing this out and will further explain this test in the figure with labels and in the legend.

• Wprowadzenie: Film przedstawia ranking 15 popularnych suplementów diety, podzielonych na cztery grupy w zależności od ich udowodnionej skuteczności i uniwersalności zastosowania [00:00:40].

• GRUPA 1: Warto przyjmować codziennie

  • Omega-3 (EPA i DHA) – z uwagi na szerokie korzyści zdrowotne i rzadkie spożywanie ich źródeł w diecie [00:19:41].
  • Witamina D – uznawana za hormon, jest kluczowa z uwagi na jej wielokierunkowe działanie i powszechne niedobory (większość osób w Polsce ma jej zbyt niski poziom) [00:20:20].

• GRUPA 2: Szeroki, korzystny wpływ na zdrowie

  • Cynk
  • Magnez (wskazany ze względu na to, że Polacy spożywają go o 20-30% za mało) [00:13:44].
  • Witamina C
  • Błonnik pokarmowy (większość Polaków spożywa go za mało, choć jest powszechny w żywności) [00:16:56].
  • Probiotyki (ważne dla regulacji pracy jelit, odporności, a także w łagodzeniu objawów depresyjnych i usprawnianiu mózgu) [00:18:32].

• GRUPA 3: Potwierdzona skuteczność, ale wąskie zastosowanie

  • Preparaty wysokobiałkowe (np. odżywki białkowe) – przydatne dla osób aktywnych fizycznie, budujących masę mięśniową, w rekonwalescencji oraz dla osób starszych zagrożonych sarkopenią [00:07:45].
  • Kreatyna – wspomaga wzrost masy i siły mięśni, wzmacnia kości, poprawia sprawność umysłową i pamięć [00:08:40].
  • Melatonina – ułatwia zasypianie, a także łagodzi objawy refluksowe i może obniżać ciśnienie tętnicze [00:10:32].
  • Kolagen – poprawia kondycję stawów, skóry, wzmacnia kości i naczynia krwionośne [00:11:42].

• GRUPA 4: Znikoma skuteczność działania, niepolecane

  • L-Karnityna – jej efekt odchudzający jest marginalny (ok. 1,1 kg redukcji masy ciała w ciągu 8–30 tygodni) [00:01:56].
  • Buzdyganek naziemny (Tribulus Terrestris) – nie ma solidnych dowodów na to, że podnosi poziom testosteronu u większości osób [00:02:50].
  • Woda alkaliczna – promowana głównie marketingowo, organizm sam reguluje równowagę kwasowo-zasadową [00:03:30].
  • Wapń – suplementacja u dorosłych i starszych ma niewielki wpływ na gęstość kości, a może nieść nieznaczne ryzyko dla układu krążenia [00:05:06].

• Tajemnicza substancja BDNF: Kluczowym elementem chroniącym mózg jest BDNF (neurotroficzny czynnik pochodzenia mózgowego), białko działające jak „naturalny nawóz” dla komórek nerwowych [00:00:55]–[00:01:13].
• Wytwarzanie nowych komórek: BDNF stymuluje powstawanie nowych komórek nerwowych i połączeń, zwiększając sprawność umysłową, pojemność pamięci i odporność na zmiany neurodegeneracyjne [00:00:24].
• Mniejsze ryzyko demencji: Wyższy poziom BDNF we krwi wiąże się z aż o 51% mniejszym ryzykiem rozwoju demencji i o 54% mniejszym ryzykiem choroby Alzheimera [00:01:30].
• Osoby szczególnie potrzebujące BDNF: Na podniesieniu poziomu BDNF mogą skorzystać osoby starsze, osoby po udarze mózgu (gdzie poziom spada o ok. 55%) [00:02:14], osoby z depresją [00:02:54], cukrzycą (w kontekście neuropatii) [00:03:30], żyjące w ciągłym stresie [00:05:12] oraz z nadwagą/otyłością [00:05:48].
• Czynniki obniżające BDNF: Negatywny wpływ na poziom BDNF ma przewlekły stres (poprzez kortyzol) [00:05:21] oraz alkohol (niezależnie od dawki) [00:06:33].
• Co podnosi BDNF (Dieta i Suplementacja):
  • Ser pleśniowy (np. Brie, Camembert, 30 g dziennie) [00:07:35].
  • Produkty bogate w kwas alfa-linolenowy (olej lniany, nasiona lnu, orzechy włoskie, nasiona chia) [00:08:00].
  • Kwasy tłuszczowe Omega-3 (tłuste ryby: łosoś, śledź, sardynki, makrela) [00:08:34].
  • Kurkumina (powyżej 500 mg dziennie) [00:08:50].
  • Cynk (30 mg glukonianu cynku dziennie lub z pożywienia: mięso, ryby, podroby, pestki dyni) [00:09:42]–[00:10:06].
  • Probiotyki (mieszanka szczepów Lactobacillus i Bifidobacterium) [00:10:25].
  • Dieta ketogeniczna (w badaniu po 3 tygodniach poziom BDNF był wyższy o 47%) [00:11:12].
  • Borówki, kakao i gorzka czekolada (badania na zwierzętach) [00:11:46].
• Co podnosi BDNF (Aktywność):
  • Ruch i ćwiczenia fizyczne (spacery, bieganie, siłownia, 3-4 razy w tygodniu, łącznie min. 2,5 godz. tygodniowo) [00:11:54]–[00:12:21].

Author response:

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

Reviewer #1 (Public Review):

Summary:

Ravichandran et al investigate the regulatory panels that determine the polarization state of macrophages. They identify regulatory factors involved in M1 and M2 polarization states by using their network analysis pipeline. They demonstrate that a set of three regulatory factors (RFs) i.e., CEBPB, NFE2L2, and BCL3 can change macrophage polarization from the M1 state to the M2 state. They also show that siRNA-mediated knockdown of those 3-RF in THP1-derived M0 cells, in the presence of M1 stimulant increases the expression of M2 markers and showed decreased bactericidal effect. This study provides an elegant computational framework to explore the macrophage heterogeneity upon different external stimuli and adds an interesting approach to understanding the dynamics of macrophage phenotypes after pathogen challenge.

Strengths:

This study identified new regulatory factors involved in M1 to M2 macrophage polarization. The authors used their own network analysis pipeline to analyze the available datasets. The authors showed 13 different clusters of macrophages that encounter different external stimuli, which is interesting and could be translationally relevant as in physiological conditions after pathogen challenge, the body shows dynamic changes in different cytokines/chemokines that could lead to different polarization states of macrophages. The authors validated their primary computational findings with in vitro assays by knocking down the three regulatory factors-NCB.

We thank the reviewer for reading our manuscript and for the encouraging comments.

Weaknesses:

One weakness of the paper is the insufficient analysis performed on all the clusters. They used macrophages treated with 28 distinct stimuli, which included a very interesting combination of pro- and anti-inflammatory cytokines/factors that can be very important in the context of in vivo pathogen challenge, but they did not characterize the full spectrum of clusters. 

We have performed a functional enrichment analysis of all the clusters and added a section describing the results (Fig 1B). We believe this work will provide a basis for future experiments to characterize other clusters.

We have also performed a Principal Component Analysis (PCA) using hall mark genes of inflammation and the NCB panel alone to show the relative position of all clusters with respect to each other

Although they mentioned that their identified regulatory panels could determine the precise polarization state, they restricted their analysis to only the two well-established macrophage polarization states, M1 and M2. Analyzing the other states beyond M1 and M2 could substantially advance the field. They mentioned the regulatory factors involved in individual clusters but did not study the potential pathway involving the target genes of these regulatory factors, which can show the importance of different macrophage polarization states. Importantly, these findings were not validated in primary cells or using in vivo models.

We agree it would be useful to demonstrate the polarization switch in other systems as well. However, it is currently infeasible for us to perform these experiments. 

Reviewer #2 (Public Review):

Summary:

The authors of this manuscript address an important question regarding how macrophages respond to external stimuli to create different functional phenotypes, also known as macrophage polarization. Although this has been studied extensively, the authors argue that the transcription factors that mediate the change in state in response to a specific trigger remain unknown. They create a "master" human gene regulatory network and then analyze existing gene expression data consisting of PBMC-derived macrophage response to 28 stimuli, which they sort into thirteen different states defined by perturbed gene expression networks. They then identify the top transcription factors involved in each response that have the strongest predicted association with the perturbation patterns they identify. Finally, using S. aureus infection as one example of a stimulus that macrophages respond to, they infect THP-1 cells while perturbing regulatory factors that they have identified and show that these factors have a functional effect on the macrophage response.

Strengths:

The computational work done to create a "master" hGRN, response networks for each of the 28 stimuli studied, and the clustering of stimuli into 13 macrophage states is useful. The data generated will be a helpful resource for researchers who want to determine the regulatory factors involved in response to a particular stimulus and could serve as a hypothesis generator for future studies.

The streamlined system used here - macrophages in culture responding to a single stimulus - is useful for removing confounding factors and studying the elements involved in response to each stimulus.

The use of a functional study with S. aureus infection is helpful to provide proof of principle that the authors' computational analysis generates data that is testable and valid for in vitro analysis.

We thank the reviewer for reading our manuscript and for the encouraging comments

Weaknesses:

Although a streamlined system is helpful for interrogating responses to a stimulus without the confounding effects of other factors, the reality is that macrophages respond to these stimuli within a niche and while interacting with other cell types. The functional analysis shown is just the first step in testing a hypothesis generated from this data and should be followed with analysis in primary human cells or in an in vivo model system if possible.

It would be helpful for the authors to determine whether the effects they see in the THP-1 immortalized cell line are reproduced in another macrophage cell line, or ideally in PBMC-derived macrophages.

We agree; It would be useful in the future to demonstrate the polarization switch in other systems as well. We believe the results we provide here will inform future studies on other systems. 

The paper would benefit from an expanded explanation of the network mining approach used, as well as the cluster stability analysis and the Epitracer analysis. Although these approaches may be published elsewhere, readers with a non-computational background would benefit from additional descriptions.

We have elaborated on the network mining approach and added a schematic diagram (Fig S13) to describe the EpiTracer algorithm.

Although the authors identify 13 different polarization states, they return to the iM0/M1/M2 paradigm for their validation and functional assays. It would be useful to comment on the broader applications of a 13-state model.

We have included a new figure panel describing the functional enrichment analysis of all the clusters (Fig 1B) and added a section describing the results. We have also performed a Principal Component Analysis (PCA) using hallmark gene of inflammation and the NCB panel alone to show the relative position of all clusters with respect to each other. The PCA plot shows that C11(M1) and C3(M2) are roughly at two extreme ends, with other clusters between them, forming something resembling a punctuated continuum of states.

The relative contributions of each "switching factor" to the phenotype remain unclear, especially as knocking out each individual factor changes different aspects of the model (Fig. S5).

Fig S5 shows the effect on phenotype upon individual knockdown of the switching factors, from which we deduce that CEBPB has the largest contribution in determining the phenotype. However, we maintain that all three genes are necessary as a panel for M1/M2 switching. 

Reviewer #1 (Recommendations For The Authors):

The manuscript by Ravichandran et al describes the networks of genes that they named j"RF" associated with M1 to M2 polarization of macrophages by using their computational pipelines. They have shown 13 clusters of human macrophage polarization state by using an available database of different combinatorial treatments with cytokines, endotoxin, or growth factors, which is interesting and could be useful in the research field. However, there are a few comments which will help to understand the subject more precisely.

(1,2) The authors claimed to identify key regulatory factors involved in the human macrophage polarization from M1 to M2. However, recent advances suggest that macrophage polarization cannot be restricted to M1 and M2 only, which is also supported by the authors' data that shows 13 clusters of macrophages. However, they only focused on the difference between clusters 11 and 3 considering conventional M1 and M2. It will be more interesting to analyze the other clusters and how they relate to the established and simplistic M1 and M2 paradigms.

It will be interesting to know if they found any difference in the enriched pathways among these different clusters considering the exclusive regulatory factors and their targets.

We appreciate the point and have addressed it as follows. In the revised manuscript, we have discussed the clusters in detail and have provided the key regulatory factors (RF) combinations and target genes that define distinct macrophage population states (Please refer: Data file S2, S3). We have also discussed the associated immunological processes with each cluster, particularly in relation to the C11 and C3 clusters. We have added a new panel in Fig 1 to illustrate a heatmap indicating the enrichment of pathways relevant to inflammation in each of the clusters (Fig 1B).   Indeed, there is a substantial difference in the enrichment terms between the extreme ends (M1, M2) and significant differences in some of the pathways between clusters.   

(3) The authors have shown the involvement of NCB at 72h post LPS treatment. Are these RF involved in late response genes or act at the earlier time point of LPS treatment? Understanding the RF involvement in the dynamic response of macrophages to any stimulant will be important.

Using the data available for different time points (30 mins to 72 hours), we plotted the fold change (with respect to unstimulated cells) in M1 and M2 clusters for each of the NCB genes and observe clear divergence in the trend at 24 hours and have provided them as newly added (Supplementary Figure 9  A, B, C).

(4) The authors showed that the knockdown of RF- NCB can switch the M1 to M2. However, they showed a few conventional markers known to be M2 markers. What happens if NCB is overexpressed or knocked down in other treatment conditions/other clusters? Is the RF-NCB only involved in these two specific stimulations or their overexpression can promote M2 polarization in any given stimuli?

It is an interesting question but for practical reasons, experimental work was limited to M1 and M2 clusters as the aim was to establish proof of concept and could not be scaled up for all clusters, which would require a large amount of work and possibly a separate study.  We believe the description of the clusters that we have provided will enable the design of future experiments that will throw light on the significance of the intermediate clusters.  

(5) The authors have shown that knockdown of RF- NCB decreases pathogen clearance, but what are their altered functions? Are they more efficient in cellular debris clearance or resolution of inflammation? The authors can check the mRNA expression of markers/cytokines involved in those processes, in the NCB knockdown condition.

Indeed. Expression levels were measured for the following genes: CXCL2, IL1B, iNOS, SOCS3 (which are pro-inflammatory markers), as well as MRC1, ARG1, TGFB, IL10 (anti-inflammatory markers), as shown in Fig 4B.  

Minor comments:

(1, 2). How the authors evaluate the performance of their knowledge-based gene network. The authors should write the methods in detail, how they generated the simulated network, and evaluated the simulated dataset.

Gene network construction and module detection have many tools available. The authors need to mention which one they used. The authors should show whether their findings are consistent with at least another two module-detection methods (eg; "RedeR") to strengthen their claim.

We have added a schematic figure (Supplementary Fig S11) and detailed description of network construction and mining in the Methods section, as follows: We have reconstructed a comprehensive knowledge-based human Gene Regulatory Network (hGRN), which consists of Regulatory Factors (RF) to Target Gene (TG) and RF to RF interactions. To achieve this, we curated experimentally determined regulatory interactions (RF-TG, RF-RF) associated with human regulatory factors (Wingender et al., 2013). These interactions were sourced from several resources, including: (a) literature-curated resources like the Human Transcriptional Regulation Interactions database (HTRIdb) (Bovolenta et al., 2012), Regulatory Network Repository (RegNetwork) (Liu et al., 2015), Transcriptional Regulatory Relationships Unraveled by Sentence-based Text-mining (TRRUST) (Han et al., 2015), and the TRANSFAC resource from Harmonizome (Rouillard et al., 2016);  (b) ChEA3, which contains ChIP-seq determined interactions (Keenan et al., 2019); and (c) high-confidence protein-protein binding interactions (RF-RF) from the human protein-protein interaction network-2 (hPPiN2) (Ravichandran et al., 2021). As a result, our hGRN comprises 27,702 nodes and 890,991 interactions.  It is important to note that none of the edges/interactions in the hGRN are data-driven. We utilized this extensive hGRN, which encompasses the experimentally determined interactions/edges, to infer stimulant-specific hGRNs and top paths using our in-house network mining algorithm, ResponseNet. We have previously demonstrated that ResponseNet, which utilizes a knowledge-based network and a sensitive interrogation algorithm, outperformed data-driven network inference methods in capturing biologically relevant processes and genes, whose validation is reported earlier (Ravichandran and Chandra, 2019; Sambaturu et al., 2021).

We utilized our in-house response network approach to identify the stimulant-specific top active and repressed perturbations (Ravichandran and Chandra, 2019; Sambaturu et al., 2021). This is clearly described in the revised manuscript. To summarize, we generated stimulant-specific Gene Regulatory Networks (GRNs) by applying weights to the master human Gene Regulatory Network (hGRN) based on differential transcriptomic responses to stimulants (i.e., comparing stimulant-treated conditions to baseline). We then produced individually weighted networks for each stimulant and implemented a refined network mining technique to extract the most significant pathways. Furthermore, we have previously conducted a systematic comparison of our network mining strategy with other data-driven module detection methods, including jActiveModules (Ideker et al, 2002), WGCNA (Langfelder et al, 2008), and ARACNE (Margolin et al, 2006). Our findings demonstrated that our approach outperformed conventional data-driven network inference methods in capturing the biologically pertinent processes and genes (Ravichandran and Chandra, 2019). Since we have experimentally validated what we predicted from the network analysis, we do not see a need for performing the computational analysis with another algorithm. Moreover, different network analyses are based on different aspects of identifying functionally relevant genes or subnetworks. While each of them output useful information, given the scale of the network and the number of different biologically significant subnetworks and genes that could be present in an unbiased network such as what we have used, the output from different methods need not agree with each other as they may capture different aspects all together and hence is not guaranteed to be informative.  

(3) Representation of Fig 2B is difficult to understand the authors' interpretation of 'the 3-RF combination has 1293 targets, 359 covering about 53% of the top-perturbed network' for general readers. If the authors can simplify the interpretation will be helpful for the readers.

This is replaced with clearer figures in the revised manuscript (Figure 2A, 2B), and the associated text is also rephrased for clarity.

Reviewer #2 (Recommendations For The Authors):

Major comments:

(1) It would be helpful for the authors to determine whether the effects they see in the THP-1 immortalized cell line are reproduced in another macrophage cell line, or ideally in PBMC-derived macrophages if this is feasible. If using PBMC- or bone marrow-derived macrophages is beyond the scope of what the authors can reasonably perform, they could consider using another macrophage cell line such as RAW 264.7 cells, which would also provide orthogonal validation from a mouse model.

At this point of time, it is unfortunately infeasible for us to perform these experiments, due to resource limitation.  Moreover, it would require a lot of time. We hope that our work provides pointers for anyone working on mouse models or other model systems to design their studies on regulatory controls and the aspect of generalizability of our findings in Thp-1 cell lines to other systems will eventually emerge.

(2) It would be helpful for the authors to provide an expanded explanation of the network mining approach used, as well as the cluster stability analysis and the Epitracer analysis. Although these approaches may be published elsewhere, readers with a non-computational background would benefit from additional descriptions. A schematic figure would also be helpful to clarify their approach.

We have added a new schematic diagram in Supplementary figures (S13) and a detailed text in the Methods section describing the network mining analysis and epitracer identification in the revised manuscript. 

(3) It would be helpful for the authors to comment on whether the thirteen polarization states that they identify align with other analyses that have been performed using data collected from stimulated macrophages, or whether this is a novel finding, especially as the original paper from which the primary data are derived identified 9 clusters. More broadly, since the authors eventually return to the M1-M2 paradigm, it is unclear whether there is any functional support for a 13-state model - it is also possible that macrophages exist along a continuum of stimulation states rather than in discrete clusters. This at least merits further discussion, which could focus on different axes of polarization as discussed and shown in the original paper.

As described in the manuscript, Clustering based on the differential transcriptome profile of RF-set1, which contains 265 transcription factors (TFs), in response to 28 stimulants, resulted in 13 distinct clusters. The cluster member associations inferred from RF-set1 were similar in number and pattern to those inferred from the entire differential transcriptome (n=12,164; Fig. S2, cophenetic coefficient = 0.68; p-value = 1.25e−51). Furthermore, the inferred cluster pattern largely matched the clustering pattern previously described for the same dataset  (Xue et al., 2014).  Our contribution: The pattern we observed from the top-ranked epicenters in each cluster suggests that a subset of differentially expressed genes (DEGs) present in our top networks is sufficient for achieving differentiation. Our gene-regulatory models suggest that saturated (SA and PA) and unsaturated (LA, LiA, and OA) fatty acids, which were previously grouped together, mediate distinct modes of resolution and are now separated into two sub-branches. Similarly, the effects of IFNγ and sLPS, previously combined, are now distinctly resolved, aligning with known regulatory differences (Hoeksema et al., 2015; Kang et al., 2019). 

The principal takeaway from this analysis is not the exact number of clusters but rather the molecular basis it provides for the differentiation of functional states, with M1 and M2 representing two ends of the spectrum. Several other states are dispersed within the polarization spectrum, which we describe as a punctuated continuum. For our switching studies, we focused on clusters C11 (M1-like) and C2 (M2-like) due to their established functional relevance. However, future studies are required to explore the functional relevance of other clusters. We have added a discussion on this aspect as suggested.

(4) It would be helpful to define the contribution of each component of the NCB group to M1 polarization.

We assessed the impact of CEBPB, NFE2L2, and BCL3 on C2 (M1-like) polarization states by quantifying the expression levels of M1 and M2 markers. Our findings indicate that knocking down CEBPB led to a significant downregulation in the expression of M1 markers and an increase in M2 marker expression. In contrast, NFE2L2 and BCL3 knockdown resulted in decreased expression of M1 markers without a corresponding significant increase in M2 markers. These results suggest that CEBPB is crucial for M1 to the M2 transition. We have added a note on pg 22 to emphasize this better.

(5) NRF2, CEBPb, and BCL3 all have well-described roles in macrophage polarization. To add clarity to their discussion, the authors should cite relevant literature (eg PMIDs 15465827, 27211851, and others) and discuss how their findings extend what is currently known about the contribution of these individual proteins to macrophage responses.

The role of NFE2L2, CEBPB and BCL3 in macrophage polarization and state transition are described in the discussion section. The PMIDs mentioned by the reviewer are added as well. 

(6) The effect size of NCB knockdown in the in vitro Staph aureus model shown in 4C is fairly small - bacterial killing assays typically require at least a log of difference to demonstrate a convincing effect. It would be helpful for the authors to include a positive control for this experiment (for example, STAT4) to frame the magnitude of their effect.

We thank the reviewer for the comment, however, we would like to point out that the difference in CFU plotted in log₁₀ scale, as per common practice. The CFUs are therefore almost halved due to the knockdown in absolute scale and reproduced multiple times with statistically significant results (p-value <0.01). We feel it is sufficient to demonstrate that the NCB geneset by themselves bring out a change in polarization and hence the killing effect. We have used STAT4 as a control for marker measurements as shown in Fig 3C. While carrying out CFU with siSTAT4 may add additional information, we have proceeded to perform the infection experiments with and without the NCB knockdown as that remains the main focus of the study. 

Minor recommendations:

(1) Is there a difference between the data represented in Figure 1A-B and Figure S1? If this is the same data, there is no need to repeat it, and Figure 1 could be composed only of the current panels C and D.

We have removed Figure1 A and B as it illustrates the same point as Figure S1. We have retained Figures C and D and renamed them as new Figure 1A and C. In addition, we have added a new panel Fig 1B (in response to earlier points). 

(2) Could Figure 2B be represented in a different way? The circles do not contain any readable information about the genes, and it may be less visually overwhelming to represent this with just the large and small triangles. Perhaps the individual genes represented by the circles could be listed in a supplemental table or Excel file.

We have provided a new Figure 2 A and B panels for the M1 and M2 clusters respectively, which has only the barcode genes along with a functional annotation. The full network is already provided in supplementary data. 

(3) When indicating the N for all experiments performed in the figure legends, the authors should indicate whether these were technical or biological replicates.

We appreciate the reviewers for the suggestion. We have indicated what N is for all figure legends.

(4) Fig 3B: the y-axis is confusing - it appears that normalization is actually to the untreated cells.

Yes indeed. The normalization is with respect to the untreated cells as per standard practice. We have indicated this clearly in the legend.

(5) The 72-hour time point in Fig S8 shows unexpected results. Could the authors explain or propose a hypothesis for why CXCL2 and IL1b abruptly decrease while iNOS and MRC1 abruptly increase?

The purpose of the mentioned experiment was to standardize the time point of M1 polarization post S. aureus  infection. In this regard,  we profiled the expression levels of markers at various time points. We chose to study the 24 hour time point for all the future experiments based on the significant upregulation of NCB seen in the macrophages.  We believe that the 72 hour time point may show effects that are different since the initial immune response would have waned leading to differences in cytokine dynamics. However, as this is not the focus of our study, we are not discussing this aspect further.

Author response:

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

Reviewer #1 (Public Review):

Summary:

Crohn's disease is a prevalent inflammatory bowel disease that often results in patient relapse post anti-TNF blockades. This study employs a multifaceted approach utilizing single-cell RNA sequencing, flow cytometry, and histological analyses to elucidate the cellular alterations in pediatric Crohn's disease patients pre and post-anti-TNF treatment and comparing them with non-inflamed pediatric controls. Utilizing an innovative clustering approach, the research distinguishes distinct cellular states that signify the disease's progression and response to treatment. Notably, the study suggests that the anti-TNF treatment pushes pediatric patients towards a cellular state resembling adult patients with persistent relapses. This study's depth offers a nuanced understanding of cell states in CD progression that might forecast the disease trajectory and therapy response.

Robust Data Integration: The authors adeptly integrate diverse data types: scRNA-seq, histological images, flow cytometry, and clinical metadata, providing a holistic view of the disease mechanism and response to treatment.

Novel Clustering Approach: The introduction and utilization of ARBOL, a tiered clustering approach, enhances the granularity and reliability of cell type identification from scRNA-seq data.

Clinical Relevance: By associating scRNA-seq findings with clinical metadata, the study offers potentially significant insights into the trajectory of disease severity and anti-TNF response; which might help with the personalized treatment regimens.

Treatment Dynamics: The transition of the pediatric cellular ecosystem towards an adult, more treatment-refractory state upon anti-TNF treatment is a significant finding. It would be beneficial to probe deeper into the temporal dynamics and the mechanisms underlying this transition.

Comparative Analysis with Adult CD: The positioning of on-treatment biopsies between treatment-naïve pediCD and on-treatment adult CD is intriguing. A more in-depth exploration comparing pediatric and adult cellular ecosystems could provide valuable insights into disease evolution.

Areas of improvement:

(1) The legends accompanying the figures are quite concise. It would be beneficial to provide a more detailed description within the legends, incorporating specifics about the experiments conducted and a clearer representation of the data points. 

We agree that it is beneficial to have descriptive figure legends that balance elements of experimental design, methodology, and statistical analyses employed in order to have a clear understanding throughout the manuscript. We have gone through and clarified areas throughout.  

(2) Statistical significance is missing from Fig. 1c WBC count plot, Fig. 2 b-e panels. Please provide it even if it's not significant. Also, the legend should have the details of stat test used.

We have now added details of statistical significance data in the Figure 1 legends. Please note that Mann-Whitney U-test was used for clinical categorical data.

(3) In the study, the NOA group is characterized by patients who, after thorough clinical evaluations, were deemed to exhibit milder symptoms, negating the need for anti-TNF prescriptions. This mild nature could potentially align the NOA group closer to FGID-a condition intrinsically defined by its low to non-inflammatory characteristics. Such an alignment sparks curiosity: is there a marked correlation between these two groups? A preliminary observation suggesting such a relationship can be spotted in Figure 6, particularly panels A and B. Given the prevalence of FGID among the pediatric population, it might be prudent for the authors to delve deeper into this potential overlap, as insights gained from mild-CD cases could provide valuable information for managing FGID.

Thank you for this insightful point. On histopathology and endoscopy, the NOA exhibited microscopic and macroscopic inflammation which landed these patients with the CD diagnosis, albeit mild on both micro and macro accounts. By contrast, the FGID group by definition will not have inflammation of microscopic and macroscopic evaluation. There is great interest in the field of adult and pediatric gastroenterology to understand why patients develop symptoms without evidence of inflammation. However, in 2023 the diagnostic tools of endoscopy with biopsy and histopathology is not sensitive enough to detect transcript level inflammation, positioning single-cell technology to be able to reveal further information in both disease processes.

Based on the reviewer’s suggestions, we have calculated a heatmap of overlapping NOA and FGID cell states along the Figure 6a joint-PC1, showing where NOA CD patients and FGID patients overlap in terms of cell states. This is displayed in Supplemental Figure 15d. This revealed a set of T, Myeloid, and Epithelial cell states that were most important in describing variance along the FGID-CD axis, allowing us to hone in on similarities at the boundary between FGID and CD. By comparing the joint cell states with CD atlas curated cluster names, we identified CCR7-expressing T cell states and GSTA2-expressing epithelial states associated with this overlap. 

(4) Furthermore, Figure 7 employs multi-dimensional immunofluorescence to compare CD, encompassing all its subtypes, with FGID. If the data permits, subdividing CD into PR, FR, and NOA for this comparison could offer a more nuanced understanding of the disease spectrum. Such a granular perspective is invaluable for clinical assessments. The key question then remains: do the sample categorizations for the immunofluorescence study accommodate this proposed stratification?

Thank you for the thoughtful discussion. We agree that stratifying Crohn’s disease by PR, FR, and NOA would provide valuable clinical insight. Unfortunately our multiplex IF cohort was designed to maximize overall CD versus FGID comparisons and does not contain enough samples in patient subgroups to power such an analysis. We have highlighted this limitation in the text.  

(5)The study's most captivating revelation is the proximity of anti-TNF-treated pediatric CD (pediCD) biopsies to adult treatment-refractory CD. Such an observation naturally raises the question: How does this alignment compare to a standard adult colon, and what proportion of this similarity is genuinely disease-specific versus reflective of an adult state? To what degree does the similarity highlight disease-specific traits?

Delving deeper, it will be of interest to see whether anti-TNF treatment is nudging the transcriptional state of the cells towards a more mature adult stage or veering them into a treatment-resistant trajectory. If anti-TNF therapy is indeed steering cells toward a more adult-like state, it might signify a natural maturation process; however, if it's directing them toward a treatment-refractory state, the long-term therapeutic strategies for pediatric patients might need reconsideration.

Thank you to the reviewer for another insightful point. We agree that age-matched samples are critical to evaluate disease cell states and hence we have age-matched controls in our pediatric cohort. Our timeline of follow-up only spans 3 years and patients remain in the pediatric age range at times of follow-up endoscopy and biopsy and would not be reflective of an adult GI state. We believe that the cellular behavior from naïve to treatment biopsy to on treatment biopsy is reflective of disease state rather than movement towards and adult-like state. We would also like to point out that pediatric onset IBD (Crohn’s and ulcerative colitis) traditionally has been harder to treat and presents with more extensive disease state (PMID: 22643596) and the ability to detect need for therapy escalation/change would be an invaluable tool for clinicians.  

We share the reviewer’s interest in disentangling a natural maturation process from disease and treatment-specific changes. Because the patients who were not given treatment did not move towards the adult-like phenotype, it could point to a push towards a treatment-resistant trajectory. To further support these findings, we generated a new disease-pseudotime figure Supplemental Figure 17, using cross-validation methods and the TradeSeq package. This figure was designed to track how each pediatric sample shifts from the treatment-naïve state through antiTNF therapy and to test the robustness of these shifts across samples. The new visualizations show patterns that do not recapitulate natural aging processes but rather shifts across all cell types associated with antiTNF treatment.

Reviewer #2 (Public Review):

Summary:

Through this study, the authors combine a number of innovative technologies including scRNAseq to provide insight into Crohn's disease. Importantly samples from pediatric patients are included. The authors develop a principled and unbiased tiered clustering approach, termed ARBOL. Through high-resolution scRNAseq analysis the authors identify differences in cell subsets and states during pediCD relative to FGID. The authors provide histology data demonstrating T cell localisation within the epithelium. Importantly, the authors find anti-TNF treatment pushes the pediatric cellular ecosystem toward an adult state.

Strengths:

This study is well presented. The introduction clearly explains the important knowledge gaps in the field, the importance of this research, the samples that are used, and study design.

The results clearly explain the data, without overstating any findings. The data is well presented. The discussion expands on key findings and any limitations to the study are clearly explained.

I think the biological findings from, and bioinformatic approach used in this study, will be of interest to many and significantly add to the field.

Weaknesses:

(1) The ARBOL approach for iterative tiered clustering on a specific disease condition was demonstrated to work very well on the datasets generated in this study where there were no obvious batch effects across patients. What if strong batch effects are present across donors where PCA fails to mitigate such effects? Are there any batch correction tools implemented in ARBOL for such cases?

We thank the reviewer for their insightful point, the full extent to which ARBOL can address batch effects requires further study. To this end we integrated Harmony into the ARBOL architecture and used it in the paper to integrate a previous study with the data presented (Figure 8). We have added to ARBOL’s github README how to use Harmony with the automated clustering method. With ARBOL, as well as traditional clustering methods, batch effects can cause artifactual clustering at any tier of clustering. Due to iteration, this can cause batch effects to present themselves in a single round of clustering, followed by further rounds of clustering that appear highly similar within each batch subset. Harmony addresses this issue, removing these batch-related clustering rounds. The later arrangement of fine-grained clusters using the bottom-up approach can use the batch-corrected latent space to calculate relationships between cell states, removing the effects from both sides of the algorithm. As stated, the extent to which ARBOL can be used to systematically address these batch effects requires further research, but the algorithmic architecture of ARBOL is well suited to address these effects.

(2) The authors mentioned that the clustering tree from the recursive sub-clustering contained too much noise, and they therefore used another approach to build a hierarchical clustering tree for the bottom-level clusters based on unified gene space. But in general, how consistent are these two trees?

Thank you for this thoughtful question. The two tree methodologies are not consistent due to their algorithmic differences, but both are important for several reasons: 

(1) The clustering tree is top-down, meaning low resolution lineage-related clusters are calculated first. Doublets and quality differences can cause very small clusters of different lineages (endothelial vs fibroblast) to fall under the incorrect lineage at first in the sub clustering tree, but these are recaptured during further sub clustering rounds, and then disentangled by the cluster-centroid tree.

(2) The hierarchical tree is a rose tree, meaning each branching point can contain several daughter branches, while taxonomies based on distances between species (or cell types in this case) are binary trees with only 2 branches per branching point, because distances between each cluster are unique. Because this taxonomy, or bottom-up, is different from the top-down approach, it is useful to then look at how these bottom-level clusters are similar. To that end, we performed pair-wise differential expression between all end clusters and clustered based on those genes. 

(3) Calculation of a binary tree represents a quantitative basis for comparing the transcriptomic distance between clusters as opposed to relying on distances calculated within a heuristic manifold such as UMAP or algorithmic similarity space such as cluster definitions based on KNN graphs.

In practice, this dual view rescues small clusters that may have been mis-grouped by technical artifacts and gives a quantitative distance based hierarchy that can be compared across metadata covariates.

Author response:

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

Reviewer #1 (Public review): 

Summary:

In their previous publication (Dong et al. Cell Reports 2024), the authors showed that citalopram treatment resulted in reduced tumor size by binding to the E380 site of GLUT1 and inhibiting the glycolytic metabolism of HCC cells, instead of the classical citalopram receptor. Given that C5aR1 was also identified as the potential receptor of citalopram in the previous report, the authors focused on exploring the potential of the immune-dependent anti-tumor effect of citalopram via C5aR1. C5aR1 was found to be expressed on tumor-associated macrophages (TAMs) and citalopram administration showed potential to improve the stability of C5aR1 in vitro. Through macrophage depletion and adoptive transfer approaches in HCC mouse models, the data demonstrated the potential importance of C5aR1-expressing macrophage in the anti-tumor effect of citalopram in vivo. Mechanistically, their in vitro data suggested that citalopram may regulate the phagocytosis potential and polarization of macrophages through C5aR1. Next, they tried to investigate the direct link between citalopram and CD8+T cells by including an additional MASH-associated HCC mouse model. Their data suggest that citalopram may upregulate the glycolytic metabolism of CD8+T cells, probability via GLUT3 but not GLUT1-mediated glucose uptake. Lastly, as the systemic 5-HT level is down-regulated by citalopram, the authors analyzed the association between a low 5-HT and a superior CD8+T cell function against a tumor. Although the data is informative, the rationale for working on additional mechanisms and logical links among different parts is not clear. In addition, some of the conclusion is also not fully supported by the current data. 

We thank the reviewer for their comprehensive summary of our study and appreciate the valuable feedback. We have made improvements based on these comments, and a detailed response addressing each point is presented below.

Strengths: 

The idea of repurposing clinical-in-used drugs showed great potential for immediate clinical translation. The data here suggested that the anti-depression drug, citalopram displayed an immune regulatory role on TAM via a new target C5aR1 in HCC.

We thank the reviewer for recognizing the strengths of our study.

Weaknesses: 

(1) The authors concluded that citalopram had a 'potential immune-dependent effect' based on the tumor weight difference between Rag-/- and C57 mice in Figure 1. However, tumor weight differences may also be attributed to a non-immune regulatory pathway. In addition, how do the authors calculate relative tumor weight? What is the rationale for using relative one but not absolute tumor weight to reflect the anti-tumor effect? 

We appreciate your insights into the potential contributions of non-immune regulatory pathways to the observed tumor weight differences between Rag1^-/-and wild type C57BL/6 mice. Indeed, the anti-tumor effects of citalopram involve non-immune mechanisms. Previously, we have demonstrated the direct effects of citalopram on cancer cell proliferation, apoptosis, and metabolic processes (PMID: 39388353). In this study, we focused on immune-dependent mechanisms, utilizing Rag1^-/- mice to investigate a potential immune-mediated effect. The relative tumor weight was calculated by assigning an arbitrary value of 1 to the Rag1^-/- mice in the DMSO treatment group, with all other tumor weights expressed relative to this baseline. As suggested, we have included absolute tumor weight data in the revised Figure 1B, 1E, 1F, and 3B.

(2) The authors used shSlc6a4 tumor cell lines to demonstrate that citalopram's effects are independent of the conventional SERT receptor (Figure 1C-F). However, this does not entirely exclude the possibility that SERT may still play a role in this context, as it can be expressed in other cells within the tumor microenvironment. What is the expression profiling of Slc6a4 in the HCC tumor microenvironment? In addition, in Figure 1F, the tumor growth of shSlc6a4 in C57 mice displayed a decreased trend, suggesting a possible role of Slc6a4. 

As suggested, we probed the expression pattern of SERT in HCC and its tumor microenvironment. Using a single cell sequencing dataset of HCC (GSE125449), we revealed that SERT is also expressed by T cells, tumor-associated endothelial cells, and cancer-associated fibroblasts (see revised Figure S2G). Therefore, we cannot fully rule out the possibility that citalopram may influence these cellular components within the TME and contribute to its therapeutic effects. In the revised manuscript, we have included and discussed this result. In Figure 1F, SERT knockdown led to a 9% reduction in tumor growth, however, this difference was not statistically significant (0.619 ± 0.099 g vs. 0.594 ± 0.129 g; p = 0.75).

(3) Why did the authors choose to study phagocytosis in Figures 3G-H? As an important player, TAM regulates tumor growth via various mechanisms. 

We choose to investigate phagocytosis because citalopram targets C5aR1-expressing TAM. C5aR1 is a receptor for the complement component C5a, which plays a crucial role in mediating the phagocytosis process in macrophages. In the revised manuscript, we have highlighted this rationale.

(4) The information on unchanged deposition of C5a has been mentioned in this manuscript (Figures 3D and 3F), the authors should explain further in the manuscript, for example, C5a could bind to receptors other than C5aR1 and/or C5a bind to C5aR1 by different docking anchors compared with citalopram.

Thank you for your insightful comment. In Figure 3D, tumor growth was attenuated in C5ar1^-/- recipients compared with C5ar1^-/- recipients, whereas C5a deposition remained unchanged. This suggests that while C5a is still present, its interaction with C5aR1 is critical for influencing tumor growth dynamics. In Figure 3F, C5a deposition was not affected by citalopram treatment. Indeed, docking analysis and DARTS assay revealed that citalopram binds to the D282 site of C5aR1. Previous report has shown that mutations on E199 and D282 reduce C5a binding affinity to C5aR1 (PMID: 37169960). Therefore, the impact of citalopram is primarily on C5a/C5aR1 interactions and downstream signaling pathways, rather than on altering C5a levels. In the revised manuscript, we have included this interpretation.

(5) Figure 3I-M - the flow cytometry data suggested that citalopram treatment altered the proportions of total TAM, M1 and M2 subsets, CD4⁺ and CD8⁺T cells, DCs, and B cells. Why does the author conclude that the enhanced phagocytosis of TAM was one of the major mechanisms of citalopram? As the overall TAM number was regulated, the contribution of phagocytosis to tumor growth may be limited. 

We thank the reviewer’s valuable input. Indeed, recent studies have demonstrated that targeting C5aR1⁺ TAMs can induce many anti-tumor effects, such as macrophage polarization and CD8⁺ T cell infiltration (PMID: 30300579, PMID: 38331868, and PMID: 38098230). In the revised manuscript, we have clarified our conclusion to better articulate the relationship between citalopram treatment, TAM populations, and their phagocytic activity, with particular emphasis on the role of CD8⁺ T cells. For macrophage phagocytosis, one possible explanation is that citalopram targets C5aR1 to enhance macrophage phagocytosis and subsequent antigen presentation and/or cytokine production, which promotes T cell recruitment and activity as well as modulate other aspects of tumor immunity. Given that the anti-tumor effects of citalopram are largely dependent on CD8⁺ T cells, we conclude that CD8⁺ T cells are essential for the effector mechanisms of citalopram.

(6) Figure 4 - what is the rationale for using the MASH-associated HCC mouse model to study metabolic regulation in CD8⁺ T cells? The tumor microenvironment and tumor growth would be quite different. In addition, how does this part link up with the mechanisms related to C5aR1 and TAM? The authors also brought GLUT1 back in the last part and focused on CD8⁺ T cell metabolism, which was totally separated from previous data. 

We chose the MASH-associated HCC mouse model because it closely mimics the etiology of metabolic-associated fatty liver disease (MAFLD), which is a significant contributor to the development of cirrhosis and HCC. In addition to the MASH-associated HCC mouse model, the study also incorporated the orthotopic Hepa1-6 tumor model. In our previous publication (Dong et al., Cell Reports 2024), we employed both of these HCC models. Therefore, we utilized the same two mouse models in this study. The inclusion of CD8⁺ T cells in our study is based on the understanding that citalopram targets GLUT1, which plays a crucial role in glucose uptake (PMID: 39388353). CD8⁺T cell function is heavily reliant on glycolytic metabolism, making it essential to investigate how citalopram’s effects on GLUT1 influence the metabolic pathways and functionality of these immune cells. In this study, we identified that the primary glucose transporter in CD8⁺ T cells is GLUT3, rather than GLUT1. The data presented in Figure 4 aim to illustrate the additional effect of citalopram on peripheral 5-HT levels, which, in turn, influences CD8⁺ T cell functionality. By linking these findings, we clarify how citalopram impacts both TAMs and CD8⁺ T cells. CD8⁺ T cells can be influenced by citalopram through various mechanisms, including TAM-dependent mechanisms, reduced systemic serum 5-HT concentrations, and unidentified direct effects. In the revised manuscript, we have enhanced the background information to avoid any gaps.

(7) Figure 5, the authors illustrated their mechanism that citalopram regulates CD8⁺ T cell anti-tumor immunity through proinflammatory TAM with no experimental evidence. Using only CD206 and MHCII to represent TAM subsets obviously is not sufficient. 

Thank you for your valuable comments. As noted by the reviewer, TAMs can influence CD8⁺ T cell anti-tumor immunity through various mechanisms. In this study, we focused on elucidating the impact of citalopram on pro-inflammatory TAMs, which in turn affect CD8⁺ T cell anti-tumor immunity and ultimately influence tumor outcomes. Therefore, in the mechanistic diagram, we highlighted the effect of citalopram on pro-inflammatory TAMs, while the causal relationship between TAMs and CD8⁺ T cell anti-tumor immunity was indicated with a dotted line due to the limited evidence presented in this study. Additionally, we have expanded our discussion on how citalopram regulates CD8⁺ T cell anti-tumor immunity through pro-inflammatory TAMs.

For the analysis of TAMs, we initially sorted CD45⁺F4/80⁺CD11b⁺ cells and assessed M1/M2 polarization by measuring CD206 and MHCII expression. As an added strength, we isolated TAMs from the orthotopic GLUT1^KD Hepa1-6 model using CD11b microbeads and conducted real-time qPCR analysis of M1-oriented (Il6, Ifnb1, and Nos2) and M2-oriented (Mrc1, Il10, and Arg1) markers. Consistent with our flow cytometry data, the qPCR results confirmed that citalopram induces a pro-inflammatory TAM phenotype (revised Figure S9A).

Reviewer #2 (Public review): Summary: 

Dong et al. present a thorough investigation into the potential of repurposing citalopram, an SSRI, for hepatocellular carcinoma (HCC) therapy. The study highlights the dual mechanisms by which citalopram exerts anti-tumor effects: reprogramming tumor-associated macrophages (TAMs) toward an anti-tumor phenotype via C5aR1 modulation and suppressing cancer cell metabolism through GLUT1 inhibition while enhancing CD8+ T cell activation. The findings emphasize the potential of drug repurposing strategies and position C5aR1 as a promising immunotherapeutic target. However, certain aspects of experimental design and clinical relevance could be further developed to strengthen the study's impact. 

We thank the reviewer’s thoughtful review and constructive feedback. As suggested, we have made improvements based on the feedback provided.

Strength: 

It provides detailed evidence of citalopram's non-canonical action on C5aR1, demonstrating its ability to modulate macrophage behavior and enhance CD8+ T cell cytotoxicity. The use of DARTS assays, in silico docking, and gene signature network analyses offers robust validation of drug-target interactions. Additionally, the dual focus on immune cell reprogramming and metabolic suppression presents a thorough strategy for HCC therapy. By emphasizing the potential for existing drugs like citalopram to be repurposed, the study also underscores the feasibility of translational applications. 

We sincerely appreciate the reviewer’s recognition of the detailed evidence supporting citalopram’s non-canonical action on C5aR1, along with the innovative methodologies employed and the promising potential for repurposing existing drugs in HCC therapy.

Major weaknesses/suggestions: 

The dataset and signature database used for GSEA analyses are not clearly specified, limiting reproducibility. The manuscript does not fully explore the potential promiscuity of citalopram's interactions across GLUT1, C5aR1, and SERT1, which could provide a deeper understanding of binding selectivity. The absence of GLUT1 knockdown or knockout experiments in macrophages prevents a complete assessment of GLUT1's role in macrophage versus tumor cell metabolism. Furthermore, there is minimal discussion of clinical data on SSRI use in HCC patients. Incorporating survival outcomes based on SSRI treatment could strengthen the study's translational relevance. 

By addressing these limitations, the manuscript could make an even stronger contribution to the fields of cancer immunotherapy and drug repurposing. 

We appreciate the reviewer’s valuable suggestions. As suggested, we have included the following revisions:

(a) GSEA analyses: For GSEA analyses, we conducted RNA sequencing (RNA-seq) analysis on HCC-LM3 cells treated with citalopram or fluvoxamine, which led to the identification of 114 differentially expressed genes (DEGs; 80 co-upregulated and 34 co-downregulated), as reported previously (PMID: 39388353). These DEGs were then utilized to create an SSRI-related gene signature. Subsequently, we analyzed RNA-seq data from liver HCC (LIHC) samples in The Cancer Genome Atlas (TCGA) cohort, comprising 371 samples, categorizing them into high and low expression groups based on the median expression levels of each candidate target gene (such as C5AR1). Finally, we performed GSEA on the grouped samples (C5AR1-high versus C5AR1-low) using the SSRI-related gene signature. In the revised manuscript, we have included this information in the “Materials and Methods” section.

(b) Exploration of binding selectivity: We acknowledge the importance of exploring the potential promiscuity of citalopram’s interactions across GLUT1, C5aR1, and SERT1. While we cannot provide further experimental data to support this aspect, we have included the following points in the revised manuscript: 1) We emphasize the significance of exploring the relative binding affinities of citalopram to GLUT1, C5aR1, and SERT, as varying affinities could influence the drug’s overall efficacy. As highlighted in the current manuscript and our previous publication (PMID: 39388353), citalopram interacts with C5aR1 and GLUT1 through distinct binding sites and mechanisms, whereas its interaction with SERT is characterized by a more direct inhibition of serotonin binding (PMID: 27049939). To gain deeper insights into these interactions, employing techniques such as surface plasmon resonance or biolayer interferometry could provide valuable quantitative data on binding kinetics and affinities for each target. 2) We discuss how citalopram’s interactions with multiple targets may contribute to its therapeutic effects, particularly in the context of immune modulation and tumor progression. The potential for citalopram to exhibit diverse mechanisms of action through its interactions with these proteins warrants further investigation. A comprehensive understanding of these pathways could lead to the development of improved therapeutic strategies.

(c) GLUT1 knockdown in macrophages: In the revised manuscript, we revealed that TAMs predominantly express GLUT3 but not GLUT1 (Figures S8B and S8C). GLUT1 knockdown in THP-1 cells did not significantly impact their glycolytic metabolism (Figure S8D), whereas GLUT3 knockdown led to a marked reduction in glycolysis in THP-1 cells.

(d) Clinical data on SSRI use in HCC patients: Previously, we have reported that SSRIs use is associated with reduced disease progression in HCC patients (PMID: 39388353) (Cell Rep. 2024 Oct 22;43(10):114818.). As detailed below:

“We determined whether SSRIs for alleviating HCC are supported by real-world data. A total of 3061 patients with liver cancer were extracted from the Swedish Cancer Register. Among them, 695 patients had been administrated with post-diagnostic SSRIs. The Kaplan-Meier survival analysis suggested that patients who utilized SSRIs exhibited a significantly improved metastasis-free survival compared to those who did not use SSRIs, with a P value of log-rank test at 0.0002. Cox regression analysis showed that SSRI use was associated with a lower risk of metastasis (HR = 0.78; 95% CI, 0.62-0.99)”.

Reviewer #1 (Recommendations for the authors):

(1) Add experiments to address the questions listed in the weaknesses.

As suggested, related experiments are performed to strengthen the conclusions.

(2) It would be appreciated to show the expression profile of SERT or employ KO mouse models to eliminate the effect of SERT.

As suggested, analysis of a single-cell sequencing dataset of HCC (GSE125449) revealed that SERT is expressed not only in HCC cells but also in T cells, tumor-associated endothelial cells, and cancer-associated fibroblasts (Figure S2G). Consistently, SERT has been reported as an immune checkpoint restricting CD8 T cell antitumor immunity (PMID: 40403728). Furthermore, SERT KO mice (Cyagen Biosciences, S-KO-02549) was employed to investigate the effects of citalopram. However, the Slc6a4 gene knockout in mice resulted in a significant decrease in 5-HT levels in the brain and a lack of cortical columnar structures. Importantly, the mice exhibited an intolerance to citalopram treatment. Therefore, we did not pursue further investigation into the effects of citalopram in SERT KO mice.

(3) Due to the concern of specificity and animal health, it would be more direct if the authors could use, for example, C5ar1-fl/fl x Adgre1-Cre mouse models.

Thank you for your valuable suggestion. We fully agree with your comment regarding the value of introducing C5ar1-fl/fl and Adgre1-Cre mouse models, along with the necessary experimental setups, to substantiate this point. However, in our study, the C5ar1 KO mice exhibited normal overall appearance and viability, indicating that the model is generally healthy. Furthermore, we have validated the specific role of C5aR1 in macrophages through bone marrow reconstitution experiments, reinforcing the importance of C5aR1 in these cells. Therefore, we chose the current model to balance experimental effectiveness with considerations for animal health.

(4) For example, a GSEA or GO analysis of comparison of macrophages from C5ar1-/- or C5ar1+/- mice may point to the enriched pathway of phagocytosis in macrophages derived from C5ar1-/- rather than C5ar1+/- mice, and this information is helpful for the integrity of this work. Besides, it would be more reliable if a nucleus staining is included in Figures 3G and 3H.

As suggested, macrophages were isolated from tumor-bearing C5ar1^-/- and C5ar1^+/- mice and subsequently analyzed using RNA sequencing. The Gene Set Enrichment Analysis (GSEA) revealed a significant enrichment of the phagocytosis pathway in macrophages derived from C5ar1^-/- mice compared to those from C5ar1^+/- mice (see revised Figure S6A). While we acknowledge that the addition of a nucleus staining would enhance reliability, we would like to point out that this style of presentation is also commonly found in articles related to phagocytosis. Furthermore, this experiment involved a significant number of experimental mice, and in accordance with the 3Rs principle for animal experiments, we did not obtain additional sorted TAMs to perform the phagocytosis assay. Thank you for your understanding.

(5) In line 122, there is a typo, and it should be 'analysis'.

Thank you for pointing out the typo. It has been corrected to "analysis" in the revised manuscript.

(6) In line 217, there is no causal relationship between the contexts, and using 'as a result' may lead to misunderstanding.

As suggested, ‘as a result’ has been removed to avoid any misunderstanding.

(7) In line 322, please make sure if it should be HBS or PBS.

It is PBS, and revisions have been made.

(8) Figure S7, the calculation of cell proportions needs to use a consistent denominator.

As suggested, we calculated cell proportions using a consistent denominator (CD45⁺ cells).

(9) Figure 4C, label error.

Thanks for your careful review. It has been corrected to "MASH".

Reviewer #2 (Recommendations for the authors):

Dong et al. present compelling evidence for repurposing citalopram, a selective serotonin reuptake inhibitor (SSRI), as a potential therapeutic for hepatocellular carcinoma (HCC). While the concept of SSRI repurposing is not novel, this manuscript provides valuable insights into the drug's dual mechanisms: targeting tumor-associated macrophages (TAMs) via C5aR1 modulation and enhancing CD8+ T cell activity, alongside inhibiting cancer cell metabolism through GLUT1 suppression. The findings underscore the promise of drug repurposing strategies and identify C5aR1 as a noteworthy immunotherapeutic target. Addressing the following points will enhance the manuscript's impact and relevance to cancer immunotherapy.

Specific Comments:

(1) The authors identify C5aR1 on TAMs as a direct target of citalopram, independent of its classical SERT target, using drug-induced gene signature network analysis and co-immunofluorescence of CD163+ macrophages with C5aR1. The DARTS assay further supports the binding of C5aR1 to citalopram, complemented by in silico docking analysis adapted from their previous GLUT1 study. Since GLUT1 and SERT1 are transporter proteins while C5aR1 is a GPCR, these heterogeneous binding interactions suggest potential promiscuity in SSRI-target engagement.

(a) Figure 2A: The authors identify C5aR1 as a target using GSEA but do not specify the dataset used (e.g., cancer or immune cells) or the signature database consulted. Providing this context would enhance reproducibility.

For GSEA, we performed RNA sequencing (RNA-seq) on HCC-LM3 cells treated with citalopram or fluvoxamine and identified 114 differentially expressed genes (DEGs), which included 80 genes that were co-upregulated and 34 that were co-downregulated, as previously documented (PMID: 39388353). These DEGs were subsequently used to develop an SSRI-related gene signature. We then employed the RNA-seq data from liver hepatocellular carcinoma (LIHC) samples within The Cancer Genome Atlas (TCGA) cohort, which included 371 samples. HCC samples in the TCGA cohort were categorized into high and low expression groups based on the median expression levels of each candidate target gene, such as C5AR1. Finally, we conducted GSEA on the grouped samples (such as C5AR1-high versus C5AR1-low) using the SSRI-related gene signature. For reproducibility, detailed information has been added to the “Materials and Methods” section of the revised manuscript.

(b) Figure 2F: Given citalopram's reported role in inhibiting GLUT1, a comparative discussion on the relative contributions of GLUT1 inhibition versus C5aR1 modulation in tumor suppression is warranted. Performing a DARTS assay for GLUT1 in THP-1 cells, which express high GLUT1 levels and exhibit upregulation in M1 macrophages (https://doi.org/10.1038/s41467-022-33526-z), would clarify SSRI interactions with macrophage metabolism.

As suggested, we first investigated citalopram treatment in THP-1 cells. The result showed the glycolytic metabolism of THP-1 cells remained largely unaffected following citalopram treatment, as evidenced by glucose uptake, lactate release, and extracellular acidification rate (ECAR) (Figure S8A). Next, we mined a single cell sequencing datasets of HCC and revealed that TAMs predominantly express GLUT3 but not GLUT1 (Figure S8B). Consistently, Western blotting analysis showed a higher expression of GLUT3 and minimal levels of GLUT1 in THP-1 cells (Figure S8C). Consistently, it has been well documented that GLUT1 expression increased after M1 polarization stimuli an GLUT3 expression increased after M2 stimulation in macrophages (PMID: 37721853, PMID: 36216803). GLUT1 knockdown in THP-1 cells did not significantly impact their glycolytic metabolism (Figure S8D), whereas GLUT3 knockdown led to a marked reduction in glycolysis in THP-1 cells. Based on these findings, we conclude that the effects of citalopram on macrophages are primarily mediated through targeting C5aR1 rather than GLUT1.

(c) Figures 2H-I: A comparison of drug-protein interactions across GLUT1, C5aR1, and SERT1 would be valuable to identify potential shared or distinct binding features.

Citalopram exhibits distinct binding characteristics across its various targets, including GLUT1, C5aR1, and its classical target, SERT. In the case of C5aR1, our in silico docking analysis identified two key binding conformations at the orthosteric site. The interactions involved significant electrostatic contacts between citalopram’s amino group and negatively charged residues like E199 and D282. Notably, D282’s accessibility and orientation towards the binding cavity suggest it plays a crucial role in citalopram binding, highlighting the importance of specific amino acid interactions at this site. For GLUT1 (PMID: 39388353), citalopram’s interaction also demonstrated notable hydrophobic contacts, particularly through the fluorophenyl group with residues V328, P385, and L325. The cyanophtalane group penetrated the substrate-binding cavity, indicating that citalopram could occupy a similar binding site as glucose, which is distinct from the binding mechanism observed in C5aR1. The involvement of E380 in both poses for GLUT1 further emphasizes the role of electrostatic interactions in mediating citalopram’s binding to this transporter. In contrast, for SERT (PMID: 27049939), citalopram locks the transporter in an outward-open conformation by occupying the central binding site, which is located between transmembrane helices 1, 3, 6, 8 and 10. This binding directly obstructs serotonin from accessing its binding site, illustrating a more definitive blockade mechanism. Additionally, the allosteric site at SERT, positioned between extracellular loops 4 and 6 and transmembrane helices 1, 6, 10, and 11, enhances this blockade by sterically hindering ligand unbinding, thus providing a clear explanation for the allosteric modulation of serotonin transport. In summary, while citalopram interacts with C5aR1 and GLUT1 through distinct binding sites and mechanisms, its interaction with SERT is characterized by a more straightforward blockade of serotonin binding. The unique structural and functional attributes of each target highlight the versatility of citalopram and suggest that its pharmacological effects may vary significantly depending on the specific protein being targeted. In the revised manuscript, we have included detailed information in the revised manuscript.

(2) The manuscript presents evidence that citalopram reprograms TAMs to an anti-tumor phenotype, enhancing their phagocytic capacity.

(a) Bone Marrow Reconstitution Experiments (Figure 3): The use of donor (dC5aR1) and recipient (rC5aR1) mice is significant but requires clarification. Explicitly defining donor and recipient terminology and including a schematic of the experimental design would improve reader comprehension.

We appreciate your valuable feedback. As suggested, the terminology for donor (dC5aR1) and recipient (rC5aR1) mice was defined: “we injected GLUT1^KD Hepa1-6 cells into syngeneic recipient C5ar1^-/- (rC5ar1^-/- ) mice that had been reconstituted with donor C5ar1^+/- (dC5ar1^+/-) or C5ar1^-/- (dC5ar1^-/-) bone marrow (BM) cells to analyze the therapeutic effect of citalopram”. Additionally, we have included a schematic of the experimental design to enhance reader comprehension (see revised Figure 3E).

(b) GLUT1 Knockdown (KD) Tumor Cells: While GLUT1 KD tumor cells are utilized, the authors do not assess GLUT1 KD or knockout (KO) in macrophages. Testing the effect of citalopram on macrophages with GLUT1 KO/KD would help determine the relative importance of C5aR1 versus GLUT1 in mediating SSRI effects.

As responded above, GLUT1 knockdown in THP-1 cells did not significantly alter their glycolytic metabolism (Figure S8D). This observation can be explained by the predominant expression of GLUT3 in TAMs rather than GLUT1 (Figures S8B and S8C). Indeed, knockdown of GLUT3 led to a significant reduction in glycolysis in THP-1 cells (Figure S8C).

(c) C5aR1's Pro-Tumoral Role: The authors state that C5aR1 fosters an immunosuppressive microenvironment but omit a discussion of current literature on C5aR1's pro-tumoral role (e.g., https://doi.org/10.1038/s41467-024-48637-y, https://www.nature.com/articles/s41419-024-06500-4, https://doi.org/10.1016/j.ymthe.2023.12.010). Including this background in both the introduction and discussion would contextualize their findings.

Thanks for your valuable feedback. As suggested, we have revised the manuscript to include discussions on C5aR1’s pro-tumoral role, referencing the suggested studies in both the introduction and discussion sections for better context. As detailed below:

(1) Targeting C5aR1⁺ TAMs effectively reverses tumor progression and enhances anti-tumor response;

(2) Targeting C5aR1 reprograms TAMs from a protumor state to an antitumor state, promoting the secretion of CXCL9 and CXCL10 while facilitating the recruitment of cytotoxic CD8⁺ T cells;

(3) Moreover, citalopram induces TAM phenotypic polarization towards to a M1 proinflammatory state, which supports anti-tumor immune response within the TME.

(d) C5aR1 Expression in TAMs: Is C5aR1 expression constitutive in TAMs? Further details on C5aR1 expression dynamics in TAMs under different conditions could strengthen the discussion. Public datasets on TAMs in various states (e.g., https://www.nature.com/articles/s41586-023-06682-5, https://www.cell.com/cell/abstract/S0092-8674(19)31119-5, https://pubmed.ncbi.nlm.nih.gov/36657444/) may offer useful insights.

Thank you for your valuable suggestions. As suggested, we investigated the expression patterns of C5aR1 in TAMs using a HCC cohort (http://cancer-pku.cn:3838/HCC/). In the study conducted by Qiming Zhang et al. (PMID: 31675496), six distinct macrophage subclusters were identified, with M4-c1-THBS1 and M4-c2-C1QA showing significant enrichment in tumor tissues. M4-c1-THBS1 was enriched with signatures indicative of myeloid-derived suppressor cells (MDSCs), while M4-c2-C1QA exhibited characteristics that resembled those of TAMs as well as M1 and M2 macrophages. Our subsequent analysis revealed that C5aR1 is highly expressed in these two clusters, while expression levels in the other macrophage clusters were notably lower (see revised Figure S3).

(3) The manuscript shows that citalopram-induced reductions in systemic serotonin levels enhance CD8+ T cell activation and cytotoxicity, as evidenced by increased glycolytic metabolism and elevated IFN-γ, TNF-α, and GZMB expression.

(a) How CD8+ T cell activation is done in serotonin-deficient environments?

As reported (PMID: 34524861), one possible explanation is that serotonin may enhance PD-L1 expression on cancer cells, thereby impairing CD8⁺ T cell function. A deficiency of serotonin in the tumor microenvironment can delay tumor growth by promoting the accumulation and effector functions of CD8⁺ T cells while reducing PD-L1 expression. In addition to the SERT-mediated transport and 5-HT receptor signaling, CD8⁺ T cells can express TPH1 (PMID: 38215751, PMID: 40403728), enabling them to synthesize endogenous 5-HT, which activates their activity through serotonylation-dependent mechanisms (PMID: 38215751). In the revised manuscript, we have incorporated these interpretations.

(4) Suggestions for the model figure revision-C5aR1 in TAMs without Citalopram (Figure 5).

(a) Including a control scenario depicting receptor status and function in TAMs without citalopram treatment would provide a clearer baseline for understanding citalopram's effects.

Thank you for your valuable input regarding the model figure revision. We have included a revised mechanism model that depicts the receptor status and function of C5aR1 in TAMs without citalopram treatment, as you suggested.

(5) Suggestions for addressing clinical relevance.

The study predominantly uses preclinical mouse models, although some human HCC data is analyzed (Figures 2B and 3O). However, there is no discussion of clinical data on SSRI use in HCC patients.

Incorporating an analysis of patient survival outcomes based on SSRI treatment (e.g., https://pmc.ncbi.nlm.nih.gov/articles/PMC5444756/, https://pmc.ncbi.nlm.nih.gov/articles/PMC10483320/) would enhance the translational relevance of the findings.

Previously, we reported that the use of SSRIs is associated with reduced disease progression in HCC patients, based on real-world data from the Swedish Cancer Register (PMID: 39388353). As suggested, we have further discussed the clinical relevance of SSRIs in the revised manuscript. As detailed below:

“In a study involving 308,938 participants with HCC, findings indicated that the use of antidepressants following an HCC diagnosis was linked to a decreased risk of both overall mortality and cancer-specific mortality (PMID: 37672269). These associations were consistently observed across various subgroups, including different classes of antidepressants and patients with comorbidities such as hepatitis B or C infections, liver cirrhosis, and alcohol use disorders. Similarly, our analysis of real-world data from the Swedish Cancer Register demonstrated that SSRIs are correlated with slower disease progression in HCC patients (PMID: 39388353). Given these insights, antidepressants, especially SSRIs, show significant potential as anticancer therapies for individuals diagnosed with HCC”.

Santé Mentale : Fausses Promesses et Solutions Collectives – Synthèse du Briefing

Résumé Exécutif

Ce document synthétise les analyses et propositions issues d'une table ronde sur la santé mentale, organisée par Psycom au ministère de la Santé.

Le constat central est la nécessité urgente de dépasser une vision individualiste de la santé mentale, où le fardeau repose sur l'individu et la psychiatrie, pour adopter une approche collective et systémique.

Les discussions ont mis en lumière plusieurs problématiques majeures : * l'expansion d'un marché du "bien-être" non réglementé, proposant des solutions pseudoscientifiques dangereuses qui engendrent une "perte de chance" pour les personnes en souffrance ; * la montée des dérives sectaires qui exploitent les vulnérabilités psychiques à des fins financières et d'emprise ; et * l'impact prépondérant sur la santé psychique (estimé à 50 %) des déterminants socio-économiques tels que * la précarité, * les discriminations ou * le logement

Face à ces défis, les experts proposent des solutions multi-niveaux.

Celles-ci incluent un renforcement de la régulation des pratiques non conventionnelles et des titres de "thérapeutes", le développement de l'esprit critique et de la métacognition au sein de la population, et une transformation profonde du soin psychiatrique vers des modèles plus humains, participatifs et moins coercitifs, à l'image de l'approche "Open Dialogue".

Enfin, le rôle crucial des collectivités locales est souligné, celles-ci pouvant agir concrètement sur l'environnement social et urbain pour promouvoir le bien-être et recréer du lien, incarnant ainsi le passage d'une "société du soin" à une "société du prendre soin" attentive aux inégalités et aux vulnérabilités.

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I. Introduction : Contexte de la Table Ronde

La présente analyse se fonde sur les échanges d'une table ronde filmée en septembre 2025 au ministère de la Santé, lors de la journée "Full Santé Mentale :

de l'intime au collectif" organisée par Psycom, un organisme public de lutte contre la stigmatisation en santé mentale.

Question centrale :

Comment sortir d’une vision trop individualiste de la santé mentale pour aller vers une réflexion plus collective ?

Comment passer d’une société du soin à une société du "prendre soin", attentive aux vulnérabilités et aux inégalités ?

Participants :

Nom

Fonction

Organisation

Sophia Feuillère

Responsable de l'innovation pédagogique

Psychom

Elisabeth Fetti

Documentariste, créatrice du podcast sur la métacognition

Méta de Choc

Samir Calfa

Conseiller santé

Miviludes (Mission interministérielle de vigilance)

Maeva Musso

Psychiatre, présidente de l'association des jeunes psychiatres

Hôpitaux Paris Est Val-de-Marne / AJPJA

Marie-Christine Sanier Coavran

Adjointe à la santé et à la lutte contre les exclusions, vice-présidente du réseau Ville Santé

Ville de Lille

II. Constats et Problématiques Actuelles

A. Déconstruire les Idées Reçues sur la Santé Mentale

Sophia Feuillère identifie trois idées reçues persistantes qui freinent une approche collective :

1. La frontière rigide entre santé mentale et psychiatrie : Le public perçoit souvent la psychiatrie comme un état figé réservé aux "malades", et la santé mentale comme un état tout aussi figé pour les "bien-portants".

Pour contrer cela, Psychom promeut une notion de mouvement et de rétablissement, notamment via son outil de la "boussole de la santé mentale".

2. La seule responsabilité de l'individu : Une croyance répandue veut qu'il suffirait d'outiller les individus (cohérence cardiaque, compétences psychosociales) pour qu'ils prennent soin d'eux. Cette vision omet les déterminants extérieurs.

L'approche systémique, illustrée par l'outil du "cosmos mental", est donc essentielle pour réintégrer le contexte collectif.

3. L'exclusivité de l'expertise médicale : L'idée que seuls les soignants peuvent parler de santé mentale reste forte.

Il est crucial de légitimer la posture du "prendre soin", que chaque citoyen peut adopter, distincte de celle du "soin", qui relève des professionnels qualifiés.

B. L'Expansion du Marché du Bien-être et ses Dangers

Elisabeth Fetti observe une explosion des offres de "bien-être" sur les médias sociaux, portées par des influenceurs souvent sans expertise.

• Narratif dominant : Le discours s'appuie sur l'expérience personnelle ("J'ai touché le fond et j'ai rebondi, donc faites comme moi"), mêlant développement personnel (sans fondement scientifique) et spiritualité.

• instrumentalisation de la science : Des termes comme "neurosciences" ou "physique quantique" sont utilisés pour conférer une fausse légitimité aux discours.

• Mécanismes de persuasion : L'"effet Barnum" est massivement utilisé.

Il s'agit de formuler des généralités vagues dans lesquelles chacun peut se reconnaître ("Tu veux réussir mais parfois tu te sens empêché"), créant un sentiment de confiance et de compréhension.

• Risques avérés :

◦ Perte de chance : Le risque le plus grave est le retard de diagnostic et de prise en charge adéquate pour des pathologies réelles (dépression, endométriose, addictions).  

◦ Escalade de l'engagement : Les clients sont entraînés dans un cycle d'engagement financier et émotionnel croissant (séance gratuite, puis livre, puis stage, etc.), rendant difficile la remise en question et la réorientation.   

◦ Culpabilisation : En cas d'échec, la responsabilité est retournée contre l'individu :

"Si ça ne marche pas, c'est que tu n'as pas assez travaillé sur toi".  

◦ Effets paradoxaux : Certaines pratiques, comme la "pensée positive", peuvent aggraver l'anxiété chez les personnes les plus vulnérables, comme le montrent des études scientifiques.

C. Les Dérives Sectaires : Emprise Mentale et Perte de Chance

Samir Calfa alerte sur l'émergence d'un "système de santé parallèle" où les dérives sectaires prolifèrent, notamment dans le champ de la santé mentale qui représente 40 % des signalements à la Miviludes.

• Mécanisme central : Il ne peut y avoir de dérive sectaire sans emprise mentale, une relation singulière entre le gourou et sa victime.

• Vide juridique : N'importe qui peut aujourd'hui inventer et proposer une méthode de prise en charge psychologique sans réglementation.

• Profil des victimes et motivations des gourous : Neuf victimes sur dix sont des femmes.

Les gourous recherchent systématiquement trois choses : l'argent, les faveurs sexuelles et le travail dissimulé (les victimes devenant des "sergents recruteurs").

• Double impact psychologique : La vulnérabilité psychique est une porte d'entrée vers ces dérives, et la sortie de l'emprise laisse des séquelles psychologiques profondes et durables ("l'organisation sectaire ne sort jamais de votre tête").

Une augmentation des suicides liés à ces phénomènes est constatée.

D. L'Impact des Déterminants Sociaux et des Inégalités