Exactly the same

Convergence isn't about everything becoming stable or perfectly unified. It's always pushing media and culture together, but change is still happening at the same time. Things are always shifting, so convergence is more about an ongoing movement and tension than a final, fixed outcome.

This article reflects on Zuckerberg's character and his campaign to spread the internet across the world. Zuckerberg defends his capitalistic model, saying that in order to do the actions he is doing right now, he has to make money somehow. He says that a free ad model is much better than a pay model which builds many barriers. For something as basic as social media and internet, I cannot help to agree with him, but for the case of facebook, I think the lack of transparency and consent is the main problem than the model itself as you still get collect data and advertise while allowing users to have some limited levels of privacy

Kayla Jimenez interviewed a 12th grade teacher from South Dakota who found a new solution to old writing prompts. Lisa Parry was having issues with worn out and un-inspiring prompts for her students' essays, but she found that ChatGPT could give students fresh new ideas(Jimenez 1-5)

In an interview with Jen Roberts, an English teacher at a San Diego high school, Jimenez found that Roberts allows her students to use AI in her classroom to aid her in teaching, "Would it be better if I could read their writing and give them feedback? Yes," she said. "But there’s only one of me and with 160 students – 36 at a time – it's a better substitute for a private tutor."(Jimenez 14)

Peter Greenes suggestion "If it (ChatGPT) can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped.” I think this is very interesting, having no experience with ChatGPT, the little I do have with other forms of AI is that it often makes small but obvious mistakes like 6 fingers, or images appear from thin air. Does this also apply in ChatGPT? Where an essay might have all of the information correct factually, but maybe the grammar doesn't make sense, or is structured so well it reads like it was written by a college professor as opposed to a high school English student? If either or even both end results are true, then like Pondiscio I feel that AI generated essays shouldn't necessarily be a realistic concern.

Pondiscio warns that AI should be utilized as a learning tool for students and teachers, not shunned as some cheat to easy learning. He closes his argument with a short, but impactful statement"____"(Pondiscio 8)

Pondiscio often references Daniel Herman, an English teacher and writer for The Atlantic. Here, Pondiscio argues against Hermans worry that AI will allow students to cheat through school, without doing any of the actual hard work"_____"(Pondiscio 7).

I believe this quote gives insight to the current situation between Jews and Arabs and gives insight on the future between the two. It explains that the high birthrate of the younger Arab leads to anxiety among the Israeli polity. They fear that this birthrate will lead to the Arab population to surpass that of the Jewish population. They believe that this change in population will eventually lead to Israeli control on the area to come to an end. There are many solutions being thought up for this problem but all of them will most likely lead to more conflict between the two groups, which shows how peace does not look likely for the foreseeable future.

This is very true that we all have our own unique styles but we are all also held to the same standards as educators.

It’s interesting to see that the pope promised land, wealth, and forgiveness of sins to men who joined the quest.

This is a very common concern for new Tailwind users, and the answer is twofold:

1. No, you do not have to memorize all the classes. The developer workflow is built around powerful code editor tools that make this unnecessary.
2. Yes, interactive editors like you described absolutely exist. They are excellent for certain workflows, especially for building pages quickly.

Here is a detailed breakdown of the solutions available.

Solution 1: The Standard Developer Workflow (Code Editor Extensions)

This is how the vast majority of developers use Tailwind CSS. Instead of memorizing classes, you rely on an intelligent plugin in your code editor (like VS Code).

The most essential tool is the Tailwind CSS IntelliSense extension for Visual Studio Code.

This plugin solves the "memorization" problem in three specific ways:

1. Autocomplete: You rarely type the full class name. You start typing a prefix, and the editor shows you all possible options.
   • If you type bg-, it will pop up a list of all available colors (bg-blue-500, bg-red-700, etc.), complete with a color swatch.
   • If you type p-, it will list all padding options (p-1, p-2, p-4, p-6, etc.).
2. Hover-to-Preview: If you are unsure what a class does, you can hover your mouse over it. The plugin will show you the exact CSS it generates.
   • Hovering over p-4 will show a popup that says padding: 1rem;.
   • Hovering over rounded-lg will show border-radius: 0.5rem;.
   • This feature turns the editor into a powerful learning tool.
3. Linting (Error Checking): The plugin will underline conflicting classes, helping you avoid mistakes. For example, if you accidentally type p-2 and p-4 on the same element, it will flag this as an issue because you are applying two different padding values.

This workflow is not based on memory, but on a logical, discoverable system. The class names are consistent: * p is padding, m is margin. * t is top, b is bottom, l is left, r is right. * Therefore, pt-4 is padding-top of 1rem. This logic becomes second nature very quickly.

Solution 2: Visual (WYSIWYG) Editors

For the "PowerPoint-like" scenario you described, several tools provide a full graphical user interface (GUI) for styling with Tailwind. These are often called visual builders or page builders.

With these tools, you would click an element, and then use a properties panel on the side to adjust its padding, color, or margin. The tool then writes the correct Tailwind HTML for you.

Examples of these tools include:

• Windframe: A visual editor and AI tool designed specifically for Tailwind CSS. It features a drag-and-drop interface and a properties panel to adjust styles, then exports production-ready code.
• Pinegrow: A professional desktop web editor that has a dedicated Tailwind Visual Editor add-on. It allows you to visually edit your project and provides controls for all Tailwind properties.
• Shuffle (and Tailwind.build): An online editor with a large library of pre-built UI components. It allows you to drag components onto a canvas, customize their styles with visual controls, and export the final HTML.
• GrayGrids: Another online tool that functions as a "Tailwind CSS Website UI Builder" with drag-and-drop functionality.

These tools are excellent for rapidly building landing pages or prototyping. The primary trade-off is that for complex, dynamic applications, many developers find it faster and more precise to work directly in the code using the IntelliSense plugin (Solution 1).

Solution 3: Component Libraries (The Middle Ground)

There is a third option that also reduces the need to "memorize" individual classes: using pre-built component libraries.

The official Tailwind UI is the most popular example.

This is not a visual editor, but a paid library of over 500+ professionally designed components (navbars, forms, buttons, page sections, etc.).

• Your Workflow: Instead of building a complex form from 100 different utility classes, you find the form you need in the Tailwind UI library, copy its HTML, and paste it into your project.
• How it Helps: This solves the problem by giving you large, complete, and perfectly-styled blocks, so you only need to make minor adjustments (like changing bg-blue-500 to bg-indigo-500) rather than building everything from scratch.

Would you like me to elaborate on how to install and configure the Tailwind CSS IntelliSense plugin for VS Code?

This shows how fast Hollywood became the center of filmmaking. It wasn't even about the glamour we hear of now, but rather about the practicality. The weather, space, and lower costs made it easier and cheaper to produce movies, which brought most of the industry to one place.

IQ testing is often considered problematic, but in situations like these, neglecting IQ testing means also neglecting proper diagnosis of gifted children or disabled children.

This is an important line to note. Do not mindlessly give your gifted students meaningless and repetitive tasks, but rather thoughtful and engaging assignments that benefit themselves and the class and ensemble.

Children are naturally musical and love to explore through music. But Hollie's rapid rate of musical expressive language became her first doorway into communication. It makes me think about how many children might respond similarly if we paid closer attention to the ways they naturally express themselves.

This reflects the concept I saw about twice-exceptional students often having “islands of strength.” In this case, her emotional and social cognition is outpacing her academic development, which complicates decision-making about grade placement.

This challenged my thinking. I grew up in a system where “more worksheets” or “faster pace” = gifted challenge. But this reframes the idea of a challenge as depth, creativity, and problem-solving over quantity.

This helped me think more deeply about how physical needs intersect with musical participation. We usually think cognitively about twice exceptional learners but the physical component is equally important. It also makes me wonder should PE and music teachers collaborate more intentionally for students like Hollie?

The IQ categories mirror special education. Yet, gifted students rarely receive differentiated service. Why do schools provide nuanced support for lower IQ ranges but not for for higher ones?

The chapter advocates for "label-free learning" but admits that labels can be useful in cognition. How can teachers have a balance between avoiding stigma and making sure gifted students receive support?

This is an important point to consider. A student may be gifted in one area, but that doesn’t necessarily mean they excel in all other areas.

I agree with this statement. Curriculums can be adjusted to fit different learning styles, but I don’t think students should be separated from their peers unless it’s absolutely necessary and the school can provide for their needs.

I think it is important to distinguish between how the student learns and if they actually enjoy it. While it's true that some students may get bored because they are more advanced than others, the same can happen if they are put in more advanced classes but more is expected of them and they don't enjoy the workload, which in turn they can become frustrated. People have different skills and different capabilities.

This is because a SNS is a private business, so they don't have to host any thing they don't want to. But this is compilated more because they don't take ownership of the speech, so law breaking speech can be hosted.

I think that the playbook has changed but censorship is still possible.

A good explanation of freedom of speech

this is a really good point, sometimes there is a topic that I am seeing all over social media, but my friends have never heard of it.

Is this what trends are?

MLK uses moral philosophy, especially drawing on St. Thomas Aquinas, to differentiate just and unjust laws. By grounding his argument in universal ethics rather than personal opinion, he justifies civil disobedience as a moral duty. This connects segregation not only to legal injustice but to spiritual and human harm, strengthening his claim that breaking unjust laws is actually an act of respect for true justice.

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

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): * Summary: * In this manuscript, Turner AH. et al. demonstrated the viral replication in cells depleting Rab11B small GTPase, which is a paralogue of Rab11A. It has been reported that Rab11A is responsible for the intracellular transport of viral RNP via recycling endosomes. The authors showed that Rab11B knockdown reduced the viral protein expression and viral titer. This may be caused by reduced attachment of viral particles on Rab11B knockdown cells.

• Major comments:*
• Comment 1 Fig 2-4: The authors should provide Western blot results with equal amount of loading control (GAPDH). The bands shown in these figures lack quantifiability and are not reliable as data.*

We have rerun these western blots with more equal loading, and included a second loading control (beta-actin) in addition to the GAPDH. These blots can be seen in new Figures 2 and 3, and the quantification against both GAPDH (Figure 2/3) as well as actin (Fig S2) is now included. We have also included additional biological replicates for Fig 2 B-D. These additional experiments have strengthened our conclusion that Rab11B is required for efficient protein production in cells infected with recent H3N2, but not H1N1, isolates.

Comment 2 Fig 2-4: Why are the results different between Rab11B knockdown alone and Rab11A/B double knockdown? If the authors claims are correct, the results of Rab11B knockdown should be reproducible in Rab11A/B double knockdown cells.

Prior literature indicates that the Rab11A and Rab11B isoforms can play opposing roles in the trafficking of some cargos (ie, with one isoform transporting a molecule to the cell surface, while the other isoform takes it off again). In this scenario, it is possible that removing both 'halves' of the trafficking loop can ablate a phenotype. However, since our double knockdown used half the amount of siRNA for each isoform (for the same total amount), it is also possible this observation is simply the result of less efficient knockdown. In order to distinguish between these possibilities we depleted Rab11A or Rab11B individually, with this same 'half dose' of siRNA (see new Figure S3). We observed that Rab11B was still robustly required for H3N2 viral protein production. These results suggest that Rab11A and Rab11B could be playing mutually opposing roles in this case, which is consistent with prior Rab11 literature.

Comment 3 Fig 6: For better understanding, please provide a schematic illustration of experimental setting.

We have added a new graphical overview to this figure (see new Figure 6A).

Comment 4: It is necessary to test other siRNA sequences or perform a rescue experiment by expressing an siRNA-resistant clone in the knockdown cells. There seems to be an activation of host defense system, such as IFN pathways.

In order to rule out the possibility of off-target effects we created a novel cell line that inducibly expresses a Rab11B shRNA sequence (see new Fig 4). This knockdown strategy used a completely different method (shRNA delivered by lentiviral vector vs transient transfection of siRNA), in a different cellular background (H441 "club like" cells vs A549 lung adenocarcinoma). This new depletion strategy showed that the Rab11B dependent H3N2 protein production phenotype is seen across multiple knockdown strategies and cellular backgrounds.

**Referees cross-commenting**

I agree with other reviewers' comments in part.

Reviewer #1 (Significance (Required)):

The authors propose a novel role for Rab11B in modulating attachment pathway of H3N2 influenza A virus by unknown mechanism. Although previous studies focus on the function of Rab11A on endocytic transport, the function and specificity of Rab11B has remained less clear. The findings may be of interest to a broad audience, including researchers in cell biology, immunology, and host-pathogen interactions. However, the study remains at a superficial level of analysis and does not lead to a deeper understanding of the underlying mechanisms.

We agree with the reviewer that a strength of this manuscript is its multi-disciplinary nature, particularly with regard to advances in our understanding of Rab11B function. We have added a significant number of experiments and new figures to bolster the rigor and reproducibility of our findings. We have also added a new figure (Fig 7) that uses reverse genetics to map the Rab11B phenotype to the HA gene of the H3N2 isolate under study. By creating '7+1' reassortant viruses with the H3 HA or the N2 NA on a PR8 (H1N1) background (see Fig 7E-H) we were able to demonstrate that Rab11B is acting specifically on one of the HA-mediated entry steps. This provides additional mechanistic insight, by mapping the Rab11B-phenotype to a step at or prior to fusion. Fundamentally, we believe the novelty and rigor of our observation that recent H3N2 viruses enter through a different route than H1N1 isolates is worthy of observation in this updated form, so that the field can begin follow up studies.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary: The authors compare the effect of RAB11A and RAB11B knockdown on replication of contemporary H1N1 and H3N2 influenza A virus strains in A549 cells (human lung epithelials cells). They find a reduction in viral protein expression for tested H3N2 but not for H1N1 isolates. Mechanistically they suggest that RAB11A affects virion attachment to the cell surface.

Major comments: The provided data do not conclusively support the suggested mechanism of action and essential controls are missing to substantiate the authors claims: • Knockdown efficacy has to be confirmed on protein level, showing reduced levels of RAB11A and B by Western blot. This is a standard in the field. Off target effects cannot be avoided by RNAi approaches and are usually ruled out by using multiple siRNAs or by complementing the targeted protein in trans.

We have verified knockdown efficacy at the protein level in new Fig 1A/B. However, due to the high degree of protein level conservation between Rab11A and Rab11B it is very difficult to develop isoform specific antibodies, and we were unable to obtain a Rab11B-specific antibody that can detect endogenous protein (despite testing 6 commercially available antibodies for specificity). Using an antibody that detects both 11A and 11B (Fig1A) we were able to observe very slight changes in the molecular weight of the Rab11 band(s) detected upon knockdown of 11A vs 11B (suggestive of the two isoforms running as a dimer, with Rab11A the lower band and Rab11B the upper band). Cells depleted of both isoforms simultaneously showed a near complete loss of signal. Using a Rab11A antibody (that we confirmed as specific) we were able to observe loss of the Rab11A signal in both the 11A and 11A+B knockdowns (Fig 1B).

• Viral titers should be presented as absolute titers not as % (here the labelling is actually misleading in all graphs indicating pfu/ml)

This data is now shown in new Figure S1, where it is clear that the trends remain consistent across biological replicates. The axis labels of Fig 1D/E and Fig 3A have been corrected as requested to make clear we are normalizing to account for experiment-to-experiment variation in peak titer.

• Reduction of viral protein expression goes hand in hand with a reduction in GAPDH. While this is accounted for in the quantification a general block of protein expression cannot be ruled out since the stability of house keeper proteins and viral proteins might be different. Testing multiple house keeping proteins could overcome this issue.

We have included a second loading control (beta-actin) in addition to the GAPDH for new Figure 2 and 3. The quantification of viral protein production compared to beta actin is now included in new Fig S2. We have also included additional biological replicates for Fig 2 B-D. These additional experiments have strengthened our conclusion that Rab11B is required for efficient protein production in cells infected with recent H3N2, but not H1N1, isolates.

• The FACS data in Fig 5 are not convincing. The previous figures showed modest reduction in viral protein expression and the fluorescence is indicated here on a logarithmic scale. Quantification and indication of mean fluorescence intensity from the same data would be a better readout to convincingly show that less cells are infected.

We have reanalyzed the existing data to quantify the geometric mean of viral protein expression in the infected cell populations (new Figure 5D, E). This analysis shows no significant difference in geometric mean of HA (Fig 5D) or M2 (Fig 5E) expression between cells treated with NT, 11A or 11B siRNA. This additional analysis strengthens our original conclusion that when Rab11B is knocked down, fewer cells get infected, but those that do produce the same level of viral proteins.

• During the time of addition experiment in Fig 6, the authors are testing for HA/M2 positive cells after 16h of infection. This is a multicycle scnario so in a second round they would measure the effect of knockdown in absence of amonium chloride. Shorter infections up to 8h with higher MOI would overcome this problem.

By maintaining cells in ammonium chloride throughout the infection we are preventing endosomal acidification at any point in the infection period, so this experiment should be measuring solely the effect of one round of infection. The 16 hr timepoint was chosen to allow for optimized staining and analysis of samples by flow cytometry, within the available hours of the flow cytometry facility.

• Standard error of mean is not an appropriate way of representing experimental error for the provided results and should be replaced by SD. Correct labeling of axis with units is required.

We have updated the axes throughout the manuscript as requested. We have obtained additional statistical expertise (reflected in the updated author list) regarding the issue of SD vs SEM. Standard deviation (SD) would show a measure of the spread of the data, however the full distribution can be clearly seen as we plotted every individual data point. Standard error of the mean (SEM) is a measure of confidence for the mean of the population which takes into account SD and also sample size. SEM is not obvious to estimate by eye in the same way as SD, and we feel is more helpful to the reader to understand how likely the two population means differ from each other on a given graph.

Minor comments: • The authors show a rescue of viral replication upon double knockdown of RAB11A and B. Maybe this is just a consequence of inefficient knockdown since only half of the siRNAs were used?

In order to determine if this was the case we depleted Rab11A or Rab11B individually, with this same 'half dose' of siRNA (see new Figure S3). We observed that Rab11B was still robustly required for H3N2 viral protein production. These results suggest that Rab11A and Rab11B could be playing mutually opposing roles in this case (ie, Rab11B transporting a molecule to the surface, while Rab11A recycles it off), which is consistent with prior Rab11 literature.

• Specific experimental issues that are easily addressable. • Are prior studies referenced appropriately? • Are the text and figures clear and accurate? • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

• Reviewer #2 (Significance (Required)): Significance The authors claim an H3N2 specific dependency on RAB11B for early steps of infection. While this is per se interesting the provided data do not fully support the claims and lack a mechanistic explanation. What is the difference between H1 and H3 strains (virion shape, HA load per virion, attachment force of H1 vs H3). The readouts used are not close enough to the events with regards to timing and could be supported by established entry assays in the field.

We have provided additional discussion of the differences between H1s and H3s, including sialic acid binding preferences and changes in the HA-sialic acid avidity (lines 76-84). Notably, we have included a new assay (new Fig 7) that provides additional mechanistic insight into the observation that recent H3N2 but not H1N1 isolates depend on Rab11B early in infection. Using reverse genetics we were able to map the Rab11B phenotype to the HA gene of the H3N2 isolate under study. By creating '7+1' reassortant viruses with either the H3 HA or the N2 NA on a PR8 (H1N1) background (see Fig 7E) we are able to demonstrate that Rab11B is acting specifically at one of the HA-mediated entry steps. This excludes several non-HA dependent steps early in the life cycle (uncoating, RNP transport to the nucleus, nuclear import), thus providing additional confirmation that Rab11B acts at one of the earliest steps in the viral life cycle (and by definition, at or prior to fusion). Fundamentally, we believe the novelty and rigor of our observation that recent H3N2 viruses enter through a different route than H1N1 isolates is worthy of observation in this updated form, so that the field can begin follow up studies.

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

Manuscript Reference: RC-2025-03007 TITLE: Rab11B is required for binding and entry of recent H3N2, but not H1N1, influenza A isolates Allyson Turner, Sara Jaffrani, Hannah Kubinski, Deborah Ajayi, Matthew Owens, Madeline McTigue, Conor Fanuele, Cailey Appenzeller, Hannah Despres, Madaline Schmidt, Jessica Crothers, and Emily Bruce

Summary Here, Turner et al. build upon existing knowledge of Influenza A virus (IAV) dependence on the Rab11 family of proteins and provide insights into the specific role of Rab11B isoform in H3N2 virus binding and entry. The introduction is clearly written and provides sufficient background on prior research involving Rab11. It effectively identifies the current gap in knowledge and justifies the investigation of more clinically relevant, circulating strains of IAV. The methods section provides sufficient detail to ensure reproducibility. Similarly, the discussion is well structured, aligns with the introduction, and thoughtfully outlines relevant follow-up experiments. The authors present data from a series of experiments which suggest that the reduced H3N2 infection and viral protein production in Rab11B-depleted cells is due to impaired virus binding. While the evidence supports a Rab11B-specific phenotype in the context of H3N2 infection, we recommend additional experiments (outlined below), to further validate and strengthen these findings. These would help solidify the mechanistic link between Rab11B depletion and the observed phenotype for H3N2 strains of IAV.

Major comments Figure 1. (B) & (C) The authors normalise viral titers to the non-targeting control (NTC) siRNA set at 100. While this approach allows for relative comparisons, we recommend including the corresponding raw PFU/ml values, at least in the supplementary materials. This will better illustrate the biological significance of gene depletion and variability of the results.

We have included the raw PFU/mL values in new Figure S1, while peak viral production varied by biological replicate (pasted below, with each biological replicate having a differently shaped data point). While the depletion-induced trends are clearly visible across biological replicates, normalization to average titer in the NT condition for each replicate allows for cleaner visualization.

In addition, the current protocol uses a high MOI (1), and a relatively short infection period (16 hours) to capture single-cycle replication. However, to better assess the impact of gene knockdown on virus production and spread, we suggest performing a multicycle replication assay using a lower MOI (e.g, 0.01-0.001) over an extended time period, such as 48 hours before titration, provided that cell viability under these conditions is acceptable.

We appreciate this suggestion and repeatedly attempted to carry out a multicycle growth curve to obtain this data. Unfortunately, out of four independent biological replicates we attempted, we were only able to maintain cell viability and adherence in one biological replicate (shown below). We have not included this data in the revised manuscript due to the limited replicates we were able to obtain, though we can add it in a further revision if the reviewer feels it is warranted.

Figure 7. (B) & (C) The authors present interesting data showing that siRNA-mediated depletion of Rab11B reduces virion binding of a recently circulating strain of H3N2, but not H1N1, suggesting a subtype-specific role. However, we strongly recommend complementing this assay with a single-cell resolution approach such as immunofluorescence detection of surface-bound viruses through HA staining and image quantification. This would allow the authors to directly assess virion binding per cell and visualise the phenotype, strengthening the mechanistic insight on H3N2 binding in Rab11B-depleted cells. Furthermore, the data, particularly for H1N1 (Figure 7.C), shows substantial variance, which suggests a suboptimal assay sensitivity and limits the strength of the conclusion that the knockdown does not affect H1N1 binding, this limitation may be overcome by implementing the above experimental suggestion.

We have made substantial efforts to include this data, but were ultimately unable to include this assay due to technical difficulties in implementation (NA stripping caused cells to lift off coverslips, difficulties in antibody sensitivity and specificity, among other issues). We also piloted single cell-based flow cytometry assays to attempt to measure signal from bound virions, but were unable to achieve sufficient differentiation between mock and bound samples with the antibodies we could obtain. However, we have included a new experimental approach that is able to genetically map the 11B-dependent phenotype to the HA gene, thus providing additional mechanistic insight and confirming that Rab11B acts on one of the earliest steps in the viral life cycle (prior to or at fusion).

Minor comments General The authors should state which statistical test was used for each dataset in the respective figure legends.

This information is now included in each figure legend.

Figure 1. Suggest changing Y axis title to PFU/ml [relative to NTC]

We have changed the axis titles of normalized data to "PFU as % of NT" throughout.

The co-depletion of Rab11A and Rab11B appears to be less efficient than individual knockdowns, based on RT- qPCR data (Figure 1.A). It is possible that the partial 'rescue' phenotype observed in Figures 2-4 is due to incomplete knockdown, rather than a true biological interaction. This possibility should be acknowledged.

In order to distinguish between a partial 'rescue' and inefficient knockdown, we depleted Rab11A or Rab11B individually, with the same 'half dose' of siRNA used in the double knockdown (see new Figure S3). We observed that Rab11B was still robustly required for H3N2 viral protein production. These results suggest that Rab11A and Rab11B could be playing mutually opposing roles in this case, which is consistent with prior Rab11 literature, rather than simply inefficient knockdown.

Furthermore, knockdown efficiency is assessed only at the mRNA level. To strengthen the conclusions, the authors are encouraged to provide western blot data confirming protein-level depletion of Rab11A and Rab11B, particularly in the double knockdown condition. This would help clarify whether co-transfection of siRNAs affect the efficiency of each individual knockdown at the protein level.

We have verified knockdown efficacy at the protein level in new Fig 1A/B. However, due to the high degree of protein level conservation between Rab11A and Rab11B it is very difficult to develop isoform specific antibodies, and we were unable to obtain a Rab11B-specific antibody that can detect endogenous protein (despite testing 6 commercially available antibodies for specificity). Using an antibody that detects both 11A and 11B (Fig1A) we were able to observe very slight changes in the molecular weight of the Rab11 band(s) detected upon knockdown of 11A vs 11B (suggestive of the two isoforms running as a dimer, with Rab11A the lower band and Rab11B the upper band). Cells depleted of both isoforms simultaneously showed a near complete loss of signal. Using a Rab11A antibody (that we confirmed as specific) we were able to observe loss of the Rab11A signal in both the 11A and 11A+B knockdowns (Fig 1B).

Figure 6. (A) & (B) are missing error bars, particularly the Rab11B knockdown data points.

Error bars are plotted in each graph, but due to very limited experimental variation these error bars are too small to appear on the graph (11B points in Fig 6B, D).

Figure 7. If including any repeats in the binding assay, authors are encouraged to use appropriate controls in each experiment such as exogenous neuraminidase treatment or sialidase treatment.

When attempting to establish a microscopy based binding assay we included exogenous neuraminidase in each experiment. Unfortunately, the combination of glass coverslips and treatment with exogenous neuraminidase at incubation times sufficient to strip virus also removed cells from the coverslips.

Reviewer #3 (Significance (Required)):

General assessment: Provides a conceptual advancement of subtype specific receptor preferences.

Advance: The study raises interesting observations regarding influenza virus subtype differences in cell surface receptor binding, in a Rab11B-dependent manner.

Audience: Influenza virologists, respiratory virologists

Expertise: Virus entry, Virus cell biology

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

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Referee #3

Evidence, reproducibility and clarity

Title: Rab11B is required for binding and entry of recent H3N2, but not H1N1, influenza A isolates

Allyson Turner, Sara Jaffrani, Hannah Kubinski, Deborah Ajayi, Matthew Owens, Madeline McTigue, Conor Fanuele, Cailey Appenzeller, Hannah Despres, Madaline Schmidt, Jessica Crothers, and Emily Bruce

Summary

Here, Turner et al. build upon existing knowledge of Influenza A virus (IAV) dependence on the Rab11 family of proteins and provide insights into the specific role of Rab11B isoform in H3N2 virus binding and entry. The introduction is clearly written and provides sufficient background on prior research involving Rab11. It effectively identifies the current gap in knowledge and justifies the investigation of more clinically relevant, circulating strains of IAV. The methods section provides sufficient detail to ensure reproducibility. Similarly, the discussion is well structured, aligns with the introduction, and thoughtfully outlines relevant follow-up experiments. The authors present data from a series of experiments which suggest that the reduced H3N2 infection and viral protein production in Rab11B-depleted cells is due to impaired virus binding. While the evidence supports a Rab11B-specific phenotype in the context of H3N2 infection, we recommend additional experiments (outlined below), to further validate and strengthen these findings. These would help solidify the mechanistic link between Rab11B depletion and the observed phenotype for H3N2 strains of IAV.

Major comments

Figure 1. (B) & (C)

The authors normalise viral titers to the non-targeting control (NTC) siRNA set at 100. While this approach allows for relative comparisons, we recommend including the corresponding raw PFU/ml values, at least in the supplementary materials. This will better illustrate the biological significance of gene depletion and variability of the results. In addition, the current protocol uses a high MOI (1), and a relatively short infection period (16 hours) to capture single-cycle replication. However, to better assess the impact of gene knockdown on virus production and spread, we suggest performing a multicycle replication assay using a lower MOI (e.g, 0.01-0.001) over an extended time period, such as 48 hours before titration, provided that cell viability under these conditions is acceptable.

Figure 7. (B) & (C)

The authors present interesting data showing that siRNA-mediated depletion of Rab11B reduces virion binding of a recently circulating strain of H3N2, but not H1N1, suggesting a subtype-specific role. However, we strongly recommend complementing this assay with a single-cell resolution approach such as immunofluorescence detection of surface-bound viruses through HA staining and image quantification. This would allow the authors to directly assess virion binding per cell and visualise the phenotype, strengthening the mechanistic insight on H3N2 binding in Rab11B-depleted cells. Furthermore, the data, particularly for H1N1 (Figure 7.C), shows substantial variance, which suggests a suboptimal assay sensitivity and limits the strength of the conclusion that the knockdown does not affect H1N1 binding, this limitation may be overcome by implementing the above experimental suggestion.

Minor comments

General

The authors should state which statistical test was used for each dataset in the respective figure legends.

Figure 1.

Suggest changing Y axis title to PFU/ml [relative to NTC] The co-depletion of Rab11A and Rab11B appears to be less efficient than individual knockdowns, based on RT- qPCR data (Figure 1.A). It is possible that the partial 'rescue' phenotype observed in Figures 2-4 is due to incomplete knockdown, rather than a true biological interaction. This possibility should be acknowledged. Furthermore, knockdown efficiency is assessed only at the mRNA level. To strengthen the conclusions, the authors are encouraged to provide western blot data confirming protein-level depletion of Rab11A and Rab11B, particularly in the double knockdown condition. This would help clarify whether co-transfection of siRNAs affect the efficiency of each individual knockdown at the protein level.

Figure 6.

(A) & (B) are missing error bars, particularly the Rab11B knockdown data points.

Figure 7.

If including any repeats in the binding assay, authors are encouraged to use appropriate controls in each experiment such as exogenous neuraminidase treatment or sialidase treatment.

Significance

General assessment: Provides a conceptual advancement of subtype specific receptor preferences.

Advance: The study raises interesting observations regarding influenza virus subtype differences in cell surface receptor binding, in a Rab11B-dependent manner.

Audience: Influenza virologists, respiratory virologists

Expertise: Virus entry, Virus cell biology

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors compare the effect of RAB11A and RAB11B knockdown on replication of contemporary H1N1 and H3N2 influenza A virus strains in A549 cells (human lung epithelials cells). They find a reduction in viral protein expression for tested H3N2 but not for H1N1 isolates. Mechanistically they suggest that RAB11A affects virion attachment to the cell surface.

Major comments:

The provided data do not conclusively support the suggested mechanism of action and essential controls are missing to substantiate the authors claims:

• Knockdown efficacy has to be confirmed on protein level, showing reduced levels of RAB11A and B by Western blot. This is a standard in the field. Off target effects cannot be avoided by RNAi approaches and are usually ruled out by using multiple siRNAs or by complementing the targeted protein in trans.
• Viral titers should be presented as absolute titers not as % (here the labelling is actually misleading in all graphs indicating pfu/ml)
• Reduction of viral protein expression goes hand in hand with a reduction in GAPDH. While this is accounted for in the quantification a general block of protein expression cannot be ruled out since the stability of house keeper proteins and viral proteins might be different. Testing multiple house keeping proteins could overcome this issue.
• The FACS data in Fig 5 are not convincing. The previous figures showed modest reduction in viral protein expression and the fluorescence is indicated here on a logarithmic scale. Quantification and indication of mean fluorescence intensity from the same data would be a better readout to convincingly show that less cells are infected.
• During the time of addition experiment in Fig 6, the authors are testing for HA/M2 positive cells after 16h of infection. This is a multicycle scnario so in a second round they would measure the effect of knockdown in absence of amonium chloride. Shorter infections up to 8h with higher MOI would overcome this problem.
• Standard error of mean is not an appropriate way of representing experimental error for the provided results and should be replaced by SD. Correct labeling of axis with units is required.

Minor comments:

• The authors show a rescue of viral replication upon double knockdown of RAB11A and B. Maybe this is just a consequence of inefficient knockdown since only half of the siRNAs were used?
• Specific experimental issues that are easily addressable.
• Are prior studies referenced appropriately?
• Are the text and figures clear and accurate?
• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Significance

The authors claim an H3N2 specific dependency on RAB11B for early steps of infection. While this is per se interesting the provided data do not fully support the claims and lack a mechanistic explanation. What is the difference between H1 and H3 strains (virion shape, HA load per virion, attachment force of H1 vs H3). The readouts used are not close enough to the events with regards to timing and could be supported by established entry assays in the field.

I wonder just how much value bias is involved in certain sociological studies. I wonder if that affects the group of people who conduct studies together and clash opinions

I thought this was a pretty powerful sentence in this section of the chapter. I think it's important to realize that as an older adult with the responisbility of a child, whilst also responsible for his own work ethic and future, that the only difference is there is someone looking up to you now. That there is an even greater motivation to succeed.

text- the words are in a regular text but in all black to get their message across but not in a harsh forcing way, it talks about property and hunger. color- the red and blue kind of remind me of the usa flag, the red reminds me of urgency,and hunger, while the blue reminds me of coldness and isolation. meaning- the target for this poster is for someone who is passionate about their nation

more than one antonio

The American Revolution made the United States an independent country and inspired other revolutions around the world. People from all walks of life helped in the fight, including women, Black Americans, and regular colonists. Even though the Revolution did not fix all inequalities, especially for Native Americans and enslaved people, its ideas about freedom and equality became important for future movements. Over time, these ideas influenced changes like ending slavery, giving women the vote, civil rights, and rights for LGBTQ+ people. The Revolution’s main impact was not just political, it also shaped ideas about fairness and freedom in American life.

colonisits opposed tea act because one company had exclusive trade.

colonists had paid indirect taxes, but the stamp act imposed an internal tax on documents and goods.

the people wanted the british lifestyle and good but also wanted there rights. and that caused so much tension.

this shows how a revolution wasn't just based on the famous textbook people but regular civilians that lived everyday lives.

they said liberty but kept slavery going. the same double standard we have today. hypocrisy at its finest.

i didn't know colonists were actually loyal like this. its funny how they were so into the king at first, but then switched up later. it shows how fast things can change when people feel disrespected.

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Reply to the reviewers

We will provide the revised manuscript as a PDF with highlighted changes, the Word file with tracked changes linked to reviewer comments, and all updated figures.

To address the reviewers' suggestions, we have conducted additional experiments that are now incorporated into new figures, or we have added new images to several existing figures where appropriate.

Please note that all figures have been renumbered to improve clarity and facilitate cross-referencing throughout the text. As recommended by Referee #3, all figure legends have been thoroughly revised to reflect these updates and are now labeled following the standard A-Z panel format, enhancing readability and ensuring easier identification. In addition, all figure legends now include the sample size for each statistical analysis.

For clarity and ease of reference, we provide below a comprehensive list of all figures included in the revised version. Figures that have undergone modifications are underlined.

Figure 1____. The first spermatogenesis wave in prepuberal mice.

This figure now includes amplified images of representative spermatocytes and a summary schematic illustrating the timeline of spermatogenesis. In addition, it now presents the statistical analysis of spermatocyte quantification to support the visual data.

__Figure 2.____ Cilia emerge across all stages of prophase I in spermatocytes during the first spermatogenesis wave. __

The images of this figure remain unchanged from the original submission, but all the graphs present now the statistical analysis of spermatocyte quantification.

Figure 3. Ultrastructure and markers of prepuberal meiotic cilia.

This figure remains unchanged from the original submission; however, we have replaced the ARL3-labelled spermatocyte image (A) with one displaying a clearer and more representative signal.

__Figure 4. Testicular tissue presents spermatocyte cysts in prepuberal mice and adult humans. __

This figure remains unchanged from the original submission.

__Figure 5. Cilia and flagella dynamics are correlated during prepuberal meiosis. __

This figure remains unchanged from the original submission.

__Figure 6. Comparative proteomics identifies potential regulators of ciliogenesis and flagellogenesis. __

This figure remains unchanged from the original submission.

Figure 7.____ Deciliation induces persistence of DNA damage in meiosis.

This figure has been substantially revised and now includes additional experiments analyzing chloral hydrate treatment, aimed at more accurately assessing DNA damage under both control and treated conditions. Images F-I and graph J are new.

Figure 8____. Aurora kinase A is a regulator of cilia disassembly in meiosis.

This figure is remodelled as the original version contained a mistake in previous panel II, for this, graph in new Fig.8 I has been corrected. In addition, it now contains additional data of αTubulin staining in arrested ciliated metaphases I after AURKA inhibition (new panel L1´).

__Figure 9. Schematic representation of the prepuberal versus adult seminiferous epithelium. __

This figure remains unchanged from the original submission.

__Supplementary Figure 1. Meiotic stages during the first meiotic wave. __

This figure remains unchanged from the original submission.

__Supplementary Figure 2 (new)____. __

This is a new figure that includes additional data requested by the reviewers. It includes additional markers of cilia in spermatocytes (glutamylated Tubulin/GT335), and the control data of cilia markers in non-ciliated spermatocytes. It also includes now the separated quantification of ciliated spermatocytes for each stage, as requested by reviewers, complementing graphs included in Figure 2.

Please note that with the inclusion of this new Supplementary Figure 2, the numbering of subsequent supplementary figures has been updated accordingly.

Supplementary Figure 3 (previously Suppl. Fig. 2)__. Ultrastructure of prophase I spermatocytes. __

This figure is equal in content to the original submission, but some annotations have been included.

Supplementary Figure 4 (previously Suppl. Fig. 3).__ Meiotic centrosome under the electron microscope. __

This figure remains unchanged from the original submission, but additional annotations have been included.

Supplementary Figure 5 (previously Suppl. Fig. 4)__. Human testis contains ciliated spermatocytes. __

This figure has been revised and now includes additional H2AX staining to better determine the stage of ciliated spermatocytes and improve their identification.

Supplementary Figure 6 (previously Suppl. Fig. 5). GLI1 and GLI3 readouts of Hedgehog signalling are not visibly affected in prepuberal mouse testes.

This figure has been remodeled and now includes the quantification of GLI1 and GLI3 and its corresponding statistical analysis. It also includes the control data for Tubulin, instead of GADPH.

Supplementary Figure 7 (previously Suppl. Fig. 6)__. CH and MLN8237 optimization protocol. __

This figure has been remodeled to incorporate control experiments using 1-hour organotypic culture treatment.

Supplementary Figure 8 (previously Suppl. Fig. 7)__. Tracking first meiosis wave with EdU pulse injection during prepubertal meiosis. __This figure remains unchanged from the original submission.

Supplementary Figure 9 (previously Suppl. Fig. 8)__. PLK1 and AURKA inhibition in cultured spermatocytes. __

This figure has been remodeled and now includes additional data on spindle detection in control and AURKA-inhibited spermatocytes (both ciliated and non ciliated).

__Response to the reviewers __

We will submit both the PDF version of the revised manuscript and the Word file with tracked changes relative to the original submission. Each modification made in response to reviewers' suggestions is annotated in the Word document within the corresponding section of the text.

A detailed, point-by-point response to each reviewer's comments is provided in the following section.

Response to the Referee #1

In this manuscript by Perez-Moreno et al., titled "The dynamics of ciliogenesis in prepubertal mouse meiosis reveal new clues about testicular maturation during puberty", the authors characterize the development of primary cilia during meiosis in juvenile male mice. The authors catalog a variety of testicular changes that occur as juvenile mice age, such as changes in testis weight and germ cell-type composition. They next show that meiotic prophase cells initially lack cilia, and ciliated meiotic prophase cells are detected after 20 days postpartum, coinciding with the time when post-meiotic spermatids within the developing testes acquire flagella. They describe that germ cells in juvenile mice harbor cilia at all substages of meiotic prophase, in contrast to adults where only zygotene stage meiotic cells harbor cilia. The authors also document that cilia in juvenile mice are longer than those in adults. They characterize cilia composition and structure by immunofluorescence and EM, highlighting that cilia polymerization may initially begin inside the cell, followed by extension beyond the cell membrane. Additionally, they demonstrate ciliated cells can be detected in adult human testes. The authors next perform proteomic analyses of whole testes from juvenile mice at multiple ages, which may not provide direct information about the extremely small numbers of ciliated meiotic cells in the testis, and is lacking follow up experiments, but does serve as a valuable resource for the community. Finally, the authors use a seminiferous tubule culturing system to show that chemical inhibition of Aurora kinase A likely inhibits cilia depolymerization upon meiotic prophase I exit and leads to an accumulation of metaphase-like cells harboring cilia. They also assess meiotic recombination progression using their culturing system, but this is less convincing.

Author response: We sincerely thank Ref #1 for the thorough and thoughtful evaluation of our manuscript. We are particularly grateful for the reviewer's careful reading and constructive feedback, which have helped us refine several sections of the text and strengthen our discussion. All comments and suggestions have been carefully considered and addressed, as detailed below.

__Major comments: __

1. There are a few issues with the experimental set up for assessing the effects of cilia depolymerization on DNA repair (Figure 7-II). First, how were mid pachytene cells identified and differentiated from early pachytene cells (which would have higher levels of gH2AX) in this experiment? I suggest either using H1t staining (to differentiate early/mid vs late pachytene) or the extent of sex chromosome synapsis. This would ensure that the authors are comparing similarly staged cells in control and treated samples. Second, what were the gH2AX levels at the starting point of this experiment? A more convincing set up would be if the authors measure gH2AX immediately after culturing in early and late cells (early would have higher gH2AX, late would have lower gH2AX), and then again after 24hrs in late cells (upon repair disruption the sampled late cells would have high gH2AX). This would allow them to compare the decline in gH2AX (i.e., repair progression) in control vs treated samples. Also, it would be informative to know the starting gH2AX levels in ciliated vs non-ciliated cells as they may vary.

Response:

We thank Ref #1 for this valuable comment, which significantly contributed to improving both the design and interpretation of the cilia depolymerization assay.

Following this suggestion, we repeated the experiment including 1-hour (immediately after culturing), and 24-hour cultures for both control and chloral hydrate (CH)-treated samples (n = 3 biological replicates). To ensure accurate staging, we now employ triple immunolabelling for γH2AX, SYCP3, and H1T, allowing clear distinction of zygotene (H1T−), early pachytene (H1T−), and late pachytene (H1T+) cells. The revised data (Figure 7) now provide a more complete and statistically robust analysis of DNA damage dynamics. These results confirm that CH-induced deciliation leads to persistence of the γH2AX signal at 24 hours, indicating impaired DNA repair progression in pachytene spermatocytes. The new images and graphs are included in the revised Figure 7.

Regarding the reviewer's final point about the comparison of γH2AX levels between ciliated and non-ciliated cells, we regret that direct comparison of γH2AX levels between ciliated and non-ciliated cells is not technically feasible. To preserve cilia integrity, all cilia-related imaging is performed using the squash technique, which maintains the three-dimensional structure of the cilia but does not allow reliable quantification of DNA damage markers due to nuclear distortion. Conversely, the nuclear spreading technique, used for DNA damage assessment, provides optimal visualization of repair foci but results in the loss of cilia due to cytoplasmic disruption during the hypotonic step. Given that spermatocytes in juvenile testes form developmentally synchronized cytoplasmic cysts, we consider that analyzing a statistically representative number of spermatocytes offers a valid and biologically meaningful measure of tissue-level effects.

In conclusion, we believe that the additional experiments and clarifications included in revised Figure 7 strengthen our conclusion that cilia depolymerization compromises DNA repair during meiosis. Further functional confirmation will be pursued in future works, since we are currently generating a conditional genetic model for a ciliopathy in our laboratory.

The authors analyze meiotic progression in cells cultured with/without AURKA inhibition in Figure 8-III and conclude that the distribution of prophase I cells does not change upon treatment. Is Figure 8-III A and B the same data? The legend text is incorrect, so it's hard to follow. Figure 8-III A shows a depletion of EdU-labelled pachytene cells upon treatment. Moreover, the conclusion that a higher proportion of ciliated zygotene cells upon treatment (Figure 8-II C) suggests that AURKA inhibition delays cilia depolymerization (page 13 line 444) does not make sense to me.

Response:

We thank Ref#1 for identifying this issue and for the careful examination of Figure 8. We discovered that the submitted version of Figure 8 contained a mismatch between the figure legend and the figure panels. The legend text was correct; however, the figure inadvertently included a non-corresponding graph (previously panel II-A), which actually belonged to Supplementary Figure 7 in the original submission. We apologize for this mistake.

This error has been corrected in the revised version. The updated Figure 8 now accurately presents the distribution of EdU-labelled spermatocytes across prophase I substages in control and AURKA-inhibited cultures (previously Figure 8-II B, now Figure 8-A). The corrected data show no significant differences in the proportions of EdU-labelled spermatocytes among prophase I substages after 24 hours of AURKA inhibition, confirming that meiotic progression is not delayed and that no accumulation of zygotene cells occurs under this treatment. Therefore, the observed increase in ciliated zygotene spermatocytes upon AURKA inhibition (new Figure 8 H-I) is best explained by a delay in cilia disassembly, rather than by an arrest or slowdown in meiotic progression. The figure legend and main text have been revised accordingly.

How do the authors know that there is a monopolar spindle in Figure 8-IV treated samples? Perhaps the authors can use a different Tubulin antibody (that does not detect only acetylated Tubulin) to show that there is a monopolar spindle.

Response:

We appreciate Ref#1 for this excellent suggestion. In the original submission (lines 446-447), we described that ciliated metaphase I spermatocytes in AURKA-inhibited samples exhibited monopolar spindle phenotypes. This description was based on previous reports showing that AURKA or PLK1 inhibition produces metaphases with monopolar spindles characterized by aberrant yet characteristic SYCP3 patterns, abnormal chromatin compaction, and circular bivalent alignment around non-migrated centrosomes (1). In our study, we observed SYCP3 staining consistent with these characteristic features of monopolar metaphases I.

However, we agree with Ref #1 that this could be better sustained with data. Following the reviewer's suggestion, we performed additional immunostaining using α-Tubulin, which labels total microtubules rather than only the acetylated fraction. For clarity purposes, the revised Figure 8 now includes α-Tubulin staining in the same ciliated metaphase I cells shown in the original submission, confirming the presence of defective microtubule polymerization and defective spindle organization. For clarity, we now refer to these ciliated metaphases I as "arrested MI". This new data further support our conclusion that AURKA inhibition disrupts spindle bipolarization and prevents cilia depolymerization, indicating that cilia maintenance and bipolar spindle organization are mechanistically incompatible events during male meiosis. The abstract, results, and discussion section has been expanded accordingly, emphasizing that the persistence of cilia may interfere with microtubule polymerization and centrosome separation under AURKA inhibition. The Discussion has been expanded to emphasize that persistence of cilia may interfere with centrosome separation and microtubule polymerization, contrasting with invertebrate systems -e.g. Drosophila (2) and P. brassicae (3)- in which meiotic cilia persist through metaphase I without impairing bipolar spindle assembly.

1. Alfaro, et al. EMBO Rep 22, (2021). DOI: 15252/embr.202051030 (PMID: 33615693)
2. Riparbelli et al . Dev Cell (2012) DOI: 1016/j.devcel.2012.05.024 (PMID: 22898783)
3. Gottardo et al, Cytoskeleton (Hoboken) (2023) DOI: 1002/cm.21755 (PMID: 37036073)

The authors state in the abstract that they provide evidence suggesting that centrosome migration and cilia depolymerization are mutually exclusive events during meiosis. This is not convincing with the data present in the current manuscript. I suggest amending this statement in the abstract.

Response:

We thank Ref#1 for this valuable observation, with which we fully agree. To avoid overstatement, the original statement has been removed from the Abstract, Results, and Discussion, and replaced with a more accurate formulation indicating that cilia maintenance and bipolar spindle formation are mutually exclusive events during mouse meiosis.

This revised statement is now directly supported by the new data presented in Figure 8, which demonstrate that AURKA inhibition prevents both spindle bipolarization and cilia depolymerization. We are grateful to the reviewer for highlighting this important clarification.

Minor comments:

The presence of cilia in all stages of meiotic prophase I in juvenile mice is intriguing. Why is the cellular distribution and length of cilia different in prepubertal mice compared to adults (where shorter cilia are present only in zygotene cells)? What is the relevance of these developmental differences? Do cilia serve prophase I functions in juvenile mice (in leptotene, pachytene etc.) that are perhaps absent in adults?

Related to the above point, what is the relevance of the absence of cilia during the first meiotic wave? If cilia serve a critical function during prophase I (for instance, facilitating DSB repair), does the lack of cilia during the first wave imply differing cilia (and repair) requirements during the first vs latter spermatogenesis waves?

In my opinion, these would be interesting points to discuss in the discussion section.

Response:

We thank the reviewer for these thoughtful observations, which we agree are indeed intriguing.

We believe that our findings likely reflect a developmental role for primary cilia during testicular maturation. We hypothesize that primary cilia at this stage might act as signaling organelles, receiving cues from Sertoli cells or neighboring spermatocytes and transmitting them through the cytoplasmic cysts shared by spermatocytes. Such intercellular communication could be essential for coordinating tissue maturation and meiotic entry during puberty. Although speculative, this hypothesis aligns with the established role of primary cilia as sensory and signaling hubs for GPCR and RTK pathways regulating cell differentiation and developmental patterning in multiple tissues (e.g., 1, 2). The Discussion section has been expanded to include these considerations.

1. Goetz et al, Nat Rev Genet (2010)- DOI: 1038/nrg2774 (PMID: 20395968)
2. Naturky et al , Cell (2019) DOI: 1038/s41580-019-0116-4 (PMID: 30948801) Our study focuses on the first spermatogenic wave, which represents the transition from the juvenile to the reproductive phase. It is therefore plausible that the transient presence of longer cilia during this period reflects a developmental requirement for external signaling that becomes dispensable in the mature testis. Given that this is only the second study to date examining mammalian meiotic cilia, there remains a vast area of research to explore. We plan to address potential signaling cascades involved in these processes in future studies.

On the other hand, while we cannot confirm that the cilia observed in zygotene spermatocytes persist until pachytene within the same cell, it is reasonable to speculate that they do, serving as longer-lasting signaling structures that facilitate testicular development during the critical pubertal window. In addition, the observation of ciliated spermatocytes at all prophase I substages at 20 dpp, together with our proteomic data, supports the idea that the emergence of meiotic cilia exerts a significant developmental impact on testicular maturation.

In summary, although we cannot yet define specific prophase I functions for meiotic cilia in juvenile spermatocytes, our data demonstrate that the first meiotic wave differs from later waves in cilia dynamics, suggesting distinct regulatory requirements between puberty and adulthood. These findings underscore the importance of considering developmental context when using the first meiotic wave as a model for studying spermatogenesis.

The authors state on page 9 lines 286-288 that the presence of cytoplasmic continuity via intercellular bridges (between developmentally synchronous spermatocytes) hints towards a mechanism that links cilia and flagella formation. Please clarify this statement. While the correlation between the timing of appearance of cilia and flagella in cells that are located within the same segment of the seminiferous tubule may be hinting towards some shared regulation, how would cytoplasmic continuity participate in this regulation? Especially since the cytoplasmic continuity is not between the developmentally distinct cells acquiring the cilia and flagella?

Response:

We thank Ref#1 for this excellent question and for the opportunity to clarify our statement.

The presence of intercellular bridges between spermatocytes is well known and has long been proposed to support germ cell communication and synchronization (1,2) as well as sharing mRNA (3) and organelles (4). A classic example is the Akap gene, located on the X chromosome and essential for the formation of the sperm fibrous sheath; cytoplasmic continuity through intercellular bridges allows Akap-derived products to be shared between X- and Y-bearing spermatids, thereby maintaining phenotypic balance despite transcriptional asymmetry (5). In addition, more recent work has further demonstrated that these bridges are critical for synchronizing meiotic progression and for processes such as synapsis, double-strand break repair, and transposon repression (6).

In this context, and considering our proteomic data (Figure 6), our statement did not intend to imply direct cytoplasmic exchange between ciliated and flagellated cells. Although our current methods do not allow comprehensive tracing of cytoplasmic continuity from the basal to the luminal compartment of the seminiferous epithelium, we plan to address this limitation using high-resolution 3D and ultrastructural imaging approaches in future studies.

Based on our current data, we propose that cytoplasmic continuity within developmentally synchronized spermatocyte cysts could facilitate the coordinated regulation of ciliogenesis, and similarly enable the sharing of regulatory factors controlling flagellogenesis within spermatid cysts. This coordination may occur through the diffusion of centrosomal or ciliary proteins, mRNAs, or signaling intermediates involved in the regulation of microtubule dynamics. However, we cannot exclude the possibility that such cytoplasmic continuity extends across all spermatocytes derived from the same spermatogonial clone, potentially providing a larger regulatory network.]] This mechanism could help explain the temporal correlation we observe between the appearance of meiotic cilia and the onset of flagella formation in adjacent spermatids within the same seminiferous segment.

We have revised the Discussion to explicitly clarify this interpretation and to note that, although hypothetical, it is consistent with established literature on cytoplasmic continuity and germ cell coordination.

1. Dym, et al. * Reprod.*(1971) DOI: 10.1093/biolreprod/4.2.195 (PMID: 4107186)
2. Braun et al. Nature. (1989) DOI: 1038/337373a0 (PMID: 2911388)
3. Greenbaum et al. * Natl. Acad. Sci. USA*(2006). DOI: 10.1073/pnas.0505123103 (PMID: 16549803)
4. Ventelä et al. Mol Biol Cell. (2003) DOI: 1091/mbc.e02-10-0647 (PMID: 12857863)
5. Turner et al. Journal of Biological Chemistry (1998). DOI: 1074/jbc.273.48.32135 (PMID: 9822690)
6. Sorkin, et al. Nat Commun (2025). DOI: 1038/s41467-025-56742-9 (PMID: 39929837)
7. *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.*

Individual germ cells in H&E-stained testis sections in Figure 1-II are difficult to see. I suggest adding zoomed-in images where spermatocytes/round spermatids/elongated spermatids are clearly distinguishable.

Response:

Ref#1 is very right in this suggestion. We have revised Figure 1 to improve the quality of the H&E-stained testis sections and have added zoomed-in panels where spermatocytes, round spermatids, and elongated spermatids are clearly distinguishable. These additions significantly enhance the clarity and interpretability of the figure.

In Figure 2-II B, the authors document that most ciliated spermatocytes in juvenile mice are pachytene. Is this because most meiotic cells are pachytene? Please clarify. If the data are available (perhaps could be adapted from Figure 1-III), it would be informative to see a graph representing what proportions of each meiotic prophase substages have cilia.

Response:

We thank the reviewer for this valuable observation. Indeed, the predominance of ciliated pachytene spermatocytes reflects the fact that most meiotic cells in juvenile testes are at the pachytene stage (Figure 1). We have clarified this point in the text and have added a new supplementary figure (Supplementary Figure 2, new figure) presenting a graph showing the proportion of spermatocytes at each prophase I substage that possess primary cilia. This visualization provides a clearer quantitative overview of ciliation dynamics across meiotic substages.

I suggest annotating the EM images in Sup Figure 2 and 3 to make it easier to interpret.

Response:

We thank the reviewer for this helpful suggestion. We have now added annotations to the EM images in Supplementary Figures 3 and 4 to facilitate their interpretation. These visual guides help readers more easily identify the relevant ultrastructural features described in the text.

The authors claim that the ratio between GLI3-FL and GLI3-R is stable across their analyzed developmental window in whole testis immunoblots shown in Sup Figure 5. Quantifying the bands and normalizing to the loading control would help strengthen this claim as it hard to interpret the immunoblot in its current form.

Response:

We thank the reviewer for this valuable suggestion. Following this recommendation, Supplementary Figure 5 has been revised to include quantification of GLI1 and GLI3 protein levels, normalized to the loading control.

After quantification, we observed statistically significant differences across developmental stages. Specifically, GLI1 expression is slightly higher at 21 dpp compared to 8 dpp. For GLI3, we performed two complementary analyses:

• Total GLI3 protein (sum of full-length and repressor forms normalized to loading control) shows a progressive decrease during development, with the lowest levels at 60 dpp (Supplementary Figure 5D).
• GLI3 activation status, assessed as the GLI3-FL/GLI3-R ratio, is highest during the 19-21 dpp window, compared to 8 dpp and 60 dpp. Although these results suggest a possible transient activation of GLI3 during testicular maturation, we caution that this cannot automatically be attributed to increased Hedgehog signaling, as GLI3 processing can also be affected by other processes, such as changes in ciliogenesis. Furthermore, because the analysis was performed on whole-testis protein extracts, these changes cannot be specifically assigned to ciliated spermatocytes.

We have expanded the Discussion to address these findings and to highlight the potential involvement of the Desert Hedgehog (DHH) pathway, which plays key roles in testicular development, Sertoli-germ cell communication, and spermatogenesis (1, 2, 3). We plan to investigate these pathways further in future studies.

1. Bitgood et al. Curr Biol. (1996). DOI: 1016/s0960-9822(02)00480-3 (PMID: 8805249)
2. Clark et al. Biol Reprod. (2000) DOI: 1095/biolreprod63.6.1825 (PMID: 11090455)
3. O'Hara et al. BMC Dev Biol. (2011) DOI: 1186/1471-213X-11-72 (PMID: 22132805) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

There are a few typos throughout the manuscript. Some examples: page 5 line 172, Figure 3-I legend text, Sup Figure 5-II callouts, Figure 8-III legend, page 15 line 508, page 17 line 580, page 18 line 611.

Response:

We thank the reviewer for detecting this. All typographical errors have been corrected, and figure callouts have been reviewed for consistency.

__ ____Response to the Referee #2__

__ __This study focuses on the dynamic changes of ciliogenesis during meiosis in prepubertal mice. It was found that primary cilia are not an intrinsic feature of the first wave of meiosis (initiating at 8 dpp); instead, they begin to polymerize at 20 dpp (after the completion of the first wave of meiosis) and are present in all stages of prophase I. Moreover, prepubertal cilia (with an average length of 21.96 μm) are significantly longer than adult cilia (10 μm). The emergence of cilia coincides temporally with flagellogenesis, suggesting a regulatory association in the formation of axonemes between the two. Functional experiments showed that disruption of cilia by chloral hydrate (CH) delays DNA repair, while the AURKA inhibitor (MLN8237) delays cilia disassembly, and centrosome migration and cilia depolymerization are mutually exclusive events. These findings represent the first detailed description of the spatiotemporal regulation and potential roles of cilia during early testicular maturation in mice. The discovery of this phenomenon is interesting; however, there are certain limitations in functional research.

We thank Ref#2 for taking the time to evaluate our manuscript and for summarizing its main findings. We regret that the reviewer did not find the study sufficiently compelling, but we respectfully clarify that the strength of our work lies precisely in addressing a largely unexplored aspect of mammalian meiosis for which virtually no prior data exist. Given the extremely limited number of studies addressing cilia in mammalian meiosis (only five to date, including our own previous publication on adult mouse spermatogenesis) (1-5), we consider that the present work provides the first robust and integrative evidence on the emergence, morphology, and potential roles of primary cilia during prepubertal testicular development. The study combines histology, high-resolution microscopy, proteomics, and pharmacological perturbations, supported by quantitative analyses, thereby establishing a solid and much-needed reference framework for future functional studies.

We emphasize that this manuscript constitutes the first comprehensive characterization of ciliogenesis during prepubertal mouse meiosis, complemented by functional in vitro assays that begin to address potential roles of these cilia. For this reason, we want to underscore the importance of this study in providing a solid framework that will support and guide future research

Major points:

1. The prepubertal cilia in spermatocytes discovered by the authors lack specific genetic ablation to block their formation, making it impossible to evaluate whether such cilia truly have functions. Because neither in the first wave of spermatogenesis nor in adult spermatogenesis does this type of cilium seem to be essential. In addition, the authors also imply that the formation of such cilia appears to be synchronized with the formation of sperm flagella. This suggests that the production of such cilia may merely be transient protein expression noise rather than a functionally meaningful cellular structure.

Response:

We agree that a genetic ablation model would represent the ideal approach to directly test cilia function in spermatogenesis. However, given the complete absence of prior data describing the dynamics of ciliogenesis during testis development, our priority in this study was to establish a rigorous structural and temporal characterization of this process in the main mammalian model organism, the mouse. This systematic and rigorous phenotypic characterization is a necessary first step before any functional genetics could be meaningfully interpreted.

To our knowledge, this study represents the first comprehensive analysis of ciliogenesis during prepubertal mouse meiosis, extending our previous work on adult spermatogenesis (1). Beyond these two contributions, only four additional studies have addressed meiotic cilia-two in zebrafish (2, 3), with Mytlys et al. also providing preliminary observations relevant to prepubertal male meiosis that we discuss in the present work, one in Drosophila (4) and a recent one in butterfly (5). No additional information exists for mammalian gametogenesis to date.

1. López-Jiménez et al. Cells (2022) DOI: 10.3390/cells12010142 (PMID: 36611937)
2. Mytlis et al. Science (2022) DOI: 10.1126/science.abh3104 (PMID: 35549308)
3. Xie et al. J Mol Cell Biol (2022) DOI: 10.1093/jmcb/mjac049 (PMID: 35981808)
4. Riparbelli et al . Dev Cell (2012) DOI: 10.1016/j.devcel.2012.05.024 (PMID: 22898783)
5. Gottardo et al, Cytoskeleton (Hoboken) (2023) DOI: 10.1002/cm.21755 (PMID: 37036073) We therefore consider this descriptive and analytical foundation to be essential before the development of functional genetic models. Indeed, we are currently generating a conditional genetic model for a ciliopathy in our laboratory. These studies are ongoing and will directly address the type of mechanistic questions raised here, but they extend well beyond the scope and feasible timeframe of the present manuscript.

We thus maintain that the present work constitutes a necessary and timely contribution, providing a robust reference dataset that will facilitate and guide future functional studies in the field of cilia and meiosis.

Taking this into account, we would be very pleased to address any additional, concrete suggestions from Ref#2 that could further strengthen the current version of the manuscript

The high expression of axoneme assembly regulators such as TRiC complex and IFT proteins identified by proteomic analysis is not particularly significant. This time point is precisely the critical period for spermatids to assemble flagella, and TRiC, as a newly discovered component of flagellar axonemes, is reasonably highly expressed at this time. No intrinsic connection with the argument of this paper is observed. In fact, this testicular proteomics has little significance.

Response:

We appreciate this comment but respectfully disagree with the reviewer's interpretation of our proteomic data. To our knowledge, this is the first proteomic study explicitly focused on identifying ciliary regulators during testicular development at the precise window (19-21 dpp) when both meiotic cilia and spermatid flagella first emerge.

While Piprek et al (1) analyzed the expression of primary cilia in developing gonads, proteomic data specifically covering the developmental transition at 19-21 dpp were not previously available. Furthermore, a recent cell-sorting study (2), detected expression of cilia proteins in pachytene spermatocytes compared to round spermatids, but did not explore their functional relevance or integrate these data with developmental timing or histological context.

In contrast, our dataset integrates histological staging, high-resolution microscopy, and quantitative proteomics, revealing a set of candidate regulators (including DCAF7, DYRK1A, TUBB3, TUBB4B, and TRiC) potentially involved in cilia-flagella coordination. We view this as a hypothesis-generating resource that outlines specific proteins and pathways for future mechanistic studies on both ciliogenesis and flagellogenesis in the testis.

Although we fully agree that proteomics alone cannot establish causal function, we believe that dismissing these data as having little significance overlooks their value as the first molecular map of the testis at the developmental window when axonemal structures arise. Our dataset provides, for the first time, an integrated view of proteins associated with ciliary and flagellar structures at the developmental stage when both axonemal organelles first appear. We thus believe that our proteomic dataset represents an important and novel contribution to the understanding of testicular development and ciliary biology.

Considering this, we would again welcome any specific suggestions from Ref#2 on additional analyses or clarifications that could make the relevance of this dataset even clearer to readers.

1. Piprek et al. Int J Dev Biol. (2019) doi: 10.1387/ijdb.190049rp (PMID: 32149371).
2. Fang et al. Chromosoma. (1981) doi: 10.1007/BF00285768 (PMID: 7227045).

Response to the Referee #3

In "The dynamics of ciliogenesis in prepubertal mouse meiosis reveals new clues about testicular development" Pérez-Moreno, et al. explore primary cilia in prepubertal mouse spermatocytes. Using a combination of microscopy, proteomics, and pharmacological perturbations, the authors carefully characterize prepubertal spermatocyte cilia, providing foundational work regarding meiotic cilia in the developing mammalian testis.

Response: We sincerely thank Ref#3 for their positive assessment of our work and for the thoughtful suggestions that have helped us strengthen the manuscript. We are pleased that the reviewer recognizes both the novelty and the relevance of our study in providing foundational insights into meiotic ciliogenesis during prepubertal testicular development. All specific comments have been carefully considered and addressed as detailed below.

Major concerns:

1. The authors provide evidence consistent with cilia not being present in a larger percentage of spermatocytes or in other cells in the testis. The combination of electron microscopy and acetylated tubulin antibody staining establishes the presence of cilia; however, proving a negative is challenging. While acetylated tubulin is certainly a common marker of cilia, it is not in some cilia such as those in neurons. The authors should use at least one additional cilia marker to better support their claim of cilia being absent.

Response:

We thank the reviewer for this helpful suggestion. In the revised version, we have strengthened the evidence for cilia identification by including an additional ciliary marker, glutamylated tubulin (GT335), in combination with acetylated tubulin and ARL13B (which were included in the original submission). These data are now presented in the new Supplementary Figure 2, which also includes an example of a non-ciliated spermatocyte showing absence of both ARL13B and AcTub signals.

Taken together, these markers provide a more comprehensive validation of cilia detection and confirm the absence of ciliary labelling in non-ciliated spermatocytes.

The conclusion that IFT88 localizes to centrosomes is premature as key controls for the IFT88 antibody staining are lacking. Centrosomes are notoriously "sticky", often sowing non-specific antibody staining. The authors must include controls to demonstrate the specificity of the staining they observe such as staining in a genetic mutant or an antigen competition assay.

Response:

We appreciate the reviewer's concern and fully agree that antibody specificity is critical when interpreting centrosomal localization. The IFT88 antibody used in our study is commercially available and has been extensively validated in the literature as both a cilia marker (1, 2), and a centrosome marker in somatic cells (3). Labelling of IFT88 in centrosomes has also been previously described using other antibodies (4, 5). In our material, the IFT88 signal consistently appears at one of the duplicated centrosomes and at both spindle poles-patterns identical to those reported in somatic cells. We therefore consider the reported meiotic IFT88 staining as specific and biologically reliable.

That said, we agree that genetic validation would provide the most definitive confirmation. We would like to inform that we are currently since we are currently generating a conditional genetic model for a ciliopathy in our laboratory that will directly assess both antibody specificity and functional consequences of cilia loss during meiosis. These experiments are in progress and will be reported in a follow-up study.

1. Wong et al. Science (2015). DOI: 1126/science.aaa5111 (PMID: 25931445)
2. Ocbina et al. Nat Genet (2011). DOI: 1038/ng.832 (PMID: 21552265)
3. Vitre et al. EMBO Rep (2020). DOI: 15252/embr.201949234 (PMID: 32270908)
4. Robert A. et al. J Cell Sci (2007). DOI: 1242/jcs.03366 (PMID: 17264151)
5. Singla et al, Developmental Cell (2010). DOI: 10.1016/j.devcel.2009.12.022 (PMID: 20230748) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

There are many inconsistent statements throughout the paper regarding the timing of the first wave of spermatogenesis. For example, the authors state that round spermatids can be detected at 21dpp on line 161, but on line 180, say round spermatids can be detected a 19dpp. Not only does this lead to confusion, but such discrepancies undermine the validity of the rest of the paper. A summary graphic displaying key events and their timing in the first wave of spermatogenesis would be instrumental for reader comprehension and could be used by the authors to ensure consistent claims throughout the paper.

Response:

We thank the reviewer for identifying this inconsistency and apologize for the confusion. We confirm that early round spermatids first appear at 19 dpp, as shown in the quantitative data (Figure 1J). This can be detected in squashed spermatocyte preparations, where individual spermatocytes and spermatids can be accurately quantified. The original text contained an imprecise reference to the histological image of 21 dpp (previous line 161), since certain H&E sections did not clearly show all cell types simultaneously. However, we have now revised Figure 1, improving the image quality and adding a zoomed-in panel highlighting early round spermatids. Image for 19 dpp mice in Fig 1D shows early, yet still aflagellated spermatids. The first ciliated spermatocytes and the earliest flagellated spermatids are observed at 20 dpp. This has been clarified in the text.

In addition, we also thank the reviewer for the suggestion of adding a summary graphic, which we agree greatly facilitates reader comprehension. We have added a new schematic summary (Figure 1K) illustrating the key stages and timing of the first spermatogenic wave.

In the proteomics experiments, it is unclear why the authors assume that changes in protein expression are predominantly due to changes within the germ cells in the developing testis. The analysis is on whole testes including both the somatic and germ cells, which makes it possible that protein expression changes in somatic cells drive the results. The authors need to justify why and how the conclusions drawn from this analysis warrant such an assumption.

Response:

We agree with the reviewer that our proteomic analysis was performed on whole testis samples, which contain both germ and somatic cells. Although isolation of pure spermatocyte populations by FACS would provide higher resolution, obtaining sufficient prepubertal material for such analysis would require an extremely large number of animals. To remain compliant with the 3Rs principle for animal experimentation, we therefore used whole-testis samples from three biological replicates per age.

We acknowledge that our assumption-that the main differences arise from germ cells-is a simplification. However, germ cells constitute the vast majority of testicular cells during this developmental window and are the population undergoing major compositional changes between 15 dpp and adulthood. It is therefore reasonable to expect that a substantial fraction of the observed proteomic changes reflects alterations in germ cells. We have clarified this point in the revised text and have added a statement noting that changes in somatic cells could also contribute to the proteomic profiles.

The authors should provide details on how proteins were categorized as being involved in ciliogenesis or flagellogenesis, specifically in the distinction criteria. It is not clear how the categorizations were determined or whether they are valid. Thus, no one can repeat this analysis or perform this analysis on other datasets they might want to compare.

Response:

We thank the reviewer for this opportunity to clarify our approach. The categorization of protein as being involved in ciliogenesis or flagellogenesis was based on their Gene Ontology (GO) cellular component annotations obtained from the PANTHER database (Version 19.0), using the gene IDs of the Differentially Expressed Proteins (DEPs). Specifically, we used the GO terms cilium (GO:0005929) and motile cilium (GO:0031514). Since motile cilium is a subcategory of cilium, proteins annotated only with the general cilium term, but not included under motile cilium, were considered to be associated with primary cilia or with shared structural components common to different types of cilia. These GO terms are represented in the bottom panel of the Figure 6.

This information has been added to the Methods section and referenced in the Results for transparency and reproducibility.

In the pharmacological studies, the authors conclude that the phenotypes they observe (DNA damage and reduced pachytene spermatocytes) are due to loss of or persistence of cilia. This overinterprets the experiment. Chloral hydrate and MLN8237 certainly impact ciliation as claimed, but have additional cellular effects. Thus, it is possible that the observed phenotypes were not a direct result of cilia manipulation. Either additional controls must address this or the conclusions need to be more specific and toned down.

Response:

We thank the reviewer for this fair observation and have taken steps to strengthen and refine our interpretation. In the revised version, we now include data from 1-hour and 24-hour cultures for both control and chloral hydrate (CH)-treated samples (n = 3 biological replicates). The triple immunolabelling with γH2AX, SYCP3, and H1T allows accurate staging of zygotene (H1T⁻), early pachytene (H1T⁻), and late pachytene (H1T⁺) spermatocytes.

The revised Figure 7 now provides a more complete and statistically supported analysis of DNA damage dynamics, confirming that CH-induced deciliation leads to persistent γH2AX signal at 24 hours, indicative of delayed or defective DNA repair progression. We have also toned down our interpretation in the Discussion, acknowledging that CH could affect other cellular pathways.

As mentioned before, the conditional genetic model that we are currently generating will allow us to evaluate the role of cilia in meiotic DNA repair in a more direct and specific way.

Assuming the conclusions of the pharmacological studies hold true with the proper controls, the authors still conflate their findings with meiotic defects. Meiosis is not directly assayed, which makes this conclusion an overstatement of the data. The conclusions need to be rephrased to accurately reflect the data.

Response:

We agree that this aspect required clarification. As noted above, we have refined both the Results and Discussion sections to make clear that our assays specifically targeted meiotic spermatocytes.

We now present data for meiotic stages at zygotene, early pachytene and late pachytene. This is demonstrated with the labelling for SYCP3 and H1T, both specific marker for meiosis that are not detectable in non meiotic cells. We believe that this is indeed a way to assay the meiotic cells, however, we have specified now in the text that we are analysing potential defects in meiosis progression. We are sorry if this was not properly explained in the original manuscript: it is now rephrased in the new version both in the results and discussion section.

It is not clear why the authors chose not to use widely accepted assays of Hedgehog signaling. Traditionally, pathway activation is measured by transcriptional output, not GLI protein expression because transcription factor expression does not necessarily reflect transcription levels of target genes.

Response:

We agree with the reviewer that measuring mRNA levels of Hedgehog pathway target genes, typically GLI1 and PTCH1, is the most common method for measuring pathway activation, and is widely accepted by researchers in the field. However, the methods we use in this manuscript (GLI1 and GLI3 immunoblots) are also quite common and widely accepted:

Regarding GLI1 immunoblot, many articles have used this method to monitor Hedgehog signaling, since GLI1 protein levels have repeatedly been shown to also go up upon pathway activation, and down upon pathway inhibition, mirroring the behavior of GLI1 mRNA. Here are a few publications that exemplify this point:

• Banday et al. 2025 Nat Commun. DOI: 10.1038/s41467-025-56632-0 (PMID: 39894896)
• Shi et al 2022 JCI Insight DOI: 10.1172/jci.insight.149626 (PMID: 35041619)
• Deng et al. 2019 eLife, DOI: 10.7554/eLife.50208 (PMID: 31482846)
• Zhu et al. 2019 Nat Commun, DOI: 10.1038/s41467-019-10739-3 (PMID: 31253779)
• Caparros-Martin et al 2013 Hum Mol Genet, DOI: 10.1093/hmg/dds409 (PMID: 23026747) *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.

As for GLI3 immunoblot, Hedgehog pathway activation is well known to inhibit GLI3 proteolytic processing from its full length form (GLI3-FL) to its transcriptional repressor (GLI3-R), and such processing is also commonly used to monitor Hedgehog signal transduction, of which the following are but a few examples:

• Pedraza et al 2025 eLife, DOI: 10.7554/eLife.100328 (PMID: 40956303)
• Somatilaka et al 2020 Dev Cell, DOI: 10.1016/j.devcel.2020.06.034 (PMID: 32702291)
• Infante et al 2018, Nat Commun, DOI: 10.1038/s41467-018-03339-0 (PMID: 29515120)
• Wang et al 2017 Dev Biol DOI: 10.1016/j.ydbio.2017.08.003 (PMID: 28800946)
• Singh et al 2015 J Biol Chem DOI: 10.1074/jbc.M115.665810 (PMID: 26451044)
• *note: due to manuscript-length limitations, not all cited references can be included in the text; they are listed here to substantiate our response.*

In summary, we think that we have used two well established markers to look at Hedgehog signaling (three, if we include the immunofluorescence analysis of SMO, which we could not detect in meiotic cilia).

These Hh pathway analyses did not provide any convincing evidence that the prepubertal cilia we describe here are actively involved in this pathway, even though Hh signaling is cilia-dependent and is known to be active in the male germline (Sahin et al 2014 Andrology PMID: 24574096; Mäkelä et al 2011 Reproduction PMID: 21893610; Bitgood et al 1996 Curr Biol. PMID: 8805249).

That said, we fully agree that our current analyses do not allow us to draw definitive conclusions regarding Hedgehog pathway activity in meiotic cilia, and we now state this explicitly in the revised Discussion.

Also in the Hedgehog pathway experiment, it is confusing that the authors report no detection of SMO yet detect little to no expression of GLIR in their western blot. Undetectable SMO indicates Hedgehog signaling is inactive, which results in high levels of GLIR. The impact of this is that it is not clear what is going on with Hh signaling in this system.

Response:

It is true that, when Hh signaling is inactive (and hence SMO not ciliary), the GLI3FL/GLI3R ratio tends to be low.

Although our data in prepuberal mouse testes show a strong reduction in total GLI3 protein levels (GLI3FL+GLI3R) as these mice grow older, this downregulation of total GLI3 occurs without any major changes in the GLI3FL/GLI3R ratio, which is only modestly affected (suppl. Figure 6).

Hence, since it is the ratio that correlates with Hh signaling rather than total levels, we do not think that the GLI3R reduction we see is incompatible with our non-detection of SMO in cilia: it seems more likely that overall GLI3 expression is being downregulated in developing testes via a Hh-independent mechanism.

Also potentially relevant here is the fact that some cell types depend more on GLI2 than on GLI3 for Hh signaling. For instance, in mouse embryos, Hh-mediated neural tube patterning relies more heavily on GLI2 processing into a transcriptional activator than on the inhibition of GLI3 processing into a repressor. In contrast, the opposite is true during Hh-mediated limb bud patterning (Nieuwenhuis and Hui 2005 Clin Genet. PMID: 15691355). We have not looked at GLI2, but it is conceivable that it could play a bigger role than GLI3 in our model.

Moreover, several forms of GLI-independent non-canonical Hh signaling have been described, and they could potentially play a role in our model, too (Robbins et al 2012 Sci Signal. PMID: 23074268).

We have revised the discussion to clarify some of these points.

All in all, we agree that our findings regarding Hh signaling are not conclusive, but we still think they add important pieces to the puzzle that will help guide future studies.

There are multiple instances where it is not clear whether the authors performed statistical analysis on their data, specifically when comparing the percent composition of a population. The authors need to include appropriate statistical tests to make claims regarding this data. While the authors state some impressive sample sizes, once evaluated in individual categories (eg specific cell type and age) the sample sizes of evaluated cilia are as low as 15, which is likely underpowered. The authors need to state the n for each analysis in the figures or legends.

We thank the reviewer for highlighting this important issue. We have now included the sample size (n) for every analysis directly in the figure legends. Although this adds length, it improves transparency and reproducibility.

Regarding the doubts of Ref#3 about the different sample sizes, the number of spermatocytes quantified in each stage is in agreement with their distribution in meiosis (example, pachytene lasts for 10 days this stage is widely represented in the preparations, while its is much difficult to quantify metaphases I that are less present because the stage itself lasts for less than 24hours). Taking this into account, we ensured that all analyses remain statistically valid and representative, applying the appropriate statistical tests for each dataset. These details are now clearly indicated in the revised figures and legends.

Minor concerns:

1. The phrase "lactating male" is used throughout the paper and is not correct. We assume this term to mean male pups that have yet to be weaned from their lactating mother, but "lactating male" suggests a rare disorder requiring medical intervention. Perhaps "pre-weaning males" is what the authors meant.

Response:

We thank the reviewer for noticing this terminology error. The expression has been corrected to "pre-weaning males" throughout the manuscript.

The convention used to label the figures in this paper is confusing and difficult to read as there are multiple panels with the same letter in the same figure (albeit distinct sections). Labeling panels in the standard A-Z format is preferred. "Panel Z" is easier to identify than "panel III-E".

Response:

We thank the reviewer for this suggestion. All figures have been relabelled using the standard A-Z panel format, ensuring consistency and easier readability across the manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In "The dynamics of ciliogenesis in prepubertal mouse meiosis reveals new clues about testicular development" Pérez-Moreno, et al. explore primary cilia in prepubertal mouse spermatocytes. Using a combination of microscopy, proteomics, and pharmacological perturbations, the authors carefully characterize prepubertal spermatocyte cilia, providing foundational work regarding meiotic cilia in the developing mammalian testis.

Major concerns:

1. The authors provide evidence consistent with cilia not being present in a larger percentage of spermatocytes or in other cells in the testis. The combination of electron microscopy and acetylated tubulin antibody staining establishes the presence of cilia; however, proving a negative is challenging. While acetylated tubulin is certainly a common marker of cilia, it is not in some cilia such as those in neurons. The authors should use at least one additional cilia marker to better support their claim of cilia being absent.

2. The conclusion that IFT88 localizes to centrosomes is premature as key controls for the IFT88 antibody staining are lacking. Centrosomes are notoriously "sticky", often sowing non-specific antibody staining. The authors must include controls to demonstrate the specificity of the staining they observe such as staining in a genetic mutant or an antigen competition assay.

3. There are many inconsistent statements throughout the paper regarding the timing of the first wave of spermatogenesis. For example, the authors state that round spermatids can be detected at 21dpp on line 161, but on line 180, say round spermatids can be detected a 19dpp. Not only does this lead to confusion, but such discrepancies undermine the validity of the rest of the paper. A summary graphic displaying key events and their timing in the first wave of spermatogenesis would be instrumental for reader comprehension and could be used by the authors to ensure consistent claims throughout the paper.

4. In the proteomics experiments, it is unclear why the authors assume that changes in protein expression are predominantly due to changes within the germ cells in the developing testis. The analysis is on whole testes including both the somatic and germ cells, which makes it possible that protein expression changes in somatic cells drive the results. The authors need to justify why and how the conclusions drawn from this analysis warrant such an assumption.

5. The authors should provide details on how proteins were categorized as being involved in ciliogenesis or flagellogenesis, specifically in the distinction criteria. It is not clear how the categorizations were determined or whether they are valid. Thus, no one can repeat this analysis or perform this analysis on other datasets they might want to compare.

6. In the pharmacological studies, the authors conclude that the phenotypes they observe (DNA damage and reduced pachytene spermatocytes) are due to loss of or persistence of cilia. This overinterprets the experiment. Chloral hydrate and MLN8237 certainly impact ciliation as claimed, but have additional cellular effects. Thus, it is possible that the observed phenotypes were not a direct result of cilia manipulation. Either additional controls must address this or the conclusions need to be more specific and toned down.

7. Assuming the conclusions of the pharmacological studies hold true with the proper controls, the authors still conflate their findings with meiotic defects. Meiosis is not directly assayed, which makes this conclusion an overstatement of the data. The conclusions need to be rephrased to accurately reflect the data.

8. It is not clear why the authors chose not to use widely accepted assays of Hedgehog signaling. Traditionally, pathway activation is measured by transcriptional output, not GLI protein expression because transcription factor expression does not necessarily reflect transcription levels of target genes.

9. Also in the Hedgehog pathway experiment, it is confusing that the authors report no detection of SMO yet detect little to no expression of GLIR in their western blot. Undetectable SMO indicates Hedgehog signaling is inactive, which results in high levels of GLIR. The impact of this is that it is not clear what is going on with Hh signaling in this system.

10. There are multiple instances where it is not clear whether the authors performed statistical analysis on their data, specifically when comparing the percent composition of a population. The authors need to include appropriate statistical tests to make claims regarding this data. While the authors state some impressive sample sizes, once evaluated in individual categories (eg specific cell type and age) the sample sizes of evaluated cilia are as low as 15, which is likely underpowered. The authors need to state the n for each analysis in the figures or legends.

Minor concerns:

1. The phrase "lactating male" is used throughout the paper and is not correct. We assume this term to mean male pups that have yet to be weaned from their lactating mother, but "lactating male" suggests a rare disorder requiring medical intervention. Perhaps "pre-weaning males" is what the authors meant.

2. The convention used to label the figures in this paper is confusing and difficult to read as there are multiple panels with the same letter in the same figure (albeit distinct sections). Labeling panels in the standard A-Z format is preferred. "Panel Z" is easier to identify than "panel III-E".

Significance

Overall, this is a well-done body of work that deserves recognition for the novel and implicative discoveries it presents. Assuming the conclusions hold true following appropriate statistical analysis and rephrasing, this paper would report the first documented evidence of meiotic cilia in the developing mammalian testis with sufficient rigor to become the foundational work on this topic.

This paper will be of interest to communities focused on germ cell development, cilia, and Hedgehog signaling. It may prompt a new perspective on Desert Hedgehog signaling as it pertains to spermatogenesis. Further, this work will be of interest to those studying male fertility, as it highlights the potential role of cilia in spermatogenesis.

Further, the proteomic analysis presented has the potential to invoke hypotheses and experimentation investigating the role of several proteins with previously uncharacterized roles in ciliogenesis, flagellogenesis, and/or spermatogenesis. The finding that the onset of ciliogenesis and flagellogenesis appear to be temporally linked has the potential to prompt research regarding shared molecular mechanisms dictating axonemal formation. We believe this paper has the potential to have an impact in its respective field, underscored by the exquisite microscopy and detailed characterization of meiotic cilia.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript by Perez-Moreno et al., titled "The dynamics of ciliogenesis in prepubertal mouse meiosis reveal new clues about testicular maturation during puberty", the authors characterize the development of primary cilia during meiosis in juvenile male mice. The authors catalog a variety of testicular changes that occur as juvenile mice age, such as changes in testis weight and germ cell-type composition. They next show that meiotic prophase cells initially lack cilia, and ciliated meiotic prophase cells are detected after 20 days postpartum, coinciding with the time when post-meiotic spermatids within the developing testes acquire flagella. They describe that germ cells in juvenile mice harbor cilia at all substages of meiotic prophase, in contrast to adults where only zygotene stage meiotic cells harbor cilia. The authors also document that cilia in juvenile mice are longer than those in adults. They characterize cilia composition and structure by immunofluorescence and EM, highlighting that cilia polymerization may initially begin inside the cell, followed by extension beyond the cell membrane. Additionally, they demonstrate ciliated cells can be detected in adult human testes. The authors next perform proteomic analyses of whole testes from juvenile mice at multiple ages, which may not provide direct information about the extremely small numbers of ciliated meiotic cells in the testis, and is lacking follow up experiments, but does serve as a valuable resource for the community. Finally, the authors use a seminiferous tubule culturing system to show that chemical inhibition of Aurora kinase A likely inhibits cilia depolymerization upon meiotic prophase I exit and leads to an accumulation of metaphase-like cells harboring cilia. They also assess meiotic recombination progression using their culturing system, but this is less convincing.

Few suggestions/comments are listed below:

Major comments

1. There are a few issues with the experimental set up for assessing the effects of cilia depolymerization on DNA repair (Figure 7-II). First, how were mid pachytene cells identified and differentiated from early pachytene cells (which would have higher levels of gH2AX) in this experiment? I suggest either using H1t staining (to differentiate early/mid vs late pachytene) or the extent of sex chromosome synapsis. This would ensure that the authors are comparing similarly staged cells in control and treated samples. Second, what were the gH2AX levels at the starting point of this experiment? A more convincing set up would be if the authors measure gH2AX immediately after culturing in early and late cells (early would have higher gH2AX, late would have lower gH2AX), and then again after 24hrs in late cells (upon repair disruption the sampled late cells would have high gH2AX). This would allow them to compare the decline in gH2AX (i.e., repair progression) in control vs treated samples. Also, it would be informative to know the starting gH2AX levels in ciliated vs non-ciliated cells as they may vary.

2. The authors analyze meiotic progression in cells cultured with/without AURKA inhibition in Figure 8-III and conclude that the distribution of prophase I cells does not change upon treatment. Is Figure 8-III A and B the same data? The legend text is incorrect, so it's hard to follow. Figure 8-III A shows a depletion of EdU-labelled pachytene cells upon treatment. Moreover, the conclusion that a higher proportion of ciliated zygotene cells upon treatment (Figure 8-II C) suggests that AURKA inhibition delays cilia depolymerization (page 13 line 444) does not make sense to me.

3. How do the authors know that there is a monopolar spindle in Figure 8-IV treated samples? Perhaps the authors can use a different Tubulin antibody (that does not detect only acetylated Tubulin) to show that there is a monopolar spindle.

4. The authors state in the abstract that they provide evidence suggesting that centrosome migration and cilia depolymerization are mutually exclusive events during meiosis. This is not convincing with the data present in the current manuscript. I suggest amending this statement in the abstract.

Minor comments

1. The presence of cilia in all stages of meiotic prophase I in juvenile mice is intriguing. Why is the cellular distribution and length of cilia different in prepubertal mice compared to adults (where shorter cilia are present only in zygotene cells)? What is the relevance of these developmental differences? Do cilia serve prophase I functions in juvenile mice (in leptotene, pachytene etc.) that are perhaps absent in adults?

Related to the above point, what is the relevance of the absence of cilia during the first meiotic wave? If cilia serve a critical function during prophase I (for instance, facilitating DSB repair), does the lack of cilia during the first wave imply differing cilia (and repair) requirements during the first vs latter spermatogenesis waves?

In my opinion, these would be interesting points to discuss in the discussion section.

1. The authors state on page 9 lines 286-288 that the presence of cytoplasmic continuity via intercellular bridges (between developmentally synchronous spermatocytes) hints towards a mechanism that links cilia and flagella formation. Please clarify this statement. While the correlation between the timing of appearance of cilia and flagella in cells that are located within the same segment of the seminiferous tubule may be hinting towards some shared regulation, how would cytoplasmic continuity participate in this regulation? Especially since the cytoplasmic continuity is not between the developmentally distinct cells acquiring the cilia and flagella?

2. Individual germ cells in H&E-stained testis sections in Figure 1-II are difficult to see. I suggest adding zoomed-in images where spermatocytes/round spermatids/elongated spermatids are clearly distinguishable.

3. In Figure 2-II B, the authors document that most ciliated spermatocytes in juvenile mice are pachytene. Is this because most meiotic cells are pachytene? Please clarify. If the data are available (perhaps could be adapted from Figure 1-III), it would be informative to see a graph representing what proportions of each meiotic prophase substages have cilia.

4. I suggest annotating the EM images in Sup Figure 2 and 3 to make it easier to interpret.

5. The authors claim that the ratio between GLI3-FL and GLI3-R is stable across their analyzed developmental window in whole testis immunoblots shown in Sup Figure 5. Quantifying the bands and normalizing to the loading control would help strengthen this claim as it hard to interpret the immunoblot in its current form.

6. There are a few typos throughout the manuscript. Some examples: page 5 line 172, Figure 3-I legend text, Sup Figure 5-II callouts, Figure 8-III legend, page 15 line 508, page 17 line 580, page 18 line 611.

Significance

This work provides new information about an important but poorly understood cellular structure present in meiotic cells, the primary cilium. More generally, this work expands on our understanding of testis development in juvenile mice. The microscopy images presented here are beautiful. The work is mostly descriptive but lays the groundwork for future investigations. I believe that this study would of interest to the germ cell, meiosis, and spermatogenesis communities, and with a few modifications, is suitable for publication.

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Reply to the reviewers

We would like to thank the three reviewers for their careful reading of our manuscript and suggested modifications. We have incorporated their suggestions as described below; these changes have significantly improved the structure and focus of the manuscript.

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

The possibility of observing 3D cellular organisation in tissues at nanometre resolution is a hope for many cell biologists. Here, the authors have combined two volume electron microscopy approaches with scanning electron microscopy: Focused Ion Beam (FIB-SEM) and Array Tomography (AT-SEM) to study the evolution of the shape and organisation of cytoplasmic bridges, the 'ring canals' (RCs) in the Drosophila ovarian follicle that connect nurse cells and oocyte. This type of cytoplasmic link, found in insects and humans, is essential for oocyte development.

RCs have mainly been studied using light microscopy with various markers that constitute them, but this approach does not fully capture an overall view of their organization. Due to their three-dimensional arrangement within the ovarian follicle, characterizing their organization using transmission electron microscopy (TEM) has been very limited until now. This v-EM study allows the authors to document the evolution of RC size and thickness during the development of germline cysts, from the germarium to stage 4, and potentially beyond. This study confirmed previous findings, namely that RC size correlates with lineage: the largest RC is formed after the first division, while the smallest is formed during the last division.

Furthermore, this work allowed a better characterisation of the membrane interdigitation surrounding the RCs. In addition, the authors highlight the important potential of v-EM for further structural analysis of the fusome, migrating border cells and the stem cell niche.

Majors comment

The output of this work can be divided into two parts. First, this work presents a technical challenge, involving image acquisition by volume electron microscopy and manual 3D reconstruction of the contours of the membranes, nuclei, RCs, and fusome in different cysts at different stages.

Secondly, this work is based on a structural study of the RCs and their associated membranes. This work is descriptive but important, although the results largely confirm previous findings, both for the structure of the RCs and their relationship to the division sequence of the cyst cells, and for the organisation of the membranes around the RCs.

Very interestingly, the authors report the spatial characterisation of membrane structures associated with and close to CRs that have already been identified (Loyer et al.). However, their characterisation is somewhat incomplete, as it lacks quantified data - how many CRs were analysed? and, above all, the characteristics of these membranes, their length and orientation according to their position and their connection in the lineage - these data could be obtained from the VEM data already collected and would be an important addition to the RC structural analysis in this work.

*Following the suggestions of this reviewer, we have reduced the emphasis on the technical approach to better highlight the ring canal data. We have summarized the ring canal measurements in graphs presented in Fig. 4B, C and included the sample sizes for these measurements in the figure legend. *

• To gain further insight into the membrane interdigitations, we have developed a detailed model of the oocyte and four ring canals that connect to the posterior nurse cells of the stage 4 egg chamber (Fig. 5). From this model, we see that the interdigitations are longer and more abundant that in the germarium (Fig. S5), but not as extensive as in the stage 8 egg chamber (Fig. 6). The interdigitations were not all oriented in the same direction, and we did not observe an obvious correlation between interdigitation number, orientation, and lineage. We plan to continue to explore these structures in future studies. *

In line with this, the authors importantly report the presence of an ER-like membrane structure lining the RCs. First, it would be nice to have statistics to support the observation of how many RCs..? Secondly, does this ER membrane structure vary according to the position of the RC in the cyst, are they related to the RC lineage?

*We appreciate the reviewer's interest in this novel ER-like structure lining the ring canals. We have generated a detailed model of these structures within the stage 4 egg chamber (Fig. 5D,E). However, because we do not have data from a large number of egg chambers, we believe that performing statistics would not be appropriate. *

The addition of graphs showing the quantitative data with statistics in the figures would improve understanding of the results. This is particularly the case for the characterisation of RCs according to the stage of cyst development, as shown in Figure 3. This also applies to the characterisation of RCs within a cyst and the relationship between RC size and lineage, as shown in Figure 4, and to the characterisation (thickness) of the inner part of the RC.

*We have included graphs of ring canal diameter based on stage (Fig. 4B) or lineage (Fig. 4C); however, because we only have data from a few germline cysts, we have not performed any statistical analysis. *

The part on the structural analysis of the fusome is interesting but still secondary to the characterisation of the RCs. This part should be moved to the results and figures after the various parts concerning the RCs.

        *We have deemphasized the fusome structural analysis in the results section; however, we chose to leave these images in the figures, since there could be a connection between the novel ER-like structures and the fusome.  *

Minor comments The distribution of the fusome in Figure 2 is difficult to see with Hts labelling and does not really correspond to the schematic, especially in regions 2a and 2B.

*We have modified the images and the schematic. *

In panel C of Figure 2, it is a little disturbing that the legend is directly on the image of RC. It hides some information about the images and could be placed at the bottom of the panel. This also the case for the panel G.

We understand the possible confusion and have changed the layout in the figure.

With figure 3B, it would be good to highlight the position of cyst.

We have pseudocolored the portion that corresponds to the relevant cyst in the same color used for the reconstruction (which is now Fig. 3A).

Reviewer #1 (Significance (Required)): As mentioned above, this work can be divided into two parts. The part corresponding to the acquisition of images by volume electron microscopy and manual 3D reconstruction is new and a great source of valuable information. The part related to the spatial characterisation of the RC is important, but corresponds more to an extension and reinforcement of previously available information than to the contribution of significant new insights. I think it will be of great interest to an audience interested in Drosophila oogenesis.

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

This study presents a high-resolution volumetric analysis of germline ring canals (RCs) during Drosophila oogenesis. By combining two complementary electron microscopy techniques-Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and Array Tomography Scanning Electron Microscopy (AT-SEM)-the authors compare RC structural features at different developmental stages, ranging from the relatively small germarium to the significantly larger, later-stage egg chambers.

At early stages of oogenesis, FIB-SEM analysis confirms that the average RC size increases progressively with cyst development, in agreement with previous studies. The authors further show that lineage reliably predicts RC size (an observation previously reported, but here identified at an earlier stage in region 2a) and, importantly, that the thickness of the actin rim can also be predicted by lineage (reported here for the first time, at stage 1). FIB-SEM analysis also enables a clear delineation of the fusome, allowing for detailed characterization of its assembly and disassembly. Notably, the authors report, for the first time, structural evidence of ER-like membranes capping the inner rim of actin RCs.

At later developmental stages, AT-SEM analysis reveals that the microvilli observed by FIB-SEM evolve into extensive interdigitations extending beyond the outer rim in mid-stage egg chambers, a structural feature detected earlier than previously reported. Moreover, by analyzing a sample in which tissue organization was disrupted during preparation, the authors demonstrate that these interdigitations preferentially occur in proximity to the RC. In addition to RC analysis at later stages, the authors use AT-SEM to readily identify small cell populations, such as the germline stem cell niche and border cells, and provide high-resolution volumetric EM data for these structures.

MAJOR COMMENT My main comment is that we don't learn much new about the biology of these ring canals. The results primarily confirm findings from previous studies using conventional electron microscopy.

Although TEM data has been used to perform foundational studies in the field, there are limitations to this approach. Due to the size of the ring canals, it is challenging to locate them within the large volume of the egg chamber (especially at later stages). Even if ring canals can be located, they are typically not oriented the same way, so a single section is not sufficient. *Although some of the results shown by our complementary vEM approaches do confirm results that have been previously reported by TEM or fluorescence microscopy, our approach provides important additional insight into structures that have been studied for many decades that would not be possible using other approaches. Further, this approach has identified a novel membrane structure lining the ring canals, and it has provided structural details of the membrane interdigitations that would not be possible with conventional electron microscopy. Further, this complementary set of vEM approaches would be applicable to the study of many other structures within other tissue types. *

• *

One particularly interesting biological question, which is briefly mentioned in the text, is whether the oocyte is the cell that inherits the majority of the fusome. Since the authors are able to reconstruct the fusome using their data, they could measure the fusome volume in each cell (especially in the two pro-oocytes) and investigate whether the cell with the larger fusome ultimately becomes the oocyte. This question has been discussed for some time, and recent studies have proposed opposing models based on fusome volume to explain how the oocyte is selected among the 16 sister cells (Nashchekin et al., Science, 2021; Barr et al., Genetics, 2024).

        *We appreciate the reviewer's interest in the fusome, and we agree that our approach has provided significant insight into its three dimensional structure. The rendering of the fusome was performed using a large number of small isosurface volumes, and it is therefore difficult to accurately determine the fusome volume, since additional (non-fusome) material could be included in the model. Further, the fusomes that were rendered were within the germline clusters from region 2b, where the fusome has already started to break down, so these would not provide an accurate quantification of the full fusome volume. Because the focus of the manuscript is on the germline ring canals and associated structures such as the interdigitations (which we have tried to further streamline in this revised version), we believe that additional analysis of the fusome is outside of the scope of this work. *

MINOR COMMENT • The fluorescent markers used in the fly stocks are neither described in the Materials and Methods section nor depicted in the figures.

*We apologize if this was not clear in the original manuscript. Based on the comment from Reviewer #3 (see below), we have repeated the Hts staining using flies that do not have CheerioYFP in the background. We have also clarified the materials and methods section to indicate the panels that correspond with each strain used. *

• The authors should quote (Nashchekin et al., Science, 2021) when mentioning unequal partionning of the fusome (p4) and oocyte determination (p12). *We have added the reference to these parts of the manuscript. *

• P11-12, when mentioning electron dense regions reflecting strong cell-cell adhesion, the authors could refer to (Fichelson et al. Development, 2010), where AJ have been described around ring canals. *We have added the reference to this part of the manuscript. *

• Figure 2A: The schematic diagram (4th line) is not explained in the figure legend. *We have updated the figure legend to describe this schematic. *

• Figure 2D: Please clarify whether the RC stage shown corresponds to stage 1 or stage 10, as indicated in panel 2E. Alternatively, are these examples representing the minimum and maximum RC sizes observed across the entire dataset?. *These were not meant to be examples of the minimum and maximum ring canal sizes observed across the dataset. Instead, they were used to demonstrate the significant expansion that occurs during oogenesis. In the updated version of this figure, this panel has been removed. *

• Figure 5D: Please specify which panel in 5B this corresponds to. • Figure 5E: Please specify which panels in 5B this corresponds to. The two green boxes are not defined. Why is there a grey background under the ovariole assembly? • Figures 5G, 5H: Does panel 5G correspond to the left green box in 5E, and 5H to the right green box in 5E? Please clarify. *We have modified Figure 5 and merged it with the figure 6. In this updated format, panels 5B and 5E have been removed. *

• Figure 6: The figure title is not on the same page as the figure itself.

• We have made this change. *

• Figure 6A: The black box marking the germarium is not defined. *In this revised version, we have modified Fig. 6, and this panel has been removed. *

• Figure 6B-E: The arrows point to long interdigitations. However, arrowheads (which are not mentioned in the legend) appear to indicate the RC outer rim. Please specify this clearly in the figure legend. In the updated version of Fig. 6, these arrowheads have been removed.

Reviewer #2 (Significance (Required)):

I am not an expert in electron microscopy, so I cannot comment in detail on these techniques, but they appear to bridge the gap between conventional EM and optical microscopy in terms of resolution, user-friendliness, and other aspects. This is technically interesting, although these EM approaches have been previously described and applied. The images and movies are beautiful and clearly presented. My main comment is that we don't learn much new about the biology of these ring canals. The results primarily confirm findings from previous studies using conventional electron microscopy.

One particularly interesting biological question, which is briefly mentioned in the text, is whether the oocyte is the cell that inherits the majority of the fusome. Since the authors are able to reconstruct the fusome using their data, they could measure the fusome volume in each cell (especially in the two pro-oocytes) and investigate whether the cell with the larger fusome ultimately becomes the oocyte. This question has been discussed for some time, and recent studies have proposed opposing models based on fusome volume to explain how the oocyte is selected among the 16 sister cells (Nashchekin et al., Science, 2021; Barr et al., Genetics, 2024).

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

Kolotuev et al. used two volume-based electron microscopy based approaches to identify, segment, and document the changes in intercellular bridges, or ring canals, in early egg chambers of the fruit fly, Drosophila melanogaster. Using array tomography and focused ion beam scanning electron microscopy, Kolotuev et al., provide a high resolution and content rich lineage analysis of ring canal size, shape and orientation among early and late egg chambers. Their analysis included parameters such as the presence and shape of the fusome, the recruitment of actin to the inner ring, and development of membrane fingers that presumably spatially stabilize such structures. Last, Kolotuev and co-authors highlight additional aspects of their dataset including a reconstruction of the border cell cluster in stage 9 egg chambers. The data presented are a treasure trove of the ultrastructural features of the developing dipteran germline and subsequent ovarian follicle development. The data presented represent the highest resolution 3D dataset available and thus are a valuable worthwhile contribution to the field. My overall impression is that this paper sits intellectually between a valuable method and a loose experimental manuscript. This critique is not requesting additional experimental evidence because the data are unique and are the foundation for a new experimental paradigm. But there is not sufficient detail presented to be a full method, nor any hypothesis testing to be considered experimental. I suggest the authors consider amplifying their methods in detail and then note that using these methods provide a foundation for additional future investigations (as mentioned in the discussion). Problems with data interpretation and presentation should be addressed before publication. Below are the major and minor concerns that I believe need to be considered.

Major comments: In general images in figures are thought provoking, however changes to figure layout and design should be considered to better highlight the results. For instance, I don't know how to follow figure 1a. The arrow leads from a whole ovary to an ovulated egg with an ovariole strand connecting the two. What is the purpose of the arrow? Is it to represent time? And why is the mature egg in the figure when no data regarding this stage is presented. The authors should consider removing the mature egg and helping the reader understand that the ovariole is a subset of the whole ovary. They might do this by putting a box around a single ovarile in the whole ovary to indicate their ovariole illustration. Several other figures have similar problems. Throughout the authors used black and white arrows on black and white EM data and these arrows were lost. Color should be considered to effectively point out what they want the reader to see.

We have modified the layout of Fig. 1 and added additional explanation to the introduction and figure legend to guide readers through the introduction to the system. We have also added color to some of the arrows throughout the manuscript.

Can the authors provide additional information for the genotypes used? For instance the Cherrio-YFP (which might affect actin). When what this used and can the authors provide information on how this affected the data between when it was used and when it was not used. Additionally, why was analysis done in transgenic flies over fully wild-type?

*We have repeated the Hts staining in Fig. 2A in flies that do not express Cheerio-YFP and have made the appropriate changes to the methods section. For the AT-SEM experiment, we chose to use this genetic background since it would align with that of the negative controls that we often use in RNAi or over-expression experiments. FIB-SEM datasets were collected while imaging other tissues of the fly, so the choice of that genotype was not intentional. However, these datasets provided us with the opportunity to do this proof-of-concept work without such a large financial investment in the acquisition of new image stacks. In the future, we hope to expand this work to generate additional datasets from flies of different genotypes. *

Figure 1 seeks to lay out the ovary system and narrow the reader into the stages that will be analyzed in subsequent figures. Figure 1B is meant to show the types and kinds of electron microscopy, however lacks a full detailed description and legend for each of the colored arrows. And to that fact, so does figure S1. The authors need to provide additional information so the reader can glean what the authors point they are trying to convey. In addition, the authors might add pros and cons to each. I know this was attempted in S1, but did not fully come across.

We appreciate this feedback, and we have modified the layout of Figure 1 and updated Figures S1 to better highlight the technical challenge of EM in general and benefits of vEM in particular.

Figure 1 and 2 seek to set up both the biological and technical system to be understood. The authors might consider combining the two figures and eliminate elements that don't represent a result of any kind (Figure 1B, 2B, 3D and 3F). Or more fully explain the result and point they are trying to make with these illustrations. I fully understand and appreciate what they are trying to get across, but it does not come across clearly. For example, I don't know how figure 2B effectively gets across the point that rotation of the image has an effect on how it is sliced and segmented in EM data. Not sure it is necessary. Furthermore, what is the bottom panel with a green ring canal supposed to allow us to interpret or conclude? The same for 3D and F. The result in 3E is far more interesting and should be two panels that emphasize the growth characteristics between young and old rings or those of M1 and M4.

        *We greatly appreciate these suggestions, and we have modified and reorganized several figures to make the flow of scientific ideas easier to follow.* *We have moved panel 1B to the supplementary figure and gave additional indications in the text as to the differences between the EM methods. We have moved panel 2B to the supplementary material. We have moved Fig. 3D to Fig. S5A,B. Fig. 5 now provides more extensive rendering of membrane interdigitations from the stage 4 egg chamber. We have chosen to leave Fig. 3F to allow readers to compare the novel ER-like structures within the ring canals to the fusome that is present within younger germline clusters. *

The HTS and actin stain in figure 2A overlap significantly and obscure the fusome staining. Can the authors confirm that there is no bleed through in their staining and imaging procedure?

*We have repeated this staining and can confirm that there was no bleed through between the two channels. *

The data in Figure 2C are critical to showing the z-resolution enhancement of sectioned EM. However, the use of green psuedocolor only in one panel is confusing. Can the authors duplicate the whole panel and provide one without and one with psuedocolor? This would be ideal for fully orienting the reader to the sectioning and setting them up to understand the rest of the figures.

*In the revised version of Figure 2, we have split the sections into two rows of panels; we have added the pseudocolor to every other section (in the bottom row of panels). *

• *

The results section for figure 2 does outline the results presented. For example, the germarium contains syncytia of differing stages and ring canals with intervening fusomes... It does more to talk about the pros and cons of different technical aspects and their difficulty This should be saved for the rationale or the discussion. Rather the section should outline the results presented.

*We have modified the layout of figure 2 in order to describe the system in a more straightforward manner with a smoother transition from Figure 1 while further explaining technical points. *

I appreciate the color coding of the differentially segment cysts in Figure 3. The color coding helped orient me to which cysts were being evaluated. However I found the lack of detail bothersome. For instance, which ring canals are in the two panels of D? Are they M1 or M4?

*With the additional analysis of the interdigitations in the stage 4 cluster, we have moved panel D to Fig. S5. We did not have enough coverage of the region 2a cluster (red) to determine lineage, but we have added a statement to the legend to indicate that the ring canal shown in Fig. S5B is an M1 ring canal. *

Also, the presentation of ring canal size and distribution should be presented in a graph. Statistics are not necessary, but a dot-plot would go a long way to presenting the result. Two plots can add value, one in which the ring canals for each phase is shown, and the other is the distribution of sizes for each cyst.

*We have added these graphs in Fig. 4B, C. *

Lastly, the results section for figure 3 interprets the membrane bound vesicles in the ring canal as "ER-like". This should be removed since they neither look ER-like to me, nor have been shown to be ER in the data.

*We appreciate this suggestion, and although we cannot be absolutely certain of the identity of these structures without further study, with our additional analysis of the stage 4 egg chamber, we are further convinced of the similar appearance of these novel structures and the ER in other regions of the nurse cell (Fig. 5). We have clarified this point in the text. *

Figure 4A is not called out specifically in the results and thus should be interpreted or removed from the figure.

In this revised version, we have removed panel 4A.

Figure 5 was confusing. I understand the authors wanted to show the wafer and the ribbons, however, this is not a result and does not offer any interpretation of a result and is thus confusing on why it is in the figure. If this were a method paper, I would understand its presence.

*We have removed this panel from the figure. *

Can the authors comment on the shape of the nuclei in older egg chambers? They are not round at all. I am interested in whether this is a fixation artifact or the real ultrastructure of the nuclei. Of the border cell nuclei for instance. If it is an artifact, this should be added to the discussion.

*Some of the nuclei appear to have a peculiar shape in the cross-section. We cannot entirely exclude the role of the fixation in the shape irregularities. However, since not all the nuclei are subject to this phenomenon, we are inclined to attribute it to the intrinsic qualities of the late-stage nuclei. In numerous cases, different tissue and cell stages determine the shape of the nucleus, which frequently deviates from a spherical shape. *

Although data from "imperfect" samples is interesting, consider relegating Figure 6 to the supplement section, as it takes away from the pre-existing narrative flow established in the paper.

• In this draft, we have combined parts of figures 5 and 6, and much of the data from the imperfect sample has been removed. *

Interpretation of the data throughout the results should be left to the discussion section. For instance, interpretation of Figure 4 results on page 14 beginning with "these data demonstrate the importance...". The importance is not related to the result, but rather discussion of past and future studies.

We have removed this sentence from the results.

In another example, Figure 5I is introduced and discussed in the results section on page 15, second whole paragraph with an overall introduction/discussion on junctions, which convolutes the actual result. Discussion of future studies or how structures like the novel membrane fingers should be viewed in a larger biological context, should not be in the results.

We have made this change.

Minor comments: Remove words such as "pseudo-timelapse", they invoke precision on a point that is imprecise.

*This has been removed. *

Re-consider the acronyms for ring canal and egg chamber.

*We have removed these acronyms. *

Consider finding another way to call out each supplemental movie other than with another acronym.

*We have added small icons to indicate that a supplemental movie is associated with a given figure or panel. *

Reviewer #3 (Significance (Required)): The present manuscript is a technical advance in the field. The use of serial EM imaging with two separate modalities, on what is considered to be a challenging problem in the field, represents a useful technical advance. Light microscopy has thus far limited the resolution to which we can understand the spatial organization and the cellular features there in that regulate germline development. This manuscript brings to bear two serial EM methods to begin approaching this problem. The audience for this work are those working at the forefront of understanding germline architecture and development. I make these statements as an expert in live and super resolution of fruit fly egg chamber development, in addition to having performed 3D SEM in past works.

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Referee #3

Evidence, reproducibility and clarity

Kolotuev et al. used two volume-based electron microscopy based approaches to identify, segment, and document the changes in intercellular bridges, or ring canals, in early egg chambers of the fruit fly, Drosophila melanogaster. Using array tomography and focused ion beam scanning electron microscopy, Kolotuev et al., provide a high resolution and content rich lineage analysis of ring canal size, shape and orientation among early and late egg chambers. Their analysis included parameters such as the presence and shape of the fusome, the recruitment of actin to the inner ring, and development of membrane fingers that presumably spatially stabilize such structures. Last, Kolotuev and co-authors highlight additional aspects of their dataset including a reconstruction of the border cell cluster in stage 9 egg chambers. The data presented are a treasure trove of the ultrastructural features of the developing dipteran germline and subsequent ovarian follicle development. The data presented represent the highest resolution 3D dataset available and thus are a valuable worthwhile contribution to the field. My overall impression is that this paper sits intellectually between a valuable method and a loose experimental manuscript. This critique is not requesting additional experimental evidence because the data are unique and are the foundation for a new experimental paradigm. But there is not sufficient detail presented to be a full method, nor any hypothesis testing to be considered experimental. I suggest the authors consider amplifying their methods in detail and then note that using these methods provide a foundation for additional future investigations (as mentioned in the discussion). Problems with data interpretation and presentation should be addressed before publication. Below are the major and minor concerns that I believe need to be considered.

Major comments:

• In general images in figures are thought provoking, however changes to figure layout and design should be considered to better highlight the results. For instance, I don't know how to follow figure 1a. The arrow leads from a whole ovary to an ovulated egg with an ovariole strand connecting the two. What is the purpose of the arrow? Is it to represent time? And why is the mature egg in the figure when no data regarding this stage is presented. The authors should consider removing the mature egg and helping the reader understand that the ovariole is a subset of the whole ovary. They might do this by putting a box around a single ovarile in the whole ovary to indicate their ovariole illustration. Several other figures have similar problems. Throughout the authors used black and white arrows on black and white EM data and these arrows were lost. Color should be considered to effectively point out what they want the reader to see.

• Can the authors provide additional information for the genotypes used? For instance the Cherrio-YFP (which might affect actin). When what this used and can the authors provide information on how this affected the data between when it was used and when it was not used. Additionally, why was analysis done in transgenic flies over fully wild-type? Figure 1 seeks to lay out the ovary system and narrow the reader into the stages that will be analyzed in subsequent figures. Figure 1B is meant to show the types and kinds of electron microscopy, however lacks a full detailed description and legend for each of the colored arrows. And to that fact, so does figure S1. The authors need to provide additional information so the reader can glean what the authors point they are trying to convey. In addition, the authors might add pros and cons to each. I know this was attempted in S1, but did not fully come across. Figure 1 and 2 seek to set up both the biological and technical system to be understood. The authors might consider combining the two figures and eliminate elements that don't represent a result of any kind (Figure 1B, 2B, 3D and 3F). Or more fully explain the result and point they are trying to make with these illustrations. I fully understand and appreciate what they are trying to get across, but it does not come across clearly. For example, I don't know how figure 2B effectively gets across the point that rotation of the image has an effect on how it is sliced and segmented in EM data. Not sure it is necessary. Furthermore, what is the bottom panel with a green ring canal supposed to allow us to interpret or conclude? The same for 3D and F. The result in 3E is far more interesting and should be two panels that emphasize the growth characteristics between young and old rings or those of M1 and M4.

• The HTS and actin stain in figure 2A overlap significantly and obscure the fusome staining. Can the authors confirm that there is no bleed through in their staining and imaging procedure?

• The data in Figure 2C are critical to showing the z-resolution enhancement of sectioned EM. However, the use of green psuedocolor only in one panel is confusing. Can the authors duplicate the whole panel and provide one without and one with psuedocolor? This would be ideal for fully orienting the reader to the sectioning and setting them up to understand the rest of the figures.

• The results section for figure 2 does outline the results presented. For example, the germarium contains syncytia of differing stages and ring canals with intervening fusomes... It does more to talk about the pros and cons of different technical aspects and their difficulty This should be saved for the rationale or the discussion. Rather the section should outline the results presented.

• I appreciate the color coding of the differentially segment cysts in Figure 3. The color coding helped orient me to which cysts were being evaluated. However I found the lack of detail bothersome. For instance, which ring canals are in the two panels of D? Are they M1 or M4? Also, the presentation of ring canal size and distribution should be presented in a graph. Statistics are not necessary, but a dot-plot would go a long way to presenting the result. Two plots can add value, one in which the ring canals for each phase is shown, and the other is the distribution of sizes for each cyst. Lastly, the results section for figure 3 interprets the membrane bound vesicles in the ring canal as "ER-like". This should be removed since they neither look ER-like to me, nor have been shown to be ER in the data.

• Figure 4A is not called out specifically in the results and thus should be interpreted or removed from the figure.

• Figure 5 was confusing. I understand the authors wanted to show the wafer and the ribbons, however, this is not a result and does not offer any interpretation of a result and is thus confusing on why it is in the figure. If this were a method paper, I would understand its presence.

• Can the authors comment on the shape of the nuclei in older egg chambers? They are not round at all. I am interested in whether this is a fixation artifact or the real ultrastructure of the nuclei. Of the border cell nuclei for instance. If it is an artifact, this should be added to the discussion.

• Although data from "imperfect" samples is interesting, consider relegating Figure 6 to the supplement section, as it takes away from the pre-existing narrative flow established in the paper. Interpretation of the data throughout the results should be left to the discussion section. For instance, interpretation of Figure 4 results on page 14 beginning with "these data demonstrate the importance...". The importance is not related to the result, but rather discussion of past and future studies. In another example, Figure 5I is introduced and discussed in the results section on page 15, second whole paragraph with an overall introduction/discussion on junctions, which convolutes the actual result. Discussion of future studies or how structures like the novel membrane fingers should be viewed in a larger biological context, should not be in the results.

Minor comments:

• Remove words such as "pseudo-timelapse", they invoke precision on a point that is imprecise.

• Re-consider the acronyms for ring canal and egg chamber.

• Consider finding another way to call out each supplemental movie other than with another acronym.

Significance

The present manuscript is a technical advance in the field. The use of serial EM imaging with two separate modalities, on what is considered to be a challenging problem in the field, represents a useful technical advance. Light microscopy has thus far limited the resolution to which we can understand the spatial organization and the cellular features there in that regulate germline development. This manuscript brings to bear two serial EM methods to begin approaching this problem. The audience for this work are those working at the forefront of understanding germline architecture and development. I make these statements as an expert in live and super resolution of fruit fly egg chamber development, in addition to having performed 3D SEM in past works.

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Referee #2

Evidence, reproducibility and clarity

This study presents a high-resolution volumetric analysis of germline ring canals (RCs) during Drosophila oogenesis. By combining two complementary electron microscopy techniques-Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and Array Tomography Scanning Electron Microscopy (AT-SEM)-the authors compare RC structural features at different developmental stages, ranging from the relatively small germarium to the significantly larger, later-stage egg chambers. At early stages of oogenesis, FIB-SEM analysis confirms that the average RC size increases progressively with cyst development, in agreement with previous studies. The authors further show that lineage reliably predicts RC size (an observation previously reported, but here identified at an earlier stage in region 2a) and, importantly, that the thickness of the actin rim can also be predicted by lineage (reported here for the first time, at stage 1). FIB-SEM analysis also enables a clear delineation of the fusome, allowing for detailed characterization of its assembly and disassembly. Notably, the authors report, for the first time, structural evidence of ER-like membranes capping the inner rim of actin RCs. At later developmental stages, AT-SEM analysis reveals that the microvilli observed by FIB-SEM evolve into extensive interdigitations extending beyond the outer rim in mid-stage egg chambers, a structural feature detected earlier than previously reported. Moreover, by analyzing a sample in which tissue organization was disrupted during preparation, the authors demonstrate that these interdigitations preferentially occur in proximity to the RC. In addition to RC analysis at later stages, the authors use AT-SEM to readily identify small cell populations, such as the germline stem cell niche and border cells, and provide high-resolution volumetric EM data for these structures.

MAJOR COMMENT

My main comment is that we don't learn much new about the biology of these ring canals. The results primarily confirm findings from previous studies using conventional electron microscopy. One particularly interesting biological question, which is briefly mentioned in the text, is whether the oocyte is the cell that inherits the majority of the fusome. Since the authors are able to reconstruct the fusome using their data, they could measure the fusome volume in each cell (especially in the two pro-oocytes) and investigate whether the cell with the larger fusome ultimately becomes the oocyte. This question has been discussed for some time, and recent studies have proposed opposing models based on fusome volume to explain how the oocyte is selected among the 16 sister cells (Nashchekin et al., Science, 2021; Barr et al., Genetics, 2024).

MINOR COMMENTS

• The fluorescent markers used in the fly stocks are neither described in the Materials and Methods section nor depicted in the figures.

• The authors should quote (Nashchekin et al., Science, 2021) when mentioning unequal partionning of the fusome (p4) and oocyte determination (p12).

• P11-12, when mentioning electron dense regions reflecting strong cell-cell adhesion, the authors could refer to (Fichelson et al. Development, 2010), where AJ have been described around ring canals.

• Figure 2A: The schematic diagram (4th line) is not explained in the figure legend.

• Figure 2D: Please clarify whether the RC stage shown corresponds to stage 1 or stage 10, as indicated in panel 2E. Alternatively, are these examples representing the minimum and maximum RC sizes observed across the entire dataset?.

• Figure 5D: Please specify which panel in 5B this corresponds to.

• Figure 5E: Please specify which panels in 5B this corresponds to. The two green boxes are not defined. Why is there a grey background under the ovariole assembly?

• Figures 5G, 5H: Does panel 5G correspond to the left green box in 5E, and 5H to the right green box in 5E? Please clarify.

• Figure 6: The figure title is not on the same page as the figure itself.

• Figure 6A: The black box marking the germarium is not defined.

• Figure 6B-E: The arrows point to long interdigitations. However, arrowheads (which are not mentioned in the legend) appear to indicate the RC outer rim. Please specify this clearly in the figure legend.

Significance

I am not an expert in electron microscopy, so I cannot comment in detail on these techniques, but they appear to bridge the gap between conventional EM and optical microscopy in terms of resolution, user-friendliness, and other aspects. This is technically interesting, although these EM approaches have been previously described and applied. The images and movies are beautiful and clearly presented.

My main comment is that we don't learn much new about the biology of these ring canals. The results primarily confirm findings from previous studies using conventional electron microscopy. One particularly interesting biological question, which is briefly mentioned in the text, is whether the oocyte is the cell that inherits the majority of the fusome. Since the authors are able to reconstruct the fusome using their data, they could measure the fusome volume in each cell (especially in the two pro-oocytes) and investigate whether the cell with the larger fusome ultimately becomes the oocyte. This question has been discussed for some time, and recent studies have proposed opposing models based on fusome volume to explain how the oocyte is selected among the 16 sister cells (Nashchekin et al., Science, 2021; Barr et al., Genetics, 2024).

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Referee #1

Evidence, reproducibility and clarity

Summary

The possibility of observing 3D cellular organisation in tissues at nanometre resolution is a hope for many cell biologists. Here, the authors have combined two volume electron microscopy approaches with scanning electron microscopy: Focused Ion Beam (FIB-SEM) and Array Tomography (AT-SEM) to study the evolution of the shape and organisation of cytoplasmic bridges, the 'ring canals' (RCs) in the Drosophila ovarian follicle that connect nurse cells and oocyte. This type of cytoplasmic link, found in insects and humans, is essential for oocyte development. RCs have mainly been studied using light microscopy with various markers that constitute them, but this approach does not fully capture an overall view of their organization. Due to their three-dimensional arrangement within the ovarian follicle, characterizing their organization using transmission electron microscopy (TEM) has been very limited until now. This v-EM study allows the authors to document the evolution of RC size and thickness during the development of germline cysts, from the germarium to stage 4, and potentially beyond. This study confirmed previous findings, namely that RC size correlates with lineage: the largest RC is formed after the first division, while the smallest is formed during the last division. Furthermore, this work allowed a better characterisation of the membrane interdigitation surrounding the RCs. In addition, the authors highlight the important potential of v-EM for further structural analysis of the fusome, migrating border cells and the stem cell niche.

Major comments

• The output of this work can be divided into two parts. First, this work presents a technical challenge, involving image acquisition by volume electron microscopy and manual 3D reconstruction of the contours of the membranes, nuclei, RCs, and fusome in different cysts at different stages. Secondly, this work is based on a structural study of the RCs and their associated membranes. This work is descriptive but important, although the results largely confirm previous findings, both for the structure of the RCs and their relationship to the division sequence of the cyst cells, and for the organisation of the membranes around the RCs.

• Very interestingly, the authors report the spatial characterisation of membrane structures associated with and close to CRs that have already been identified (Loyer et al.). However, their characterisation is somewhat incomplete, as it lacks quantified data - how many CRs were analysed? and, above all, the characteristics of these membranes, their length and orientation according to their position and their connection in the lineage - these data could be obtained from the VEM data already collected and would be an important addition to the RC structural analysis in this work. In line with this, the authors importantly report the presence of an ER-like membrane structure lining the RCs. First, it would be nice to have statistics to support the observation of how many RCs..? Secondly, does this ER membrane structure vary according to the position of the RC in the cyst, are they related to the RC lineage? The addition of graphs showing the quantitative data with statistics in the figures would improve understanding of the results. This is particularly the case for the characterisation of RCs according to the stage of cyst development, as shown in Figure 3. This also applies to the characterisation of RCs within a cyst and the relationship between RC size and lineage, as shown in Figure 4, and to the characterisation (thickness) of the inner part of the RC.

• The part on the structural analysis of the fusome is interesting but still secondary to the characterisation of the RCs. This part should be moved to the results and figures after the various parts concerning the RCs.

Minor comments

• The distribution of the fusome in Figure 2 is difficult to see with Hts labelling and does not really correspond to the schematic, especially in regions 2a and 2B.

• In panel C of Figure 2, it is a little disturbing that the legend is directly on the image of RC. It hides some information about the images and could be placed at the bottom of the panel. This also the case for the panel G.

• With figure 3B, it would be good to highlight the position of cyst.

Significance

As mentioned above, this work can be divided into two parts.

The part corresponding to the acquisition of images by volume electron microscopy and manual 3D reconstruction is new and a great source of valuable information. The part related to the spatial characterisation of the RC is important, but corresponds more to an extension and reinforcement of previously available information than to the contribution of significant new insights.

I think it will be of great interest to an audience interested in Drosophila oogenesis.

The article shows why I don’t trust chatbots with mental health: even the therapists quoted say AI should only be a supplement, not a substitute, and the piece documents cases where bots mishandled suicidal ideation. It’s telling that states have started banning or restricting “AI therapy,” and that researchers found some bots literally listed New York bridges when a user hinted at self-harm—proof that safety breaks exactly when it matters most. A real clinician can read body language, tone, and silence; as one therapist in the story puts it, “the actual relationship with another person is where good work happens.” The article itself concedes as much when a therapist warns he “strongly dissuades patients from attempting to diagnose themselves with any mental health condition using AI,” because humans integrate nonverbal cues that bots miss. Yes, journaling with a bot may feel helpful at 2 a.m., but that convenience shouldn’t replace the accountability and care of talking to trained people who can intervene responsibly. Bottom line: for mental health, I’m against AI chatbots; talk to real humans.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript by Dufour et al. is a follow-up on the groups' previous publication that introduced the photo-inducible Cre recombinase, LiCre. In the present work, the authors further characterize the properties and kinetics of their optogenetic switch. Initially, the authors show that light affects only LiCre-mediated recombination itself and not DNA binding. Following these observations, they measure and mathematically model LiCre kinetics demonstrating high efficiency in vivo and a surprising temperature sensitivity. Finally, Dufour et al. evaluate several mutations that affect the LOV photo-cycle and provide recommendation for LiCre applications. The study thoroughly investigates various aspects of the function of LiCre, confirming some previously known characteristics (i.e. temperature-dependence of Cre activity and functionality of LOV-based optogenetic tools in yeast without co-factor supplementation), while providing new LiCre-specific insights (kinetics, light-independent DNA binding). Please note that the reviewer is no expert in mathematical modeling and cannot fully judge the methodological details of the models. While I have some concerns as listed below, I believe study should be well-suited for publication after a revision.

Major comments:

1. After completing the initial experiment, the authors discovered that their plasmids carry different numbers of V5 epitopes. I am wondering whether this was due to a recombination event happening during the experiment or whether the constructs were not sequence verified prior to use? In any case, an additional ChIP experiment using Cre and LiCre constructs with the identical number of tag-repeats will be necessary. The result, i.e. the strong reduction of DNA-binding of LiCre (which is close to the negative control), is quite remarkable given that LiCre is still considerably active and high DNA affinities were observed in SPR experiments. In light of these counterindications, identical experiment conditions for test and reference group become even more important.
2. The conclusion that DNA-binding of LiCre is completely light-independent is not entirely convincing to me. The differences between the light and dark conditions in Fig. 2d are indeed small, but the values for LiCre are almost on par with the vector control and therefore hard to interpret. Based on this experiment alone, one could even be inclined to argue that LiCre does not bind DNA at all (which is of course falsified by the later experiments), showing that the resolution of the corresponding dataset is too low to draw final conclusions. Light-independent DNA binding should either be confirmed by a more sensitive method or the conclusion statements on this matter should be revised accordingly.
3. If I understand the explanations correctly, replicates and plotted data points refer to multiple samples (different colonies), that were handled in a single experiment, i.e. by one researcher at the same time/same day. As already mentioned by the authors in the main text, this workflow explains the considerable differences between some of the results in the present manuscript and an identical experiment in a previous publication by the same authors. Providing truly independent experiments (performed on different days) that are therefore independent towards variables such as the fluctuation in incubation temperature (which was the issue in the described experiments) will be crucial, at least for the key datasets.

Minor comments:

1. At the end of the Introduction, the authors mention that the interaction of the Cre heptamers was weakened via point mutations in LiCre. A short sentence about the engineering rationale behind this weakened interaction would help readers, who are not familiar with the author's prior work.
2. Fig. 2a-b depicts images relating to the purification procedure. These could be moved to the supplements as they don't provide any insight apart from the fact that the proteins were successfully purified.
3. The kinetic characterization was only performed for LiCre. Especially for scientists, who have worked with wildtype Cre before, a side-by-side comparison with wt Cre would be valuable to judge the loss in reaction speed that has to be expected when switching from Cre to LiCre.
4. The difference between the ChIP results and the SPR results is striking but not mentioned in the discussion section. Also, the statement: "Finally, our results have practical implications on experimental protocols employing LiCre. First, given its high affinity for loxP (Fig. 5b), over-expressing LiCre at high levels will probably not increase its efficiency." (line 502) refers only to the affinity but seems to ignore the low DNA-occupancy of LiCre observed in Fig. 2d. Adapting the discussion section accordingly would improve the manuscript.

Significance

General assessment and advance:

The present study provides a large set of experiments and analyses characterizing the optogenetic LiCre recombinase. In general, the study is well conceived and executed. Although some of my concerns listed above affect key aspects of the study, they should be straightforward to address. The manuscript is a follow-up study providing a more detailed characterization of an optogenetic tool previously developed by the same authors. Its novelty is therefore somewhat limited. While the study provides a rich body of additional data, many of the findings merely confirmed aspects that were to be expected based on the two proteins LiCre is built of (temperature-dependent activity of Cre, optogenetics in yeast w/o the need of co-factor supplementation, weaker DNA-affinity of the Cre fusion protein as compared to wildtype Cre). New insights are provided by the facts that (i) light only controls recombination but not DNA binding and (ii) light activation of only some protomers within the LiCre heptamer is likely to be sufficient to activate recombination. The former aspect is, however, not entirely evident from the results as described above.

Audience:

The study will be of interest for researchers focusing on inducible DNA recombination and especially relevant to those who plan to work with LiCre and can now rely on a more detailed and extended characterization compared to the original LiCre publication.

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Referee #1

Evidence, reproducibility and clarity

Dufour et al describe characterization of the light-activated recombinase LiCre. This work combines the yeast reporter assay, surface plasmon resonance (SPR) and kinetic modeling to provide a comprehensive study of how LiCre functions both in vivo in yeast and in vitro. The authors show that LiCre binds to loxP sites in the dark with high affinity, but reduced cooperativity compared to wild-type Cre, and that recombination efficiency is affected by temperature and illumination regime. Importantly, the authors establish a kinetic model that not only explains these observations but also predicts the altered behavior of a mutant (T418S), which was experimentally validated. It would be valuable to highlight what other predictions the model could make, even if for future work. Overall, this work combines quantitative experiments and modeling to provide new insights into the biochemical and kinetic properties of LiCre.

Specific comments:

Line 110-115: Although described in the Methods section, a brief statement of dark and light treatment conditions would help readers better follow the experiments. Likewise, listing the three unrelated positions would improve the clarity.

Line 185: Is there a typo?

Line 216: Have the authors considered performing surface plasmon resonance (SPR) to confirm the binding affinity of LiCre-V5 DNA?

Line 233-234: To determine whether the observed difference in recombination efficiency is due to the genomic context of the reporter loci or due to the measurement accuracy of GFP and RFP signals, have the authors considered swapping the positions of GFP and RFP?

Line 236: The sentence "Importantly, we never observed recombination in the entire cell population" is ambiguous. I believe it means recombination was never observed in 100% of the cells. Please rephrase it.

Line 245-249: The hypothesis of plasmid loss based on plating samples on selective and non-selective media without illumination assumes that loss of growth on selective media is only due to plasmid loss, without considering other factors like burden or toxicity. Moreover, the broad range of 10-30% makes it difficult to justify that the ~15% recombination-negative fraction falls within expected variation. The conclusion that LiCre-mediated recombination efficiency is close to 100% after prolonged photoactivation (Line 249, 301-303) is not fully convincing unless more evidence is provided.

Line 275-276: The authors suspect that the decrease in recombination efficiency at very high light intensity is possibly attributed to phototoxicity. Could photobleaching also contribute to this effect? A viability assay would help to validate the phototoxicity explanation.

Line 345-346: While the model with x=2 provides a slightly better fit comparing to the others, the possibility of x=4 cannot be excluded. The inference that "photo-activation of at least two LiCre protomers enables recombination" is not sufficiently proven.

Figure 1e: Please clarify whether the Western blots shown represent biological replicates.

Figure 4: Please include the error bars. Panel a - The authors integrated GFP and mCherry reporters at two different loci to avoid positional bias. Why then is only mCherry used as the ON readout in most experiments, rather than analyzing both reporters in parallel? Please clarify. For panel 4h and line 272, the statement that maximal activation was reached at 12 mW/cm² should be rephrased more cautiously, as no intermediate intensities between 12 and 35.6 mW/cm² were tested.

Significance

This study provides a quantitative experimental and predictive analysis of the light-activated recombinase LiCre, offering new insights into its binding, activation and recombination properties. The predictive validation of the mutant is a strength of this work. While the modeling part is an innovative aspect, more clarification is needed, especially regarding the conclusion that photo-activation of at least two LiCre protomers enables recombination. More mechanistic investigations are needed to support the conclusions. The work will be of interest to researchers in optogenetics, genome engineering, and DNA-protein interactions. My expertise is in yeast genome engineering and applications of Cre-mediated recombination system. Modeling is outside my primary area of expertise.

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Reply to the reviewers

1. General Statements

Thank you for providing an assessment of our manuscript. We suggest here a revision plan to address the points raised by the reviewers regarding code documentation, benchmarking, and biological applications.

As part of the revisions implemented we have:

Clarified the management of dependencies of our package Fixed the data download run times of test data Clarified the parameters of the normalization and optimization functions We plan to:

Extend our manuscript to include a section on cross-condition analysis that builds on our tutorials, where we will illustrate how ParTIpy can quantify shifts in the distribution of fibroblasts across the functional space defined by archetypal analysis between healthy and failing hearts. Extend our benchmarks of scalability of coresets, by reporting wall-clock time and peak memory usage across distinct data sizes. Extend our benchmarks of stability of coresets, by reporting the similarity of the estimated archetypes based on the original versus the sampled data. Include the original enrichment analysis of ParTI to provide users with distinct options to work with the archetypes, and provide a larger discussion on the distinct strategies. We believe these revisions will strengthen our__ software manuscript__ and will help us to provide a robust and practical tool to analyze functional trade-offs from biological data.

2. Description of the planned revisions

Reviewer #1

Summary

The paper "ParTIpy: A Scalable Framework for Archetypal Analysis and Pareto Task Inference" presents ParTIpy, an open-source Python package that modernizes and scales the Pareto Task Inference (ParTI) framework for analyzing biological trade-offs and functional specialization. Unlike the earlier MATLAB implementation, which required a commercial license and was limited in scalability, ParTIpy leverages Python's open ecosystem and integration with tools such as scverse to make archetypal analysis more accessible, flexible, and compatible with modern biological data workflows. Through advanced optimization and coreset algorithms, it efficiently handles large scale single cell and spatial transcriptomics datasets. ParTIpy identifies "archetypes", or optimal phenotypic extremes, to reveal how cells balance competing functional programs. The paper demonstrates its application in modeling hepatocyte specialization across the liver lobule, highlighting spatial patterns of metabolic division of labor.

Overall, ParTIpy represents a modern, accessible, and scalable Python-based solution for exploring biological trade-offs and resource allocation in high-dimensional data. The paper is clearly written and addresses an important methodological gap. However, the enrichment analysis differs from the original ParTI framework and should be discussed more explicitly, and the documentation and tutorials, while helpful, could be refined to improve usability and reproducibility.

Major Comments

1. The archetype enrichment analysis used in this paper differs from the original enrichment analysis implemented in ParTI. This is acceptable, but: a) The authors should explicitly state and discuss the differences between the two approaches. b) The enrichment analysis should be made more systematic. For each tested feature (e.g. gene or pathway), the analysis should report a p-value for the hypothesis that the feature is enriched near an archetype - that is, its expression (or value) is high close to the archetype and decreases with distance. Appropriate multiple-hypothesis correction should also be applied.

We thank the reviewer for this valuable comment and agree that the differences between our enrichment analysis and the original ParTI implementation should be stated more explicitly. We will incorporate the original enrichment algorithm into ParTIpy, enabling users to select their preferred method. In the revised manuscript, we will note that two enrichment algorithms are available and describe both in greater detail in the supplementary methods section. We also note that the current enrichment analysis already reports p-values adjusted for multiple hypothesis testing.

Reviewer #2

Summary

This paper introduces the software ParTIpy, a scalable Python implementation of Pareto Task Inference (ParTI), designed to infer functional trade-offs in biological systems through archetypal analysis. The framework modernizes the previous toolbox with efficient optimization, memory-saving coreset construction, and integration with the scverse ecosystem for single-cell transcriptomic data.

Using hepatocytes scRNA-seq data as a test case, the authors identify archetypes corresponding to distinct gene expression patterns. These archetypes align with known liver domains in spatial transcriptomics data, validating both the method's interpretability and its biological relevance.

Major comments

(1) Conclusions

The core computational and biological claims are well supported. ParTIpy clearly scales better than earlier implementations and reproduces known biological structure. However, claims about "scalability to large datasets" should be further qualified (see below).

We will implement further performance benchmarks as discussed below.

(2) Claims

Archetypal analysis based on current matrix computation formulation is non-parametric, and new data require recomputation of archetypes. Therefore, the method cannot generalize to unseen data in the way deep learning approaches, which could be further acknowledged and clarified.

We thank the reviewer for this insightful comment. We agree that deep learning frameworks are typically amortized, allowing them to generalize to unseen data without retraining, and we will clarify this distinction in the discussion of the revised manuscript. However, we note that mapping new cells into an existing archetypal space is computationally inexpensive, as it only requires solving a single convex optimization problem.

(3) Additional suggested analyses or experiments

1） Absolute performance benchmarks : it's suggested to report wall-clock time and memory for a few dataset sizes (10k, 100k, 1M cells).

We thank the reviewer for this helpful suggestion. We will extend the coreset benchmark to quantify how coreset size affects both archetype positions and biological interpretation. Specifically, we will match archetypes across coreset sizes by solving the linear sum assignment problem, as we currently do when comparing bootstrap samples. We will then compare the distances between archetypes inferred from the full dataset and those obtained from different coreset sizes. In addition to measuring displacement, we will assess biological stability by comparing the gene expression vectors of corresponding archetypes as well as their enriched pathways (using metrics such as cosine similarity and Jaccard index).

**Referee cross-commenting**

I agree with the other reviewer's suggestion to check consistency and reproducibility with previous implementation, and enhance the tutorial of the software for users from a biological background. Combined with my comments to further improve the biological application showcase, the revised manuscript could be an impactful contribution to the field, if these comments could be properly addressed.

(1) Advance

This paper is primarily a technical contribution. It modernizes the Pareto Task Inference framework into a scalable and user-friendly Python implementation, which is valuable. However, to further improve its significance especially for the broader biological audience, more detailed analysis could be performed (see below)

(2) Biological scope and applications [optional]

The current biological validation in hepatocyte is technically fine but limited in breadth and impact. It demonstrates that ParTIpy works but falls in short of showing what new insights it can reveal. Several promising applications could be further explored:

1) Cross-condition comparisons: could ParTIpy quantify how the Pareto front shifts between conditions (e.g., normal vs. tumor, treated vs. control)?

We thank the reviewer for this valuable suggestion. We have shown ParTIpy's applicability to cross-condition settings in our online tutorials (https://partipy.readthedocs.io/en/latest/notebooks/cross_condition_lupus.html). However, we agree that a more explicit mention in the manuscript is needed. Thus, we will include a cross-condition analysis as a second application in the revised manuscript, focusing on fibroblasts from heart failure patients from Amrute, et. al. (2023) 1. This will illustrate how ParTIpy can quantify shifts in the distribution of cells across the functional space defined by archetypal analysis.

Because the manuscript does not explore these scenarios, the biological impact remains narrow, and the framework's broader interpretive power is somehow underrepresented.

We hope that the additional application included in the revised manuscript helps better illustrate the framework's strength. We would also like to note that the online tutorials provide a comprehensive overview of ParTIpy's functionality, as we expect these will serve as a primary entry point for many researchers interested in archetypal analysis and Pareto Task Inference.

(3) Audience and impact

The paper will interest computational biologists, systems biologists, and bioinformaticians focused on single-cell analysis, and its impact will grow substantially if the authors demonstrate more biological applications.

(4) Reviewer expertise

Computational biology, single-cell transcriptomics, machine learning, computational math

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

Reviewer #1

2. The package documentation on GitHub and ReadTheDocs is a major strength, but the tutorials can be improved for clarity and accessibility:

We thank the reviewer for this positive feedback. Indeed, providing comprehensive documentation to facilitate ease of adoption was a major motivation behind this project. In response to the reviewer's suggestions, we have revised the tutorials to further improve their clarity, structure, and accessibility, as detailed below.

a) The documentation should list external dependencies that need to be installed seperately, e.g. pybiomart.

We thank the reviewer for pointing this out. We had added all dependencies under the optional-dependencies.extra header, which allows users to run pip install partipy[extra] to be able to run all tutorial notebooks. However, we forgot to explain that in the tutorial or Readme page, which we corrected now. The Readme now reads:

Install the latest stable full release from PyPI with the extra dependencies (e.g., pybiomart, squidpy, liana) that are required to run every tutorial:

``` pip install partipy[extra]

```

Additionally we include clarifications in every tutorial notebook that uses additional dependencies: "To run this notebook, install ParTIpy with the tutorial extras: pip install partipy[extra]".

b) The dataset used in the Quickstart demo appears to be inaccessible or extremely slow to download (the function load_hepatocyte_data_2() did not complete even after 30 minutes, at least in my experience). The authors should verify data availability on Zenodo and consider providing a smaller or cached version to make the demo more reliable and reproducible.

We thank the reviewer for this helpful comment. We agree that the previous implementation of load_hepatocyte_data_2() was not reliable due to slow download speeds from Zenodo. To address this, we now host the required AnnData object on figshare (https://figshare.com/articles/dataset/scRNA-seq_hepatocyte_data_from_Ben-Moshe_et_al_2022_/30588713?file=59459459), ensuring faster and more stable access for the Quickstart tutorial via scanpy.read:

```

adata = sc.read("data/hepatocyte_processed.h5ad", backup_url="https://figshare.com/ndownloader/files/59459459")

adata

```

c) The tutorial order could be more intuitive - for instance, "archetype crosstalk network" appears before "archetypal analysis". Consider starting with the simulated dataset and presenting the full pipeline before moving to more complex real-world examples.

We thank the reviewer for this helpful suggestion and agree that the previous ordering was not intuitive. We have reordered the tutorials such that the notebook introducing archetypal analysis now appears first, followed by the Quickstart tutorial and the subsequent applied examples.

Minor comments

1. In the Python function, the parameter "optim" could use more descriptive option names - for example, renaming "projected_gradients" to "PCHA" would make it clearer and more consistent with terminology used in the paper.

We thank the reviewer for this helpful suggestion. We agree that the previous naming could be misleading. While PCHA does not precisely describe the underlying algorithm, it is the term most users are familiar with from the literature. We have therefore updated the function to accept both "PCHA" and "projected_gradients", which now map to the same underlying optimization routine.

In the Quickstart preprocessing, the authors use the following code:

sc.pp.normalize_total(adata)

sc.pp.log1p(adata)

However, they do not specify the target sum in the normalize_total function. The authors should ensure that the data values before the logarithmic transformation span several orders of magnitude (e.g., 0-10,000); if normalization is performed to a sum of 1, the log transformation becomes ineffective.

We thank the reviewer for this helpful comment. By default, sc.pp.normalize_total scales the counts in each cell to the median total counts across all cells, which preserves the typical range of expression values prior to logarithmic transformation. We therefore consider this default behavior appropriate for the Quickstart example. Nonetheless, we will clarify this explicitly in the tutorial to avoid confusion.

**Referee cross-commenting**

I agree with Reviewer #2 observation that the paper's contribution is primarily technical; however, I consider this technical advance to be an important and timely one that will enable many biologists to apply archetypal analysis more effectively in their own work.

We thank the reviewer for this positive and encouraging assessment.

Reviewer #1 (Significance (Required)):

This study presents ParTIpy, a Python-based implementation of Pareto Task Inference (ParTI) that makes archetypal analysis more accessible, scalable, and compatible with modern single-cell and spatial transcriptomics workflows. Its main strength lies in translating a conceptually powerful but technically limited MATLAB framework into an open-source, efficient Python package, enabling wider use in computational biology. The package is well-documented, which further enhances its accessibility and adoption potential, though documentation could be improved to enhance reproducibility and ease of use. It will be of interest to computational systems biologists, particularly those working with omics data, and those interested in studying functional trade-offs and resource allocation.

We appreciate the reviewer's positive evaluation and are encouraged by their recognition of ParTIpy's relevance and potential impact in computational biology.

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

Reviewer #2

The current biological validation in hepatocyte is technically fine but limited in breadth and impact. It demonstrates that ParTIpy works but falls in short of showing what new insights it can reveal. Several promising applications could be further explored:

2) Transient or plastic states: Cells with mixed archetype weights or high mixture entropy can be interpreted as transient, functionally flexible states. ParTIpy can quantify such transience geometrically, even in static data, which providing a competitive counterpart to models like CellRank or CellSimplex (https://doi.org/10.1093/bioinformatics/btaf119).

We thank the reviewer for this interesting suggestion. While we agree that quantifying transient or plastic states based on archetype mixtures is an intriguing idea, validating whether cells with mixed archetype weights ("generalists") truly represent transient states would require additional data modalities such as temporal or lineage-tracing measurements. Although we find this direction highly interesting, given that the manuscript is intended as a software paper, we prefer to focus on more directly supported applications of cross-condition data, where labeled data is available.

However, we will expand our discussion to relate ParTIpy with CellSimplex since we believe this is an interesting angle that future users could explore.

5. References

1. Amrute, J. M. et al. Defining cardiac functional recovery in end-stage heart failure at single-cell resolution. Nat. Cardiovasc. Res. 2, 399-416 (2023).

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Referee #2

Evidence, reproducibility and clarity

Summary

This paper introduces the software ParTIpy, a scalable Python implementation of Pareto Task Inference (ParTI), designed to infer functional trade-offs in biological systems through archetypal analysis. The framework modernizes the previous toolbox with efficient optimization, memory-saving coreset construction, and integration with the scverse ecosystem for single-cell transcriptomic data.

Using hepatocytes scRNA-seq data as a test case, the authors identify archetypes corresponding to distinct gene expression patterns. These archetypes align with known liver domains in spatial transcriptomics data, validating both the method's interpretability and its biological relevance.

Major comments

(1) Conclusions

The core computational and biological claims are well supported. ParTIpy clearly scales better than earlier implementations and reproduces known biological structure. However, claims about "scalability to large datasets" should be further qualified (see below).

(2) Claims

Archetypal analysis based on current matrix computation formulation is non-parametric, and new data require recomputation of archetypes. Therefore, the method cannot generalize to unseen data in the way deep learning approaches, which could be further acknowledged and clarified.

(3) Additional suggested analyses or experiments

1. Absolute performance benchmarks : it's suggested to report wall-clock time and memory for a few dataset sizes (10k, 100k, 1M cells).
2. Coreset sensitivity analysis: Could authors show how coreset size affects archetype positions and biological interpretation?

Referee cross-commenting

I agree with the other reviewer's suggestion to check consistency and reproducibility with previous implementation, and enhance the tutorial of the software for users from a biological background. Combined with my comments to further improve the biological application showcase, the revised manuscript could be an impactful contribution to the field, if these comments could be properly addressed.

Significance

(1) Advance

This paper is primarily a technical contribution. It modernizes the Pareto Task Inference framework into a scalable and user-friendly Python implementation, which is valuable. However, to further improve its significance especially for the broader biological audience, more detailed analysis could be performed (see below)

(2) Biological scope and applications [optional]

The current biological validation in hepatocyte is technically fine but limited in breadth and impact. It demonstrates that ParTIpy works but falls in short of showing what new insights it can reveal. Several promising applications could be further explored:

1) Cross-condition comparisons: could ParTIpy quantify how the Pareto front shifts between conditions (e.g., normal vs. tumor, treated vs. control)?

2) Transient or plastic states: Cells with mixed archetype weights or high mixture entropy can be interpreted as transient, functionally flexible states. ParTIpy can quantify such transience geometrically, even in static data, which providing a competitive counterpart to models like CellRank or CellSimplex (https://doi.org/10.1093/bioinformatics/btaf119).

Because the manuscript does not explore these scenarios, the biological impact remains narrow, and the framework's broader interpretive power is somehow underrepresented.

(3) Audience and impact

The paper will interest computational biologists, systems biologists, and bioinformaticians focused on single-cell analysis, and its impact will grow substantially if the authors demonstrate more biological applications.

(4) Reviewer expertise Computational biology, single-cell transcriptomics, machine learning, computational math

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Referee #1

Evidence, reproducibility and clarity

Summary

The paper "ParTIpy: A Scalable Framework for Archetypal Analysis and Pareto Task Inference" presents ParTIpy, an open-source Python package that modernizes and scales the Pareto Task Inference (ParTI) framework for analyzing biological trade-offs and functional specialization. Unlike the earlier MATLAB implementation, which required a commercial license and was limited in scalability, ParTIpy leverages Python's open ecosystem and integration with tools such as scverse to make archetypal analysis more accessible, flexible, and compatible with modern biological data workflows. Through advanced optimization and coreset algorithms, it efficiently handles large scale single cell and spatial transcriptomics datasets. ParTIpy identifies "archetypes", or optimal phenotypic extremes, to reveal how cells balance competing functional programs. The paper demonstrates its application in modeling hepatocyte specialization across the liver lobule, highlighting spatial patterns of metabolic division of labor. Overall, ParTIpy represents a modern, accessible, and scalable Python-based solution for exploring biological trade-offs and resource allocation in high-dimensional data. The paper is clearly written and addresses an important methodological gap. However, the enrichment analysis differs from the original ParTI framework and should be discussed more explicitly, and the documentation and tutorials, while helpful, could be refined to improve usability and reproducibility.

Major Comments

1. The archetype enrichment analysis used in this paper differs from the original enrichment analysis implemented in ParTI. This is acceptable, but:

a. The authors should explicitly state and discuss the differences between the two approaches.

b. The enrichment analysis should be made more systematic. For each tested feature (e.g. gene or pathway), the analysis should report a p-value for the hypothesis that the feature is enriched near an archetype - that is, its expression (or value) is high close to the archetype and decreases with distance. Appropriate multiple-hypothesis correction should also be applied. 2. The package documentation on GitHub and ReadTheDocs is a major strength, but the tutorials can be improved for clarity and accessibility:

a. The documentation should list external dependencies that need to be installed seperately, e.g. pybiomart.

b. The dataset used in the Quickstart demo appears to be inaccessible or extremely slow to download (the function load_hepatocyte_data_2() did not complete even after 30 minutes, at least in my experience). The authors should verify data availability on Zenodo and consider providing a smaller or cached version to make the demo more reliable and reproducible.

c. The tutorial order could be more intuitive - for instance, "archetype crosstalk network" appears before "archetypal analysis". Consider starting with the simulated dataset and presenting the full pipeline before moving to more complex real-world examples.

Minor comments

1. In the Python function, the parameter "optim" could use more descriptive option names - for example, renaming "projected_gradients" to "PCHA" would make it clearer and more consistent with terminology used in the paper.
2. In the Quickstart preprocessing, the authors use the following code: sc.pp.normalize_total(adata) sc.pp.log1p(adata) However, they do not specify the target sum in the normalize_total function. The authors should ensure that the data values before the logarithmic transformation span several orders of magnitude (e.g., 0-10,000); if normalization is performed to a sum of 1, the log transformation becomes ineffective.

Referee cross-commenting

I agree with Reviewer #2 observation that the paper's contribution is primarily technical; however, I consider this technical advance to be an important and timely one that will enable many biologists to apply archetypal analysis more effectively in their own work.

Significance

This study presents ParTIpy, a Python-based implementation of Pareto Task Inference (ParTI) that makes archetypal analysis more accessible, scalable, and compatible with modern single-cell and spatial transcriptomics workflows. Its main strength lies in translating a conceptually powerful but technically limited MATLAB framework into an open-source, efficient Python package, enabling wider use in computational biology. The package is well-documented, which further enhances its accessibility and adoption potential, though documentation could be improved to enhance reproducibility and ease of use. It will be of interest to computational systems biologists, particularly those working with omics data, and those interested in studying functional trade-offs and resource allocation.

Document d'information : Analyse des mécanismes de sortie de conflit

Résumé analytique

Ce document synthétise les perspectives d'experts sur les mécanismes de résolution des conflits et de construction de la paix, basées sur des recherches en sciences comportementales et des expériences de médiation sur le terrain.

L'analyse part du constat d'une "spirale tragique" du conflit, où l'agression et la représaille s'auto-alimentent, nourries par des biais psychologiques comme la déshumanisation de l'ennemi.

Les points à retenir sont les suivants :

1. L'inversion de la spirale : Le cycle destructeur peut être inversé pour devenir un "cercle vertueux".

La clé de cette transformation est l'humanisation de l'autre, qui consiste à le percevoir comme un acteur avec qui collaborer, une partie ayant des intérêts légitimes ou un semblable.

2. Le rôle central des victimes : De manière contre-intuitive, les études, notamment en Colombie, montrent que les victimes de conflits violents sont souvent plus prosociales, plus enclines à la coopération et à la réconciliation que les non-victimes.

Cette attitude s'explique par une forte aversion à la perte — ayant tant perdu, elles sont déterminées à empêcher la violence de se répéter — et une capacité à reconnaître la souffrance partagée.

3. Transformer la violence, pas éliminer le conflit : Les experts s'accordent à dire que l'objectif n'est pas d'éliminer le conflit, qui est inhérent aux sociétés humaines, mais de le transformer d'une forme violente à une forme non-violente et constructive, gérée par des moyens politiques et institutionnels.

4. Recommandations stratégiques : Pour favoriser la paix, les recommandations clés incluent une communication qui reconnaît la souffrance de toutes les parties, l'utilisation du cadre de l'aversion à la perte pour motiver l'action collective, la promotion du contact direct entre les groupes pour humaniser l'autre, et le fait de s'attaquer aux causes profondes des conflits (ex: inégalités).

5. Le changement climatique comme analogie : Le défi climatique est présenté comme un exemple de conflit global non-violent qui exige une "collaboration radicale".

La solution ne réside pas dans la création de nouveaux mouvements, mais dans la capacité à capter et à renforcer les énergies positives et les initiatives déjà existantes au sein de la société.

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1. La "Spirale Tragique" du Conflit

L'analyse des conflits commence par le concept de "spirale tragique", un mécanisme d'escalade auto-entretenu. Ce cycle destructeur se déroule selon les étapes suivantes :

• Stress initial : Des tensions ou des difficultés génèrent un stress collectif.

• Attribution et accusation : En raison du "biais fondamental d'attribution", les humains ont tendance à attribuer la cause des problèmes à des personnes plutôt qu'à des situations. Cela mène à l'identification et à l'accusation d'un ennemi.

• Déshumanisation et agression : L'autre groupe est déshumanisé, ce qui lève les inhibitions et permet l'agression et la violence. Ces actes permettent de libérer la tension accumulée.

• Destruction et représailles : La violence entraîne la destruction, ce qui génère davantage de stress et de souffrance, alimentant un désir de représailles de la part de l'autre camp.

• Auto-alimentation : Chaque partie, se percevant comme répondant à une agression initiale, perpétue un cycle sans fin de violence et de souffrance croissante, renforçant la dichotomie "nous contre eux".

Ce modèle, alimenté par des propensions humaines universelles, explique comment les conflits s'enracinent et s'intensifient.

2. Inverser la Spirale : L'Humanisation comme Moteur du Changement

La même dynamique de boucle de rétroaction qui alimente la violence peut être inversée pour créer un "cercle vertueux" où "le mieux mène au mieux". La clé de cette inversion réside dans le processus d'humanisation.

Selon Adam Kahane, l'humanisation consiste à choisir de voir les autres non pas comme des objets ou des non-humains, mais à travers des perspectives constructives :

• Perspective technocratique : Voir l'autre comme un co-acteur dans la résolution d'un problème commun.

• Perspective politique : Voir l'autre comme une partie ayant des intérêts légitimes dans le cadre d'une négociation.

• Perspective relationnelle : Voir l'autre comme un semblable ou un parent, reconnaissant une humanité partagée.

Ce changement de perspective est souvent déclenché par une prise de conscience pragmatique : la reconnaissance qu'aucune partie ne peut l'emporter unilatéralement et que la collaboration, même avec des adversaires, est indispensable pour assurer son propre avenir.

3. Le Rôle Contre-Intuitif des Victimes dans la Réconciliation

Un des constats les plus frappants issus des recherches menées en Colombie est le rôle moteur des victimes dans les processus de paix. Contrairement à l'idée reçue, les personnes ayant directement souffert de la violence sont souvent plus enclines à la coopération et à la réconciliation que celles qui n'ont pas été directement affectées.

Les Mécanismes Comportementaux sous-jacents

Les recherches d'Enrique Fatas et Lina Restrepo mettent en lumière plusieurs explications comportementales à ce phénomène :

• Aversion à la perte : Conformément à la théorie des perspectives, les pertes sont ressenties plus intensément que les gains équivalents. Les victimes ont subi des pertes immenses (famille, biens, sécurité) et sont donc extrêmement motivées à éviter que cette souffrance ne se répète, ce qui les rend plus ouvertes à la concession pour garantir la paix.

• Prosocialité accrue : Il est documenté à travers l'Afrique, l'Asie et l'Amérique latine que l'exposition à un conflit violent augmente la prosocialité des victimes envers les membres de leur propre groupe (in-group) mais aussi envers d'autres groupes vulnérables qu'elles perçoivent comme similaires. Si les ex-combattants sont perçus comme un autre groupe vulnérable plutôt que comme des ennemis déshumanisés, cette prosocialité peut s'étendre à eux.

• "Victimisation inclusive" : Dans des contextes comme la Colombie, où le conflit a été long et irrégulier, la victimisation est si répandue qu'elle transcende les clivages. Il n'y a pas un "nous" et un "eux" clairement définis, ce qui favorise une identification partagée et réduit la pensée conflictuelle.

La Reconnaissance de la Souffrance Partagée

Adam Kahane corrobore cette observation en soulignant que les participants qui avaient le plus souffert dans les ateliers de paix en Colombie étaient les plus déterminés à trouver une solution non-violente. La reconnaissance de la souffrance partagée avec l'adversaire permet de le voir comme un être humain. Citant Carl Rogers, il affirme que "ce qui est le plus personnel est le plus universel".

Distinction entre Attitudes et Comportements

Une étude de Lina Restrepo sur le financement participatif pour des entrepreneurs (victimes vs. ex-combattants) a révélé une nuance importante.

• Comportement : Les participants ont donné des sommes d'argent similaires aux deux groupes, ne montrant aucune différence comportementale.

• Attitudes : Cependant, les attitudes exprimées (peur, anxiété) envers les ex-combattants restaient négatives.

Cette dissociation montre que même les personnes non directement affectées sont capables de surmonter leurs préjugés et leurs peurs pour s'engager dans des actions coopératives lorsqu'une solution pacifique est en jeu.

4. Recommandations Stratégiques pour la Construction de la Paix

Les experts proposent une série de recommandations pour sortir des conflits violents et faire prévaloir la paix.

Recommandation

Description

Expert(s)

Communication efficace

Communiquer sur les politiques de réconciliation de manière à légitimer l'aide aux victimes et aux ex-combattants, en reconnaissant explicitement la souffrance de l'autre pour éviter le "renversement du stigmate" (une réaction négative de la part de ceux qui ne bénéficient pas des politiques).

Enrique Fatas

Gestion de la mémoire

Ne pas utiliser la mémoire du conflit de manière partisane, car cela perpétue le conflit et peut nuire aux compétences cognitives et aux perspectives économiques des victimes, même des années plus tard.

Enrique Fatas

Cadre de l'aversion à la perte

Communiquer non pas sur les gains de la paix, mais sur ce que la société a à perdre si le conflit violent persiste. Ce cadre est plus puissant pour motiver l'action.

Lina Restrepo

Empathie et perspective

Intégrer activement le point de vue des victimes dans le discours public pour que les non-victimes développent une plus grande empathie envers une solution pacifique.

Lina Restrepo

Hypothèse du contact

Faciliter le contact direct entre les membres des groupes opposés. Apprendre à connaître l'autre en tant que personne (avec une famille, une histoire) est un puissant antidote à la déshumanisation.

Lina Restrepo

S'attaquer aux causes profondes

S'assurer que les raisons sous-jacentes qui ont déclenché le conflit en premier lieu (inégalités, manque de confiance dans les institutions) sont résolues pour éviter une résurgence de la violence.

Lina Restrepo

Canaliser les énergies existantes

Au lieu d'essayer de "pousser les gens à agir", il est plus efficace d'identifier, de soutenir et d'aider à coordonner les énergies, les mouvements sociaux et les initiatives positives qui existent déjà au sein de la société.

Adam Kahane

Transformer le conflit

Accepter que le but n'est pas d'éliminer le conflit mais de le transformer en un processus non-violent. Le conflit est inévitable ; la violence ne l'est pas.

Adam Kahane, Enrique Fatas

5. Le Changement Climatique : Une Analogie pour la "Collaboration Radicale"

Le changement climatique est utilisé comme une analogie puissante pour les conflits complexes du 21e siècle.

• C'est un problème non-unilatéral et non-local : aucune nation ou groupe ne peut le résoudre seul.

• Il représente un "conflit sans violence" où des intérêts divergents (agriculteurs, industries, gouvernements) s'affrontent.

• Il est caractérisé par une urgence temporelle ("ticking clock") qui rend l'inaction catastrophique.

Face à ce défi, Adam Kahane préconise une "collaboration radicale" qui intègre la vitesse, l'ampleur et la justice. Cependant, un risque majeur, souligné par Lina Restrepo, est la normalisation : à force d'entendre parler de la crise, les populations s'y habituent et l'urgence perçue diminue, ce qui paralyse l'action.

Conclusion : De l'Espoir à l'Action

La discussion se conclut sur une note pragmatique et pleine d'espoir.

La clé pour résoudre les conflits les plus complexes, qu'il s'agisse de guerres civiles ou de crises globales comme le changement climatique, ne réside pas dans la création de solutions ex nihilo.

Elle réside plutôt dans notre capacité à "capter les énergies qui circulent déjà".

Des mouvements positifs, des leaders et des initiatives existent toujours.

Le véritable défi est de les identifier, de les unir et de les amplifier pour transformer les dynamiques de conflit en collaboration constructive.

Reviewer #2 (Public review):

This study by Anttonen, Christensen-Dalsgaard, and Elemans describes the development of hearing thresholds in an altricial songbird species, the zebra finch. The results are very clear and along what might have been expected for altricial birds: at hatch (2 days post-hatch), the chicks are functionally deaf. Auditory evoked activity in the form of auditory brainstem responses (ABR) can start to be detected at 4 days post-hatch, but only at very loud sound levels. The study also shows that ABR response matures rapidly and reaches adult-like properties around 25 days post-hatch. The functional development of the auditory system is also frequency dependent, with a low-to-high frequency time course. All experiments are very well performed. The careful study throughout development and with the use of multiple time-points early in development is important to further ensure that the negative results found right after hatching are not the result of the experimental manipulation. The results themselves could be classified as somewhat descriptive, but, as the authors point out, they are particularly relevant and timely. Since 2016, there have been a series of studies published in high-profile journals that have presumably shown the importance of prenatal acoustic communication in altricial birds, mostly in zebra finches. This early acoustic communication would serve various adaptive functions. Although acoustic communication between embryos in the egg and parents has been shown in precocial birds (and crocodiles), finding an important function for prenatal communication in altricial birds came as a surprise. Unfortunately, none of those studies performed a careful assessment of the chicks' hearing abilities. This is done here, and the results are clear: zebra finches at 2 and 6 days post-hatch are functionally deaf. Since it is highly improbable that the hearing in the egg is more developed than at birth, one can only conclude that zebra finches in the egg (or at birth) cannot hear the heat whistles. The paper also ruled out the detection on egg vibrations as an alternative path. The prior literature will have to be corrected, or further studies conducted to solve the discrepancies. For this purpose, the "companion" paper on bioRxiv that studies the bioacoustical properties of heat calls from the same group will be particularly useful. Researchers from different groups will be able to precisely compare their stimuli.

Beyond the quality of the experiments, I also found that the paper was very well written. The introduction was particularly clear and complete (yet concise).

Weaknesses:

My only minor criticism is that the authors do not discuss potential differences between behavioral audiograms and ABRs. Optimally, one would need to repeat the work of Okanoya and Dooling with your setup and using the same calibration. The ~20dB difference might be real, or it might be due to SPL measured with different instruments, at different distances, etc. Either way, you could add a sentence in the discussion that states that even with the 20 dB difference in audiogram heat whistles would not be detected during the early days post-hatch. But adding a (novel) behavioral assay in young birds could further resolve the issue.

More Minor Points:

(1) As mentioned in the main text, the duration of pips (from pips to bursts) affects the effective bandwidth of the stimulus. I believe that the authors could give an estimate of this effective bandwidth, given what is known from bird auditory filters. I think that this estimate could be useful to compare to the effective bandwidth of the heat-call, which can now also be estimated.

(2) Figure 5b. Label the green and pink areas as song and heat-call spectrum. Also note that in the legend the authors say: "Green and red areas display the frequency windows related to the best hearing sensitivity of zebra finches and to heat calls, respectively". I don't think this is what they meant. I agree that 1-4 kHz is the best frequency sensitivity of zebra finches, but they probably meant green == "song frequency spectrum" and pink == "heat call spectrum". In either case, the figure and the legend need clarification.

(3) Figure 5c. Here also, I would change the song and heat-call labels to "song spectrum", "heat call spectrum". The authors would not want readers to think that they used song and heat calls in these experiments (maybe next time?). For the same reason, maybe in 5a you could add a cartoon of the oscillogram of a frequency sweep next to your speaker.

(4) Methods. In the description of the stimulus, the authors describe "5ms long tone bursts", but these are the tone pips in the main part of the manuscript. Use the same terms.

Reviewer #3 (Public review):

Summary

Following recent findings that exposure to natural sounds and anthropogenic noise before hatching affects development and fitness in an altricial songbird, this study attempts to estimate the hearing capacities of zebra finch nestlings and the perception of high frequencies in that species. It also tries to estimate whether airborne sound can make zebra finch eggs vibrate, although this is not relevant to the question.

Strength

That prenatal sounds can affect the development of altricial birds clearly challenges the long-held assumption that altricial avian embryos cannot hear. However, there is currently no data to support that expectation. Investigating the development of hearing in songbirds is therefore important, even though technically challenging. More broadly, there is accumulating evidence that some bird species use sounds beyond their known hearing range (especially towards high frequencies), which also calls for a reassessment of avian auditory perception.

Weaknesses

Rather than following validated protocols, the study presents many experimental flaws and two major methodological mistakes (see below), which invalidate all results on responses to frequency-specific tones in nestlings and those on vibration transmission to eggs, as well as largely underestimating hearing sensitivity. Accordingly, the study fails to detect a response in the majority of individuals tested with tones, including adults, and the results are overall inconsistent with previous studies in songbirds. The text throughout the preprint is also highly inaccurate, often presenting only part of the evidence or misrepresenting previous findings (both qualitatively and quantitatively; some examples are given below), which alters the conclusions.

Conclusion and impact

The conclusion from this study is not supported by the evidence. Even if the experiment had been performed correctly, there are well-recognised limitations and challenges of the method that likely explain the lack of response. The preprint fails to acknowledge that the method is well-known for largely underestimating hearing threshold (by 20-40dB in animals) and that it may not be suitable for a 1-gram hatchling. Unlike what is claimed throughout, including in the title, the failure to detect hearing sensitivity in this study does not invalidate all previous findings documenting the impacts of prenatal sound and noise on songbird development. The limitations of the approach and of this study are a much more parsimonious explanation. The incorrect results and interpretations, and the flawed representation of current knowledge, mean that this preprint regrettably creates more confusion than it advances the field.

Detailed assessment

For brevity, only some references are included below as examples, using, when possible, those cited in the preprint (DOI is provided otherwise). A full review of all the studies supporting the points below is beyond the scope of this assessment.

(A) Hearing experiment

The study uses the Auditory Brainstem Response (ABR), which measures minute electrical signals transmitted to the surface of the skull from the auditory nerve and nuclei in the brainstem. ABR is widely used, especially in humans, because it is non-invasive. However, ABR is also a lot less sensitive than other methods, and requires very specific experimental precautions to reliably detect a response, especially in extremely small animals and with high-frequency sounds, as here.

(1) Results on nestling frequency sensitivity are invalid, for failing to follow correct protocols:

The results on frequency testing in nestlings are invalid, since what might serve as a positive control did not work: in adults, no response was detected in a majority of individuals, at the core of their hearing range, with loud 95dB sounds (Figure S1), when testing frequency sensitivity with "tone burst".

This is mostly because the study used a stimulation duration 5 times larger than the norm. It used 25ms tone bursts, when all published avian studies (in altricial or precocial birds) used stimulation of 5ms or less (when using subdermal electrodes as here; e.g., cited: Brittan-Powell et al 2004; not cited: Brittan-Powell et al 2002 (doi: 10.1121/1.1494807), Henry & Lucas 2008 (doi: 10.1016/j.anbehav.2008.08.003)). Long stimulations do not make sense and are indeed known to interfere with the detection of an ABR response, especially at high frequencies, as, for example, explicitly tested and stated in Lauridsen et al 2021 (cited).

Adult response was then re-tested with a correct 5ms tone duration ("tone-pip"), which showed that, for the few individuals that responded to 25ms tones, thresholds were abnormally high (c.a. by 30dB; Figure 2C).<br /> Yet, no nestlings were retested with a correct protocol. There is therefore no valid data to support any conclusion on nestling frequency hearing. Under these circumstances, the fact that some nestlings showed a response to 25ms tones from day 8 would argue against them having very low sensitivity to sound.

(2) Responses to clicks underestimate hearing onset by several days:

Without any valid nestling responses to tones (see # 1), establishing the onset of hearing is not possible based on responses to clicks only, since responses to clicks occur at least 4 days after responses to tones during development (Saunders et al, 1973). Here, 60% of 4-day-old individuals responding to clicks means most would have responded to tones at and before 2 days post-hatch, had the experiment been done correctly.<br /> Responses to tones are indeed observed in other songbirds at 1day post-hatch (see #6).

In budgerigars, hearing onset occurs before 5 days post hatch, since responses to both clicks and tones were detectable at the first age tested at 5dph (Brittan-Powell et al, 2004).

(3) Experimental parameters chosen lower ABR detectability, specifically in younger birds:

Very fast stimulus repetition rate inhibits the ABR response, especially in young:

(a) The stimulus presentation rate (25 stim/ sec) is 6 times faster than zebra finch heat-calls, and 5 to 25 times faster than most previous studies in young birds (e.g., cited: Saunders et al 1973, 1974: 1 stim/sec or less; Katayama 1985: 3.3 clicks/sec; Brittan-Powell et al 2004: 4 stim/sec). Faster rates saturate the neurons and accordingly are known to decrease ABR amplitude and increase ABR latency, especially in younger animals with an immature nervous system. In birds, this occurs especially in the range from 5 to 30 stim/sec (e.g., cited: Saunder et al 1973, Brittan-Powell et al 2004). Values here with 25 rather than 1-4 stim/min are therefore underestimating true sensitivity.

(b) Averaging over only 400 measures is insufficient to reliably detect weak ABR signals:

The study uses 2 to 3 times fewer measures per stimulation type than the recommended value of 1,000 (e.g., Brittan-Powell et al 2002, 2024; Henry & Lucas 2008). This specifically affects the detection of weak signals, as in small hatchlings with tiny brains (adult zebra finches are 12-14g).

(c) Body temperature is not specified and strongly affects the ABR:

Controlling the body temperature of hatchlings of 1-4 grams (with a temperature probe under a 5mm-wide wing) would be very challenging. Low body temperature entirely eliminates the ABR, and even slight deviance from optimal temperature strongly increases wave latency and decreases wave amplitude (e.g., cited: Katayama 1985).

(d) Other essential information is missing on parameters known to affect the ABR:

This includes i) the weight of the animals, ii) whether and how the response signal was amplified and filtered, iii) how the automatised S/N>2 criteria compared to visual assessment for wave detection, and iv) what measures were taken to allow the correct placement of electrodes on hatchlings less than 5 grams.

(4) Results in adults largely underestimate sensitivity at high frequencies, and are not the correct reference point:

(a) Thresholds measured here at high frequencies for adults (using the correct stimulus duration, only done on adults) are 10-30dB higher than in all 3 other published ABR studies in adult zebra finches (cited: Zevin et al 2004; Amin et al 2007; not cited: Noirot et al 2011 (10.1121/1.3578452)), for both 4 and 6 kHz tone pips.

(b) The underlying assumption used throughout the preprint that hearing must be adult-like to be functional in nestlings does not make sense. Slower and smaller neural responses are characteristic of immature systems, but it does not mean signals are not being perceived.

(5) Failure to account for ABR underestimation leads to false conclusions:

(a) Whether the ABR method is suitable to assess hearing in very small hatchlings is unknown. No previous avian study has used ABR before 5 days post-hatch, and all have used larger bird species than the zebra finch.

(b) Even when performed correctly on large enough animals, the ABR systematically underestimates actual auditory sensitivity by 20-40 dB, especially at high frequencies, compared to behavioural responses (e.g., none cited: Brittan-Powell et al 2002, Henry & Lucas 2008, Noirot et al 2011). Against common practice, the preprint fails to account for this, leading to wrong interpretations. For example, in Figure 1G (comparing to heat call levels), actual hearing thresholds would be 30-40dB below those displayed. In addition, the "heat whistle" level displayed here (from the same authors) is 15dB lower than their second measure that they do not mention, and than measures obtained by others (unpublished data). When these two corrections are made - or even just the first one - the conclusion that heat-call sound levels are below the zebra finch hearing threshold does not hold.

(c) Rather than making appropriate corrections, the preprint uses a reference in humans (L180), where ABR is measured using a much more powerful method (multi-array EEG) than in animals, and from a larger brain. The shift of "10-20dB" obtained in humans is not applicable to animals.

(6) Results are inconsistent with previous findings in developing songbirds:

As expected from all of the above, results and conclusions in the preprint are inconsistent with findings in other songbirds, which, using other methods, show for example, auditory sensitivity in:

(a) zebra finch embryos, in response to song vs silence (not cited: Rivera et al 2018, doi: 10.1097/WNR.0000000000001187)

(b) flycatcher hatchlings at 2-3d post hatch (first age tested), across a wide range of frequencies (0.3 to 5kHz), at low to moderate sound levels (45-65dB) (cited: Aleksandrov and Dmitrieva 1992, not cited: Korneeva et al 2006 (10.1134/S0022093006060056)).

(c) songbird nestlings at 2-6d post hatch, which discriminate and behaviourally respond to relevant parental calls or even complex songs. This level of discrimination requires good hearing across frequencies (e.g., not cited: Korneeva et al 2006; Schroeder & Podos 2023 (doi: 10.1016/j.anbehav.2023.06.015)).

(d) zebra finch nestlings at 13d post-hatch, which show adult-like processing of songs in the auditory cortex (CNM) (Schroeder & Remage‐Healey 2021, doi: 10.1002/dneu.22802).

(e) zebra finch juveniles, which are able to perceive and learn song syllables at 5-7kHz (fundamental frequency) with very similar acoustic properties to heat calls, and also produced during inspiration (Goller & Daley 2001, doi: 10.1098/rspb.2001.1805).

NONE of these results - which contradict results and claims in the preprint - are mentioned. Instead, the preprint focuses on very slow-developing species (parrots and owls), which take 2-4 times longer than songbirds to fledge (cited: Brittan-Powell et al 2004; Köppl & Nickel 2007; Kraemer et al 2017).

(7) Results in figures are misreported in the text, and conclusions in the abstract and headers are not supported by the data:

For example:

(a) The data on Figure 1E shows that at 4 days old, 8 out of 13 nestlings (60%) responded to clicks, but the text says only 5/13 responded (L89). When 60% (4dph) and 90% (6dph) of individuals responded, the correct term would be that "most animals", rather than "some animals" responded (L89). Saying that ABR to loud sound appeared "in the majority only after one week" (L93) is also incorrect, given the data. It follows that the title of the paragraph is also erroneous.

(b) The hearing threshold is underestimated by 40dB at 6 and 8Kz on Fig 2C, not by "10-20dB" as reported in the text (L178).

(B) Egg vibration experiment

(8) Using airborne sound to vibrate eggs is biologically irrelevant:

The measurement of airborne sound levels to vibrate eggs misunderstands bone conduction hearing and is not biologically meaningful: zebra finch parents are in direct contact with the eggs when producing heat calls during incubation, not hovering in front of the nest. This misunderstanding affects all extrapolations from this study to findings in studies on prenatal communication.

(C) Misrepresentation of current knowledge

(9) Values from published papers are misreported, which reverses the conclusions:

Most critical examples:

(a) Preprint: "Zebra finch most sensitive hearing range of 1-to-4 kHz (Amin et al., 2007; Okanoya and Dooling, 1987; Yeh et al., 2023)" (L173).<br /> Actual values in the studies cited are:

1-to-7kHz, in Amin et al 2007 (threshold [=50dB with ABR] is the same at 7kHz and 1KHz).

1-to-6 kHz, in Okanoya and Dooling (the threshold [=30dB with behaviour] is actually lower at 6kHz than at 1KHz).

1-to-7kHz, in Yeh et al (threshold [=35-38dB with behaviour] is the same at 7kHz and 1KHz).

Note that zebra finch nestlings' begging calls peaking at 6kHz (Elie & Theunissen 2015, doi: 10.1007/s10071-015-0933-6), would fall 2kHz above the parents' best hearing range if it were only up to 4kHz.

(b) The preprint incorrectly states throughout (e.g., L139, L163, L248) that heat-calls are 7-10kHz, when the actual value is 6-10kHz in the paper cited (Katsis et al, 2018).

(c) Using the correct values from these studies, and heat-calls at 45 dB SLP (as measured by others (unpublished data), or as measured by the authors themselves, but which is not reported here (Anttonen et a,l 2025), the correct conclusion is that heat calls fall within the known zebra finch hearing range.

(10) Published evidence towards high-frequency hearing, including in early development, is systematically omitted:

(a) Other studies showing birds use high frequencies above the known avian hearing range are ignored. This includes oilbirds (7-23kHz; Brinklov et al 2017; by 1 of the preprint authors, doi: 10.1098/rsos.170255) and hummingbirds (10-20kHz; Duque et al 2020, doi: 10.1126/sciadv.abb9393), and in a lesser extreme, zebra finches' inspiratory song syllables at 5-7kHz (Goller & Dalley, 2001).

(b) The discussion of anatomical development (L228-241) completely omits the well-known fact that the avian basilar papilla develops from high to low frequencies (i.e., base to apex), which - as many have pointed out - is opposite to the low-to-high development of sensitivity (e.g., cited: Cohen & Fermin 1978; Caus Capdevila et al 2021).

(c) High frequency hearing in songbirds at hatching is several orders of magnitude better than in chickens and ducks at the same age, even though songbirds are altricial (e.g., at 4kHz, flycatcher: 47dB, chicken-duck: 90dB; at 5kHz, flycatcher: 65dB, chicken-duck: 115dB; Korneeva et al 2006, Saunders et al 1974). That is because Galliformes are low-frequency specialists, according to both anatomical and ecological evidence, with calls peaking at 0.8 to 1.2kHz rather than 2-6kHz in songbirds. It is incorrect to conclude that altricial embryos cannot perceive high frequencies because low-frequency specialist precocial birds do not (L250;261).

The references used to support the statement on a very high threshold for precocial birds above 6kHz are also wrong (L250). Katayama 1985 did not test embryos, nor frequency tones. Neither of these two references tested ducks.

(11) Incorrect statements do not reflect findings from the references cited

For example:

(a) "in altricial bird species hearing typically starts after hatching" (L12, in abstract), "with little to no functional hearing during embryonic stages (Woolley, 2017)." (L33).

There is no evidence, in any species, to support these statements. This is only a - commonly repeated - assumption, not actually based on any data. On the contrary, the extremely limited evidence to date shows the opposite, with zebra finch embryos showing ZENK activation in the auditory cortex in response to song playback (Rivera et al, 2018, not cited).

The book chapter cited (Woolley 2017) acknowledges this lack of evidence, and, in the context of song learning, provides as only references (prior to 2018), 2 studies showing that songbirds do not develop a normal song if the song tutor is removed before 10d post-hatch. That nestlings cannot memorise (to later reproduce) complex signals heard before d10 does not mean that they are deaf to any sound before day 10.

Studies showing hearing in young songbird nestlings (see point 6 above) also contradict these statements.

(b) "Zebra finch embryos supposedly are epigenetically guided to adapt to high temperatures by their parents high-frequency "heat calls" " (L36 and L135).

This is an extremely vague and meaningless description of these results, which cannot be assessed by readers, even though these results are presented as a major justification for the present study. Rather than giving an interpretation of what "supposedly" may occur, it would be appropriate to simply synthesize the empirical evidence provided in these papers. They showed that embryonic exposure to heat-calls, as opposed to control contact calls, alters a suite of physiological and behavioural traits in nestlings, including how growth and cellular physiology respond to high temperatures. This also leads to carry-over effects on song learning and reproductive fitness in adulthood.

(c) "The acoustic communication in precocial mallard ducks depends specifically on the low-frequency auditory sensitivity of the embryo (Gottlieb, 1975)" (L253)

The study cited (Gottlieb, 1975) demonstrates exactly the opposite of this statement: it shows that duckling embryos, not only perceive high frequency sounds (relative to the species frequency range), but also NEED this exposure to display normal audition and behaviour post-hatch. Specifically, it shows that duckling embryos deprived of exposure to their own high-frequency calls (at 2 kHz), failed to identify maternal calls post-hatch because of their abnormal insensitivity to higher frequencies, which was later confirmed by directly testing their auditory perception of tones (Dimitrieva & Gottlieb, 1994).

(12) Considering all of the mistakes and distortions highlighted above, it would be very premature to conclude, based on these results and statements, that altricial avian embryos are not sensitive to sound. This study provides no actual scientific ground to support this conclusion.

Author Response:

We thank all reviewers for their time and effort to carefully review our paper and for the constructive comments on our manuscript. Below we outline our planned revisions to the public reviews of the three reviewers.

In our revision, we will include more details regarding our ABR measurements (including temperature, animal metadata), analysis (including filter settings) and lay out a much more detailed motivation for our ABR signal design. Furthermore, we will provide a more detailed discussion on the caveats of the technique and the interpretation of ABR data in general and our data specifically. Furthermore, we will add more discussion on differences between ABR based audiograms and behavioural data. The authors have extensive experience with the ABR technique and are well aware of its limitations, but also its strengths for use in animals that cannot be trained on behavioural tasks such as the very young zebra finches in this study. These additions will strengthen our paper. We think our conclusions remain justified by our data.

Reviewer #1 and #2:

We thank both reviewers for their positive words and suggested improvements. The planned general improvements listed above will take care of all suggestions and comments in the public review.

Reviewer #3:

We thank the reviewer for the detailed critique of our manuscript and many suggestions for improvement. The planned general improvements listed above will take care of many of the suggestions and comments listed in the public review. Here we will highlight a few first responses that we will address in detail in our resubmission.

The reviewer’s major critiques can be condensed to the following four points.

(1) ABR cannot be done in such small animals.

This critique is unfounded. ABR measures the summed activity in the auditory pathway, and with smaller distance from brainstem to electrodes in small animals, the ABR signals are expected to have higher amplitude and consequently better SNR.  Thus, smaller animals should lead to higher amplitude ABR signals. We have successfully recorded ABR in animals smaller than 2 DPH zebra finches to support this claim (zebrafish (Jørgensen et al., 2012), 10 mm froglets (Goutte et al., 2017) and 5 mm salamanders (Capshaw et al., 2020). It is more surprising the technique still provides robust signals even in very large animals such as Minke whales (Houser et al., 2024).

(2) The ABR methods used does not follow protocol for other published work in birds. Particularly the 25 ms long duration tone bursts may have underestimated high frequency hearing.

There is no fixed protocol for ABR measurements, and several studies of bird ABR have used as long or even longer durations. Longer-duration signals were chosen deliberately and are necessary to have a sufficient number of cycles and avoid frequency splatter at our lowest frequencies used (see Lauridsen et al., 2021).

(3) Sensitivity data should be corrected from ABR to behavioural data.

We present the results of our measurements on hearing sensitivity using ABR, and ABR based thresholds are generally less sensitive than thresholds based on behavioural studies (presented in Fig 2c). Correcting for these measurements to behavioural thresholds is of course possible, but presenting only the corrected thresholds would be a misrepresentation of our sensitivity data. Even so it should be done only within species and age group and such data is currently not available. In our revision, we will include elaborate discussion on this topic.

(4) Results are inconsistent with papers in developing songbirds.

We agree that our results do not support and even question the claims in earlier work. These papers however do either 1) not measure hearing physiology or 2) do so in different species. To our best knowledge there is presently no data published on the auditory physiology development in songbird embryos. Our data are consistent with what is known about the physiology of auditory development in all birds studied so far. We will provide a detailed discussion on this topic in our revision.

References

Capshaw et al. (2020) J Exp Biol 223: jeb236489

Goutte et al. (2017) Sci Rep 7: 12121, doi 10.1038/s41598-017-12145-5

Houser et al. (2024) Science 386, 902-906. DOI:10.1126/science.ado7580).

Jørgensen et al. (2012) Adv Exp Med Biol 730: 117-119

Lauridsen et al (2021) J Exp Biol 224: jeb237313. https://doi.org/10.1242/jeb.237313

I think this is an interesting thing to point out as well. Plagiarism isn't always purposeful or with mal-intent but it doesn't make it any less harmful.

This is a limiting factor that could potentially change your adventure but it is a good point.

It is important to do research in where you ant to go, you want to save yourself an unfortunate surprise.

No natter ehere you go if planned right it will turn out amazing

Reviewer #1 (Public review):

Summary:

Dorrego-Rivas et al. investigated two different DA neurons and their neurotransmitter release properties in the main olfactory bulb. They found that the two different DA neurons in mostly glomerular layers have different morphologies as well as electrophysiological properties. The anaxonic DA neurons are able to self-inhibit but the axon-bearing ones are not. The findings are interesting and important to increase the understanding both of the synaptic transmissions in the main olfactory bulb and the DA neuron diversity. However, there are some major questions that the authors need to address to support their conclusions.

(1) It is known that there are two types of DA neurons in the glomerular layer with different diameters and capacitances (Kosaka and Kosaka, 2008; Pignatelli et al., 2005; Angela Pignatelli and Ottorino Belluzzi, 2017). In this manuscript, the authors need to articulate better which layer the imaging and ephys recordings took place, all glomerular layers or with an exception. Meanwhile, they have to report the electrophysiological properties of their recordings, including capacitances, input resistance, etc.

(2) It is understandable that recording the DA neurons in the glomerular layer is not easy. However, the authors still need to increase their n's and repeat the experiments at least three times to make their conclusion more solid. For example (but not limited to), Fig 3B, n=2 cells from 1 mouse. Fig.4G, the recording only has 3 cells.

(3) The statistics also use pseudoreplicates. It might be better to present the biology replicates, too.

(4) In Figure 4D, the authors report the values in the manuscript. It is recommended to make a bar graph to be more intuitive.

(5) In Figure 4F and G, although the data with three cells suggest no phenotype, the kinetics looked different. So, the authors might need to explore that aside from increasing the n.

(6) Similarly, for Figure 4I and J, L and M, it is better to present and analyze it like F and G, instead of showing only the after-antagonist effect.

Comments on revisions:

In the rebuttal, the authors argued that it had been extremely hard to obtain recordings stable enough for before-and-after effects on the same cell. Alternatively, they could perform the before-and-after comparison on different cells.

Author response:

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

Reviewer #1 (Public review):

This Reviewer was positive about the study, stating ‘The findings are interesting and important to increase the understanding both of the synaptic transmissions in the main olfactory bulb and the DA neuron diversity.’ They provided a number of helpful suggestions for improving the paper, which we have incorporated as follows:

(1) It is known that there are two types of DA neurons in the glomerular layer with different diameters and capacitances (Kosaka and Kosaka, 2008; Pignatelli et al., 2005; Angela Pignatelli and Ottorino Belluzzi, 2017). In this manuscript, the authors need to articulate better which layer the imaging and ephys recordings took place, all glomerular layers or with an exception. Meanwhile, they have to report the electrophysiological properties of their recordings, including capacitances, input resistance, etc.

We thank the Reviewer for this clarification. Indeed, the two dopaminergic cell types we study here correspond directly to the subtypes previously identified based on cell size. Our previous work showed that axon-bearing OB DA neurons have significantly larger somas than their anaxonic neighbours (Galliano et al. 2018), and we replicate this important result in the present study (Figure 3D). In terms of electrophysiological correlates of cell size, we now provide full details of passive membrane properties in the new Supplementary Figure 4, as requested. Axon-bearing DA neurons have significantly lower input resistance and show a non-significant trend towards higher cell capacitance. Both features are entirely consistent with the larger soma size in this subtype. We apologise for the oversight in not fully describing previous categorisations of OB DA neurons, and have now added this information and the appropriate citations to the Introduction (lines 56 to 59 of the revised manuscript). 

In terms of cell location, all cells in this study were located in the OB glomerular layer. We sampled the entire glomerular layer in all experiments, including the glomerular/EPL border where the majority of axon-bearing neurons are located (Galliano et al. 2018). This is now clarified in the Materials and Methods section (lines 535 to 537 and 614 to 616 of the revised manuscript).

(2) It is understandable that recording the DA neurons in the glomerular layer is not easy. However, the authors still need to increase their n's and repeat the experiments at least three times to make their conclusion more solid. For example (but not limited to), Fig 3B, n=2 cells from 1 mouse. Fig.4G, the recording only has 3 cells.

Despite the acknowledged difficulty of these experiments, we have now added substantial extra data to the study as requested. We have increased the number of cells and animals to further support the following findings:

Fig 3B: we now have n=5 cells from N=3 mice. We have created a new Supplementary Figure 1 to show all the examples.

Figure 4G: we now have n=6 cells from N=4 mice.

Figure 5G: we now have n=3 cells from N=3 mice.

The new data now provide stronger support for our original conclusions. In the case of auto-evoked inhibition after the application of D1 and D2 receptor antagonists, a nonsignificant trend in the data suggests that, while dopamine is clearly not necessary for the response, it may play a small part in its strength. We have now included this consideration in the Results section (lines 256 to 264 of the revised manuscript).

(3) The statistics also use pseudoreplicates. It might be better to present the biology replicates, too.

Indeed, in a study focused on the structural and functional properties of individual neurons, we performed all comparisons with cell as the unit of analysis. This did often (though not always) involve obtaining multiple data points from individual mice, but in these low-throughput experiments n was never hugely bigger than N. The potential impact of pseudoreplicates and their associated within-animal correlations was therefore low. We checked this in response to the Reviewer’s comment by running parallel nested analyses for all comparisons that returned significant differences in the original submission. These are the cases in which we would be most concerned about potential false positive results arising from intra-animal correlations, which nested tests specifically take into account (Aarts et al., 2013). In every instance we found that the nested tests also reported significant differences between anaxonic and axonbearing cell types, thus fully validating our original statistical approach. We now report this in the relevant section of the Materials and Methods (lines 686 to 691 of the revised manuscript).

(4) In Figure 4D, the authors report the values in the manuscript. It is recommended to make a bar graph to be more intuitive.

This plot does already exist in the original manuscript. We originally describe these data to support the observation that an auto-evoked inhibition effect exists in anaxonic neurons (corresponding to now lines 240 to 245 of the revised manuscript). We then show them visually in their entirety when we compare them to the lack of response in axon-bearing neurons, depicted in Figure 5C. We still believe that this order of presentation is most appropriate for the flow of information in the paper, so have maintained it in our revised submission.

(5) In Figure 4F and G, although the data with three cells suggest no phenotype, the kinetics looked different. So, the authors might need to explore that aside from increasing the n.

We thank the Reviewer for this suggestion. To quantify potential changes in the autoevoked inhibition response kinetics, we fitted single exponential functions and compared changes in the rate constant (k; Methods, lines 650 to 652 of the revised manuscript). Overall, we observed no consistent or significant change in rate constant values after adding DA receptor antagonists. This finding is now reported in the Results section (lines 260 to 263 of the revised manuscript) and shown in a new Supplementary Figure 3.

(6) Similarly, for Figure 4I and J, L and M, it is better to present and analyze it like F and G, instead of showing only the after-antagonist effect.

We agree that the ideal scenario would have been to perform the experiments in Figure 4J and 4M the same way as those in Figure 4G, with a before vs after comparison. Unfortunately, however, this was not practically possible. 

When attempting to apply carbenoxelone to already-patched cells, we found that this drug highly disrupted the overall health and stability of our recordings immediately after its application. This is consistent with previous reports of similar issues with this compound (e.g. Connors 2012, Epilepsy Currents; Tovar et al., 2009, Journal of Neurophysiology). After many such attempts, the total yield of this experiment was one single cell from one animal. Even so, as shown in the traces below, we were able to show that the auto-evoked inhibition response was not eliminated in this specific case:

Author response image 1.

Traces of an AEI response recorded before (magenta) and after (green) the application of carbenoxolone (n=1 cell from N=1 mouse).

In light of these issues, we instead followed published protocols in applying the carbenoxolone directly in the bath without prior recording for 20 minutes (following Samailova et al., 2003, Journal of Neurochemistry) and ran the protocol after that time. Given that our main question was to ask whether gap junctions were strictly necessary for the presence of any auto-evoked inhibition response, our positive findings in these experiments still allowed us to draw clear conclusions.

In contrast, the issue with the NKCC1 antagonist bumetanide was time. As acknowledged by this Reviewer, obtaining and maintaining high-quality patch recordings from OB DA neurons is technically challenging. Bumetanide is a slow-acting drug when used to modify neuronal chloride concentrations, because in addition to the time it takes to reach the neurons and effectively block NKCC1, the intracellular levels of chloride subsequently change slowly. Studies using this drug in slice physiology experiments typically use an incubation time of at least 20 minutes (e.g. Huberfeld et al., 2007, Journal of Neuroscience), which was incompatible with productive data collection in OB DA neurons. Again, after many unsuccessful efforts, we were forced instead to include bumetanide in the bath without prior recording for 20-30 minutes. As with the carbenoxolone experiment, our goal here was to establish whether autoevoked inhibition was in any way retained in the presence of this drug, so our positive result again allowed us to draw clear conclusions.

Reviewer #1 (Recommendations for the authors):

(1) I suggest the authors reconsider the terminology. For example, they use "strikingly" in their title. The manuscript reported two different transmitter release strategies but not the mechanisms, and the word "strikingly" is not professional, either.

We appreciate the Reviewer’s attention to clarity and tone in the manuscript title, and have nevertheless decided to retain the original wording. The almost all-or-nothing differences between closely related cell types shown in structural and functional properties here (Figures 3F & 5C) are pronounced, extremely clear and easily spotted – all properties appropriate for the word ‘striking.’ In addition, we note that the use of this term is not at all unprofessional, with a PubMed search for ‘strikingly’ in the title of publications returning over 200 hits.

(2) Similarly, almost all confocal scopes are 3D because images can be taken at stacks. So "3D confocal" is misleading.

We understand that this is misleading. We have now replaced the sentence ‘Example snapshot of a 3D confocal stack of…’ by ‘Example confocal images of…’ in all the figure legends that apply.

(3) It is recommended to present the data in bar graphs with data dots instead of showing the numbers in the manuscript directly.

We agree entirely, and now present data plots for all comparisons reported in the study (Supplementary Figures 2, 4 and 5).

Reviewer #2 (Recommendations for the authors):

(1) Several experiments report notably small sample sizes, such as in Figures 3B and 5G, where data from only 2 cells derived from 1-2 mice are presented. Figures 4E-G also report the experimental result only from 3 cells derived from 3 mice. To enhance the statistical robustness and reliability of the findings, these experiments should be replicated with larger sample sizes.

As per our response to Reviewer 1’s comment #2 above, and to directly address the concern that some evidence was ‘incomplete’, we have now added significant extra data and analysis to this revised submission (Figures 4 and 5; and Supplementary Figure 1). We believe that this has further enhanced the robustness and reliability of our findings, as requested.

(2) The authors utilize vGAT-Cre for Figures 1-3 and DAT-tdTomato for Figures 4-5, raising concerns about consistency in targeting the same population of dopaminergic neurons. It remains unclear whether all OB DA neurons express vGAT and release GABA. Clarification and additional evidence are needed to confirm whether the same neuronal population was studied across these experiments.

Although we indeed used different mouse lines to investigate structural and functional aspects of transmitter release, we can be very confident that both approaches allowed us to study the same two distinct DA cell types being compared in this paper. Existing data to support this position are already clear and strong, so in this revision we have focused on the Reviewer’s suggestion to clarify the approaches we chose.

First, it is well characterised that in mouse and many other species all OB DA neurons are also GABAergic. This has been demonstrated comprehensively at the level of neurochemical identity and in terms of dopamine/GABA co-release, and is true across both small-soma/anaxonic and large-soma/axon-bearing subclasses (Kosaka & Kosaka 2008; 2016; Maher & Westbrook 2008; Borisovska et al., 2013; Vaaga et al., 2016; Liu et al. 2013). To specifically confirm vGAT expression, we have also now provided additional single-cell RNAseq data and immunohistochemical label in a revised Figure 1 (see also Panzanelli et al., 2007, now referenced in the paper, who confirmed endogenous vGAT colocalisation in TH-positive OB neurons). Most importantly, by using vGAT-cre mice here we were able to obtain sufficient numbers of both anaxonic and axon-bearing DA neurons among the vGAT-cre-expressing OB population. We could unambiguously identify these cells as dopaminergic because of their expression of TH protein which, due to the absence of noradrenergic neurons in the OB, is a specific and comprehensive marker for dopaminergic cells in this brain region (Hokfelt et al., 1975; Rosser et al., 1986; Kosaka & Kosaka 2016). Crucially, both axon-bearing and anaxonic OB DA subtypes strongly express TH (Galliano et al., 2018, 2021). We have now added additional text to the relevant Results section (lines 99 to 108 of the revised manuscript) to clarify these reasons for studying vGAT-cre mice here.

We were also able to clearly identify and sample both subtypes of OB DA neuron using DAT-tdT mice. Our previous published work has thoroughly characterised this exact mouse line at the exact ages studied in the present paper (Galliano et al., 2018; Byrne et al., 2022). We know that DAT-tdT mice provide rather specific label for TH-expressing OB DA neurons (75% co-localisation; Byrne et al., 2022), but most importantly we know which non-DA neurons are labelled in this mouse line and how to avoid them. All nonTH-expressing but tdT-positive cells in juvenile DAT-tdT mice are small, dimly fluorescent and weakly spiking neurons of the calretinin-expressing glomerular subtype (Byrne et al., 2022). These cells are easily detected during physiological recordings, and were excluded from our study here. This information is now provided in the relevant Methods section (lines 616 to 619 of the revised manuscript, also referenced in lines 236 to 240 of the results section), and we apologise for its previous omission. Finally, we have shown both structurally and functionally that both axon-bearing and anaxonic OB DA subtypes are labelled in DAT-tdT mice (Galliano et al., 2018, Tufo et al., 2025; present study). Overall, these additional clarifications firmly establish that the same neuronal populations were indeed studied across our experiments.

(3) The low TH+ signal in Figure 1D raises questions regarding the successful targeting of OB DA neurons. Further validation, such as additional staining, is required to ensure that the targeted neurons are accurately identified.

As noted in our response to the previous comment, TH is a specific marker for dopaminergic neurons in the mouse OB, and is widely used for this purpose. Labelling for TH in our tissue is extremely reliable, and in fact gives such strong signal that we were forced to reduce the primary antibody concentration to 1:50,000 to prevent bleedthrough into other acquisition channels. Even at this concentration it was extremely straightforward to unambiguously identify TH-positive cells based on somatic immunofluorescence. We recognise, however, that the original example image in Figure 1D was not sufficiently clear, and have now provided a new example which illustrates the TH-based identification of these cells much more effectively. 

(4) Estimating the total number of dopaminergic neurons in the olfactory bulb, along with the relative proportions of anaxonic and axon-bearing neuron subtypes, would provide valuable context for the study. Presenting such data is crucial to underscore the biological significance of the findings.

This information has already been well characterised in previous studies. Total dopaminergic cell number in the OB is ~90,000 (Maclean & Shipley, 1988; Panzanelli et al., 2007; Parrish-Aungst et al., 2007). In terms of proportions, anaxonic neurons make up the vast majority of these cells, with axon-bearing neurons representing only ~2.5% of all OB dopaminergic neurons at P28 (Galliano et al., 2018). Of course, the relatively low number of the axon-bearing subtype does not preclude its having a potentially large influence on glomerular networks and sensory processing, as demonstrated by multiple studies showing the functional effects of inter-glomerular inhibition (Kosaka & Kosaka, 2008; Liu et al., 2013; Whitesell et al., 2013; Banerjee et al., 2015). This information has now been added to the Introduction (line 47 and lines 59 to 62 of the revised manuscript).

(5) The authors report that in-utero injection was performed based on the premise that the two subclasses of dopaminergic neurons in the olfactory bulb are generated during embryonic development. However, it remains unclear whether in-utero injection is essential for distinguishing between these two subclasses. While the manuscript references a relevant study, the explanation provided is insufficient. A more detailed justification for employing in-utero injection would enhance the manuscript's clarity and methodological rigor.

We apologise for the lack of clarity in explaining the approach. In utero injection is not absolutely essential for distinguishing between the two subclasses, but it does have two major advantages. 1) Because infection happens before cells migrate to their final positions, it produces sparse labelling which permits later unambiguous identification of individual cells’ processes; and 2) Because both subclasses are generated embryonically (compared to the postnatal production of only anaxonic DA neurons), it allows effective targeting of both cell types. We have now expanded the relevant section of the Results to explain the rationale for our approach in more detail (lines 109 to 116 of the revised manuscript).

(6) In Figures 1A and 4A, it appears that data from previously published studies were utilized to illustrate the differential mRNA expression in dopaminergic neurons of the olfactory bulb. However, the Methods section and the manuscript lack a detailed description of how these dopaminergic neurons were classified or analyzed. Given that these figures contribute to the primary dataset, providing additional explanation and context is essential to ensure clarity of the findings.

We apologise for the lack of clarity. We have now extended the part of the methods referring to the RNAseq data analysis (lines 666 to 678 of the revised manuscript). 

(7) In Figure 2C, anaxonic dopamine neurons display considerable variability in the number of neurotransmitter release sites, with some neurons exhibiting sparse sites while others exhibit numerous sites. The authors should address the potential biological or methodological reasons for this variability and discuss its significance.

We thank the Reviewer for highlighting this feature of our data. We have now outlined potential methodological reasons for the variability, whilst also acknowledging that it is consistent with previous reports of presynaptic site distributions in these cells (Kiyokage et al., 2017; Results, lines 169 to 172 of the revised manuscript). We have also added a brief discussion of the potential biological significance (Discussion, lines 446 to 450).

(8) In the images used to differentiate anaxonic and axon-bearing neurons, the soma, axons, and dendrites are intermixed, making it difficult to distinguish structures specific to each subclass. Employing subclass-specific labeling or sparse labeling techniques could enhance clarity and accuracy in identifying these structures.

Distinguishing these structures is indeed difficult, and was the main reason we used viral label to produce sparse labelling (see response to comment #5 above). In all cases we were extremely careful, including cells only when we could be absolutely certain of their anaxonic or axon-bearing identity, and could also be certain of the continuity of all processes. Crucially, while the 2D representations we show in our figures may suggest a degree of intermixing, we performed all analyses on 3D image stacks, significantly improving our ability to accurately assign structures to individual cells. We have now added extra descriptions of this approach in the relevant Methods section (lines 546 to 548 of the revised manuscript).

(9) In Figure 3, the soma area and synaptophysin puncta density are compared between axon-bearing and anaxonic neurons. However, the figure only presents representative images of axon-bearing neurons. To ensure a fair and accurate comparison, representative images of both neuron subtypes should be included.

The original figures did include example images of puncta density (or lack of puncta) in both cell types (Figure 2B and Figure 3E). For soma area, we have now included representative images of axon-bearing and anaxonic neurons with an indication of soma area measurement in a new Supplementary Figure 2A.

(10) In Figure 4B, the authors state that gephyrin and synaptophysin puncta are in 'very close proximity.' However, it is unclear whether this proximity is sufficient to suggest the possibility of self-inhibition. Quantifying the distance between gephyrin and synaptophysin puncta would provide critical evidence to support this claim. Additionally, analyzing the distribution and proportion of gephyrinsynaptophysin pairs in close proximity would offer further clarity and strengthen the interpretation of these findings.

We thank the Reviewer for raising this issue. We entirely agree that the example image previously shown did not constitute sufficient evidence to claim either close proximity of gephyrin and synaptophysin puncta, nor the possibility of self-inhibition. We are not in a position to perform a full quantitative analysis of these spatial distributions, nor do we think this is necessary given previous direct evidence for auto-evoked inhibition in OB dopaminergic cells (Smith and Jahr, 2002; Murphy et al., 2005; Maher and Westbrook, 2008; Borisovska et al., 2013) and our own demonstration of this phenomenon in anaxonic neurons (Figure 4). We have therefore removed the image and the reference to it in the text. 

(11) In Figures 4J and 4M, the effects of the drugs are presented without a direct comparison to the control group (baseline control?). Including these baseline control data is essential to provide a clear context for interpreting the drug effects and to validate the conclusions drawn from these experiments.

We appreciate the Reviewer’s attention to this important point. As this concern was also raised by Reviewer 1 (their point #6), we have provided a detailed response fully addressing it in our replies to Reviewer 1 above. 

(12) In Lines 342-344, the authors claim that VMAT2 staining is notoriously difficult. However, several studies (e.g., Weihe et al., 2006; Cliburn et al., 2017) have successfully utilized VMAT2 staining. Moreover, Zhang et al., 2015 - a reference cited by the authors - demonstrates that a specific VMAT2 antibody effectively detects VMAT2. Providing evidence of VMAT2 expression in OB DA neurons would substantiate the claim that these neurons are GABA-co-releasing DA neurons and strengthen the study's conclusions.

As noted in response to this Reviewer’s comment #2 above, there is clear published evidence that OB DA neurons are GABA- and dopamine-releasing cells. These cells are also known to express VMAT2 (Cave et al., 2010; Borisovska et al., 2013; Vergaña-Vera et al., 2015). We do not therefore believe that additional evidence of VMAT2 expression is necessary to strengthen our study’s conclusions. We did make every effort to label VMAT2-positive release sites in our neurons, but unfortunately all commercially available antibodies were ineffective. The successful staining highlighted by the Reviewer was either performed in the context of virally driven overexpression (Zhang et al., 2015) or was obtained using custom-produced antibodies (Weihe et al., 2006; Cliburn et al., 2017). We have now modified the Discussion text to provide more clarification of these points (lines 393 to 395 of the revised manuscript).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This paper investigates the physical mechanisms underlying cell intercalation, which then enables collective cell flows in confluent epithelia. The authors show that T1 transitions (the topological transitions responsible for cell intercalation) correspond to the unbinding of groups of hexatic topological defects. Defect unbinding, and hence cell intercalation and collective cell flows, are possible when active stresses in the tissue are extensile. This result helps to rationalize the observation that many epithelial cell layers have been found to exhibit extensile active nematic behavior.

Strengths

The authors obtain their results based on a combination of active hexanematic hydrodynamics and a multiphase field (MPF) model for epithelial layers, whose connection is a strength of the paper. With the hydrodynamic approach, the authors find the active flow fields produced around hexatic topological defects, which can drive defect unbinding. Using the MPF simulations, the authors show that T1 transitions tend to localize close to hexatic topological defects.

We are grateful to Reviewer #1, for appreciating and highlighting the strengths of work.

Weaknesses

Citations are sometimes not comprehensive. Cases of contractile behavior found in collective cell flows, which would seemingly contradict some of the authors’ conclusions, are not discussed.

I encourage the authors to address the comments and questions below.

We are thankful to Reviewer #1, for their questions and comments. We have addressed them point by point below, and have amended the manuscript accordingly.

(1) In Equation 1, what do the authors mean by the cluster’s size ℓ? How is this quantity defined? The calculations in the Methods suggest that ℓ indicates the distance between the p-atic defects and the center of the T1 cell cluster, but this is not clearly defined.

We are thank Reviewer #1 for their question. We define the cluster size as the initial distance between the center of the quadrupole and any defect (see Methods). In a primary cell cluster, where cells themselves are the defects, the cluster’s size is the distance between the center of the central junction and the center of any cell in the cluster. Hence, this is half the diameter of an cell which, for example in a typical, confluent MDCK epithelial monolayer, would be about 10µm. We have added this clarification in the definition of the cluster size, above Eq. (1).

(2) The multiphase field model was developed and reviewed already, before the Loewe et al. 2020 paper that the authors cite. Earlier papers include Camley et al. PNAS 2014, Palmieri et al. Sci. Rep. 2015, Mueller et al. PRL 2019, and Peyret et al. Biophys. J. 2019, as reviewed in Alert and Trepat. Annu. Rev. Condens. Matter Phys. 2020.

We thank the referee for their suggestion to incorporate further MPF literature. We have done so in the amended manuscript.

(3) At what time lag is the mean-squared displacement in Figure 3f calculated? How does the choice of a lag time affect these data and the resulting conclusions?

The scatter plot in Fig. 3f was constructed by dividing the system into square subregions of size ∆ℓ = 35 l.u., each containing approximately 4 cells. For each subregion, we analyzed a time window of ∆t = 25 × 10³ iterations, measuring both the normalized mean square displacement of cells (relative to the subregion area ∆ℓ²) and the average defect density. The normalized displacement is calculated as m.s.d. , where t∗ denotes the start time of the observation window. We chose the time window ∆t used to compute the mean square displacement to match the characteristic duration of T1 events and defect lifetimes in our simulations. Observation times much longer (∆t > 35 × 10³) than the typical T1 event duration would cause the two sets of data points to merge into a single group, suggesting no correlation between cell motility and defect density beyond defect life-time.

(4) The authors argue that their results provide an explanation for the extensile behavior of cell layers. However, there are also examples of contractile behavior, such as in Duclos et al., Nat. Phys., 2017 and in P´erez-Gonz´alez et al., Nat. Phys., 2019. In both cases, collective cell flows were observed, which in principle require cell intercalations. How would these observations be rationalized with the theory proposed in this paper? Can these experiments and the theory be reconciled?

The contractile or extensile nature of stress in epithelia depends crucially on the specific tissue type and its biological context. Different cell populations, depending on their position along the epithelial/mesenchymal spectrum, can exhibit either contractile or extensile behaviors. Our theory applies to tissues where hexatic order dominates at the cellular scale, particularly in confluent systems where neighbor exchanges occur primarily through T1 transitions. In contrast, the systems studied by Duclos et al., Nat. Phys. (2018) and Perez-Gonzalez et al. (Nat. Phys., 2019) exhibit nematic order at the cellular level, meaning their dynamics are governed by fundamentally different mechanisms. Since our framework is derived for hexatic-dominated tissues, it does not directly apply to those cases, though a hybrid hexanematic descriptions previously developed by some of the authors in Armengol-Collado et al. eLife 13:e86400 (2024) could help reconcile these observations. In general, a key distinction must be made between the contractility of individual cells and the extensile/contractile nature of the collective force network. To illustrate this, consider a cell exerting a 6- fold symmetric force distribution: each vertex force arises from an imbalance in junctional tensions with neighboring cells, which are themselves contractile due to actomyosin activity. However, the resulting vertex forces can be either contractile or extensile depending on network geometry and tension distribution. This is captured in our coarse-grained description [see Armengol-Collado et al. eLife 13:e86400 (2024)], where the active stress emerges from higher-order moments of cellular forces. Specifically, the deviatoric part of the hexatic active stress tensor , where is the cell radius, the number cell density and the intensity of cellular tension. The negative sign of the coefficient of the active stress shows that the active stress is extensile—consistently with observations in various epithelial systems (e.g., Saw et al., Nature 2017; Blanch-Mercader et al., Phys. Rev. Lett. 2018). Finally, we note that the connection between cellular-scale forces and large-scale extensility has been rationalized in other contexts, such as active nematics (Balasubramaniam et al., Nat. Mater. 2021).

Reviewer #2 (Public Review):

This paper studies the role of hexatic defects in the collective migration of epithelia. The authors emphasize that epithelial migration is driven by cell intercalation events and not just isolated T1 events, and analyze this through the lens of hexatic topological defects. Finally, the authors study the effect of active and passive forces on the dynamics of hexatic defects using analytical results, and numerical results in both continuum and phase-field models.

The results are very interesting and highlight new ways of studying epithelial cell migration through the analysis of the binding and unbinding of hexatic defects.

We are grateful to Reviewer #2, for their interest and for emphasizing the novelty of our work.

Strengths

(1) The authors convincingly argue that intercalation events are responsible for collective cell migration, and that these events are accompanied by the formation and unbinding of hexatic topological defects.

(2) The authors clearly explain the dynamics of hexatic defects during T1 transitions, and demonstrate the importance of active and passive forces during cell migration.

(3) The paper thoroughly studies the T1 transition through the viewpoint of hexatic defects. A continuum model approach to study T1 transitions in cell layers is novel and can lead to valuable new insights.

We thank the Reviewer for their kind and supporting words, and for highlighting the clarity, persuasiveness, and thoroughness.

Weaknesses

(1) The authors could expand on the dynamics of existing hexatic defects during epithelial cell migration, in addition to how they are created during T1 transitions.

We thank the referee for their comment. The detailed analysis of dislocation-pair unbinding modes and their statistical impact on the transition to collective migration is comprehensively addressed in our subsequent work Puggioni et al., arXiv:2502.09554. In the present study, we focus specifically on the fundamental mechanism enabling dislocation unbinding: active extensile stresses generate flows that drive dislocation pairs apart, while passive elastic stresses tend to pull them together (Krommydas et al., Phys. Rev. Lett. 2023; Armengol- Collado et al., arXiv:2502.13104). When active forces dominate over passive restoring forces, the dislocations unbind. This represents a crucial distinction from classical Berezinskii–Kosterlitz–Thouless or Kosterlitz–Thouless–Halperin–Nelson–Youn transitions, where thermal fluctuations drive defect unbinding. In our system, the process is fundamentally activity-driven. Nevertheless, the resulting state - characterized by unbound defects and collective migration - bears strong analogy to the melting transition in equilibrium systems. We emphasize that the dynamics of passive defects has been previously examined in Krommydas et al., Phys. Rev. Lett. 2023. A discussion of these aspects can be found in the Appendix “Numerical simulations of defect annihilation and unbinding”.

(2) The different terms in the MPF model used to study cell layer dynamics are not fully justified. In particular, it is not clear why the model includes self-propulsion and rotational diffusion in addition to nematic and hexatic stresses, and how these quantities are related to each other.

We thank the referee for their comment. The MPF model’s terms (e.g., self-propulsion, rotational diffusion), reflect the stochastic, deformable nature of cells as active droplets migrating with near-constant speed. We emphasize that self-propulsion is the only non-equilibrium mechanism in our model — no additional active stresses (nematic or hexatic) are imposed. We have clarified this point in the revised manuscript and expanded our discussion of the MPF model.

(3) The authors could provide some physical intuition on what an active extensile or contractile term in the hexatic order parameter means, and how this is related to extensility and contractility in active nematics and/or for cell layers.

We thank the referee for their comment. As we explain in the reply to comment [4] of Reviewer #1, the contractile or extensile nature of stress in epithelia depends crucially on the specific tissue type and its biological context. Different cell populations, depending on their position along the epithelial/mesenchymal spectrum, can exhibit either contractile or extensile behaviors. Our theory applies to tissues where hexatic order dominates at the cellular scale, particularly in confluent systems where neighbor exchanges occur primarily through T1 transitions. In contrast, the systems studied by Duclos et al., Nat. Phys. (2018) and Perez-Gonzalez et al. (Nat. Phys., 2019) exhibit nematic order at the cellular level, meaning their dynamics are governed by fundamentally different mechanisms. Since our framework is derived for hexatic-dominated tissues, it does not directly apply to those cases, though a hybrid hexanematic descriptions previously developed by some of the authors in Armengol-Collado et al. eLife 13:e86400 (2024) could help reconcile these observations. In general, a key distinction must be made between the contractility of individual cells and the extensile/contractile nature of the collective force network. To illustrate this, consider a cell exerting a 6-fold symmetric force distribution: each vertex force arises from an imbalance in junctional tensions with neighboring cells, which are themselves contractile due to actomyosin activity. However, the resulting vertex forces can be either contractile or extensile depending on network geometry and tension distribution. This is captured in our coarse-grained description [see Armengol-Collado et al. eLife 13:e86400 (2024)], where the active stress emerges from higher-order moments of cellular forces. Specifically, the deviatoric part of the hexatic active stress tensor , where is the cell radius, the number cell density and the intensity of cellular tension. The negative sign of the coefficient of the active stress shows that the active stress is extensile—consistently with observations in various epithelial systems (e.g., Saw et al., Nature 2017; Blanch-Mercader et al., Phys. Rev. Lett. 2018). Finally, we note that the connection between cellular-scale forces and large-scale extensility has been rationalized in other contexts, such as active nematics (Balasubramaniam et al., Nat. Mater. 2021).

Recommendations for the Authors: Reviewer #2 (Recommendations for the Authors):

(1) The authors point out that hexatic topological defects are produced in quadrupoles (L109). Does this also mean that these defects can be annihilated only in quadrupoles as well? In the same vein, are hexatic defects always bound in pairs, as suggested by the schematics, or is it possible to observe an isolated hexatic defect?

We thank the referee for their question. Hexatic disclinations (the defect monopoles discussed in this work), much like electrons and positrons, can annihilate in any number of neutral charge configuration (dipole, quadrupole, octupole, etc.). Unbinding a pair of hexatic disinclination, however, costs much more energy than unbinding a quadrupole to dipoles. Hence isolated defects appear in abundance only in late, fully disordered phase, where the system has completely “melted”. For more details on how defect unbinding modes affect tissue dynamics, please see our subsequent work Puggioni et al., arXiv:2502.09554.

(2) Could you clarify if the flows described in Figures 2(a)-(b), panel (i) are driven by a passive backflow term without activity? Could you compare the magnitudes of these flows compared to the typical active terms?

We thank the referee for their question. In panel 2(b) there is only passive backflow. In 2(a) instead, both terms are included, and are in a regime of parameters where the active flow overcomes the active flow (and hence the active force overcomes the passive force as delineated in the discussions section). In turn, the magnitude of the passive flows, is studied in detail in our previous work Krommydas et al., (Phys. Rev. Lett. 2023).

(3) Could you clarify how the continuum hexatic model and MPF model are related to each other? What are the similarities and differences in the dynamics of these models?

We thank the referee for this insightful question. A key point of our work is precisely that the continuum hexatic model and the MPF (Multi-Phase Field) model are distinct in nature.

The MPF model is an established agent-based framework used to simulate tissue dynamics at the cellular level. It captures individual cell behaviors and interactions through phase-field variables. In our work, we use the MPF model as a benchmark to extract statistical features of tissue dynamics, such as defect motion and orientational correlations. In contrast, our continuum hexatic model is a coarse-grained hydrodynamic theory that describes the dynamics of orientational order in active tissues. It is built on symmetry principles and conservation laws, and it does not rely on microscopic cell-level details. Instead, it captures the collective behavior of the system through a hexatic order parameter and its coupling to flow and activity.

Despite their conceptual differences, the MPF model and our hydrodynamic theory exhibit similar statistical features. This agreement—also observed in the independent study by Jain et al. (Phys. Rev. Res. 2024)—provides strong support for the validity and generality of our continuum description.

(4) When multiple references by the same author and year are cited using alphabets, the second alphabet is not in bold e.g. Giomi et al., 2022b, a in Line 75, and others.

We are grateful to the referee carefully going through the manuscript and pointing out these typos. We have corrected them in the amended manuscript.

Reviewer #3 (Public Review):

In this manuscript, the authors discuss epithelial tissue fluidity from a theoretical perspective. They focus on the description of topological transitions whereby cells change neighbors (T1 transitions). They explain how such transitions can be described by following the fate of hexatic defects. They first focus on a single T1 transition and the surrounding cells using a hydrodynamic model of active hexatics. They show that successful T1 intercalations, which promote tissue fluidity, require a sufficiently large extensile hexatic activity in the neighborhood of the cells attempting a T1 transition. If such activity is contractile or not sufficiently extensile, the T1 is reversed, hexatic defects annihilate, and the epithelial network configuration is unchanged. They then describe a large epithelium, using a phase field model to describe cells. They show a correlation between T1 events and hexatic defects unbinding, and identify two populations of T1 cells: one performing T1 cycles (failed T1), and not contributing to tissue migration, and one performing T1 intercalation (successful T1) and leading to the collective cell migration.

Strengths

The manuscript is scientifically sound, and the variety of numerical and analytical tools they use is impressive. The approach and results are very interesting and highlight the relevance of hexatic order parameters and their defects in describing tissue dynamics.

We thank the Reviewer for recognizing the scientific soundness of the manuscript, the breadth of numerical and analytical tools employed, as well as their interest in our work.

Weaknesses

(1) Goal and message of the paper. (a) In my opinion, the article is mainly theoretical and should be presented as such. For instance, their conclusions and the consequences of their analysis in terms of biology are not extremely convincing, although they would be sufficient for a theory paper oriented to physicists or biophysicists. The choice of journal and potential readership should be considered, and I am wondering whether the paper structure should be re-organized, in order to have side-by-side the methods and the results, for instance (see also below).

We thank the referee for their criticism. In response, we have made an effort to reword certain parts of the manuscript. As with any theoretical study, the biological implications of our work can only be fully assessed through experimental validation — a prospect we look forward to. Nevertheless, we have submitted our work to the subsection of Physics of Life, which we believe is perfectly suited to our content.

(b) Currently, the two main results sections are somewhat disconnected, because they use different numerical models, and because the second section only marginally uses the results from the first section to identify/distinguish T1.

We thank the referee, for their comment. In the second section we are using statistics from the MPF model, to support the analytical and numerical findings of our hydrodynamic theory of cell intercalation. In the time between our submission, further qualitative evidence have been brought to light in the work of Jain et al. (Phys. Rev. Res. 2024).

(2) Quite surprisingly, the authors use a cell-based model to describe the macroscopic tissuescale behavior, and a hydrodynamic model to describe the cell-based events. In particular, their hydrodynamic description (the active hexatic model) is supposed to be a coarse-grained description, valid to capture the mesoscopic physics, and yet, they use it to describe cellscale events (T1 transitions). For instance, what is the meaning of the velocity field they are discussing in Figure 2? This makes me question the validity of the results of their first part.

We thank the referee for their comment. There are many excellent discrete models of epithelial tissues in the literature (e.g., Bi et al., Phys. Rev. X 2016; Pasupalak et al., Soft Matter 2020; Graner et al., Phys. Rev. Lett. 1992), each capturing essential biological features such as cell division, apoptosis and sorting. While these models have provided invaluable insights, our work takes a different approach by developing a continuum theory aimed at describing epithelial dynamics at two levels: (1) mesoscopic intercalation events and (2) macroscopic collective migration. Crucially, our goal is not to replicate a specific discrete model — which would risk constructing a “model of a model” — but rather to derive a hydrodynamic description of tissue dynamics grounded in symmetry principles and conservation laws. Along this logic, the velocity field in our theory should be interpreted as an Eulerian (continuum) velocity, representing the coarse-grained flow of the tissue rather than the Lagrangian motion of individual cells. This distinction is central to our framework, which operates at scales where cellular details are averaged out, yet retains the essential physics of hexatic order and active stresses. We validate our predictions against the Multiphase Field (MPF) model. [We thank Reviewer 1 for their suggestion to incorporate further MPF literature.] Furthermore, Jain et al. (Phys. Rev. Res. 2024) have used the MPF to predict flow patterns around T1 transitions and obtained results compatible with those of our hydrodynamic theory. From this comparison we can conclude that both the MPF and our theory are able to capture the same aspect of cell intercalation in epithelial layer. This, however, does not imply that other discrete models of epithelia can reproduce this aspect too, nor that our theory is specifically tailored to the MPF model. We have clarified these points in the revised manuscript and expanded our discussion of the MPF model.

(3) The quality of the numerical results presented in the second part (phase field model) could be improved. (a) In terms of analysis of the defects. It seems that they have all the tools to compare their cell-resolved simulations and their predictions about how a T1 event translates into defects unbinding. However, their analysis in Figure 3e is relatively minimal: it shows a correlation between T1 cells and defects. But it says nothing about the structure and evolution of the defects, which, according to their first section, should be quite precise.

We thank the referee for their comment. Further qualitative evidence have been brought to light in the work of Jain et al. (Phys. Rev. Res. 2024), were the exact flow pattern predicted by our hydrodynamic theory is obtained, in the MPF, around cells undergoing T1 rearrangements.

(b) In terms of clarity of the presentation. For instance, in Figure 3f, they plot the mean-square displacement as a function of a defect density. I thought that MSD was a time-dependent quantity: they must therefore consider MSD at a given time, or averaged over time. They should be explicit about what their definition of this quantity is.

We thank the referee for raising this point. As clarified in our response to Reviewer 1, point 3, the mean square displacement (MSD) plotted in Fig. 3f is computed over a fixed time window of ∆t = 25×103 iterations, chosen to match the typical duration of T1 events and defect lifetimes. [See also reply to Reviewer #1, point (3).] The MSD is normalized by the subregion area and averaged over time within each window. We have now made this explicit in the amended version of the manuscript.

(c) In terms of statistics. For instance, Figure 3g is used to study the role of rotational diffusion on the average time between T1s. The error bars in this figure are huge and make their claims hardly supported. Their claim of a ”monotonic decay” of the average time between intercalations is also not fully supported given their statistics.

We appreciate the Reviewer’s comment regarding the statistical robustness of Fig. 3g. While we acknowledge that the error bars are substantial – reflecting the inherent variability in cell intercalation dynamics – the yellow curve does exhibit a consistent downward trend in the average time between T1 transitions as rotational diffusion increases. This monotonic decrease is visible across the entire range of variation of the rotational diffusion Dr, and is statistically supported when considering the trend over independent simulations. To address this concern, we have revised the main text to adjusted the wording: instead of stating that “the former is a monotonically decreasing function of Dr,” we now write that “the former displays a decreasing trend with Dr,” which better reflects the statistical variability while preserving the observed behavior.

Reviewer #3 (Recommendations for the Authors):

(1) Section 1 is difficult to follow due to multiple reasons: early but delayed definitions, unclear use of T1 intercalation vs. T1 cycles, disconnected figures and unclear simulation descriptions. We recommend including simulation setup details earlier and restructuring the flow of arguments.

We thank the referee for their comment. We have made an effort in rewording and clarifying things in our amended manuscript. We are slightly confused by what they mean by “early but delayed definitions”, if they could clarify, we would be happy to amend the position and phrasing of these definitions accordingly.

(2) It could be useful to have an additional figure early on defining schematically hexatic defects and an illustration showing an epithelium (or a simulation), similar to what the authors have produced in some of their other publications on this topic.

We thank the referee for their comment. Figures 3c and 3d show what a hexatic defect looks like in a simulation of the epithelium. Following the referee’s recommendation, we have added a note in the caption of figure 3, citing our work were we show the same defects in MDCK epithelial monolayers (Armengol et al., Nat. Phys. 2023).

(3) Minor points and typos:

Line 88: the bond between vertices shrinks, not the vertices.

Figure 1: the 1/6 is displayed as 1 6 (fraction bar missing).

Line 232: “and order” → “one/an order”.

Line 237: Fig. 3g) → Fig. 3g

Line 298: ”nu” and ”v” hard to distinguish in eLife font.

Methods: define all notation clearly (e.g., tensor product exponent, D/Dt in Eq. 3c).

Methods: ”cell orientation, coarse-graining and topological defects” section is difficult to follow, schematic would help.

Line 457 onward: unclear how panels (ii-iv) of Fig. 2ab are obtained.

Line 480 onward: not referenced in main text.

Figure 2: “avalancHe” typo.

Figure 2 caption: “cell intercalaTION” typo.

Movies are neither referenced nor explained.

Figure 5 and 6 are not referenced in the main text.

We thank the referee for their detailed read of the paper. We have corrected all typos.

If amygdala size correlates with empathy and aggression, how do environmental factors or genetics influence its development?

If the occipital lobe is damaged, can the person still be able to see but just not interpret what they are seeing?

1. those things that add value but which aren’t part of your formal job description. These “extras” are called extra-role performance or organizational citizenship behaviors (OCBs)

Reviewer #1 (Public Review):

Summary:

This study by Park and colleagues uses longitudinal saliva viral load data from two cohorts (one in the US and one in Japan from a clinical trial) in the pre-vaccine era to subset viral shedding kinetics and then use machine learning to attempt to identify clinical correlates of different shedding patterns. The stratification method identifies three separate shedding patterns discriminated by peak viral load, shedding duration, and clearance slope. The authors also assess micro-RNAs as potential biomarkers of severity but do not identify any clear relationships with viral kinetics.

Strengths:

The cohorts are well developed, the mathematical model appears to capture shedding kinetics fairly well, the clustering seems generally appropriate, and the machine learning analysis is a sensible, albeit exploratory approach. The micro-RNA analysis is interesting and novel.

Reviewer #3 (Public Review):

The article presents a comprehensive study on the stratification of viral shedding patterns in saliva among COVID-19 patients. The authors analyze longitudinal viral load data from 144 mildly symptomatic patients using a mathematical model, identifying three distinct groups based on the duration of viral shedding. Despite analyzing a wide range of clinical data and micro-RNA expression levels, the study could not find significant predictors for the stratified shedding patterns, highlighting the complexity of SARS-CoV-2 dynamics in saliva. The research underscores the need for identifying biomarkers to improve public health interventions and acknowledges several limitations, including the lack of consideration of recent variants, the sparsity of information before symptom onset, and the focus on symptomatic infections.

The manuscript is well-written, with the potential for enhanced clarity in explaining statistical methodologies. This work could inform public health strategies and diagnostic testing approaches.

Comments on the revised version from the editor:

The authors comprehensively addressed the concerns of all 3 reviewers. We are thankful for their considerable efforts to do so. Certain limitations remain unavoidable such as the lack of immunologic diversity among included study participants and lack of contemporaneous variants of concern.

One remaining issue is the continued use of the target cell limited model which is sufficient in most cases, but misses key datapoints in certain participants. In particular, viral rebound is poorly described by this model. Even if viral rebound does not place these cases in a unique cluster, it is well understood that viral rebound is of clinical significance.

In addition, the use of microRNAs as a potential biomarker is still not fully justified. In other words, are there specific microRNAs that have a pre-existing mechanistic basis for relating to higher or lower viral loads? As written it still feels like microRNA was included in the analysis simply because the data existed.

Author response:

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

Reviewer #1 (Public review)

Summary:

This study by Park and colleagues uses longitudinal saliva viral load data from two cohorts (one in the US and one in Japan from a clinical trial) in the pre-vaccine era to subset viral shedding kinetics and then use machine learning to attempt to identify clinical correlates of different shedding patterns. The stratification method identifies three separate shedding patterns discriminated by peak viral load, shedding duration, and clearance slope. The authors also assess micro-RNAs as potential biomarkers of severity but do not identify any clear relationships with viral kinetics.

Strengths:

The cohorts are well developed, the mathematical model appears to capture shedding kinetics fairly well, the clustering seems generally appropriate, and the machine learning analysis is a sensible, albeit exploratory approach. The micro-RNA analysis is interesting and novel.

Weaknesses:

The conclusions of the paper are somewhat supported by the data but there are certain limitations that are notable and make the study's findings of only limited relevance to current COVID-19 epidemiology and clinical conditions.

We sincerely appreciate the reviewer’s thoughtful and constructive comments, which have been invaluable in improving the quality of our study. We have carefully revised the manuscript to address all points raised.

(1) The study only included previously uninfected, unvaccinated individuals without the omicron variant. It has been well documented that vaccination and prior infection both predict shorter duration shedding. Therefore, the study results are no longer relevant to current COVID-19 conditions. This is not at all the authors' fault but rather a difficult reality of much retrospective COVID research.

Thank you for your comment. We agree with the review’s comment that some of our results could not provide insight into the current COVID-19 conditions since most people have either already been infected with COVID-19 or have been vaccinated. We revised our manuscript to discuss this (page 22, lines 364-368). Nevertheless, we believe it is novel that we have extensively investigated the relationship between viral shedding patterns in saliva and a wide range of clinical and microRNA data, and that developing a method to do so remains important. This is important for providing insight into early responses to novel emerging viral diseases in the future. Therefore, we still believe that our findings are valuable.

(2) The target cell model, which appears to fit the data fairly well, has clear mechanistic limitations. Specifically, if such a high proportion of cells were to get infected, then the disease would be extremely severe in all cases. The authors could specify that this model was selected for ease of use and to allow clustering, rather than to provide mechanistic insight. It would be useful to list the AIC scores of this model when compared to the model by Ke.

Thank you for your feedback and suggestion regarding our mathematical model. As the reviewer pointed out, in this study, we adopted a simple model (target cell-limited model) to focus on reconstruction of viral dynamics and stratification of shedding patterns rather than exploring the mechanism of viral infection in detail. Nevertheless, we believe that the target cell-limited model provides reasonable reconstructed viral dynamics as it has been used in many previous studies. We revised manuscript to clarify this point (page 10, lines 139-144). Also, we revised our manuscript to provide more detailed description of the model comparison along with information about AIC (page 10, lines 130-135).

(3) Line 104: I don't follow why including both datasets would allow one model to work better than the other. This requires more explanation. I am also not convinced that non-linear mixed effects approaches can really be used to infer early model kinetics in individuals from one cohort by using late viral load kinetics in another (and vice versa). The approach seems better for making populationlevel estimates when there is such a high amount of missing data.

Thank you for your feedback. We recognized that our explanation was insufficient by your comment. We intended to describe that, rather than comparing performance of the two models, data fitting can be performed with same level for both models by including both datasets. We revised the manuscript to clarify this point (page 10, lines 135-139).

Additionally, we agree that nonlinear mixed effects models are a useful approach for performing population-level estimates of missing data. On the other hand, in addition, the nonlinear mixed effects model has the advantage of making the reasonable parameter estimation for each individual with not enough data points by considering the distribution of parameters of other individuals. Paying attention to these advantages, we adopted a nonlinear mixed effects model in our study. We also revised the manuscript to clarify this (page 27, lines 472-483).

(4) Along these lines, the three clusters appear to show uniform expansion slopes whereas the NBA cohort, a much larger cohort that captured early and late viral loads in most individuals, shows substantial variability in viral expansion slopes. In Figure 2D: the upslope seems extraordinarily rapid relative to other cohorts. I calculate a viral doubling time of roughly 1.5 hours. It would be helpful to understand how reliable of an estimate this is and also how much variability was observed among individuals.

We appreciate your detailed feedback on the estimated up-slope of viral dynamics. As the reviewer noted, the pattern differs from that observed in the NBA cohort, which may be due to their measurement of viral load from upper respiratory tract swabs. In our estimation, the mean and standard deviation of the doubling time (defined as ln2/(𝛽𝑇₀𝑝𝑐⁻¹ − 𝛿)) were 1.44 hours and 0.49 hours, respectively. Although direct validation of these values is challenging, several previous studies, including our own, have reported that viral loads in saliva increase more rapidly than in the upper respiratory tract swabs, reaching their peak sooner. Thus, we believe that our findings are consistent with those of previous studies. We revised our manuscript to discuss this point with additional references (page 20, lines 303-311).

(5) A key issue is that a lack of heterogeneity in the cohort may be driving a lack of differences between the groups. Table 1 shows that Sp02 values and lab values that all look normal. All infections were mild. This may make identifying biomarkers quite challenging.

Thank you for your comment regarding heterogeneity in the cohort. Although the NFV cohort was designed for COVID-19 patients who were either mild or asymptomatic, we have addressed this point and revised the manuscript to discuss it (page 21, lines 334-337).

(6) Figure 3A: many of the clinical variables such as basophil count, Cl, and protein have very low pre-test probability of correlating with virologic outcome.

Thank you for your comment regarding some clinical information we used in our study. We revised our manuscript to discuss this point (page 21, lines 337-338).

(7) A key omission appears to be micoRNA from pre and early-infection time points. It would be helpful to understand whether microRNA levels at least differed between the two collection timepoints and whether certain microRNAs are dynamic during infection.

Thank you for your comment regarding the collection of micro-RNA data. As suggested by the reviewer, we compared micro-RNA levels between two time points using pairwise t-tests and Mann-Whitney U tests with FDR correction. As a result, no micro-RNA showed a statistically significant difference. This suggests that micro-RNA levels remain relatively stable during the course of infection, at least for mild or asymptomatic infection, and may therefore serve as a biomarker independent of sampling time. We have revised the manuscript to include this information (page 17, lines 259-262).

(8) The discussion could use a more thorough description of how viral kinetics differ in saliva versus nasal swabs and how this work complements other modeling studies in the field.

We appreciate the reviewer’s thoughtful feedback. As suggested, we have added a discussion comparing our findings with studies that analyzed viral dynamics using nasal swabs, thereby highlighting the differences between viral dynamics in saliva and in the upper respiratory tract. To ensure a fair and rigorous comparison, we referred to studies that employed the same mathematical model (i.e., Eqs.(1-2)). Accordingly, we revised the manuscript and included additional references (page 20, lines 303-311).

Furthermore, we clarified the significance of our study in two key aspects. First, it provides a detailed analysis of viral dynamics in saliva, reinforcing our previous findings from a single cohort by extending them across multiple cohorts. Second, this study uniquely examines whether viral dynamics in saliva can be directly predicted by exploring diverse clinical data and micro-RNAs. Notably, cohorts that have simultaneously collected and reported both viral load and a broad spectrum of clinical data from the same individuals, as in our study, are exceedingly rare. We revised the manuscript to clarify this point (page 20, lines 302-311).

(9) The most predictive potential variables of shedding heterogeneity which pertain to the innate and adaptive immune responses (virus-specific antibody and T cell levels) are not measured or modeled.

Thank you for your comment. We agree that antibody and T cell related markers may serve as the most powerful predictors, as supported by our own study [S. Miyamoto et al., PNAS (2023), ref. 24] as well as previous reports. While this point was already discussed in the manuscript, we have revised the text to make it more explicit (page 21, lines 327-328).

(10) I am curious whether the models infer different peak viral loads, duration, expansion, and clearance slopes between the 2 cohorts based on fitting to different infection stage data.

Thank you for your comment. We compared features between 2 cohorts as reviewer suggested. As a result, a statistically significant difference between the two cohorts (i.e., p-value ≤ 0.05 from the t-test) was observed only at the peak viral load, with overall trends being largely similar. At the peak, the mean value was 7.5 log₁₀ (copies/mL) in the Japan cohort and 8.1 log₁₀ (copies/mL) in the Illinois cohort, with variances of 0.88 and 0.87, respectively, indicating comparable variability.

Reviewer #2 (Public review)

Summary:

This study argues it has found that it has stratified viral kinetics for saliva specimens into three groups by the duration of "viral shedding"; the authors could not identify clinical data or microRNAs that correlate with these three groups.

Strengths:

The question of whether there is a stratification of viral kinetics is interesting.

Weaknesses:

The data underlying this work are not treated rigorously. The work in this manuscript is based on PCR data from two studies, with most of the data coming from a trial of nelfinavir (NFV) that showed no effect on the duration of SARS-CoV-2 PCR positivity. This study had no PCR data before symptom onset, and thus exclusively evaluated viral kinetics at or after peak viral loads. The second study is from the University of Illinois; this data set had sampling prior to infection, so has some ability to report the rate of "upswing." Problems in the analysis here include:

We are grateful to the reviewer for the constructive feedback, which has greatly enhanced the quality of our study. In response, we have carefully revised the manuscript to address all comments.

The PCR Ct data from each study is treated as equivalent and referred to as viral load, without any reports of calibration of platforms or across platforms. Can the authors provide calibration data and justify the direct comparison as well as the use of "viral load" rather than "Ct value"? Can the authors also explain on what basis they treat Ct values in the two studies as identical?

Thank you for your comment regarding description of viral load data. We recognized the lack of explanation for the integration of viral load data by reviewer's comment. We calculated viral load from Ct value using linear regression equations between Ct and viral load for each study's measurement method, respectively. We revised the manuscript to clarify this point in the section of Saliva viral load data in Methods.

The limit of detection for the NFV PCR data was unclear, so the authors assumed it was the same as the University of Illinois study. This seems a big assumption, as PCR platforms can differ substantially. Could the authors do sensitivity analyses around this assumption?

Thank you for your comment regarding the detection limit for viral load data. As reviewer suggested, we conducted sensitivity analysis for assumption of detection limit for the NFV dataset. Specifically, we performed data fitting in the same manner for two scenarios: when the detection limit of NFV PCR was lower (0 log₁₀ copies/mL) or higher (2 log₁₀ copies/mL) than that of the Illinois data (1.08 log₁₀ copies/mL), and compared the results.

As a result, we obtained largely comparable viral dynamics in most cases (Supplementary Fig 6). When comparing the AIC values, we observed that the AIC for the same censoring threshold was 6836, whereas it increased to 7403 under the low censoring threshold and decreased to 6353 under the higher censoring threshold. However, this difference may be attributable to the varying number of data points treated as below the detection limit. Specifically, when the threshold is set higher, more data are treated as below the detection limit, which may result in a more favorable error calculation. To discuss this point, we have added a new figure (Supplementary Fig 6) and revised the manuscript accordingly (page 25, lines 415-418).

The authors refer to PCR positivity as viral shedding, but it is viral RNA detection (very different from shedding live/culturable virus, as shown in the Ke et al. paper). I suggest updating the language throughout the manuscript to be precise on this point.

We appreciate the reviewer’s feedback regarding the terminology used for viral shedding. In response, we have revised all instances of “viral shedding” to “viral RNA detection” throughout the manuscript as suggested.

Eyeballing extended data in Figure 1, a number of the putative long-duration infections appear to be likely cases of viral RNA rebound (for examples, see S01-16 and S01-27). What happens if all the samples that look like rebound are reanalyzed to exclude the late PCR detectable time points that appear after negative PCRs?

We sincerely thank the reviewer for the valuable suggestion. In response, we established a criterion to remove data that appeared to exhibit rebound and subsequently performed data fitting

(see Author response image 1 below). The criterion was defined as: “any data that increase again after reaching the detection limit in two measurements are considered rebound and removed.” As a result, 15 out of 144 cases were excluded due to insufficient usable data, leaving 129 cases for analysis. Using a single detection limit as the criterion would have excluded too many data points, while defining the criterion solely based on the magnitude of increase made it difficult to establish an appropriate “threshold for increase.”

The fitting result indicates that the removal of rebound data may influence the fitting results; however, direct comparison of subsequent analyses, such as clustering, is challenging due to the reduced sample size. Moreover, the results can vary substantially depending on the criterion used to define rebound, and establishing a consistent standard remains difficult. Accordingly, we retained the current analysis and have added a discussion of rebound phenomena in the Discussion section as a limitation (page 22, lines 355-359). We once again sincerely appreciate the reviewer’s insightful and constructive suggestion.

Author response image 1.

Comparison of model fits before and after removing data suspected of rebound. Black dots represent observed measurements, and the black and yellow curves show the fitted viral dynamics for the full dataset and the dataset with rebound data removed, respectively.

There's no report of uncertainty in the model fits. Given the paucity of data for the upslope, there must be large uncertainty in the up-slope and likely in the peak, too, for the NFV data. This uncertainty is ignored in the subsequent analyses. This calls into question the efforts to stratify by the components of the viral kinetics. Could the authors please include analyses of uncertainty in their model fits and propagate this uncertainty through their analyses?

We sincerely appreciate the reviewer’s detailed feedback on model uncertainty. To address this point, we revised Extended Fig 1 (now renumbered as Supplementary Fig 1) to include 95% credible intervals computed using a bootstrap approach. In addition, to examine the potential impact of model uncertainty on stratified analyses, we reconstructed the distance matrix underlying stratification by incorporating feature uncertainty. Specifically, for each individual, we sampled viral dynamics within the credible interval and averaged the resulting feature, and build the distance matrix using it. We then compared this uncertainty-adjusted matrix with the original one using the Mantel test, which showed a strong correlation (r = 0.72, p < 0.001). Given this result, we did not replace the current stratification but revised the manuscript to provide this information through Result and Methods sections (page 11, lines 159-162 and page 28, lines 512-519). Once again, we are deeply grateful for this insightful comment.

The clinical data are reported as a mean across the course of an infection; presumably vital signs and blood test results vary substantially, too, over this duration, so taking a mean without considering the timing of the tests or the dynamics of their results is perplexing. I'm not sure what to recommend here, as the timing and variation in the acquisition of these clinical data are not clear, and I do not have a strong understanding of the basis for the hypothesis the authors are testing.

We appreciate the reviewers' feedback on the clinical data. We recognized that the manuscript lacked description of the handling of clinical data by your comment. In this research, we focused on finding “early predictors” which could provide insight into viral shedding patterns. Thus, we used clinical data measured in the earliest time (date of admission) for each patient. Another reason is that the date of admission is the almost only time point at which complete clinical data without any missing values are available for all participants. We revised our manuscript to clarify this point (page 5, lines 90-95).

It's unclear why microRNAs matter. It would be helpful if the authors could provide more support for their claims that (1) microRNAs play such a substantial role in determining the kinetics of other viruses and (2) they play such an important role in modulating COVID-19 that it's worth exploring the impact of microRNAs on SARS-CoV-2 kinetics. A link to a single review paper seems insufficient justification. What strong experimental evidence is there to support this line of research?

We appreciate the reviewer’s comments regarding microRNA. Based on this feedback, we recognized the need to clarify our rationale for selecting microRNAs as the analyte. The primary reason was that our available specimens were saliva, and microRNAs are among the biomarkers that can be reliably measured in saliva. At the same time, previous studies have reported associations between microRNAs and various diseases, which led us to consider the potential relevance of microRNAs to viral dynamics, beyond their role as general health indicators. To better reflect this context, we have added supporting references (page 17, lines 240-243).

Reviewer #3 (Public review)

The article presents a comprehensive study on the stratification of viral shedding patterns in saliva among COVID-19 patients. The authors analyze longitudinal viral load data from 144 mildly symptomatic patients using a mathematical model, identifying three distinct groups based on the duration of viral shedding. Despite analyzing a wide range of clinical data and micro-RNA expression levels, the study could not find significant predictors for the stratified shedding patterns, highlighting the complexity of SARS-CoV-2 dynamics in saliva. The research underscores the need for identifying biomarkers to improve public health interventions and acknowledges several limitations, including the lack of consideration of recent variants, the sparsity of information before symptom onset, and the focus on symptomatic infections. 

The manuscript is well-written, with the potential for enhanced clarity in explaining statistical methodologies. This work could inform public health strategies and diagnostic testing approaches. However, there is a thorough development of new statistical analysis needed, with major revisions to address the following points:

We sincerely appreciate the thoughtful feedback provided by Reviewer #3, particularly regarding our methodology. In response, we conducted additional analyses and revised the manuscript accordingly. Below, we address the reviewer’s comments point by point.

(1) Patient characterization & selection: Patient immunological status at inclusion (and if it was accessible at the time of infection) may be the strongest predictor for viral shedding in saliva. The authors state that the patients were not previously infected by SARS-COV-2. Was Anti-N antibody testing performed? Were other humoral measurements performed or did everything rely on declaration? From Figure 1A, I do not understand the rationale for excluding asymptomatic patients. Moreover, the mechanistic model can handle patients with only three observations, why are they not included? Finally, the 54 patients without clinical data can be used for the viral dynamics fitting and then discarded for the descriptive analysis. Excluding them can create a bias. All the discarded patients can help the virus dynamics analysis as it is a population approach. Please clarify. In Table 1 the absence of sex covariate is surprising.

We appreciate the detailed feedback from the reviewer regarding patient selection. We relied on the patient's self-declaration to determine the patient's history of COVID-19 infection and revised the manuscript to specify this (page 6, lines 83-84).

In parameter estimation, we used the date of symptom onset for each patient so that we establish a baseline of the time axis as clearly as possible, as we did in our previous works. Accordingly, asymptomatic patients who do not have information on the date of symptom onset were excluded from the analysis. Additionally, in the cohort we analyzed, for patients excluded due to limited number of observations (i.e., less than 3 points), most patients already had a viral load close to the detection limit at the time of the first measurement. This is due to the design of clinical trial, as if a negative result was obtained twice in a row, no further follow-up sampling was performed. These patients were excluded from the analysis because it hard to get reasonable fitting results. Also, we used 54 patients for the viral dynamics fitting and then only used the NFV cohort for clinical data analysis. We acknowledge that our description may have confused readers. We revised our manuscript to clarify these points regarding patient selecting for data fitting (page 6, lines 96-102, page 24, lines 406-407, and page 7, lines 410-412). In addition, we realized, thanks to the reviewer’s comment, that gender information was missing in Table 1. We appreciate this observation and have revised the table to include gender (we used gender in our analysis). 

(2) Exact study timeline for explanatory covariates: I understand the idea of finding « early predictors » of long-lasting viral shedding. I believe it is key and a great question. However, some samples (Figure 4A) seem to be taken at the end of the viral shedding. I am not sure it is really easier to micro-RNA saliva samples than a PCR. So I need to be better convinced of the impact of the possible findings. Generally, the timeline of explanatory covariate is not described in a satisfactory manner in the actual manuscript. Also, the evaluation and inclusion of the daily symptoms in the analysis are unclear to me.

We appreciate the reviewer’s feedback regarding the collection of explanatory variables. As noted, of the two microRNA samples collected from each patient, one was obtained near the end of viral shedding. This was intended to examine potential differences in microRNA levels between the early and late phases of infection. No significant differences were observed between the two time points, and using microRNA from either phase alone or both together did not substantially affect predictive accuracy for stratified groups. Furthermore, microRNA collection was motivated primarily by the expectation that it would be more sensitive to immune responses, rather than by ease of sampling. We have revised the manuscript to clarify these points regarding microRNA (page 17, lines 243-245 and 259-262).

Furthermore, as suggested by the reviewer, we have also strengthened the explanation regarding the collection schedule of clinical information and the use of daily symptoms in the analysis (page 6, lines 90-95, page 14, lines 218-220,).

(3) Early Trajectory Differentiation: The model struggles to differentiate between patients' viral load trajectories in the early phase, with overlapping slopes and indistinguishable viral load peaks observed in Figures 2B, 2C, and 2D. The question arises whether this issue stems from the data, the nature of Covid-19, or the model itself. The authors discuss the scarcity of pre-symptom data, primarily relying on Illinois patients who underwent testing before symptom onset. This contrasts earlier statements on pages 5-6 & 23, where they claim the data captures the full infection dynamics, suggesting sufficient early data for pre-symptom kinetics estimation. The authors need to provide detailed information on the number or timing of patient sample collections during each period.

Thank you for the reviewer’s thoughtful comments. The model used in this study [Eqs.(1-2)] has been employed in numerous prior studies and has successfully identified viral dynamics at the individual level. In this context, we interpret the rapid viral increase observed across participants as attributable to characteristics of SARS-CoV-2 in saliva, an interpretation that has also been reported by multiple previous studies. We have added the relevant references and strengthened the corresponding discussion in the manuscript (page 20, lines 303-311).

We acknowledge that our explanation of how the complementary relationship between the two cohorts contributes to capturing infection dynamics was not sufficiently clear. As described in the manuscript, the Illinois cohort provides pre-symptomatic data, whereas the NFV cohort offers abundant end-phase data, thereby compensating for each other’s missing phases. By jointly analyzing the two cohorts with a nonlinear mixed-effects model, we estimated viral dynamics at the individual-level. This approach first estimates population-level parameters (fixed effects) using data from all participants and then incorporates random effects to account for individual variability, yielding the most plausible parameter values.

Thus, even when early-phase data are lacking in the NFV cohort, information from the Illinois cohort allows us to infer most reasonable dynamics, and the reverse holds true for the end phase. In this context, we argued that combining the two cohorts enables mathematical modeling to capture infection dynamics at the individual level. Recognizing that our earlier description could be misleading, we have carefully reinforced the relevant description (page 27, lines 472-483). In addition, as suggested by the reviewer, we have added information on the number of data samples available for each phase in both cohorts (page 7, lines 106-109).

(4) Conditioning on the future: Conditioning on the future in statistics refers to the problematic situation where an analysis inadvertently relies on information that would not have been available at the time decisions were made or data were collected. This seems to be the case when the authors create micro-RNA data (Figure 4A). First, when the sampling times are is something that needs to be clarified by the authors (for clinical outcomes as well). Second, proper causal inference relies on the assumption that the cause precedes the effect. This conditioning on the future may result in overestimating the model's accuracy. This happens because the model has been exposed to the outcome it's supposed to predict. This could question the - already weak - relation with mir-1846 level.

We appreciate the reviewer’s detailed feedback. As noted in Reply to Comments 2, we collected micro-RNA samples at two time points, near the peak of infection dynamics and at the end stage, and found no significant differences between them. This suggests that micro-RNA levels are not substantially affected by sampling time. Indeed, analyses conducted using samples from the peak, late stage, or both yielded nearly identical results in relation to infection dynamics. To clarify this point, we revised the manuscript by integrating this explanation with our response in Reply to Comments 2 (page 17, lines 259-262). In addition, now we also revised manuscript to clarify sampling times of clinical information and micro-RNA (page 6, lines 90-95).

(5) Mathematical Model Choice Justification and Performance: The paper lacks mention of the practical identifiability of the model (especially for tau regarding the lack of early data information). Moreover, it is expected that the immune effector model will be more useful at the beginning of the infection (for which data are the more parsimonious). Please provide AIC for comparison, saying that they have "equal performance" is not enough. Can you provide at least in a point-by-point response the VPC & convergence assessments?

We appreciate the reviewer’s detailed feedback regarding the mathematical model. We acknowledge the potential concern regarding the practical identifiability of tau (incubation period), particularly given the limited early-phase data. In our analysis, however, the nonlinear mixed-effects model yielded a population-level estimate of 4.13 days, which is similar with previously reported incubation periods for COVID-19. This concordance suggests that our estimate of tau is reasonable despite the scarcity of early data.

For model comparison, first, we have added information on the AIC of the two models to the manuscript as suggested by the reviewer (page 10, lines 130-135). One point we would like to emphasize is that we adopted a simple target cell-limited model in this study, aiming to focus on reconstruction of viral dynamics and stratification of shedding patterns rather than exploring the mechanism of viral infection in detail. Nevertheless, we believe that the target cell-limited model provides reasonable reconstructed viral dynamics as it has been used in many previous studies. We revised manuscript to clarify this (page 10, lines 135-144). 

Furthermore, as suggested, we have added the VPC and convergence assessment results for both models, together with explanatory text, to the manuscript (Supplementary Fig 2, Supplementary Fig 3, and page 10, lines 130-135). In the VPC, the observed 5th, 50th, and 95th percentiles were generally within the corresponding simulated prediction intervals across most time points. Although minor deviations were noted in certain intervals, the overall distribution of the observed data was well captured by the models, supporting their predictive performance (Supplementary Fig 2). In addition, the log-likelihood and SAEM parameter trajectories stabilized after the burn-in phase, confirming appropriate convergence (Supplementary Fig 3).

(6) Selected features of viral shedding: I wonder to what extent the viral shedding area under the curve (AUC) and normalized AUC should be added as selected features.

We sincerely appreciate the reviewer’s valuable suggestion regarding the inclusion of additional features. Following this recommendation, we considered AUC (or normalized AUC) as an additional feature when constructing the distance matrix used for stratification. We then evaluated the similarity between the resulting distance matrix and the original one using the Mantel test, which showed a very high correlation (r = 0.92, p < 0.001). This indicates that incorporating AUC as an additional feature does not substantially alter the distance matrix. Accordingly, we have decided to retain the current stratification analysis, and we sincerely thank the reviewer once again for this interesting suggestion.

(7) Two-step nature of the analysis: First you fit a mechanistic model, then you use the predictions of this model to perform clustering and prediction of groups (unsupervised then supervised). Thus you do not propagate the uncertainty intrinsic to your first estimation through the second step, ie. all the viral load selected features actually have a confidence bound which is ignored. Did you consider a one-step analysis in which your covariates of interest play a direct role in the parameters of the mechanistic model as covariates? To pursue this type of analysis SCM (Johnson et al. Pharm. Res. 1998), COSSAC (Ayral et al. 2021 CPT PsP), or SAMBA ( Prague et al. CPT PsP 2021) methods can be used. Did you consider sampling on the posterior distribution rather than using EBE to avoid shrinkage?

Thank you for the reviewer’s detailed suggestions regarding our analysis. We agree that the current approach does not adequately account for the impact of uncertainty in viral dynamics on the stratified analyses. As a first step, we have revised Extended Data Fig 1 (now renumbered as Supplementary Fig 1) to include 95% credible intervals computed using a bootstrap approach, to present the model-fitting uncertainty more explicitly. Then, to examine the potential impact of model uncertainty on stratified analyses, we reconstructed the distance matrix underlying stratification by incorporating feature uncertainty. Specifically, for each individual, we sampled viral dynamics within the credible interval and averaged the resulting feature, and build the distance matrix using it. We then compared this uncertainty-adjusted matrix with the original one using the Mantel test, which showed a strong correlation (r = 0.72, p < 0.001). Given this result, we did not replace the current stratification but revised the manuscript to provide this information (page 11, lines 159-162 and page 28, 512-519).

Furthermore, we carefully considered the reviewer’s proposed one-step analysis. However, implementation was constrained by data-fitting limitations. Concretely, clinical information is available only in the NFV cohort. Thus, if these variables are to be entered directly as covariates on the parameters, the Illinois cohort cannot be included in the data-fitting process. Yet the NFV cohort lacks any pre-symptomatic observations, so fitting the model to that cohort alone does not permit a reasonable (well-identified/robust) fitting result. While we were unable to implement the suggestion under the current data constraints, we sincerely appreciate the reviewer’s thoughtful and stimulating proposal.

(8) Need for advanced statistical methods: The analysis is characterized by a lack of power. This can indeed come from the sample size that is characterized by the number of data available in the study. However, I believe the power could be increased using more advanced statistical methods. At least it is worth a try. First considering the unsupervised clustering, summarizing the viral shedding trajectories with features collapses longitudinal information. I wonder if the R package « LongituRF » (and associated method) could help, see Capitaine et al. 2020 SMMR. Another interesting tool to investigate could be latent class models R package « lcmm » (and associated method), see ProustLima et al. 2017 J. Stat. Softwares. But the latter may be more far-reached.

Thank you for the reviewer’s thoughtful suggestions regarding our unsupervised clustering approach. The R package “LongitiRF” is designed for supervised analysis, requiring a target outcome to guide the calculation of distances between individuals (i.e., between viral dynamics). In our study, however, the goal was purely unsupervised clustering, without any outcome variable, making direct application of “LongitiRF” challenging.

Our current approach (summarizing each dynamic into several interpretable features and then using Random Forest proximities) allows us to construct a distance matrix in an unsupervised manner. Here, the Random Forest is applied in “proximity mode,” focusing on how often dynamics are grouped together in the trees, independent of any target variable. This provides a practical and principled way to capture overall patterns of dynamics while keeping the analysis fully unsupervised.

Regarding the suggestion to use latent class mixed models (R package “lcmm”), we also considered this approach. In our dataset, each subject has dense longitudinal measurements, and at many time points, trajectories are very similar across subjects, resulting in minimal inter-individual differences. Consequently, fitting multi-class latent class mixed models (ng ≥ 2) with random effects or mixture terms is numerically unstable, often producing errors such as non-positive definite covariance matrices or failure to generate valid initial values. Although one could consider using only the time points with the largest differences, this effectively reduces the analysis to a feature-based summary of dynamics. Such an approach closely resembles our current method and contradicts the goal of clustering based on full longitudinal information.

Taken together, although we acknowledge that incorporating more longitudinal information is important, we believe that our current approach provides a practical, stable, and informative solution for capturing heterogeneity in viral dynamics. We would like to once again express our sincere gratitude to the reviewer for this insightful suggestion.

(9) Study intrinsic limitation: All the results cannot be extended to asymptomatic patients and patients infected with recent VOCs. It definitively limits the impact of results and their applicability to public health. However, for me, the novelty of the data analysis techniques used should also be taken into consideration.

We appreciate your positive evaluation of our research approach and acknowledge that, as noted in the Discussion section as our first limitation, our analysis may not provide valid insights into recent VOCs or all populations, including asymptomatic individuals. Nonetheless, we believe it is novel that we extensively investigated the relationship between viral shedding patterns in saliva and a wide range of clinical and micro-RNA data. Our findings contribute to a deeper and more quantitative understanding of heterogeneity in viral dynamics, particularly in saliva samples. To discuss this point, we revised our manuscript (page 22, lines 364-368).

Strengths are:

Unique data and comprehensive analysis.

Novel results on viral shedding.

Weaknesses are:

Limitation of study design.

The need for advanced statistical methodology.

Reviewer #1 (Recommendations For The Authors):

Line 8: In the abstract, it would be helpful to state how stratification occurred.

We thank the reviewer for the feedback, and have revised the manuscript accordingly (page 2, lines 8-11).

Line 31 and discussion: It is important to mention the challenges of using saliva as a specimen type for lab personnel.

We thank the reviewer for the feedback, and have revised the manuscript accordingly (page 3, lines 36-41).

Line 35: change to "upper respiratory tract".

We thank the reviewer for the feedback, and have revised the manuscript accordingly (page 3, line 35).

Line 37: "Saliva" is not a tissue. Please hazard a guess as to which tissue is responsible for saliva shedding and if it overlaps with oral and nasal swabs.

We thank the reviewer for the feedback, and have revised the manuscript accordingly (page 3, lines 42-45).

Line 42, 68: Please explain how understanding saliva shedding dynamics would impact isolation & screening, diagnostics, and treatments. This is not immediately intuitive to me.

We thank the reviewer for the feedback, and have revised the manuscript accordingly (page 3, lines 48-50).

Line 50: It would be helpful to explain why shedding duration is the best stratification variable.

We thank the reviewer for the feedback. We acknowledge that our wording was ambiguous. The clear differences in the viral dynamics patterns pertain to findings observed following the stratification, and we have revised the manuscript to make this explicit (page 4, lines 59-61).

Line 71: Dates should be listed for these studies.

We thank the reviewer for the feedback, and have revised the manuscript accordingly (page 6, lines 85-86).

Reviewer #2 (Recommendations For The Authors):

Please make all code and data available for replication of the analyses.

We appreciate the suggestion. Due to ethical considerations, it is not possible to make all data and code publicly available. We have clearly stated in the manuscript about it (Data availability section in Methods).

Reviewer #3 (Recommendations For The Authors):

Here are minor comments / technical details:

(1) Figure 1B is difficult to understand.

Thank you for the comment. We updated Fig 1B to incorporate more information to aid interpretation.

(2) Did you analyse viral load or the log10 of viral load? The latter is more common. You should consider it. SI Figure 1 please plot in log10 and use a different point shape for censored data. The file quality of this figure should be improved. State in the material and methods if SE with moonlit are computed with linearization or importance sampling.

Thank you for the comment. We conducted our analyses using log10-transformed viral load. Also, we revised Supplementary Fig 1 (now renumbered as Supplementary Fig 4) as suggested. We also added Supplementary Fig 3 and clarified in the Methods that standard errors (SE) were obtained in Monolix from the Fisher information matrix using the linearization method (page 28, lines 498-499).

(3) Table 1 and Figure 3A could be collapsed.

Thank you for the comment, and we carefully considered this suggestion. Table 1 summarizes clinical variables by category, whereas Fig 3A visualizes them ordered by p-value of statistical analysis. Collapsing these into a single table would make it difficult to apprehend both the categorical summaries and the statistical ranking at a glance, thereby reducing readability. We therefore decided to retain the current layout. We appreciate the constructive feedback again. 

(4) Figure 3 legend could be clarified to understand what is 3B and 3C.

We thank the reviewer for the feedback and have reinforced the description accordingly.

(5) Why use AIC instead of BICc?

Thank you for your comment. We also think BICc is a reasonable alternative. However, because our objective is predictive adequacy (reconstruction of viral dynamics), we judged AIC more appropriate. In NLMEM settings, the effective sample size required by BICc is ambiguous, making the penalty somewhat arbitrary. Moreover, since the two models reconstruct very similar dynamics, our conclusions are not sensitive to the choice of criterion.

(6) Bibliography. Most articles are with et al. (which is not standard) and some are with an extended list of names. Provide DOI for all.

We thank the reviewer for the feedback, and have revised the manuscript accordingly.

(7) Extended Table 1&2 - maybe provide a color code to better highlight some lower p-values (if you find any interesting).

We thank the reviewer for the feedback. Since no clinical information and micro-RNAs other than mir-1846 showed low p-values, we highlighted only mir-1846 with color to make it easier to locate.

(8) Please make the replication code available.

We appreciate the suggestion. Due to ethical considerations, it is not possible to make all data and code publicly available. We have clearly stated in the manuscript about it (Data availability section in Methods).

Reviewer #2 (Public review):

This study investigated the impact of early HIV specific CD8 T cell responses on the viral reservoir size after 24 weeks and 3 years of follow up in individuals who started ART during acute infection. Viral reservoir quantification showed that total and defective HIV DNA, but not intact, declined significantly between 24 weeks and 3 years post-ART. The authors also showed that functional HIV-specific CD8⁺ T-cell responses persisted over three years and that early CD8⁺ T-cell proliferative capacity was linked to reservoir decline, supporting early immune intervention in the design of curative strategies.

The paper is well written, easy to read, and the findings are clearly presented. The study is novel as it demonstrates the effect of HIV specific CD8 T cell responses on different states of the HIV reservoir, that is HIV-DNA (intact and defective), the transcriptionally active and inducible reservoir. Although small, the study cohort was relevant and well-characterized as it included individuals who initiated ART during acute infection, 12 of whom were followed longitudinally for 3 years, providing unique insights into the beneficial effects of early treatment on both immune responses and the viral reservoir. The study uses advanced methodology. I enjoyed reading the paper.

The study's limitations are minor and well acknowledged. While the cohort included only male participants-potentially limiting generalizability-the authors have clarified this limitation in the discussion. Although a chronic infection control group was not yet available, the authors explained that their protocol includes plans to add this comparison in future studies. These limitations are appropriately addressed and do not undermine the strength or validity of the study's conclusions.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

In this work, van Paassen et al. have studied how CD8 T cell functionality and levels predict HIV DNA decline. The article touches on interesting facets of HIV DNA decay, but ultimately comes across as somewhat hastily done and not convincing due to the major issues. 

(1) The use of only 2 time points to make many claims about longitudinal dynamics is not convincing. For instance, the fact that raw data do not show decay in intact, but do for defective/total, suggests that the present data is underpowered. The authors speculate that rising intact levels could be due to patients who have reservoirs with many proviruses with survival advantages, but this is not the parsimonious explanation vs the data simply being noisy without sufficient longitudinal follow-up. n=12 is fine, or even reasonably good for HIV reservoir studies, but to mitigate these issues would likely require more time points measured per person. 

(1b) Relatedly, the timing of the first time point (6 months) could be causing a number of issues because this is in the ballpark for when the HIV DNA decay decelerates, as shown by many papers. This unfortunate study design means some of these participants may already have stabilized HIV DNA levels, so earlier measurements would help to observe early kinetics, but also later measurements would be critical to be confident about stability. 

The main goal of the present study was to understand the relationship of the HIV-specific CD8 T-cell responses early on ART with the reservoir changes across the subsequent 2.5-year period on suppressive therapy. We have revised the manuscript in order to clarify this.  We chose these time points because the 24 week time point is past the initial steep decline of HIV DNA, which takes place in the first weeks after ART initiation. It is known that HIV DNA continues to decay for years after (Besson, Lalama et al. 2014, Gandhi, McMahon et al. 2017). 

(2) Statistical analysis is frequently not sufficient for the claims being made, such that overinterpretation of the data is problematic in many places. 

(2a) First, though plausible that cd8s influence reservoir decay, much more rigorous statistical analysis would be needed to assert this directionality; this is an association, which could just as well be inverted (reservoir disappearance drives CD8 T cell disappearance). 

To correlate different reservoir measures between themselves and with CD8+ T-cell responses at 24 and 156 weeks, we now performed non-parametric (Spearman) correlation analyses, as they do not require any assumptions about the normal distribution of the independent and dependent variables. Benjamini-Hochberg corrections for multiple comparisons (false discovery rate, 0.25) were included in the analyses and did not change the results. 

Following this comment we would like to note that the association between the T-cell response at 24 weeks and the subsequent decrease in the reservoir cannot be bi-directional (that can only be the case when both variables are measured at the same time point). Therefore, to model the predictive value of T-cell responses measured at 24 weeks for the decrease in the reservoir between 24 and 156 weeks, we fitted generalized linear models (GLM), in which we included age and ART regimen, in addition to three different measures of HIV-specific CD8+ T-cell responses, as explanatory variables, and changes in total, intact, and total defective HIV DNA between 24 and 156 weeks ART as dependent variables.

(2b) Words like "strong" for correlations must be justified by correlation coefficients, and these heat maps indicate many comparisons were made, such that p-values must be corrected appropriately. 

We have now used Spearman correlation analysis, provided correlation coefficients to justify the wording, and adjusted the p-values for multiple comparisons (Fig. 1, Fig 3., Table 2). Benjamini-Hochberg corrections for multiple comparisons (false discovery rate, 0.25) were included in the analyses and did not change the results.  

(3) There is not enough introduction and references to put this work in the context of a large/mature field. The impacts of CD8s in HIV acute infection and HIV reservoirs are both deep fields with a lot of complexity. 

Following this comment we have revised and expanded the introduction to put our work more in the context of the field (CD8s in acute HIV and HIV reservoirs). 

Reviewer #2 (Public review): 

Summary: 

This study investigated the impact of early HIV specific CD8 T cell responses on the viral reservoir size after 24 weeks and 3 years of follow-up in individuals who started ART during acute infection. Viral reservoir quantification showed that total and defective HIV DNA, but not intact, declined significantly between 24 weeks and 3 years post-ART. The authors also showed that functional HIV-specific CD8⁺ T-cell responses persisted over three years and that early CD8⁺ T-cell proliferative capacity was linked to reservoir decline, supporting early immune intervention in the design of curative strategies. 

Strengths: 

The paper is well written, easy to read, and the findings are clearly presented. The study is novel as it demonstrates the effect of HIV specific CD8 T cell responses on different states of the HIV reservoir, that is HIV-DNA (intact and defective), the transcriptionally active and inducible reservoir. Although small, the study cohort was relevant and well-characterized as it included individuals who initiated ART during acute infection, 12 of whom were followed longitudinally for 3 years, providing unique insights into the beneficial effects of early treatment on both immune responses and the viral reservoir. The study uses advanced methodology. I enjoyed reading the paper. 

Weaknesses: 

All participants were male (acknowledged by the authors), potentially reducing the generalizability of the findings to broader populations. A control group receiving ART during chronic infection would have been an interesting comparison. 

We thank the reviewer for their appreciation of our study. Although we had indeed acknowledged the fact that all participants were male, we have clarified why this is a limitation of the study (Discussion, lines 296-298). The reviewer raises the point that it would be useful to compare our data to a control group. Unfortunately, these samples are not yet available, but our study protocol allows for a control group (chronic infection) to ensure we can include a control group in the future.

Reviewer #1 (Recommendations for the authors): 

Minor: 

On the introduction: 

(1) One large topic that is mostly missing completely is the emerging evidence of selection on HIV proviruses during ART from the groups of Xu Yu and Matthias Lichterfeld, and Ya Chi Ho, among others. 

Previously, it was only touched upon in the Discussion. Now we have also included this in the Introduction (lines 77-80).

(2) References 4 and 5 don't quite match with the statement here about reservoir seeding; we don't completely understand this process, and certainly, the tissue seeding aspect is not known. 

Line 61-62: references were changed and this paragraph was rewritten to clarify.

(3) Shelton et al. showed a strong relationship with HIV DNA size and timing of ART initiation across many studies. I believe Ananwaronich also has several key papers on this topic. 

References by Ananwaronich are included (lines 91-94).

(4) "the viral levels decline within weeks of AHI", this is imprecise, there is a peak and a decline, and an equilibrium. 

We agree and have rewritten the paragraph accordingly.

(5) The impact of CD8 cells on viral evolution during primary infection is complex and likely not relevant for this paper. 

We have left viral evolution out of the introduction in order to keep a focus on the current subject.

(6) The term "reservoir" is somewhat polarizing, so it might be worth mentioning somewhere exactly what you think the reservoir is, I think, as written, your definition is any HIV DNA in a person on ART? 

Indeed, we refer to the reservoir when we talk about the several aspects of the reservoir that we have quantified with our assays (total HIV DNA, unspliced RNA, intact and defective proviral DNA, and replication-competent virus). In most instances we try to specify which measurement we are referring to. We have added additional reservoir explanation to clarify our definition to the introduction (lines 55-58).

(7) I think US might be used before it is defined. 

We thank the reviewer for this notification, we have now also defined it in the Results section (line 131).

(8) In Figure 1 it's also not clear how statistics were done to deal with undetectable values, which can be tricky but important. 

We have now clarified this in the legend to Figure 2 (former Figure 1). Paired Wilcoxon tests were performed to test the significance of the differences between the time points. Pairs where both values were undetectable were always excluded from the analysis. Pairs where one value was undetectable and its detection limit was higher than the value of the detectable partner, were also excluded from the analysis. Pairs where one value was undetectable and its detection limit was lower than the value of the detectable partner, were retained in the analysis.

In the discussion: 

(1) "This confirms that the existence of a replication-competent viral reservoir is linked to the presence of intact HIV DNA." I think this statement is indicative of many of the overinterpretations without statistical justification. There are 4 of 12 individuals with QVOA+ detectable proviruses, which means there are 8 without. What are their intact HIV DNA levels? 

We thank the reviewer for the question that is raised here. We have now compared the intact DNA levels (measured by IPDA) between participants with positive vs. negative QVOA output, and observed a significant difference. We rephrased the wording as follows: “We compared the intact HIV DNA levels at the 24-week timepoint between the six participants, from whom we were able to isolate replicating virus, and the fourteen participants, from whom we could not. Participants with positive QVOA had significantly higher intact HIV DNA levels than those with negative QVOA (p=0.029, Mann-Whitney test; Suppl. Fig. 3). Five of six participants with positive QVOA had intact DNA levels above 100 copies/106 PBMC, while thirteen of fourteen participants with negative QVOA had intact HIV DNA below 100 copies/106 PBMC (p=0.0022, Fisher’s exact test). These findings indicate that recovery of replication-competent virus by QVOA is more likely in individuals with higher levels of intact HIV DNA in IPDA, reaffirming a link between the two measurements.”

(2) "To determine whether early HIV-specific CD8+ T-cell responses at 24 weeks were predictive for the change in reservoir size". This is a fundamental miss on correlation vs causation... it could be the inverse. 

We thank the reviewer for the remark. We have calculated the change in reservoir size (the difference between the reservoir size at 24 weeks and 156 weeks ART) and analyzed if the HIVspecific CD8+ T-cell response at 24 weeks ART are predictive for this change. We do not think it can be inverse, as we have a chronological relationship (CD8+ responses at week 24 predict the subsequent change in the reservoir).

(3) "This may suggest that active viral replication drives the CD8+ T-cell response." I think to be precise, you mean viral transcription drives CD8s, we don't know about the full replication cycle from these data. 

We agree with the reviewer and have changed “replication” to “transcription” (line 280).

(4) "Remarkably, we observed that the defective HIV DNA levels declined significantly between 24 weeks and 3 years on ART. This is in contrast to previous observations in chronic HIV infection (30)". I don't find this remarkable or in contrast: many studies have analyzed and/or modeled defective HIV DNA decay, most of which have shown some negative slope to defective HIV DNA, especially within the first year of ART. See White et al., Blankson et al., Golob et al., Besson et al., etc In addition, do you mean in long-term suppressed? 

The point we would like to make is that,  compared to other studies, we found a significant, prominent decrease in defective DNA (and not intact DNA) over the course of 3 years, which is in contrast to other studies (where usually the decrease in intact is significant and the decrease in defective less prominent). We have rephrased the wording (lines 227-230) as follows:

“We observed that the defective HIV DNA levels decreased significantly between 24 and 156 weeks of ART. This is different from studies in CHI, where no significant decrease during the first 7 years of ART (Peluso, Bacchetti et al. 2020, Gandhi, Cyktor et al. 2021), or only a significant decrease during the first 8 weeks on ART, but not in the 8 years thereafter, was observed (Nühn, Bosman et al. 2025).”

Reviewer #2 (Recommendations for the authors): 

(1) Page 4, paragraph 2 - will be informative to report the statistics here. 

(2) Page 4, paragraph 4 - "General phenotyping of CD4+ (Suppl. Fig. 3A) and CD8+ (Supplementary Figure 3B) T-cells showed no difference in frequencies of naïve, memory or effector CD8+ T-cells between 24 and 156 weeks." - What did the CD4+ phenotyping show? 

We thank the reviewer for the remark. Indeed, there were also no differences in frequencies of naïve, memory or effector CD4+ T-cells between 24 and 156 weeks. We have added this to the paragraph (now Suppl. Fig 4), lines 166-168.

(3) Page 5, paragraph 3 - "Similarly, a broad HIV-specific CD8+ T-cell proliferative response to at least three different viral proteins was observed in the majority of individuals at both time points" - should specify n=? for the majority of individuals. 

At time point 24 weeks, 6/11 individuals had a response to env, 10/11 to gag, 5/11 to nef, and 4/11 to pol. At 156 weeks, 8/11 to env, 10/11 to gag, 8/11 to nef and 9/11 to pol. We have added this to the text (lines 188-191).

(4) Seven of 22 participants had non-subtype B infection. Can the authors explain the use of the IPDA designed by Bruner et. al. for subtype B HIV, and how this may have affected the quantification in these participants? 

Intact HIV DNA was detectable in all 22 participants. We cannot completely exclude influence of primer/probe-template mismatches on the quantification results, however such mismatches could also have occurred in subtype B participants, and droplet digital PCR that IPDA is based on is generally much less sensitive to these mismatches than qPCR.

(5) Page 7, paragraph 2 - the authors report a difference in findings from a previous study ("a decline in CD8 T cell responses over 2 years" - reference 21), but only provide an explanation for this on page 9. The authors should consider moving the explanation to this paragraph for easier understanding. 

We agree with the reviewer that this causes confusion. Therefore, we have revised and changed the order in the Discussion.

(6) Page 7, paragraph 2 - Following from above, the previous study (21) reported this contradicting finding "a decline in CD8 T cell responses over 2 years" in a CHI (chronic HIV) treated cohort. The current study was in an acute HIV treated cohort. The authors should explain whether this may also have resulted in the different findings, in addition to the use of different readouts in each study.

We thank the reviewer for this attentiveness. Indeed, the study by Takata et al. investigates the reservoir and HIV-specific CD8+ T-cell responses in both the RV254/ SEARCH010 study who initiated ART during AHI and the RV304/ SEARCH013 who initiated ART during CHI. We had not realized that the findings of the decline in CD8 T cell responses were solely found in the RV304/ SEARCH013 (CHI cohort). It appears functional HIV specific immune responses were only measured in AHI at 96 weeks, so we have clarified this in the Discussion. 

Besson, G. J., C. M. Lalama, R. J. Bosch, R. T. Gandhi, M. A. Bedison, E. Aga, S. A. Riddler, D. K. McMahon, F. Hong and J. W. Mellors (2014). "HIV-1 DNA decay dynamics in blood during more than a decade of suppressive antiretroviral therapy." Clin Infect Dis 59(9): 1312-1321.

Gandhi, R. T., J. C. Cyktor, R. J. Bosch, H. Mar, G. M. Laird, A. Martin, A. C. Collier, S. A. Riddler, B. J. Macatangay, C. R. Rinaldo, J. J. Eron, J. D. Siliciano, D. K. McMahon and J. W. Mellors (2021). "Selective Decay of Intact HIV-1 Proviral DNA on Antiretroviral Therapy." J Infect Dis 223(2): 225-233.

Gandhi, R. T., D. K. McMahon, R. J. Bosch, C. M. Lalama, J. C. Cyktor, B. J. Macatangay, C. R. Rinaldo, S. A. Riddler, E. Hogg, C. Godfrey, A. C. Collier, J. J. Eron and J. W. Mellors (2017). "Levels of HIV-1 persistence on antiretroviral therapy are not associated with markers of inflammation or activation." PLoS Pathog 13(4): e1006285.

Nühn, M. M., K. Bosman, T. Huisman, W. H. A. Staring, L. Gharu, D. De Jong, T. M. De Kort, N. Buchholtz, K. Tesselaar, A. Pandit, J. Arends, S. A. Otto, E. Lucio De Esesarte, A. I. M. Hoepelman, R. J. De Boer, J. Symons, J. A. M. Borghans, A. M. J. Wensing and M. Nijhuis (2025). "Selective decline of intact HIV reservoirs during the first decade of ART followed by stabilization in memory T cell subsets." Aids 39(7): 798-811.

Peluso, M. J., P. Bacchetti, K. D. Ritter, S. Beg, J. Lai, J. N. Martin, P. W. Hunt, T. J. Henrich, J. D. Siliciano, R. F. Siliciano, G. M. Laird and S. G. Deeks (2020). "Differential decay of intact and defective proviral DNA in HIV-1-infected individuals on suppressive antiretroviral therapy." JCI Insight 5(4).

Reviewer #1 (Public review):

Summary:

The authors attempt to study how oocyte incomplete cytokinesis occurs in the mouse ovary.

Strengths:

The finding that UPR components are highly expressed during zygotene is an interesting result that has broad implications for how germ cells navigate meiosis. The findings that proteasome activity increases in germ cells compared to somatic cells suggest that the germline might have a quantitatively different response for protein clearance.

Weaknesses:

(1) The microscopy images look saturated, for example, Figure 1a, b, etc? Is this a normal way to present fluorescent microscopy?

(2) The authors should ensure that all claims regarding enrichment/lower vs lower values have indicated statistical tests.

(a) In Figure 2f, the authors should indicate which comparison is made for this test. Is it comparing 2 vs 6 cyst numbers?

(b) Figures 4d and 4e do not have a statistical test indicated.

(3) Because the system is developmentally dynamic, the major conclusions of the work are somewhat unclear. Could the authors be more explicit about these and enumerate them more clearly in the abstract?

(4) The references for specific prior literature are mostly missing (lines 184-195, for example).

(5) The authors should define all acronyms when they are first used in the text (UPR, EGAD, etc).

(6) The jumping between topics (EMA, into microtubule fragmentation, polarization proteins, UPR/ERAD/EGAD, GCNA, ER, balbiani body, etc) makes the narrative of the paper very difficult to follow.

(7) The heading title "Visham participates in organelle rejuvenation during meiosis" in line 241 is speculative and/or not supported. Drawing upon the extensive, highly rigorous Drosophila literature, it is safe to extrapolate, but the claim about regeneration is not adequately supported.

Reviewer #2 (Public review):

This study identifies Visham, an asymmetric structure in developing mouse cysts resembling the Drosophila fusome, an organelle crucial for oocyte determination. Using immunofluorescence, electron microscopy, 3D reconstruction, and lineage labeling, the authors show that primordial germ cells (PGCs) and cysts, but not somatic cells, contain an EMA-rich, branching structure that they named Visham, which remains unbranched in male cysts. Visham accumulates in regions enriched in intercellular bridges, forming clusters reminiscent of fusome "rosettes." It is enriched in Golgi and endosomal vesicles and partially overlaps with the ER. During cell division, Visham localizes near centrosomes in interphase and early metaphase, disperses during metaphase, and reassembles at spindle poles during telophase before becoming asymmetric. Microtubule depolymerization disrupts its formation.

Cyst fragmentation is shown to be non-random, correlating with microtubule gaps. The authors propose that 8-cell (or larger) cysts fragment into 6-cell and 2-cell cysts. Analysis of Pard3 (the mouse ortholog of Par3/Baz) reveals its colocalization with Visham during cyst asymmetry, suggesting that mammalian oocyte polarization depends on a conserved system involving Par genes, cyst formation, and a fusome-like structure.

Transcriptomic profiling identifies genes linked to pluripotency and the unfolded protein response (UPR) during cyst formation and meiosis, supported by protein-level reporters monitoring Xbp1 splicing and 20S proteasome activity. Visham persists in meiotic germ cells at stage E17.5 and is later transferred to the oocyte at E18.5 along with mitochondria and Golgi vesicles, implicating it in organelle rejuvenation. In Dazl mutants, cysts form, but Visham dynamics, polarity, rejuvenation, and oocyte production are disrupted, highlighting its potential role in germ cell development.

Overall, this is an interesting and comprehensive study of a conserved structure in the germline cells of both invertebrate and vertebrate species. Investigating these early stages of germ cell development in mice is particularly challenging. Although primarily descriptive, the study represents a remarkable technical achievement. The images are generally convincing, with only a few exceptions.

Major comments:

(1) Some titles contain strong terms that do not fully match the conclusions of the corresponding sections.

(1a) Article title "Mouse germline cysts contain a fusome-like structure that mediates oocyte development":

The term "mediates" could be misleading, as the functional data on Visham (based on comparing its absence to wild-type) actually reflects either a microtubule defect or a Dazl mutant context. There is no specific loss-of-function of visham only.

(1b) Result title, "Visham overlaps centrosomes and moves on microtubules":

The term "moves" implies dynamic behavior, which would require live imaging data that are not described in the article.

(1c) Result title, "Visham associates with Golgi genes involved in UPR beginning at the onset of cyst formation":

The presented data show that the presence of Visham in the cyst coincides temporally with the expression and activity of the UPR response; the term "associates" is unclear in this context.

(1d) Result title, "Visham participates in organelle rejuvenation during meiosis":

The term "participates" suggests that Visham is required for this process, whereas the conclusion is actually drawn from the Dazl mutant context, not a specific loss-of-function of visham only.

(2) The authors aim to demonstrate that Visham is a fusome-like structure. I would suggest simply referring to it as a "fusome-like structure" rather than introducing a new term, which may confuse readers and does not necessarily help the authors' goal of showing the conservation of this structure in Drosophila and Xenopus germ cells. Interestingly, in a preprint from the same laboratory describing a similar structure in Xenopus germ cells, the authors refer to it as a "fusome-like structure (FLS)" (Davidian and Spradling, BioRxiv, 2025).

Reviewer #3 (Public review):

This manuscript provides evidence that mice have a fusome, a conserved structure most well studied in Drosophila that is important for oocyte specification. Overall, a myriad of evidence is presented demonstrating the existence of a mouse fusome that the authors term visham. This work is important as it addresses a long-standing question in the field of whether mice have fusomes and sheds light on how oocytes are specified in mammals. Concerns that need to be addressed revolve around several conclusions that are overstated or unclear and are listed below.

(1) Line 86 - the heading for this section is "PGCs contain a Golgi-rich structure known as the EMA granule" but there is nothing in this section that shows it is Golgi-rich. It does show that the structure is asymmetric and has branches.

(2) Line 105-106, how do we know if what's seen by EM corresponds to the EMA1 granule?

(3) Line 106-107-states "Visham co-stained with the Golgi protein Gm130 and the recycling endosomal protein Rab11a1". This is not convincing as there is only one example of each image, and both appear to be distorted.

(4) Line 132-133---while visham formation is disrupted when microtubules are disrupted, I am not convinced that visham moves on microtubules as stated in the heading of this section.

(5) Line 156 - the heading for this section states that Visham associates with polarity and microtubule genes, including pard3, but only evidence for pard3 is presented.

(6) Lines 196-210 - it's strange to say that UPR genes depend on DAZ, as they are upregulated in the mutants. I think there are important observations here, but it's unclear what is being concluded.

(7) Line 257-259---wave 1 and 2 follicles need to be explained in the introduction, and how this fits with the observations here clarified.

Author response:

Reviewer #1 (Public Review):

Summary

We thank the reviewer for the constructive and thoughtful evaluation of our work. We appreciate the recognition of the novelty and potential implications of our findings regarding UPR activation and proteasome activity in germ cells.

(1) The microscopy images look saturated, for example, Figure 1a, b, etc. Is this a normal way to present fluorescent microscopy?

The apparent saturation was not present in the original images, but likely arose from image compression during PDF generation. While the EMA granule was still apparent, in the revised submission, we will provide high-resolution TIFF files to ensure accurate representation of fluorescence intensity and will carefully optimize image display settings to avoid any saturation artifacts.

(2) The authors should ensure that all claims regarding enrichment/lower vs. lower values have indicated statistical tests.

We fully agree. In the revised version, we will correct any quantitative comparisons where statistical tests were not already indicated, with a clear statement of the statistical tests used, including p-values in figure legends and text.

(a) In Figure 2f, the authors should indicate which comparison is made for this test. Is it comparing 2 vs. 6 cyst numbers?

We acknowledge that the description was not sufficiently detailed. Indeed, the test was not between 2 vs 6 cyst numbers, but between all possible ways 8-cell cysts or the larger cysts studied could fragment randomly into two pieces, and produce by chance 6-cell cysts in 13 of 15 observed examples. We will expand the legend and main text to clarify that a binomial test was used to determine that the proportion of cysts producing 6-cell fragments differed very significantly from chance.

Revised text:

“A binomial test was used to assess whether the observed frequency of 6-cell cyst products differed from random cyst breakage. Production of 6-cell cysts was strongly preferred (13/15 cysts; ****p < 0.0001).”

(b) Figures 4d and 4e do not have a statistical test indicated.

We will include the specific statistical test used and report the corresponding p-values directly in the figure legends.

(3) Because the system is developmentally dynamic, the major conclusions of the work are somewhat unclear. Could the authors be more explicit about these and enumerate them more clearly in the abstract?

We will revise the abstract to better clarify the findings of this study. We will also replace the term Visham with mouse fusome to reflect its functional and structural analogy to the Drosophila and Xenopus fusomes, making the narrative more coherent and conclusive.

(4) The references for specific prior literature are mostly missing (lines 184-195, for example).

We appreciate this observation of a problem that occurred inadvertently when shortening an earlier version.  We will add 3–4 relevant references to appropriately support this section.

(5) The authors should define all acronyms when they are first used in the text (UPR, EGAD, etc).

We will ensure that all acronyms are spelled out at first mention (e.g., Unfolded Protein Response (UPR), Endosome and Golgi-Associated Degradation (EGAD)).

(6)  The jumping between topics (EMA, into microtubule fragmentation, polarization proteins, UPR/ERAD/EGAD, GCNA, ER, balbiani body, etc) makes the narrative of the paper very difficult to follow.

We are not jumping between topics, but following a narrative relevant to the central question of whether female mouse germ cells develop using a fusome.  EMA, microtubule fragmentation, polarization proteins, ER, and balbiani body are all topics with a known connection to fusomes. This is explained in the general introduction and in relevant subsections. We appreciate this feedback that further explanations of these connections would be helpful. In the revised manuscript, use of the unified term mouse fusome will also help connect the narrative across sections.  UPR/ERAD/EGAD are processes that have been studied in repair and maintenance of somatic cells and in yeast meiosis.  We show that the major regulator XbpI is found in the fusome, and that the fusome and these rejuvenation pathway genes are expressed and maintained throughout oogenesis, rather than only during limited late stages as suggested in previous literature.

(7) The heading title "Visham participates in organelle rejuvenation during meiosis" in line 241 is speculative and/or not supported. Drawing upon the extensive, highly rigorous Drosophila literature, it is safe to extrapolate, but the claim about regeneration is not adequately supported.

We believe this statement is accurate given the broad scope of the term "participates." It is supported by localization of the UPR regulator XbpI to the fusome. XbpI is the ortholog of HacI a key gene mediating UPR-mediated rejuvenation during yeast meiosis.  We also showed that rejuvenation pathway genes are expressed throughout most of meiosis (not previously known) and expanded cytological evidence of stage-specific organelle rejuvenation later in meiosis, such as mitochondrial-ER docking, in regions enriched in fusome antigens. However, we recognize the current limitations of this evidence in the mouse, and want to appropriately convey this, without going to what we believe would be an unjustified extreme of saying there is no evidence. 

Reviewer #2 (Public Review):

We thank the reviewer for the comprehensive summary and for highlighting both the technical achievement and biological relevance of our study. We greatly appreciate the thoughtful suggestions that have helped us refine our presentation and terminology.

(1) Some titles contain strong terms that do not fully match the conclusions of the corresponding sections.

(1a) Article title “Mouse germline cysts contain a fusome-like structure that mediates oocyte development”

We will change the statement to: “Mouse germline cysts contain a fusome that supports germline cyst polarity and rejuvenation.”

(1b) Result title “Visham overlaps centrosomes and moves on microtubules” We acknowledge that “moves” implies dynamics. We will include additional supplementary images showing small vesicular components of the mouse fusome on spindle-derived microtubule tracks.

(1c) Result title “Visham associates with Golgi genes involved in UPR beginning at the onset of cyst formation”

We will revise this title to: “The mouse fusome associates with the UPR regulatory protein Xbp1 beginning at the onset of cyst formation” to reflect the specific UPR protein that was immunolocalized. 

(1d) Result title “Visham participates in organelle rejuvenation during meiosis”

We will revise this to: “The mouse fusome persists during organelle rejuvenation in meiosis.”

(2) The authors aim to demonstrate that Visham is a fusome-like structure. I would suggest simply referring to it as a "fusome-like structure" rather than introducing a new term, which may confuse readers and does not necessarily help the authors' goal of showing the conservation of this structure in Drosophila and Xenopus germ cells. Interestingly, in a preprint from the same laboratory describing a similar structure in Xenopus germ cells, the authors refer to it as a "fusome-like structure (FLS)" (Davidian and Spradling, BioRxiv, 2025).

We appreciate the reviewer’s insightful comment. To maintain conceptual clarity and align with existing literature, we will refer to the structure as the mouse fusome throughout the manuscript, avoiding introduction of a new term.

Reviewer #3 (Public Review):

We thank the reviewer for emphasizing the importance of our study and for providing constructive feedback that will help us clarify and strengthen our conclusions.

(1) Line 86 - the heading for this section is "PGCs contain a Golgi-rich structure known as the EMA granule" 

We agree that the enrichment of Golgi within the EMA PGCs was not shown until the next section. We will revise this heading to:

“PGCs contain an asymmetric EMA granule.”

(2)  Line 105-106, how do we know if what's seen by EM corresponds to the EMA1 granule?

We will clarify that this identification is based on co-localization with Golgi markers (GM130 and GS28) and response to Brefeldin A treatment, which will be included as supplementary data. These findings support that the mouse fusome is Golgi-derived and can therefore be visualized by EM. The Golgi regions in E13.5 cyst cells move close together and associate with ring canals as visualized by EM (Figure 1E), the same as the mouse fusomes identified by EMA.

(3) Line 106-107-states "Visham co-stained with the Golgi protein Gm130 and the recycling endosomal protein Rab11a1". This is not convincing as there is only one example of each image, and both appear to be distorted.

Space is at a premium in these figures, but we have no limitation on data documenting this absolutely clear co-localization. We will replace the existing images with high-resolution, non-compressed versions for the final figures to clearly illustrate the co-staining patterns for GM130 and Rab11a1.

(4) Line 132-133---while visham formation is disrupted when microtubules are disrupted, I am not convinced that visham moves on microtubules as stated in the heading of this section.

We will include additional supplementary data showing small mouse fusome vesicles aligned along microtubules.

(5) Line 156 - the heading for this section states that Visham associates with polarity and microtubule genes, including pard3, but only evidence for pard3 is presented.

We agree and will revise the heading to: “Mouse fusome associates with the polarity protein Pard3.” We are adding data showing association of small fusome vesicles on microtubules.  

(6)  Lines 196-210 - it's strange to say that UPR genes depend on DAZ, as they are upregulated in the mutants. I think there are important observations here, but it's unclear what is being concluded.

UPR genes are not upregulated in DAZ in the sense we have never documented them increasing. We show that UPR genes during this time behave like pleuripotency genes and normally decline, but in DAZ mutants their decline is slowed.  We will rephrase the paragraph to clarify that Dazl mutation partially decouples developmental processes that are normally linked, which alters UPR gene expression relative to cyst development.

(7) Line 257-259-wave 1 and 2 follicles need to be explained in the introduction, and how these fits with the observations here clarified.

Follicle waves are too small a focus of the current study to explain in the introduction, but we will request readers to refer to the cited relevant literature (Yin and Spradling, 2025) for further details.

We sincerely thank all reviewers for their insightful and constructive feedback. We believe that the planned revisions—particularly the refined terminology, improved image quality, clarified statistics, and restructured abstract—will substantially strengthen the manuscript and enhance clarity for readers.

Reviewer #1 (Public review):

Summary:

In this paper, the authors conduct both experiments and modeling of human cytomegalovirus (HCMV) infection in vitro to study how the infectivity of the virus (measured by cell infection) scales with the viral concentration in the inoculum. A naïve thought would be that this is linear in the sense that doubling the virus concentration (and thus the total virus) in the inoculum would lead to doubling the fraction of infected cells. However, the authors show convincingly that this is not the case for HCMV, using multiple strains, two different target cells, and repeated experiments. In fact, they find that for some regimens (inoculum concentration), infected cells increase faster than the concentration of the inoculum, which they term "apparent cooperativity". The authors then provided possible explanations for this phenomenon and constructed mathematical models and simulations to implement these explanations. They show that these ideas do help explain the cooperativity, but they can't be conclusive as to what the correct explanation is. In any case, this advances our knowledge of the system, and it is very important when quantitative experiments involving MOI are performed.

Strengths:

Careful experiments using state-of-the-art methodologies and advancing multiple competing models to explain the data.

Weaknesses:

There are minor weaknesses in explaining the implementation of the model. However, some specific assumptions, which to this reviewer were unclear, could have a substantial impact on the results. For example, whether cell infection is independent or not. This is expanded below.

Suggestions to clarify the study:

(1) Mathematically, it is clear what "increase linearly" or "increase faster than linearly" (e.g., line 94) means. However, it may be confusing for some readers to then look at plots such as in Figure 2, which appear linear (but on the log-log scale) and about which the authors also say (line 326) "data best matching the linear relationship on a log-log scale".

(2) One of the main issues that is unclear to me is whether the authors assume that cell infection is independent of other cells. This could be a very important issue affecting their results, both when analyzing the experimental data and running the simulations. One possible outcome of infection could be the generation of innate mediators that could protect (alter the resistance) of nearby cells. I can imagine two opposite results of this: i) one possibility is that resistance would lead to lower infection frequencies and this would result in apparent sub-linear infection (contrary to the observations); or ii) inoculums with more virus lead to faster infection, which doesn't allow enough time for the "resistance" (innate effect) to spread (potentially leading to results similar to the observations, supra-linear infection).

(3) Another unclear aspect of cell infection is whether each cell only has one chance to be infected or multiple chances, i.e., do the authors run the simulation once over all the cells or more times?

(4) On the other hand, the authors address the complementary issue of the virus acting independently or not, with their clumping model (which includes nice experimental measurements). However, it was unclear to me what the assumption of the simulation is in this case. In the case of infection by a clump of virus or "viral compensation", when infection is successful (the cell becomes infected), how many viruses "disappear" and what happens to the rest? For example, one of the viruses of the clump is removed by infection, but the others are free to participate in another clump, or they also disappear. The only thing I found about this is the caption of Figure S10, and it seems to indicate that only the infected virus is removed. However, a typical assumption, I think, is that viruses aggregate to improve infection, but then the whole aggregate participates in infection of a single cell, and those viruses in the clump can't participate in other infections. Viral cooperativity with higher inocula in this case would be, perhaps, the result of larger numbers of clumps for higher inocula. This seems in agreement with Figure S8, but was a little unclear in the interpretation provided.

(5) In algorithm 1, how does P_i, as defined, relate to equation 1?

(6) In line 228, and several other places (e.g., caption of Table S2), the authors refer to the probability of a single genome infecting a cell p(1)=exp(-lambda), but shouldn't it be p(1)=1-exp(-lambda) according to equation 1?

(7) In line 304, the accrued damage hypothesis is defined, but it is stated as a triggering of an antiviral response; one would assume that exposure to a virion should increase the resistance to infection. Otherwise, the authors are saying that evolution has come up with intracellular viral resistance mechanisms that are detrimental to the cell. As I mentioned above, this could also be a mechanism for non-independent cell infection. For example, infected cells signal to neighboring cells to "become resistance" to infection. This would also provide a mechanism for saturation at high levels.

(8) In Figure 3, and likely other places, t-tests are used for comparisons, but with only an n=5 (experiments). Many would prefer a non-parametric test.

Reviewer #2 (Public review):

In their article, Peterson et al. wanted to show to what extent the classical "single hit" model of virion infection, where one virion is required to infect a cell, does not match empirical observations based on human cytomegalovirus in vitro infection model, and how this would have practical impacts in experimental protocols.

They first used a very simple experimental assay, where they infected cells with serially diluted virions and measured the proportion of infected cells with flow cytometry. From this, they could elegantly show how the proportion of infected cells differed from a "single hit" model, which they simulated using a simple mathematical model ("powerlaw model"), and better fit a model where virions need to cooperate to infect cells. They then explore which mechanism could explain this apparent cooperation:

(1) Stochasticity alone cannot explain the results, although I am unsure how generalizable the results are, because the mathematical model chosen cannot, by design, explain such observations only by stochasticity.

(2) Virion clumping seemed not to be enough either to generally explain such a pattern. For that, they first use a mathematical model showing that the apparent cooperation would be small. However, I am unsure how extreme the scenario of simulated virion clumping is. They then used dynamic light scattering to measure the distribution of the sizes of clumps. From these estimates, they show that virion clumps cannot reproduce the observed virion cooperation in serial dilution assays. However, the authors remain unprecise on how the uncertainty of these clumps' size distribution would impact the results, as most clumps have a size smaller than a single virion, leaving therefore a limited number of clumps truly containing virions.

The two models remain unidentifiable from each other but could explain the apparent virion cooperativity: either due to an increase in susceptibility of the cell each time a virion tries to infect it, or due to viral compensation, where lesser fit viruses are able to infect cells in co-infection with a better fit virion. Unfortunately, the authors here do not attempt to fit their mathematical model to the experimental data but only show that theoretical models and experimental data generate similar patterns regarding virion apparent cooperation.

Finally, the authors show that this virions cooperation could make the relationship between the estimated multiplicity of infection and viruses/cell deviate from the 1:1 relationship. Consequently, the dilution of a virion stock would lead to an even stronger decrease in infectivity, as more diluted virions can cooperate less for infection.

Overall, this work is very valuable as it raises the general question of how the estimate of infectivity can be biased if extrapolated from a single virus titer assay. The observation that HCMV virions often cooperate and that this cooperation varies between contexts seems robust. The putative biological explanations would require further exploration.

This topic is very well known in the case of segmented viruses and the semi-infectious particles, leading to the idea of studying "sociovirology", but to my knowledge, this is the first time that it was explored for a nonsegmented virus, and in the context of MOI estimation.

Author response:

Reviewer #1 (Public review):

Summary:

In this paper, the authors conduct both experiments and modeling of human cytomegalovirus (HCMV) infection in vitro to study how the infectivity of the virus (measured by cell infection) scales with the viral concentration in the inoculum. A naïve thought would be that this is linear in the sense that doubling the virus concentration (and thus the total virus) in the inoculum would lead to doubling the fraction of infected cells. However, the authors show convincingly that this is not the case for HCMV, using multiple strains, two different target cells, and repeated experiments. In fact, they find that for some regimens (inoculum concentration), infected cells increase faster than the concentration of the inoculum, which they term "apparent cooperativity". The authors then provided possible explanations for this phenomenon and constructed mathematical models and simulations to implement these explanations. They show that these ideas do help explain the cooperativity, but they can't be conclusive as to what the correct explanation is. In any case, this advances our knowledge of the system, and it is very important when quantitative experiments involving MOI are performed.

Strengths:

Careful experiments using state-of-the-art methodologies and advancing multiple competing models to explain the data.

Weaknesses:

There are minor weaknesses in explaining the implementation of the model. However, some specific assumptions, which to this reviewer were unclear, could have a substantial impact on the results. For example, whether cell infection is independent or not. This is expanded below.

Suggestions to clarify the study:

(1) Mathematically, it is clear what "increase linearly" or "increase faster than linearly" (e.g., line 94) means. However, it may be confusing for some readers to then look at plots such as in Figure 2, which appear linear (but on the log-log scale) and about which the authors also say (line 326) "data best matching the linear relationship on a log-log scale". 

This is a good point. In our revision, we will include a clarification to indicate that linear on the log-log scale relationship does not imply linear relationship on the linear-linear scale.

(2) One of the main issues that is unclear to me is whether the authors assume that cell infection is independent of other cells. This could be a very important issue affecting their results, both when analyzing the experimental data and running the simulations. One possible outcome of infection could be the generation of innate mediators that could protect (alter the resistance) of nearby cells. I can imagine two opposite results of this: i) one possibility is that resistance would lead to lower infection frequencies and this would result in apparent sub-linear infection (contrary to the observations); or ii) inoculums with more virus lead to faster infection, which doesn't allow enough time for the "resistance" (innate effect) to spread (potentially leading to results similar to the observations, supra-linear infection). 

In our models we assumed cells to be independent of each other (see also responses to other similar points). Because we measure infection in individual cells, assuming cells are independent is a reasonable first approximation. However, the reviewer makes an excellent point that there may be some between-cell signaling happening in the culture that “alerts” or “conditions” cells to change their “resistance”. It is also possible that at higher genome/cell numbers, exposure of cells to virions or virion debris may change the state of cells in the culture, and more cells become “susceptible” to infection. This is a good point that we will list in Limitations subsection of Discussion; it is a good hypothesis to test in our future experiments.

(3) Another unclear aspect of cell infection is whether each cell only has one chance to be infected or multiple chances, i.e., do the authors run the simulation once over all the cells or more times? 

Each cell has only one chance to be infected. Algorithm 1 clearly states that; we will add an extra sentence in “Agent-based simulations” to indicate this point.

(4) On the other hand, the authors address the complementary issue of the virus acting independently or not, with their clumping model (which includes nice experimental measurements). However, it was unclear to me what the assumption of the simulation is in this case. In the case of infection by a clump of virus or "viral compensation", when infection is successful (the cell becomes infected), how many viruses "disappear" and what happens to the rest? For example, one of the viruses of the clump is removed by infection, but the others are free to participate in another clump, or they also disappear. The only thing I found about this is the caption of Figure S10, and it seems to indicate that only the infected virus is removed. However, a typical assumption, I think, is that viruses aggregate to improve infection, but then the whole aggregate participates in infection of a single cell, and those viruses in the clump can't participate in other infections. Viral cooperativity with higher inocula in this case would be, perhaps, the result of larger numbers of clumps for higher inocula. This seems in agreement with Figure S8, but was a little unclear in the interpretation provided. 

This is a good point. We did not remove the clump if one of the virions in the clump manages to infect a cell, and indeed, this could be the reason why in some simulations we observe apparent cooperativity when modeling viral clumping. This is something we will explore in our revision.

(5) In algorithm 1, how does P_i, as defined, relate to equation 1? 

These are unrelated because eqn.(1) is a phenomenological model that links infection per cell to genomes per cell. P_i in algorithm 1 is “physics-inspired” potential barrier.

(6) In line 228, and several other places (e.g., caption of Table S2), the authors refer to the probability of a single genome infecting a cell p(1)=exp(-lambda), but shouldn't it be p(1)=1-exp(-lambda) according to equation 1?

Indeed, it was a typo, p(1)=1-exp(-lambda) per eqn 1. Thank you, it will be corrected in the revised paper.

(7) In line 304, the accrued damage hypothesis is defined, but it is stated as a triggering of an antiviral response; one would assume that exposure to a virion should increase the resistance to infection. Otherwise, the authors are saying that evolution has come up with intracellular viral resistance mechanisms that are detrimental to the cell. As I mentioned above, this could also be a mechanism for non-independent cell infection. For example, infected cells signal to neighboring cells to "become resistance" to infection. This would also provide a mechanism for saturation at high levels. 

We do not know how exposure of a cell to one virion would change its “antiviral state”, i.e., to become more or less resistant to the next infection. If a cell becomes more resistant, there is no possibility to observe apparent cooperativity in infection of cells, so this hypothesis cannot explain our observations with n>1. Whether this mechanism plays a role in saturation of cell infection rate at lower than 1 value when genome/cell is large is unclear but is a possibility. We will add this point to Discussion in revision.

(8) In Figure 3, and likely other places, t-tests are used for comparisons, but with only an n=5 (experiments). Many would prefer a non-parametric test. 

We repeated the analyses in Fig 3 with Mann-Whitney test, results were the same, so we would like to keep results from the t-test in the paper.

Reviewer #2 (Public review):

In their article, Peterson et al. wanted to show to what extent the classical "single hit" model of virion infection, where one virion is required to infect a cell, does not match empirical observations based on human cytomegalovirus in vitro infection model, and how this would have practical impacts in experimental protocols.

They first used a very simple experimental assay, where they infected cells with serially diluted virions and measured the proportion of infected cells with flow cytometry. From this, they could elegantly show how the proportion of infected cells differed from a "single hit" model, which they simulated using a simple mathematical model ("powerlaw model"), and better fit a model where virions need to cooperate to infect cells. They then explore which mechanism could explain this apparent cooperation:

(1) Stochasticity alone cannot explain the results, although I am unsure how generalizable the results are, because the mathematical model chosen cannot, by design, explain such observations only by stochasticity. 

Our null model simulations are not just about stochasticity; they also include variability in virion infectivity and cell resistance to infection. We agree that simulations cannot truly prove that such variability cannot result in apparent cooperativity; however, we also provide a mathematical proof that increase in frequency of infected cells should be linear with virion concentration at small genome/cell numbers.

(2) Virion clumping seemed not to be enough either to generally explain such a pattern. For that, they first use a mathematical model showing that the apparent cooperation would be small. However, I am unsure how extreme the scenario of simulated virion clumping is. They then used dynamic light scattering to measure the distribution of the sizes of clumps. From these estimates, they show that virion clumps cannot reproduce the observed virion cooperation in serial dilution assays. However, the authors remain unprecise on how the uncertainty of these clumps' size distribution would impact the results, as most clumps have a size smaller than a single virion, leaving therefore a limited number of clumps truly containing virions. 

As we stated in the paper, clumping may explain apparent cooperativity in simulations depending on how stock dilution impacts distribution of virions/clump. This could be explored further, however, better experimental measurements of virions/clump would be highly informative (but we do not have resources to do these experiments at present). Our point is that the degree of apparent cooperativity is dependent on the target cell used (n is smaller on epithelial cells than on fibroblasts) that is difficult to explain by clumping which is a virion property. Per comment by reviewer 1, we will do some more analyses of the clumping model to investigate importance of clump removal per successful infection on the detected degree of apparent cooperativity.

The two models remain unidentifiable from each other but could explain the apparent virion cooperativity: either due to an increase in susceptibility of the cell each time a virion tries to infect it, or due to viral compensation, where lesser fit viruses are able to infect cells in co-infection with a better fit virion. Unfortunately, the authors here do not attempt to fit their mathematical model to the experimental data but only show that theoretical models and experimental data generate similar patterns regarding virion apparent cooperation. 

In the revision we will provide examples of simulations that “match” experimental data with a relatively high degree of apparent cooperativity; we have done those before but excluded them from the current version since they are a bit messy. Fitting simulations to data may be an overkill.

Finally, the authors show that this virions cooperation could make the relationship between the estimated multiplicity of infection and viruses/cell deviate from the 1:1 relationship. Consequently, the dilution of a virion stock would lead to an even stronger decrease in infectivity, as more diluted virions can cooperate less for infection.

Overall, this work is very valuable as it raises the general question of how the estimate of infectivity can be biased if extrapolated from a single virus titer assay. The observation that HCMV virions often cooperate and that this cooperation varies between contexts seems robust. The putative biological explanations would require further exploration.

This topic is very well known in the case of segmented viruses and the semi-infectious particles, leading to the idea of studying "sociovirology", but to my knowledge, this is the first time that it was explored for a nonsegmented virus, and in the context of MOI estimation. 

Thank you.

Reviewer #3 (Public review): 

Summary:

The authors dilute fluorescent HCMV stocks in small steps (df ≈ 1.3-1.5) across 23 points, quantify infections by flow cytometry at 3 dpi, and fit a power-law model to estimate a cooperativity parameter n (n > 1 indicates apparent cooperativity). They compare fibroblasts vs epithelial cells and multiple strains/reporters, and explore alternative mechanisms (clumping, accrued damage, viral compensation) via analytical modeling and stochastic simulations. They discuss implications for titer/MOI estimation and suggest a method for detecting "apparent cooperativity," noting that for viruses showing this behavior, MOI estimation may be biased.

Strengths:

(1) High-resolution titration & rigor: The small-step dilution design (23 serial dilutions; tailored df) improves dose-response resolution beyond conventional 10× series.

(2) Clear quantitative signal: Multiple strain-cell pairs show n > 1, with appropriate model fitting and visualization of the linear regime on log-log axes.

(3) Mechanistic exploration: Side-by-side modeling of clumping vs accrued damage vs compensation frames testable hypotheses for cooperativity. 

Thank you.

Weaknesses:

(1) Secondary infection control: The authors argue that 3 dpi largely avoids progeny-mediated secondary infection; this claim should be strengthened (e.g., entry inhibitors/control infections) or add sensitivity checks showing results are robust to a small secondary-infection contribution. 

This is an important point. We do believe that the current knowledge about HCMV virion production time – it takes 3-4 days to make virions per multiple papers (see Fig 7 in Vonka and Benyesh-Melnick JB 1966; Fig 3B in Stanton et al JCI 2010; and Fig 1A in Li et al. PNAS 2015) – is sufficient to justify our experimental design but we do agree that an additional control to block novel infections with would be useful. We had previously performed experiments with a HCMV TB-gL-KO that cannot make infectious virions (but the stock virions can be made from complemented target cells). We will investigate if our titration experiments with this virus strain have sufficient resolution to detect apparent cooperativity. However, at present we do not have the resources to perform novel experiments.  

(2) Discriminating mechanisms: At present, simulations cannot distinguish between accrued damage and viral compensation. The authors should propose or add a decisive experiment (e.g., dual-color coinfection to quantify true coinfection rates versus "priming" without coinfection; timed sequential inocula) and outline expected signatures for each mechanism. 

Excellent suggestion. Because infection of a cell is a result of the joint viral infectivity and cell resistance, it may be hard to discriminate between these alternatives unless we specify them as particular molecular mechanisms. But we will try our best and list potential future experiments in the revised version of the paper.

(3) Decline at high genomes/cell: Several datasets show a downturn at high input. Hypotheses should be provided (cytotoxicity, receptor depletion, and measurement ceiling) and any supportive controls. 

Another good point. We do not have a good explanation, but we do not believe this is because of saturation of available target cells.  It seemed to only happen (or was most pronounced) with the ME stocks, which are typically lower in titer and so the higher MOI were nearly undiluted stock. It may be the effect of the conditioned medium.  Or perhaps there are non-infectious particles like dense bodies (enveloped particles that lack a capsid and genome) and non-infectious, enveloped particles (NIEPs) that compete for receptors or otherwise damage cells and these don’t get diluted out at the higher doses.  We plan to include these points in Discussion of the revised version of the paper.

(4) Include experimental data: In Figure 6, please include the experimentally measured titers (IU/mL), if available. 

This is a model-simulated scenario, and as such, there is no measured titers.

(5) MOI guidance: The practical guidance is important; please add a short "best-practice box" (how to determine titer at multiple genomes/cell and cell densities; when single-hit assumptions fail) for end-users. 

Good suggestion. We will include best-practice box using guidelines developed in Ryckman lab over the years in the revised version of the paper.

Overall note to all reviews: We have deposited our codes and the data on github; yet, none of the reviewers commented on it.

Reviewer #4 (Public review):

Summary:

This manuscript by Qian and colleagues utilizes ribosome profiling, and reporter assays to dissect translation termination. Unfortunately, the data do not support the conclusions of the paper, controls are missing and several assays are not well validated and do not reproduce previous findings from others.

Specific comments:

• Translation termination has been studied in several organisms including mammalian cells and yeast. In those cases what is analyzed is not the peak height at the stop codon, but rather the difference in the ribosome density before and after the stop. Thus, analyzing peak height is not validated. I understand that this is relevant only for the ribosome profiling experiments (and Ezra-seq) not the RF1 profiling. But much of the data was acquired that way.

• Moreover, the data do not reproduce previous findings and no effort is made to connect them to previous data. Previous data has shown that stop codon efficacy varies. This is not reproduced (S1C). Similarly, an effect from the +1 residue is not reproduced. The data isn't even stratified by different stop codons as previous work has shown that different surrounding residues have different effects in the context of different stop codons. Thus, none of the sequencing data is validated or trusted and does not reproduce previous findings.

• The GA-rich sequence identified by Ezra-Seq and RF1 seq is not the same and it differs from previous sequences (Wangen &Green).

• The authors claim that the majority of Rf1 peaks is at stop codons, but that is not true. It is only about 30% of the peaks. Also, not all mRNAs have peaks at the stop codons. That is at best problematic. Finally, there are mRNAs that are known to "suffer" from NMD, what do these look like in the Ezra-Seq and RF1-Seq? How about mRNAs that have programmed frameshifts? This raises questions on the validity of the eRF1 data.

• Figure 4: First, instead of M/P ratio, one should analyze M/M+P, to normalize out differences in the loading and effects from collisions, which are guaranteed to occur here, but not considered or analyzed. Second, the data are analyzed as if what matters are codons in the P and E site (and beyond, where there are definitely NOT recognized codons). While there is evidence for some interactions, one would think that an additional analysis based on sequence would be helpful. Also, the supplemental data indicates that very rarely are there reciprocal changes (as should be the case), and as seen for stop codons.

• Regarding the HiBit reporter assay: The two sequecnes clearly have effects on translation without considering stop codon context (Figure 4C), which need to be taken into account. Also, the effect from the sequences varies in the context of the assay in 4C and 4D (2-fold vs .5 fold), further questioning the assay. Moreover, the authors claim that re-initiation cannot account for Hibit levels, but that is clearly incorrect. The western in Figure 4E does not reproduce the data in 4D. While Hibit goes up (as in 4D, the putative GFP-fusion goes down. Finally, while the second reading frame should be more efficient is not explained and further argues for an artifact. Previous work (and work herein) suggests that read-through occurs equally in each reading frame. No controls for these assays are presented: e.g. stimulation by antibiotics, ABCE1 depletion, etc.

• Figure 5 has similar problems. I don't understand how the Figure in 5A is made, but when you overlay the cited structures on Rps26, the molecules are identical. I guess the authors used some fantasy to build non-existing sequences differently into the structure. There is no basis for that. In panel C and the same in Figure 7, the number of analyzed mRNAs varies. This could influence the outcome and the EXACT same set of mRNAs should be analyzed. But the main problem here is that the authors need to analyze readthrough and not peak height as detailed above. Essential controls are missing that show what fraction of the 18S rRNA is mutated. Previous work has shown that 2 nt truncated 18S rRNA is actively degraded. It is hard to believe how 15% of altered ribosomes can abolish 100% of the effect from the C-rich sequences. Important validation is missing: the authors should analyze rRNA sequences in their ribo-seq dataset to demonstrate that they have the mutated rRNAs, and that these enrich and de-enrich as predicted.

• In Figure 5-7 the authors develop a model that the sequence selectivity arises from base pairing between 18S rRNA and the mRNA. If so, then they should really stratify the data by number of WC pairs that can be formed. And only WC pairs, as GU pairs have a totally different geometry that will likely be discriminated against in this context. Also, the mutation is in a part of the helix that has no effect (Figure S3G). Thus, the data within the manuscript are inconsistent.

• Figure 6 does not agree with published data (Li et al., Nature 2022). Previous work did not show testis-depletion of Rps26 in purified ribosomes. This is the critical difference as the authors here did not purify ribosomes. Also, another Rps is an essential control, even if purified ribosomes are used. The validity of this dataset is thus questionable . Depletion from polysomes is hard to believe, as overall there is less signal in the polysomes.

• Figure 7 has similar problems as figure 5. Different pools of mRNAs are analyzed; peak height is not validated. Overexpression of Rps26 is not shown, as only Myc is shown, not Rps26. Beyond that, increased occupancy in ribosomes needs to be shown for the effect to come from ribosomes. Given how sick the cells are it is most likely that all effects are secondary and arise from whatever else is going on in the overexpression or depletion of Rps26. No controls are presented to show specific effects from Rps26.

• The authors need to check Rli1/ABCE levels in their cells. Their data have features that are indicative of low ABCE1 levels. These include a very small effect from ABCE1 depletion. These could be responsible for some of the effects they observe.

Reviewer #1 (Public review):

Microglia are mononuclear phagocytes in the CNS and play essential roles in physiology and pathology. In some conditions, circulating monocytes may infiltrate in the CNS and differentiated into microglia or microglia-like cells. However, the specific mechanism is large unknown. In this study, the authors explored the epigenetic regulation of this process. The quality of this study will be significantly improved if a few questions are addressed.

(1) The capacity of circulating myeloid cell-derived microglia are controversial. In this study, the authors utilized CX3CR1-GFP/CCR2-DsRed (hetero) mice as a lineage tracing line. However, this animal line is not an appropriate approach for this purpose. For example, when the CX3CR1-GFP/CCR2-DsRed as the undifferentiated donor cell, they are GFP+ and DsRed+. When the cell fate has been changed to microglia, they will change into GFP+ and DsRed- cells. However, this process is mediated with busulfan and artificially introduced bone marrow cells in the circulating cell, which is not existed in physiological and pathological conditions. These artifacts will potentially bring in artifacts and confound the conclusion, as the classical wrong text book knowledge of the bone marrow derived microglia theory and subsequently corrected by Fabio Rossi lab1,2. This is the most risk for drawing this conclusion. The top evidence is from the parabiosis animal model. Therefore, A parabiosis study before making this conclusion, combining a CX3CR1-GFP (hetero) mouse with a WT mouse without busulfan conditioning and looking at whether there are GFP+ microglia in the GFP- WT mouse brain. If there are no GFP+ microglia, the author should clarify this is not a physiological or pathological condition, but a defined artificial host condition, as previously study did3.

(2) In some conditions, peripheral myeloid cells can infiltrate and replace the brain microglia4,5. Discuss it would be helpful to better understand the mechanism of microglia replacement.

References:

(1) Ajami, B., Bennett, J.L., Krieger, C., Tetzlaff, W., and Rossi, F.M. (2007). Local self-renewal can sustain CNS microglia maintenance and function throughout adult life. Nature neuroscience 10, 1538-1543. 10.1038/nn2014.

(2) Ajami, B., Bennett, J.L., Krieger, C., McNagny, K.M., and Rossi, F.M.V. (2011). Infiltrating monocytes trigger EAE progression, but do not contribute to the resident microglia pool. Nature neuroscience 14, 1142-1149. http://www.nature.com/neuro/journal/v14/n9/abs/nn.2887.html#supplementary-information.

(3) Mildner, A., Schmidt, H., Nitsche, M., Merkler, D., Hanisch, U.K., Mack, M., Heikenwalder, M., Bruck, W., Priller, J., and Prinz, M. (2007). Microglia in the adult brain arise from Ly-6ChiCCR2+ monocytes only under defined host conditions. Nature neuroscience 10, 1544-1553. 10.1038/nn2015.

(4) Wu, J., Wang, Y., Li, X., Ouyang, P., Cai, Y., He, Y., Zhang, M., Luan, X., Jin, Y., Wang, J., et al. (2025). Microglia replacement halts the progression of microgliopathy in mice and humans. Science 389, eadr1015. 10.1126/science.adr1015.

(5) Xu, Z., Rao, Y., Huang, Y., Zhou, T., Feng, R., Xiong, S., Yuan, T.F., Qin, S., Lu, Y., Zhou, X., et al. (2020). Efficient strategies for microglia replacement in the central nervous system. Cell reports 32, 108041. 10.1016/j.celrep.2020.108041.

Reviewer #2 (Public review):

In this manuscript by Han et al, the authors assess the binding of SARS-CoV-2 to heparan sulfate clusters via advanced light microscopy of viral particles. The authors claim that the SARS-CoV-2 spike (in the context of pseudovirus and in authentic virus) engages heparan sulfate clusters on the cell surface, which then promotes endocytosis and subsequent infection. The finding that HSPGs are important for SARS-CoV-2 entry in some cell types is well-described, but the authors attempt to make the claim here that HS represents an alternative "receptor" and that HS engagement is far more important than the field appreciates. The data itself appears to be of appropriate quality and would be of interest to the field, but the overly generalized conclusions lack adequate experimental support. This significantly diminishes enthusiasm for this manuscript as written. The manuscript is imprecise and far overstates the actual findings shown by the data. Additional controls would be of great benefit.

Further, it is this reviewer's opinion that the findings do not represent a novel paradigm as claimed. HS has been well described for SARS-CoV-2 and other viruses to serve as attachment factors to promote initial virus attachment. While the manuscript provides new insight into the details of this process, the manuscript attempts to oversell this finding by applying new words rather than new molecular details. The authors would be better served by presenting a more balanced and nuanced view of their interesting data. In this reviewer's opinion, the salesmanship significantly detracts from the data and manuscript.

Major Comments:

The authors need to rigorously define a "receptor" vs an "attachment factor." They also should avoid ambiguous terms such as "receptor underlying ...attachment" and "attachment receptor" (or at least clearly define them). Much of their argument hinges on the specific definition of these terms. This reviewer would argue that a receptor is a host factor that is necessary and sufficient for active promotion of viral entry (genome release into the cytoplasm), while an attachment factor is a host factor that enhances initial viral attachment/endocytosis but is neither necessary nor sufficient. The evidence does NOT implicate HS as a receptor under this fairly textbook definition. This is proven in Figure 1 (and elsewhere) in which ACE2 is absolutely required for viral entry.

The authors should genetically perturb HS biosynthesis in their key assays to demonstrate necessity. HS biosynthesis genes have been shown to be important for SARS-CoV-2 entry into some cells but not others (Huh7.5 cells PMID 33306959, but not in Vero cells PMID 33147444, Calu3 cells 35879413, A549 cells 33574281, and others 36597481. The authors need to discuss this important information and reconcile it with their data and model if they want to claim that HS is broadly important.

Is targeting HS really a compelling anti-viral strategy? The data show a ~5-fold reduction, which likely won't excite a drug company. The strengths and limitations of HS targeting should be presented in a more balanced discussion. Animal data showing anti-viral activity of PIX is warranted. This would enhance this claim and also provide key evidence of a relevant role for HS in a more physiologic model.

The authors provide little discussion of the fact that these studies rely exclusively on cell lines (which also happen to be TMPRSS2-deficient). The role of proteases in the role of HS should be tested in the cell lines and primary cells used, as protease expression is a key determinant of the site of fusion.

The claim that "SARS-CoV2 JN.1 variant binds to heparan sulfate, not hACE2, in primary human airway cells" is extraordinary and thus requires extraordinary evidence.

First, PIX reduces attachment by 5-fold, which is not the same as "nearly abolished." Also, anti-ACE2 "nearly abolished" entry in 7D, while PIX did not. If the authors want to make these claims, an alternative method to disrupt HS (other than PIX) is needed in primary airway cells. A genetic approach would be much more convincing. The authors should also demonstrate whether entry in their primary cell assays is TMPRSS2 vs Cathepsin L dependent (using E64d and camostat, for instance) as mentioned above.

Each figure should clearly state how many independent experiments and replicates per experiment were performed. What does "3 experiments" mean? Are these three independent experiments or three wells on one day?

Synthèse : L'Ascension de la Diversité comme Valeur Politique

Résumé

Ce document de synthèse analyse l'exposé de la professeure Lorraine Daston sur l'ascension extraordinairement rapide de la diversité en tant que valeur politique fondamentale.

Le point de départ est un paradoxe : alors que les changements de valeurs morales sont généralement des processus séculaires, voire millénaires (ex. l'abolition de l'esclavage, l'égalité des sexes), la diversité s'est imposée comme un bien allant de soi en quelques décennies seulement, à partir des années 1970.

L'hypothèse centrale de Daston est que cette ascension fulgurante n'est pas un événement ex nihilo. La valeur politique actuelle de la diversité "s'est appuyée" (piggybacked) sur des incarnations antérieures et bien établies de cette même valeur dans d'autres domaines.

Le document retrace cette généalogie en trois étapes clés :

1. La Diversité Esthétique : Depuis l'Antiquité (Pline l'Ancien), la "fécondité exubérante" de la nature, notamment la variété infinie des fleurs, a été perçue comme une forme de beauté pure, gratuite et admirable.

Cette valeur a atteint son apogée aux XVIe-XVIIe siècles avec l'afflux de nouveautés et les cabinets de curiosités (Wunderkammern).

2. La Diversité Économique : À partir du XVIIIe siècle, la diversité change de nature et s'associe à l'efficacité. L'exemple de la manufacture d'épingles d'Adam Smith illustre comment la division du travail – une forme de diversité des tâches – devient synonyme de productivité et d'innovation.

3. La Synthèse Biologique : Au XIXe siècle, les biologistes, notamment Henri Milne-Edwards et Charles Darwin, fusionnent ces deux conceptions.

Ils appliquent le principe de la division du travail à l'organisme vivant et à l'évolution des espèces, présentant la nature non plus comme un simple terrain de jeu esthétique, mais comme une "économie sauvagement compétitive" et efficace.

C'est la naissance conceptuelle de la "biodiversité".

La valeur politique contemporaine de la diversité, née aux États-Unis dans le sillage des mouvements pour les droits civiques des années 1960, puise sa force et son évidence dans ce double héritage.

Elle invoque à la fois l'efficacité économique (les équipes diverses sont plus performantes) et la beauté esthétique, comme l'illustre la métaphore de la "Nation Arc-en-ciel" de Nelson Mandela, qui évoque simultanément la splendeur de la flore sud-africaine et l'harmonie multiraciale.

La session de questions-réponses explore les critiques contemporaines (de gauche comme de droite), les contextes nationaux spécifiques et les distinctions conceptuelles cruciales avec des notions comme le pluralisme, l'égalité et l'équité.

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Introduction : Une Ascension "Météorique"

L'analyse de Lorraine Daston part d'un constat qu'elle qualifie d'« étonnant » : la rapidité avec laquelle la diversité s'est établie comme une valeur politique, non seulement dans les arguments et la législation, mais aussi comme une intuition morale viscérale.

• Un changement de valeur exceptionnellement rapide : Les changements de valeurs fondamentales sont des processus extrêmement lents. Daston cite plusieurs exemples :

◦ L'esclavage : Il a fallu des millénaires pour passer d'une acceptation quasi universelle dans l'Antiquité à une réprobation quasi universelle aujourd'hui.   

◦ L'égalité des femmes : Les arguments en sa faveur remontent au XVIIe siècle en Europe, mais la législation sur le droit de vote n'est intervenue qu'au XXe siècle, et l'enracinement de cette valeur dans la conscience collective reste discutable.  

◦ L'égalité économique : Défendue depuis le XVIIIe siècle, elle n'a pas encore franchi le seuil de la législation, et encore moins celui de l'intuition morale.

• Un indicateur quantitatif : L'analyse des données de Google Ngram, qui mesure la fréquence des mots dans un corpus de millions de livres, montre une augmentation "météorique" de l'usage du mot "diversité" à partir des années 1970.

◦ Années 1970 : La hausse est principalement liée à la biodiversité.  

◦ Années 1980 : Le terme commence à être appliqué à des contextes sociaux et politiques.  

◦ Influence américaine : Les courbes pour le français (diversité) et l'allemand (Diversität) suivent celles de l'anglais avec un décalage d'environ cinq ans, suggérant une direction d'influence des États-Unis vers l'Europe.

En allemand, le mot "Diversity" est d'abord importé de l'anglais avant d'être naturalisé en "Diversität".

L'Hypothèse Centrale : Une Préhistoire de la Valeur

Pour expliquer cette ascension rapide, Daston avance que "l'incarnation la plus récente de la diversité dans le domaine politique puise son évidence en partie dans des versions antérieures de la diversité, d'abord comme valeur esthétique, puis comme valeur économique".

Chaque nouvelle version s'est appuyée sur la précédente, créant une sorte de palimpseste de significations qui confère à la valeur politique actuelle sa force d'évidence.

Les Incarnations Historiques de la Diversité

1. La Diversité comme Valeur Esthétique : La Surabondance de la Nature

Depuis l'Antiquité, la nature, par sa "fécondité débordante" et son "excès exubérant", a été le premier exemple de la diversité en tant que beauté.

• Pline l'Ancien (~78 ap. J.-C.) : Il s'émerveillait de la prolifération "magnifique mais apparemment inutile" des fleurs, qu'il considérait comme la preuve que la nature est "dans son humeur la plus enjouée".

• Emmanuel Kant (XVIIIe siècle) : Pour illustrer la beauté pure, qui ne sert aucun but et ne peut être subsumée sous aucun concept, il choisit les fleurs comme exemple premier.

• L'expansion européenne (XVIe-XVIIe siècles) : L'arrivée de produits exotiques (tulipes du Levant, porcelaines de Chine, coquilles de nautile de l'Indo-Pacifique) a enrichi cette esthétique de la diversité, visible dans les natures mortes et les peintures de l'époque.

• Les cabinets de curiosités (Wunderkammern) : Considérés comme l'apogée de cette esthétique, ils rassemblaient des objets hétéroclites (artefacts, animaux empaillés, etc.) dans un esprit d'extravagance et de mépris pour la frugalité.

2. La Diversité comme Valeur Économique : L'Efficacité et la Division du Travail

À la fin du XVIIIe siècle, la diversité est associée à un concept radicalement différent : l'efficacité économique.

• La manufacture d'épingles : Décrite dans l'Encyclopédie de Diderot et D'Alembert, cette usine normande illustre comment la division de la fabrication en 18 opérations distinctes permet une efficacité "époustouflante" (jusqu'à 48 000 épingles par jour).

• Adam Smith (1776) : Dans La Richesse des Nations, il utilise cet exemple pour démontrer comment la division du travail favorise l'efficacité et l'innovation technologique.

• Applications étendues : Au XIXe siècle, ce principe est appliqué bien au-delà de l'industrie :

◦ Charles Babbage : S'en inspire pour concevoir le premier ordinateur, la machine analytique.    ◦ Émile Durkheim : L'utilise pour sa théorie de la solidarité organique dans les sociétés avancées.

3. La Synthèse Biologique : De la Physiologie à la Biodiversité

Ce sont les biologistes qui ont réuni les conceptions esthétique et économique de la diversité.

• Henri Milne-Edwards : Confronté à l'infinie variété des organismes, ce zoologiste français y a décelé un principe organisateur fondamental : la division du travail.

Pour lui, "c'est surtout par la division du travail que la perfection est obtenue".

Le corps d'un organisme complexe est comme une usine où chaque organe a sa fonction (le cerveau ne digère pas, l'estomac ne pense pas).

• Charles Darwin (1859) : En lisant Milne-Edwards, il relie le principe de la division du travail à la spéciation dans L'Origine des espèces.

La nature n'est plus seulement un terrain de jeu, mais une "économie sauvagement compétitive" et extrêmement efficace.

C'est le moment où la "corne d'abondance de Pline fusionne avec la manufacture d'épingles d'Adam Smith", donnant naissance à l'idée moderne de biodiversité.

L'Émergence de la Diversité comme Valeur Politique

Origines aux États-Unis : De l'Égalité à la Diversité

Le consensus académique situe le début de l'ascension de la diversité politique aux États-Unis dans les années 1960.

• Le Mouvement des Droits Civiques : Les campagnes pour les droits des Afro-Américains, puis des femmes, se sont menées sous la bannière de l'égalité pour tous les citoyens, indépendamment de la race, du genre ou de la sexualité.

L'argument était démographique : si un groupe représente X% de la population, il devrait être représenté à hauteur de X% dans toutes les sphères de la société.

• La controverse de l'Affirmative Action : Les programmes conçus pour appliquer ce principe (quotas, discrimination positive) se sont avérés politiquement controversés.

• Le tournant de la "Diversity Management" : Après que la Cour Suprême a jugé l'affirmative action inconstitutionnelle dans plusieurs décisions marquantes, une nouvelle spécialité a émergé : la gestion de la diversité.

Dans les années 1990, le terme "diversité" a supplanté celui d'"égalité" dans les politiques publiques et privées.

Influence et Exemples Mondiaux

Cette nouvelle valeur s'est ensuite propagée à l'échelle mondiale.

• Union Européenne : Le concept est intégré dans les directives aux États membres vers 2012.

• Afrique du Sud post-apartheid : Cet exemple est particulièrement révélateur de la fusion des différentes couches de la valeur.

◦ L'archevêque Desmond Tutu a qualifié les Sud-Africains de "peuple arc-en-ciel de Dieu", un symbole religieux évoquant l'alliance après le Déluge.  

◦ Nelson Mandela a repris cette phrase à des fins civiques, soulignant les connotations multiraciales de l'arc-en-ciel.

Dans son discours présidentiel, il déclare : "Nous contractons une alliance : nous construirons une société dans laquelle tous les Sud-Africains, noirs et blancs, pourront marcher la tête haute... une nation arc-en-ciel en paix avec elle-même et avec le monde."

Cette métaphore puise sa force dans le double héritage de la diversité :

• Efficacité économique : L'argument selon lequel des équipes diverses obtiennent de meilleurs résultats par la combinaison des perspectives.

• Beauté esthétique : Mandela a souvent associé l'arc-en-ciel à la flore de son pays, comme "les célèbres jacarandas de Pretoria".

Le cœur de la valeur politique de la diversité reste "la splendeur de la prairie en fleurs".

Analyses et Critiques Contemporaines (Session Q&R)

La discussion qui a suivi l'exposé a permis d'explorer plusieurs nuances et critiques contemporaines de la notion de diversité.

Thème

Analyse et Points Clés

Déclin et Critiques

L'observation d'un léger déclin dans l'usage du mot "diversité" après 2010 pourrait s'expliquer par l'émergence de critiques venant des deux côtés du spectre politique :<br>\

• Critique de gauche : Au nom de l'universalisme, arguant que la diversité accorde un statut politique sur la base de caractéristiques distinctives, alors que l'égalité se fonde sur ce qui est commun à tous les êtres humains.<br>\

• Critique de droite : Au nom de la méritocratie, considérant que le principe de diversité s'y oppose.

Contextes Nationaux et Résistances

L'application de la diversité varie considérablement selon les contextes nationaux :<br>\

• France : Réticence à collecter des statistiques ethniques en raison de forts principes universalistes.<br>\

• États-Unis : Le débat est centré sur la question raciale.<br>- Europe Centrale : La discussion porte souvent sur les populations Roms.<br>\

• Résistances pratiques : La définition des groupes "divers" à inclure est souvent un "champ de bataille", une "guerre de tous contre tous" hobbesienne, loin de l'image d'un défilé arc-en-ciel.

Distinctions Conceptuelles Clés

Des distinctions importantes ont été établies avec des termes voisins :<br>\

• Diversité vs. Pluralisme : La diversité tend à s'appliquer aux identités individuelles ou de groupe, tandis que le pluralisme est une catégorie plus large incluant la pluralité des opinions et des idées ("marketplace of ideas" de John Stuart Mill) au sein même de ces groupes.<br>\

• Égalité vs. Équité : L'égalité (des chances) est compatible avec une méritocratie sur un "terrain de jeu équitable".

L'équité (des résultats) devient très controversée dans un contexte de contraction économique (post-2008), où le gain d'un groupe est perçu comme la perte d'un autre, menant à la fragmentation.

Le Pouvoir de la Métaphore Esthétique

La métaphore de l'arc-en-ciel est qualifiée de "brillante" car elle désamorce la stratégie de l'altérité et du dénigrement.

Personne ne hiérarchise les couleurs de l'arc-en-ciel ; au contraire, leur mélange est considéré comme plus beau que chaque couleur prise isolément.

Cela démontre le rôle actif de la valeur esthétique de la diversité dans la sphère politique.

Reviewer #1 (Public review):

Summary:

In this manuscript, Bisht et al address the hypothesis that protein folding chaperones may be implicated in aggregopathies and in particular Tau aggregation, as a means to identify novel therapeutic routes for these largely neurodegenerative conditions.

The authors conducted a genetic screen in the Drosophila eye, which facilitates identification of mutations that either enhance or suppress a visible disturbance in the nearly crystalline organization of the compound eye. They screened by RNA-interference all 64 known Drosophila chaperones and revealed that mutations in 20 of them exaggerate the Tau-dependent phenotype, while 15 ameliorated it. The enhancer of degeneration group included 2 subunits of the typically heterohexameric prefoldin complex and other co-translational chaperones.

The authors characterized in depth one of the prefoldin subunits, Pfdn5 and convincingly demonstrated that this protein functions in regulation of microtubule organization, likely due to its regulation of proper folding of tubulin monomers. They demonstrate convincingly using both immunohistochemistry in larval motor neurons and microtubule binding assays that Pfdn5 is a bona fide microtubule associated protein contributing to the stability of the axonal microtubule cytoskeleton, which is significantly disrupted in the mutants.

Similar phenotypes were observed in larvae expressing the Frontotemporal dementia with Parkinsonism on chromosome 17-associated mutations of the human Tau gene V377M and R406W. On the strength of the phenotypic evidence and the enhancement of the TauV377M-induced eye degeneration they demonstrate that loss of Pfdn5 exaggerates the synaptic deficits upon expression of the Tau mutants. Conversely, overexpression of Pfdn5 or Pfdn6 ameliorates the synaptic phenotypes in the larvae, the vacuolization phenotypes in the adult, even memory defects upon TauV377M expression.

Strengths:

The phenotypic analyses of the mutant and its interactions with TauV377M at the cell biological, histological, and behavioral levels are precise, extensive, and convincing and achieve the aims of characterization of a novel function of Pfdn5.

Regarding this memory defect upon V377M tau expression. Kosmidis et al (2010) pmid: 20071510, demonstrated that pan-neuronal expression of TauV377M disrupts the organization of the mushroom bodies, the seat of long-term memory in odor/shock and odor/reward conditioning. If the novel memory assay the authors use depends on the adult brain structures, then the memory deficit can be explained in this manner.

If the mushroom bodies are defective upon TauV377M expression does overexpression of Pfdn5 or 6 reverse this deficit? This would argue strongly in favor of the microtubule stabilization explanation.

The discovery that Pfdn5 (and 6 most likely) affect tauV377M toxicity is indeed a novel and important discovery for the Tauopathies field. It is important to determine whether this interaction affects only the FTDP-17-linked mutations, or also WT Tau isoforms, which are linked to the rest of the Tauopathies. Also, insights on the mode(s) that Pfdn5/6 affect Tau toxicity, such as some of the suggestions above are aiming at, will likely be helpful towards therapeutic interventions.

Weaknesses:

What is unclear however is how Pfdn5 loss or even overexpression affects the pathological Tau phenotypes.

Does Pfdn5 (or 6) interact directly with TauV377M? Colocalization within tissues is a start, but immunoprecipitations would provide additional independent evidence that this is so.

Does Pfdn5 loss exacerbate TauV377M phenotypes because it destabilizes microtubules, which are already at least partially destabilized by Tau expression?<br /> Rescue of the phenotypes by overexpression of Pfdn5 agrees with this notion.

However, Cowan et al (2010) pmid: 20617325 demonstrated that wild-type Tau accumulation in larval motor neurons indeed destabilizes microtubules in a Tau phosphorylation-dependent manner.

So, is TauV377M hyperphosphorylated in the larvae?? What happens to TauV377M phosphorylation when Pfdn5 is missing and presumably more Tau is soluble and subject to hyperphosphorylation as predicted by the above?

Expression of WT human Tau (which is associated with most common Tauopathies other than FTDP-17) as Cowan et al suggest has significant effects on microtubule stability, but such Tau-expressing larvae are largely viable. Will one mutant copy of the Pfdn5 knockout enhance the phenotype of these larvae?? Will it result in lethality? Such data will serve to generalize the effects of Pfdn5 beyond the two FDTP-17 mutations utilized.

Does the loss of Pfdn5 affect TauV377M (and WTTau) levels?? Could the loss of Pfdn5 simply result in increased Tau levels? And conversely, does overexpression of Pfdn5 or 6 reduce Tau levels?? This would explain the enhancement and suppression of TauV377M (and possibly WT Tau) phenotypes. It is an easily addressed, trivial explanation at the observational level, which if true begs for a distinct mechanistic approach.

Finally, the authors argue that TauV377M forms aggregates in the larval brain based on large puncta observed especially upon loss of Pfdn5. This may be so, but protocols are available to validate this molecularly the presence of insoluble Tau aggregates (for example, pmid: 36868851) or soluble Tau oligomers as these apparently differentially affect Tau toxicity. Does Pfdn5 loss exaggerate the toxic oligomers and overexpression promotes the more benign large aggregates??

Comments on revisions:

In the revised manuscript Βisht et al have provided extensive new experimental evidence in support of previously more tenuous claims. These fully satisfy my comments and suggestions, and in my view, have significantly strengthened the manuscript with compelling new evidence.

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this manuscript, Bisht et al address the hypothesis that protein folding chaperones may be implicated in aggregopathies and in particular Tau aggregation, as a means to identify novel therapeutic routes for these largely neurodegenerative conditions.

The authors conducted a genetic screen in the Drosophila eye, which facilitates the identification of mutations that either enhance or suppress a visible disturbance in the nearly crystalline organization of the compound eye. They screened by RNA interference all 64 known Drosophila chaperones and revealed that mutations in 20 of them exaggerate the Tau-dependent phenotype, while 15 ameliorated it. The enhancer of the degeneration group included 2 subunits of the typically heterohexameric prefoldin complex and other co-translational chaperones.

The authors characterized in depth one of the prefoldin subunits, Pfdn5, and convincingly demonstrated that this protein functions in the regulation of microtubule organization, likely due to its regulation of proper folding of tubulin monomers. They demonstrate convincingly using both immunohistochemistry in larval motor neurons and microtubule binding assays that Pfdn5 is a bona fide microtubule-associated protein contributing to the stability of the axonal microtubule cytoskeleton, which is significantly disrupted in the mutants.

Similar phenotypes were observed in larvae expressing Frontotemporal dementia with Parkinsonism on chromosome 17-associated mutations of the human Tau gene V377M and R406W. On the strength of the phenotypic evidence and the enhancement of the TauV377Minduced eye degeneration, they demonstrate that loss of Pfdn5 exaggerates the synaptic deficits upon expression of the Tau mutants. Conversely, the overexpression of Pfdn5 or Pfdn6 ameliorates the synaptic phenotypes in the larvae, the vacuolization phenotypes in the adult, and even memory defects upon TauV377M expression.

Strengths

The phenotypic analyses of the mutant and its interactions with TauV377M at the cell biological, histological, and behavioral levels are precise, extensive, and convincing and achieve the aims of characterization of a novel function of Pfdn5. 

Regarding this memory defect upon V377M tau expression. Kosmidis et al (2010), PMID: 20071510, demonstrated that pan-neuronal expression of Tau^V377M disrupts the organization of the mushroom bodies, the seat of long-term memory in odor/shock and odor/reward conditioning. If the novel memory assay the authors use depends on the adult brain structures, then the memory deficit can be explained in this manner. 

(1) If the mushroom bodies are defective upon Tau^V377M. expression, does overexpression of Pfdn5 or 6 reverse this deficit? This would argue strongly in favor of the microtubule stabilization explanation.

We thank the reviewer for this insightful comment. Consistent with Kosmidis et al. (2010), we confirm that expression of hTau^V377M disrupts the architecture of mushroom bodies.   In addition, we find, as suggested by the reviewer, that coexpression of either Pfdn5 or Pfdn6 with hTau^V377M significantly restores the organization of the mushroom bodies. These new findings strongly support the hypothesis that Pfdn5 or Pfdn6 mitigate hTau^V377M -induced memory deficits by preserving the structure of the mushroom body, likely through stabilizing the microtubule network. This data has now been included in the revised manuscript (Figure 7H-O).

(2) The discovery that Pfdn5 (and 6 most likely) affects tauV377M toxicity is indeed a novel and important discovery for the Tauopathies field. It is important to determine whether this interaction affects only the FTDP-17-linked mutations or also WT Tau isoforms, which are linked to the rest of the Tauopathies. Also, insights on the mode(s) that Pfdn5/6 affect Tau toxicity, such as some of the suggestions above, are aiming at will likely be helpful towards therapeutic interventions.

We agree that determining whether prefoldin modulates the toxicity of both mutant and wildtype Tau is critical for understanding its broader relevance to Tauopathies. We have now performed additional experiments required to address this issue. These new data show that loss of Pfdn5 also exacerbates toxicity associated with wildype Tau (hTau^WT), in a manner similar to that observed with hTau^V337M or hTau^R406W. Specifically, overexpression of hTau^WT in a Pfdn5 mutant background leads to Tau aggregate formation (Figure S7G-I), and coexpression of Pfdn5 with hTau^WT reduces the associated synaptic defects (Figure S11F-L). These findings underscore a general role for Pfdn5 in modulating diverse Tauopathy-associated phenotypes and suggest that it could be a broadly relevant therapeutic target. 

Weakness

(3) What is unclear, however, is how Pfdn5 loss or even overexpression affects the pathological Tau phenotypes. Does Pfdn5 (or 6) interact directly with TauV377M? Colocalization within tissues is a start, but immunoprecipitations would provide additional independent evidence that this is so.

We appreciate this important suggestion. To investigate a potential direct interaction between Pfdn5 and Tau^V377M, we performed co-immunoprecipitation experiments using lysates from adult fly brain expressing hTau^V337M. Under the conditions tested, we did not detect a direct physical interaction. While this does not support a direct interaction, it does not strongly refute it either. We note that Pfdn5 and Tau are colocalized within axons (Figure S13J-K). At this stage, we are unable to resolve the issue of direct vs indirect association. If indirect, then Tau and Pfdn5 act within the same subcellular compartments (axon); if direct, then either only a small fraction of the total cellular proteins is in the Tau-Pfdn5 complex and therefore difficult to detect in bulk protein westerns, or the interactions are dynamic or occur in conditions that we have not been able to mimic in vitro. 

(4) Does Pfdn5 loss exacerbate Tau^V377M phenotypes because it destabilizes microtubules, which are already at least partially destabilized by Tau expression? Rescue of the phenotypes by overexpression of Pfdn5 agrees with this notion. 

However, Cowan et al (2010) pmid: 20617325 demonstrated that wildtype Tau accumulation in larval motor neurons indeed destabilizes microtubules in a Tau phosphorylation-dependent manner. So, is Tau^V377M hyperphosphorylated in the larvae?? What happens to Tau^V377M phosphorylation when Pfdn5 is missing and presumably more Tau is soluble and subject to hyperphosphorylation as predicted by the above?

We completely agree that it is important to link Tau-induced phenotypes with the microtubule destabilization and phosphorylation state of Tau.   We performed immunostaining using futsch antibody to check the microtubule organization at the NMJ and observed a severe reduction in futsch intensity when Tau^V337M was expressed in the Pfdn5 mutant (ElavGal4>Tau^V337M; DPfdn5^15/40), suggesting that Pfdn5 absence exacerbates the hTau^V337M defects due to more microtubule destabilization (Figure S6F-J). 

We have performed additional experiments to examine the phosphorylation state of hTau in Drosophila larval axons. Immunocytochemistry indicated that only a subset of hTau aggregates in Pfdn5 mutants (Elav-Gal4>Tau^V337M; DPfdn5^15/40) are recognized by phospho-hTau antibodies.   For instance, the AT8 antibody (targeting pSer202/pThr205) (Goedert et al., 1995) labelled only a subset of aggregates identified by the total hTau antibody (D5D8N) (Figure S9AE). Moreover, feeding these larvae (Elav-Gal4>Tau<sup>V337M</sup; DPfdn5^15/40) with LiCl, which blocks GSK3b, still showed robust Tau aggregation (Figure S9F-J). 

These results imply that: a) soluble phospho-hTau levels in Pfdn5 mutants are low and not reliably detected with a single phospholylation-specific antibody; b) Loss of Pfdn5 results in Tau aggregation in a hyperphosphorylation-independent manner similar to what has been reported earlier (LI et al. 2022); and c) the destabilization of microtubules in Elav-Gal4>Tau^V337M; DPfdn5^15/40 results in Tau dissociation and aggregate formation. These data and conclusions have been incorporated into the revised manuscript.

(5) Expression of WT human Tau (which is associated with most common Tauopathies other than FTDP-17) as Cowan et al suggest has significant effects on microtubule stability, but such Tauexpressing larvae are largely viable. Will one mutant copy of the Pfdn5 knockout enhance the phenotype of these larvae?? Will it result in lethality? Such data will serve to generalize the effects of Pfdn5 beyond the two FDTP-17 mutations utilized.

We have now examined whether heterozygous loss of Pfdn5 (∆Pfdn5/+) enhances the effect of Tau expression. While each genotype (hTau^V337M, hTau^WT or ∆Pfdn5/+) alone is viable, Elav-Gal4 driven expression of hTau^V337M or hTau^WT in Pfdn5 heterozygous background does not cause lethality. 

(6) Does the loss of Pfdn5 affect TauV377M (and WTTau) levels?? Could the loss of Pfdn5 simply result in increased Tau levels? And conversely, does overexpression of Pfdn5 or 6 reduce Tau levels?? This would explain the enhancement and suppression of Tau^V377M (and possibly WT Tau) phenotypes. It is an easily addressed, trivial explanation at the observational level, which, if true, begs for a distinct mechanistic approach.

To test whether Pfdn5 modulates Tau phenotypes by altering Tau protein levels, we performed western blot analysis under Pfdn5 or Pfdn6 overexpression conditions and observed no change in hTau^V337M levels (Figure 6O). However, in the absence of Pfdn5, both hTau^V337M and hTau^WT form large, insoluble aggregates that are not detected in soluble lysates by standard western blotting but are visualized by immunocytochemistry (Figure S7G-I). Thus, the apparent reduction in Tau levels on western blots reflects a solubility shift, not an actual decrease in Tau expression. These findings argue against a simple model in which Pfdn5 regulates Tau abundance and instead support a mechanism in which Pfdn5 loss leads to change in Tau conformation, leading to its sequesteration away for already destabilized microtubules.  

(7) Finally, the authors argue that Tau^V377M forms aggregates in the larval brain based on large puncta observed especially upon loss of Pfdn5. This may be so, but protocols are available to validate this molecularly the presence of insoluble Tau aggregates (for example, pmid: 36868851) or soluble Tau oligomers, as these apparently differentially affect Tau toxicity. Does Pfdn5 loss exaggerate the toxic oligomers, and overexpression promote the more benign large aggregates??

We have performed additional experiments to analyze the nature of these aggregates using 1,6-HD. The 1,6-hexanediol can dissolve the Tau aggregate seeds formed by Tau droplets, but cannot dissolve the stable Tau aggregates (WEGMANN et al. 2018). We observed that 5% 1,6hexanediol failed to dissolve these Tau aggregates (Figure S8), demonstrating the formation of stable filamentous flame-shaped NFT-like aggregates in the absence of Pfdn5 (Figure 5D and Figure S9).

Reviewer #2 (Public review):

Bisht et al detail a novel interaction between the chaperone, Prefoldin 5, microtubules, and taumediated neurodegeneration, with potential relevance for Alzheimer's disease and other tauopathies. Using Drosophila, the study shows that Pfdn5 is a microtubule-associated protein, which regulates tubulin monomer levels and can stabilize microtubule filaments in the axons of peripheral nerves. The work further suggests that Pfdn5/6 may antagonize Tau aggregation and neurotoxicity. While the overall findings may be of interest to those investigating the axonal and synaptic cytoskeleton, the detailed mechanisms for the observed phenotypes remain unresolved and the translational relevance for tauopathy pathogenesis is yet to be established. Further, a number of key controls and important experiments are missing that are needed to fully interpret the findings.

The strength of this study is the data showing that Pfdn5 localizes to axonal microtubules and the loss-of-function phenotypic analysis revealing disrupted synaptic bouton morphology. The major weakness relates to the experiments and claims of interactions with Tau-mediated neurodegeneration. 

In particular, it is unclear whether knockdown of Pfdn5 may cause eye phenotypes independent of Tau. 

Our new experiments confirm that knockdown of Pfdn5 alone does not cause eye phenotypes.

Further, the GMR>tau phenotype appears to have been incorrectly utilized to examine agedependent, neurodegeneration.

In response, we have modulated and explained our conclusions in this regard as described later in our “rebuttal.”

This manuscript argues that its findings may be relevant to thinking about mechanisms and therapies applicable to tauopathies; however, this is premature given that many questions remain about the interactions from Drosophila, the detailed mechanisms remain unresolved, and absent evidence that Tau and Pfdn may similarly interact in the mammalian neuronal context. Therefore, this work would be strongly enhanced by experiments in human or murine neuronal culture or supportive evidence from analyses of human data.

The reviewer is correct that the impact would be greater if Pfdn5-Tau interactions were also examined in human tissue.   While we have not attempted these experiments ourselves, we hope that our observations will stimulate others to test the conservation of phenomena we describe. There are, however, several lines of circumstantial evidence from human Alzheimer’s disease datasets that implicate PFDN5 in disease pathology. For example, recent compilations and analyses of proteomic data show reductions of CCT components, TBCE, as well as Prefoldin subunits, including PFDN5, in AD tissue (HSIEH et al. 2019; TAO et al. 2020; JI et al. 2022; ASKENAZI et al. 2023; LEITNER et al. 2024; SUN et al. 2024). Furthermore, whole blood mRNA expression data from Alzheimer's patients revealed downregulation of PFDN5 transcript (JI et al. 2022). Together, these findings from human data are consistent with the roles of PFDN5 in suppressing diverse neurodegenerative processes. We have incorporated these points into the discussion section of the revised manuscript.

Reviewer #1 (Recommendations for the authors):

See public review for experimental recommendations focusing on the Tau Pfdn interactions.  I would refrain from using the word aggregates, I would call them puncta, unless there is molecular or visual (ie AFM) evidence that they are indeed insoluble aggregates.  Finally, although including the full genotypes written out below the axis in the bar graphs is appreciated, it nevertheless makes them difficult to read due to crowding in most cases and somewhat distracting from the figure. 

In my opinion, a more reader-friendly manner of reporting the phenotypes will be highly helpful. For example, listing each component of the genotype on the left of each bar graph and adding a cross or a filled circle under the bar to inform of the full genotype of the animals used.

As described in the response to the previous comment, we now have strong direct evidences to support our view that the observed puncta are stable Tau aggregates. Thus, we feel justified to use the term Tau-aggregates in preference to Tau puncta. 

We have tried to write the genotypes to make them more reader-friendly.

Reviewer #2 (Recommendations for the authors):

(1) Lines 119-121: 35 modifiers from 64 seem like an unusually high hit rate. Are these individual genes or lines? Were all modifiers supported by at least 2 independent RNAi strains targeting non-overlapping sequences? A supplemental table should be included detailing all genes and specific strains tested, with corresponding results.

We agree with the reviewer that 35 modifiers from 64 genes may be too high. However, since the genes knocked down in the study are chaperones, crucial for maintaining proteostasis, we may have got unusually high hits. The information related to individual genes and lines is provided in Supplemental Table 1. We have now included an additional Supplemental Table 3, which lists the genes and the RNAi lines used in Figure 1, detailing the sequence target information. The table also specifies the number of independent RNAi strains used and the corresponding results. 

(2) Figure 1: The authors quantify the areas of ommatidial fusion and necrosis as degeneration, but it is difficult to appreciate the aberrations in the photos provided. Was any consideration given to also quantifying eye size?

We have processed the images to enhance their contrast and make the aberrations clearer. The percentage of degenerated eye area (Figure 1M) was normalized with total eye area. The method for quantifying degenerated area has been explained in the materials and methods section.

(3) Figure 1: a) Only enhancers of rough eyes are shown but no controls are included to evaluate whether knockdown of these genes causes eye toxicity in the absence of Tau. These are important missing controls. All putative Tau enhancers, including Pdn5/6, need to be tested with GMR-GAL4 independently of Tau to determine whether they cause a rough eye. In a previous publication from some of the same investigators (Raut et al 2017), knockdown of Pfdn using eyGAL4 was shown to induce severe eye morphology defects - this raises questions about the results shown here. 

We agree that assessing the effects of HSP knockdown independent of Tau is essential to confirm modifier specificity. We have now performed these knockdowns, and the data are reported in Supplemental Table 1. For RNAi lines represented in Figure 1, which enhanced Tau-induced degeneration/eye developmental defect, except for one of the RNAi lines against Pfdn6 (GD34204), no detectable eye defects were observed when knocked down with GMR-Gal4 at 25°C, suggesting that enhancement is specific to the Tau background. 

Use of a more eye-specific GMR-Gal4 driver at 25°C versus broader expressing ey-Gal4 at 29°C in prior work (Raut et al. 2017) likely reflects the differences in the eye morphological defects.

(b) Besides RNAi, do the classical Pdn5 deletion alleles included in this work also enhance the tau rough eye when heterozygous? Please also consider moving the Pfdn5/6 overexpression studies to evaluate possible suppression of the Tau rough eye to Figure 1, as it would enhance the interpretation of these data (but see also below).

GMR-Gal4 driven expression of hTau^V337M or hTau^WT in Pfdn5 heterozygous background does not enhance rough eye phenotype. 

(4) For genes of special interest, such as Pdn5, and other genes mentioned in the results, the main figure, or discussion, it is also important to perform quantitative PCR to confirm that the RNAi lines used actually knock down mRNA expression and by how much. These studies will establish specificity.

We agree that confirming RNAi efficiency via quantitative PCR (qPCR) is essential for validating the knockdown efficiency. We have now included qPCR data, especially for key modifiers, confirming effective knockdown (Figure S2).

(5) Lines 235-238: how do you conclude whether the tau phenotype is "enhanced" when Pfdn5 causes a similar phenotype on its own? Could the combination simply be additive? Did overexpression of Pdn5 suppress the UAS-hTau NMJ bouton phenotype (see below)? 

Although Pfdn5 mutants and hTau expression individually increase satellite boutons, their combination leads to a significantly more severe and additional phenotype, such as significantly decreased bouton size and increased bouton number, indicating an enhancing rather than purely additive interaction (Figure 4 and Figure S6C). Moreover, we now show that overexpression of Pfdn5 significantly suppressed the hTau^V337M-induced NMJ phenotypes. This new data has been incorporated as Figure S11F-L in the revised manuscript. 

Alternatively, did the authors consider reducing fly tau in the Pdn5 mutant background?

In new additional experiments, we observe that double mutants for Drosophila Tau (dTau) and Pfdn5 also exhibit severe NMJ defects, suggesting genetic interactions between dTau and Pfdn5. This data is shown below for the reviewer.

Author response image 1.

A double mutant combination of dTau and Pfdn5 aggravates the synaptic defects at the Drosophila NMJ. (A-D') Confocal images of NMJ synapses at muscle 4 of A2 hemisegment showing synaptic morphology in (A-A') control, (B-B') ΔPfdn5<SUP>15/40</SUP>, (C-C') dTauKO/dTauKO (Drosophila Tau mutant), (D-D') dTauKO/dTauKO; ∆Pfdn5<SUP>15/40</SUP> double immunolabeled for HRP (green), and CSP (magenta). The scale bar in D for (A-D') represents 10 µm. 

(6) It may be important to further extend the investigation to the actin cytoskeleton. It is noted that Pfdn5 also stabilizes actin. Importantly, tau-mediated neurodegeneration in Drosophila also disrupts the actin cytoskeleton, and many other regulators of actin modify tau phenotypes.

We appreciate the suggestion to examine the actin cytoskeleton. While prior studies indicate that Pfdn5 might regulate the actin cytoskeleton and that Tau^V377M hyperstabilizes the actin cytoskeleton, we did not observe altered actin levels in Pfdn5 mutants (Figure 2G). However, actin dynamics may represent an additional mechanism through which Pfdn5 might temporally influence Tauopathy. Future work will address potential actin-related mechanisms in Tauopathy.

(7) Figure 2: in the provided images, it is difficult to appreciate the futsch loops. Please include an image with increased magnification. It appears that fly strains harboring a genomic rescue BAC construct are available for Pfdn-this would be a complementary reagent to test besides Pfdn overexpression.

We have updated Figure 2 to include high magnification NMJ images as insets, clearly showing the Futsch loops. While we have not yet tested a genomic rescue BAC construct for Pfdn5, we plan to use the fly line harboring this construct in future work.

(8) Figure 3: Some of the data is not adequately explained. The use of Ran as a loading control seems rather unusual. What is the justification? Pfdn appears to only partially co-localize with a-tubulin in the axon; can the authors discuss or explain this? Further, in Pfdn5 mutants, there appears to be a loss of a-tubulin staining (3b'); this should also be discussed.

We appreciate the reviewer's concern regarding the choice of loading control for our Western blot analysis. Importantly, since Tubulin levels and related pathways were the focus of our analysis, traditional loading controls such as α- or β-tubulin or actin were deemed unsuitable due to potential co-regulation. Ran, a nuclear GTPase involved in nucleocytoplasmic transport, is not known to be transcriptionally or post-translationally regulated by Tubulin-associated signaling pathways. To ensure its reliability as a loading control, we confirmed by densitometric analysis that Ran expression showed minimal variability across all samples. Hence, we used Ran for accurate normalization in the Western blot data represented in this manuscript. We have also used GAPDH as a loading control and found no difference with respect to Ran as a loading control across samples.

We appreciate the reviewer's comment regarding the interpretation of our Pearson's correlation coefficient (PCC) results. While the mean colocalization value of 0.6 represents a moderate positive correlation (MUKAKA 2012), which may not reach the conventional threshold for "high positive" colocalization (usually considered 0.7-0.9), it nonetheless indicates substantial spatial overlap between the proteins of interest. Importantly, colocalization analysis provides supportive but indirect evidence for molecular proximity.  To further validate the interaction, we performed a microtubule binding assay, which directly demonstrates the binding of Pfdn5 to stabilized microtubules.

In accordance with the western blot analysis shown in Figure 2G-I, the levels of Tubulin are reduced in the Pfdn5 mutants (Figure 3B''). We have incorporated and discussed this in the revised manuscript.

(9) Figure 4: Overexpression of Pfdn appears to rescue the supernumerary satellite bouton numbers induced by human Tau; however, interpretation of this experiment is somewhat complicated as it is performed in Pfdn mutant genetic background. Can overexpression of Pfdn on its own rescue the Tau bouton defect in an otherwise wildtype background?

We have now coexpressed Pfdn5 and hTau<SUP>V337M</SUP> in an otherwise wild-type background. As shown in Figure S11F-L, Pfdn5 overexpression suppresses Tau-induced bouton defects. We have incorporated the data in the Results section to support the role of Pfdn5 as a modifier of Tau toxicity.

(10) Lines 256-263 / Figure 5: (a) What exactly are these tau-positive structures (punctae) being stained in larval brains in Fig 5C-E? Most prior work on tau aggregation using Drosophila models has been done in the adult brain, and human wildtype or mutant Tau is not known to form significant numbers of aggregates in neurons (although aggregates have been described following glia tau expression). 

Therefore, the results need to be further clarified. Besides the provided schematic, a zoomed-out image showing the whole larval brain is needed here for orientation. Have these aggregates been previously characterized in the literature? 

We agree with the reviewer that the expression of the wildtype or mutant form of human Tau in Drosophila is not known to form aggregates in the larval brain, in contrast to the adult brain (JACKSON et al. 2002; OKENVE-RAMOS et al. 2024). Consistent with previous reports, we also observed that Tau expression on its own does not form aggregates in the Drosophila larval brain.

However, in the absence of Pfdn5, microtubule disruption is severe, leading to reduced Taumicrotubule binding and formation of globular/round or flame-shaped tangles like aggregates in the larval brain. Previous studies have reported that 1,6-hexanediol can dissolve the Tau aggregate seeds formed by Tau droplets, but cannot dissolve the stable Tau aggregates (WEGMANN et al. 2018). We observed that 5% 1,6-Hexanediol failed to dissolve these Tau puncta, demonstrating the formation of stable aggregates in the absence of Pfdn5. Additionally, we now performed a Tau solubility assay and show that in the absence of Pfdn5, a significant amount of Tau goes in the pellet fraction, which could not be detected by phospho-specific AT8 Tau antibody (targeting pSer202/pThr205) but was detected by total hTau antibody (D5D8N) on the western blots (Figure S8). These data further reinforce our conclusion that  Pfdn5 prevents the transition of hTau from soluble and/or microtubule-associated state to an aggregated, insoluble, and pathogenic state. These new data have been incorporated into the revised manuscript.

(b) Can additional markers (nuclei, cell membrane, etc.) be used to highlight whether the taupositive structures are present in the cell body or at synapses?

We performed the co-staining of Tau and Elav to assess the aggregated Tau localization. We found that in the presence of Pfdn5, Tau is predominantly cytoplasmic and localised to the cell body and axons. In the absence of Pfdn5, Tau forms aggregates but is still localized to the cell body or axons. However, some of the aggregates are very large, and the subcellular localization could not be determined (Figure S8M-N'). These might represent brain regions of possible nuclear breakdown and cell death (JACKSON et al. 2002).

(c) It would also be helpful to perform western blots from larval (and adult) brains examining tau protein levels, phospho-tau species, possible higher-molecular weight oligomeric forms, and insoluble vs. soluble species. These studies would be especially important to help interpret the potential mechanisms of observed interactions.

Western blot analysis revealed that overexpression of Pfdn5 does not alter total Tau levels (Figure 6O). In Pfdn5 mutants, however, hTau^V337M levels were reduced in the supernatant fraction and increased in the pellet fraction, indicating a shift from soluble monomeric Tau to aggregated Tau.

(d) Does overexpression of Pdn5 (UAS-Pdn5) suppress the formation of tau aggregates? I would therefore recommend that additional experiments be performed looking at adult flies (perhaps in Pfdn5 heterozygotes or using RNAi due to the larval lethality of Pdn5 null animals).

Overexpression of Pfdn5 significantly reduced Tau-aggregates (Elav-Gal4/UASTau^V337M; UAS-Pfdn5; DPfdn5^15/40) observed in Pfdn5 mutants (Figure 5E). Coexpression of Pfdn5 and hTau^V337M suppresses the Tau aggregates/puncta in 30-day adult brain. Since heterozygous DPfdn¹⁵/+ did not show a reduction in Pfdn5 levels, we did not test the suppression of Tau aggregates in  DPfdn¹⁵/+; Elav>UAS-Pfdn5, UAS-Tau^V337M.

(11) Figure 6, panels A-N: The GMR>Tau rough eye is not a "neurodegenerative" but rather a predominantly developmental phenotype. It results from aberrant retinal developmental patterning and the subsequent secretion/formation of the overlying eye cuticle (lenslets). I am confused by the data shown suggesting a "shrinking eye size" and increasing roughened surface over time (a GMR>tau eye similar to that shown in panel B cannot change to appear like the one in panel H with aging). The rough eye can be quite variable among a population of animals, but it is usually fixed at the time the adult fly ecloses from the pupal case, and quite stable over time in an individual animal. Therefore, any suppression of the Tau rough eye seen at 30 days should be appreciable as soon as the animals eclose. These results need to be clarified. If indeed there is robust suppression of Tau rough eye, it may be more intuitive and clearer to include these data with Figure 1, when first showing the loss-of-function enhancement of the Tau rough eye. Also, why is Pfdn6 included in these experiments but not in the studies shown in Figures 2-5?

We thank the reviewer for their careful and knowledgeable assessment of the GMR>Tau rough eye model. We appreciate the clarification that the rough eye phenotype could be “developmental” rather than neurodegenerative.”  Our initial observations regarding "shrinking eye size" and "increased surface roughness" clearly show age-related progression of structural change.   Such progression has been observed and reported by others (IIJIMA-ANDO et al. 2012; PASSARELLA AND GOEDERT 2018).   We observed an age-dependent increase in the number of fused ommatidia in GMR-Gal4 >Tau, which were rescued by Pfdn5 or Pfdn6 expression. We noted that adult-specific induction of hTau^V337M adult flies using the Gal80^ts and GMR-GeneSwitch (GMR-GS) systems was not sufficient to induce a significant eye phenotype; thus, early expression of Tau in the developing eye imaginal disc appears to be required for the adult progressive phenotype that we observe. We feel that it is inadequate to refer to this adult progressive phenotype as “developmental,” and while admittedly arguable whether this can be termed “degenerative.”   

To address neurodegeneration more directly, we focused on 30-day-old adult fly brains and demonstrated that Pfdn5 overexpression suppresses age-dependent Tau-induced neurodegeneration in the central nervous system (Figure 6H-N and Figure S12). This supports our central conclusion regarding the neuroprotective role of Pfdn5 in age-associated Tau pathology. Since we found an enhancement in the Tau-induced synaptic and eye phenotypes by Pfdn6 knockdown, we also generated CRISPR/Cas9-mediated loss-of-function mutants for Pfdn6. However, loss of Pfdn6 resulted in embryonic/early first instar lethality, which precluded its detailed analysis at the larval stages.

(12) Figure 6, panels O-T: the elav>tau image appears to show a different frontal section plane compared to the other panels. It is advisable to show images at a similar level in all panels since vacuolar pathology can vary by region. It is also useful to be able to see the entire brain at a lower power, but the higher power inset view is obscuring these images. I would recommend creating separate panels rather than showing them as insets.

In the revised figure, we now display the low- and high-magnification images as separate, clearly labeled panels instead of using insets. This improves visibility of the brain morphology while providing detailed views of the vacuolar pathology (Figure 6H-L).

(13) Figure 6/7: For the experiments in which Pfdn5/6 is overexpressed and possibly suppresses tau phenotypes (brain vacuoles and memory), it is important to use controls that normalize the number of UAS binding sites, since increased UAS sites may dilute GAL4 and reduced Tau expression levels/toxicity. Therefore, it would be advisable to compare with Elav>Tau flies that also include a chromosome with an empty UAS site or other transgenes, such as UAS-GFP or UAS-lacZ.

We thank the reviewer for the suggestion. Now we have incorporated proper controls in the brain vacuolization, the mushroom body, and ommatidial fusion rescue experiments. Also, we have independently verified whether Gal4 dilution has any effect on the Tau phenotypes (Figure 6H-L, Figure 7, and Figure S11A-B).

(14) Lines 311-312: the authors say vacuolization occurs in human neurodegenerative disease, which is not really true to my knowledge and definitely not stated in the citation they use. Please re-phrase.

Now we have made the appropriate changes in the revised manuscript.

(15) Figure 7: The authors claim that Pfdn5/6 expression does not impact memory behavior, but there in fact appears to be a decrease in preference index (panel D vs panel B). Does this result complicate the interpretation of the potential interaction with Tau (panel F). Are data from wildtype control flies available?

In our memory assay, a decrease in performance index (PI) of the trained flies compared to the naïve flies indicates memory formation (normal memory in control flies, Figure 7B). In contrast, a lack of significant difference in PI indicates a memory defect (Figure 7C: hTau^V337M overexpressed flies). "Decrease in preference index (panel D vs panel B)" is not a sign of memory defect; it may be interpreted as a better memory instead. Hence, neuronal overexpression of Pfdn5 (Figure 7D) or Pfdn6 (Figure 7E) in wildtype neurons does not cause memory deficits. In addition, coexpression of Pfdn5/6 and hTau^V337M successfully rescues the Tau-induced memory defect (significant drop in PI compared to the PI of naïve flies in Figure 7F-G). Moreover, almost complete rescue of the Tau-induced mushroom body defect on Pfdn5 or Pfdn6 expression further establishes potential interaction between Pfdn5/6 and Tau. This data has been incorporated into the revised manuscript.

The memory assay itself with extensive data on wildtype flies and various other genotype will shortly be submitted for publication in another manuscript (Majumder et al, manuscript under preparation); However, we can confirm for the reviewer that wildtype flies, trained and assayed by the protocol described, show a significant decrease in performance index compared to the naïve flies, indicative of strong learning and memory performance, very similar to the control genotype data shown in Figure 7B. 

Additional minor considerations

(16) Lines 50-52: there are many therapeutic interventions for treating tauopathies, but not curative or particularly effective ones.

Now we have made the appropriate changes in the revised manuscript.

(17) Lines 87-106 seem like a duplication of the abstract. Consider deleting or condensing.

We have made the appropriate changes in the revised manuscript.

(18) Where is pfdn5 expressed? Development v. adult? Neuron v. glia? Conservation?

Prefoldin5 is expressed throughout development but strongly localized to the larval trachea and neuronal axons. Drosophila Pfdn5 shows 35% overall identity with human PFDN5. 

(19) Liine 187: is pfdn5 truly "novel"?

The role of Pfdn5 as microtubule-binding and stabilizing is a new finding and has not been predicted or described before. Hence, it is a novel neuronal microtubule-associated protein.  

(20) Figure 5, panel F, genotype labels on the x-axis are confusing; consider simplifying to Control, DPfdn, and Rescue.

We have made appropriate changes in the figure for better readability.

(21) Figures 5/8: it might be preferable to use consistent colors for Tau/HRP--Tau is labeled green in Figure 5 and then purple in Figure 8.

We have made these changes where possible. 

(22) Lines 311-312: Vacuolar neuropathology is NOT typically observed in human Tauopathy.

We thank the reviewer for pointing this out. We have made the appropriate changes in the revised manuscript.

(23) Lines 328-349: The explanation could be made more clear. Naïve flies should not necessarily be called controls. Also, a more detailed explanation of how the preference index is computed would be helpful. Why are some datapoints negative values?

(a) We have rewritten this paragraph to make the description and explanation clearer. The detailed method and formula to calculate the Preference index have been incorporated in the Materials and Methods section.

(b) We have replaced the term Control with Naïve. 

(c) Datapoints with negative values appeared in some of the 'Trained' group flies. It indicates that post-CuSO₄ training, some groups showed repulsion towards the otherwise attractive odor 2,3B. As 2,3B is an attractive odorant, naïve or control flies show attraction towards it compared to air, which is evident from a higher number of flies in the Odor arm (O) compared to that of the Air arm (A) of the Y-maze; thus, the PI [(O-A/O+A)*100] is positive in case of naïve fly groups. Training of the flies led to an association of the attractive odorant with bitter food, leading to a decrease of attraction, and even repulsion towards the odorant in a few instances, resulting in less fly count in the odor arm compared to the air arm. Hence, the PI becomes negative as (O-A) is negative in such instances. Thus, it is not an anomaly but indicates strong learning. 

(24) Line 403: misspelling "Pdfn"

We have corrected this.

(25) Lines 423-425: recommend re-phrasing, since tauopathies are human diseases. Mice and other animal models may be susceptible to tau-mediated neuronal dysfunction but not Tauopathy, per see.

We have made the appropriate changes in the revised manuscript.

(26) Lines 468-469: "tau neuropathology" rather than "tau associated neuropathies".

We have made the appropriate changes in the revised manuscript. 

References

Askenazi, M., T. Kavanagh, G. Pires, B. Ueberheide, T. Wisniewski et al., 2023 Compilation of reported protein changes in the brain in Alzheimer's disease. Nat Commun 14: 4466.

Hsieh, Y. C., C. Guo, H. K. Yalamanchili, M. Abreha, R. Al-Ouran et al., 2019 Tau-Mediated Disruption of the Spliceosome Triggers Cryptic RNA Splicing and Neurodegeneration in Alzheimer's Disease. Cell Rep 29: 301-316 e310.

Iijima-Ando, K., M. Sekiya, A. Maruko-Otake, Y. Ohtake, E. Suzuki et al., 2012 Loss of axonal mitochondria promotes tau-mediated neurodegeneration and Alzheimer's disease-related tau phosphorylation via PAR-1. PLoS Genet 8: e1002918.

Jackson, G. R., M. Wiedau-Pazos, T. K. Sang, N. Wagle, C. A. Brown et al., 2002 Human wildtype tau interacts with wingless pathway components and produces neurofibrillary pathology in Drosophila. Neuron 34: 509-519.

Ji, W., K. An, C. Wang and S. Wang, 2022 Bioinformatics analysis of diagnostic biomarkers for Alzheimer's disease in peripheral blood based on sex differences and support vector machine algorithm. Hereditas 159: 38.

Leitner, D., G. Pires, T. Kavanagh, E. Kanshin, M. Askenazi et al., 2024 Similar brain proteomic signatures in Alzheimer's disease and epilepsy. Acta Neuropathol 147: 27.

Li, L., Y. Jiang, G. Wu, Y. A. R. Mahaman, D. Ke et al., 2022 Phosphorylation of Truncated Tau Promotes Abnormal Native Tau Pathology and Neurodegeneration. Mol Neurobiol 59: 6183-6199.

Mershin, A., E. Pavlopoulos, O. Fitch, B. C. Braden, D. V. Nanopoulos et al., 2004 Learning and memory deficits upon TAU accumulation in Drosophila mushroom body neurons. Learn Mem 11: 277-287.

Mukaka, M. M., 2012 Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24: 69-71.

Okenve-Ramos, P., R. Gosling, M. Chojnowska-Monga, K. Gupta, S. Shields et al., 2024 Neuronal ageing is promoted by the decay of the microtubule cytoskeleton. PLoS Biol 22: e3002504.

Passarella, D., and M. Goedert, 2018 Beta-sheet assembly of Tau and neurodegeneration in Drosophila melanogaster. Neurobiol Aging 72: 98-105.

Sun, Z., J. S. Kwon, Y. Ren, S. Chen, C. K. Walker et al., 2024 Modeling late-onset Alzheimer's disease neuropathology via direct neuronal reprogramming. Science 385: adl2992.

Tao, Y., Y. Han, L. Yu, Q. Wang, S. X. Leng et al., 2020 The Predicted Key Molecules, Functions, and Pathways That Bridge Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). Front Neurol 11: 233.

Wegmann, S., B. Eftekharzadeh, K. Tepper, K. M. Zoltowska, R. E. Bennett et al., 2018 Tau protein liquid-liquid phase separation can initiate tau aggregation. EMBO J 37.

1. the P-O-L-C functions might be ideal but that they do not accurately depict the day-to-day actions of actual managers

1. the P-O-L-C functions might be ideal but that they do not accurately depict the day-to-day actions of actual managers

A sad ending for someone who was in slavery for their entire life without any recognition,

board game: mid-weight, beginner-friendly

less dependent getting energy

Back to the double-edged sword metaphor, if used poorly, it can very negatively affect workers, but if used correctly, it can boost both workers and company performance.

AI threatens many jobs in the market, which can cause people to pick up more skills and attempt to adapt, but those who cannot may lose their jobs and not be able to recover or adapt.

AI is the future, and we have to learn to adapt, but also how to apply it to the workplace effectively to have it move us forward rather than hinder us.

When looking at a poll of the last month, it will look like Trump's approval is unchanged. But if you zoom out, you can see the decline in approval sharply. The framing of the data is just as important as the data itself.

AI willingly was deviating from the information it was given, being harsher intentionally despite being trained by human responses.

Effective use of AI, as it still includes human touch, but with the collaboration of AI to enhance it.

The reasoning behind this is because it represents the amount of energy per given unit charge (In this case Coulombs, but Joule / Volt could work too). A great analogy I saw online because this topic can be confusing for intro to electrochemistry is the height analogy. That is an object on a high ledge does not have any kinetic energy but rather potential energy (the unit here being per g or lb that increases with increased potential energy).

TEAR PIE

Evidence

Characteristics

I think this is important that it is pointed out that EVERY citizens wasn't involved in this poll but rather a sample of citizens and this could mean that polls are constructed using certain methods, and the choices made in sampling and weighting affect the final results.

I thought this was important to spot out because of how divided our country is and how much discourse democrats and republicans have but in this time of the government shutdown, both parties have almost the same view on this topic and how everyone should still get the benefits needed to get through these food insecure times.

I love that the teachers used multiple ways for the students to learn. I have tried to learn multiple languages however there was nothing but book work and repeat what the teacher said. I did not have a practical way to learn and use what I was learning. It was difficult to understand and I believe using multiple ways to approach learning is best.

Who conducted the survey? The poll was conducted by the Quinnipiac University Poll, an independent, non-partisan polling organization based at Quinnipiac University.

This week's readings and video are really making me reflect on my career goals. I someday want to teach writing, but as a white woman who speaks English and took a few years of ASL courses, how can I best show up for students who don't share the same background as me? I think this will be a lifelong question I ask myself, and with more experience will come more answers, but I'm grateful that these readings have opened a sort of door for me to think about.

How wicked of him, to resist being a puppet

We can give an example as a Buddhism. It is another religion. It is different from Christianity and Islam because it does not teach belief in one God. But it is similar because it also teaches people to live peacefully and to be kind to others.

My family and I speak Turkish. There are many differences, but If I need to say some I can say two basic differences, one of them is that sentence elements and subject usage. For example, while in Turkish we use subject+object+verb, in English it is used as subject+verb+object. Secondly, Turkish is an agglutinative language. New meanings are created by adding suffixes to word roots. However, in English, word roots undergo changes or separate words are added.

This highlights how gaps in teacher understanding allow harassment to persist. Many educators don’t realize that peer actions can still count as sexual harassment under Title IX. Better training on these laws could help schools respond more effectively and protect students from genderbased harm

One time when I was filling out my Social Security application, I noticed something that felt a bit off. The form asked for “Parent” information, and then gave two boxes—one labeled “Father” and the other “Mother.” I didn’t think much of it at first, but later I started to wonder. What if someone has two moms? Or what if they don’t know who their biological parents are? How would they fill that out?

To be honest, I keep wondering if she wasn’t a lesbian, would the school have acted differently? Would they have held a memorial for her right away or shown more sympathy from the start? Part of me feels like they probably would have. It’s sad to say, but sometimes it feels like people only show respect when the victim fits into what they see as “normal.” That double standard is exactly what makes LGBTQ+ students feel invisible or unimportant.

I think one of the reasons homophobia persists today is not necessarily because people are against LGBTQ+ identities, but because of how identity politics sometimes shapes public discourse. For example, in the film industry, when a highly anticipated project is handled by LGBTQ+ directors, writers, or cinematographers and the final result doesn't meet public expectations, any criticism toward the work is sometimes labeled as homophobic. However, people rarely ask whether the criticism is about the quality of the work rather than the creator’s identity. In many other cases, straight directors also receive harsh critiques without their identity being part of the conversation. As a result, some neutral audiences feel silenced or unfairly accused, which creates resentment and eventually contributes to homophobia—not out of hate, but out of frustration with not being able to express honest opinions freely. I believe this is a misunderstanding rooted in overprotectiveness and a lack of space for dialogue.

In 2019, I was still in middle school in China. That year, I saw how hard it was for classmates who didn’t fit the “normal” expectations of gender and sexuality. I remember one boy who performed a Blackpink dance during an event—he danced with so much emotion and confidence, but a lot of people laughed at him or called him names. At the time, I didn’t really understand him either. But as I grew older and met more people from the LGBTQ+ community, I started to understand their experiences and slowly began to accept them.

This quote shocked me because it shows that bias is not only coming from peers but from adults who are supposed to model respectful behavior. When teachers or staff use biased language, students receive the message that harassment is normal or acceptable. It makes me think about how important professional training is—not just for protecting LGBTQ students but for shifting the entire school climate.

This section highlights how rigid gender norms harm everyone not just women. It’s powerful how Mayo connects toxic masculinity to both harassment and the silencing of LGBTQ+ children. Schools play a big role in reinforcing these ideas especially through outdated sex ed that excludes queer students and fails to hold boys accountable.

This sentence reminds me of the meaning of a religion.There are nice religions that give people hope and tide people together for spiritual comfort, but there are some other religions, or maybe cult, tend to control people and force them to do things that hurt themselves. When religions trying to disapprove people from homosexuality, is it because of the religion or is it because of the group of people not want homosexuality to appear and view them as 'heretic'? I think the idea of religion represents the mindset for 'what is good' of a group of people.

With the fast growth of the internet, online communities easily form their own circles and often define themselves by opposing other circles. This makes the narratives around gender even more amplified. Biological sex is something we are born with, but identity and self-understanding come from personal experience and practice. Society usually does not care about the reasons behind someone’s identity; it only reacts to the result. When people do not accept the way someone understands themselves, they often respond with judgment or hostility, which creates even more division.

This social consensus was formed over a long period of time. Early societies viewed heterosexual marriage as the basis for survival and continuation. Later, religion, law, and education reinforced this idea, shaping clear expectations for how men and women should behave. Media and cultural messages kept repeating these images, making people believe this is the norm. Although modern society is becoming more open and diverse, these frameworks still exist, only in more subtle ways within daily communication and thinking. New habits, ideas, values, and even technologies all take time to move from being questioned to being accepted and finally recognized. The world keeps moving forward, but it always takes time for people to truly adapt and accept change.

This line stood out to me because it reveals how schools reinforce gender binaries through small, everyday routines. Something as common as lining up by “boys” and “girls” teaches children early on that gender is the first and most important way to categorize themselves. It makes me think about how many biases come not from explicit discrimination but from “normal” institutional habits that go unquestioned.

This section shows how gender norms are not taught through big rules but through small everyday practices. What I find important here is that children learn “how to be a boy” or “how to be a girl” before they even understand these labels themselves. When teachers divide students into boys vs. girls for games, it teaches kids that gender difference is more important than cooperation. This early separation also trains children to see gender as competition, not as a flexible part of identity. What seems like an innocent classroom routine actually becomes the foundation for later gender stereotypes and pressure to “fit” into one gender role.

Reviewer #2 (Public review):

Summary:

The role of PRC2 in post neural crest induction was not well understood. This work developed an elegant mouse genetic system to conditionally deplete EED upon SOX10 activation. Substantial developmental defects were identified for craniofacial and bone development. The authors also performed extensive single-cell RNA sequencing to analyze differentiation gene expression changes upon conditional EED disruption.

Strengths:

(1) Elegant genetic system to ablate EED post neural crest induction.

(2) Single-cell RNA-seq analysis is extremely suitable for studying the cell type specific gene expression changes in developmental systems.

Original Weaknesses:

(1) Although this study is well designed and contains state-of-art single cell RNA-seq analysis, it lacks the mechanistic depth in the EED/PRC2-mediated epigenetic repression. This is largely because no epigenomic data was shown.

(2) The mouse model of conditional loss of EZH2 in neural crest has been previously reported, as the authors pointed out in the discussion. What is novelty in this study to disrupt EED? Perhaps a more detailed comparison of the two mouse models would be beneficial.

(3) The presentation of the single-cell RNA-seq data may need improvement. The complexity of the many cell types blurs the importance of which cell types are affected the most by EED disruption.

(4) While it's easy to identify PRC2/EED target genes using published epigenomic data, it would be nice to tease out the direct versus indirect effects in the gene expression changes (e.g Fig. 4e)

Comments on latest version:

The authors have addressed weaknesses 2 and 3 of my previous comment very well. For weaknesses 1 and 4, the authors have added a main Fig 5 and its associated supplemental materials, which definitely strengthen the mechanistic depth of the story. However, I think the audience would appreciate if the following questions/points could be further addressed regarding the Cut&Tag data (mostly related to main Figure 5):

(1) The authors described that Sox10-Cre would be expressed at E8.75, and in theory, EED-FL would be ablated soon after that. Why would E16.5 exhibit a much smaller loss in H3K27me3 compared to E12.5? Shouldn't a prolong loss of EED lead to even worse consequence?

(2) The gene expression change at E12.5 upon loss of EED (shown in Fig. 4h) seems to be massive, including many PRC2-target genes. However, the H3K27me3 alteration seems to be mild even at E12.5. Does this infer a PRC2 or H3K27 methylation - independent role of EED? To address this, I suggest the authors re-consider addressing my previously commented weakness #4 regarding the RNA-seq versus Cut&Tag change correlation. For example, a gene scatter plot with X-axis of RNA-seq changes versus Y-axis of H3K27me3 level changes.

(3) The CUT&Tag experiments seem to contain replicates according to the figure legend, but no statistical analysis was presented including the new supplemental tables. Also, for Fig. 5c-d, instead of showing the MRR in individual conditions, I think the audience would really want to know the differential MRR between Fl/WT and Fl/Fl. In other words, how many genes/ MRR have statistically lower H3K27me3 level upon EED loss.

Author response:

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

Reviewer #1 (Public review):

Epigenetic regulation complex (PRC2) is essential for neural crest specification, and its misregulation has been shown to cause severe craniofacial defects. This study shows that Eed, a core PRC2 component, is critical for craniofacial osteoblast differentiation and mesenchymal proliferation after neural crest induction. Using mouse genetics and single-cell RNA sequencing, the researcher found that conditional knockout of Eed leads to significant craniofacial hypoplasia, impaired osteogenesis, and reduced proliferation of mesenchymal cells in post-migratory neural crest populations.

Overall, the study is superficial and descriptive. No in-depth mechanism was analyzed and the phenotype analysis is not comprehensive.

We thank the reviewer for sharing their expertise and for taking the time to provide helpful suggestions to improve our study. We are gratified that the striking phenotypes we report from Eed loss in post-migratory neural crest craniofacial tissues were appreciated. The breadth and depth of our phenotyping techniques, including skeletal staining, micro-CT, echocardiogram, immunofluorescence, histology, and primary craniofacial cell culture provide comprehensive data in support our hypothesis that PRC2 is required for epigenetic control of craniofacial osteoblast differentiation. To provide mechanistic data in support of this hypothesis, we have now performed CUT&Tag H3K27me3 chromatin profiling on nuclei harvested from E12.5 or E16.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissue. These new data, which are presented in Fig. 5, Supplementary Fig. 9, and Supplementary Tables 7-10 of our revised manuscript, validate our hypothesis that epigenetic regulation of chromatin architecture downstream of PRC2 activity underlies craniofacial osteoblast differentiation. In particular, we now show that Eed-dependent H3K27me3 methylation is associated with correct temporal expression of transcription factors that are necessary for craniofacial differentiation and patterning, such as including Msx1, Pitx1, Pax7, which were initially nominated by single-cell RNA sequencing of E12.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissues in Fig. 4, Supplementary Fig. 5-7, and Supplementary Tables 1-6.

Reviewer #2 (Public review):

Summary:

The role of PRC2 in post-neural crest induction was not well understood. This work developed an elegant mouse genetic system to conditionally deplete EED upon SOX10 activation. Substantial developmental defects were identified for craniofacial and bone development. The authors also performed extensive single-cell RNA sequencing to analyze differentiation gene expression changes upon conditional EED disruption.

Strengths:

(1) Elegant genetic system to ablate EED post neural crest induction.

(2) Single-cell RNA-seq analysis is extremely suitable for studying the cell type-specific gene expression changes in developmental systems.

We thank the reviewer for their generous and helpful comments on our study. We are happy that our mouse genetic and single-cell RNA sequencing approaches were appropriate in pairing the craniofacial phenotypes we report with distinct gene expression changes in post-migratory neural crest tissues upon Eed deletion.

Weaknesses:

(1) Although this study is well designed and contains state-of-the-art single-cell RNA-seq analysis, it lacks the mechanistic depth in the EED/PRC2-mediated epigenetic repression. This is largely because no epigenomic data was shown.

Thank you for this suggestion. As described in response to Reviewer #1, we have now performed CUT&Tag H3K27me3 chromatin profiling on nuclei harvested from E12.5 or E16.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissues to provide mechanistic epigenomic data in support of our hypothesis that hat PRC2 is required for craniofacial osteoblast differentiation. These new data, which are presented in Fig. 5, Supplementary Fig. 9, and Supplementary Tables 7-10 of our revised manuscript, integrate genome-wide and targeted metaplot visualizations across genotypes with in-depth analyses of methylation rich regions and genes associated with methylation rich loci. Broadly, these new data reveal that changes in H3K27me3 occupancy correlate with gene expression changes from single-cell RNA sequencing of E12.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissues in Fig. 4, Supplementary Fig. 5-7, and Supplementary Tables 1-6.

(2) The mouse model of conditional loss of EZH2 in neural crest has been previously reported, as the authors pointed out in the discussion. What is novel in this study to disrupt EED? Perhaps a more detailed comparison of the two mouse models would be beneficial.

We acknowledge and cite the study the reviewer has indicated (Schwarz et al. Development 2014) in our initial and revised manuscripts. This elegant investigation uses Wnt1-Cre to delete Ezh2 and reports a phenotype similar to the one we observed with Sox10-Cre deletion of Eed, but our study adds depth to the understanding of PRC2’s vital role in neural crest development by ablating Eed, which has a unique function in the PRC2 complex by binding to H3K27me3 and allosterically activating Ezh2. In this sense, our study sheds light on whether phenotypes arising from deletion of Eed, the PRC2 “reader”, differ from phenotypes arising from deletion of Ezh2, the PRC2 “writer”, in neural crest derived tissues. Moreover, we provide the first single-cell RNA sequencing and epigenomic investigations of craniofacial phenotypes arising from PRC2 activity in the developing neural crest. Due to limitations associated with the Wnt1-Cre transgene (Lewis et al. Developmental Biology 2013), which targets pre-migratory neural crest cells, our investigations used Sox10Cre, which targets the migratory neural crest and is completely recombined by E10.5. We have included a detailed comparison of these mouse models in the Discussion section of our revised manuscript, and we thank the reviewer for this thoughtful suggestion. 

(3) The presentation of the single-cell RNA-seq data may need improvement. The complexity of the many cell types blurs the importance of which cell types are affected the most by EED disruption.

We thank the reviewer for the opportunity to improve the presentation of our single-cell RNA sequencing data. In response, we have added Supplementary Fig. 8 to our revised manuscript, which shows the cell clusters most affected by EED disruption in UMAP space across genotypes. Because we wanted to capture the fill diversity of cell types underlying the phenotypes we report, we did not sort Sox10+ cells (via FACS, for example) from craniofacial tissues before single-cell RNA sequencing. Our resulting single-cell RNA sequencing data are therefore inclusive of a diversity of cell types in UMAP space, and the prevalence of many of these cell types was unaffected by epigenetic disruption of neural crest derived tissues. The prevalence of the cell clusters that are most affected across genotypes and which are most relevant to our analyses of the developing neural crest are shown in Fig. 4c (and now also in Supplementary Fig. 8), including C0 (differentiating osteoblasts), C4 (mesenchymal stem cells), C5 (mesenchymal stem cells), and C7 (proliferating mesenchymal stem cells). Marker genes and pseudobulked differential expression analyses across these clusters are shown in Fig. 4d and Fig. 4e-h, respectively. 

(4) While it's easy to identify PRC2/EED target genes using published epigenomic data, it would be nice to tease out the direct versus indirect effects in the gene expression changes (e.g Figure 4e).

We agree with the reviewer that the single-cell RNA sequencing data in our initial submission do not provide insight into direct versus indirect changes in gene expression downstream of PRC2. In contrast, the CUT&Tag chromatin profiling data that we have generated for this revision provides mechanistic insight into H3K27me3 occupancy and direct effects on gene expression resulting from PRC2 inactivation in our mouse models.

REVIEWING EDITOR COMMENTS

The following are recommended as essential revisions

(1) The study is overall superficial and primarily descriptive, lacking in-depth mechanistic analysis and comprehensive phenotype evaluation.

Please see responses to Reviewer #1 and Reviewer #2 (weaknesses 1 and 4) above. 

(2) The authors did not investigate the temporal and spatial expression of Eed during cranial neural crest development, which is crucial for explaining the observed phenotypes.

The temporal and spatial expression of Eed during embryogenesis is well studied. Eed is ubiquitously expressed starting at E5.5, peaks at E9.5, and is downregulated but maintained at a high basal expression level through E18.5 (Schumacher et al. Nature 1996). Although comprehensive analysis of Eed expression in neural crest tissues has not been reported (to our knowledge), Eed physically and functionally interacts with Ezh2 (Sewalt et al. Mol Cell Biol 1998), which is enriched at a diversity of timepoints throughout all developing craniofacial tissues (Schwarz et al. Development 2014). In our study, we confirmed enrichment of Eed expression in craniofacial tissues throughout development using QPCR, and have provided a more detailed description of these published and new findings in the Discussion section of our revised manuscript. 

(3) There is no apoptosis analysis provided for any of the samples.

We evaluated the presence of apoptotic cells in E12.5 craniofacial sections using immunofluorescence for Cleaved Caspase 3 in Supplementary Fig. 3d. Although we found a modest increase in the labeling index of apoptotic cells, there was insufficient evidence to conclude that apoptosis is a substantial factor in craniofacial hypoplasia resulting from Eed loss in post-migratory neural crest craniofacial tissues. We have clarified these findings in the Results and Discussion sections of our revised manuscript. 

(4) As Eed is a core component of the PRC2 complex, were any other components altered in the Eed cKO mutant? How does Eed regulation influence osteogenic differentiation and proliferation through known pathways?

We thank the editors for this thoughtful inquiry. Although we did not specifically investigate expression or stability of other PRC2 components in Eed conditional mutants, and little is known about how Eed regulates osteogenic differentiation or proliferation through any pathway, our single-cell RNA sequencing data presented in Fig. 4, Supplementary Fig. 5-7, and Supplementary Tables 1-6 provide a significant conceptual advance with mechanistic implications for understanding bone development downstream of Eed and do not reveal any alterations in the expression of other PRC2 components across genotypes. We have clarified these important details in the Discussion section of our revised manuscript. 

(5) The authors may compare the Eed cKO phenotype with that of the previous EZH2 cKO mouse model since both Eed and EZH2 are essential subunits of PRC2.

Please see responses to editorial comment 2 above and the last paragraph of the Discussion section of our revised manuscript for comparisons between Eed and Ezh2 knockout phenotypes.

Author response:

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

Reviewer #1 (Public review):

Summary:

The authors validate the contribution of RAP2A to GB progression. RAp2A participates in asymmetric cell division, and the localization of several cell polarity markers, including cno and Numb.

Strengths:

The use of human data, Drosophila models, and cell culture or neurospheres is a good scenario to validate the hypothesis using complementary systems.

Moreover, the mechanisms that determine GB progression, and in particular glioma stem cells biology, are relevant for the knowledge on glioblastoma and opens new possibilities to future clinical strategies.

Weaknesses:

While the manuscript presents a well-supported investigation into RAP2A's role in GBM, several methodological aspects require further validation. The major concern is the reliance on a single GB cell line (GB5), which limits the generalizability of the findings. Including multiple GBM lines, particularly primary patient-derived 3D cultures with known stem-like properties, would significantly enhance the study's relevance.

Additionally, key mechanistic aspects remain underexplored. Further investigation into the conservation of the Rap2l-Cno/aPKC pathway in human cells through rescue experiments or protein interaction assays would be beneficial. Similarly, live imaging or lineage tracing would provide more direct evidence of ACD frequency, complementing the current indirect metrics (odd/even cell clusters, Numb asymmetry).

Several specific points require attention:

(1) The specificity of Rap2l RNAi needs further confirmation. Is Rap2l expressed in neuroblasts or intermediate neural progenitors? Can alternative validation methods be employed?

There are no available antibodies/tools to determine whether Rap2l is expressed in NB lineages, and we have not been able either to develop any. However, to further prove the specificity of the Rap2l phenotype, we have now analyzed two additional and independent RNAi lines of Rap2l along with the original RNAi line analyzed. We have validated the results observed with this line and found a similar phenotype in the two additional RNAi lines now analyzed. These results have been added to the text ("Results section", page 6, lines 142-148) and are shown in Supplementary Figure 3.

(2) Quantification of phenotypic penetrance and survival rates in Rap2l mutants would help determine the consistency of ACD defects.

In the experiment previously mentioned (repetition of the original Rap2l RNAi line analysis along with two additional Rap2l RNAi lines) we have substantially increased the number of samples analyzed (both the number of NB lineages and the number of different brains analyzed). With that, we have been able to determine that the penetrance of the phenotype was 100% or almost 100% in the 3 different RNAi lines analyzed (n>14 different brains/larvae analyzed in all cases). Details are shown in the text (page 6, lines 142-148), in Supplementary Figure 3 and in the corresponding figure legend.

(3) The observations on neurosphere size and Ki-67 expression require normalization (e.g., Ki-67+ cells per total cell number or per neurosphere size). Additionally, apoptosis should be assessed using Annexin V or TUNEL assays.

The experiment of Ki-67+ cells was done considering the % of Ki-67+ cells respect the total cell number in each neurosphere. In the "Materials and methods" section it is well indicated: "The number of Ki67+ cells with respect to the total number of nuclei labelled with DAPI within a given neurosphere were counted to calculate the Proliferative Index (PI), which was expressed as the % of Ki67+ cells over total DAPI+ cells"

Perhaps it was not clearly showed in the graph of Figure 5A. We have now changed it indicating: "% of Ki67+ cells/ neurosphere" in the "Y axis". 

Unfortunately, we currently cannot carry out neurosphere cultures to address the apoptosis experiments. 

(4) The discrepancy in Figures 6A and 6B requires further discussion.

We agree that those pictures can lead to confusion. In the analysis of the "% of neurospheres with even or odd number of cells", we included the neurospheres with 2 cells both in the control and in the experimental condition (RAP2A). The number of this "2 cell-neurospheres" was very similar in both conditions (27,7 % and 27 % of the total neurospheres analyzed in each condition), and they can be the result of a previous symmetric or asymmetric division, we cannot distinguish that (only when they are stained with Numb, for example, as shown in Figure 6B). As a consequence, in both the control and in the experimental condition, these 2-cell neurospheres included in the group of "even" (Figure 6A) can represent symmetric or asymmetric divisions. However, in the experiment shown in Figure 6B, it is shown that in these 2 cellneurospheres there are more cases of asymmetric divisions in the experimental condition (RAP2A) than in the control.

Nevertheless, to make more accurate and clearer the conclusions, we have reanalyzed the data taking into account only the neurospheres with 3-5-7 (as odd) or 4-6-8 (as even) cells. Likewise, we have now added further clarifications regarding the way the experiment has been analyzed in the methods.

(5) Live imaging of ACD events would provide more direct evidence.

We agree that live imaging would provide further evidence. Unfortunately, we currently cannot carry out neurosphere cultures to approach those experiments.

(6) Clarification of terminology and statistical markers (e.g., p-values) in Figure 1A would improve clarity.

We thank the reviewer for pointing out this issue. To improve clarity, we have now included a Supplementary Figure (Fig. S1) with the statistical parameters used. Additionally, we have performed a hierarchical clustering of genes showing significant or not-significant changes in their expression levels.

(7) Given the group's expertise, an alternative to mouse xenografts could be a Drosophila genetic model of glioblastoma, which would provide an in vivo validation system aligned with their research approach.

The established Drosophila genetic model of glioblastoma is an excellent model system to get deep insight into different aspects of human GBM. However, the main aim of our study was to determine whether an imbalance in the mode of stem cell division, favoring symmetric divisions, could contribute to the expansion of the tumor. We chose human GBM cell lines-derived neurospheres because in human GBM it has been demonstrated the existence of cancer stem cells (glioblastoma or glioma stem cells -GSCs--). And these GSCs, as all stem cells, can divide symmetric or asymmetrically. In the case of the Drosophila model of GBM, the neoplastic transformation observed after overexpressing the EGF receptor and PI3K signaling is due to the activation of downstream genes that promote cell cycle progression and inhibit cell cycle exit. It has also been suggested that the neoplastic cells in this model come from committed glial progenitors, not from stem-like cells.

With all, it would be difficult to conclude the causes of the potential effects of manipulating the Rap2l levels in this Drosophila system of GBM. We do not discard this analysis in the future (we have all the "set up" in the lab). However, this would probably imply a new project to comprehensively analyze and understand the mechanism by which Rap2l (and other ACD regulators) might be acting in this context, if it is having any effect. 

However, as we mentioned in the Discussion, we agree that the results we have obtained in this study must be definitely validated in vivo in the future using xenografts with 3D-primary patient-derived cell lines.

Reviewer #2 (Public review):

This study investigates the role of RAP2A in regulating asymmetric cell division (ACD) in glioblastoma stem cells (GSCs), bridging insights from Drosophila ACD mechanisms to human tumor biology. They focus on RAP2A, a human homolog of Drosophila Rap2l, as a novel ACD regulator in GBM is innovative, given its underexplored role in cancer stem cells (CSCs). The hypothesis that ACD imbalance (favoring symmetric divisions) drives GSC expansion and tumor progression introduces a fresh perspective on differentiation therapy. However, the dual role of ACD in tumor heterogeneity (potentially aiding therapy resistance) requires deeper discussion to clarify the study's unique contributions against existing controversies. Some limitations and questions need to be addressed.

(1) Validation of RAP2A's prognostic relevance using TCGA and Gravendeel cohorts strengthens clinical relevance. However, differential expression analysis across GBM subtypes (e.g., MES, DNA-methylation subtypes ) should be included to confirm specificity.

We have now included a Supplementary figure (Supplementary Figure 2), in which we show the analysis of RAP2A levels in the different GBM subtypes (proneural, mesenchymal and classical) and their prognostic relevance (i.e. the proneural subtype that presents RAP2A levels significantly higher than the others is the subtype that also shows better prognostic).

(2) Rap2l knockdown-induced ACD defects (e.g., mislocalization of Cno/Numb) are well-designed. However, phenotypic penetrance and survival rates of Rap2l mutants should be quantified to confirm consistency.

We have now analyzed two additional and independent RNAi lines of Rap2l along with the original RNAi line. We have validated the results observed with this line and found a similar phenotype in the two additional RNAi lines now analyzed. To determine the phenotypic penetrance, we have substantially increased the number of samples analyzed (both the number of NB lineages and the number of different brains analyzed). With that, we have been able to determine that the penetrance of the phenotype was 100% or almost 100% in the 3 different Rap2l RNAi lines analyzed (n>14 different brains/larvae analyzed in all cases). These results have been added to the text ("Results section", page 6, lines 142-148) and are shown in Supplementary Figure 3 and in the corresponding figure legend. 

(3) While GB5 cells were effectively used, justification for selecting this line (e.g., representativeness of GBM heterogeneity) is needed. Experiments in additional GBM lines (especially the addition of 3D primary patient-derived cell lines with known stem cell phenotype) would enhance generalizability.

We tried to explain this point in the paper (Results). As we mentioned, we tested six different GBM cell lines finding similar mRNA levels of RAP2A in all of them, and significantly lower levels than in control Astros (Fig. 3A). We decided to focus on the GBM cell line called GB5 as it grew well (better than the others) in neurosphere cell culture conditions, for further analyses. We agree that the addition of at least some of the analyses performed with the GB5 line using other lines (ideally in primary patientderive cell lines, as the reviewer mentions) would reinforce the results. Unfortunately, we cannot perform experiments in cell lines in the lab currently. We will consider all of this for future experiments.

(4) Indirect metrics (odd/even cell clusters, NUMB asymmetry) are suggestive but insufficient. Live imaging or lineage tracing would directly validate ACD frequency.

We agree that live imaging would provide further evidence. Unfortunately, we cannot approach those experiments in the lab currently.

(5) The initial microarray (n=7 GBM patients) is underpowered. While TCGA data mitigate this, the limitations of small cohorts should be explicitly addressed and need to be discussed.

We completely agree with this comment. We had available the microarray, so we used it as a first approach, just out of curiosity of knowing whether (and how) the levels of expression of those human homologs of Drosophila ACD regulators were affected in this small sample, just as starting point of the study. We were conscious of the limitations of this analysis and that is why we followed up the analysis in the datasets, on a bigger scale. We already mentioned the limitations of the array in the Discussion:

"The microarray we interrogated with GBM patient samples had some limitations. For example, not all the human genes homologs of the Drosophila ACD regulators were present (i.e. the human homologs of the determinant Numb). Likewise, we only tested seven different GBM patient samples. Nevertheless, the output from this analysis was enough to determine that most of the human genes tested in the array presented altered levels of expression"[....] In silico analyses, taking advantage of the existence of established datasets, such as the TCGA, can help to more robustly assess, in a bigger sample size, the relevance of those human genes expression levels in GBM progression, as we observed for the gene RAP2A."

(6) Conclusions rely heavily on neurosphere models. Xenograft experiments or patient-derived orthotopic models are critical to support translational relevance, and such basic research work needs to be included in journals.

We completely agree. As we already mentioned in the Discussion, the results we have obtained in this study must be definitely validated in vivo in the future using xenografts with 3D-primary patient-derived cell lines.

(7) How does RAP2A regulate NUMB asymmetry? Is the Drosophila Rap2l-Cno/aPKC pathway conserved? Rescue experiments (e.g., Cno/aPKC knockdown with RAP2A overexpression) or interaction assays (e.g., Co-IP) are needed to establish molecular mechanisms.

The mechanism by which RAP2A is regulating ACD is beyond the scope of this paper. We do not even know how Rap2l is acting in Drosophila to regulate ACD. In past years, we did analyze the function of another Drosophila small GTPase, Rap1 (homolog to human RAP1A) in ACD, and we determined the mechanism by which Rap1 was regulating ACD (including the localization of Numb): interacting physically with Cno and other small GTPases, such as Ral proteins, and in a complex with additional ACD regulators of the "apical complex" (aPKC and Par-6). Rap2l could be also interacting physically with the "Ras-association" domain of Cno (domain that binds small GTPases, such as Ras and Rap1). We have added some speculations regarding this subject in the Discussion:

"It would be of great interest in the future to determine the specific mechanism by which Rap2l/RAP2A is regulating this process. One possibility is that, as it occurs in the case of the Drosophila ACD regulator Rap1, Rap2l/RAP2A is physically interacting or in a complex with other relevant ACD modulators."

(8) Reduced stemness markers (CD133/SOX2/NESTIN) and proliferation (Ki-67) align with increased ACD. However, alternative explanations (e.g., differentiation or apoptosis) must be ruled out via GFAP/Tuj1 staining or Annexin V assays.

We agree with these possibilities.  Regarding differentiation, the potential presence of increased differentiation markers would be in fact a logic consequence of an increase in ACD divisions/reduced stemness markers. Unfortunately, we cannot approach those experiments in the lab currently.

(9) The link between low RAP2A and poor prognosis should be validated in multivariate analyses to exclude confounding factors (e.g., age, treatment history).

We have now added this information in the "Results section" (page 5, lines 114-123).

(10) The broader ACD regulatory network in GBM (e.g., roles of other homologs like NUMB) and potential synergies/independence from known suppressors (e.g., TRIM3) warrant exploration.

The present study was designed as a "proof-of-concept" study to start analyzing the hypothesis that the expression levels of human homologs of known Drosophila ACD regulators might be relevant in human cancers that contain cancer stem cells, if those human homologs were also involved in modulating the mode of (cancer) stem cell division. 

To extend the findings of this work to the whole ACD regulatory network would be the logic and ideal path to follow in the future.

We already mentioned this point in the Discussion:

"....it would be interesting to analyze in the future the potential consequences that altered levels of expression of the other human homologs in the array can have in the behavior of the GSCs. In silico analyses, taking advantage of the existence of established datasets, such as the TCGA, can help to more robustly assess, in a bigger sample size, the relevance of those human genes expression levels in GBM progression, as we observed for the gene RAP2A."

(11) The figures should be improved. Statistical significance markers (e.g., p-values) should be added to Figure 1A; timepoints/culture conditions should be clarified for Figure 6A.

Regarding the statistical significance markers, we have now included a Supplementary Figure (Fig. S1) with the statistical parameters used. Additionally, we have performed a hierarchical clustering of genes showing significant or notsignificant changes in their expression levels. 

Regarding the experimental conditions corresponding to Figure 6A, those have now been added in more detail in "Materials and Methods" ("Pair assay and Numb segregation analysis" paragraph).

(12) Redundant Drosophila background in the Discussion should be condensed; terminology should be unified (e.g., "neurosphere" vs. "cell cluster").

As we did not mention much about Drosophila ACD and NBs in the "Introduction", we needed to explain in the "Discussion" at least some very basic concepts and information about this, especially for "non-drosophilists". We have reviewed the Discussion to maintain this information to the minimum necessary.

We have also reviewed the terminology that the Reviewer mentions and have unified it.

Reviewer #1 (Recommendations for the authors):

To improve the manuscript's impact and quality, I would recommend:

(1) Expand Cell Line Validation: Include additional GBM cell lines, particularly primary patient-derived 3D cultures, to increase the robustness of the findings.

(2) Mechanistic Exploration: Further examine the conservation of the Rap2lCno/aPKC pathway in human cells using rescue experiments or protein interaction assays.

(3) Direct Evidence of ACD: Implement live imaging or lineage tracing approaches to strengthen conclusions on ACD frequency.

(4) RNAi Specificity Validation: Clarify Rap2l RNAi specificity and its expression in neuroblasts or intermediate neural progenitors.

(5) Quantitative Analysis: Improve quantification of neurosphere size, Ki-67 expression, and apoptosis to normalize findings.

(6) Figure Clarifications: Address inconsistencies in Figures 6A and 6B and refine statistical markers in Figure 1A.

(7) Alternative In Vivo Model: Consider leveraging a Drosophila glioblastoma model as a complementary in vivo validation approach.

Addressing these points will significantly enhance the manuscript's translational relevance and overall contribution to the field.

We have been able to address points 4, 5 and 6. Others are either out of the scope of this work (2) or we do not have the possibility to carry them out at this moment in the lab (1, 3 and 7). However, we will complete these requests/recommendations in other future investigations.

Reviewer #2 (Recommendations for the authors):

Major Revision /insufficient required to address methodological and mechanistic gaps.

(1) Enhance Clinical Relevance

Validate RAP2A's prognostic significance across multiple GBM subtypes (e.g., MES, DNA-methylation subtypes) using datasets like TCGA and Gravendeel to confirm specificity.

Perform multivariate survival analyses to rule out confounding factors (e.g., patient age, treatment history).

(2) Strengthen Mechanistic Insights

Investigate whether the Rap2l-Cno/aPKC pathway is conserved in human GBM through rescue experiments (e.g., RAP2A overexpression with Cno/aPKC knockdown) or interaction assays (e.g., Co-IP).

Use live-cell imaging or lineage tracing to directly validate ACD frequency instead of relying on indirect metrics (odd/even cell clusters, NUMB asymmetry).

(3) Improve Model Systems & Experimental Design

Justify the selection of GB5 cells and include additional GBM cell lines, particularly 3D primary patient-derived cell models, to enhance generalizability.

It is essential to perform xenograft or orthotopic patient-derived models to support translational relevance.

(5) Address Alternative Interpretations

Rule out other potential effects of RAP2A knockdown (e.g., differentiation or apoptosis) using GFAP/Tuj1 staining or Annexin V assays.

Explore the broader ACD regulatory network in GBM, including interactions with NUMB and TRIM3, to contextualize findings within known tumor-suppressive pathways.

(6) Improve Figures & Clarity

Add statistical significance markers (e.g., p-values) in Figure 1A and clarify timepoints/culture conditions for Figure 6A.

Condense redundant Drosophila background in the discussion and ensure consistent terminology (e.g., "neurosphere" vs. "cell cluster").

We have been able to address points 1, partially 3 and 6. Others are either out of the scope of this work or we do not have the possibility to carry them out at this moment in the lab. However, we are very interested in completing these requests/recommendations and we will approach that type of experiments in other future investigations.

Not sure if we have staging Event Reference docs, but this one points to the 13.0.0 version directly.

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Le vocabulaire employé — scientifique et maîtrisé — atténue la dimension violente de la scène. Le taureau devient un objet d’étude ou un corps à réguler, plutôt qu’un être souffrant. Même la notion de “bravoure” lui attribue une valeur héroïque qui justifie la souffrance infligée.

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Over time, families who have money and time can sue the district and move their kids to private schools, while families without resources have to stay in under-resourced schools. This makes the gap between public schools and social classes even bigger.

This opening immediately shows the urgency of T.J.’s situation. It is heartbreaking because his lack of progress is not due to a lack of effort, but a failure of the system around him. The quote highlights how early delays compound over time when appropriate support is not provided. It connects to class themes about how institutional neglect—rather than individual deficits—creates long-term educational harm.