Reviewer #2 (Public review):

The authors set out to resolve the high-resolution structure of a glutamine synthetase (GS) decamer using cryo-EM, investigate glutamine binding at the decamer interface, and validate structural observations through biochemical assays of ATP hydrolysis linked to enzyme activity. Their work sits at the intersection of structural and functional biology, aiming to bridge atomic-level details with biological mechanisms - a goal with clear relevance to researchers studying enzyme catalysis and metabolic regulation.

Strengths and weaknesses of methods and results:

A key strength of the study lies in its use of cryo-EM, a technique well-suited for resolving large, dynamic macromolecular complexes like the GS decamer. The reported resolutions (down to 2.15 Å) initially suggest the potential for detailed structural insights, such as side-chain interactions and ligand density. However, several methodological limitations significantly undermine the reliability of the results:

(1) Cryo-EM data processing: The absence of critical details about B-factor sharpening - a standard step to enhance map interpretability - is a major concern. For high-resolution maps (<3 Å), sharpening is typically applied to resolve side-chain features, yet the submitted maps (e.g., those in Figures 1D, 2D, and supplementary figures) appear unprocessed, with density quality inconsistent with the claimed resolutions. This makes it difficult to evaluate whether observed features (e.g., glutamine binding) are genuine or artifacts of unsharpened data.

(2) Modeling and density consistency: The structural models, particularly for glutamine binding at the decamer interface, do not align with the reported resolution. The maps shown in Figure 2D and Supplementary Figure S7 lack sufficient density to confidently place glutamine or even surrounding residues, conflicting with claims of 2.15 Å resolution. Additionally, fitting a non-symmetric ligand (glutamine) into a symmetry-refined map requires justification, as symmetry constraints may distort ligand placement.

(3) Biochemical assay controls: While the enzyme activity assays aim to link structure to function, they lack essential controls (e.g., blank reactions without GS or substrates, substrate omission tests) to confirm that ATP hydrolysis is GS-dependent. The use of TCEP, a reducing agent, is also not paired with experiments to rule out unintended effects on the PK/LDH system, further limiting confidence in activity measurements.

Achievement of aims and support for conclusions:

The study falls short of convincingly achieving its goals. The claimed high-resolution structural details (e.g., side-chain densities, ligand binding) are not supported by the provided maps, which lack sharpening and show inconsistencies in density quality. Similarly, the biochemical data do not robustly validate the structural claims due to missing controls. As a result, the evidence is insufficient to confirm glutamine binding at the decamer interface or the functional relevance of the observed structural features.

Likely impact and utility:

If these methodological gaps are addressed, the work could make a meaningful contribution to the field. A well-resolved GS decamer structure would advance understanding of enzyme assembly and ligand recognition, while validated biochemical assays would strengthen the link between structure and function. Improved data processing and clearer reporting of validation steps would also make the structural data more reliable for the community, providing a resource for future studies on GS or related enzymes.

Additional context:

Cryo-EM has transformed structural biology by enabling high-resolution analysis of large complexes, but its success hinges on rigorous data processing and validation steps that are critical to ensuring reproducibility. The challenges highlighted here are not unique to this study; they reflect broader issues in the field where incomplete reporting of methods can obscure the reliability of results. By addressing these points, the authors would not only strengthen their current work but also set a positive example for transparent and rigorous structural biology research.

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

(1) In Figure 2D and Supplementary Figure 7, the extra density observed between the two decamers does not appear to have the defining features of a glutamine. A less defined density may be expected given the nature of the complex, but even though mutagenesis assays were performed to support this assignment, none of these results constitutes direct and conclusive evidence for glutamine binding at this site. I would thus suggest showing the density maps at multiple contour thresholds to allow readers to also better evaluate the various small molecules under turnover conditions that cannot be well fitted based on this density map, helping to provide a more balanced interpretation of the results.

(2) On the same point regarding the density for the enzyme under turnover conditions, more details should be provided about the symmetry expansion and classification performed, and also show the approximate ratio of reconstructions that include this density. Did you try symmetry expansion followed by focused classification, especially on the interface region?

(3) The interface between the two decamers of the model needs to be double-checked and reassigned, especially for the residues surrounding the fitted glutamine. For example, the side chain of the Lys residue shown in the attached figure is most likely modeled incorrectly.

Author response:

Reviewer #1 (Public review):

Summary:

The study is methodologically solid and introduces a compelling regulatory model. However, several mechanistic aspects and interpretations require clarification or additional experimental support to strengthen the conclusions.

Strengths:

(1) The manuscript presents a compelling structural and biochemical analysis of human glutamine synthetase, offering novel insights into product-induced filamentation.

(2) The combination of cryo-EM, mutational analysis, and molecular dynamics provides a multifaceted view of filament assembly and enzyme regulation.

(3) The contrast between human and E. coli GS filamentation mechanisms highlights a potentially unique mode of metabolic feedback in higher organisms.

Weaknesses:

(1) The mechanism underlying spontaneous di-decamer formation in the absence of glutamine is insufficiently explored and lacks quantitative biophysical validation.

(2) Claims of decamer-only behavior in mutants rely solely on negative-stain EM and are not supported by orthogonal solution-based methods.

We thank the reviewer for the summary and noting of the strengths. We agree that the evolutionary divergence of metabolic feedback in GS homologs is a fruitful avenue for future studies. With regard to the weaknesses, the di-decamer in the absence of glutamine only forms under high (higher than physiological) concentrations of enzyme. Our primary evidence for the mutant behavior was the lack of crosslinking (Figure 1E), with supplementary support from the negative stain. In the revised version we will soften the language to say “reduced” rather than “did not support” filament formation.

Reviewer #2 (Public review):

The authors set out to resolve the high-resolution structure of a glutamine synthetase (GS) decamer using cryo-EM, investigate glutamine binding at the decamer interface, and validate structural observations through biochemical assays of ATP hydrolysis linked to enzyme activity. Their work sits at the intersection of structural and functional biology, aiming to bridge atomic-level details with biological mechanisms - a goal with clear relevance to researchers studying enzyme catalysis and metabolic regulation.

Strengths and weaknesses of methods and results:

A key strength of the study lies in its use of cryo-EM, a technique well-suited for resolving large, dynamic macromolecular complexes like the GS decamer. The reported resolutions (down to 2.15 Å) initially suggest the potential for detailed structural insights, such as side-chain interactions and ligand density. However, several methodological limitations significantly undermine the reliability of the results:

(1) Cryo-EM data processing: The absence of critical details about B-factor sharpening - a standard step to enhance map interpretability - is a major concern. For high-resolution maps (<3 Å), sharpening is typically applied to resolve side-chain features, yet the submitted maps (e.g., those in Figures 1D, 2D, and supplementary figures) appear unprocessed, with density quality inconsistent with the claimed resolutions. This makes it difficult to evaluate whether observed features (e.g., glutamine binding) are genuine or artifacts of unsharpened data.

(2) Modeling and density consistency: The structural models, particularly for glutamine binding at the decamer interface, do not align with the reported resolution. The maps shown in Figure 2D and Supplementary Figure S7 lack sufficient density to confidently place glutamine or even surrounding residues, conflicting with claims of 2.15 Å resolution. Additionally, fitting a non-symmetric ligand (glutamine) into a symmetry-refined map requires justification, as symmetry constraints may distort ligand placement.

(3) Biochemical assay controls: While the enzyme activity assays aim to link structure to function, they lack essential controls (e.g., blank reactions without GS or substrates, substrate omission tests) to confirm that ATP hydrolysis is GS-dependent. The use of TCEP, a reducing agent, is also not paired with experiments to rule out unintended effects on the PK/LDH system, further limiting confidence in activity measurements.

Achievement of aims and support for conclusions:

The study falls short of convincingly achieving its goals. The claimed high-resolution structural details (e.g., side-chain densities, ligand binding) are not supported by the provided maps, which lack sharpening and show inconsistencies in density quality. Similarly, the biochemical data do not robustly validate the structural claims due to missing controls. As a result, the evidence is insufficient to confirm glutamine binding at the decamer interface or the functional relevance of the observed structural features.

Likely impact and utility:

If these methodological gaps are addressed, the work could make a meaningful contribution to the field. A well-resolved GS decamer structure would advance understanding of enzyme assembly and ligand recognition, while validated biochemical assays would strengthen the link between structure and function. Improved data processing and clearer reporting of validation steps would also make the structural data more reliable for the community, providing a resource for future studies on GS or related enzymes.

We disagree with the reviewer’s overall assessment.

With regard to sharpening and resolution: we examined sharpened maps and in a revised version will present additional supplementary figures showing these maps side by side. We note that the resolutions reported are global and that the most interesting features are, of course, in the periphery and subject to conformational and compositional heterogeneity. We will include supplementary figures of core side chain densities that are more like what are expected by the reviewer in the revision. 

With regard to modeling: the apo filament and turnover filament datasets were handled nearly identically. The additional density is therefore likely not artefactual to the symmetry operator - however, the lower resolution in this region noted by the reviewer is worthy of further exploration. The maps are public and we think this is the most plausible interpretation of the density, which we based primarily on the biochemical data and will include more speculation in the version.

With regard to the biochemical controls: we point the reviewer to Figure S1, which shows that omission of ammonia or glutamate in the wild-type (tagless) system removes any coupling of the reactions. We will perform the additional controls to publication quality in the revised version along with the TCEP control. We note that the reducing agent is present across all experiments, ruling out an effect on any specific result. The inclusion of TCEP is also very standard in other published uses of the Coupled ATPase assay (e.g. PMID: 31778111 and PMID: 32483380 by our first author)

Additional context:

Cryo-EM has transformed structural biology by enabling high-resolution analysis of large complexes, but its success hinges on rigorous data processing and validation steps that are critical to ensuring reproducibility. The challenges highlighted here are not unique to this study; they reflect broader issues in the field where incomplete reporting of methods can obscure the reliability of results. By addressing these points, the authors would not only strengthen their current work but also set a positive example for transparent and rigorous structural biology research.

All the data is public and the reviewer or anyone is free to reinterpret the maps and models - and we encourage that rather than just an interpretation of our static figures. In addition, we will upload the raw micrograph data for the apo filament and turnover filament datasets to EMPIAR prior to submitting the revision.

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

(1) In Figure 2D and Supplementary Figure 7, the extra density observed between the two decamers does not appear to have the defining features of a glutamine. A less defined density may be expected given the nature of the complex, but even though mutagenesis assays were performed to support this assignment, none of these results constitutes direct and conclusive evidence for glutamine binding at this site. I would thus suggest showing the density maps at multiple contour thresholds to allow readers to also better evaluate the various small molecules under turnover conditions that cannot be well fitted based on this density map, helping to provide a more balanced interpretation of the results.

(2) On the same point regarding the density for the enzyme under turnover conditions, more details should be provided about the symmetry expansion and classification performed, and also show the approximate ratio of reconstructions that include this density. Did you try symmetry expansion followed by focused classification, especially on the interface region?

(3) The interface between the two decamers of the model needs to be double-checked and reassigned, especially for the residues surrounding the fitted glutamine. For example, the side chain of the Lys residue shown in the attached figure is most likely modeled incorrectly.

We thank the reviewer for the feedback. As noted above, we will include supplemental figures that show maps at multiple thresholds and sharpening schemes. We noted in the manuscript and above that our interpretation here is based on integrating biochemical evidence alongside the density and will make that even more clear in the revised manuscript. The filaments +/- the putative glutamine density were processed nearly identically, but we will attempt various schemes of focused classification/symmetry expansion in the revision as well. However, we point out that there is extensive averaging there that makes modeling a bit trickier than expected given the global resolution.

DOI: 10.1038/s41598-025-91689-3

Resource: Mutant Mouse Regional Resource Center (RRID:SCR_002953)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:SCR_002953

What is this?

Reviewer #3 (Public review):

Summary:

Using a specparam (1/f) analysis of task-evoked activity, the authors propose that "substantial changes traditionally attributed to theta oscillations in working memory tasks are, in fact, due to shifts in the spectral slope of aperiodic activity." This is a very bold and ambitious statement, and the field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. Unfortunately, the data shown here does not support the main conclusion advanced by the authors.

Strengths:

The field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. The authors perform a number of additional control analyses, including different types of baseline correction, ERP subtraction, as well as replication of the experiment with two additional datasets.

Comments on previous revisions:

The authors have completed a substantial revision based on the comments from all of the reviewers. Overall, the major claims of the initial report have been profoundly tempered.

[Editors' note: We determined that this revised version appropriately tempers some of the prior claims and addresses the concerns raised by the reviewers through two rounds of review.]

Ev 3, death due to disease

I know we have 3 dimensions that are well known

Commercialism has followed this as a mantra as most things made are inexpensive and serves its purposes and quickly.

This passage is important because it shows the differences between high school and college, making the reader consider things they may have never thought about before. It points out to unique customs and language of college, which is super important for a smooth transition.

this passage is especialy important to me, my instructors are going to be crutial for contacts in the industry, and for their intimate knowlage of how things work and how people connect and operate with each other, without their willingness to help me navigate the first few years of my career, i would be floundering alone without direction.

After I read this part, it made me think about how my access to help will grow, and I should try to reach out for help whenever I need it rather than wait for it to come to me. There will be peers going through the same struggles as me and probably have the same questions as me. Earlier in the passage, it said the relationship you have with your professor will be one of the most important ones you have, and I find it interesting, and I'm curious how a relationship with a professor is online rather than a teacher in person. I think I will use the office hours and any extra help I can get from professors.

...civility makes our disagreements tolerable so that we can share a life together even if we don't share a faith- (3:50)

I think its so cool how we have the technology to understand languages created thousands of years ago.

The floods actually benefited them instead of causing a problem which I've never really heard before

It seemed like everyone did their own thing and what worked for them.

It seems like depending on their roles/job their bodies were in different conditions.

I did not know that River Valley Civilizations created the worlds first cities.

How the native cultures lived in the northeastern woodlands

I’m amazed at how labor-intensive silk production was. It must have taken serious knowledge and skill just to get usable threads.

It’s really cool how trade and farming surpluses could make these small villages grow into complex societies. I wonder how connected these villages actually were—was it more like independent towns trading with each other, or more of a coordinated network?

It makes me think about what factors allow a city to survive thousands of years while others disappear. Maybe its trade location, natural resources, and harbor all helped. Also, it’s kind of wild to imagine that the same place people lived in 8,000 years ago is still inhabited today.

Makes sense that hunter-gatherers and herders did well there. I wonder how many people lived in that area compared to along the Nile.

I wonder how much everyday Egyptians participated in these religious interpretations versus just following orders.

The number of symbols is crazy to think about, it must have required extensive training to use. I wonder how scribes learned such a complex system and whether literacy was limited to an elite class. Also, why did the language and script eventually give way to Elamite...was it political, social, or linguistic pressures maybe?

I think its so cool how they created their own type of writing to keep track of everything.

Materialism, slippery slope into greed/corruption

inferiors, placeholders

Worried about Adam's safety

Group G Ben Braniff, Kim Maynard, Nick Devic, Maria Echeverri Solis, Sam Yalda

1. Design has a major impact on the world and society. Even the little things can add up to a lot. Sustainability is a revolutionary Idea that should be at the core of every design now.

2. Society is another bottom line meaning all design inherently affects humans and/or is designed for humans. It's important to design for the extremes and the edge cases like people with disabilities.

3. Corporations output a lot of waste. When they make small changes to be more sustainable, it results in big changes and saving a lot of material. Small changes can include anything from using 2% less plastic per water bottle to using wood buttons instead of plastic ones.

4. A lot of people don't consider themselves disabled, but it's very common at some point in people's lives to have a certain level of impairment. It's important to keep this in mind when designing as you're designing for the general population--not just a specific individual.

5. Addressing issues like world hunger may require rethinking the way we design food production. As they stated for example, choosing kangaroo meat over beef as a more environmentally sustainable option.

6. Thoughtful design choices per the example in the video such as adding white circles inside letters to reduce ink use, can improve efficiency and conserve resources.

7. It is interesting how he opens up his discussion to slowly introduce that design isn't just about doing it for marketing or 'profit' as he pointed out. When watching this it helps a person realize that design is so much more powerful than that if you put it towards another cause. Design could end up being the solution to some of the biggest problems in society.

8. A very important point he made was that improving accessibility is beneficial to many more people than just the people that initially needed it such as people with disabilities. From this i think a good takeaway is that design should always be considerate of any disabilities/needs that the audience might have because sometimes that design is just better for everyone in general.

9. My first take is design should go beyond money and aesthetics. By thinking about sustainability and accessibility the designers can create solutions that are socially responsible and environmentally friendly.

10. My second take is when you design with people with disabilities you end up with solutions that are more usable and inclusive

Group C:

1.) Canada and Norway both depend on exporting natural resources. We need to build sustainable value locally not just send raw goods elsewhere.

2.) Designers cannot just follow orders. They are responsible for what their work does in society. Every design choice affects people.

3.) Design is not decoration. It is about safety fairness access and improving lives for all users regardless of their condition.

4.) Designing for people with special needs often improves the product for everyone. Accessibility is not only for some but for all.

5.) Our design work affects future generations. We must think about the full life of what we create and the culture we leave behind.

6.) Whether a disability is permanent, temporary, or situational almost everyone will experience it. Accessible design should be normal for all.

7.) We all design in some way through how we communicate organize spaces or build systems. With that power comes responsibility to shape better experiences.

8.) Designers today have bigger reach and tools than ever before. That means their decisions matter more than ever for how we treat each other and our planet.

9.) A product is not just its production and launch. It has sourcing use reuse and disposal. We should design with all those phases in mind and aim to leave positive outcomes.

10.) Tiny design decisions like choice of material font or layout can multiply into big environmental and social effects when many people use them.

Group B 1. In the 20th century, designers helped make profit, which is how design emerged as a means to a capitalist outcome.

1. Accessibility and universal design are framed as having a core to people. Making products and services usable for everyone is a central social responsibility.

2. People tend to think of design as “life and death,” with colors used in modern signage to depict warnings, danger, etc. While other times the same colors will depict guidance and help.

3. Harlem Bruntland invented the idea of sustainability and Norway contributed to the growth of this idea. She is considered a “design thinker”.

4. As mentioned, designers and design thinking has evolved from profit lead design, to environmental sustainability design. Design thinking evolves with the times and depends on the designers, society, and the environment.

5. “Over the next 10 to 15 years, technology has the capacity to virtually eliminate barriers faced by people with disabilities in the workplace.” This quote was said by Steve Ballmer. We already see this with the way workplaces are designed, such as flexible workstations with adjustable desks, multisensory alarms, high-contrast color palettes for people with visual impairments, and many more.

6. Culture is presented as the fourth bottom line, highlighting what makes humans unique is that we don’t just evolve alone. Humans design their responses to challenges; sustaining a culture means sustaining adaptability.

7. The closing of the digital divide has liberated more people through information access, elevating the importance of ethics in design choices and accessible methodologies.

8. The quadruple bottom line is designed to include profit, planet, people, and culture. It is aimed to ensure a holistic approach to design.

9. Even small changes, like changing/thinning a font, can have positive impacts on safety, sustainability, and accessibility.

Group A 1. designers are beginning to consider ethics and necessity instead of just profit 2. Well designed solutions can solve more than one problem at a time

• trees for clients and fish for employees
• When we design to increase accessibility, we are helping everyone, not just people with obvious disabilities

-Full list for group a in page notes-

1. 4x bottom line nowadays: profit, environment, people, and ???.
2. Small changes in design result in a big impact.
3. The way things are designed can make their argument more convincing, even if what they are arguing for is outlandish.
4. Designers, through strategic thinking, can find a way to a better world.
5. When designers design for the extreme, it benefits all. (a) Examples of small changes like these can benefit many people, For example the colorblind traffic light changes in canada would not only save those with the disability but those who get in car accidents due to a color blinded individual.
6. Designers should focus more on doing good than on making good designs.
7. Profit only firms may win short term, but sustainable first firms gain long term reliance, loyalty, and savings.
8. “Over the next 10 to 15 years, technology has the capacity to virtually eliminate barriers faced by people with disabilities in the workplace” (24:57)
9. Sustainable design must entwine with strategic thinking: especially at the early stages. (a) One company focused on defining their success solely off profit will lead to companies chasing efficiency and aesthetics. (b) Another company designing for accessibility and sustainability, may not be profitable in the start as much as the other company, but in the long term, generally speaking the will be more successful than those other companies who now have to catch up and focus on accessibility and sustainability.
10. There is a fear of a divide of people, those with technology and those without. This is called the digital divide.

Group A:

1) We live in a world today where everyone is a designer.

2) Every design speaker will always mention about the environment.

3) In todays world it is easier to create ideas.

4) A design can help change how we can change the world.

5) A subtle design can have a huge impact such as traffic lights

6) The Ghana medical problem being a simple fix with just a website saying if it was a real or fake medicine

7) designers are beginning to consider ethics and necessity instead of just profit

8) The events of September 22, 2011 are a lot worse now, the numbers would be astronomically higher than it was before

9) Well designed solutions can solve more than one problem at a time - trees for clients and fish for employees

10) When we design to increase accessibility, we are helping everyone, not just people with obvious disabilities

GROUP D:

1.everyone needs to have empathy, especially in today’s world where mostly everything is online. designers can use accessibility to help people. like the example with the girl and the tube in her mouth. she can now go on the internet, like everyone else, because someone designed the tube for her

1. I think the eco fonts are a great idea. he said that using them, it can use 25% less ink and toner. this is great for companies and regular people. they will be able to save money, since the ink and toner won’t be used up as fast.

2. "Designers must be cognizant of their power and impact on the world"

3. "Small changes executed on a large scale can contribute massively to sustainability"

4. sustainability isn't just about people, profit, and planet culture also matters, should be treated as a fourth bottom line

5. accessibility helps many people and hurts nobody"

6. Ethical codes matter. Berman emphasizes aligning design practice with professional ethics, similar to medicine or law, to reduce harm and increase trust.

7. Profit isn’t the only measure of success. Good design must be judged by social benefit and long-term impact, not just short-term financial gain.

8. Global perspective is essential. Designers must think beyond local markets, recognizing how their work contributes to worldwide challenges such as climate change and inequality.

9. Designers as change agents. Berman encourages professionals to see themselves as leaders who can steer society toward justice, sustainability, and fairness through thoughtful design.

GROUP D: 1. everyone needs to have empathy, especially in today’s world where mostly everything is online. designers can use accessibility to help people. like the example with the girl and the tube in her mouth. she can now go on the internet, like everyone else, because someone designed the tube for her

1. I think the eco fonts are a great idea. he said that using them, it can use 25% less ink and toner. this is great for companies and regular people. they will be able to save money, since the ink and toner won’t be used up as fast.

2. "Designers must be cognizant of their power and impact on the world"

3. "Small changes executed on a large scale can contribute massively to sustainability"

4. sustainability isn't just about people, profit, and planet culture also matters, should be treated as a fourth bottom line

5. accessibility helps many people and hurts nobody"

6. Ethical codes matter. Berman emphasizes aligning design practice with professional ethics, similar to medicine or law, to reduce harm and increase trust.

7. Profit isn’t the only measure of success. Good design must be judged by social benefit and long-term impact, not just short-term financial gain.

8. Global perspective is essential. Designers must think beyond local markets, recognizing how their work contributes to worldwide challenges such as climate change and inequality.

9. Designers as change agents. Berman encourages professionals to see themselves as leaders who can steer society toward justice, sustainability, and fairness through thoughtful design.

Group B 1. In the 20th century, designers helped make profit, which is how design emerged as a means to a capitalist outcome.

1. Accessibility and universal design are framed as having a core to people. Making products and services usable for everyone is a central social responsibility.

2. People tend to think of design as “life and death,” with colors used in modern signage to depict warnings, danger, etc. While other times the same colors will depict guidance and help.

3. Harlem Bruntland invented the idea of sustainability and Norway contributed to the growth of this idea. She is considered a “design thinker”.

4. As mentioned, designers and design thinking has evolved from profit lead design, to environmental sustainability design. Design thinking evolves with the times and depends on the designers, society, and the environment.

5. “Over the next 10 to 15 years, technology has the capacity to virtually eliminate barriers faced by people with disabilities in the workplace.” This quote was said by Steve Ballmer. We already see this with the way workplaces are designed, such as flexible workstations with adjustable desks, multisensory alarms, high-contrast color palettes for people with visual impairments, and many more.

6. Culture is presented as the fourth bottom line, highlighting what makes humans unique is that we don’t just evolve alone. Humans design their responses to challenges; sustaining a culture means sustaining adaptability.

7. The closing of the digital divide has liberated more people through information access, elevating the importance of ethics in design choices and accessible methodologies.

8. The quadruple bottom line is designed to include profit, planet, people, and culture. It is aimed to ensure a holistic approach to design.

9. Even small changes, like changing/thinning a font, can have positive impacts on safety, sustainability, and accessibility.

GROUP E: 1. Design has to move beyond profit. Whereas before, it was tied to mostly just to increasing profit. 2. Sustainability is now a mainstream thing that you need to think about, whereas just a decade ago, it was questioned as even being necessary. 3. Design things with accessibility in mind. By ensuring that your design is accessible to all people, it can often even improve the experience for everyone. 4. The internet can either feed into consumerism and insecurity or, help spread knowledge.

5.Design should be about culture, and innovating and adapting, which creates meaning.

6.accessibility is very important design to consider. The video talks about a trafic light example where fixing traffic lights for colorblind people made them clearer for everyone else as well.

7.design should cater to everyone as caring and fairness is a part of a good design.

8.before planning, think about goals and sustainability to insure the correct results.

9.everyone can be a designer, by our choices we all shape the world.

10.even tiny changes make a difference like using different font sizes and color choices.

1. If you manufacture a problem and then create a solution, people will buy the solution to your artificial problem

2.Double bottom lines, financial (profit) + environmental (planet)

3.It's never been easier for us to create an d share ideas than today

1. Simple innovations to previous designs can change things tremendously, for example a dutch font has tiny white dots goign through the letters, which saves companies 25% percent on ink and toner, however the result is still legible

2. Designers, as humans, want to create projects that are sustainable and can be innovated, as it helps leave a legacy, something every person wants

3. Simply following orders is not enough, designers must fight for innovation and creativity, especially to innovate or invent

4. Baerekraft = sustainability, a term coined by Gro Harlem Brundtland, a Norwegian designer show pushed for this to be a major focus if design

5. Carteret Islands was the first civilization to be completely wiped out, due to not being sustainable, showing sustainability is a key component to design

6. Events, whether good or bad, are caused by designs and designers creating and innovating

7. Designs are integral to sustaining life, to the point that it can determine life and death, such as traffic lights using distinct colors as well as shapes to save the lives of colorblind drivers

• Informativo 1.190
• RE 1469887 / AL - Tema 1.424
• Órgão julgador: Tribunal Pleno
• Relator(a): Min. PRESIDENTE
• Julgamento: 12/09/2025 (Virtual)
• Ramo do Direito: Administrativo
• Matéria: Militar; Concurso Público; Requisitos Básicos para a Investidura; Altura Mínima

Polícia Militar: altura mínima para investidura em cargo da carreira Tese fixada - A exigência de altura mínima para ingresso em cargo do Sistema Único de Segurança Pública pressupõe a existência de lei e da observância dos parâmetros fixados para a carreira do exército (Lei federal nº 12.705/2012, 1,60m para homens e 1,55m para mulheres).

Resumo - É inconstitucional — por violar o princípio da razoabilidade — lei estadual que exige, como requisito para ingresso na Polícia Militar, altura mínima superior à prevista para ingresso nas carreiras do Exército.

• A imposição, pelo legislador estadual, de requisitos mais rigorosos do que os previstos na legislação federal para o Exército, sem qualquer justificativa relacionada às atribuições do cargo, configura afronta aos princípios da razoabilidade e da proporcionalidade (1).
• Na espécie, o Tribunal de Justiça do Estado de Alagoas manteve a eliminação de candidata em concurso público para a Polícia Militar por ela não possuir a altura mínima de 1,65m exigida pela legislação estadual (2).
• Com base nesses entendimentos, o Plenário, por unanimidade, reconheceu a existência de repercussão geral da questão constitucional suscitada (Tema 1.424 da repercussão geral) e, no mérito, por maioria: (i) reafirmou a jurisprudência dominante sobre a matéria (3); (ii) deu provimento ao recurso extraordinário para reformar o acórdão recorrido e determinar o prosseguimento da candidata no concurso público; e, por fim, (iii) fixou a tese anteriormente citada.

(1) Lei nº 12.705/2012: “Art. 2º A matrícula para o ingresso nos cursos de formação de oficiais e sargentos de carreira do Exército depende de aprovação prévia em concurso público, atendidos os seguintes requisitos, dentre outros estabelecidos na legislação vigente: (...) XIII - ter altura mínima de 1,60 m (um metro e sessenta centímetros) ou, se do sexo feminino, a altura mínima de 1,55 m (um metro e cinquenta e cinco centímetros).” (2) Lei nº 5.346/1992 do Estado de Alagoas: “Art. 7º O ingresso na Polícia Militar do Estado de Alagoas é facultado a todos os brasileiros, sem distinção de raça, sexo, cor ou credo religioso, mediante matrícula ou nomeação, após aprovação em concurso público de prova ou provas e títulos, desde que observadas as seguintes condições: (...) III – altura mínima de 1,65m (um metro e sessenta e cinco centímetros), se do sexo masculino, e 1,60m (um metro e sessenta centímetros), se do sexo feminino;” (3) Precedentes citados: ADI 5.044, ARE 1.459.395 AgR, RE 1.465.829 AgR e RE 1.480.201, bem como ARE 1.562.570, ARE 1.511.877 e RE 1.500.883 (decisões monocráticas).

Legislação: Lei nº 12.705/2012: art. 2º, XIII. Lei nº 5.346/1992 do Estado de Alagoas: art. 7º, III.

Precedentes: ADI 5.044, ARE 1.459.395 AgR, RE 1.465.829 AgR e RE 1.480.201, bem como ARE 1.562.570, ARE 1.511.877 e RE 1.500.883 (decisões monocráticas).

• Informativo 1163
• ADI 3516 / CE
• Órgão julgador: Tribunal Pleno
• Relator(a): Min. EDSON FACHIN
• Julgamento: 13/12/2024 (Virtual)
• Ramo do Direito: Administrativo
• Matéria: Servidor Público; Inativos e Pensionistas; Gratificações; Proibição de Vinculação da Receita de Impostos; Atividades de Administração Tributária; Princípio da Eficiência

Impossibilidade de vinculação de receita de imposto a pagamento de Prêmio por Desempenho Fiscal a inativos e pensionistas

Resumo - São inconstitucionais — pois afrontam o art. 167, IV, da CF/1988 — dispositivos de lei estadual que vinculam a receita de impostos ao pagamento de Prêmio por Desempenho Fiscal (PDF) ou de gratificação a inativos e pensionistas.

• A ressalva contida no dispositivo acima citado (1) autoriza a vinculação da receita tributária ao pagamento do PDF apenas aos servidores <u>em atividade</u> na administração tributária. Ela tem respaldo no princípio da eficiência (CF/1988, art. 37, caput), na medida em que visa ao aumento da produtividade dos fiscais, e se fundamenta no incremento da arrecadação, no alcance de metas fixadas em regulamento, bem como na instituição de programas de qualidade e produtividade no serviço público, a ser viabilizado sob a forma de adicional ou prêmio de produtividade (2).

• À luz do caráter contributivo do sistema previdenciário, a concessão de vantagem remuneratória a servidores inativos sem o devido desconto da contribuição previdenciária também é inconstitucional, sob pena de desvirtuamento do equilíbrio atuarial e financeiro.

• Com base nesses e em outros entendimentos, o Plenário, por unanimidade, conheceu em parte da ação e, nessa extensão, a julgou parcialmente procedente para declarar a inconstitucionalidade dos arts. 1º, § 1º; 1º-A e 5º-A, da Lei nº 13.439/2004, com a redação da Lei nº 14.969/2011, ambas do Estado do Ceará (3).

(1) CF/1988: “Art. 167. São vedados: (...) IV - a vinculação de receita de impostos a órgão, fundo ou despesa, ressalvadas a repartição do produto da arrecadação dos impostos a que se referem os arts. 158 e 159, a destinação de recursos para as ações e serviços públicos de saúde, para manutenção e desenvolvimento do ensino e para realização de atividades da administração tributária, como determinado, respectivamente, pelos arts. 198, § 2º, 212 e 37, XXII, e a prestação de garantias às operações de crédito por antecipação de receita, previstas no art. 165, § 8º, bem como o disposto no § 4º deste artigo;”

(2) CF/1988: “Art. 39. (...) § 7º Lei da União, dos Estados, do Distrito Federal e dos Municípios disciplinará a aplicação de recursos orçamentários provenientes da economia com despesas correntes em cada órgão, autarquia e fundação, para aplicação no desenvolvimento de programas de qualidade e produtividade, treinamento e desenvolvimento, modernização, reaparelhamento e racionalização do serviço público, inclusive sob a forma de adicional ou prêmio de produtividade.”

(3) Lei nº 13.439/2004 do Estado do Ceará: “Art. 1º Fica instituído para os servidores públicos ativos, integrantes do Grupo Ocupacional Tributação, Arrecadação e Fiscalização - TAF, o Prêmio por Desempenho Fiscal - PDF, a ser concedido mensalmente, desde que implementadas as condições previstas para a sua concessão, nos valores e limites fixados nesta Lei, com o objetivo de estimular os aumentos de produtividade da Secretaria da Fazenda que impliquem no incremento. (Redação dada pela Lei n.º 14.969, de 01.08.11) I - da arrecadação tributária anual, inclusive multas e juros e outras receitas previstas na legislação tributária; II - de outros indicadores de desempenho referidos nesta Lei ou que venham a ser estabelecidos em regulamento. § 1º. O Prêmio por Desempenho Fiscal (PDF) de que trata o caput será extensivo a pensionistas de servidores fazendários, conforme disposto em regulamento. (...) Art. 1º-A Aos aposentados na data da publicação desta Lei e aos que estejam em processo de aposentadoria instaurados nesta mesma data, bem como aos pensionistas de ex-servidores fazendários é devida gratificação em substituição ao valor percebido no mesmo título, na data de vigência desta Lei, totalmente desvinculado da sistemática de apuração e distribuição prevista na Lei n° 13.439, de 16 de janeiro de 2004, correspondente a 97,34% (noventa e sete vírgula trinta e quatro por cento) do valor da 1ª Classe, referência ‘C’ da Tabela B, do anexo III, da Lei n° 13.778, de 6 de junho de 2006, com a redação dada pela Lei n° 14.350, de 19 de maio de 2009, e alterações posteriores, observando-se, para os pensionistas, a proporcionalidade da pensão, submetida exclusivamente à revisão geral dos servidores, a serem custeados com recursos do PDF, Grupo I, conforme disposição em regulamento. Parágrafo único. No prazo de 90 (noventa) dias, a contar da data da publicação da presente Lei, a Secretaria da Fazenda - SEFAZ, juntamente com a Secretaria de Planejamento e Gestão – SEPLAG, e Procuradoria Geral do Estado-PGE, deverão apresentar os atos normativos e legais necessários à realização dos ajustes dos atos de aposentadoria, concedidas até a data de publicação desta Lei. (redação dada pela Lei n.º 14.969, de 01.08.11) (Revogado pela Lei n.º 17.393, de 26/02/2021) (...) Art. 5º-A O Prêmio por Desempenho Fiscal - PDF, será devido ao servidor efetivo do grupo TAF que venha a se aposentar após a publicação desta Lei, nos seguintes termos: I – aos servidores que implementarem as regras dos arts. 3º ou 6º da Emenda Constitucional nº 41, de 19 de dezembro de 2003, ou do art. 3º da Emenda Constitucional nº 47, de 5 de julho de 2005, o Prêmio por Desempenho Fiscal – PDF, será calculado pela média aritmética simples de valores mensais percebidos, a esse título, pelo servidor fazendário nos 24 (vinte e quatro) meses anteriores ao pedido de aposentadoria; II – para os servidores que implementarem as regras dos arts. 3º ou 6º da Emenda Constitucional nº 41, de 19 de dezembro de 2003, ou do art. 3º da Emenda Constitucional nº 47, de 5 de julho de 2005, cujo período de percepção por ocasião do pedido de aposentadoria seja menor do que 24 (vinte e quatro) meses, será observada a média aritmética do período de percepção, multiplicado pela fração cujo numerador será o número correspondente ao total de meses trabalhado e o denominador será sempre o numeral 24; III – para os que implementarem os requisitos de aposentadoria previstos no art. 40, da Constituição Federal, com a redação dada pela Emenda Constitucional nº 41, de 19 de dezembro de 2003, nos termos da legislação federal. Parágrafo único. Nas hipóteses dos incisos I e II deste artigo, o PDF não poderá ser inferior ao limite mínimo definido no art. 4º-A desta Lei. (Redação dada pela Lei n.º 14.969, de 01.08.11)”

Legislação: CF/1988: art. 37, caput, XXII; art. 39, § 7º; art. 165, §§ 4º e 8º; art. 167, IV; art. 198, § 2º; art. 212. Lei nº 13.439/2004 do Estado do Ceará: art. 1º, § 1º; 1º-A e 5º-A.

Reviewer #1 (Public review):

The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

(1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only).

(2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead.

(3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants.

(4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role.

(5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional?

(6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals?

Reviewer #2 (Public review):

Summary:

The authors introduce a gene-level framework to detect shared genetic architecture between complex traits by integrating GWAS summary statistics with eQTL data via a new algorithm, Sherlock-II, which aggregates signals from multiple (cis/trans) eSNPs to produce gene-phenotype p-values. Shared pathways are identified with Partial-Pearson-Correlation Analysis (PPCA).

Strengths:

The authors show the gene-based approach is complementary and often more sensitive than SNP-level methods, and discuss limitations (in terms of no directionality, dependence on eQTL coverage).

Weaknesses:

(1) How do the authors explain data where missing tissues or sparse eQTL mapping are available? Would that bias as to which genes/traits can be linked and may produce false negatives or tissue-specific false positives?

(2) Aggregating SNP-level signals into gene scores can be confounded by LD; for example, a nearby causal variant for a different gene or non-expression mechanism may drive a gene's score, producing spurious gene-trait links. How do the authors prevent this?

(3) How the SNPs are assigned to genes would affect results, this is because different choices can change which genes appear shared between traits. The authors can expand on these.

(4) Many reported novel trait links remain speculative without functional or orthogonal validation (e.g., colocalization, perturbation data). Thus, the manuscript's claims are inconclusive and speculative.

(5) It would be best to run LD-aware colocalization and power-matched simulations to check for robustness.

Author response:

Reviewer #1 (Public review):

The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

(1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only). 

The reviewer is correct that in this version of the tool, we focused on SNPs with effect on gene expression, as the majority of the SNPs identified by GWASs are non-coding SNPs. In the future improvement, we should also include coding SNPs that change the amino acid sequence of genes. We will discuss this point more in the revised manuscript.

(2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead. 

This is a simple scheme to incorporate eQTL data from multiple tissues, assuming that the tissue that gives the strongest association is most relevant, or mainly mediates the effect from the SNP to the phenotype. This is a reasonable approach given that the tissues of origin for most of the phenotypes are unknown. In the future improvement, we should incorporate eQTL data from the appropriate tissue(s) if that is known.

(3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants. 

This is due to one of the limitations of the current algorithm: no direction of association is predicted explicitly. It could be that increasing the expression of a gene reduced the risk of one disease but increase the risk of another. Currently we have to analyze the details of the SNPs to infer direction once overlapping genes are found. This needs improvement in the future.  

(4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role. 

We plan to incorporate reviewer's suggestion in the revised manuscript.

(5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional? 

We will consider a different presentation in the revised manuscript.

(6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals? 

We will add these information in the revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors introduce a gene-level framework to detect shared genetic architecture between complex traits by integrating GWAS summary statistics with eQTL data via a new algorithm, Sherlock-II, which aggregates signals from multiple (cis/trans) eSNPs to produce gene-phenotype p-values. Shared pathways are identified with Partial-Pearson-Correlation Analysis (PPCA).

Strengths:

The authors show the gene-based approach is complementary and often more sensitive than SNP-level methods, and discuss limitations (in terms of no directionality, dependence on eQTL coverage).

Weaknesses:

(1) How do the authors explain data where missing tissues or sparse eQTL mapping are available? Would that bias as to which genes/traits can be linked and may produce false negatives or tissue-specific false positives?

Missing tissues or sparse eQTL certainly can produce false negatives as the signals linking the two phenotypes are simply not captured in the data. It is less likely to produce false positives as long as the statistical test is well controlled.   

(2) Aggregating SNP-level signals into gene scores can be confounded by LD; for example, a nearby causal variant for a different gene or non-expression mechanism may drive a gene's score, producing spurious gene-trait links. How do the authors prevent this? 

When there are multiple SNPs in LD with multiple genes nearby, it is generally difficult to map the causal SNP and the causal gene it affected, and thus there will be spurious gene-trait links. When we calculate the global similarity based on the gene-trait association profiles,  we tried to control this by simulating with random GWASs that have the same power as the real GWAS and preserve the LD structure, as the spurious links will also be present in the simulated data (but may appear in different loci) that are used to calibrate the statistical significance. 

(3) How the SNPs are assigned to genes would affect results, this is because different choices can change which genes appear shared between traits. The authors can expand on these. 

We assign SNPs to genes based on their strongest eQTL association from the available data. Improvement can be made if the relevant tissues for a trait are known (see response to Reviewer 1 above).

(4) Many reported novel trait links remain speculative without functional or orthogonal validation (e.g., colocalization, perturbation data). Thus, the manuscript's claims are inconclusive and speculative. 

We agree with the reviewer that the reported trait links are speculative, and they should be treated as hypotheses generated from the computational analyses. To truly validate some of these proposed relationships, deeper functional analyses and experimental tests are needed.

(5) It would be best to run LD-aware colocalization and power-matched simulations to check for robustness. 

We agree more control on LD and power-matched simulations will be important for testing the robustness of the predictions.

Structure and function

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this review, the author covered several aspects of the inflammation response, mainly focusing on the mechanisms controlling leukocyte extravasation and inflammation resolution.

Strengths:

This review is based on an impressive number of sources, trying to comprehensively present a very broad and complex topic.

Weaknesses:

(1) This reviewer feels that, despite the title, this review is quite broad and not centred on the role of the extracellular matrix.

(2) The review will benefit from a stronger focus on the specific roles of matrix components and dynamics, with more informative subheadings.

(3) The macrophage phenotype section doesn't seem well integrated with the rest of the review (and is not linked to the ECM).

(4) Table 1 is difficult to follow. It could be reformatted to facilitate reading and understanding

(5) Figure 2 appears very complex and broad.

(6) Spelling and grammar should be thoroughly checked to improve the readability.

This review focuses on the whole extravasation journey of leukocyte and highlights involvement of extracellular matrix (ECM) in multiple phases of the process. ECM may exert their roles either as a collective structure or as individual components. In the revision, for those functions involving specific matrix components, we will emphasize the matrix components and incorporate this information to subheadings as suggested. The parts of macrophage phenotype (Section 10-11) are included for its pivotal roles on deciding the tissue fate following inflammation (ie. to resolve / to regenerate damages incurred or to sustain inflammation), which is an important aspect of this review. ECM could modify macrophage phenotypes either directly (section 10) or indirectly via modulations of tissue stiffness or other cell types like fibroblasts (section 9). However, as pointed out by other reviewers as well, we acknowledge that Section 11 does not integrate well enough to the rest of the review. We plan to reorganize this part and to emphasize its link to ECM during the revision for better integration. We will reformat Table 1 for easier comprehension. We will consider restructuring Figure 2, which outlines various events influencing tissue decision of resolution/inflammation, perhaps by breaking up into two separate figures, to better focus the message. We will also check the language to improve readability.

Reviewer #2 (Public review):

Summary:

The manuscript is a timely and comprehensive review of how the extracellular matrix (ECM), particularly the vascular basement membrane, regulates leukocyte extravasation, migration, and downstream immune function. It integrates molecular, mechanical, and spatial aspects of ECM biology in the context of inflammation, drawing from recent advances. The framing of ECM as an active instructor of immune cell fate is a conceptual strength.

Strengths:

(1) Comprehensive synthesis of ECM functions across leukocyte extravasation and post-transmigration activity.

(2) Incorporation of recent high-impact findings alongside classical literature.

(3) Conceptually novel framing of ECM as an active regulator of immune function.

(4) Effective integration of molecular, mechanical, and spatial perspectives.

Weaknesses:

(1) Insufficient narrative linkage between the vascular phase (Sections 2-6) and the in-tissue phase (Sections 7-10).

(2) Underrepresentation of lymphocyte biology despite mention in early sections.

(3) The MIKA macrophage identity framework is only loosely tied to ECM mechanisms.

(4) Limited discussion of translational implications and therapeutic strategies.

(5) Overly dense figure insets and underdeveloped links between ECM carryover and downstream immune phenotypes.

(6) Acronyms and some mechanistic details may limit accessibility for a broader readership.

We will add a transition paragraph between Section 6 and Section 7 to provide a narrative that the extravasation processes affect downstream leukocyte functions. While lymphocytes follow a similar extravasation principle, their in-tissue activities differ from innate leukocytes. We will thus include discussion of lymphocyte-ECM crosstalk to Section 8 and/or 9 in the revision. We will restructure Section 11 and Figure 3 to better integrate to the rest of the review: In the current manuscript, we merely describe the capability of the MIKA framework to describe identity of any tissue macrophages and thus the framework could serve as a roadmap to facilitate identity normalization of pathological macrophages. We plan, in the revision, by employing the MIKA framework, to discuss and demonstrate linkage between macrophage identities and expression/production of modulators to functional ECM effectors described in Section 8-9. Regarding the comment of limited discussion of translational implications / therapeutic strategies, we will try to enrich this aspect throughout the manuscript where appropriate, in addition to the existing ones (eg. line 293-297; 388-391; 460-463; 512-517) We will also revise figure structure in general to avoid too dense information and to improve clarity. We will consider to provide a glossary explaining specialized terms to expand readership accessibility.

Reviewer #3 (Public review):

Summary & Strengths:

This review by Yu-Tung Li sheds new light on the processes involved in leukocyte extravasation, with a focus on the interaction between leukocytes and the extracellular matrix. In doing so, it presents a fresh perspective on the topic of leukocyte extravasation, which has been extensively covered in numerous excellent reviews. Notably, the role of the extracellular matrix in leukocyte extravasation has received relatively little attention until recently, with a few exceptions, such as a study focusing on the central nervous system (J Inflamm 21, 53 (2024) doi.org/10.1186/s12950-024-00426-6) and another on transmigration hotspots (J Cell Sci (2025) 138 (11): jcs263862 doi.org/10.1242/jcs.263862). This review synthesizes the substantial knowledge accumulated over the past two decades in a novel and compelling manner.

The author dedicates two sections to discussing the relevant barriers, namely, endothelial cell-cell junctions and the basement membrane. The following three paragraphs address how leukocytes interact with and transmigrate through endothelial junctions, the mechanisms supporting extravasation, and how minimal plasma leakage is achieved during this process. The subsequent question of whether the extravasation process affects leukocyte differentiation and properties is original and thought-provoking, having received limited consideration thus far. The consequences of the interaction between leukocytes and the extracellular matrix, particularly regarding efferocytosis, macrophage polarization, and the outcome of inflammation, are explored in the subsequent three chapters. The review concludes by examining tissue-specific states of macrophage identity.

Weaknesses:

Firstly, the first ten sections provide a comprehensive overview of the topic, presenting logical and well-formulated arguments that are easily accessible to a general audience. In stark contrast, the final section (Chapter 11) fails to connect coherently with the preceding review and is nearly incomprehensible without prior knowledge of the author's recent publication in Cell. Mol. Life Sci. CMLS 772 82, 14 (2024). This chapter requires significantly more background information for the general reader, including an introduction to the Macrophage Identity Kinetics Archive (MIKA), which is not even introduced in this review, its basis (meta-analysis of published scRNA-seq data), its significance (identification of major populations), and the reasons behind the revision of the proposed macrophage states and their further development. Secondly, while the attempt to integrate a vast amount of information into fewer figures is commendable, it results in figures that resemble a complex puzzle. The author may consider increasing the number of figures and providing additional, larger "zoom-in" panels, particularly for the topics of clot formation at transmigration hotspots and the interaction between ECM/ECM fragments and integrins. Specifically, the color coding (purple for leukocyte α6-integrins, blue for interacting laminins, also blue for EC α6 integrins, and red for interacting 5-1-1 laminins) is confusing, and the structures are small and difficult to recognize.

We agree with and appreciate the specific and helpful suggestions by the reviewer. During the revision, we will provide the requested background description of MIKA to enhance accessibility of general readership. As pointed out by other reviewers, since this part (Section 11) is less well-integrated to the rest of the review, we will restructure this part by linking tissue macrophage identities under MIKA framework to modulation of functional ECM effectors described in previous sections (Section 8-9). We acknowledge the current figure organization might be overly information-dense and will consider breaking down the contents to multiple figures. The size and color-coding issues will also be addressed.

Reviewer #1 (Public review):

Summary:

In this review, the author covered several aspects of the inflammation response, mainly focusing on the mechanisms controlling leukocyte extravasation and inflammation resolution.

Strengths:

This review is based on an impressive number of sources, trying to comprehensively present a very broad and complex topic.

Weaknesses:

(1) This reviewer feels that, despite the title, this review is quite broad and not centred on the role of the extracellular matrix.

(2) The review will benefit from a stronger focus on the specific roles of matrix components and dynamics, with more informative subheadings.

(3) The macrophage phenotype section doesn't seem well integrated with the rest of the review (and is not linked to the ECM).

(4) Table 1 is difficult to follow. It could be reformatted to facilitate reading and understanding

(5) Figure 2 appears very complex and broad.

(6) Spelling and grammar should be thoroughly checked to improve the readability.

Reviewer #2 (Public review):

Summary:

The manuscript is a timely and comprehensive review of how the extracellular matrix (ECM), particularly the vascular basement membrane, regulates leukocyte extravasation, migration, and downstream immune function. It integrates molecular, mechanical, and spatial aspects of ECM biology in the context of inflammation, drawing from recent advances. The framing of ECM as an active instructor of immune cell fate is a conceptual strength.

Strengths:

(1) Comprehensive synthesis of ECM functions across leukocyte extravasation and post-transmigration activity.

(2) Incorporation of recent high-impact findings alongside classical literature.

(3) Conceptually novel framing of ECM as an active regulator of immune function.

(4) Effective integration of molecular, mechanical, and spatial perspectives.

Weaknesses:

(1) Insufficient narrative linkage between the vascular phase (Sections 2-6) and the in-tissue phase (Sections 7-10).

(2) Underrepresentation of lymphocyte biology despite mention in early sections.

(3) The MIKA macrophage identity framework is only loosely tied to ECM mechanisms.

(4) Limited discussion of translational implications and therapeutic strategies.

(5) Overly dense figure insets and underdeveloped links between ECM carryover and downstream immune phenotypes.

(6) Acronyms and some mechanistic details may limit accessibility for a broader readership.

Reviewer #3 (Public review):

Summary & Strengths:

This review by Yu-Tung Li sheds new light on the processes involved in leukocyte extravasation, with a focus on the interaction between leukocytes and the extracellular matrix. In doing so, it presents a fresh perspective on the topic of leukocyte extravasation, which has been extensively covered in numerous excellent reviews. Notably, the role of the extracellular matrix in leukocyte extravasation has received relatively little attention until recently, with a few exceptions, such as a study focusing on the central nervous system (J Inflamm 21, 53 (2024) doi.org/10.1186/s12950-024-00426-6) and another on transmigration hotspots (J Cell Sci (2025) 138 (11): jcs263862 doi.org/10.1242/jcs.263862). This review synthesizes the substantial knowledge accumulated over the past two decades in a novel and compelling manner.

The author dedicates two sections to discussing the relevant barriers, namely, endothelial cell-cell junctions and the basement membrane. The following three paragraphs address how leukocytes interact with and transmigrate through endothelial junctions, the mechanisms supporting extravasation, and how minimal plasma leakage is achieved during this process. The subsequent question of whether the extravasation process affects leukocyte differentiation and properties is original and thought-provoking, having received limited consideration thus far. The consequences of the interaction between leukocytes and the extracellular matrix, particularly regarding efferocytosis, macrophage polarization, and the outcome of inflammation, are explored in the subsequent three chapters. The review concludes by examining tissue-specific states of macrophage identity.

Weaknesses:

Firstly, the first ten sections provide a comprehensive overview of the topic, presenting logical and well-formulated arguments that are easily accessible to a general audience. In stark contrast, the final section (Chapter 11) fails to connect coherently with the preceding review and is nearly incomprehensible without prior knowledge of the author's recent publication in Cell. Mol. Life Sci. CMLS 772 82, 14 (2024). This chapter requires significantly more background information for the general reader, including an introduction to the Macrophage Identity Kinetics Archive (MIKA), which is not even introduced in this review, its basis (meta-analysis of published scRNA-seq data), its significance (identification of major populations), and the reasons behind the revision of the proposed macrophage states and their further development. Secondly, while the attempt to integrate a vast amount of information into fewer figures is commendable, it results in figures that resemble a complex puzzle. The author may consider increasing the number of figures and providing additional, larger "zoom-in" panels, particularly for the topics of clot formation at transmigration hotspots and the interaction between ECM/ECM fragments and integrins. Specifically, the color coding (purple for leukocyte α6-integrins, blue for interacting laminins, also blue for EC α6 integrins, and red for interacting 5-1-1 laminins) is confusing, and the structures are small and difficult to recognize.

contribute your own thinking

OJO, MIRAR

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work aims to elucidate the molecular mechanisms affected in hypoxic conditions, causing reduced cortical interneuron migration. They use human assembloids as a migratory assay of subpallial interneurons into cortical organoids and show substantially reduced migration upon 24 hours of hypoxia. Bulk and scRNA-seq show adrenomedullin (ADM) up-regulation, as well as its receptor RAMP2, confirmed atthe protein level. Adding ADM to the culture medium after hypoxic conditions rescues the migration deficits, even though the subtype of interneurons affected is not examined. However, the authors demonstrate very clearly that ineffective ADM does not rescue the phenotype, and blocking RAMP2 also interferes with the rescue. The authors are also applauded for using 4 different cell lines and using human fetal cortex slices as an independent method to explore the DLXi1/2GFP-labelled iPSC-derived interneuron migration in this substrate with and without ADM addition (after confirming that also in this system ADM is up-regulated). Finally, the authors demonstrate PKA-CREB signalling mediating the effect of ADM addition, which also leads to up-regulation of GABAreceptors. Taken together, this is a very carefully done study on an important subject - how hypoxia affects cortical interneuron migration. In my view, the study is of great interest.

Strengths:

The strengths of the study are the novelty and the thorough work using several culture methods and 4 independent lines.

Weaknesses:

The main weakness is that other genes regulated upon hypoxia are not confirmed, such that readers will not know until which fold change/stats cut-off data are reliable.

Reviewer #2 (Public review):

Summary

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids, and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM.

Strengths:

The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies. The authors use sufficient iPSC lines including both XX and XY, so the analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of validation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall, this is a very nice manuscript.

Weaknesses:

I have a few comments and suggestions for the authors. See below.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether hypoxia disrupts the migration of human cortical interneurons, a process long suspected to underlie brain injury in preterm infants but previously inaccessible for direct study. Using human forebrain assembloids and ex vivo developing brain tissue, they visualized and quantified interneuron migration under hypoxic conditions, identified molecular components of the response, and explored the effect of pharmacological intervention (specifically ADM) on restoring the migration deficits.

Strengths:

The major strength of this study lies in its use of human forebrain assembloids and ex vivo prenatal brain tissue, which provide a direct system to study interneuron migration under hypoxic conditions. The authors combine multiple approaches: long-term live imaging to directly visualize interneuron migration, bulk and single-cell transcriptomics to identify hypoxia-induced molecular responses, pharmacological rescue experiments with ADM to establish therapeutic potential, and mechanistic assays implicating the cAMP/PKA/pCREB pathway and GABA receptor expression in mediating the effect. Together, this rigorous and multifaceted strategy convincingly demonstrates that hypoxia disrupts interneuron migration and that ADM can restore this defect through defined molecular mechanisms.

Overall, the authors achieve their stated aims, and the results strongly support their  conclusions. The work has a significant impact by providing the first direct evidence of hypoxia-induced interneuron migration deficits in the human context, while also nominating a candidate therapeutic avenue. Beyond the specific findings, the methodological platform - particularly the combination of assembloids and live imaging - will be broadly useful to the community for probing neurodevelopmental processes in health and disease.

Weaknesses:

The main weakness of the study lies in the extent to which forebrain assembloids

recapitulate in vivo conditions, as the migration of interneurons from hSO to hCO does not fully reflect the native environment or migratory context of these cells. Nevertheless, this limitation is tempered by the fact that the work provides the first direct observation of human interneuron migration under hypoxia, representing a major advance for the field. In addition, while the transcriptomic analyses are valuable and highlight promising candidates, more in-depth exploration will be needed to fully elucidate the molecular mechanisms governing neuronal migration and maturation under hypoxic conditions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors should examine if all cortical interneurons are affected by ADM or only subtypes (Parvalbumin/Somatostatin).

We thank the reviewer for raising this important question. In our study, we utilized the Dlx1/2b::eGFP reporter to broadly label cortical interneurons; however, this system does not distinguish specific interneuron subtypes. To address this, in the revised version of the manuscript we will use the single-cell RNA sequencing data and immunostainings to provide this information. Based on previous analyses from Birey et al (Cell Stem Cell, 2022), we expect interneurons within assembloids to express mostly calbindin (CALB2) and somatostatin (SST) at this in vitro stage of development; parvalbumin subtype appears later based on data from Birey et al (Nature, 2017) and more recently from Varela et al, (bioRxiv, 2025).

In parallel, we will analyze available scRNA-seq data from developing human primary brain tissue a similar age as the one used in the manuscript, and check whether these subtypes of interneurons are similar to the ones within assembloids.

(2) The authors should test more candidates from their bulk RNA-seq data with different fold changes for regulation after hypoxia, to allow the reader to judge at which cut-off the DEGs may be reproducible. This would make this database much more valuable for the field of hypoxia research.

We appreciate the reviewers’ thoughtful suggestion. In addition to the bulk RNA-seq analysis, we did validate several upregulated hypoxia-responsive genes with varying fold changes by qPCR; these include PDK1, PFKP, VEGFA (Figure S1). 

We go agree that in-depth investigation of specific cut-offs would be interesting, however, this could be the focus of a different manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Can the authors comment on the possibility of inflammatory response pathways being activated by hypoxia? Has this been shown before? While not the focus of the manuscript, it could be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

We thank the reviewer this important comment about inflammation. Indeed, hypoxia has been shown to activate the inflammatory response pathways. In various studies, it was found that HIF-1a can interact with NF-κB signaling, leading to the upregulation of pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α (Rius et al., Cell, 2008; Hagberg et al., Nat Rev Neurol, 2015).

In our transcriptomics data (Figure 2D), and to the reviewers’ point, we identified enrichment of inflammatory signaling response following the hypoxic exposure. Since hSO at the time of analyses do contain astrocytes, we think these glia contribute to the observed pro-inflammatory changes. Based on these results and because ADM is known to have strong anti-inflammatory properties, the effects of ADM on hypoxic astrocytes should be investigated in future studies focused on hypoxia-induced inflammation. In the revision, we will address this comment in the discussion section and cite the appropriate papers.

(2) Could the authors comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms in place in ventral vs dorsal areas.

Based on our scRNA-seq data in hSOs showing significant upregulation of ADM expression in astrocytes and progenitors, we speculate that the primary mechanism is likely to involve paracrine interactions. However, we cannot exclude autocrine mechanisms with the included experiments. Dissecting these interactions in a cell-type specific manner could be an important focus for future ADM-related studies.

To address the question about the possible different mechanisms in ventral versus dorsal areas, in the revision we will plot and include in the figures the data about the cell-type expression of ADM and its receptors in hCOs.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Figure 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

We thank the reviewer for this comment and the observation. Although we did not include a traditional positive control in these ELISA assays, several lines of evidence indicate that the measurements are reliable. First, the standard curves behaved as expected, and all sample values fell within the assay’s dynamic range. Second, technical replicates showed low variability, and the observed changes across experimental conditions (e.g., hypoxia vs. control) were consistent with the expected biological responses based on previous literature. We agree that including western blot validation would strengthen the findings, and we will note this for our future studies focused on CREB and ADM.

(4) Could the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

We appreciate the reviewers’ insightful question. Currently, not much is known about the molecular pathways and downstream cellular events triggered by ADM binding to RAMP2 in inhibitory neurons, and in general in brain cells. The data from our study brings the first information about the cell-type specific expression of ADM in baseline and hypoxic conditions and is one of the key novelties of our study.

While the signaling landscape of ADM in interneurons is largely unexplored, several studies in other (non-brain) cell types have demonstrated that ADM binding to RAMP2 can activate downstream cascades such as the cAMP/PKA/CREB pathway, PI3K/AKT, and ERK/MAPK, all of which are also known to be critical regulators of neuronal development and survival. These previously published data along with our CREB-targeted findings in hypoxic interneurons, suggest ADM–RAMP2 signaling could influence multiple aspects of interneuron biology, but these remain to be evaluated in future studies.

We agree with the reviewer that CREB has a wide range of transcriptional targets. We decided to focus on GABA as a target of CREB for two main reasons, including: (i) GABA signaling has been previously shown to play an important role in the migration of cortical interneurons, and (ii) a previous study by Birey et al. (Cell Stem Cell, 2022) demonstrated that CREB pathway activity is essential for regulating interneuron migration in assembloid models of Timothy Syndrom, thus further providing evidence that dysregulation of CREB activity disrupts migration dynamics.

While our study provides a first step toward uncovering the mechanisms of interneuron migration protection by ADM, we fully acknowledge that future work will be needed to delineate the full spectrum of ADM–RAMP2 downstream signaling events in inhibitory neurons and other brain cells.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known, but was not discussed.

We appreciate this question from the reviewer; however, this was not something that we focused on in this manuscript due to the already large amount of data included. A separate study focusing on neurogenesis defects and the molecular mechanisms of injury for that specific developmental process would be an important next step.

(6) In the Discussion section, it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might result in, in terms of functional consequences for neural circuit development.

We thank the Reviewer for the suggestion of detailing the functional impact of reduced inhibitory neuron migration. We will revise the manuscript by incorporating a paragraph about this in the Discussion section.

Reviewer #3 (Recommendations for the authors):

Most of the evidence presented is convincing in supporting the conclusions, and I have only minor suggestions for improvement:

(1) The bulk RNA-seq was performed in hSOs only, which may not fully capture the phenotypes of migrating or migrated interneurons. It would be valuable, if feasible, to sort migrated cells from hSO-hCO assembloids and specifically examine their molecular mediators.

We thank the reviewer for this suggestion. While it is likely that the cellular environment will have some influence on a subset of the molecular changes, based on all the data from the manuscript and our specific target, the RNA-sequencing on hCOs was sufficient to capture essential changes like ADM upregulation. The in-depth exploration on differential responses of migrated versus non-migrated interneurons to hypoxia could be the focus of a different project.

(2) In Figure 3, it is striking that cell-type heterogeneity dominates over hypoxia vs. control conditions. A joint embedding of hSO and hCO cells could provide further insight into molecular differences between migrated and non-migrated interneurons.

We thank the reviewer for this observation and opportunity to clarify. Since we manually separated the assembloids before the analyses, we processed these samples separately. That is why they separate like this. In the revision, we will add data about ADM expression and its receptors’ expression in the hCOs.

(3) It would be helpful to expand the discussion on how closely the migration observed in hSO-hCO assembloids reflects in vivo conditions, and what environmental aspects are absent from this model. This would better frame the interpretation and translational relevance of the findings.

We thank the Reviewer for bringing up this important point. Although the assembloid model offers the unique advantage of allowing the direct investigation of migration patterns of hypoxic interneurons, we fully agree it does not fully recapitulate the in vivo environment. While there are multiple aspects that cannot be recapitulated in vitro at this time (e.g. cellular complexity, vasculature, immune response, etc), we are encouraged by the validation of our main findings in ex vivo developing human brain tissue, which strongly supports the validity of our findings for in vivo conditions.

We will expand our discussion to include more details and the need to validate these findings using in vivo models, while also acknowledging that different species (e.g. rodents versus non-human primates versus humans) might have different responses to hypoxia.

(4) The authors suggest that hypoxia is also associated with delayed interneuron maturation, yet the bulk RNA-seq data primarily reveal stress and hypoxia-related genes. A more detailed discussion of why genes linked to interneuron maturation and function were not strongly affected would clarify this point.

We thank the Reviewer for the opportunity to clarify.

The RNAseq data was performed during the acute stages of hypoxia/reoxygenation and we think a maturation phenotype might be difficult to capture at this point and would require analysis at later in vitro assembloid maturation stages.

Our speculation about a possible maturation defect is based on data from previous studies from developmental biology that showed failure of interneurons to reach their final cortical location within a specified developmental window will impair their integration within the neuronal network, and thus lead to maturation defects and possible elimination by apoptosis.

Since preterm infants suffer from countless hypoxic events over multiple months, we suggest these repetitive events are likely to induce cumulative delays in migration, inability of interneurons to reach their target in time, followed by abnormal integration within the excitatory network, and eventual elimination of some of these interneurons through apoptosis. However, the direct demonstration of this effect following a hypoxic insult would require prolonged in vivo experiments in rodents to follow the migration, network integration and apoptosis of interneurons; to our knowledge this experimental design is not technically feasible at this time.

(5) Relatedly, while the focus on interneuron migration is well justified, acknowledging how hypoxia might also impact other aspects of cortical development (e.g., progenitor proliferation, neuronal maturation, or circuit integration) would place the findings in a broader developmental framework and strengthen their relevance.

We appreciate the Reviewer’s suggestion to discuss the role of hypoxia on other processes during cortical development. In the revised manuscript, we will include citations about the effects of hypoxia on interneuron proliferation, maturation and circuit integration as available, and also expand to other cell types known to be affected.

(6) Very minor: in Figure S3C and D, it was not stated what the colors mean (grey: control, yellow: hypoxia)

Thank you for pointing out this error and we will correct it in our revision.

Reviewer #2 (Public review):

Summary

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids, and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM. The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies. The authors use sufficient iPSC line,s including both XX and XY, so the analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of validation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall, this is a very nice manuscript. I have a few comments and suggestions for the authors.

Strengths and Weaknesses:

(1) Can the authors comment on the possibility of inflammatory response pathways being activated by hypoxia? Has this been shown before? While not the focus of the manuscript, it could be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

(2) Could the authors comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms in place in ventral vs dorsal areas.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Figure 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

(4) Could the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known, but was not discussed.

(6) In the Discussion section, it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might result in, in terms of functional consequences for neural circuit development.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether hypoxia disrupts the migration of human cortical interneurons, a process long suspected to underlie brain injury in preterm infants but previously inaccessible for direct study. Using human forebrain assembloids and ex vivo developing brain tissue, they visualized and quantified interneuron migration under hypoxic conditions, identified molecular components of the response, and explored the effect of pharmacological intervention (specifically ADM) on restoring the migration deficits.

Strengths:

The major strength of this study lies in its use of human forebrain assembloids and ex vivo prenatal brain tissue, which provide a direct system to study interneuron migration under hypoxic conditions. The authors combine multiple approaches: long-term live imaging to directly visualize interneuron migration, bulk and single-cell transcriptomics to identify hypoxia-induced molecular responses, pharmacological rescue experiments with ADM to establish therapeutic potential, and mechanistic assays implicating the cAMP/PKA/pCREB pathway and GABA receptor expression in mediating the effect. Together, this rigorous and multifaceted strategy convincingly demonstrates that hypoxia disrupts interneuron migration and that ADM can restore this defect through defined molecular mechanisms.

Overall, the authors achieve their stated aims, and the results strongly support their conclusions. The work has a significant impact by providing the first direct evidence of hypoxia-induced interneuron migration deficits in the human context, while also nominating a candidate therapeutic avenue. Beyond the specific findings, the methodological platform - particularly the combination of assembloids and live imaging - will be broadly useful to the community for probing neurodevelopmental processes in health and disease.

Weaknesses:

The main weakness of the study lies in the extent to which forebrain assembloids recapitulate in vivo conditions, as the migration of interneurons from hSO to hCO does not fully reflect the native environment or migratory context of these cells. Nevertheless, this limitation is tempered by the fact that the work provides the first direct observation of human interneuron migration under hypoxia, representing a major advance for the field. In addition, while the transcriptomic analyses are valuable and highlight promising candidates, more in-depth exploration will be needed to fully elucidate the molecular mechanisms governing neuronal migration and maturation under hypoxic conditions.

Reviewer #3 (Public review):

Summary

This manuscript, from the developers of the novel DREADD-selective agonist DCZ (Nagai et al., 2020), utilizes a unique dataset where multiple PET scans in a large number of monkeys, including baseline scans before AAV injection, 30-120 days post-injection, and then periodically over the course of the prolonged experiments, were performed to access short- and long-term dynamics of DREADD expression in vivo, and to associate DREADD expression with the efficacy of manipulating the neuronal activity or behavior. The goal was to provide critical insights into practicality and design of multi-year studies using chemogenetics, and to elucidate factors affecting expression stability.

Strengths are systematic quantitative assessment of the effects of both excitatory and inhibitory DREADDs, quantification of both the short-term and longer-term dynamics, a wide range of functional assessment approaches (behavior, electrophysiology, imaging), and assessment of factors affecting DREADD expression levels, such as serotype, promoter, titer (concentration), tag, and DREADD type.

These finding will undoubtedly have a very significant impact on the rapidly growing, but still highly challenging field of primate chemogenetic manipulations. As such, the work represents an invaluable resource for the community.

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Reply to the reviewers

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

The manuscript by Xu et al. investigated split gene drive systems by targeting multiple female essential genes involved in fertility and viability in Drosophila. The authors evaluate the suppression efficiency through individual corsses and cage trials. Resistance allele formation and fitness costs are explored by examining the sterility and fertility of each line. Overall, the experimental design is sound and methods are feasible. The work is comprehensive, and conclusions are well supported by the data. This work offers informative insights that could guide the design of suppression gene drive systems in other invasive disease vectors or agricultural pests.

However, several points requiring clarification or improvement:

1 Methodological clarity: Some experimental details are indufficiently described, for example, regarding the setup of genetic crosses involving different Cas9 derivatives. In line 197-198, "the mated females, together with females that were mated with Cas9 only males", it is unclear whether the latter group refers to gRNA-females.

-We thank the reviewer for pointing out this ambiguity. The latter group refers to Cas9 females crossed to Cas9 males. We have clarified this both in the methods (line 207) and results (line 505-509).

2.Regarding the inheritance rates, you included the reverse orientation of CG4415-Cas9, as I understood, it means this component is in reverse orientation with fluorescent marker. Since it is standard to design adjacent components in opposite direction to avoid transcriptional interference, the rationale for including this comparison should be better justified.

• In our construct, ‘CG4415 (reverse orientation)’ indicates that Cas9 was oriented in the same direction as the fluorescent marker, while the other Cas9 constructs (nanos-Cas9 and CG4415-Cas9) places them in opposite directions. “reverse” just indicates a change from a “standard” in another study. Our previous publication showed that Cas9 orientation relative to the marker had little apparent effect on drive performance at the yellow-G locus. In this study, we compared both orientations in a fertility gene and again observed similar results, suggesting that orientation relative to the marker does not substantially affect drive efficiency in our system. We have clarified this in the figure legend text.

Embryo resistance is inferred from the percentage of sterile drive females derived from drive mothers. How many female individuals were analysed per line and why deep sequencing was not employed to directly detect resistance alleles.

-Embryo resistance can mean slightly different things for different applications. The most important is probably the fraction of females that have little to no fertility due to embryo resistance. Some of these may not have complete embryo resistance alleles, but instead, have mosaicism, with a sufficient level of resistance to still cause sterility. It is unclear exactly what proportion of resistance to wild-type may cause this, and thus, proportions from pooled sequencing, which could include both complete and all levels of mosaicism, may not be sufficient to measure this parameter. Another relevant parameter that we did not measure is the fraction of males rendered unable to do drive conversion (this value should be closer to the complete resistance rate, but probably still lower because of the multiple gRNAs). Even in this case, deep sequencing would not allow us to determine exactly what is happening in males, making individual sequencing a preferred approached. It is very nice, of course, for characterizing which resistance alleles are present overall, but in this study, we wanted to put a bit more emphasis on the effect of resistance, rather than its sequence characterizing.

We analyzed 30 females per line for lines targeting nox, oct, dec and stl, 9 females for ndl and 276 individuals for line tra-v2 (Data Set S4). We believe such individual analyses sufficiently detected embryo resistance causing sterility within reasonable error. Note that we did also randomly genotype several sterile females and found mutations at target sites that disrupted gene functions.

In response to this comment, we have added some text to justify our measurement of resistance alleles and include some of this discussion:

“Note also that this defines embryo resistance as sufficient to induce sterility, but these may be mosaic rather than complete resistance. Further, note that the multiplex gRNA design in males may allow for continued drive conversion with a complete (as opposed to mosaic) embryo resistance allele, if some sites remain wild-type.”

Masculinisation phenotypes were observed upon disruption of tra gene. How strong intersexes were distinguished from males? What molecular markers were used to determine genetic sex. This information should be clearly provided.

-We observed two types of strong masculinisation phenotypes (Figure S2), one with bigger body size than wildtype males, and the other was identical to wildtype males. The homozygosity of the drive allele could be assessed by the brightness of red fluorescence in the eyes. However, we also randomly genotyped these masculinized females (as part of a batch that included males) to confirm their sex using primers for the Y-linked gene PP1Y2. A specific band was detected in wild-type males but not in masculinized females, confirming their genetic sex. This information has been added to the manuscript (lines 477-480).

It would be more appropriate to use "hatchability"rather than "fertility" when referring to egg-to-larva viability.

-Thank you for the suggestion. We used egg-to-adult survival rates as a proxy for the fertility of their parents because they usually laid similar number of eggs. However, it still technically incorrect language. We have fixed this in line 582 and elsewhere in the section.

In cage trials, a complete gene drive is mimicked by introducing Cas9 to the background population, but this differs from actual complete gene drive, due to potential effects from separate insertion sites (different chromosome or loci). These difference could impact the system's performance and should be discussed.

-We appreciate this point and have added discussion on the limitations of mimicking a complete gene drive using split components (line 766-779).

7.Given the large amount of data presented, it would improve readability and interpretation if each result section concluded with a concise summary highlighting the key findings and implications.

-Thank you for the suggestion. We have added brief summaries at the end of each results section to highlight the key findings and their significance.

Reviewer #1 (Significance (Required)):

The authors evaluate suppression efficiency through individual courses and cage trials. Resistance allele formation and fitness costs are explored by examining the sterility and fertility of each line. Overall, the experimental design is sound and methods are feasible. The work is comprehensive, and conclusions are well supported by the data. This work offers informative insights that could guide the design of suppression gene drive systems in other invasive disease vectors or agricultural pests.

-We appreciate the reviewer’s positive assessment of our work.

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

Paper summary

The manuscript by Xu. et al presents an insightful and valuable contribution to the field of gene drive research. The manuscript by Xu et al. presents an insightful and valuable contribution to the field of gene drive research. The strategy of targeting and disrupting female fertility genes using selfish homing genetic elements was first proposed by Burt in 2003. However, for this approach to be effective, the phenotypic constraints associated with gene disruption have meant that the pool of suitable target genes remains relatively small - notwithstanding the significant expansion in accessible targets enabled by CRISPR-based genome editing nucleases. Population suppression gene drives are well developed as proof-of-principle systems, with some now in the late stages of development as genetic control strains. However, advancing the pipeline will require a broader set of validated target genes - both to ensure effectiveness across diverse species and to build redundancy into control strategies, reducing reliance on any single genetic target.

In their paper, the authors conduct a systematic review of nine female fertility genes in Drosophila melanogaster to assess their potential as targets for homing-based suppression gene drives. The authors first conduct a thorough bioinformatic review to select candidate target genes before empirically testing candidates through microinjection and subsequent in vivo analyses of drive efficiency, population dynamics, and fitness costs relating to fecundity and fertility. After finalising their results, the authors identify two promising candidate target genes - oct and stl - which both demonstrate high gene conversion rates and, regarding the latter, can successfully suppress a cage population at a high release frequency. However, the manuscript suffers from a lack of in-depth discussion of a key limitation in its experimental design - namely, that the authors utilise a split-drive design to assess population dynamics and fitness effects when such a drive will not reflect release scenarios in the field. The review below highlights some major strengths and weaknesses of the paper, with suggestions for improvement.

Key strengths

The study's most significant strength is in its systematic selection and empirical testing of nine distinct genes as targets for homing-based gene drive, hence providing a valuable resource that substantially expands the pool of potential targets beyond the more commonly studied target genes (e.g. nudel, doublesex, among others). The identification of suitable target genes presents a significant bottleneck in the development of gene drives and the work presented here provides a foundational dataset for future research. The authors bolster the utility of their results by assessing the conservation of candidate genes across a range of pest species, suggesting the potential for broader application.

A key finding in the paper is the successful suppression of a cage population using a stl-targeting gene drive (albeit at a high release frequency). This provides a critical proof-of-principal result demonstrating that stl is a viable target for a suppression drive. While in the paper suppression was not possible at lower release frequencies, together, the results provide evidence for complex population dynamics and threshold effects that may govern the success or failure of a gene drive release strategy - hence moving the conversation from a technical perspective ("can it work") to how a gene drive may be implemented. Moreover, the authors also employ a multiplexed gRNA strategy for all their gene drive designs and in particular their population suppressive gene drive targeting stl. This provides further proof-of-principal evidence for multiplexed gRNAs in order to combat the evolution of functional resistance following gene drive deployment.

Finally, a further strength of this paper is in the clever dissection of fitness effects resulting from maternal Cas9 deposition. The authors design and perform a robust set of crosses to elucidate the parental source of fitness effects (i.e. maternally, paternally, or biparentally derived Cas9), finding (as they and others have before) that embryonic fitness was significantly reduced when Cas9 was inherited from a maternal source. As discussed, the authors conclude that maternal deposition is particularly pronounced in the context of split drives as opposed to complete drives, with the implication being that a complete drive might succeed where a split-drive has failed; thus providing a key directive for future study.

Concerns

The manuscript's central weakness lies in its interpretation of the results from the cage experiments - namely that a split-drive system was used to "mimic the release of a complete drive". In the study, mosquitoes carrying the drive element (i.e. the gRNA) were introduced into a population homozygous for the Cas9 element over several generations. This design is likely not representative of a real-world scenario and, as the authors state, likely exaggerates fitness costs. This is because the females carrying Cas9 will maternally deposit Cas9 protein into her eggs, with activity spanning several generations. When mated with a drive-carrying male the gRNA will immediately co-exist with maternally deposited Cas9, leading to early somatic cleavage and significant fitness costs (reflected in the author's own fertility crosses). This is fundamentally different to how a complete drive would function in a real-world release, where complete-drive males would mate with wild-type females not carrying Cas9. Their offspring would carry the drive element but would not be exposed to maternally deposited cas9, thus deleterious maternal effects would only begin to appear in the subsequent generation from females carrying the drive. Fitness costs measured from split-drive designs are therefore likely substantially overestimated compared to what would occur during the initial but critical release phase of a complete drive. This flaw weakens the paper's ability to predict the failure or success of the screened targets in a complete drive design, thus weakening the interpretation of the results from the cage trials. As a suggestion for improvement, the authors should explicitly and more prominently discuss the limitations of their split-drive model compared to complete drive models, both in the Results and Discussion. It is also recommended to include a schematic for both strategies that contrasts the experimental setup design (i.e. release of the drive into a Cas9 homozygous background) with a complete-drive release, clearly illustrating differences in maternal deposition pathways. This will not only contextualise the results and support the author's conclusion that observed fitness costs are likely an overestimate but will further strengthen the arguments that the candidate target genes found in this study may still be viable in a complete-drive system.

-We sincerely appreciate the thoughtful review and the valuable comments and suggestions provided, which have helped improve both the clarity and readability of this study. We have revised several parts in the discussion of the manuscript and hope that these changes adequately address the concerns raised. We have also made Figure S5 to illustrate the differences between two release strategies (biparental-Cas9 split drive in our study and complete drive in real release).

Please note that this type of fitness cost may have partially undermined our cage study (the fitness effect is notable, but still small compared to total fitness costs), but this is also among the first studies to propose and investigate this phenomenon in the first place (it is also noted in another preprint from our lab but to our knowledge not proposed elsewhere). Thus, part of the impact of our manuscript is showing that this is important, which may inform future cage studies in our lab and elsewhere.

A second weakness in the manuscript relates to its limited explanation and discussion of key concepts. For example, the manuscript reports a stark difference in outcome of the two stl-targeting drives, where a high initial release in cage 1 led to population elimination versus a failure of the drive to spread in cage 2. The authors attribute this to vague "allele effects" and stochastic factors such as larval competition; however the results appear reminiscent of the Allee effect, which is a well-characterised phenomenon describing the correlation of population size (or density) and individual fitness (or per capita population growth rate). Using their results as an example, is it plausible that the high-frequency initial release in cage 1 imposed enough genetic load to quickly drive the population density below the Allee threshold thus quickly leading to population eradication. In cage 2, the low-frequency at initial release was insufficient to cross the Allee threshold. Omitting mention of this ecological principal greatly weakens the Discussion, and further presents a missed opportunity to discuss one of the more crucial strengths of the paper - that is, in providing a deeper insight into the practical requirements for successful field implementation.

-While we do indeed mention this Allee effect (the “allele effect” noted above is a misspelling that we have corrected), we were hesitant to give it much discussion, considering that the specific Allee effect in our cages is likely of a very different nature than one would find in nature (we explain that it is likely due to bacterial growth that occurs when fewer larvae are present). However, it is perhaps still a good excuse to cover it in the discussion, while still noting that the specific Allee effect in our cage may not be representative. We have added the following text: “Nonetheless, the successful result in the cage with high release study may point to a potential field strategy for a drive that is less efficient (perhaps even one found to be less efficient in initial field tests compared to laboratory tests). If the initial release frequency of the drive is sufficiently high and widespread, then short-term high genetic load may substantially reduce the population, perhaps enough for Allee effects to become important. At this point, even if average genetic load is slowly declining without additional drive releases, persistent moderate genetic load coupled with the Allee effect may be sufficient to ensure population elimination.”

In a similar vein, the authors provide only a superficial mechanistic discussion into the fitness costs associated with drives targeting key candidate genes. The paper would benefit from a deeper discussion regarding the specific molecular functions of top-performing genes (stl, oct, nox) and how unintended Cas9 activity could disrupt their activity, integrating known molecular functions with observed fitness costs. For instance, oct encodes a G-protein coupled receptor essential for ovulation and oviduct muscle relaxation, thus disruption to the oct gene would directly impair egg-laying which would account for the observed phenotypic effects. A deeper discussion linking unintended Cas9 activity to the specific, sensitive functions of target genes would elevate the paper from a descriptive screen to a more insightful mechanistic study.

-We appreciate the reviewer’s comment. We have added a discussion to further explain fitness cost caused by unintended Cas9 activity disrupting target gene functions. However, keep in mind that the exact timing of Cas9 cleavage and the exact timing of these gene’s essential functions is still somewhat uncertain, which may limit insights from this line of analysis compared to a situation where ideal, high quality data is available for both of these. Here is the new material in the discussion:

“The functions of the top-performing genes suggests a mechanistic basis for the observed fitness costs. Aside from germline cells, nanos has expression in other ovary cells as well. CG4415 lacks this expression, but our Cas9 construct with this promoter may have a different expression pattern that the native gene, as evidenced by its support for good drive conversion in females. stl is essential for ovarian follicle development, and its disruption likely in non-germline ovary cells could compromise egg chamber development and fertility. oct encodes the octopamine β2 receptor, a G-protein coupled receptor critical for ovulation and fertilization, so if it were similarly lost, egg-laying would be directly impaired. nox, which encodes NADPH oxidase, contributes to calcium flux and smooth muscle contraction during ovulation, so its disruption may prevent egg laying. tra is needed in the whole body for sexual development, but may also play an important role in ovary function. Thus, unintended Cas9 activity at these non-germline ovary cells can directly interfere with sensitive reproductive functions, potentially explaining the fertility costs observed in drive carriers. This issue could potentially be overcome if promoters were available that were truly restricted to germline cells rather than other reproductive cells, though it remains unclear if such promoters both exist and would retain their expression pattern at a non-native locus.”

It is curious that the authors chose two genes on the X chromosome as targets. In insects (such as Drosophila here) that have heterogametic sex chromosomes, homing is not possible in the heterogametic sex as there is no chromosome to home to - so there will be no homing in males. On top of that, there is usually some fitness effect in carrier (heterozygous) females, so in a population these are nearly always bad targets for drives - unless there is some other compelling reason to choose that target?

-Our rationale for testing X-linked targets is twofold. First, these genes are likely to play important roles in sex-specific functions and may have a different expression pattern (which is why specifically Dec was included), potentially reducing fitness costs. Although homing cannot occur in males, if drive conversion at these sites in females is very high and fitness costs are minimal, the resulting genetic load could still be sufficient to suppress populations (thus, such candidates could be superior even in diploids if they happen to have a lower fitness costs). Second, X-linked targets may have broader relevance for suppression drives in haplodiploid pests (e.g., fire ants), which has the same population dynamics as an X-linked target in a diploid populations. Our results therefore could have provided useful insights for such scenarios (such as for fire ants: Liu et al., bioRxiv 2025) if drive performance was sufficient for followup testing.

Minor comments

• Enhanced clarity in the Figures and data presentation would greatly improve readability. For example, Figure 5 is critical yet difficult to interpret; consider changing x-axis labels from icons to explicit text (e.g. "biparental Cas9", "maternal cas9", "paternal Cas9"). Similarly, Figure 4 is difficult to read and the y-axis label "population size" is ambiguous; consider adding shapes or dashes (rather than relying solely on colour) and clarifying the y-axis (e.g. no. adults collected) in the legend.

-We appreciate the reviewer’s comment and have revised Figure 4 as suggested. Regarding Figure 5, we attempted to replace the icons with text labels; however, this was not possible because there is very little horizontal space and two generations to specify. Instead, we have revised the figure legend to provide a clearer explanation, which can hopefully improve clarity..

• Expand on or include a schematic to show the differences in construction between the tra-v1 and tra-v2 constructs to better contextualise the discrepancies in results (e.g. inheritance rates of 61%-66% for tra-v1 and 81%-83% for tra-v2 between the two.

-We have expanded Figure 2 to compare the constructs of tra-v1 and tra-v2. The further explanation of these two constructs was added into the result section: ‘When targeting tra, we originally tested the 4-gRNA construct tra-v1. However, the drive inheritance rate was relatively low (61%-66%), and sequencing revealed that only the middle two gRNAs were active (Table S3). Lack of cleavage at the outmost sites is particularly detrimental to achieving high drive conversion. Therefore, a second construct tra-v2 was tested that retained the two active gRNAs and included two new gRNAs. It showed substantially improved drive inheritance (81%-83%). ’

• Minor typos e.g.:

o Line 87: "form" to "from"

o Line 484: "expended" to "expanded

o Line 560: "foor" to "for"

o Line 732: "conversed" to "conserved

-We have revised these typos.

• Clarify the split drive system: the authors introduce split drive for the first time in Line 118. They should at least give a clear definition and explanation of split drive and complete drive in the introduction.

-We have included an introduction of split drive and complete drive in the introduction (line 47-53).

• Line 237-238., The fitness evaluation lacks a clear description of controls. How were non-drive flies generated and validated as controls?

-Drive heterozygotes were crossed with Cas9 homozygotes to generate the flies used for fitness evaluation. From the same cross, non-drive progeny were obtained and used as controls, ensuring they shared a comparable genetic background and rearing conditions with the drive-carrying individuals. We have now clarified in the manuscript results that “these served as the controls because they had the same environment and parents as the drive flies”.

• Line 409-412.,line 423.,The high inheritance rates of stl and oct drives are impressive; however, variation in results across Cas9 promoters should be explained further in the discussion.

-In the discussion section (lines 751-765), we included a dedicated paragraph addressing the variation observed between the nanos and CG4415 promoters. We have now expanded it to briefly note some differences:

“Our previous works showed that both nanos and CG4415 have high drive conversion rates8, but nanos failed to suppress target populations in a homing drive targeting the female fertility gene yellow-G due to its fitness cost in drive females27. CG4415 had much lower maternal deposition, which allowed the elimination of cage populations by targeting yellow-G8. Here, we tested both promoters with drives targeting oct and stl, with both showing slightly higher drive efficiency than the drive targeting yellow-G in small-scale crosses. CG4415 has slightly worse though still good performance in females, likely due to male-biased expression compared to nanos.”

• Line 414: The CG4415 promoter yielded reduced drive conversion rates in females, yet is still referred to as a promising promoter. This conclusion seems optimistic and should be clarified/more justified.

-Based on our previous study cited in this context, CG4415 shows relatively lower germline conversion rates compared to nanos, although still remaining at a high level. Importantly, CG4415 also exhibits reduced maternal deposition relative to nanos, which could help mitigate fitness costs associated with maternal deposition—an important consideration for suppression systems. Taken together, while its conversion efficiency is lower (but only slightly), the potential benefits of reduced maternal deposition and perhaps even fitness costs provide a rationale for regarding CG4415 as a promising promoter. We state this when first introducing the promoter in the “Drive efficiency assessment” results subsection.

• Specify the number of flies released, sex ratio, and cage size per generation (Line 466). This is essential for reproducibility.

-We appreciate the reviewer’s comment and have revised the text to clarify our release approach, which differed from that used in other studies (which tend to have substantial fitness differences between lines in the first generation that can complicate analysis and change results). Rather than directly releasing drive males or females into cages, we first crossed drive males with non-drive females and then mixed them with non-drive females mated to non-drive males. The offspring (including males and females) from these crosses were recorded as the G0 generation, and their ratios were recorded as release frequency. We have specified the release ratio adult numbers in the following paragraph and supplementary file.

Reviewer #2 (Significance (Required)):

Overall the manuscript presents a valuable and timely resource for gene drive research, in particular for its systematic appraisal of potential target genes for population suppression drives and its rigorous assessment of the impact of maternal Cas9 deposition. The value in the generation and empirical testing of a novel multiplexed stl-targeting gene drive that led to population eradication in a cage trial should not be understated. While several key aspects of the discussion of the manuscript should be strengthened, the study presents a meaningful contribution to the field, extending previous work and and outlines important considerations for the design and implementation of effective gene drive systems.

-We thank the reviewer for their encouraging and constructive comments. We are pleased that the systematic evaluation of target genes, the analysis of maternal Cas9 deposition, and the multiplexed stl-targeting drive were recognized as valuable contributions. We have strengthened the discussion as suggested, and we believe these revisions further enhance the manuscript as an aid for the design and implementation of future gene drive systems.

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

In this study, Xu and colleagues explored how CRISPR-based homing gene drives could be used to suppress insect populations by targeting female fertility genes in Drosophila melanogaster. They engineered split gene drives with multiplexed guide RNAs to target nine candidate genes, seeking to prevent functional resistance and achieve high drive conversion with minimal fitness costs.

Here my comments about this work:

Abstract: While the stated aim of the study on line 16 is to "maintain high drive conversion efficiency with low fitness costs in female drive carriers," the conclusion in lines 29-31 shifts focus toward the broader challenges and future optimization of gene drive systems. This conclusion does not clearly highlight the specific results of the study or how they relate directly to the original objective. It would be more effective to emphasize the actual findings, such as which target genes performed best and under what conditions, and how these findings support or contradict the stated goals. The study primarily aimed to assess the efficiency of specific female fertility genes and to evaluate strategies for minimizing the formation of functional resistance alleles, rather than proposing a protocol for optimization. Therefore, better alignment is needed between the study's aim, experimental design, and concluding statements. Clarifying this alignment would also help refine the paper's focus and more accurately communicate its contribution, including whether it is exploratory, comparative, or methodologically driven.

-We have revised the abstract to clarify the alignment as suggested by the reviewer. We note that this discrepancy is due to the initial aim of our study being different than some of the important lessons learned along the way regarding fitness effects from Cas9 deposition in split drives. Still, we agree that it would be better to be more consistent in our wording and conclusions.

Introduction: One of the key design elements in this study is the use of multiplexed gRNAs. It is reasonable to assume that this strategy may influence fitness costs, potentially in more than one way. Given that assessing fitness cost is a major focus of the study, it would be helpful to include a brief discussion of previous research examining how multiplexed gRNAs may impact fitness in gene drive systems. A short review of relevant studies, if available, would provide important context for interpreting the results and could help clarify whether any observed fitness costs might be attributed, at least in part, to the multiplexing strategy itself. This addition could be appropriately placed around line 102, where gRNA design is discussed.

-We have added an explanation in the Discussion to mention this. However, it has not been conclusively shown that multiplexed gRNAs have any effect on fitness. Indeed, there have been some multiplexed constructs that seem to have no fitness effect, and some that have high fitness costs. This doesn’t rule out the potential for multiplexed gRNAs to influence fitness itself, but it means that the mechanism may be complex. The new text reads:

“Another potential though unconfirmed source of fitness cost arises from increased cleavage events associated with multiplexed gRNAs, where the greater number of gRNAs can enhance the overall cut rate compared to single-gRNA designs.”

Line 42: Cas12a also showed efficacy using gene drives in yeast and Drosophila.

-We now mention Cas12a at the beginning of the introduction.

Line 133: The paragraph begins by stating that homologs of the target genes were identified and aligned. To improve clarity, especially for readers who are new to gene drive research, it would be helpful to begin the paragraph with a brief introductory sentence explaining the purpose of this step. For example, you could state the importance of identifying and aligning homologs to assess the conservation of target sites across species, which is critical for evaluating the broader applicability of gene drive strategies. This context would guide the reader and clarify the relevance of the analysis.

-We have added the explanation as suggested.

Lines 144-145: You mention that "the exception was tra, for which two constructs containing different gRNA sets were generated." For clarity, it would be helpful to provide a brief explanation of why two different gRNA sets were used for tra, and whether this differs from the approach taken with the other target genes. It's currently unclear whether all other genes were targeted using a single, standardized set of gRNAs, and this should be explicitly stated here for consistency, even though it is mentioned later in the plasmid construction section. Additionally, I suggest combining the sections on gRNA target design and plasmid construction. Since these components are closely related and sequential in the experimental workflow, presenting them together would improve the logical flow and help readers follow the methodology more smoothly.

-We have combined both the gRNA target design and plasmid construction sections. We also discuss the two tra constructs early in the results section (see response to reviewer 2).

Line 210: The analysis of the cage experiments was based on models from previous studies that used a simplified assumption of a single gRNA at the target site. While I understand this approach has precedent, it raises important questions about potential limitations. Specifically, could simplifying the analysis to one gRNA affect the conclusions of this study, given that the experimental design involves multiplexed gRNAs with four distinct target sites? The implications of using this simplified model should be clearly addressed, as the dynamics of drive efficiency, resistance formation, and fitness effects may differ when multiple gRNAs are employed. Additionally, while I am not a statistician, it is worth asking whether more sophisticated modeling approaches could be applied to account for all four gRNAs, rather than reducing the system to a single-gRNA framework. A discussion of the modeling choices and their potential consequences would strengthen the interpretation of the results.

-We have clarified this. While we have modeled multiple gRNAs with high fidelity in SLiM, the maximum likelihood method is not very amenable to such treatment. It may cause our fitness estimate to be a small overestimate, but give the low fitness inferences, would certainly not have a large enough effect to fundamentally change any conclusion (and should be of a consistent level across all cages). We now discuss this in the methods section.

Lines 297-300: Your results show that the expression of all target genes was higher in females, except for oct, which had higher expression in males. Additionally, oct expression decreased in adults. Given that oct is functionally important for ovulation and fertilization, processes that are primarily required in adult females, this pattern is somewhat unexpected. Could there be a possible explanation for the lower expression of oct, particularly in females and especially in adults, where its function would presumably be most critical? A brief discussion or hypothesis addressing this discrepancy would help clarify the biological relevance and interpretation of the expression data.

-Based on transcriptome data from FlyBase, derived from Graveley et al. (2011), Oct is indeed expressed slightly higher in adult males than in adult females. This difference may be attributed to the fact that the female flies used in the study were virgins; Oct expression could be upregulated post-mating to mediate ovulation. Additionally, Oct is expressed not only in reproductive tissues but also in other organs such as the nervous system, where sex-specific differences in cell type composition or neural activity may contribute to the observed expression bias. However, high expression does not necessarily correlate with essential expression. Though Oct could have multiple functions, it’s still possible that the only apparent phenotype upon knockout is female sterility. We have added the following text: “This male-biased expression may result from the use of virgin females in the dataset, as oct is likely upregulated after mating. Moreover, oct is also expressed in non-reproductive tissues such as the nervous system, which may contribute to sex-specific differences in expression38. While oct may have multiple functions, it is possible that it is only essential for female fertility.”

Lines 346-347: What is the distance between the gRNA target sites within each gene? Are all of the gRNAs confirmed to be active? It would be valuable to include a table summarizing the distance between target sites for each gene, the activity levels of the individual gRNAs, and the corresponding homing rates. This would help determine whether there is a correlation between gRNA spacing and drive efficiency. For example, Lopez del Amo et al. (Nature Communications, 2020) demonstrated that even a 20-nucleotide mismatch at each homology arm can significantly reduce drive conversion. Including such a comparative analysis in your study could provide important insights into how gRNA arrangement influences overall drive performance and would be incredibly helpful for future multiplexing designs.

-We have showed previously that close spacing of gRNAs should help maintain high drive conversion efficiency, and this is alluded to indirectly in the introduction (we now mention it more directly). In our study, gRNAs were positioned in close proximity without overlap, with the general distance between the outermost cut sites within each gene being We have added a summary table (Table S3) presenting the sequencing results, which also showed gRNA activity levels. Notably, most but not all gRNAs were active, at least for embryo resistance (low to moderate activity may still be present in the germline). Coupled with varying activity levels for those that were active, this likely contributed to reduced drive conversion due to mismatches at the homology arms. This observation supports the notion that drive performance could be optimized by selecting and arranging more active gRNAs. Consistent with this, our second construct targeting tra (tra-v2) exhibited a higher inheritance rate than the original construct, suggesting that gRNA arrangement and activity critically influence drive efficiency. Testing the activity of every single gRNA requires the construction of multiple gRNA lines, since in vitro or ex vivo tests will not be accurate as in vivo transformation test. However, in our study, as long as drive conversion rates were reasonably high, further optimization was not needed. Therefore, the multiplexing gRNA design can not only maximize drive conversion, but also reduce labor filtering an increased number of 1-gRNA designs with lower performance.

Line 434: I was not able to find any sequencing data. This is important to evaluate gRNA activities and establish correlations with drive efficiency.

-We have added a summary of the sequencing results in Table S3, though these are for embryo resistance alleles. Note that while high gRNA activity is correlated with high drive inheritance, these are not directly related. For suppression drives, germline resistance rates are usually of low importance compared to drive inheritance, so we did not assess these in detail (and pessimistically assumed complete germline resistance in our cage models).

Line 482: Did the authors test Cas9-only individuals (without the drive) against a wild-type population? This would help determine whether Cas9 alone has any unintended fitness effects. Additionally, is Cas9 expression stable over time and across generations? It would be helpful to include any observations or thoughts on the long-term stability and potential fitness impact of Cas9 in the absence of the drive element.

-We did not perform a direct comparison of Cas9-only individuals and wild-type flies in this study. However, previous studies (Champer et al., Nature Communications, 2020 - Langmuller et al., eLife 2022), which we now cite in the discussion, found no significant fitness difference between very similar Cas9-expressing lines and wild type in the absence of a drive element, indicating no significant fitness impact from Cas9 alone (though we cannot exclude a small effect, it certainly could not come close to explaining our results). In our experiments, Cas9 expression was generally stable across generations, as indicated by consistent drive inheritance and fertility test results obtained from independent batches. Separate from this study, we did observe rare instability in one nanos-Cas9 line, which had remained stable for over five years but recently became inactive (low population maintenance size may have caused stochastic removal of the functional allele). It is something to watch out for, but probably not on the timescale of a single study.

Discussion: I would appreciate a more direct and clearly stated conclusion that summarizes the key findings of the study. While the discussion addresses the main outcomes in depth, presenting a concise concluding paragraph, either at the end of the discussion or as a standalone conclusion section, would provide a stronger and more definitive closing statement. This would help reinforce what the study ultimately achieved and ensure the main takeaways are clearly communicated to the reader.

-We have revised and expanded the last paragraph of the discussion section to make our findings more direct and clear.

Overall, I believe this is an important study that offers valuable insights for advancing the design of CRISPR-based gene drives. The findings contribute to the development of more efficient and practical gene drive prototypes, bringing the field closer to real-world applications.

Reviewer #3 (Significance (Required)):

In this study, Xu and colleagues explored how CRISPR-based homing gene drives could be used to suppress insect populations by targeting female fertility genes in Drosophila melanogaster. They engineered split gene drives with multiplexed guide RNAs to target nine candidate genes, seeking to prevent functional resistance and achieve high drive conversion with minimal fitness costs. Among the targets, the stall (stl) and octopamine β2 receptor (oct) genes performed better, showing the highest inheritance rates in lab crosses. When tested in population cages, the stl drive was able to completely eliminate a fly population, but only when released at a high enough frequency, while other cages failed. These failures were traced and explained by fitness cost in drive-carrying females, caused largely by maternally deposited Cas9, which led to embryo resistance and reduced fertility. Through additional fertility assays and modeling, the team confirmed that the origin and timing of Cas9 expression, particularly from mothers, significantly impacted drive success. Surprisingly, even when Cas9 was driven by promoters with supposedly low somatic activity, such as nanos, fitness still persisted. The study revealed that while gene drives can be powerful, their effectiveness relies on finely balanced factors like promoter choice, drive architecture, and gene function. Overall, the research offers valuable lessons for designing robust, next-generation gene drives aimed at ecological pest control.

-We sincerely appreciate the reviewer’s positive and thoughtful comments. We agree that the points raised highlight the importance of our findings and hope that our revisions have further improved both the clarity and overall content of the manuscript.

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

In this study, Xu and colleagues explored how CRISPR-based homing gene drives could be used to suppress insect populations by targeting female fertility genes in Drosophila melanogaster. They engineered split gene drives with multiplexed guide RNAs to target nine candidate genes, seeking to prevent functional resistance and achieve high drive conversion with minimal fitness costs.

Here my comments about this work:

Abstract: While the stated aim of the study on line 16 is to "maintain high drive conversion efficiency with low fitness costs in female drive carriers," the conclusion in lines 29-31 shifts focus toward the broader challenges and future optimization of gene drive systems. This conclusion does not clearly highlight the specific results of the study or how they relate directly to the original objective. It would be more effective to emphasize the actual findings, such as which target genes performed best and under what conditions, and how these findings support or contradict the stated goals. The study primarily aimed to assess the efficiency of specific female fertility genes and to evaluate strategies for minimizing the formation of functional resistance alleles, rather than proposing a protocol for optimization. Therefore, better alignment is needed between the study's aim, experimental design, and concluding statements. Clarifying this alignment would also help refine the paper's focus and more accurately communicate its contribution, including whether it is exploratory, comparative, or methodologically driven.

Introduction: One of the key design elements in this study is the use of multiplexed gRNAs. It is reasonable to assume that this strategy may influence fitness costs, potentially in more than one way. Given that assessing fitness cost is a major focus of the study, it would be helpful to include a brief discussion of previous research examining how multiplexed gRNAs may impact fitness in gene drive systems. A short review of relevant studies, if available, would provide important context for interpreting the results and could help clarify whether any observed fitness costs might be attributed, at least in part, to the multiplexing strategy itself. This addition could be appropriately placed around line 102, where gRNA design is discussed.

Line 42: Cas12a also showed efficacy using gene drives in yeast and Drosophila.

Line 133: The paragraph begins by stating that homologs of the target genes were identified and aligned. To improve clarity, especially for readers who are new to gene drive research, it would be helpful to begin the paragraph with a brief introductory sentence explaining the purpose of this step. For example, you could state the importance of identifying and aligning homologs to assess the conservation of target sites across species, which is critical for evaluating the broader applicability of gene drive strategies. This context would guide the reader and clarify the relevance of the analysis.

Lines 144-145: You mention that "the exception was tra, for which two constructs containing different gRNA sets were generated." For clarity, it would be helpful to provide a brief explanation of why two different gRNA sets were used for tra, and whether this differs from the approach taken with the other target genes. It's currently unclear whether all other genes were targeted using a single, standardized set of gRNAs, and this should be explicitly stated here for consistency, even though it is mentioned later in the plasmid construction section. Additionally, I suggest combining the sections on gRNA target design and plasmid construction. Since these components are closely related and sequential in the experimental workflow, presenting them together would improve the logical flow and help readers follow the methodology more smoothly.

Line 210: The analysis of the cage experiments was based on models from previous studies that used a simplified assumption of a single gRNA at the target site. While I understand this approach has precedent, it raises important questions about potential limitations. Specifically, could simplifying the analysis to one gRNA affect the conclusions of this study, given that the experimental design involves multiplexed gRNAs with four distinct target sites? The implications of using this simplified model should be clearly addressed, as the dynamics of drive efficiency, resistance formation, and fitness effects may differ when multiple gRNAs are employed. Additionally, while I am not a statistician, it is worth asking whether more sophisticated modeling approaches could be applied to account for all four gRNAs, rather than reducing the system to a single-gRNA framework. A discussion of the modeling choices and their potential consequences would strengthen the interpretation of the results.

Lines 297-300: Your results show that the expression of all target genes was higher in females, except for oct, which had higher expression in males. Additionally, oct expression decreased in adults. Given that oct is functionally important for ovulation and fertilization, processes that are primarily required in adult females, this pattern is somewhat unexpected. Could there be a possible explanation for the lower expression of oct, particularly in females and especially in adults, where its function would presumably be most critical? A brief discussion or hypothesis addressing this discrepancy would help clarify the biological relevance and interpretation of the expression data.

Lines 346-347: What is the distance between the gRNA target sites within each gene? Are all of the gRNAs confirmed to be active? It would be valuable to include a table summarizing the distance between target sites for each gene, the activity levels of the individual gRNAs, and the corresponding homing rates. This would help determine whether there is a correlation between gRNA spacing and drive efficiency. For example, Lopez del Amo et al. (Nature Communications, 2020) demonstrated that even a 20-nucleotide mismatch at each homology arm can significantly reduce drive conversion. Including such a comparative analysis in your study could provide important insights into how gRNA arrangement influences overall drive performance and would be incredibly helpful for future multiplexing designs.

Line 434: I was not able to find any sequencing data. This is important to evaluate gRNA activities and establish correlations with drive efficiency.

Line 482: Did the authors test Cas9-only individuals (without the drive) against a wild-type population? This would help determine whether Cas9 alone has any unintended fitness effects. Additionally, is Cas9 expression stable over time and across generations? It would be helpful to include any observations or thoughts on the long-term stability and potential fitness impact of Cas9 in the absence of the drive element.

Discussion: I would appreciate a more direct and clearly stated conclusion that summarizes the key findings of the study. While the discussion addresses the main outcomes in depth, presenting a concise concluding paragraph, either at the end of the discussion or as a standalone conclusion section, would provide a stronger and more definitive closing statement. This would help reinforce what the study ultimately achieved and ensure the main takeaways are clearly communicated to the reader.

Overall, I believe this is an important study that offers valuable insights for advancing the design of CRISPR-based gene drives. The findings contribute to the development of more efficient and practical gene drive prototypes, bringing the field closer to real-world applications.

Significance

In this study, Xu and colleagues explored how CRISPR-based homing gene drives could be used to suppress insect populations by targeting female fertility genes in Drosophila melanogaster. They engineered split gene drives with multiplexed guide RNAs to target nine candidate genes, seeking to prevent functional resistance and achieve high drive conversion with minimal fitness costs. Among the targets, the stall (stl) and octopamine β2 receptor (oct) genes performed better, showing the highest inheritance rates in lab crosses. When tested in population cages, the stl drive was able to completely eliminate a fly population, but only when released at a high enough frequency, while other cages failed. These failures were traced and explained by fitness cost in drive-carrying females, caused largely by maternally deposited Cas9, which led to embryo resistance and reduced fertility. Through additional fertility assays and modeling, the team confirmed that the origin and timing of Cas9 expression, particularly from mothers, significantly impacted drive success. Surprisingly, even when Cas9 was driven by promoters with supposedly low somatic activity, such as nanos, fitness still persisted. The study revealed that while gene drives can be powerful, their effectiveness relies on finely balanced factors like promoter choice, drive architecture, and gene function. Overall, the research offers valuable lessons for designing robust, next-generation gene drives aimed at ecological pest control.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

The manuscript by Xu et al. investigated split gene drive systems by targeting multiple female essential genes involved in fertility and viability in Drosophila. The authors evaluate the suppression efficiency through individual corsses and cage trials. Resistance allele formation and fitness costs are explored by examining the sterility and fertility of each line. Overall, the experimental design is sound and methods are feasible. The work is comprehensive, and conclusions are well supported by the data. This work offers informative insights that could guide the design of suppression gene drive systems in other invasive disease vectors or agricultural pests.

However, several points requiring clarification or improvement:

1. Methodological clarity: Some experimental details are indufficiently described, for example, regarding the setup of genetic crosses involving different Cas9 derivatives. In line 197-198, "the mated females, together with females that were mated with Cas9 only males", it is unclear whether the latter group refers to gRNA-females.
2. Regarding the inheritance rates, you included the reverse orientation of CG4415-Cas9, as I understood, it means this component is in reverse orientation with fluorescent marker. Since it is standard to design adjacent components in opposite direction to avoid transcriptional interference, the rationale for including this comparison should be better justified.
3. Embryo resistance is inferred from the percentage of sterile drive females derived from drive mothers. How many female individuals were analysed per line and why deep sequencing was not employed to directly detect resistance alleles.
4. Masculinisation phenotypes were observed upon disruption of tra gene. How strong intersexes were distinguished from males? What molecular markers were used to determine genetic sex. This information should be clearly provided.
5. It would be more appropriate to use "hatchability"rather than "fertility" when referring to egg-to-larva viability.
6. In cage trials, a complete gene drive is mimicked by introducing Cas9 to the background population, but this differs from actual complete gene drive, due to potential effects from separate insertion sites (different chromosome or loci). These difference could impact the system's performance and should be discussed.
7. Given the large amount of data presented, it would improve readability and interpretation if each result section concluded with a concise summary highlighting the key findings and implications.

Significance

The authors evaluate suppression efficiency through individual courses and cage trials. Resistance allele formation and fitness costs are explored by examining the sterility and fertility of each line. Overall, the experimental design is sound and methods are feasible. The work is comprehensive, and conclusions are well supported by the data. This work offers informative insights that could guide the design of suppression gene drive systems in other invasive disease vectors or agricultural pests.

Through a case study of analyzing research publications supported by funding of the Dutch Research Council NOW this manuscript provides a concise description and assessment of a methodology based on utilizing open data sources for identifying which funded publications have been made available within so-called transformative open access agreements. The research gap that is addressed is a relevant and interesting one, as exact and measurable aspects of transformative agreements are still scarce despite the massive financial investments made into them and the breadth of research outputs that are impacted by them.

The introduction and literature review are good and appropriate at framing the context of the study and provide a thorough positioning of this study in relation to previous work in this area. I especially appreciate that the authors have included and given credit to the recently published study by Jahn (2025), and provide a clear argument for how the two studies are within the same topic area but come with different contributions.

The methods section provides a transparent description of the workflow utilized for the study, the data collection and analysis requires a few different steps and working with data on different levels (agreement, journal, article) but the provided narrative provides sufficient detail for the reader to follow the process.

The results section is the only area where I see some room for improvement in terms of presentation, the results themselves are valid and in my view interpreted correctly.  Figure 3 is a central visualization of the results of the project but it is hard to interpret independently from reading the text, and even by reading the text some aspects remain unclear. I would suggest to consider the following changes 1) make the venn-areas proportional, now the areas are not sized in relation to the data they represent (use e.g. https://www.deepvenn.com/, https://www.meta-chart.com/venn or a similar tool), 2) insert data labels and legends so that one can get a grip of what the different areas and colors of the visualization mean without reading the text, 3) revise the text that refers to the figure so that each part of the figure would be mention in consecutive order following a predictable structure. Currently only some of the lettered areas are mentioned and those which are not mentioned require quite a lot of effort from the reader to figure out.

The discussion and conclusions are good and I like that the authors are not over-selling the contribution in any way, rather being very realistic in what this method can and cannot achieve.

Overall I think this is a strong paper that provides a valuable and focused contribution to intersecting area of bibliometric research and science policy that still contains many unanswered questions due to the lack of comprehensive data, to which this study provides one more piece to the puzzle.

We would like to thank Najko Jahn and Mikael Laakso for their very positive and thoughtful reviews, which significantly improved our article. In response to the reviewers' specific comments, we have corrected all identified errors and made a number of improvements. In particular, regarding the presentation of results (Figure 3) and a more comprehensive speculation on the role of national and international infrastructure providers for unlocking article level metadata on transformative agreements. These are the changes we made in response to the reviewer’s comments:

Response to Reviewer #1: Najko Jahn

We agree that a broader discussion of transformative agreement complexity would be valuable, and this paper provides important insights—particularly regarding author opt-out possibilities, contract caps, and exclusion of non-research articles. However, we believe such discussion extends beyond the scope of this paper which we have meant primarily as a study to validate a method for tracking / analysing transformative agreements using open data.

Following the reviewer's advice, we have updated the concluding section to include discussion of how various national and international infrastructures could facilitate more open availability of article-level transformative agreement data.

We have implemented the reviewer's suggestion to present main results earlier in the paper. The key findings regarding matched and unmatched articles are now introduced on pages 3-4.

The reviewer correctly identified our imprecise description of journal identification methodology. We have corrected both the figure and text to accurately reflect our use of ISSN (obtained from Crossref) rather than ISSN-L.

Response to Reviewer #2: Mikael Laakso

We thank the reviewer for their insightful feedback. We have implemented all suggested improvements to our results presentation, enhancing the Venn diagram in Figure 3 by: 1) making areas proportional to data, 2) improving label clarity, 3) providing clearer caption explanations, and 4) revising the accompanying text to follow the figure's label sequence.

The revised version of the article is available here.

This manuscript examines preprint review services and their role in the scholarly communications ecosystem.  It seems quite thorough to me. In Table 1 they list many peer-review services that I was unaware of e.g. SciRate and Sinai Immunology Review Project.

To help elicit critical & confirmatory responses for this peer review report I am trialling Elsevier’s suggested “structured peer review” core questions, and treating this manuscript as a research article.

Introduction

1. Is the background and literature section up to date and appropriate for the topic?

   Yes.

2. Are the primary (and secondary) objectives clearly stated at the end of the introduction?

   No. Instead the authors have chosen to put the two research questions on page 6 in the methods section. I wonder if they ought to be moved into the introduction – the research questions are not methods in themselves. Might it be better to state the research questions first and then detail the methods one uses to address those questions afterwards? [as Elsevier’s structured template seems implicitly to prefer.

Methods

1. Are the study methods (including theory/applicability/modelling) reported in sufficient detail to allow for their replicability or reproducibility?

   I note with approval that the version number of the software they used (ATLAS.ti) was given.

   I note with approval that the underlying data is publicly archived under CC BY at figshare.

   The Atlas.ti report data spreadsheet could do with some small improvement – the column headers are little cryptic e.g. “Nº  ST “ and “ST” which I eventually deduced was Number of Schools of Thought and Schools of Thought (?)   

   Is there a rawer form of the data that could be deposited with which to evidence the work done? The Atlas.ti report spreadsheet seemed like it was downstream output data from Atlas.ti. What was the rawer input data entered into Atlas.ti? Can this be archived somewhere in case researchers want to reanalyse it using other tools and methods.

   I note with disapproval that Atlas.ti is proprietary software which may hinder the reproducibility of this work. Nonetheless I acknowledge that Atlas.ti usage is somewhat ‘accepted’ in social sciences despite this issue.

   I think the qualitative text analysis is a little vague and/or under-described: “Using ATLAS.ti Windows (version 23.0.8.0), we carried out a qualitative analysis of text from the relevant sites, assigning codes covering what they do and why they have chosen to do it that way.” That’s not enough detail. Perhaps an example or two could be given? Was inter-rater reliability performed when ‘assigning codes’ ? How do we know the ‘codes’ were assigned accurately?

2. Are statistical analyses, controls, sampling mechanism, and statistical reporting (e.g., P-values, CIs, effect sizes) appropriate and well described?

   This is a descriptive study (and that’s fine) so there aren’t really any statistics on show here other than simple ‘counts’ (of Schools of Thought) in this manuscript. There are probably some statistical processes going on within the proprietary qualitative analysis of text done in ATLAS.ti but it is under described and so hard for me to evaluate. 

Results

1. Is the results presentation, including the number of tables and figures, appropriate to best present the study findings?

   Yes. However, I think a canonical URL to each service should be given.  A URL is very useful for disambiguation, to confirm e.g. that the authors mean this Hypothesis (www.hypothes.is) and NOT this Hypothesis (www.hyp.io). I know exactly which Hypothesis is the one the authors are referring to but we cannot assume all readers are experts 😊

   Optional suggestion: I wonder if the authors couldn’t present the table data in a slightly more visual and/or compact way? It’s not very visually appealing in its current state. Purely as an optional suggestion, to make the table more compact one could recode the answers given in one or more of the columns 2, 3 and 4 in the table e.g. "all disciplines = ⬤ , biomedical and life sciences = ▲, social sciences =  ‡  , engineering and technology = † ". I note this would give more space in the table to print the URLs for each service that both reviewers have requested.

   ———————————————————————————————

   | Service name | Developed by | Scientific disciplines | Types of outputs |

   | Episciences | Other | ⬤ | blah blah blah. |

   | Faculty Opinions | Individual researcher | ▲ | blah blah blah. |

   | Red Team Market | Individual researcher | ‡ | blah blah blah. |

   ———————————————————————————————

   The "Types of outputs" column might even lend themselves to mini-colour-pictograms (?) which could be more concise and more visually appealing? A table just of text, might be scientifically 'correct' but it is incredibly dull for readers, in my opinion.

2. Are additional sub-analyses or statistical measures needed (e.g., reporting of CIs, effect sizes, sensitivity analyses)?

   No / Not applicable. 

Discussion

1. Is the interpretation of results and study conclusions supported by the data and the study design?

   Yes.

2. Have the authors clearly emphasized the limitations of their study/theory/methods/argument?

   No. Perhaps a discussion of the linguistic/comprehension bias of the authors might be appropriate for this manuscript. What if there are ‘local’ or regional Chinese, Japanese, Indonesian or Arabic language preprint review services out there? Would this authorship team really be able to find them?

Additional points:

• Perhaps the points made in this manuscript about financial sustainability (p24) are a little too pessimistic. I get it, there is merit to this argument, but there is also some significant investment going on there if you know where to look. Perhaps it might be worth citing some recent investments e.g. Gates -> PREreview (2024) https://content.prereview.org/prereview-welcomes-funding/  and Arcadia’s $4 million USD to COAR for the Notify Project which supports a range of preprint review communities including Peer Community In, Episciences, PREreview and Harvard Library.  (source: https://coar-repositories.org/news-updates/coar-welcomes-significant-funding-for-the-notify-project/ ) 

• Although I note they are mentioned, I think more needs to be written about the similarity and overlap between ‘overlay journals’ and preprint review services. Are these arguably not just two different terms for kinda the same thing? If you have Peer Community In which has it’s overlay component in the form of the Peer Community Journal, why not mention other overlay journals like Discrete Analysis and The Open Journal of Astrophysics.   I think Peer Community In (& it’s PCJ) is the go-to example of the thin-ness of the line the separates (or doesn’t!) overlay journals and preprint review services. Some more exposition on this would be useful.

In "Researchers Are Willing to Trade Their Results for Journal Prestige: Results from a Discrete Choice Experiment", the authors investigate researchers’ publication preferences using a discrete choice experiment in a cross-sectional survey of international health and medical researchers. The study investigates publishing decisions in relation to negotiation of trade-offs amongst various factors like journal impact factor, review helpfulness, formatting requirements, and usefulness for promotion in their decisions on where to publish. The research is timely; as the authors point out, reform of research assessment is currently a very active topic. The design and methods of the study are suitable and robust. The use of focus groups and interviews in developing the attributes for study shows care in the design. The survey instrument itself is generally very well-designed, with important tests of survey fatigue, understanding (dominant choice task) and respondent choice consistency (repeat choice task) included. Respondent performance was good or excellent across all these checks. Analysis methods (pMMNL and latent class analysis) are well-suited to the task. Pre-registration and sharing of data and code show commitment to transparency. Limitations are generally well-described.

In the below, I give suggestions for clarification/improvement. Except for some clarifications on limitations and one narrower point (reporting of qualitative data analysis methods), my suggestions are only that – the preprint could otherwise stand, as is, as a very robust and interesting piece of scientific work.

1. Respondents come from a broad range of countries (63), with 47 of those countries represented by fewer than 10 respondents. Institutional cultures of evaluation can differ greatly across nations. And we can expect variability in exposure to the messages of DORA (seen, for example, in level of permeation of DORA as measured by signatories in each country, https://sfdora.org/signers/)..%3B!!NVzLfOphnbDXSw!HdeyeHHei6yWQHFjhN3deSSfp82ur9i9JNOLEVOYZN0BvyslUO2S8DlvjBbautmafJEvlUsxQZbT0JLQX7lO8EcOYtZsJkA%24&data=05%7C02%7Ca.l.brasil.varandas.pinto%40cwts.leidenuniv.nl%7C9f47a111adec49d04bb608dd0614ae94%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C638673408085242099%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=by5mhPfSM0MFFG9LE2iiYjdtSs5IhvpuukqVv%2FLak2s%3D&reserved=0 "https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2F%2Fsfdora.org%2Fsigners%2F).%3B!!NVzLfOphnbDXSw!HdeyeHHei6yWQHFjhN3deSSfp82ur9i9JNOLEVOYZN0BvyslUO2S8DlvjBbautmafJEvlUsxQZbT0JLQX7lO8EcOYtZsJkA%24&data=05%7C02%7Ca.l.brasil.varandas.pinto%40cwts.leidenuniv.nl%7C9f47a111adec49d04bb608dd0614ae94%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C638673408085242099%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=by5mhPfSM0MFFG9LE2iiYjdtSs5IhvpuukqVv%2FLak2s%3D&reserved=0") In addition, some contexts may mandate or incentivise publication in some venues using measures including IF, but also requiring journals to be in certain databases like WoS or Scopus, or having preferred journal lists). I would suggest the authors should include in the Sampling section a rationale for taking this international approach, including any potentially confounding factors it may introduce, and then adding the latter also in the limitations.

2. Reporting of qualitative results: In the introduction and methods, the role of the focus groups and interviews seems to have been just to inform the design of the experiment. But then, results from that qualitative work then appear as direct quotes within the discussion to contextualise or explain results. In this sense though, the qualitative results are being used as new data. Given this, I feel that the methods section should include description of the methods and tools used for qualitative data analysis (currently it does not). But in addition, to my understanding (and this may be a question of disciplinary norms – I’m not a health/medicine researcher), generally new data should not be introduced in the discussion section of a research paper. Rather the discussion is meant to interpret, analyse, and provide context for the results that have already been presented. I personally hence feel that the paper would benefit from the qualitative results being reported separately within the results section.

3. Impact factors – Discussion section: While there is interesting new information on the relative trade-offs amongst other factors, the most emphasised finding, that impact factors still play a prominent role in publication venue decisions, is hardly surprising. More could perhaps be done to compare how the levels of importance reported here differ with previous results from other disciplines or over time (I know a like-for-like comparison is difficult but other studies have investigated these themes, e.g., https://doi.org/10.1177/01655515209585). In addition, beyond the question of whether impact factors are important, a more interesting question in my view is why they still persist. What are they used for and why are they still such important “driver[s] of researchers’ behaviour”? This was not the authors’ question, and they do provide some contextualisation by quoting their participants, but still I think they could do more to contextualise what is known from the literature on that to draw out the implications here. The attribute label in the methods for IF is “ranking”, but ranking according of what and for what? Not just average per-article citations in a journal over a given time frame. Rather, impact factors are used as a proxy indicators of less-tangible desirable qualities – certainly prestige (as the title of this article suggests), but also quality, trust (as reported by one quoted focus group member “I would never select a journal without an impact factor as I always publish in journals that I know and can trust that are not predatory”, p.6), journal visibility, importance to the field, or improved chances of downstream citations or uptake in news media/policy/industry etc. Picking apart the interactions of these various factors in researchers’ choices to make use of IFs (which is not in all cases bogus or unjustified) could add valuable context. I’d especially recommend engaging at least briefly with more work from Science and Technology Studies - especially Müller and de Rijcke’s excellent Thinking with Indicators study (doi: 10.1093/reseval/rvx023), but also those authors other work, as well as work from Ulrike Felt, Alex Rushforth (esp https://doi.org/10.1007/s11024-015-9274-5), Björn Hammerfelt and others.

4. Disciplinary coverage: (1) A lot of the STS work I talk about above emphasises epistemic diversity and the ways cultures of indicator use differ across disciplinary traditions. For this reason, I think it should be pointed out in the limitations that this is research in Health/Med only, with questions on generalisability to other fields. (2) Also, although the abstract and body of the article do make clear the disciplinary focus, the title does not. Hence, I believe the title should be slightly amended (e.g., “Health and Medical Researchers Are Willing to Trade …”)

This manuscript reports the results of an interesting discrete choice experiment designed to probe the values and interests that inform researchers’ decisions on where to publish their work.

Although I am not an expert in the design of discrete choice experiments, the methodology is well explained and the design of the study comes across as well considered, having been developed in a staged way to identify the most appropriate pairings of journal attributes to include.

The principal findings to my mind, well described in the abstract, include the observations that (1) researchers’ strongest preference was for journal impact factor and (2) that they were prepared to remove results from their papers if that would allow publication in a higher impact factor journal. The first of these is hardly surprising – and is consistent with a wide array of literature (and ongoing activism, e.g. through DORA, CoARA). The second is much more striking – and concerning for the research community (and its funders). This is the first time I have seen evidence for such a trade-off. 

Overall, the manuscript is very clearly written. I have no major issues with the methods or results. However, I think but some minor revisions would enhance the clarity and utility of the paper.

First, although it is made clear in Table 1 that the researchers included in the study are all from the medical and clinical sciences, this is not apparent from the title or the abstract. I think both should be modified to reflect the nature of the sample. In my experience researchers in these fields are among those who feel most intensely the pressure to publish in high IF journals. The authors may want also to reflect in a revised manuscript how well their findings may transfer to other disciplines.

Second, in several places I felt the discussion of the results could be enriched by reference to papers in the recent literature that are missing from the bibliography. These include (1) Muller and De Rijcke’s 2017 paper on Thinking with Indicators, which discusses how the pressure of metrics impacts the conduct of research (https://doi.org/10.1093/reseval/rvx023); (2) Bjorn Brembs’ analysis of the reliability of research published in prestige science journals (https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2018.00376/full; and (3) McKiernan’s et al.’s examination of the use of the Journal Impact Factor in academic review, promotion, and tenure evaluations (https://pubmed.ncbi.nlm.nih.gov/31364991/). 

Third, although the text and figures are nicely laid out, I would recommend using a smaller or different font for the figure legends to more easily distinguish them from body text.

هل تتفق معها؟

Passive Voice?

كان + فعل مضارع

نحن نعيش حياة عصرية في أمريكا.

والدي هو الشخص الأكثرنفوذا في حياتي.

حصل على رتبة عالية

منحت وسام الشرف الفرنسي.

فزت بجائزة صغيرة

مايكل فيلبس لديه الميدالية الذهبية الأولمبية في السباحة

زها حديد حصلت على جوائز وأوسمة مهمة في الهندسة المعمارية وكانت أول امرأة تفوز بجائزة بريتزكر

Questions

• إيه الدور اللي لعبته زها حديد في تغيير صورة الستات في مجال الهندسة المعمارية عالميًا؟
• كيف أثّر خبر وفاة زها حديد المفاجئ على طلابها ومشاريعها التي كانت قيد التنفيذ في ذلك الوقت؟

Heart attack

توفت السمك بأزمة قلبية

زها حديد المهندسة المعمارية المشهورة, توفت في ميامي بعد أزمة قلبية. ناس كتير من السياسيين والمعماريين عبروا عن حزنهم الكبير على خبر وفاتها. وقالوا إن إرثها هيعيش وممكن يكون إلهام للناس لسنين طويلة.

Idafa

Accusative case, direct object مفعول به منصوب بالفتحة

Masdar/Verbal noun of the verb "أصاب"

Account

عندي حساب على إنستغرام لمشاركة صوري

Inspiration

الفن مصدر إلهام للكثير من الشباب

Her legacy

ترك جدي إرثًا ثقافيًا غنيًا في القرية

Decades

تطورت التكنولوجيا كثيرا خلال العقود الماضية

Architect

المعماري صمم مبنى جديدا في وسط المدينة

اسم مفعول

مصدر

اسم مفعول

زها حديد معمارية عالمية صممت مشاريع في دول مختلفة مثل النمسا والصين وإيطاليا واذربيجان وقطر. من اهمّ اعمالها مركز الالعاب المائية في لندن وملعب الوكرة في قطر. رغم انها كان يفترض ان تُشرف على بناء الملعب الاولمبي في طوكيو ٢٠٢٠ لكن مشروعها اٌلغي لانه باهظ.

Peers

هي سعيدة عندما ترتاح مع نظرائها

Admiration

احب ان اسمع المديح عندما اعمل جيداً

Excessive

التعليم في بعض الجامعات الخاصة باهظ على معظم الناس

Ultimately

تعبنا كثير في الرحلة، لكن في نهاية المطاف استمتعنا

Ice Skating Rink

انا واصحابي لعبنا في منصة تزحلق على الجليد طول اليوم

Supposed

كان يفترض ان يغسل الاطباق امس

Designs

يوسف بدأ يشتغل على تصميمات في الفصل لالهندسة المعمارية

Reviewer #3 (Public review):

Summary:

This is an exciting, comprehensive paper that demonstrates the role of GATA4 on OA-like changes in chondrocytes. The authors present elegant reverse translational experiments that justify this mechanism and demonstrate the sufficiency of GATA4 in a mouse model of osteoarthritis (DMM), where GATA4 drove cartilage degeneration and pain in a manner that was significantly worse than DMM alone. This could pave the way for new therapies for OA that account for both structural changes and pain.

Strengths:

(1) GATA4 was identified from human chondrocytes.

(2) IHC and sequencing confirmed GATA4 presence.

(3) Activation of SMADs is clearly shown in vitro with GATA4 overexpression.

(4) The role of GATA4 was functionally assessed in vivo using the mouse DMM model, where the authors uncovered that GATA4 worsens OA structure and hyperalgesia in male mice.

(5) It is interesting that GATA4 is largely known to be found in cardiac cells and to have a role in cardiac repair, metabolism, and inflammation, among other things listed by the authors in the discussion (in liver, lung, pancreas). What could this new knowledge of GATA4 mean for OA as a potentially systemically mediated disease, where cardiac disease and metabolic syndrome are often co-morbid?

Weaknesses:

I do not have further comments. Thank you for addressing the previously mentioned concerns.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public review):

The only aspect that would benefit from further clarification is a more detailed discussion of aging-associated ECM changes in the context of prior literature. 

Thank you. Please refer to the new section (Lines 604-617)

Reviewer #3 (Public review):

(1) It would be useful to explain why GATA4 was chosen over HIF1a, which was the most differentially expressed. 

Thank you. Please refer to Lines 530-537.  

“Of note, Hypoxia-Inducible Factor 1α (HIF1 α) was the most differentially expressed gene predicted to regulate chondrocyte aging. The connection between HIF1 α and aging has been previously reported.[32] Furthermore, additional studies have investigated HIF1 in association with OA and assessed its use as a therapeutic target.[33,34] Therefore, we decided to focus on GATA4, which was less studied in chondrocytes but highly associated with cellular senescence, an aging hallmark. However, our selection did not dampen the importance of HIF1α and other molecules listed in Figure 1D in chondrocyte aging. They can be further studied in the future using the same strategy employed in the current work.”

(2) In Figure 5, it would be useful to demonstrate the non-surgical or naive limbs to help contextualize OARSI scores and knee hyperalgesia changes. 

In the current study, we focused on the DMM control and DMM Gata4 virus groups so we did not include a sham control group. We recognized this was a limitation of this study.  

(3) While there appear to be GATA4 small-molecule inhibitors in various stages of development that could be used to assess the effects in age-related OA, those experiments are out of scope for the current study.  

We agree with this comment that the results are still preliminary, which was the reason that we put it in the supplementary materials. However, we felt like the result is informative, which will support the potential of GATA4 as a therapeutic target and inspire the development of more specific inhibitors. Therefore, we would still keep the results in the current study.

Reviewer #3 (Public review):

The manuscript by Rios-Jimenez developed a software tool, BEHAV3D Tumor Profiler, to analyze 3D intravital imaging data and identify distinctive tumor cell migratory phenotypes based on the quantified 3D image data. Moreover, the heterogeneity module in this software tool can correlate the different cell migration phenotypes with variable features of the tumor microenvironment. Overall, this is a useful tool for intravital imaging data analysis and its open-source nature makes it accessible to all interested users.

Strengths:

An open-source software tool that can quantify cell migratory dynamics from intravital imaging data and identify distinctive migratory phenotypes that correlate with variable features of the tumor microenvironment.

Weaknesses:

Motility is the main tumor cell feature analyzed in the study together with some other tumor-intrinsic features, such as morphology. However, these features are insufficient to characterize and identify the heterogeneity of the tumor cell population that impacts their behaviors in the complex tumor microenvironment (TME). For instance, there are important non-tumor cell types in the TME, and the interaction dynamics of tumor cells with other cell types, e.g., fibroblasts and distinct immune cells, play a crucial role in regulating tumor behaviors. BEHAV3D-TP focuses on analysis of tumor-alone features, and cannot be applied to analyze important cell-cell interaction dynamics in 3D.

Reviewer #1 (Public review):

Summary:

This manuscript uses molecular dynamics simulations to understand how forces felt by the intracellular domain are coupled to opening of the mechanosensitive ion channel NOMPC. The concept is interesting - as the only clearly defined example of an ion channel that opens due to forces on a tethered domain, the mechanism by which this occur are yet to be fully elucidated. The main finding is that twisting of the transmembrane portion of the protein - specifically via the TRP domain that is conserved within the broad family of channels- is required to open the pore. That this could be a common mechanism utilised by a wide range of channels in the family, not just mechanically gated ones, makes the result significant. It is intriguing to consider how different activating stimuli can produce a similar activating motion within this family. While the authors do not see full opening of the channel, only an initial dilation, this motion is consistent with partial opening of structurally characterized members of this family.

Strengths:

Demonstrating that rotation of the TRP domain is the essential requirement for channel opening would have significant implcaitions for other members of this channel family.

Weaknesses:

The manuscript centres around 3 main computational experiments. In the first, a compression force is applied on a truncated intracellular domain and it is shown that this creates both a membrane normal (compression) and membrane parallel (twisting) force on the TRP domain. This is a point that was demonstrated in the authors prior eLife paper - so the point here is to quantify these forces for the second experiment.

The second experiment is the most important in the manuscript. In this, forces are applied directly to two residues on the TRP domain with either a membrane normal (compression) or membrane parallel (twisting) direction, with the magnitude and directions chosen to match that found in the first experiment. Only the twisting force is seen to widen the pore in the triplicate simulations, suggesting that twisting, but not compression can open the pore. This result is intriguing and there appears to be a significant difference between the dilation of pore with the two force directions. When the forces are made of similar magnitude, twisting still has a larger effect than forces along the membrane normal.

The second important consideration is that the study never sees full pore opening, rather a widening that is less than that seen in open state structures of other TRP channels and insufficient for rapid ion currents. This is something the authors acknowledge in their prior manuscript Twist may be the key to get this dilation, but we don't know if it is the key to full pore opening. Structural comparison to open state TRP channels supports that this represents partial opening along the expected pathway of channel gating.

Experiment three considers the intracellular domain and determines the link between compression and twisting of the intracellular AR domain. In this case, the end of the domain is twisted and it is shown that the domain compresses, the converse to the similar study previously done by the authors in which compression of the domain was shown to generate torque.

Reviewer #3 (Public review):

Summary:

This manuscript by Duan and Song interrogates the gating mechanisms and specifically force transmission in mechanosensitive NOMPC channels using steered molecular dynamics simulations. They propose that the ankyrin spring can transmit force to the gate through torsional forces adding molecular detail to the force transduction pathways in this channel.

Strengths:

Detailed, rigorous simulations coupled with a novel model for force transduction.

Weaknesses:

Experimental validation of reduced mechanosensitivity through mutagenesis of proposed ankyrin/TRP domain coupling interactions would greatly enhance the manuscript.

3 degree of freedom mechanism

Reviewer #1 (Public review):

Summary:

The manuscript "Synaptotagmin 1 and Synaptotagmin 7 promote MR1-mediated presentation of Mycobacterium tuberculosis antigens", authored by Kim et al., showed that the calcium-sensing trafficking proteins Synaptotagmin (Syt) 1 and Syt7 specifically promote (are critical for) MAIT cell activation in response to Mtb-infected bronchial epithelial cell line BEAS-2B (Fig. 1) and monocyte-like cell line THP-1 (Figure 3) . This work also showed co-localization of Syt1 and Syt7 with Rab7a and Lamp1, but not with Rab5a (Figure 5). Loss of Syt1 and Syt7 resulted in a larger area of MR1 vesicles (Figure 6f) and an increased number of MR1 vesicles in close proximity to an Auxotrophic Mtb-containing vacuoles during infection (Figure 7ab). Moreover, flow organellometry was used to separate phagosomes from other subcellular fractions and identify enrichment of auxotrophic Mtb-containing vacuoles in fractions 42-50, which were enriched with Lamp1+ vacuoles or phagosomes (Figures 7e-f).

Strengths:

This work nicely associated Syt1 and Syt7 with late endocytic compartments and Mtb+ vacuoles. Gene editing of Syt1 and Syt7 loci of bronchial epithelial and monocyte-like cells supported Syt1 and Syt7 facilitated maintaining a normal level of antigen presentation for MAIT cell activation in Mtb infection. Imaging analyses further supported that Syt1 and Syt7 mutants enhanced the overlaps of MR1 with Mtb fluorescence, and the MR1 proximity with Mtb-infected vacuoles, suggesting that Syt1 and Syt7 proteins help antigen presentation in Mtb infection for MAIT activation.

Weaknesses:

Additional data are needed to support the conclusion, "identify a novel pathway in which Syt1 and Syt7 facilitate the translocation of MR1 from Mtb-containing vacuoles" and some pieces of other evidence may be seen by some to contradict this conclusion.

Reviewer #3 (Public review):

Summary:

In the submitted manuscript, the authors investigate the role of Synaptotagmins (Syt1) and (Syt7) in MR1 presentation of MtB.

Strengths:

In the first series of experiments, the authors determined that knocking down Syt1 and Sy7 in antigen-presenting cells decreases IFN-γ production following cellular infection with Mtb. These experiments are well performed and controlled.

Weaknesses:

Next, they aim to mechanistically investigate how Syt1 and Syt7 affect MtB presentation. In particular, they focus on MR1, a non-classical MHC-I molecule known to present endogenous and exogenous metabolites, including MtB metabolites.

Results from these next series of experiments are less clear. Firstly, they show that knocking down Syt1 and Sy7 does not change MtB phagocytosis as well as MR1 ER-plasma membrane translocation. Based on this, they suggest that Syt1 and Syt7 may affect MR1 trafficking in endosomal compartments. However, neither subcellular compartment analysis nor flow organelleometry clearly establishes the role of Syt1 and Syt7 in MtB trafficking.

Altogether, the notion that Synaptotagmins facilitate MR1 interaction with Mtb-containing compartments and its vesicular transport was already known. As such, the manuscript should add additional insight on where/how the interaction occurs. The reviewer is left with the notion that Syt1 and Sy7 may affect MR1 presentation, facilitating the trafficking of MR1 vesicles from endosomal compartments to either the cell surface or other endosomal compartments. The analysis is observational and additional data or discussion could address what the insight gained beyond what is already known from the literature.

Reviewer #1 (Public review):

This study by Thapliyal and Glauser investigates the neural mechanisms that contribute to the progressive suppression of thermonociceptive behavior that is induced under conditions of starvation. Several previous studies have demonstrated that when starved, C. elegans alters its preferences for a variety of sensory cues, including CO2, temperature, and odors, in order to prioritize food seeking over other behavioral drives. The varied mechanisms that underlie the ability of internal states to alter behavioral responses are not fully understood, however there is growing evidence for a role by neuropeptidergic signaling as well as capacity for functionally distinct microcircuits, formed by distinct internal states, to trigger similar behavior outcomes.

Within the physiological range of C. elegans (~15-25C), starvation triggers a profound reduction in temperature-driven thermotaxis behaviors. This reduction involves the recruitment of the amphid sensory neuron pair AWC. The AWC neurons primarily act to sense appetitive chemosensory cues, however under starvation conditions begin to display temperature responses that previous studies have linked to the reduction in thermotaxis navigation. Here, Thapliyal and Glauser investigate the impact of starvation on thermonociceptive responses, innate escape behaviors that are triggered by exposure to noxious temperatures above 26C or rapid thermal stimuli below 26C. They compare the strength of thermonociceptive behaviors, specifically heat-triggered reversals, in worms experiencing either early food deprivation (1 hour off food) or prolonged starvation (6 hours off food). Their experiments demonstrate a progressive loss of heat-triggered reversals that is mediated by AWC and ASI neurons, as well as both glutamateric and neuropeptidergic signaling.

At the level of neural activity, this study reports that the transition from early food deprivation to prolonged starvation reconfigures the temperature-driven activity of AWC neurons from largely deterministic to stochastic. This finding is interesting in light of previous work that reported the opposite transition (from stochastic to deterministic) in temperature-driven AWC responses when comparing well-fed worms to those kept from food for 3 hours. This study also identifies neural and genetic mechanisms that contribute to differences in thermonociceptive responses at +1 versus +6 hours starvation; confusingly, these mechanisms are partially distinct from those that contribute to differences in negative thermotaxis behaviors in well-fed and +3 hours starvation worms (Takeishi et al 2020). A limitation of this manuscript is that these differences are not particularly acknowledged or addressed, other than the hypothesis that independent mechanisms underlie negative thermotaxis versus thermonociceptive stimuli. However, this suggestion is not experimentally verified. Multiple additional aspects of this study make the results difficult to synthesize with existing knowledge, including 1) differences in - and insufficient discussion of - the magnitude and kinetics of thermal stimuli; 2) this study's use of "heating power" rather than temperature values when presenting behavioral results; 3) the use of +1 hours starvation as a baseline instead of well-fed worms. Indeed, this last point reflects a noticeable experimental result that differs from previous studies, namely that at room temperature the basal movements of well-fed and starved worms are not different. Such a surprisingly result warrants further quantification of worm mobility in general and could have prompted a set of experiments directly testing previously published thermal conditions, to demonstrate that the new effects reported arise specifically from the use of thermonociceptive stimuli, as hypothesized. Finally, a previous report (Yeon et al 2021) demonstrated differences in the impact of chronic versus acute neural silencing on starvation-dependent plasticity in the context of negative thermotaxis. We therefore wonder whether similar developmental compensation impacts the neural circuits that contribute to starvation-dependent plasticity in the thermonociceptive responses.

A weakness of this manuscript is that the introduction is insufficiently scholarly in terms of citations and the description of current knowledge surrounding the impact of internal state on sensory behavior, particularly given previous work on the impact of feeding state on thermosensory behavioral plasticity (Takeshi et al 2020, Yeon et al 2021) and chemosensory valence (Banerjee et al 2023, Rengarajan et al 2019, etc). Similarly, the authors commanding knowledge of the distinction between thermotaxis navigation (especially negative thermotaxis) and thermonociceptive behaviors could be communicated in more depth and clarity to the readers, in order to contextualize this study's new findings within the previous literature.

Nevertheless, this study represents a solid addition to the growing evidence that C. elegans sensory behaviors are strongly impacted by internal states, and that neuropeptigergic signaling plays a key role in mediating behavioral plasticity. To that end, the authors have provided solid evidence of their claims.

Reviewer #2 (Public review):

In this work Thapliyal and Glauser tried to provide mechanistic understanding by which animals modulate their neural circuit responses to control nociceptive behavior on the basis of the dynamic internal feeding state. It is an important study that adds to growing body of evidences coming from multiple model systems. They have used elegant genetics, behavioral and Ca-imaging experiments to demonstrate how the auxiliary thermosensory neuron pair, AWC and one of the internal state sensing interneuron pair, ASI, respond to dynamic internal starvation-state to modulate behavioral response to noxious heat. Interestingly, these neuron pairs use distinct molecular mechanisms along with some other unidentified neurons to suppress heat-indued reversal response under short-term and prolonged starvations. The experiments are well performed that support most of the claims and provide important framework for future studies.

I have some queries that if answered, will certainly enhance the study,

(1) The results suggests that ASI is one of the primary drivers for the starvation-evoked behavioral plasticity, which regulates AWC activity under prolonged starvation. It raises many important questions including, a) how starvation modulates ASI response to heat? b) under prolonged starvation, whether ASI also promotes other, non-AWC, glutamatergic inhibitory neurons to suppress heat-induced reversal and how?

(2) How does ASI regulate AWC activity? In the proposed model (figure 8) authors suggested an independent, unknown signal, other than INS-32 and NLP-18, from ASI to regulate AWC activity. However, from the results the existence of another signal is not very clear.

(3) Previously, Takeishi et. al., showed that ins-1 dynamically modulates AWC-AIA mediated thermotaxis behavior based on the feeding state of the animal. It raises questions whether ins-1 also contributes to noxious heat-induced reversal behavior.

(4) Experiments with AWC fate conversion mutants (nsy-1 and nsy-7) were very good ideas, however the results obtained were confusing. flp-6 mutant data suggests AWCoff would be essential for heat induced reversal, especially at the low intensity stimulus level. However, nsy-1 mutant forming two AWCon neurons showed complete rescue at the low heat level, which is quite opposite. Similarly, although less prominent, eat-4 rescue experiments suggested both nsy-1 and nsy-7 should behave normally at high heat condition, which was not the result observed.

Reviewer #3 (Public review):

Summary:

Thapliyal and Glauser show that hunger alters how C. elegans respond to noxious thermal stimuli. Using targeted neural ablation, mutant analysis, and live-cell functional imaging the authors demonstrate that hunger changes the properties of AWC sensory neurons, which sense noxious heat. The authors further show that effects of hunger on nociception require ASI neurons, which are known to respond to hunger and mediate effects of food deprivation on behavior. Finally, the study uses mutant analysis to implicate glutamate and specific neuropeptides in thermal nociception and in modulation of nociceptors by hunger-responsive neurons.

Strengths:

The study clearly shows a strong effect of hunger on nociception and documents a striking effect of hunger on the intrinsic properties of AWC sensory neurons, which respond to noxious heat. The study also clearly and compellingly demonstrates that ablation of hunger-responsive ASI neurons blocks effects of hunger on nociceptive AWCs. These data, which constitute the kernel of the manuscript, are striking and exciting.

Weaknesses:

The study has some weaknesses that the authors should address.

(1) Ablation of AWC neurons alters the basal sensitivity to noxious heat stimuli. This should be clearly noted in the description of the result and warrants some discussion.

(2) Throughout the study it seems that data are replotted in multiple figure panels. The authors should clearly indicate in figure legends when this occurs. Also, the authors should ensure that statistical tests requiring multiple comparisons are correctly implemented and reflect the number of times experimental data are compared to a single set of control data.

(3) How ASIs modulate AWCs remains unclear. The authors find that loss of INS-6, an insulin-like peptide provided by ASIs, partially recapitulates the effect of ASI ablation. This is observation is not further developed and instead the authors characterize other secreted factors that seem to mediate sensitization of animals to noxious heat stimuli. While it is interesting that there are multiple opposing inputs into the nociceptor circuit, the essential connection between ASIs and AWCs that underlies the foundational observations in figures 1 and 2 is not sufficiently characterized.

(4) The assertion that 'starvation reshapes AWC responses from deterministic to stochastic' is not clearly supported by the data. AWC neurons seem capable of showing different responses to thermal stimuli, and the probabilities associated with these responses change after fasting. The different kinds of responses are seen under basal and fasted conditions.

• 1. O eventual acolhimento do pedido na ação de usucapião acarreta perda patrimonial imediata, ou seja, perda da propriedade do imóvel, gerando enorme prejuízo para os credores da massa falida. Assim, deve-se reconhecer a competência do juízo universal da falência para apreciar demandas dessa natureza.
• 2. "A arrecadação é ato de apreensão judicial executiva que visa à guarda e conservação dos bens do falido para futura alienação, em benefício dos credores. Sendo assim, nada mais coerente que todas as questões relacionadas aos bens arrecadados sejam decididas pelo juízo falimentar." (CC 84.752/RN, Rel. Ministra NANCY ANDRIGHI, SEGUNDA SEÇÃO, julgado em 27/06/2007, DJ 01/08/2007, p. 433)
• 3. Conflito de competência não conhecido em relação aos Juízos da 16ª e 17ª Varas Cíveis de Brasília/DF e, quanto ao incidente suscitado em face do Juízo da 11ª Vara Cível de Brasília/DF e do Tribunal de Justiça do Distrito Federal e dos Territórios, conflito conhecido para declarar a competência do Juízo da 11ª Vara Cível de Goiânia/GO. (CC n. 114.842/GO, relator Ministro Luis Felipe Salomão, Segunda Seção, julgado em 25/2/2015, DJe de 3/3/2015.)

1. Nos termos da jurisprudência consolidada pela Segunda Seção do STJ, no julgamento do CC 114.842/GO, em 25/02/2015, eventual acolhimento do pedido na ação de usucapião acarreta perda patrimonial imediata, ou seja, perda da propriedade do imóvel, gerando enorme prejuízo para os credores da massa falida, devendo ser reconhecida a competência do juízo universal da falência para apreciar demandas dessa natureza. (AgInt no REsp n. 2.004.910/CE, relatora Ministra Nancy Andrighi, Terceira Turma, julgado em 28/11/2022, DJe de 30/11/2022.)

Reviewer #1 (Public review):

Summary:

In this article, the authors develop a method to re-analyze published data measuring the transcription dynamics of developmental genes within Drosophila embryos. Using a simple framework, they identify periods of transcriptional activity from traces of MS2 signal and analyze several parameters of these traces. In the five data sets they analyzed, the authors find that each transcriptional "burst" has a largely invariant duration, both across spatial positions in the embryo and across different enhancers and genes, while the time between transcriptional bursts varies more. However, they find that the best predictor of the mean transcription levels at different spatial positions in the embryo is the "activity time" -- the total time from the first to the last transcriptional burst in the observed cell cycle.

Strengths:

(1) The algorithm for analyzing the MS2 transcriptional traces is clearly described and appropriate for the data.

(2) The analysis of the four transcriptional parameters -- the transcriptional burst duration, the time between bursts, the activity time, and the polymerase loading rate is clearly done and logically explained, allowing the reader to observe the different distributions of these values and the relationship between each of these parameters and the overall expression output in each cell. The authors make a convincing case that the activity time is the best predictor of a cell's expression output.

(3) The figures are clearly presented and easy to follow.

Weaknesses:

(1) The strength of the relationship between the different transcriptional parameters and the mean expression output is displayed visually in Figures 5 and 7, but is not formally quantified. Given that the tau_off times seem more correlated to mean activity for some enhancers (e.g., rho) than others (e.g., sna SE), the quantification might be useful.

(2) There are some mechanistic details that are not discussed in depth. For example, the authors observe that the accumulation and degradation of the MS2 signal have similar slopes. However, given that the accumulation represents the transcription of MS2 loops, while the degradation represents diffusion of nascent transcripts away from the site of transcription, there is no mechanistic expectation for this. The degradation of signal seems likely to be a property of the mRNA itself, which shouldn't vary between cells or enhancer reporters, but the accumulation rate may be cell- or enhancer-specific. Similarly, the activity time depends both on the time of transcription onset and the time of transcription cessation. These two processes may be controlled by different transcription factor properties or levels and may be interesting to disentangle.

(3) There are previous analyses of the eve stripe dynamics, which the authors cite, but do not compare the results of their work to the previous work in depth.

Reviewer #3 (Public review):

Summary:

In this paper, the authors developed a simple algorithm to analyse live imaging transcription data (MS2) and infer various kinetic parameters. They then applied it to analyse data from previous publications on Drosophila that measured the dynamics of reporter genes driven by various enhancers alone (sna, Kr, rho), or in an endogenous context (eve).

The authors find that the main correlate with mean gene expression levels is the activity time, that is, the time during which the gene is bursting. They also find a correlation with the variation of the off time.

Strengths:

(1) The findings are very clearly presented.

(2) The simplicity of the algorithm is nice, and the comparative analysis among the various enhancers can be helpful for the field.

Weaknesses:

(1) The algorithm is not benchmarked against previously used algorithms in the field to infer ON and OFF times, for example, those based on Hidden Markov models. A comparison would help strengthen the support for this algorithm (if it really works well) or show at which point one must be careful when interpreting this data.

(2) More broadly, the novelty of the findings and how those fit within the knowledge of the field is not super clear. A better account of previous findings that have already quantified ON, OFF times and so on, and how the current findings fit within those, would help better appreciate the significance of the work.

Author response:

Reviewer #1 (Public review):

(1) The strength of the relationship between the different transcriptional parameters and the mean expression output is displayed visually in Figures 5 and 7, but is not formally quantified. Given that the tau_off times seem more correlated to mean activity for some enhancers (e.g., rho) than others (e.g., sna SE), the quantification might be useful.

We re-plot Figure 5 and Figure 7 to present the correlation between the studied burst parameters. As the reviewer suggested, after quantifying the correlation we can better study the correlation between the cells averaged tau-off and the cell-averaged fluorescence signal in some of the selected enhancers. As a result of these findings we decide to change our message and instead of claiming that the burst statistics are homogeneous over the embryo domain, to claim that these statistics have weak but significant correlations with the cell-averaged mean gene fluorescence.  

(2) There are some mechanistic details that are not discussed in depth. For example, the authors observe that the accumulation and degradation of the MS2 signal have similar slopes. However, given that the accumulation represents the transcription of MS2 loops, while the degradation represents diffusion of nascent transcripts away from the site of transcription, there is no mechanistic expectation for this. The degradation of signal seems likely to be a property of the mRNA itself, which shouldn't vary between cells or enhancer reporters, but the accumulation rate may be cell- or enhancer-specific. Similarly, the activity time depends both on the time of transcription onset and the time of transcription cessation. These two processes may be controlled by different transcription factor properties or levels and may be interesting to disentangle.

The accumulation slope represents the rate of nascent transcript production, which depends on transcription initiation frequency and RNA polymerase elongation rate. While transcription initiation rates can vary between enhancers, our results show that the loading rates are relatively comparable across different enhancer sequences (Figure 5D). Instead, the primary difference observed was in activity time and burst frequency, consistent with previous findings that enhancers predominantly modulate burst frequency (Fukaya et al., 2016). The degradation slope represents the diffusion of completed transcripts away from the transcription site, which should be an intrinsic property of the mRNA molecule and therefore independent of the regulatory sequences driving transcription.

(3) There are previous analyses of the eve stripe dynamics, which the authors cite, but do not compare the results of their work to the previous work in depth.

The goal of this manuscript is to compare transcriptional bursting properties across different enhancers, rather than to provide an in-depth analysis of eve stripe dynamics specifically. We analyzed four transgenic constructs with different enhancers alongside an endogenous eve construct, focusing on comparative bursting parameters rather than detailed eve expression patterns. Additionally, the previously published eve stripe dynamics data came from BAC constructs, whereas our data comes from the endogenous eve locus. This methodological difference makes direct comparison of stripe dynamics less straightforward and less relevant to our central research question about enhancer-driven bursting variability.

Reviewer #2 (Public review):

(1) The manuscript does not clearly delineate how this analysis extends beyond the prior landmark study (citation #40: Fukaya et al., 2016). While the current manuscript offers new modeling and statistics, more explicit clarification of what is novel in terms of biological conclusions and methodological advancement would help position the work.

The prior study (Fukaya et al., 2016) characterized transcriptional bursting qualitatively, focusing on average burst properties per nucleus without systematic mathematical modeling or statistical analysis of burst-to-burst variability. While they demonstrated that enhancer strength correlates with burst frequency, no quantitative framework was developed to dissect the molecular mechanisms underlying these differences or to connect burst dynamics to spatial gene expression patterns.

(1) We developed an explicit mathematical model with rigorous inference algorithms to quantify transcriptional states from fluorescence trajectories; (2) We performed comprehensive statistical analysis of burst timing distributions, revealing that inter-burst intervals follow exponential distributions while burst durations are hypo-exponentially distributed; (3) Most importantly, we discovered that burst kinetics (τON, τOFF) remain remarkably consistent across different genes and spatial locations, while spatial expression gradients arise primarily through modulation of activity time - the temporal window during which bursting occurs. This mechanistic insight reveals that enhancers regulate spatial patterning not by changing intrinsic burst properties, but by controlling the duration of transcriptionally permissive periods.

(2) While the methods are explained in detail in the Supplementary Information, the manuscript would benefit from including a diagrammatic model and explicitly clarifying whether the model is descriptive or predictive in scope.

We plan to prepare the diagrammatic model in the formal response. 

(3) The interpretation that fluorescence decay reflects RNA degradation could be confounded by polymerase runoff or transcript diffusion from the transcription site. These potential limitations are not thoroughly discussed. (Write few lines in the discussion)

This concern, related to the interpretation of the predictive model will be addressed in a future work. The decay in the fluorescence signal can be biologically related to the transcription termination, polymerase detachment, and diffusion. A key limitation of the approach is that the model is phenomenological and does not these capture processes that can be addressed with a more mechanistic model.

(4) The so-called loading rate is used as an empirical parameter in fitting fluorescence traces, but is not convincingly linked to distinct biological processes. The manuscript would benefit from a more precise definition or reframing of this term.

We modify the language of our definition of loading rate as follows: “Loading rate is defined as the rate of increase of fluorescence signal following promoter activation. This quantity is a proxy measurement for the rate of RNA Polymerase II transcription initiation.” The full transcription process has multiple mechanisms including chromatin dynamics, 3D enhancer-promoter interactions, transcription factor binding, mRNA polymerase pausing, and interactions between developmental promoter motifs and associated proteins. We did not have access to specific measurements of these mechanisms and therefore cannot provide a solid biological meaning of the model behind the inference algorithm. However, the fact that we have reproducible results in biological replicas can support the robustness of our method at predicting the promoter state in the studied datasets. In the formal response we will compare the performance of our method with other available ones.

Reviewer #3 (Public review):

(1)The algorithm is not benchmarked against previously used algorithms in the field to infer ON and OFF times, for example, those based on Hidden Markov models. A comparison would help strengthen the support for this algorithm (if it really works well) or show at which point one must be careful when interpreting this data.

We are implementing a benchmarking protocol to compare our results with the proposed and already published models. We expect to present this comparison in the formal response.

(2) More broadly, the novelty of the findings and how those fit within the knowledge of the field is not super clear. A better account of previous findings that have already quantified ON, OFF times and so on, and how the current findings fit within those, would help better appreciate the significance of the work.

To have a better clarity of the new findings we modified the title from “Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development” to “Temporal Duration of Gene Activity is the main Regulator of Spatial Expression Patterns in Early Drosophila Embryos”.

In short, (1) We developed an explicit mathematical model with rigorous inference algorithms to quantify transcriptional states from fluorescence trajectories; (2) We performed comprehensive statistical analysis of burst timing distributions, revealing that inter-burst intervals follow exponential distributions while burst durations are hypo-exponentially distributed; (3) Most importantly, we discovered that burst kinetics (τON, τOFF) remain remarkably consistent across different genes and spatial locations, while spatial expression gradients arise primarily through modulation of activity time - the temporal window during which bursting occurs. This mechanistic insight reveals that enhancers regulate spatial patterning not by changing intrinsic burst properties, but by controlling the duration of transcriptionally permissive periods.

Reviewer #1 (Public review):

Summary:

The authors state the study's goal clearly: "The goal of our study was to understand to what extent animal individuality is influenced by situational changes in the environment, i.e., how much of an animal's individuality remains after one or more environmental features change." They use visually guided behavioral features to examine the extent of correlation over time and in a variety of contexts. They develop new behavioral instrumentation and software to measure behavior in Buridan's paradigm (and variations thereof), the Y-maze, and a flight simulator. Using these assays, they examine the correlations between conditions for a panel of locomotion parameters. They propose that inter-assay correlations will determine the persistence of locomotion individuality.

Strengths:

The OED defines individuality as "the sum of the attributes which distinguish a person or thing from others of the same kind," a definition mirrored by other dictionaries and the scientific literature on the topic. The concept of behavioral individuality can be characterized as: (1) a large set of behavioral attributes, (2) with inter-individual variability, that are (3) stable over time. A previous study examined walking parameters in Buridan's paradigm, finding that several parameters were variable between individuals, and that these showed stability over separate days and up to 4 weeks (DOI: 10.1126/science.aaw718). The present study replicates some of those findings, and extends the experiments from temporal stability to examining correlation of locomotion features between different contexts.

The major strength of the study is using a range of different behavioral assays to examine the correlations of several different behavior parameters. It shows clearly that the inter-individual variability of some parameters is at least partially preserved between some contexts, and not preserved between others. The development of high-throughput behavior assays and sharing the information on how to make the assays is a commendable contribution.

Weaknesses:

The definition of individuality considers a comprehensive or large set of attributes, but the authors consider only a handful. In Supplemental Fig. S8, the authors show a large correlation matrix of many behavioral parameters, but these are illegible and are only mentioned briefly in Results. Why were five or so parameters selected from the full set? How were these selected? Do the correlation trends hold true across all parameters? For assays in which only a subset of parameters can be directly compared, were all of these included in the analysis, or only a subset?

The correlation analysis is used to establish stability between assays. For temporal re-testing, "stability" is certainly the appropriate word, but between contexts it implies that there could be 'instability'. Rather, instead of the 'instability' of a single brain process, a different behavior in a different context could arise from engaging largely (or entirely?) distinct context-dependent internal processes, and have nothing to do with process stability per se. For inter-context similarities, perhaps a better word would be "consistency".

The parameters are considered one-by-one, not in aggregate. This focuses on the stability/consistency of the variability of a single parameter at a time, rather than holistic individuality. It would appear that an appropriate measure of individuality stability (or individuality consistency) that accounts for the high-dimensional nature of individuality would somehow summarize correlations across all parameters. Why was a multivariate approach (e.g. multiple regression/correlation) not used? Treating the data with a multivariate or averaged approach would allow the authors to directly address 'individuality stability', along with the analyses of single-parameter variability stability.

The correlation coefficients are sometimes quite low, though highly significant, and are deemed to indicate stability. For example, in Figure 4C top left, the % of time walked at 23{degree sign}C and 32{degree sign}C are correlated by 0.263, which corresponds to an R2 of 0.069 i.e. just 7% of the 32{degree sign}C variance is predictable by the 23{degree sign}C variance. Is it fair to say that 7% determination indicates parameter stability? Another example: "Vector strength was the most correlated attention parameter... correlations ranged... to -0.197," which implies that 96% (1 - R2) of Y-maze variance is not predicted by Buridan variance. At what level does an r value not represent stability?

The authors describe a dissociation between inter-group differences and inter-individual variation stability, i.e. sometimes large mean differences between contexts, but significant correlation between individual test and retest data. Given that correlation is sensitive to slope, this might be expected to underestimate the variability stability (or consistency). Is there a way to adjust for the group differences before examining correlation? For example, would it be possible to transform the values to in-group ranks prior to correlation analysis?

What is gained by classifying the five parameters into exploration, attention, and anxiety? To what extent have these classifications been validated, both in general, and with regard to these specific parameters? Is increased walking speed at higher temperature necessarily due to increased 'explorative' nature, or could it be attributed to increased metabolism, dehydration stress, or a heat-pain response? To what extent are these categories subjective?

The legends are quite brief and do not link to descriptions of specific experiments. For example, Figure 4a depicts a graphical overview of the procedure, but I could not find a detailed description of this experiment's protocol.

Using the current single-correlation analysis approach, the aims would benefit from re-wording to appropriately address single-parameter variability stability/consistency (as distinct from holistic individuality). Alternatively, the analysis could be adjusted to address the multivariate nature of individuality, so that the claims and the analysis are in concordance with each other.

The study presents a bounty of new technology to study visually guided behaviors. The Github link to the software was not available. To verify successful transfer or open-hardware and open-software, a report would demonstrate transfer by collaboration with one or more other laboratories, which the present manuscript does not appear to do. Nevertheless, making the technology available to readers is commendable.

The study discusses a number of interesting, stimulating ideas about inter-individual variability, and presents intriguing data that speaks to those ideas, albeit with the issues outlined above.

While the current work does not present any mechanistic analysis of inter-individual variability, the implementation of high-throughput assays sets up the field to more systematically investigate fly visual behaviors, their variability, and their underlying mechanisms.

Comments on revisions:

While the incorporation of a hierarchical mixed model (HMM) appears to represent an improvement over their prior single-parameter correlation approach, it's not clear to me that this is a multivariate analysis. They write that "For each trait, we fitted a hierarchical linear mixed-effects model in Matlab (using the fit lme function) with environmental context as a fixed effect and fly identity (ID) as a random intercept... We computed the intraclass correlation coefficient (ICC) from each model as the between-fly variance divided by total variance. ICC, therefore, quantified repeatability across environmental contexts."

Does this indicate that HMM was used in a univariate approach? Can an analysis of only five metrics of several dozen total metrics be characterized as 'holistic'?

Within Figure 10a, some of the metrics show high ICC scores, but others do not. This suggests that the authors are overstating the overall persistence and/or consistency of behavioral individuality. It is clear from Figure S8 that a large number of metrics were calculated for each fly, but it remains unclear, at least to me, why the five metrics in Figure 10a are justified for selection. One is left wondering how rare or common is the 0.6 repeatability of % time walked among all the other behavioral metrics. It appears that a holistic analysis of this large data set remains impossible.

The authors write: "...fly individuality persists across different contexts, and individual differences shape behavior across variable environments, thereby making the underlying developmental and functional mechanisms amenable to genetic dissection." However, presumably the various behavioral features (and their variability) are governed by different brain regions, so some metrics (high ICC) would be amenable to the genetic dissection of individuality/variability, while others (low ICC) would not. It would be useful to know which are which, to define which behavioral domains express individuality, and could be targets for genetic analysis, and which do not. At the very least, the Abstract might like to acknowledge that inter-context consistency is not a major property of all or most behavioral metrics.

I hold that inter-trial repeatability should rightly be called "stability" while inter-context repeatability should be called "consistency". In the current manuscript, "consistency" is used throughout the manuscript, except for the new edits, which use "stability". If the authors are going to use both terms, it would be preferable if they could explain precisely how they define and use these terms.

Reviewer #2 (Public review):

Summary:

The authors repeated measured the behavior of individual flies across several environmental situations in custom-made behavioral phenotyping rigs.

Strengths:

The study uses several different behavioral phenotyping devices to quantify individual behavior in a number of different situations and over time. It seems to be a very impressive amount of data. The authors also make all their behavioral phenotyping rig design and tracking software available, which I think is great and I'm sure other folks will be interested in using and adapting to their own needs.

Weaknesses/Limitations:

I think an important limitation is that while the authors measured the flies under different environmental scenarios (i.e. with different lighting, temperature) they didn't really alter the "context" of the environment. At least within behavioral ecology, context would refer to the potential functionality of the expressed behaviors so for example, an anti-predator context, or a mating context, or foraging. Here, the authors seem to really just be measuring aspects of locomotion under benign (relatively low risk perception) contexts. This is not a flaw of the study, but rather a limitation to how strongly the authors can really say that this demonstrates that individuality is generalized across many different contexts. It's quite possible that rank-order of locomotor (or other) behaviors may shift when the flies are in a mating or risky context.

I think the authors are missing an opportunity to use much more robust statistical methods. It appears as though the authors used pearson correlations across time/situations to estimate individual variation; however far more sophisticated and elegant methods exist. The problem is that pearson correlation coefficients can be anti-conservative and additionally, the authors have thus had to perform many many tests to correlate behaviors across the different trials/scenarios. I don't see any evidence that the authors are controlling for multiple testing which I think would also help. Alternatively, though, the paper would be a lot stronger, and my guess is, much more streamlined if the authors employ hierarchical mixed models to analyse these data, which are the standard analytical tools in the study of individual behavioral variation. In this way, the authors could partition the behavioral variance into its among- and within-individual components and quantify repeatability of different behaviors across trials/scenarios simultaneously. This would remove the need to estimate 3 different correlations for day 1 & day 2, day 1 & 3, day 2 & 3 (or stripe 0 & stripe 1, etc) and instead just report a single repeatability for e.g. the time spent walking among the different strip patterns (eg. figure 3). Additionally, the authors could then use multivariate models where the response variables are all the behaviors combined and the authors could estimate the among-individual covariance in these behaviors. I see that the authors state they include generalized linear mixed models in their updated MS, but I struggled a bit to understand exactly how these models were fit? What exactly was the response? what exactly were the predictors (I just don't understand what Line404 means "a GLM was trained using the environmental parameters as predictors (0 when the parameter was not change, 1 if it was) and the resulting individual rank differences as the response"). So were different models run for each scenario? for different behaviors? Across scenarios? what exactly? I just harp on this because I'm actually really interested in these data and think that updating these methods can really help clarify the results and make the main messages much clearer!

I appreciate that the authors now included their sample sizes in the main body of text (as opposed to the supplement) but I think that it would still help if the authors included a brief overview of their design at the start of the methods. It is still unclear to me how many rigs each individual fly was run through? Were the same individuals measured in multiple different rigs/scenarios? Or just one?

I really think a variance partitioning modeling framework could certainly improve their statistical inference and likely highlight some other cool patterns as these methods could better estimate stability and covariance in individual intercepts (and potentially slopes) across time and situation. I also genuinely think that this will improve the impact and reach of this paper as they'll be using methods that are standard in the study of individual behavioral variation

Author response:

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

Reviewer #1 (Public review):  

Summary:  

The authors state the study's goal clearly: "The goal of our study was to understand to what extent animal individuality is influenced by situational changes in the environment, i.e., how much of an animal's individuality remains after one or more environmental features change." They use visually guided behavioral features to examine the extent of correlation over time and in a variety of contexts. They develop new behavioral instrumentation and software to measure behavior in Buridan's paradigm (and variations thereof), the Y-maze, and a flight simulator. Using these assays, they examine the correlations between conditions for a panel of locomotion parameters. They propose that inter-assay correlations will determine the persistence of locomotion individuality.

Strengths:  

The OED defines individuality as "the sum of the attributes which distinguish a person or thing from others of the same kind," a definition mirrored by other dictionaries and the scientific literature on the topic. The concept of behavioral individuality can be characterized as: (1) a large set of behavioral attributes, (2) with inter-individual variability, that are (3) stable over time. A previous study examined walking parameters in Buridan's paradigm, finding that several parameters were variable between individuals, and that these showed stability over separate days and up to 4 weeks (DOI: 10.1126/science.aaw718). The present study replicates some of those findings and extends the experiments from temporal stability to examining correlation of locomotion features between different contexts.  

The major strength of the study is using a range of different behavioral assays to examine the correlations of several different behavior parameters. It shows clearly that the inter-individual variability of some parameters is at least partially preserved between some contexts, and not preserved between others. The development of high-throughput behavior assays and sharing the information on how to make the assays is a commendable contribution.

Weaknesses:  

The definition of individuality considers a comprehensive or large set of attributes, but the authors consider only a handful. In Supplemental Fig. S8, the authors show a large correlation matrix of many behavioral parameters, but these are illegible and are only mentioned briefly in Results. Why were five or so parameters selected from the full set? How were these selected? Do the correlation trends hold true across all parameters? For assays in which only a subset of parameters can be directly compared, were all of these included in the analysis, or only a subset?  

The correlation analysis is used to establish stability between assays. For temporal re-testing, "stability" is certainly the appropriate word, but between contexts it implies that there could be 'instability'. Rather, instead of the 'instability' of a single brain process, a different behavior in a different context could arise from engaging largely (or entirely?) distinct context-dependent internal processes, and have nothing to do with process stability per se. For inter-context similarities, perhaps a better word would be "consistency".  

The parameters are considered one-by-one, not in aggregate. This focuses on the stability/consistency of the variability of a single parameter at a time, rather than holistic individuality. It would appear that an appropriate measure of individuality stability (or individuality consistency) that accounts for the high-dimensional nature of individuality would somehow summarize correlations across all parameters. Why was a multivariate approach (e.g. multiple regression/correlation) not used? Treating the data with a multivariate or averaged approach would allow the authors to directly address 'individuality stability', along with the analyses of single-parameter variability stability.

The correlation coefficients are sometimes quite low, though highly significant, and are deemed to indicate stability. For example, in Figure 4C top left, the % of time walked at 23{degree sign}C and 32{degree sign}C are correlated by 0.263, which corresponds to an R2 of 0.069 i.e. just 7% of the 32{degree sign}C variance is predictable by the 23{degree sign}C variance. Is it fair to say that 7% determination indicates parameter stability? Another example: "Vector strength was the most correlated attention parameter... correlations ranged... to -0.197," which implies that 96% (1 - R2) of Y-maze variance is not predicted by Buridan variance. At what level does an r value not represent stability?

The authors describe a dissociation between inter-group differences and inter-individual variation stability, i.e. sometimes large mean differences between contexts, but significant correlation between individual test and retest data. Given that correlation is sensitive to slope, this might be expected to underestimate the variability stability (or consistency). Is there a way to adjust for the group differences before examining correlation? For example, would it be possible to transform the values to in-group ranks prior to correlation analysis?

What is gained by classifying the five parameters into exploration, attention, and anxiety? To what extent have these classifications been validated, both in general, and with regard to these specific parameters? Is increased walking speed at higher temperature necessarily due to increased 'explorative' nature, or could it be attributed to increased metabolism, dehydration stress, or a heat-pain response? To what extent are these categories subjective?

The legends are quite brief and do not link to descriptions of specific experiments. For example, Figure 4a depicts a graphical overview of the procedure, but I could not find a detailed description of this experiment's protocol.

Using the current single-correlation analysis approach, the aims would benefit from re-wording to appropriately address single-parameter variability stability/consistency (as distinct from holistic individuality). Alternatively, the analysis could be adjusted to address the multivariate nature of individuality, so that the claims and the analysis are in concordance with each other.

The study presents a bounty of new technology to study visually guided behaviors. The Github link to the software was not available. To verify successful transfer or open-hardware and open-software, a report would demonstrate transfer by collaboration with one or more other laboratories, which the present manuscript does not appear to do. Nevertheless, making the technology available to readers is commendable.

The study discusses a number of interesting, stimulating ideas about interindividual variability and presents intriguing data that speaks to those ideas, albeit with the issues outlined above.

While the current work does not present any mechanistic analysis of interindividual variability, the implementation of high-throughput assays sets up the field to more systematically investigate fly visual behaviors, their variability, and their underlying mechanisms.  

Comments on revisions:  

I want to express my appreciation for the authors' responsiveness to the reviewer feedback. They appear to have addressed my previous concerns through various modifications including GLM analysis, however, some areas still require clarification for the benefit of an audience that includes geneticists.  

(1) GLM Analysis Explanation (Figure 9)  

While the authors state that their new GLM results support their original conclusions, the explanation of these results in the text is insufficient. Specifically:

The interpretation of coefficients and their statistical significance needs more detailed explanation. The audience includes geneticists and other nonstatistical people, so the GLM should be explained in terms of the criteria or quantities used to assess how well the results conform with the hypothesis, and to what extent they diverge.

The criteria used to judge how well the GLM results support their hypothesis are not clearly stated.

The relationship between the GLM findings and their original correlationbased conclusions needs better integration and connection, leading the reader through your reasoning.

We thank the reviewer for highlighting this important point. We have revised the Results section in the reviseed manuscript to include a more detailed explanation of the GLM analysis. Specifically, we now clarify the interpretation of the model coefficients, including the direction and statistical significance, in relation to the hypothesized effects. We also outline the criteria we used to assess how well the GLM supports our original correlation-based conclusions—namely, whether the sign and significance of the coefficients align with the expected relationships derived from our prior analysis. Finally, we explicitly describe how the GLM results confirm or extend the patterns observed in the correlation-based analysis, to guide readers through our reasoning and the integration of both approaches.

(2) Documentation of Changes  

One struggle with the revised manuscript is that no "tracked changes" version was included, so it is hard to know exactly what was done. Without access to the previous version of the manuscript, it is difficult to fully assess the extent of revisions made. The authors should provide a more comprehensive summary of the specific changes implemented, particularly regarding:

We thank the reviewer for bringing this to our attention. We were equally confused to learn that the tracked-changes version was not visible, despite having submitted one to eLife as part of our revision. 

Upon contacting the editorial office, they confirmed that we did submit a trackedchanges version, but clarified that it did not contain embedded figures (as they were added manually to the clean version).  The editorial response said in detail: “Regarding the tracked-changes file: it appears the version with markup lacked figures, while the figure-complete PDF had markup removed, which likely caused the confusion mentioned by the reviewers.” We hope this answer from eLife clarifies the reviewers’ concern.

(2)  Statistical Method Selection  

The authors mention using "ridge regression to mitigate collinearity among predictors" but do not adequately justify this choice over other approaches. They should explain:

Why ridge regression was selected as the optimal method  

How the regularization parameter (λ) was determined  

How this choice affects the interpretation of environmental parameters' influence on individuality

We appreciate the reviewer’s thoughtful question regarding our choice of statistical method. In response, we have expanded the Methods section in the revised manuscript to provide a more detailed justification for the use of a GLM, including ridge regression. Specifically, we explain that ridge regression was selected to address collinearity and to control for overfitting.

We now also describe how the regularization parameter (λ) was selected: we used 5-fold cross-validation over a log-spaced grid (10^⁻⁶ - 10<sup>⁶</sup) to identify the optimal value that minimized the mean squared error (MSE).

Finally, we clarify in both the Methods and Results sections how this modeling choice affects the interpretation of our findings. 

Reviewer #2 (Public review):  

Summary:  

The authors repeatedly measured the behavior of individual flies across several environmental situations in custom-made behavioral phenotyping rigs.

Strengths:  

The study uses several different behavioral phenotyping devices to quantify individual behavior in a number of different situations and over time. It seems to be a very impressive amount of data. The authors also make all their behavioral phenotyping rig design and tracking software available, which I think is great, and I'm sure other folks will be interested in using and adapting to their own needs.

Weaknesses/Limitations:  

I think an important limitation is that while the authors measured the flies under different environmental scenarios (i.e. with different lighting, temperature) they didn't really alter the "context" of the environment. At least within behavioral ecology, context would refer to the potential functionality of the expressed behaviors so for example, an anti-predator context, or a mating context, or foraging. Here, the authors seem to really just be measuring aspects of locomotion under benign (relatively low risk perception) contexts. This is not a flaw of the study, but rather a limitation to how strongly the authors can really say that this demonstrates that individuality is generalized across many different contexts. It's quite possible that rank-order of locomotor (or other) behaviors may shift when the flies are in a mating or risky context.  

I think the authors are missing an opportunity to use much more robust statistical methods It appears as though the authors used pearson correlations across time/situations to estimate individual variation; however far more sophisticated and elegant methods exist. The problem is that pearson correlation coefficients can be anti-conservative and additionally, the authors have thus had to perform many many tests to correlate behaviors across the different trials/scenarios. I don't see any evidence that the authors are controlling for multiple testing which I think would also help. Alternatively, though, the paper would be a lot stronger, and my guess is, much more streamlined if the authors employ hierarchical mixed models to analyse these data, which are the standard analytical tools in the study of individual behavioral variation. In this way, the authors could partition the behavioral variance into its among- and within-individual components and quantify repeatability of different behaviors across trials/scenarios simultaneously. This would remove the need to estimate 3 different correlations for day 1 & day 2, day 1 & 3, day 2 & 3 (or stripe 0 & stripe 1, etc) and instead just report a single repeatability for e.g. the time spent walking among the different strip patterns (eg. figure 3). Additionally, the authors could then use multivariate models where the response variables are all the behaviors combined and the authors could estimate the among-individual covariance in these behaviors. I see that the authors state they include generalized linear mixed models in their updated MS, but I struggled a bit to understand exactly how these models were fit? What exactly was the response? what exactly were the predictors (I just don't understand what Line404 means "a GLM was trained using the environmental parameters as predictors (0 when the parameter was not changed, 1 if it was) and the resulting individual rank differences as the response"). So were different models run for each scenario? for different behaviors? Across scenarios? What exactly? I just harp on this because I'm actually really interested in these data and think that updating these methods can really help clarify the results and make the main messages much clearer!

I appreciate that the authors now included their sample sizes in the main body of text (as opposed to the supplement) but I think that it would still help if the authors included a brief overview of their design at the start of the methods. It is still unclear to me how many rigs each individual fly was run through? Were the same individuals measured in multiple different rigs/scenarios? Or just one?

I really think a variance partitioning modeling framework could certainly improve their statistical inference and likely highlight some other cool patterns as these methods could better estimate stability and covariance in individual intercepts (and potentially slopes) across time and situation. I also genuinely think that this will improve the impact and reach of this paper as they'll be using methods that are standard in the study of individual behavioral variation

Reviewer #3 (Public review):  

This manuscript is a continuation of past work by the last author where they looked at stochasticity in developmental processes leading to inter-individual behavioural differences. In that work, the focus was on a specific behaviour under specific conditions while probing the neural basis of the variability. In this work, the authors set out to describe in detail how stable individuality of animal behaviours is in the context of various external and internal influences. They identify a few behaviours to monitor (read outs of attention, exploration, and 'anxiety'); some external stimuli (temperature, contrast, nature of visual cues, and spatial environment); and two internal states (walking and flying).

They then use high-throughput behavioural arenas - most of which they have built and made plans available for others to replicate - to quantify and compare combinations of these behaviours, stimuli, and internal states. This detailed analysis reveals that:

(1) Many individualistic behaviours remain stable over the course of many days.  

(2) That some of these (walking speed) remain stable over changing visual cues. Others (walking speed and centrophobicity) remain stable at different temperatures.

(3) All the behaviours they tested fail to remain stable over spatially varying environment (arena shape).

(4) and only angular velocity (a read out of attention) remains stable across varying internal states (walking and flying)

Thus, the authors conclude that there is a hierarchy in the influence of external stimuli and internal states on the stability of individual behaviours.

The manuscript is a technical feat with the authors having built many new high-throughput assays. The number of animals are large and many variables have been tested - different types of behavioural paradigms, flying vs walking, varying visual stimuli, different temperature among others.  

Comments on revisions:'  

The authors have addressed my previous concerns.  

We thank the reviewer for the positive feedback and are glad our revisions have satisfactorily addressed the previous concerns. We appreciate the thoughtful input that helped us improve the clarity and rigor of the manuscript.

Reviewer #1 (Recommendations for the authors):  

Comment on Revised Manuscript  

Recommendations for Improvement  

(1) Expand the Results section for Figure 9 with a more detailed interpretation of the GLM coefficients and their biological significance

(2) Provide explicit criteria (or at least explain in detail) for how the GLM results confirm or undermine their original hypothesis about environmental context hierarchy

While the claims are interesting, the additional statistical analysis appears promising. However, clearer explanation of these new results would strengthen the paper and ensure that readers from diverse backgrounds can fully understand how the evidence supports the authors' conclusions about individuality across environmental contexts. 

We thank the reviewer for these constructive suggestions. In response to these suggestions, we have expanded both the Methods and Results sections to provide a more detailed explanation of the GLM coefficients, including their interpretation and how they relate to our original correlation-based findings.

We now clarify how the direction, magnitude, and statistical significance of specific coefficients reflect the influence of different environmental factors on the persistence of individual behavioral traits. To make this accessible to readers from diverse backgrounds, we explicitly outline the criteria we used to evaluate whether the GLM results support our hypothesis about the hierarchical influence of environmental context, namely, whether the structure and strength of effects align with the patterns predicted from our prior correlation analysis.

These additions improve clarity and help readers understand how the new statistical results reinforce our conclusions about the context-dependence of behavioral individuality.

Reviewer #2 (Recommendations for the authors):  

Thanks for the revision of the paper! I updated my review to try and provide a little more guidance by what I mean about updating your analyses. I really think this is a super cool data set and I genuinely wish this were MY dataset so that way I could really dig into it to partition the variance. These variance partitioning methods are standard in my particular subfield (study of individual behavioral variation in ecology and evolution) and so I think employing them is 1) going to offer a MUCH more elegant and holistic view of the behavioral variation (e.g. you can report a single repeatability estimate for each behavior rather than 3 different correlations) and 2) improve the impact and readership for your paper as now you'll be using methods that a whole community of researchers are very familiar with. It's just a suggestion, but I hope you consider it!

We sincerely thank the reviewer for the insightful and encouraging feedback and for introducing us to this modeling approach. In response to this suggestion, we have incorporated a hierarchical linear mixed-effects model into our analysis (now presented in Figure 10), accompanied by a new supplementary table (Table T3). We also updated the Methods, Results, and Discussion sections to describe the rationale, implementation, and implications of the mixed-model analysis.

We agree with the reviewer that this approach provides a more elegant way to quantify behavioral variation and individual consistency across contexts. In particular, the ability to estimate repeatability directly aligns well with the core questions of our study. It facilitates improved communication of our findings to ecology, evolution, and behavior researchers. We greatly appreciate the suggestion; it has significantly strengthened both the analytical framework and the interpretability of the manuscript.

Reviewer #1 (Public review):

Summary:

In this manuscript, Mahajan et.al introduce two innovative macroscopic measures-intrachromosomal gene correlation length (𝓁∗) and transition energy barrier-to investigate chromatin structural dynamics associated with aging and age-related syndromes such as Hutchinson-Gilford Progeria Syndrome (HGPS) and Werner Syndrome (WRN). The authors propose a compelling systems-level approach that complements traditional biomarker-driven analyses, offering a more holistic and quantitative framework to assess genome-wide dysregulation. The concept of 𝓁∗ as a spatial correlation metric to capture chromatin disorganization is novel and well-motivated. The use of autocorrelation on distance-binned gene expression adds depth to the interpretation of chromatin state shifts. The energy landscape framework for gene state transitions is an elegant abstraction, with the notion of "irreversibility" providing a thermodynamic interpretation of transcriptional dysregulation. The application to multiple datasets (Fleischer, Line-1) and pathological states adds robustness to the analysis. The consistency of chromosome 6 (and to some extent chromosomes 16 and X) emerging as hotspots aligns well with known histone cluster localization and disease-relevant pathways. The manuscript does an excellent job of integrating transcriptomic trends with known epigenetic hallmarks of aging, and the proposed metrics can be used in place of traditional techniques like PCA in capturing structural transcriptome features. However, a direct correlation with ATACseq/ HiC data with the present analysis will be more informative.

Strengths:

Novel inclusion of statistical metrics that can help in systems-level studies in aging and chromatin biology.

Weaknesses:

(1) In the manuscript, the authors mention "While it may be intuitive to assume that highly expressed genes originate from euchromatin, this cannot be conclusively stated as a complete representation of euchromatin genes, nor can LAT be definitively linked to heterochromatin". What percentage of LAT can be linked to heterochromatin? What is the distribution of LAT and HAT in the euchromatin?

(2) In Figure 2, the authors observe "that the signal from the HAT class is the stronger between two and the signal from the LAT class, being mostly uniform, can be constituted as background noise." Is this biologically relevant? Are low-abundance transcripts constitutively expressed? The authors should discuss this in the Results section.

(3) The authors make a very interesting observation from Figure 3: that ASO-treated LINE-1 appears to be more effective in restoring HGPS cell lines closer to wild-type compared to WRN.. This can be explained by the difference in the basal activity of L1 elements in the HGPS vs WRN cell types. The authors should comment on this.

(4) The authors report that "from the results on Fleicher dataset is the magnitude of the difference in similarity distance is more pronounced in 𝓁∗ than in gene expression." Does this mean that the alterations in gene distance and chromatin organization do not result in gene expression change during aging?

(5) "In Fleischer dataset, as evident in Figure 4a, although changes in the heterochromatin are not identical for all chromosomes shown by the different degrees of variation of 𝓁∗ in each age group." The authors should present a comprehensive map of each chromosome change in gene distance to better explain the above statement.

(6) While trends in 𝓁∗ are discussed at both global and chromosome-specific levels, stronger statistical testing (e.g., permutation tests, bootstrapping) would lend greater confidence, especially when differences between age groups or treatment states are modest.

(7) While the transition energy barrier is an insightful conceptual addition, further clarification on the mathematical formulation and its physical assumptions (e.g., energy normalization, symmetry conditions) would improve interpretability. Also, in between Figures 7 and 8, the authors first compare the energy barrier of Chromosome 1 and then for all other chromosomes. What is the rationale for only analyzing chromosome 1? How many HAT or LAT are present there?

Reviewer #2 (Public review):

The authors report that intra-chromosomal gene correlation length (spatial correlations in gene expressions along the chromosome) serves as a proxy of chromatin structure and hence gene expression. They further explore changes in these metrics with aging. These are interesting and important findings. However, there are fundamental problems at this time.

(1) The basic method lacks validation. There is no validation of the method by approaches that directly measure chromatin structure, for example ATAC-seq, ChIP-seq, or CUT n RUN.

(2) There is no validation by interventions that directly probe chromatin structure, such as HDAC inhibitors. The authors employ datasets with knockdown of LINE-1 for validation. However, this is not a specific chromatin intervention.

(3) There is no statistical analysis, e.g., in Figures 4 and 5.

(4) The authors state, "in Figure 4a changes in the heterochromatin are not identical for all chromosomes shown...." I do not see the data for individual chromosomes.

(5) In comparisons of WT vs HGPS NT or HGPS SCR (Figure S6), is this a fair comparison? The WT and HGPS are presumably from different human donors, so they have genetic and epigenetic differences unrelated to HGPS.

Author response:

Reviewer #1 (Public review):

Summary:

In this manuscript, Mahajan et. al. introduce two innovative macroscopic measures-intrachromosomal gene correlation length (𝓁∗) and transition energy barrier-to investigate chromatin structural dynamics associated with aging and age-related syndromes such as Hutchinson-Gilford Progeria Syndrome (HGPS) and Werner Syndrome (WRN). The authors propose a compelling systems-level approach that complements traditional biomarker-driven analyses, offering a more holistic and quantitative framework to assess genome-wide dysregulation. The concept of 𝓁∗ as a spatial correlation metric to capture chromatin disorganization is novel and well-motivated. The use of autocorrelation on distance-binned gene expression adds depth to the interpretation of chromatin state shifts. The energy landscape framework for gene state transitions is an elegant abstraction, with the notion of "irreversibility" providing a thermodynamic interpretation of transcriptional dysregulation. The application to multiple datasets (Fleischer, Line-1) and pathological states adds robustness to the analysis. The consistency of chromosome 6 (and to some extent chromosomes 16 and X) emerging as hotspots aligns well with known histone cluster localization and disease-relevant pathways. The manuscript does an excellent job of integrating transcriptomic trends with known epigenetic hallmarks of aging, and the proposed metrics can be used in place of traditional techniques like PCA in capturing structural transcriptome features. However, a direct correlation with ATACseq/HiC data with the present analysis will be more informative.

(1) In the manuscript, the authors mention "While it may be intuitive to assume that highly expressed genes originate from euchromatin, this cannot be conclusively stated as a complete representation of euchromatin genes, nor can LAT be definitively linked to heterochromatin". What percentage of LAT can be linked to heterochromatin? What is the distribution of LAT and HAT in the euchromatin?

Thank you for this insightful question. In the revision we will add chromatin state annotations using ChromHMM to identify overlap between HAT/LAT and corresponding chromatin state. This should provide the specific percentages and distributions you requested.

We would like to take this opportunity to clarify that based on the plots Fig S1, and differential gene expressions, HAT is most likely a subset of euchromatin and LAT may contain both euchromatin and heterochromatin. The HAT/LAT cutoff occurs around the knee point in the log-log plot (Figure S1), where the linear portion indicates scale-invariant behavior with similar relative changes across expression ranks. The non-linear portion represents departure from power-law scaling, where low-expression genes exhibit sharper decline than expected. This suggests potential biological mechanisms such as chromatin silencing, detection limits, or technical artifacts related to sequencing depth.

We will provide detailed chromatin state analysis in the revision. For reference, HAT gene lists per chromosome are available in our GitHub repository at: https://github.com/altoslabs/papers-2025-rnaseq-chrom-aging/tree/main/data/Preprocessed_dat a under /<dataset>/chromosome_{}/data_hi.

(2) In Figure 2, the authors observe "that the signal from the HAT class is the stronger between two and the signal from the LAT class, being mostly uniform, can be constituted as background noise." Is this biologically relevant? Are low-abundance transcripts constitutively expressed? The authors should discuss this in the Results section.

We apologize for the confusion arising from the usage of the term “background noise”. We agree that the distinction between high-abundance transcripts (HATs) and low-abundance transcripts (LATs) deserves more explicit discussion in the Results.

Our intention is to say that HAT has a higher signal-to-noise ratio (SNR) compared to LAT. This is coming from the power law graph of FigS1.  Our intention is to state that the HAT class provides a strong, robust signal, consistent across chromosomes and the LAT class exhibits lower SNR and a more uniform background-like distribution in the context of the problem we are solving and not rather a generic biological statement. The experiment result that led to this statement is presented in FigS3. This does not imply that low-abundance transcripts lack biological relevance, but rather that they contribute less to the spatial organization patterns we measure.

(3) The authors make a very interesting observation from Figure 3: that ASO-treated LINE-1 appears to be more effective in restoring HGPS cell lines closer to wild-type compared to WRN.. This can be explained by the difference in the basal activity of L1 elements in the HGPS vs WRN cell types. The authors should comment on this.

We thank the reviewer for this incisive biological observation. While the differential effectiveness of ASO-treated LINE-1 in HGPS versus WRN cell lines is indeed an interesting phenomenon that may relate to basal L1 activity differences, this biological mechanism falls outside the scope of our current study.

Our paper focuses on demonstrating that the 𝓁∗ metric can sensitively detect chromatin structural changes that have been independently validated. We utilize the Della Valle et al. (2022) dataset specifically because it provides experimentally confirmed chromatin structural differences (Progeroid vs wild-type vs ASO-treated Progeriod), allowing us to validate that 𝓁∗ correlates with these established changes.

For detailed discussion of the biological mechanisms underlying differential LINE-1 ASO effectiveness between progeroid syndromes, we would direct readers to Della Valle et al. (2022) and related LINE-1 biology literature. Our contribution lies in demonstrating that 𝓁∗ can capture these chromatin organizational changes with enhanced sensitivity compared to traditional expression-based approaches. We are reluctant, without further experimentation, to venture into over-interpreting these results from a biology perspective.  

(4) The authors report that "from the results on Fleischer dataset is the magnitude of the difference in similarity distance is more pronounced in 𝓁∗ than in gene expression." Does this mean that the alterations in gene distance and chromatin organization do not result in gene expression change during aging?

Thank you for this important clarification request. This observation, illustrated in Figure 3, highlights two key points: (1) 𝓁∗ shows similar trends to PCA analysis, and (2) 𝓁∗ demonstrates higher sensitivity than traditional gene expression analysis.

This enhanced sensitivity enables better discrimination between aging states, particularly in the Fleischer dataset representing natural aging where changes are more gradual. The higher sensitivity stems from 𝓁∗'s ability to capture transcriptional spatial organization through spatial autocorrelation, which can detect subtle organizational changes that may precede or accompany expression changes rather than replacing them.

We will clarify in the revision that chromatin organizational changes and gene expression changes are complementary rather than mutually exclusive phenomena during aging.

(5) "In Fleischer dataset, as evident in Figure 4a, although changes in the heterochromatin are not identical for all chromosomes shown by the different degrees of variation of 𝓁∗ in each age group." The authors should present a comprehensive map of each chromosome change in gene distance to better explain the above statement.

Thank you for the feedback. If we understand your comment correctly, we need to provide a chromosome-wise distribution for Fig3c. We will update the paper and the supplementary.

(6) While trends in 𝓁∗ are discussed at both global and chromosome-specific levels, stronger statistical testing (e.g., permutation tests, bootstrapping) would lend greater confidence, especially when differences between age groups or treatment states are modest.

Thank you for the helpful suggestion. In the revision, we will incorporate permutation-based significance testing by shuffling the gene annotation and count table to generate a null distribution for our 𝓁∗ calculation. This will allow us to more rigorously assess whether the observed differences across age groups or treatment states deviate from chance expectations and thereby lend greater statistical confidence to our findings.

(7) While the transition energy barrier is an insightful conceptual addition, further clarification on the mathematical formulation and its physical assumptions (e.g., energy normalization, symmetry conditions) would improve interpretability. Also, in between Figures 7 and 8, the authors first compare the energy barrier of Chromosome 1 and then for all other chromosomes.

What is the rationale for only analyzing chromosome 1? How many HAT or LAT are present there?

Regarding chromosome 1 focus: we initially presented chromosome 1 as a representative example, but we will include energy landscape analysis for all chromosomes in the supplementary materials

We use the same HATs that were extracted during 𝓁∗ for the energy landscape as well. The HAT details are present in the github repo, the link provided in response to 1st feedback.

The normalization of the energy barrier ensures comparability across chromosomes of different sizes and across samples with different absolute expression scales. Specifically, we normalize with respect to the total area under the two-dimensional energy landscape while using the thermal energy (k_B T) as a scaling factor to place transition energy barriers on the scale of thermal fluctuations. This is formally expressed as in Eq. (1). 

The physical consequences of symmetry in the energy landscape are discussed in lines 472-491 of the manuscript, where we also introduce the concept of irreversibility. In brief, the chromatin energy landscape (Figure 8) is constructed by quantifying the energy contributions of genes that are upregulated (lower triangular matrix) and downregulated (upper triangular matrix) between two states. If the integrated energy contributions of upregulated and downregulated genes are equal, the landscape is symmetric, representing a thermodynamically reversible process, for example, nucleosome repositioning between euchromatic and heterochromatic regions without net gain or loss of nucleosomes. However, in cases where epigenetic modifications alter nucleosome density (e.g., disease states that reduce nucleosome numbers), the integrated energies are unequal, reflecting an irreversible energy cost. In this case, restoring chromatin requires additional energy input (e.g., to replace “missing” nucleosomes), which manifests as asymmetry in the landscape.

Reviewer #2 (Public review):

The authors report that intra-chromosomal gene correlation length (spatial correlations in gene expressions along the chromosome) serves as a proxy of chromatin structure and hence gene expression. They further explore changes in these metrics with aging. These are interesting and important findings. However, there are fundamental problems at this time.

(1) The basic method lacks validation. There is no validation of the method by approaches that directly measure chromatin structure, for example ATAC-seq, ChIP-seq, or CUT n RUN.

We appreciate the reviewer’s point that direct measurements such as ATAC-seq and ChIP-seq remain the gold standard for characterizing chromatin structure. Our method is designed to complement, not replace, these approaches by leveraging RNA-seq data to detect large-scale transcriptional patterns that correlate with chromatin dynamics.

We agree that integrating datasets with paired RNA-seq and chromatin accessibility assays would strengthen the manuscript and plan to include one such dataset in the revision.

Based on this feedback, we will also take the opportunity during revision to clarify and soften certain statements. Specifically, we will reposition ℓ∗ as a sensitive, computational proxy for detecting transcriptional signatures that are suggestive of chromatin structural changes. In other words, ℓ∗ provides an indirect window into chromatin dynamics through transcriptional spatial organization, allowing detection of patterns that may precede or accompany structural changes. Direct assays such as ATAC-seq or ChIP-seq remain essential for confirming the underlying physical modifications. To make this scope clear, we will revise the title to: “Macroscopic RNA-seq Analysis to Detect Transcriptional Patterns Associated with Chromatin State Changes,” and adjust the main text.  

We would like to take this opportunity to clarify why our initial version focused on the Della Valle and Fleischer datasets rather than including new paired datasets with direct chromatin measurements. The primary objective of our paper is to introduce two macroscopic RNA-seq–based measures, ℓ∗ and the energy landscape, that are designed to detect transcriptional signatures suggestive of chromatin structural changes in the context of aging and age-related diseases. These measures explicitly model transcriptional spatial organization and provide a sensitive, scalable way to analyze RNA-seq data in domains where direct chromatin assays may not be readily available.

The datasets we used (Della Valle et al., Fleischer et al.) have been rigorously validated and independently demonstrated differences in chromatin structure between conditions. Our goal was to show that ℓ∗ and the energy landscape align with and extend these established findings, offering a more sensitive measure of transcriptional spatial organization. Specifically, in the Della Valle dataset, chromatin structural differences between progeroid and healthy donors — and their partial rescue by LINE-1 ASO treatment — were experimentally confirmed, providing a strong foundation for testing whether our metrics reflect these known changes. Similarly, the Fleischer dataset captures natural, in vivo aging, which has also been linked to chromatin alterations in prior studies.

Thus, our approach builds on this well-established biological context rather than attempting to re-demonstrate these chromatin differences from scratch. Finally, we emphasize that our current focus is aging and age-related diseases. While the framework could potentially be applied to other chromatin modification contexts, we have not tested it outside this domain and do not claim general applicability at this stage.

(2) There is no validation by interventions that directly probe chromatin structure, such as HDAC inhibitors. The authors employ datasets with knockdown of LINE-1 for validation. However, this is not a specific chromatin intervention.

We request the reviewer to refer to our response to (1) as it includes the rationale behind the selection of LINE-1 and Fleischer dataset. We would also like to state that while the focus of Della Valle et al. was LINE-1 treated ASO to show rescue of progeroid samples, it also contains data for non-treated as well as healthy samples. Importantly, untreated progeroid samples show distinctly different chromatin structure compared to healthy samples, with substantial differences detectable by both PCA and our 𝓁∗ metric.

Our 𝓁∗ method provides additional interpretability by capturing transcriptional spatial organization, resulting in shorter correlation lengths for healthy patients and longer lengths for progeroid patients.

But as mentioned in our response to (1) we will try to add an additional dataset with paired rna-seq and one of ATAC, ChIP-seq or CUT n RUN in the revision

(3) There is no statistical analysis, e.g., in Figures 4 and 5.

We have provided statistical analysis for Fig 4 (lines 237-241). We will do a similar analysis for Fig. 5. 

(4) The authors state, "in Figure 4a changes in the heterochromatin are not identical for all chromosomes shown...." I do not see the data for individual chromosomes.

The data for individual chromosomes is available in supplementary Fig. S11 – references at line 425. We will make this cross-reference clearer in the main text and consider whether some of this chromosome-specific information should be elevated to the main figures for better accessibility.

(5) In comparisons of WT vs HGPS NT or HGPS SCR (Figure S6), is this a fair comparison? The WT and HGPS are presumably from different human donors, so they have genetic and epigenetic differences unrelated to HGPS.

Figure S6 demonstrates that 𝓁∗ analysis identifies chromosome 6 as most affected, consistent with differential gene expression patterns.

Regarding donor differences in WT vs HGPS comparisons, we defer to the experimental design of Della Valle et al., which follows standard practices in progeroid research. Our review of the literature indicates that progeroid studies typically use either parent/child samples or different donor comparisons (as individuals cannot simultaneously represent both WT and HGPS states).

Importantly, the LINE-1 ASO treatment comparisons use the same cell lines, eliminating donor variability concerns. This experimental design allows us to validate that 𝓁∗ can detect rescue effects within genetically identical samples, supporting the method's sensitivity to chromatin structural changes  

Reviewing Editor Comments:

You'll note that both reviewers were very thoughtful in their comments, and in principle are supportive and excited by the work. However, their evaluation of the strength of evidence diverged substantially. I'm inclined to suggest that finding a way to support the novel method with an alternative approach would greatly improve the impact of this work. I encourage you to consider a revision that provides such data, in the context of technology currently available to the field.

We sincerely thank the editor for their thoughtful and encouraging assessment of our work. We are grateful for their recognition of the novelty of our macroscopic measures (ℓ∗ and the transition energy barrier) and their potential to provide a systems-level understanding of chromatin structural dynamics in aging and age-related syndromes. In response to the editor’s suggestion for direct validation with chromatin accessibility data, we plan to integrate an additional dataset containing paired RNA-seq and ATAC-seq or related measurements in our revision. This will help strengthen the link between our RNA-seq–based metrics and direct chromatin assays. We have also clarified and softened the manuscript text to ensure it is clear that ℓ∗ serves as a complementary, computational proxy, not a replacement, for direct experimental approaches. Very specifically, to make this scope clear, we will revise the title to: “Macroscopic RNA-seq Analysis to Detect Transcriptional Patterns Associated with Chromatin State Changes,” and adjust the main text. We thank the editor for the feedback. We have provided additional details in response to specific comments made by the reviewers.

Reviewer #1 (Public review):

Summary

This manuscript presents an updated version of rsatoolbox, a Python package for performing Representational Similarity Analysis (RSA) on neural data. The authors provide a comprehensive and well-integrated framework that incorporates a range of state-of-the-art methodological advances. The updated version extends the toolbox's capabilities.

The paper outlines a typical RSA workflow in five steps:

(1) Importing data and estimating activity patterns.

(2) Estimating representational geometries (computing RDMs).

(3) Comparing RDMs.

(4) Performing inferential model comparisons.

(5) Handling multiple testing across space and time.

For each step, the authors describe methodological advances and best practices implemented in the toolbox, including improved measures of representational distances, evaluators for representational models, and statistical inference methods.

While the relative impact of the manuscript is somewhat limited to the new contributions in this update (which are nonetheless very useful), the general toolbox - here thoroughly described and discussed - remains an invaluable contribution to the field and is well-received by the cognitive and computational neuroscience communities.

Strengths:

A key strength of the work is the breadth and integration of the implemented methods. The updated version introduces several new features, such as additional comparators and dissimilarity estimators, that closely follow recent methodological developments in the field. These enhancements build on an already extensive set of functionalities, offering seamless support for RSA analyses across a wide variety of data sources, including deep neural networks, fMRI, EEG, and electrophysiological recordings.

The toolbox also integrates effectively with the broader open-source ecosystem, providing compatibility with BIDS formats and outputs from widely used neuroscience software. This integration will make it easier for researchers to incorporate rsatoolbox into existing workflows. The documentation is extensive, and the scope of functionality - from dissimilarity estimation to statistical inference - is impressive.

For researchers already familiar with RSA, rsatoolbox offers a coherent environment that can streamline analyses, promote methodological consistency, and encourage best practices.

Weaknesses:

While I enjoyed reading the manuscript - and even more so exploring the toolbox - I have some comments for the authors. None of these points is strictly major, and I leave it to the authors' discretion whether to act on them, but addressing them could make the manuscript an even more valuable resource for those approaching RSA.

(1) While several estimators and comparators are implemented, Figure 4 appears to suggest that only a subset should be used in practice. This raises the question of whether the remaining options are necessary, and under what circumstances they might be preferable. Although it is likely that different measures are suited to different scenarios, this is not clearly explained in the manuscript. As presented, a reader following the manuscript's guidance might rely on only a few of the available comparators and estimators without understanding the rationale. It would be helpful if the authors could provide practical examples illustrating when one measure might be preferred over another, and how different measures behave under varying conditions-for instance, in what situations the user should choose manifold similarity versus Bures similarity?

(2) The comparison to other RSA tools is minimal, making it challenging to place rsatoolbox in the broader landscape of available resources. Although the authors mention some existing RSA implementations, they do not provide a detailed comparison of features or performance between their toolbox and alternatives.

(3) Finally, given the growing interest in comparing neural network models with brain data, a more detailed discussion of how the toolbox can be applied to common questions in this area would be a valuable addition.

Reviewer #1 (Public review):

This is an interesting and timely computational study using molecular dynamics simulation as well as quantum mechanical calculation to address why tyrosine (Y), as part of an intrinsically disordered protein (IDP) sequence, has been observed experimentally to be stronger than phenylalanine (F) as a promoter for biomolecular phase separation. Notably, the authors identified the aqueous nature of the condensate environment and the corresponding dielectric and hydrogen bonding effects as a key to understand the experimentally observed difference. This principle is illustrated by the difference in computed transfer free energy of Y- and F-containing pentapeptides into solvent with various degrees of polarity. The elucidation offered by this work is important. The computation appears to be carefully executed, the results are valuable, and the discussion is generally insightful. However, there is room for improvement in some parts of the presentation in terms of accuracy and clarity, including, e.g., the logic of the narrative should be clarified with additional information (and possibly additional computation), and the current effort should be better placed in the context of prior relevant theoretical and experimental works on cation-π interactions in biomolecules and dielectric properties of biomolecular condensates. Accordingly, this manuscript should be revised to address the following, with added discussion as well as inclusion of references mentioned below.

(1) Page 2, line 61: "Coarse-grained simulation models have failed to account for the greater propensity of arginine to promote phase separation in Ddx4 variants with Arg to Lys mutations (Das et al., 2020)". As it stands, this statement is not accurate, because the cited reference to Das et al. showed that although some coarse-grained model, namely the HPS model of Dignon et al., 2018 PLoS Comput did not capture the Arg to Lys trend, the KH model described in the same Dignon et al. paper was demonstrated by Das et al. (2020) to be capable of mimicking the greater propensity of Arg to promote phase separation than Lys. Accordingly, a possible minimal change that would correct the inaccuracy of this statement in the manuscript would be to add the word "Some" in front of "coarse-grained simulation models ...", i.e., it should read "Some coarse-grained simulation models have failed ...". In fact, a subsequent work [Wessén et al., J Phys Chem B 126: 9222-9245 (2022)] that applied the Mpipi interaction parameters (Joseph et al., 2021, already cited in the manuscript) showed that Mpipi is capable of capturing the rank ordering of phase separation propensity of Ddx4 variants, including a charge scrambled variant as well as both the Arg to Lys and the Phe to Ala variants (see Fig.11a of the above-cited Wessén et al. 2022 reference). The authors may wish to qualify their statements in the introduction to take note of these prior results. For example, they may consider adding a note immediately after the next sentence in the manuscript "However, by replacing the hydrophobicity scales ... (Das et al., 2020)" to refer to these subsequent findings in 2021-2022.

(2) Page 8, lines 285-290 (as well as the preceding discussion under the same subheading & Fig.4): "These findings suggest that ... is not primarily driven by differences in protein-protein interaction patterns ..." The authors' logic in terms of physical explanation is somewhat problematic here. In this regard, "Protein-protein interaction patterns" appears to be a straw man, so to speak. Indeed, who (reference?) has argued that the difference in the capability of Y and F in promoting phase separation should be reflected in the pairwise amino acid interaction pattern in a condensate that contains either only Y (and G, S) and only F (and G, S) but not both Y and F? Also, this paragraph in the manuscript seems to suggest that the authors' observation of similar contact patterns in the GSY and GSF condensates is "counterintuitive" given the difference in Y-Y and F-F potentials of mean force (Joseph et al., 2021); but there is nothing particularly counterintuitive about that. The two sets of observations are not mutually exclusive. For instance, consider two different homopolymers, one with a significantly stronger monomer-monomer attraction than the other. The condensates for the two different homopolymers will have essentially the same contact pattern but very different stabilities (different critical temperatures), and there is nothing surprising about it. In other words, phase separation propensity is not "driven" by contact pattern in general, it's driven by interaction (free) energy. The relevant issue here is total interaction energy or critical point of the phase separation. If it is computationally feasible, the authors should attempt to determine the critical temperatures for the GSY condensate versus the GSF condensate to verify that the GSY condensate has a higher critical temperature than the GSF condensate. That would be the most relevant piece of information for the question at hand.

(3) Page 9, lines 315-316: "...Our ε [relative permittivity] values ... are surprisingly close to that derived from experiment on Ddx4 condensates (45{plus minus}13) (Nott et al., 2015)". For accuracy, it should be noted here that the relative permittivity provided in the supplementary information of Nott et al. was not a direct experimental measurement but based on a fit using Flory-Huggins (FH), but FH is not the most appropriate theory for polymer with long-spatial-range Coulomb interactions. To this reviewer's knowledge, no direct measurement of relative permittivity in biomolecular condensates has been made to date. Explicit-water simulation suggests that relative permittivity of Ddx4 condensate with protein volume fraction ≈ 0.4 can have relative permittivity ≈ 35-50 (Das et al., PNAS 2020, Fig.7A), which happens to agree with the ε = 45{plus minus}13 estimate. This information should be useful to include in the authors' manuscript.

(4) As for the dielectric environment within biomolecular condensates, coarse-grained simulation has suggested that whereas condensates formed by essentially electric neutral polymers (as in the authors' model systems) have relative permittivities intermediate between that of bulk water and that of pure protein (ε = 2-4, or at most 15), condensates formed by highly charge polymers can have relative permittivity higher than that of bulk water [Wessén et al., J Phys Chem B 125:4337-4358 (2021), Fig.14 of this reference]. In view of the role of aromatic residues (mainly Y and F) in the phase separation of IDPs such as A1-LCD and LAF-1 that contain positively and negatively charged residues (Martin et al., 2020; Schuster et al., 2020, already cited in the manuscript), it should be useful to address briefly how the relationship between the relative phase-separation promotion strength of Y vs F and dielectric environment of the condensate may or may not be change with higher relative permittivities.

(5) The authors applied the dipole moment fluctuation formula (Eq.2 in the manuscript) to calculate relative permittivity in their model condensates. Does this formula apply only to an isotropic environment? The authors' model condensates were obtained from a "slab" approach (p.4) and thus the simulation box has a rectangular geometry. Did the authors apply their Eq.2 to the entire simulation box or only to the central part of the box with the condensate (see, e.g., Fig.3C in the manuscript). If the latter is the case, is it necessary to use a different dipole moment formula that distinguishes between the "parallel" and "perpendicular" components of the dipole moment (see, e.g., Eq.16 in the above-cited Wessén et al. 2021 paper). A brief added comments will be useful.

(6) With regard to the general role of Y and F in the phase separation of biomolecules containing positively charged Arg and Lys residues, the relative strength of cation-π interactions (cation-Y vs cation-F) should be addressed (in view of the generality implied by the title of the manuscript), or at least discussed briefly in the authors' manuscript if a detailed study is beyond the scope of their current effort. It has long been known that in the biomolecular context, cation-Y is slightly stronger than cation-F, whereas cation-tryptophan (W) is significantly stronger than either cation-Y and cation-F [Wu & McMahon, JACS 130:12554-12555 (2008)]. Experimental data from a study of EWS (Ewing sarcoma) transactivation domains indicated that Y is a slightly stronger promoter than F for transcription, whereas W is significantly stronger than either Y or F [Song et al., PLoS Comput Biol 9:e1003239 (2013)]. In view of the subsequent general recognition that "transcription factors activate genes through the phase-separation capacity of their activation domain" [Boija et al., Cell 175:1842-1855.e16 (2018)] which is applicable to EWS in particular [Johnson et al., JACS 146:8071-8085 (2024)], the experimental data in Song et al. 2013 (see Fig.3A of this reference) suggests that cation-Y interactions are stronger than cation-F interactions in promoting phase separation, thus generalizing the authors' observations (which focus primarily on Y-Y, Y-F and F-F interactions) to most situations in which cation-Y and cation-F interactions are relevant to biomolecular condensation.

(7) Page 9: The observation of a weaker effective F-F (and a few other nonpolar-nonpolar) interaction in a largely aqueous environment (as in an IDP condensate) than in a nonpolar environment (as in the core of a folded protein) is intimately related to (and expected from) the long-recognized distinction between "bulk" and "pair" as well as size dependence of hydrophobic effects that have been addressed in the context of protein folding [Wood & Thompson, PNAS 87:8921-8927 (1990); Shimizu & Chan, JACS 123:2083-2084 (2001); Proteins 49:560-566 (2002)]. It will be useful to add a brief pointer in the current manuscript to this body of relevant resource in protein science.

Comments on revisions:

The authors have largely addressed my previous concerns and the manuscript has been substantially improved. Nonetheless, it will benefit the readers more if the authors had included more of the relevant references provided in my previous review so as to afford a broader and more accurate context to the authors' effort. This deficiency is particularly pertinent for point number 6 in my previous report about cation-pi interactions. The authors have now added a brief discussion but with no references on the rank ordering of Y, F, and W interactions. I cannot see how providing additional information about a few related works could hurt. Quite the contrary, having the references will help readers establish scientific connections and contribute to conceptual advance.

Author response:

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

Reviewer #1 (Public review):

This is an interesting and timely computational study using molecular dynamics simulation as well as quantum mechanical calculation to address why tyrosine (Y), as part of an intrinsically disordered protein (IDP) sequence, has been observed experimentally to be stronger than phenylalanine (F) as a promoter for biomolecular phase separation. Notably, the authors identified the aqueous nature of the condensate environment and the corresponding dielectric and hydrogen bonding effects as a key to understanding the experimentally observed difference. This principle is illustrated by the difference in computed transfer free energy of Y- and F-containing pentapeptides into a solvent with various degrees of polarity. The elucidation offered by this work is important. The computation appears to be carefully executed, the results are valuable, and the discussion is generally insightful. However, there is room for improvement in some parts of the presentation in terms of accuracy and clarity, including, e.g., the logic of the narrative should be clarified with additional information (and possibly additional computation), and the current effort should be better placed in the context of prior relevant theoretical and experimental works on cation-π interactions in biomolecules and dielectric properties of biomolecular condensates. Accordingly, this manuscript should be revised to address the following, with added discussion as well as inclusion of references mentioned below.

We are grateful for the referee’s assessment of our work and insightful suggestions, which we address point by point below.

(1) Page 2, line 61: "Coarse-grained simulation models have failed to account for the greater propensity of arginine to promote phase separation in Ddx4 variants with Arg to Lys mutations (Das et al., 2020)". As it stands, this statement is not accurate, because the cited reference to Das et al. showed that although some coarse-grained models, namely the HPS model of Dignon et al., 2018 PLoS Comput did not capture the Arg to Lys trend, the KH model described in the same Dignon et al. paper was demonstrated by Das et al. (2020) to be capable of mimicking the greater propensity of Arg to promote phase separation than Lys. Accordingly, a possible minimal change that would correct the inaccuracy of this statement in the manuscript would be to add the word "Some" in front of "coarse-grained simulation models ...", i.e., it should read "Some coarse-grained simulation models have failed ...". In fact, a subsequent work [Wessén et al., J Phys Chem B 126: 9222-9245 (2022)] that applied the Mpipi interaction parameters (Joseph et al., 2021, already cited in the manuscript) showed that Mpipi is capable of capturing the rank ordering of phase separation propensity of Ddx4 variants, including a charge scrambled variant as well as both the Arg to Lys and the Phe to Ala variants (see Figure 11a of the above-cited Wessén et al. 2022 reference). The authors may wish to qualify their statements in the introduction to take note of these prior results. For example, they may consider adding a note immediately after the next sentence in the manuscript "However, by replacing the hydrophobicity scales ... (Das et al., 2020)" to refer to these subsequent findings in 2021-2022.

We agree with the referee that the wording used in the original version was inaccurate. We did not want to expand too much on the previous results on Lys/Arg, to avoid overwhelming our readers with background information that was not directly relevant to the aromatic residues Phe and Tyr. We have now introduced some of the missing details in the hope that this will provide a more accurate account of what has been achieved with different versions of coarse-grained models. In the revised version, we say the following:

Das and co-workers attempted to explain arginine’s greater propensity to phase separate in Ddx4 variants using coarse-grained simulations with two different energy functions (Das et al., 2020). The model was first parametrized using a hydrophobicity scale, aimed to capture the “stickiness” of different amino acids (Dignon et al., 2018), but this did not recapitulate the correct rank order in the stability of the simulated condensates (Das et al., 2020). By replacing the hydrophobicity scale with interaction energies from amino acid contact matrices —derived from a statistical analysis of the PDB (Dignon et al., 2018; Miyazawa and Jernigan, 1996; Kim and Hummer, 2008)— they recovered the correct trends (Das et al., 2020). A key to the greater propensity for LLPS in the case of Arg may derive from the pseudo-aromaticity of this residue, which results in a greater stabilization relative to the more purely cationic character of Lys (Gobbi and Frenking, 1993; Wang et al., 2018; Hong et al., 2022).

(2) Page 8, lines 285-290 (as well as the preceding discussion under the same subheading & Figure 4): "These findings suggest that ... is not primarily driven by differences in protein-protein interaction patterns ..." The authors' logic in terms of physical explanation is somewhat problematic here. In this regard, "Protein-protein interaction patterns" appear to be a straw man, so to speak. Indeed, who (reference?) has argued that the difference in the capability of Y and F in promoting phase separation should be reflected in the pairwise amino acid interaction pattern in a condensate that contains either only Y (and G, S) and only F (and G, S) but not both Y and F? Also, this paragraph in the manuscript seems to suggest that the authors' observation of similar contact patterns in the GSY and GSF condensates is "counterintuitive" given the difference in Y-Y and F-F potentials of mean force (Joseph et al., 2021); but there is nothing particularly counterintuitive about that. The two sets of observations are not mutually exclusive. For instance, consider two different homopolymers, one with a significantly stronger monomer-monomer attraction than the other. The condensates for the two different homopolymers will have essentially the same contact pattern but very different stabilities (different critical temperatures), and there is nothing surprising about it. In other words, phase separation propensity is not "driven" by contact pattern in general, it's driven by interaction (free) energy. The relevant issue here is total interaction energy or the critical point of the phase separation. If it is computationally feasible, the authors should attempt to determine the critical temperatures for the GSY condensate versus the GSF condensate to verify that the GSY condensate has a higher critical temperature than the GSF condensate. That would be the most relevant piece of information for the question at hand.

We are grateful for this very insightful comment by the referee. We have followed this suggestion to address whether, despite similar interaction patterns in GSY and GSF condensates, their stabilities are different. As in our previous work (De Sancho, 2022), we have run replica exchange MD simulations for both condensates and derived their phase diagrams. Our results, shown in the new Figure 5 and supplementary Figs. S6-S7, clearly indicate that the GSY condensate has a lower saturation density than the GSF condensate. This result is consistent with the trends observed in experiments on mutants of the low-complexity domain of hnRNPA1, where the relative amounts of F and Y determine the saturation concentration (Bremer et al., 2022).

(3) Page 9, lines 315-316: "...Our ε [relative permittivity] values ... are surprisingly close to that derived from experiment on Ddx4 condensates (45{plus minus}13) (Nott et al., 2015)".  For accuracy, it should be noted here that the relative permittivity provided in the supplementary information of Nott et al. was not a direct experimental measurement but based on a fit using Flory-Huggins (FH), but FH is not the most appropriate theory for a polymer with long-spatial-range Coulomb interactions. To this reviewer's knowledge, no direct measurement of relative permittivity in biomolecular condensates has been made to date. Explicit-water simulation suggests that the relative permittivity of Ddx4 condensate with protein volume fraction ≈ 0.4 can have a relative permittivity ≈ 35-50 (Das et al., PNAS 2020, Fig.7A), which happens to agree with the ε = 45{plus minus}13 estimate. This information should be useful to include in the authors' manuscript.

We thank the referee for this useful comment. We are aware that the estimate we mentioned is not direct. We have now clarified this point and added the additional estimate from Das et al. In the new version of the manuscript, we say:

Our 𝜀 values for the condensates (39 ± 5 for GSY and 47 ± 3 for GSF) are surprisingly close to that derived from experiments on Ddx condensates using Flory-Huggins theory (45±13) (Nott et al., 2015) and from atomistic simulations of Ddx4 (∼35−50 at a volume fraction of 𝜙 = 0.4) (Das et al., 2020).

(4) As for the dielectric environment within biomolecular condensates, coarse-grained simulation has suggested that whereas condensates formed by essentially electric neutral polymers (as in the authors' model systems) have relative permittivities intermediate between that of bulk water and that of pure protein (ε=2-4, or at most 15), condensates formed by highly charged polymers can have relative permittivity higher than that of bulk water [Wessén et al., J Phys Chem B 125:4337-4358 (2021), Fig.14 of this reference]. In view of the role of aromatic residues (mainly Y and F) in the phase separation of IDPs such as A1-LCD and LAF-1 that contain positively and negatively charged residues (Martin et al., 2020; Schuster et al., 2020, already cited in the manuscript), it should be useful to address briefly how the relationship between the relative phase-separation promotion strength of Y vs F and dielectric environment of the condensate may or may not be change with higher relative permittivities.

We thank the referee for their comment regarding highly charged polymers. However, we have chosen not to address these systems in our manuscript, as they are significantly different from the GSY/GSF peptide condensates under investigation. In polyelectrolyte systems, condensate formation is primarily driven by electrostatic interactions and counterion release, while we highlight the role of transfer free energies. At high dielectric constants (and dielectrics even higher than that of water), the strength of electrostatic interactions will be greatly reduced. In our approach to estimate differences between Y and F, the transfer free energy should plateau at a value of ΔΔG=0 in water. At greater values of ε>80, it becomes difficult to predict whether additional effects might become relevant. As this lies beyond the scope of our current study, we prefer not to speculate further.

(5) The authors applied the dipole moment fluctuation formula (Eq.2 in the manuscript) to calculate relative permittivity in their model condensates. Does this formula apply only to an isotropic environment? The authors' model condensates were obtained from a "slab" approach (page 4 and thus the simulation box has a rectangular geometry. Did the authors apply Equation 2 to the entire simulation box or only to the central part of the box with the condensate (see, e.g., Figure 3C in the manuscript). If the latter is the case, is it necessary to use a different dipole moment formula that distinguishes between the "parallel" and "perpendicular" components of the dipole moment (see, e.g., Equation 16 in the above-cited Wessén et al. 2021 paper). A brief added comment will be useful.

We have calculated the relative permittivity from dense phases only. These dense phases were sliced from the slab geometry and then re-equilibrated. Long simulations were then run to converge the calculation of the dielectric constant. We have clarified this in the Methods section of the paper. We say:

For the calculation of the dielectric constant of condensates, we used the simulations of isolated dense phases mentioned above.

(6) Concerning the general role of Y and F in the phase separation of biomolecules containing positively charged Arg and Lys residues, the relative strength of cation-π interactions (cation-Y vs cation-F) should be addressed (in view of the generality implied by the title of the manuscript), or at least discussed briefly in the authors' manuscript if a detailed study is beyond the scope of their current effort. It has long been known that in the biomolecular context, cation-Y is slightly stronger than cation-F, whereas cation-tryptophan (W) is significantly stronger than either cation-Y and cation-F [Wu & McMahon, JACS 130:12554-12555 (2008)]. Experimental data from a study of EWS (Ewing sarcoma) transactivation domains indicated that Y is a slightly stronger promoter than F for transcription, whereas W is significantly stronger than either Y or F [Song et al., PLoS Comput Biol 9:e1003239 (2013)]. In view of the subsequent general recognition that "transcription factors activate genes through the phase-separation capacity of their activation domain" [Boija et al., Cell 175:1842-1855.e16 (2018)] which is applicable to EWS in particular [Johnson et al., JACS 146:8071-8085 (2024)], the experimental data in Song et al. 2013 (see Figure 3A of this reference) suggests that cation-Y interactions are stronger than cation-F interactions in promoting phase separation, thus generalizing the authors' observations (which focus primarily on Y-Y, Y-F and F-F interactions) to most situations in which cation-Y and cation-F interactions are relevant to biomolecular condensation.

We thank our referee for this insightful comment. While we restrict our analysis to aromatic pairs in this work, the observed crossover will certainly affect other pairs where tyrosine or phenylalanine are involved. We now comment on this point in the discussions section of the revised manuscript. This topic will be explored in detail in a follow-up manuscript we are currently completing. We say:

We note that, although we have not included in our analysis positively charged residues that form cation-π interactions with aromatics, the observed crossover will also be relevant to Arg/Lys contacts with Phe and Tyr. Following the rationale of our findings, within condensates, cation-Tyr interactions are expected to promote phase separation more strongly than cation-Phe pairs.

(7) Page 9: The observation of weaker effective F-F (and a few other nonpolar-nonpolar) interactions in a largely aqueous environment (as in an IDP condensate) than in a nonpolar environment (as in the core of a folded protein) is intimately related to (and expected from) the long-recognized distinction between "bulk" and "pair" as well as size dependence of hydrophobic effects that have been addressed in the context of protein folding [Wood & Thompson, PNAS 87:8921-8927 (1990); Shimizu & Chan, JACS 123:2083-2084 (2001); Proteins 49:560-566 (2002)]. It will be useful to add a brief pointer in the current manuscript to this body of relevant resources in protein science.

We thank the referee for bringing this body of work to our attention. In the revised version of our work, we briefly mention how it relates to our results. We also note that the suggested references have pointed to another of the limitations of our study, that of chain connectivity, addressed in the work by Shimizu and Chan. While we were well aware of these limitations, we had not mentioned them in our manuscript. Concerning the distinction between pair and bulk hydrophobicities, we include the following in the concluding lines of our work:

The observed context dependence has deep roots in the concepts of “pair” and “bulk” hydrophobicity (Wood and Thompson, 1990; Shimizu and Chan, 2002). While pair hydrophobicity is connected to dimerisation equilibria (i.e. the second step in Figure 2B), bulk hydrophobicity is related to transfer processes (the first step). Our work stresses the importance of considering both the pair contribution that dominates at high solvation, and the transfer free energy contribution, which overwhelms the interaction strength at low dielectrics.

Reviewer #2 (Public review):

Summary:

In this preprint, De Sancho and López use alchemical molecular dynamics simulations and quantum mechanical calculations to elucidate the origin of the observed preference of Tyr over Phe in phase separation. The paper is well written, and the simulations conducted are rigorous and provide good insight into the origin of the differences between the two aromatic amino acids considered.

We thank the referee for his/her positive assessment of our work. Below, we address all the questions raised one by one.

Strengths:

The study addresses a fundamental discrepancy in the field of phase separation where the predicted ranking of aromatic amino acids observed experimentally is different from their anticipated rankings when considering contact statistics of folded proteins. While the hypothesis that the difference in the microenvironment of the condensed phase and hydrophobic core of folded proteins underlies the different observations, this study provides a quantification of this effect. Further, the demonstration of the crossover between Phe and Tyr as a function of the dielectric is interesting and provides further support for the hypothesis that the differing microenvironments within the condensed phase and the core of folded proteins is the origin of the difference between contact statistics and experimental observations in phase separation literature. The simulations performed in this work systematically investigate several possible explanations and therefore provide depth to the paper.

Weaknesses:

While the study is quite comprehensive and the paper well written, there are a few instances that would benefit from additional details. In the methods section, it is unclear as to whether the GGXGG peptides upon which the alchemical transforms are conducted are positioned restrained within the condensed/dilute phase or not. If they are not, how would the position of the peptides within the condensate alter the calculated free energies reported? 

The peptides are not restrained in our simulations and can therefore diffuse out of the condensate given sufficient time. Although the GGXGG peptide can, given sufficient time, leave the peptide condensate, we did not observe any escape event in the trajectories we used to generate starting points for switching. Hence, the peptide environment captured in our calculations reflects, on average, the protein-protein and protein-solvent interactions inside the model condensate. We believe this is the right way of performing the calculation of transfer free energy differences into the condensate. We have clarified this point when we describe the equilibrium simulation results in the revised manuscript. We say:

Also, the peptide that experiences the transformation, which is not restrained, must remain buried within the condensate for all the snapshots that we use as initial frames, to avoid averaging the work in the dilute and dense phases.

On the referee’s second point of whether there would be differences if the peptide visited the dilute phase, the answer is that, indeed, we would. We expect that the behaviour of the peptide would approach ΔΔG=0, considering the low protein concentration in the dilute phase. For mixed trajectories with sampling in both dilute and dense phases, our expectation would be a bimodal distribution in the free energy estimates from switching (see e.g. Fig. 8 in DOI:10.1021/acs.jpcb.0c10263). Because we are exclusively interested in the transfer free energies into the condensate, we do not pursue such calculations in this work.

It would also be interesting to see what the variation in the transfer of free energy is across multiple independent replicates of the transform to assess the convergence of the simulations. 

Upon submission of our manuscript, we were confident that the results we had obtained would pass the test of statistical significance. We had, after all, done many more simulations than those reported, plus the comparable values of ΔΔG_Transfer for both GSY and GSF pointed in the right direction. However, we acknowledge that the more thorough test of running replicates recommended by the referee is important, considering the slow diffusion within the Tyr peptide condensates due to its stickiness. Also, the non-equilibrium switching method had not been tested before for dense phases like the ones considered here.

We have hence followed our referee's suggestion and done three different replicates, 1 μs each, of the equilibrium runs starting from independent slab configurations, for both the GSY and GSF condensates (see the new supporting figures Fig. S1, S2 and S5). We now report the errors from the three replicates as the standard error of the mean (bootstrapping errors remain for the rest of the solvents). Our results are entirely consistent with the values reported originally, confirming the validity of our estimates.

Additionally, since the authors use a slab for the calculation of these free energies, are the transfer free energies from the dilute phase to the interface significantly different from those calculated from the dilute phase to the interior of the condensate? 

We thank the referee for this valuable comment, as it has pointed us in the direction of a rapidly increasing body of work on condensate interfaces, for example, as mediators of aggregation, that we may consider for future study with the same methodology. However, as discussed above, we have not considered this possibility in our work, as we decided to focus on the condensate environment, rather than its interface.

The authors mention that the contact statistics of Phe and Tyr do not show significant difference and thereby conclude that the more favorable transfer of Tyr primarily originates from the dielectric of the condensate. However, the calculation of contacts neglects the differences in the strength of interactions involving Phe vs. Tyr. Though the authors consider the calculation of energy contact formation later in the manuscript, the scope of these interactions are quite limited (Phe-Phe, Tyr-Tyr, Tyr-Amide, Phe-Amide) which is not sufficient to make a universal conclusion regarding the underlying driving forces. A more appropriate statement would be that in the context of the minimal peptide investigated the driving force seems to be the difference in dielectric. However, it is worth mentioning that the authors do a good job of mentioning some of these caveats in the discussion section.

We thank the referee for this important comment. Indeed, the similar contact statistics and interaction patterns that we reported originally do not necessarily imply identical interaction energies. In other words, similar statistics and patterns can still result in different stabilities for the Phe and Tyr condensates if the energetics are different. Hence, we cannot conclude that the GSF and GSY condensate environments are equivalent.

To address this point, we have run new simulations for the revised version of our paper, using the temperature-replica exchange method, as before. From the new datasets, we derive the phase diagrams for both the GSF and GSY condensates (see the new Fig. 5). We find that the tyrosine-containing condensate is more stable than that of phenylalanine, as can be inferred from the lower saturation density in the low-density branch of the phase diagram. In consequence, despite the similar contact statistics, the energetics differ, making the saturation density of the GSY slightly lower than that of GSF. This result is consistent with experimental data by Bremer et al (Nat. Chem. 2022). 

Reviewer #3 (Public review):

Summary:

In this study, the authors address the paradox of how tyrosine can act as a stronger sticker for phase separation than phenylalanine, despite phenylalanine being higher on the hydrophobicity scale and exhibiting more prominent pairwise contact statistics in folded protein structures compared to tyrosine.

We are grateful for the referee’s favourable opinion on the paper. Below, we address all of the issues raised.

Strengths:

This is a fascinating problem for the protein science community with special relevance for the biophysical condensate community. Using atomistic simulations of simple model peptides and condensates as well as quantum calculations, the authors provide an explanation that relies on the dielectric constant of the medium and the hydration level that either tyrosine or phenylalanine can achieve in highly hydrophobic vs. hydrophilic media. The authors find that as the dielectric constant decreases, phenylalanine becomes a stronger sticker than tyrosine. The conclusions of the paper seem to be solid, it is well-written and it also recognises the limitations of the study. Overall, the paper represents an important contribution to the field.

Weaknesses:

How can the authors ensure that a condensate of GSY or GSF peptides is a representative environment of a protein condensate? First, the composition in terms of amino acids is highly limited, second the effect of peptide/protein length compared to real protein sequences is also an issue, and third, the water concentration within these condensates is really low as compared to real experimental condensates. Hence, how can we rely on the extracted conclusions from these condensates to be representative for real protein sequences with a much more complex composition and structural behaviour?

We agree with the main weakness identified by the referee. In fact, all these limitations had already been stated in our original submission. Our ternary peptide condensates are just a minimal model system that bears reasonable analogies with condensates, but definitely is not identical to true LCR condensates. The analogies between peptide and protein condensates are, however, worth restating: 

(1) The limited composition of the peptide condensates is inspired by LCR sequences (see Fig. 4 in Martin & Mittag, 2018).

(2) The equilibrium phase diagram, showing a UCST, is consistent with that of LCRs from Ddx4 or hnRNPA1.

(3) The dynamical behaviour is intermediate between liquid and solid (De Sancho, 2022). 

(4) The contact patterns are comparable to those observed for FUS and LAF1 (Zheng et al, 2020).

The third issue pointed out by the referee requires particular attention. Indeed, the water content in the model condensates is low (~200 mg/mL for GSY) relative to the experiment (e.g. ~600 mg/mL for FUS and LAF-1 from simulations). Considering that both interaction patterns and solvation contribute to the favorability of Tyr relative to Phe, we speculate that a greater degree of solvation in the true protein condensates will further reinforce the trends we observe.

In any case, in the revised version of the manuscript, we have made an effort to insist on the limitations of our results, some of which we plan to address in future work.

Reviewer #3 (Recommendations for the authors):

(1) The fact that protein density is so high within GSY or GSF peptide condensates may significantly alter the conclusions of the paper. Can the authors show that for condensates in which the protein density is ~0.2-0.3 g/cm3, the same conclusions hold? Could the authors use a different peptide sequence that establishes a more realistic protein concentration/density inside the condensate?

Unfortunately, recent work with a variety of peptide sequences suggests that finding peptides in the density range proposed by the referee may be very challenging. For example, Pettit and his co-workers have extensively studied the behaviour of GGXGG peptides. In a recent work, using the CHARMM36m force field and TIP3P water, they report densities of ~1.2-1.3 g/mL for capped pentapeptide condensates (Workman et al, Biophys. J. 2024; DOI: 10.1016/j.bpj.2024.05.009). Brown and Potoyan have recently run simulations of zwitterionic GXG tripeptides with the Amber99sb-ILDNQ force field and TIP3P water, starting with a homogenous distribution in cubic simulation boxes (Biophys. J. 2024, DOI: 10.1016/j.bpj.2023.12.027). In a box with an initial concentration of 0.25 g/mL, upon phase separation, the peptide ends up occupying what would seem to be ~1/3 of the box, although we could not find exact numbers. This would imply densities of ~0.75 g/mL in the dense phase, with the additional problem of many charges. Finally, Joseph and her co-workers have recently simulated a set of hexapeptide condensates with varied compositions using a combination of atomistic and coarse-grained simulations. For the atomistic simulations, the Amber03ws force field and TIP4P water were used (see BioRxiv reference 10.1101/2025.03.04.641530). They have found values of the protein density in the dense phase ranging between 0.8 and 1.2 g/mL.  The consistency in the range of densities reported in these studies suggests that short peptides, at least up to 7-residues long, tend to form quite dense condensates, akin to those investigated in our work. While the examples mentioned do not comprehensively span the full range of peptide lengths, sequences, and force fields, they nonetheless support the general behaviour we observe. A systematic exploration of all these variables would require an extensive search in parameter space, which we believe falls outside the scope of the present study.

(2) Do the conclusions hold for phase-separating systems that mostly rely on electrostatic interactions to undergo LLPS, like protein-RNA complex coacervates? In other words, could the authors try the same calculations for a binary mixture composed of polyR-polyE, or polyK-polyE?

This is an excellent idea that we may attempt in future work, but the remit of the current work is aromatic amino acids Phe and Tyr only. Hence, we do not include calculations or discussion on polyR-polyE systems in our revised manuscript.

(3) One of the major approximations made by the authors is the length of the peptides within the condensates, which is not realistic, or their density. Specifically, could they double or triple the length of these peptides while maintaining their composition so it can be quantified the impact of sequence length in the transfer of free energies?

We thank the referee for this comment and agree with the main point, which was stated as a limitation in our original submission. The suggested calculations anticipate research that we are planning but will not include in the current work. One of the advantages of our model systems is that the small size of the peptides allows for small simulation boxes and relatively rapid sampling. Longer peptide sequences would require conformational sampling beyond our current capabilities, if done systematically. An example of these limitations is the amount of data that we had to discard from the new simulations we report, which amounts to up to 200 ns of our replica exchange runs in smaller simulation boxes (i.e. >19 μs in total for the 48 replicas of the two condensates!). As stated in the answer to point 1, we have found in the literature work on peptides in the range of 1-7 residues with consistent densities. Additionally, a recent report using alchemical transformations using equilibrium techniques with tetrapeptide condensates, pointing to the role of transfer free energy as driving force for condensate formation, further supports the observations from our work.

Minor issues:

(1) The caption of Figure 3B is not clear. It can only be understood what is depicted there once you read the main text a couple of times. I encourage the authors to clarify the caption.

We have rewritten the caption for greater clarity. Now it reads as follows:

Time evolution of the density profiles calculated across the longest dimension of the simulation box (L) in the coexistence simulations. In blue we show the density of all the peptides, and in dark red that of the F/Y residue in the GGXGG peptide.

(2) Why was the RDF from Figure 5A cut at such a short distance? Can the authors expand the figure to clearly show that it has converged?

In the updated Figure 5 (now Fig. 6), we have extended the g(r) up to r=1.75 nm so that it clearly plateaus at a value of 1.

Reviewer #1 (Public review):

Summary

In the presented paper, Lu and colleagues focus on how items held in working memory bias someone's attention. In a series of three experiments, they utilized a similar paradigm in which subjects were asked to maintain two colored squares in memory for a short and variable time. After this delay, they either tested one of the memory items or asked subjects to perform a search task.

In the search task, items could share colors with the memory items, and the authors were interested in how these would capture attention, using reaction time as a proxy. The behavioral data suggest that attention oscillates between the two items. At different maintenance intervals, the authors observed that items in memory captured different amounts of attention (attentional capture effect).

This attentional bias fluctuates over time at approximately the theta frequency range of the EEG spectrum. This part of the study is a replication of Peters and colleagues (2020).

Next, the authors used EEG recordings to better understand the neural mechanisms underlying this process. They present results suggesting that this attentional capture effect is positively correlated with the mean amplitude of alpha power. Furthermore, they show that the weighted phase lag index (wPLI) between the alpha and theta bands across different electrodes also fluctuates at the theta frequency.

Strengths

The authors focus on an interesting and timely topic: how items in working memory can bias our attention. This line of research could improve our understanding of the neural mechanisms underlying working memory, specifically how we maintain multiple items and how these interact with attentional processes. This approach is intriguing because it can shed light on neuronal mechanisms not only through behavioral measures but also by incorporating brain recordings, which is definitely a strength.<br /> Subjects performed several blocks of experiments, ranging from 4 to 30, over a few days depending on the experiment. This makes the results - especially those from behavioral experiments 2 and 3, which included the most repetitions - particularly robust.

Weaknesses

One of the main EEG results is based on the weighted phase lag index (wPLI) between oscillations in the alpha and theta bands. In my opinion, this is problematic, as wPLI measures the locking of oscillations at the same frequency. It quantifies how reliably the phase difference stays the same over time. If these oscillations have different frequencies, the phase difference cannot remain consistent. Even worse, modeling data show that even very small fluctuations in frequency between signals make wPLI artificially small (Cohen, 2015).

In response authors stated : "Additionally, the present study referenced previous research by using the wPLI index as a measure of cross-frequency coupling strength31,64-66"<br /> Unfortunately, after checking those publications, we can see that in paper 31 there is no mention of "wPLI" or "PLV." In 64 and 65, the authors use wPLI, but only to measure same-frequency coherence, whereas cross-frequency coupling is computed by phase-amplitude coupling or cross-frequency coupling also known as n:m-PS. In 66, I cannot find any cross-frequency results, only cross-species analysis. This is very problematic, as it indicates that the authors included references in their rebuttal without verifying their relevance.<br /> 31 de Vries, I. E. J., van Driel, J., Karacaoglu, M. & Olivers, C. N. L. Priority Switches in Visual Working Memory are Supported by Frontal Delta and Posterior Alpha Interactions. Cereb Cortex 28, 4090-4104, doi:10.1093/cercor/bhy223 (2018).64 Delgado-Sallent, C. et al. Atypical, but not typical, antipsychotic drugs reduce hypersynchronized prefrontal-hippocampal circuits during psychosis-like states in mice: Contribution of 5-HT2A and 5-HT1A receptors. Cerebral Cortex 32, 870 3472-3487 (2022). 65 Siebenhühner, F. et al. Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings. PLoS Biology 18, e3000685 (2020). 66 Zhang, F. et al. Cross-Species Investigation on Resting State Electroencephalogram. Brain Topogr 32, 808-824, doi:10.1007/s10548-019-00723-x (2019).

Another result from the electrophysiology data shows that the attentional capture effect is positively correlated with the mean amplitude of alpha power. In the presented scatter plot, it seems that this result is driven by one outlier. Unfortunately, Pearson correlation is very sensitive to outliers, and the entire analysis can be driven by an extreme case. I extracted data from the plot and obtained a Pearson correlation of 0.4, similar to what the authors report. However, the Spearman correlation, which is robust against outliers, was only 0.13 (p = 0.57) indicating a non-significant relationship.

Cohen, M. X. (2015). Effects of time lag and frequency matching on phase based connectivity. Journal of Neuroscience Methods, 250, 137-146

Author response:

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

Reviewer #1 (Public review):

Thank you very much for your recognition of our work and for pointing out the shortcomings. We have made revisions one by one and provided corresponding explanations regarding the issues you raised.

Weaknesses:

One of the main EEG results is based on the weighted phase lag index (wPLI) between oscillations in the alpha and theta bands. In my opinion, this is problematic, as wPLI measures the locking of oscillations at the same frequency. It quantifies how reliably the phase difference stays the same over time. If these oscillations have different frequencies, the phase difference cannot remain consistent. Even worse, modeling data show that even very small fluctuations in frequency between signals make wPLI artificially small (Cohen, 2015).

thank you for raising the question regarding the application of wPLI between the alpha and theta bands, which indeed deserves further explanation. In our study, we referred to some relevant previous literatures and adopted their approach of using wPLI to measure cross-frequency coupling strength, as this index itself can reflect the stability of phase differences. We have also considered the point you mentioned that the phase differences of oscillations with different frequencies are difficult to remain consistent. However, in this study, the presentation times of the two memory items are the same, which is fair to both from this perspective. Moreover, the study observed that the wPLI values of these two items alternately dominate over time, and this changing pattern is consistent with the regularity of behavioral data. It seems hard to explain this as a mere coincidence. 

The corresponding discussion has been added to the revised part of the paper：“the present study referenced previous research by using the wPLI index as a measure of cross-frequency coupling strength31,64-66 (this index quantifies the stability of phase differences), yet the phases of different oscillations inherently change over time. However, this is fair to the two memory items in the present study, as their presentation times were balanced. The study found that the wPLI values of the two items alternately dominated over time, consistent with the pattern of behavioral data, which is hardly explicable by coincidence”

Another result from the electrophysiology data shows that the attentional capture effect is positively correlated with the mean amplitude of alpha power. In the presented scatter plot, it seems that this result is driven by one outlier. Unfortunately, Pearson correlation is very sensitive to outliers, and the entire analysis can be driven by an extreme case. I extracted data from the plot and obtained a Pearson correlation of 0.4, similar to what the authors report. However, the Spearman correlation, which is robust against outliers, was only 0.13 (p = 0.57), indicating a non-significant relationship.

you mentioned that the correlation between the attentional capture effect and the mean amplitude of alpha power in the electrophysiological data might be influenced by an outlier, and you also compared the results of Pearson and Spearman correlation coefficients, which we fully agree with.

It is true that the small sample size of the current study makes the results vulnerable to interference from extreme data. Regarding this point, I have already explained it in the limitations section of the discussion in the revised manuscript:“the sample size of the current study is small, which may render the results vulnerable to interference from extreme cases”

The behavioral data are interesting, but in my opinion, they closely replicate Peters and colleagues (2020) using a different paradigm. In that study, participants memorized four spatial positions that formed the endpoints of two objects, and one object was cued. Similarly, reaction times fluctuated at theta frequency, and there was an anti-phase relationship between the two objects. The main novelty of the present study is that this bias can be transferred to an unrelated task. While the current study extends Peters and colleagues' findings to a different task context, the lack of a thorough, direct comparison with Peters et al. limits the clarity of the novel insights provided.

thank you very much for your attention to the behavioral data and its relevance to the study by Peters et al. (2020). We have noticed that there are similarities in some results between the two studies, which also indicates the stability of the relevant phenomena from one aspect.

However, we would also like to further explain the differences between this study and the study by Peters et al. In the study by Peters et al., participants memorized four spatial positions that formed the endpoints of two objects (one of which was cued), and their results showed that after the two objects disappeared, attention fluctuated at the theta rhythm between their original positions with an inverse correlation. In contrast, the present study explores the manner of memory maintenance indirectly by leveraging the guiding effect of working memory on attention, effectively avoiding the influence of spatial positions.

The study by Peters et al. directly examined differences in probe positions, clearly demonstrating that attention undergoes rhythmic changes at the two spatial locations and persists after the objects vanish, but it hardly clarifies the rhythmicity of working memory performance. Whereas the present study directly investigates such performance using the attention-capture effect of working memory, revealing that when maintaining multiple memory items, their attention-capturing capabilities alternate in dominance, i.e., multiple working memory items alternately become priority templates in a rhythmic manner. This is also some new attempts in the research perspective and method of this study.

The corresponding discussion has been added to the revised part of the paper

“Similar to the present study, Peters et al. had participants memorize four spatial positions forming the endpoints of two objects (one cued), and their results showed that after the two objects disappeared, attention fluctuated at the theta rhythm between their original positions with an inverse correlation; in contrast, the present study explores the manner of memory maintenance indirectly by leveraging the guiding effect of working memory on attention, effectively avoiding the influence of spatial positions—while Peters et al.’s study, which directly examined differences in probe positions, clearly demonstrates that attention undergoes rhythmic changes at the two spatial locations and persists after the objects vanish, it hardly clarifies the rhythmicity of working memory performance, whereas the present study directly investigates such performance using the attention-capture effect of working memory, revealing that when maintaining multiple memory items, their attention-capturing capabilities alternate in dominance, i.e., multiple working memory items alternately become priority templates in a rhythmic manner.”

Reviewer #2 (Public review):

The information provided in the current version of the manuscript is not sufficient to assess the scientific significance of the study.

thank you very much for pointing out the multiple issues in our manuscript. Due to several revisions of this work, including experimental adjustments, there have been some inconsistencies in details. We appreciate you identifying them one by one.  We have made corresponding revisions based on your comments:

(1) In many cases, the details of the experiments or behavioral tasks described in the main text are not consistent with those provided in the Materials and Methods section. Below, I list only a few of these discrepancies as examples:

a) For Experiment 1, the Methods section states that the detection stimulus was presented for 2000 ms (lines 494 and 498), but Figure 1 in the main text indicates a duration of 1500 ms.

we greatly appreciate you catching this inconsistency. We have made unified revisions by referring to the final implemented experimental procedures.  Corresponding revisions have been made in the paper：

b) For Experiment 2, not only is the range of SOAs mentioned in the Methods section inconsistent with that shown in the main text and the corresponding figure, but the task design also differs between sections.

Thank you for bringing this discrepancy to our attention. We have made unified revisions by referring to the final implemented experimental procedures. The correct SOAs are 233：33：867 ms.

Corresponding revisions have been made in the paper：

c) For Experiment 3, the main text indicates that EEG recordings were conducted, but in the Methods section, the EEG recording appears to have been part of Experiment 2 (lines 538-540).

we’re grateful for you noticing this mix-up. In fact, only Experiment 3 is an EEG experiment, and we have made corresponding corrections in the "Methods" section. Corresponding revisions have been made in the paper: “The remaining components after this process were then projected back into the channel space. We extracted data from -500 ms to 2000 ms relative to cue stimulus presentation in Experiment 3.”  

(2) The results described in the text often do not match what is shown in the corresponding figure. For example:

a) In lines 171-178, the SOAs at which a significant difference was found between the two conditions do not appear to match those shown in Figure 2A.

Many thanks for spotting this error. The previous results missed one SOA time, namely 33 ms, leading to a 33 ms difference in time. We have corrected it in the revised manuscript.

Corresponding revisions have been made in the paper：“Specifically, the capture effect of cued items was significantly greater than that of uncued items at SOAs of 267ms (t(24) = 2.72, p = 0.03, Cohen's d = 1.11), 667ms (t(24) = 2.37, p = 0.03, Cohen's d= 0.97) and 833ms (t(24) = 3.53, p = 0.002, Cohen's d = 1.44), while the capture effect of uncued items was significantly greater than that of cued items at SOAs of 333ms (t(24) = 2.97, p = 0.007, Cohen's d = 1.21), 367ms (t(24) = 2.14, p = 0.04, Cohen's d = 0.87), 433ms (t(24 )= 2.49, p = 0.02, Cohen's d = 1.02), 467ms (t(24)=2.37, p = 0.03, Cohen's d = 0.97) and 567ms (t(24)=2.72, p = 0.02, Cohen's d = 1.11). ”

(b) In Figure 4, the figure legend (lines 225-228) does not correspond to the content shown in the figure.

we appreciate you pointing out this oversight. When adjusting the color scheme during the revision of the manuscript, we neglected to revise the legend, which has now been corrected in the revised manuscript.

Corresponding revisions have been made in the paper：“Figure 4. The red line represents the average across all participants of the Fourier transforms of the differences in capture effects between left and right memory items at the individual level. The gray area represents values below the group average of medians derived from 1000 permutations, with each permutation involving Fourier transforms for each participant. *: p < 0.05.”

(c) In Figure 9, not sufficient information is provided within the figure or in the text, making it difficult to understand. Consequently, the results described in the text cannot be clearly linked to the figure.

Thank you for drawing our attention to this issue. We have revised Figure 9 and its legend in the revised manuscript to make them clearer and easier to understand.

Corresponding revisions have been made in the paper

(3) Insufficient information is provided regarding the data analysis procedures, particularly the permutation tests used for the data presented in Figures 2B, 4, and 10. The results shown in these figures are critical for the main conclusions drawn in the manuscript.

we’re thankful for you highlighting this gap. In the revised manuscript, we have provided a more detailed explanation in the "Methods" section, especially regarding the content related to frequency analysis, to make the expression clearer.

Corresponding revisions have been made in the paper：“As shown in Figure 8, the alpha power (8-14 Hz) induced by cued and uncued items alternated in dominance during the memory retention phase. To quantify this rhythmic alternation, we conducted a spectral analysis following these steps: First, we computed the power difference between cued and uncued items within the 8-14 Hz range during the retention phase. These differences were then downsampled to 100 Hz using a 10 ms window for averaging, generating a one-dimensional time series spanning the 0-2000 ms retention period. This time series was subsequently subjected to amplitude spectrum analysis across frequencies from 1 Hz to 50 Hz using Fourier transformation.

To assess the statistical significance of the observed spectral features, we employed a permutation test. Specifically, we randomly shuffled the temporal order of the time series of power differences between cued and uncued items—thereby preserving the amplitude distribution of the data while eliminating temporal correlations in the original sequence—and repeated the Fourier transform and spectral analysis for each shuffled time series. This permutation process was replicated 1000 times to generate a null distribution of spectral power values. A frequency component in the original data was considered statistically significant if its power ranked within the top 5% of the corresponding null distribution (p < 0.05).

We applied the same analytical pipeline to investigate differences in the weighted phase-lag index (wPLI) between the contralateral regions of the two items and the prefrontal cortex during the retention phase. Specifically, wPLI differences (i.e., the difference between the two conditions) were computed, downsampled to 100 Hz using a 10 ms window for averaging to generate a time series spanning 0-2000 ms, and then subjected to amplitude spectrum analysis (1-50 Hz) using Fourier transformation. Significance was assessed via the identical permutation test procedure described above (randomly shuffling the temporal order of the difference time series).”

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an

1- ask a seat mate 2- ask your professor 3- look over the class material or learn it online

what does the space between the elements mean? the HF/mhche...H+F-? what are those letters

How does the strength of the conjugate base relate to the strength of its acid, and why?

these are the top 3 barriers athletes chose for the survey

I thrive in structure, but I also like the idea of being flexible and allowing my students' interests to guide the classroom discussions. I want an atmosphere that is engaging. That's why I think I'll need to be flexible in some areas. It is hard to practice that in the Pre-II classroom setting when you are co-teaching with another person. I did find an article about flexible seating arrangements with movable furniture being a way that allows for a flexible learning environment.

https://www.schooloutfitters.com/blog/3-ways-to-create-an-effective-flexible-classroom?srsltid=AfmBOorDZLNZs6pVij2DHmqnQfphrTVHzZeSMPKaX-uePJufL8uT10tb

Stressors cause pressure, symptoms show how stress affects a person, and coping is how they try to deal with it.

1) Objectives and Priorities 2) Management Tools 3) Stress Generator 4) Perception of Control

Joint Public Review:

Marshall et al describe the effects of altering metabotropic glutamate receptor 5 activity on activity of D1 receptor expressing spiny projection neurons in dorsolateral striatum focusing on two states - locomotion and rest. The authors examine effects of dSPN-specific constitutive mGlu5 deletion in several motor tests to arrive at this finding. Effects of inhibiting the degradation of the endocannabinoid 2-arachidonoyl glycerol are also examined. Overall, this is a valuable study that provides solid new information of relevance to movement disorders and possibly psychosis.

The combination of in vivo cellular calcium imaging, pharmacology, receptor knockout and movement analysis is effectively used. The main findings do not involve gross firing rates or numbers of active neurons, but rather are revealed by specialized measures involving Jaccard coefficient and an assessment of coactivity. The authors conclude that mGlu5 expressed in dSPNs contributes to movement through effects on clustered spatial coactivity of dSPNs. More specifically, reduced mGluR5 increases coactivity during rest (defined as low velocity periods) but not during locomotion periods. The authors observe a role for mGlu5 expression in dSPNs in modulating the frequency of mEPSCs, suggesting a role in presynaptic neurotransmitter release. Some data suggesting the story may be different in the other major SPN subpopulation (iSPNs) are also presented but these studies are relatively underdeveloped leaving some ambiguity as to how cell-selective the findings are. In addition, an occlusion experiment in which the pharmacological mGluR5 agents are delivered to the dSPN mGluR5 KO to clarify if other sites of action are involved beyond the proposed D1-expressing neurons is missing. Finally, the authors present a working model that sets the stage for future experimentation. Overall, this study provides an important and detailed assessment of mGluR5 contributions to striatal circuit function and behavior.

Remaining concerns include:

(1) To clarify that dSPNs are sole site of action, it is necessary to examine effects of the mGlu5 NAM in the dSPN mGlu5 cKO mice. If the effects of the two manipulations occluded one another this would certainly support the hypothesis that the drug effects are mediated by receptors expressed in dSPNs. A similar argument can be made for examining effects of the JNJ PAM in the cKO mice.

(2) There is a concern that the D1 Cre line used (Ey262), which may also target cortical neurons expands the interpretation of the study beyond the striatal populations. Further discussion of this point, particularly in the interpretation of the mGluR5 cKO experiments, would provide a better understanding of the contribution of the paper.

(3) The use of CsF-based whole-cell internal solutions has caused concern in some past studies due to possible interference with G-protein, phosphatase and channel function (https://www.sciencedirect.com/science/article/abs/pii/S1044743104000296, https://www.jneurosci.org/content/jneuro/6/10/2915.full.pdf). It is reassuring the DHPG-induced LTD was still observable with this solution. However, it might be worth examining this plasticity with a different internal to ensure that the magnitude of the agonist effect is not altered by this manipulation.

(4) Behavioral resolution of actions at low velocity that are termed "rest" are not explored in this study. Thus, a remaining ambiguity is whether the activities in rest include only periods of immobility or other low-velocity activities such as grooming or rearing.

Author response:

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

Reviewer #1 (Public review):

“Can the authors offer a hypothesis as to how decreased coactivity promotes increased movement velocity.” 

In our revision we have added an additional metric measuring how spatial coactivity changes during movement onset, the spatial correlation index, which replicates a previous finding that co-activity among proximal neurons is statistically greater surrounding movement onset. We did not find, as outlined in the revision, that mGluR5 manipulations significantly altered this relationship. Our data therefore shows, consistent with that shown previously, that ensembles of dSPNs that are co-active during movement onset, in particular ambulatory movement, are more likely to contain neurons that are closer together and the neurons are highly active. In contrast, rest ensembles contain neurons that are less active but have more highly correlated activity, across all pairwise distances. Additionally, mGluR5 inhibition, genetic or pharmacological, promotes the activation of rest ensembles but does not affect the properties of movement ensembles. Previous studies (e.g. Klaus A. et al., 2017) have shown that neurons in rest ensembles are, in general, unlikely to also be members of movement ensembles, We therefore hypothesize that corticostriatal synapses onto SPNs of rest ensembles are more likely, during spontaneous behavior, to have reduced synaptic weight due to mGluR5 signaling, potentially due to eCB mediated inhibition of neurotransmitter release. Therefore, when we inhibit mGluR5 at these synapses, we increase synaptic weight and increase the probability of activation of this coordinated rest ensemble, which suppresses movement. If, on the other hand, the synapses that govern activation of neurons in movement ensembles have a higher weight, they may be unaffected by mGluR5 inhibition. 

The use of the Jaccard similarity index in this study is not intuitive and not fully explained by the methods or the diagram in Figure 1. 

We have added more detail to the paper to explain the methodology of the jaccard similarity measure. The advantage of this method is that is specifically captures cells that are jointly active, as opposed to jointly inactive and is therefore useful for capturing co-activity in our sparsely active Ca²⁺ imaging data. 

The analysis of a possible 2-AG role in the mGlu5 mediated processes is incomplete. 

We agree that, as an experiment to outline which endocannabinoids are involved in modulating synaptic strength through mGluR5, this experiment alone is not sufficient.

However, our main focus in this paper is how manipulations of mGluR5 affect the spatiotemporal dynamics of dSPNs and we chose not to focus on specific mechanisms of endocannabinoid signaling, though these would certainly be interesting to investigate further in vivo.

It would seem to be a simple experiment to examine effects of the mGlu5 NAM in the dSPN mGlu5 cKO mice. If effects of the two manipulations occluded one another this would certainly support the hypothesis that the drug effects are mediated by receptors expressed in dSPNs. A similar argument can be made for examining effects of the JNJ PAM in the cKO mice. 

We agree that this experiment would be valuable and extend our findings presented in the paper, however, it has practically been outside the scope of the current work. 

Reviewer #2 (Public review):

Pharmacological and genetic manipulations of mGluR5 do not differentially/preferentially modulate the activity of proximal vs distal dSPNs, therefore, it could also be interpreted that mGluR5 is blanketly boosting/suppressing all dSPN activity as opposed to differential proximal/distal spatial relationships. 

As in the response to reviewer 1 above, we have added additional clarification to the text explaining that our manipulations do not differentially affect the co-activity of proximal vs distal dSPNs, this is also quantified throughout the text using the spatial coordination index. However, we disagree that “it could also be interpreted that mGluR5 is blanketly boosting/suppressing all dSPN activity” as we do not observe statistically significant changes in the event rate following either pharmacological or genetic manipulations of mGluR5. Rather, we consistently observe statistically significant changes in co-activity among neurons, the extent to which activity of active neurons during either rest or movement are correlated with each other. This is the central finding of our manuscript, inhibiting or potentiating mGluR5 signaling alters behavior, not by blanket suppression or enhancement of the activity as measured using the event rate, of dSPNs, but by affecting their ensemble dynamic properties.  Co-activity during rest versus ambulatory movement is statistically greater in both proximal and distal cells and inhibiting mGluR5 increases this co-activity and decreases movement. 

For these analyses of prox vs distal and all others, please include the detail of how many proximal vs distal cells were involved and per subject. 

We have added a supplemental table that details the number of cells included per subject in all analyses

Ln. 151-152: Please provide data concerning how volumes of infectivity differ between injecting AAV vs. coating the lens? If these numbers are very different, this could impact the number of Jaccard pairings and bias results. 

While viral injection may lead to a larger volume of expression, with this one photon imaging method only those cells within ~200 microns of the edge of the lens will be able to be resolved, therefore practically, if there is an additional volume of infected tissue outside of the field of view of the lens, it would not affect the results as these neurons will not be resolved by the endoscope camera. Accordingly, the average number of cells detected per session is very similar following each approach (mean # of cells per session with coating 90.93 ± 23.69 cells, with viral injection 90.03 ± 29.29 cells)

Is mGluR5 affecting dSPN activity in other measures beyond co-activity and rate? Does the amplitude of events change?

We have added supplemental data for figures 2, 3, and 5 demonstrating that manipulations of mGluR5 do not affect the amplitude or length of Ca²⁺ events included in the analysis. 

What is the model of mGluR5 signaling in a resting state vs. movement? What other behaviors are occurring when the mouse is in a low velocity "resting state" (0-0.5 cm/s). If this includes other forms of movement (i.e. rearing, grooming) then the animal really isn't in a resting state. This is not mentioned in the open field behavior section of the methods and should be described (Ln. 486) in addition to greater explanation of what behavior measures were obtained from the video tracking software (only locomotion?)

It would be very interesting to determine if during “rest,” when the animals is not engaged in ambulatory behavior, it may be engaged in some fine motor behavior. However, the resolution of the cameras used to measure locomotor activity in this dataset does not allow us to do this. 

There is large variability in co-activity in proximal dSPNs when animals are "resting" (2j). Could this be explained by different behavior states within your definition of "rest"?

We agree that if the animal is engaging in fine motor behavior that we cannot resolve with our behavior setup, this could produce some variability in coactivity. However, as shown previously (e.g. Klaus A. et al., 2017), ensembles active when the animal is not moving (our definition of “resting”), regardless of additional fine motor behaviors the animal may be engaged in when not moving, are substantially different that those ensembles that are active when the animal is moving. We therefore expect that this may limit, although potentially not eliminate, variability due to different behavioral states we may have grouped into our “resting” category. Unfortunately, as mentioned above, we are not able resolve variations in fine motor output in this behavioral data. 

Have you performed IHC, ISH or another measure to validate D1 cell specific cKO?

The mGluR5^loxP/loxP mice used in this study were characterized previously by our lab (Xu et al., 2009), we used the same mice here with a different, but also published and characterized Cre-driver line, Drd1a-Cre Ey262 (Gerfen et al., 2013).

Why are the "Mean Norm Co-activity" values in 5e so high in this experiment relative to figures 2-4?  

In experiments where we treated the same animal with vehicle and a drug (i.e., experiments in Figure 2 and 3), we normalized the values for each animal in the drug treatment group to the distal bin of that animal following vehicle treatment. This allowed us to more clearly resolve the changes within each animal due to drug treatment. As comparisons in the data in figure 5 d–f are between different animals (rather than different treatments of the same animal) we could not perform this normalization procedure.  

Reviewer #3 (Public review):

Some D1 Cre lines have expression in the cortex. Which specific Cre line was used in this study? 

We used, Drd1a-Cre Ey262. This is included in methods. 

The text says JNJ treatment .... increased locomotor speed (Figure 3b) and increased the duration but not frequency of movement bouts (Figure 3c, d). However, the statistics of the figure legends say: however the change in mean velocity (3b) is not significant (p=0.060, U=3, Mann-Whitney U test), nor is the mean bout length during vehicle and JNJ (p=0.060, U=3, Mann-Whitney U test) (3d) Comparison of mean number of bouts of each animal during vehicle and JNJ (p=0.403, U=8, Mann-Whitney U test). 

This has been corrected to indicate only the change in time spend at rest is statistically significant.

This effect was most pronounced during periods of rest (Figure 3i, j). The decrease was only in rest? Are the colors in Figure 3J inverted? Therefore, JNJ treatment had effects that were qualitatively the inverse to the effects of fenobam on locomotion and dSPN activity. 

We have corrected the text to state that, overall, and during periods of rest but not movement, JNJ had effects that were qualitatively the opposite of fenobam.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This work addresses an important question in the field of Drosophila aggression and mating. Prior social isolation is known to increase aggression in males, manifesting as increased lunging, which is suppressed by group housing (GH). However, it is also known that single housed (SH) males, despite their higher attempts to court females, are less successful. Here, Gao et al., develop a modified aggression assay to address this issue by recording aggression in Drosophila males for 2 hours, with a virgin female immobilized by burying its head in the food. They found that while SH males frequently lunge in this assay, GH males switch to higher intensity but very low frequency tussling. Constitutive neuronal silencing and activation experiments implicate cVA sensing Or67d neurons in promoting high frequency lunging, similar to earlier studies, whereas Or47b neurons promote low frequency but higher intensity tussling. Optogenetic activation revealed that three pairs of pC1SS2 neurons increase tussling. Cell-type-specific DsxM manipulations combined with morphological analysis of pC1SS2 neurons and side-by-side tussling quantification link the developmental role of DsxM to the functional output of these aggression-promoting cells. In contrast, although optogenetic activation of P1a neurons in the dark did not increase tussling, thermogenetic activation under visible light drove aggressive tussling. Using a further modified aggression assay, GH males exhibit increased tussling and maintain territorial control, which could contribute to a mating advantage over SH males, although direct measures of reproductive success are still needed.

Strengths:

Through a series of clever neurogenetic and behavioral approaches, the authors implicate specific subsets of ORNs and pC1 neurons in promoting distinct forms of aggressive behavior, particularly tussling. They have devised a refined territorial control paradigm, which appears more robust than earlier assays using a food cup (Chen et al., 2002). This new setup is relatively clutter-free and could be amenable to future automation using computer vision approaches. The updated Figure 5, which combines cell-type-specific developmental manipulation of pC1SS2 neurons with behavioral output, provides a link between developmental mechanisms and functional aggression circuits. The manuscript is generally well written, and the claims are largely supported by the data.

Thank you for the precise summary of the manuscript and acknowledgment of the novelty and significance of the study.

Weakness:

Although most concerns have been addressed, the manuscript still lacks a rigorous, objective method for quantifying lunging and tussling. Because scoring appears to have been done manually and a single lunge in a 30 fps video spans only 2-3 frames, the 0.2 s cutoff seems arbitrary, and there are no objective criteria distinguishing reciprocal lunging from tussling. Despite this, the study offers valuable insights into the neural and behavioral mechanisms of Drosophila aggression.

Thank you for this comment. The duration of each lunge was measured by analyzing the videos frame by frame—from the frame before the initiation of the lunge to the frame after its completion—resulting in an average span of 3–5 frames. Given a frame rate of 30 fps, this corresponds to approximately 0.1–0.17 seconds. We acknowledge that there are certain limitations for manually quantifying the two types of aggressive behaviors, which has now been stated in the newly added “Limitations of the Study” section in the revised manuscript.

Reviewer #2 (Public review):

Summary:

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

Strengths:

A behavioral experiment system that allows stable observation of tussling, which could not be easily analyzed due to its low-frequency, would be very useful. The experimental setup itself is relatively simple, just the addition of a female to the platform, so it should be applicable to future research. The finding about the relationship between the social experience and the aggression mode change is quite novel. Although the intensity of aggression changes with the social experience was already reported in several papers (Liu et al., 2011 etc), the fact that the behavioral mode itself changes significantly has rarely been addressed, and is extremely interesting. The identification of sensory and central neurons required for the tussling makes appropriate use of the genetic tools and the results are clear. A major strength of this study in neurobiology is the finding that another group of neurons (Or47b-expressing olfactory neurons and pC1[SS2] neurons), distinct from the group of neurons previously thought to be involved in low-intensity aggression (i.e. lunging), function in the tussling behavior. Furthermore, the results showing that the regulation of aggression by pC1[SS2] neurons is based on the function of the dsx gene will bring a new perspective to the field. Further investigation of the detailed circuit analysis is expected to elucidate the neural substrate of the conflict between the two aggression modes. The experimental systems examining the territory control and the reproductive competition in Fig. 6 are novel and have advantages in exploring their biological significance. It is important to note that in addition to showing the effects of age and social experience on territorial and mating behaviors, the authors experimentally demonstrated that altered fighting strategy has effects with respect to these behaviors.

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

Reviewer #3 (Public review):

In this revised manuscript, Gao et al. presented a series of well-controlled behavioral data showing that tussling, a form of high-intensity fighting among male fruit flies (Drosophila melanogaster) is enhanced specifically among socially experienced and relatively old males. Moreover, results of behavioral assays led authors to suggest that increased tussling among socially experienced males may increase mating success. They also concluded that tussling is controlled by a class of olfactory sensory neurons and sexually dimorphic central neurons that are distinct from pathways known to control lunges, a common male-type attack behavior.

A major strength of this work is that it is the first attempt to characterize behavioral function and neural circuit associated with Drosophila tussling. Many animal species use both low-intensity and high-intensity tactics to resolve conflicts. High-intensity tactics are mostly reserved for escalated fights, which are relatively rare. Because of this, tussling in the flies, like high-intensity fights in other animal species, have not been systematically investigated. Previous studies on fly aggressive behavior have often used socially isolated, relatively young flies within a short observation duration. Their discovery that 1) older (14-days old) flies tend to tussle more often than younger (2 to 7-days-old) flies, 2) group-reared flies tend to tussle more often than socially isolated flies, and 3) flies tend to tussle at later stage (mostly ~15 minutes after the onset of fighting), are the result of their creativity to look outside of conventional experimental settings. These new findings are key for quantitatively characterizing this interesting yet under-studied behavior.

Newly presented data have made several conclusions convincing. Detailed descriptions of methods to quantify behaviors help understand the basis of their claims by improving transparency. However, I remain concerned about authors' persistent attempt to link the high intensity aggression to reproductive success. The authors' effort to "tone down" the link between the two phenomena remains insufficient. There are purely correlational. I reiterate this issue because the overall value of the manuscript would not change with or without this claim.

Thank you for acknowledging the novelty and significance of the study. Regarding the relationship you mentioned between high-intensity aggression and reproductive success, we further toned down the statement between them throughout the manuscript in the revised manuscript. We also modified the title to “Social Experience Shapes Fighting Strategies in Drosophila”. In addition, we now added a ‘Limitations of the Study’ section to clearly state the correlation between tussling and reproductive success.

Reviewer #1 (Recommendations for the authors):

If possible, mention the EM-connectome data showing the minimal interneuronal path from Or47b ORNs to pC1SS2 neurons (even if derived from the female connectome), which can strengthen the model of parallel sensory-central pathways.

Thank you for this comment. According to data from the EM connectome, connecting Or47b ORNs to pC1d neurons requires at least two intermediate neurons. An example minimal pathway is: ORN_VA1v (L) → AL-AST1 (L) → PLP245 (L) → pC1d (R). We have added this point in the Discussion section of the revised manuscript.

I'm not convinced that labeling lunges as "gentle" combat behavior works, either in the abstract or elsewhere. While lunging is indeed a lower-intensity form of aggression compared to tussling, applying anthropomorphic descriptors risks misleading readers.

Thank you for this comment. We now use “low-intensity” instead of “gentle” to describe lunging.

In Materials & Methods, please cross-check all figure-panel references after the recent re-numbering (e.g. "Figure 5A6A" etc.).

Thank you for this comment. We have thoroughly verified the figure panel references in the Materials & Methods section.

Ensure that Table S1 is clearly cited in the main text where you first describe fly genotypes.

Thank you for this comment. We have now cited Table S1 in the main text.

There are multiple grammatical errors and typos throughout the manuscript. Please correct them. Some examples are below, but this is not an exhaustive list:

Line 98-102 requires rephrasing as the results are already published and not being observed by the authors.

Thank you for this comment. We have revised the manuscript to “we occasionally observed the high-intensity boxing and tussling behavior in male flies as previously reported (Chen et al., 2002; Nilsen et al., 2004), which….”

line 116- lower not 'lowed'.

Corrected.

line 942 & 945- knock-down males not 'knocking down males'.

Corrected. Thank you very much for these comments.

Reviewer #2 (Recommendations for the authors):

The authors have almost completely answered the major comments I have noted on the ver.1 manuscript: (1) They clearly show changes in fighting strategy in the territory control behavior experiment in Fig. 6-figure supplements. (2) A detailed description of how aggressive behavior is measured. Thus, I am convinced by this revision.

Thank you for these comments that make the manuscript a better version.

Furthermore, in Fig. 5, which examined the relationship of pC1[SS2] characteristics with the function of dsx, is a novel data and very interesting. I look forward to further developments.

Thank you. We will continue to explore this part in our future study.

However, one point still concerns me.

Line 192: Although the authors describe it as "usage-dependent," the trans-Tango technique is essentially a postsynaptic cell-labeling technique. It is possible that the labeling intensity in postsynaptic cells increases from the change in expression levels of the Or47b gene due to GH. However, there is no difference in the expression level of the Or47b gene labeled by GFP between SH and GH. Therefore, we cannot conclude that the expression of the Or47b gene is increased by rearing conditions.

The original paper on trans-TANGO (Talay et al., 2017) does not discuss the usage-dependency. A review of trans-synaptic labeling techniques (Ni, Front Neural Circuits. 2021) discusses that the increase in trans-TANGO signaling with aging may be related to synaptic strength, but there is no experimental evidence for this. In my opinion, the results in Figure 3-figure supplement 2 only weakly suggest that the increase in trans-TANGO signaling may be explained by an increase in synaptic strength due to group rearing.

We appreciate the reviewer’s insightful comment regarding the interpretation of the trans-Tango signal. Indeed, the original trans-Tango study (Talay et al., 2017) does not claim that the method is usage-dependent. The observed increase in trans-Tango labeling with age, as reported in their supplemental figures, may reflect accumulation over time, potentially influenced by synaptic maturation or increased component expression. To avoid overstating our results, we have revised the relevant statement in the manuscript to remove the term "usage-dependent" and now describe the change in trans-Tango signal more cautiously.  

Reviewer #3 (Recommendations for the authors):

Below are the cases where their professed attempts to "tone down the statement" appear ignored:

Lines 27-29:

"Our findings... suggest how social experience shapes fighting strategies to optimize reproductive success".

We have now revised the manuscript to “Our findings… suggest that social experience may shape fighting strategies to optimize reproductive success.”

Lines 85-86:

"... discover that this infrequent yet intense form of combat is... crucial for territory dominance and mating competition".

We have now revised the manuscript to “…discover that this infrequent yet intense form of combat is enhanced by social enrichment, while the low-intensity lunging is suppressed by social enrichment.” 

Lines 335-339:

"Here, we found that... GH males tend to... increase the high-intensity tussling, which enhances their territorial and mating competition."

We have removed “which enhances their territorial and mating competition” in the revised manuscript.

Lines 343-344:

"... presenting a paradox between social experience, aggression and reproductive success. Our result resolved this paradox..."

We have now revised the manuscript to “...Our results provide an explanation for this paradox…”

Lines 355-358:

"Interestingly, we found that the mating advantage gained through social enrichment can even offset the mating disadvantage associated with aging, further supporting the vital role of shifting fighting strategies in experienced, aged males."

We have removed “further supporting the vital role of shifting fighting strategies in experienced, aged males” in the revised manuscript.

Lines 361-362:

"These results separate the function of the two fighting forms and rectify out understanding of how social experiences regulate aggression and reproductive success."

We have removed this sentence in the revised manuscript.

Some may say that a speculative statement is harmless, but I think it indeed is harmful unless it is clearly indicated as a speculation. It is regrettable that authors remain reluctant to change their claim without providing any new supporting evidence. All three reviewers raised the same concern in the first round of review.

We apologize for not making the speculative nature of the statement clearer in the previous version. In the revised manuscript, we have now explicitly rephrased sentences to only suggest a correlation but not a causal link between tussling and reproductive success.

I have no choice but to keep my evaluation of the manuscript as "Incomplete" unless the authors thoroughly eliminate any attempt to link these two. This must go beyond changing a few words in the lines listed above.

Thank you for this comment. In addition to the lines listed above, we carefully checked all statements regarding the correlation between fighting strategies and reproductive success throughout the full text. Furthermore, we have also added a “Limitations of the Study” section to address the shortcomings of this study in the revised manuscript.

I do not have the same level of concern over the interpretation of Fig. 6A-C, because this is directly linked to aggressive interactions. Even if the socially isolated males do not engage in tussling, it is not a leap to assume that a different fighting tactic of socially experienced males can give them an advantage in defending a territory. To me, this is a sufficient ethological link with the observed behavioral change.

Thank you for this insightful comment.

The following are relatively minor, although important, concerns.

I beg to differ over the authors' definition of "tussling". Supplemental movies S1 and S2 appear to include "tussling" bouts in which 2 flies lunging at each other in rapid succession, and supplemental movie S3 appears to include bouts of "holding", in which one fly holds the opponent's wings and shakes vigorously. These cases suggest that the definition of "tussling" as opposed to "lunging" has a subjective element. However, I would not delve on this matter further because it is impossible to be completely objective over behavioral classification, even by using a computational method. An important point is that the definition is applied consistently within the publication. I have no reason to doubt that this was not the case.

Thank you for this comment. Since the analysis of tussling behavior was conducted manually, it is challenging to achieve complete objectivity. However, we made every effort to apply consistent criteria throughout the analysis. We have added a “Limitations of the Study” section in the revised manuscript to clearly state this caveat. We appreciate your understanding.

Authors now state that "all tester flies were loaded by cold anesthesia" (lines 432-433). I would like to draw attention to the well-known fact that anesthesia, whether by ice or by CO2, are long known to affect fly's subsequent behaviors (for aggression, see Trannoy S. et al., Learn. Mem. 2015. 22: 64-68). It will be prudent to acknowledge the possibility that this handling method could have contributed to unusually high levels of spontaneous tussling, which has not been reported elsewhere before.

Thank you for this comment. The increased tussling behavior observed in our study is unlikely due to cold anesthesia, as noted by Trannoy S. et al. (2015), cold anesthesia profoundly reduces locomotion and general aggressiveness in flies. We acknowledge that the use of cold anesthesia in behavioral experiments may have potential effects on aggression. To minimize this influence, we allowed the flies to recover and adapt for at least 30 minutes before behavioral recording. Moreover, both control and experimental groups were treated in exactly the same manner to ensure consistency.

It is intriguing that pC1SS2 neurons are dsx+ but fru-. Authors convincingly demonstrated that these neurons are clearly distinct from the P1a neurons, a well-characterized hub for male social behaviors. It is possible that pC1SS2 neurons overlap with previously characterized dsx+ neurons that are important for male aggressions (measured by lunges), such as in Koganezawa et al., Curr. Biol. 2016 and Chiu et al., Cell 2020, a point authors could have explicitly raised.

Thank you for this comment. We have added this point into the Discussion section of the revised manuscript, as follows: “That tussling-promoting… aggression (Koganezawa et al., 2016). Moreover, the anatomical features of pC1^SS2 neurons are highly similar to the male-specific aggression-promoting (MAP) neurons identified by another previous study (Chiu et al., 2021).

I acknowledge the authors' courage to initiate an investigation to a less characterized, high intensity fighting behavior. Tussling requires the simultaneous engagement of two flies. Even if there are confusion over the distinction between lunges and tussling, authors' conclusion that socially experienced flies and socially isolated flies employ distinct fighting strategy is convincing. The concern I raised above is about the interpretation of the data, not about the quality of data.

Thank you for your constructive comments to make this manuscript better.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary: 

This study provides compelling evidence suggesting that ghrelin, a molecule released in the surroundings of the major adult brain neurogenic niche (V-SVZ) by blood vessels with high blood flow, controls the migration of newborn interneurons towards the olfactory bulbs. 

Strengths:

This study is a tour de force as it provides a solid set of data obtained by time-lapse recordings in vivo. The data demonstrate that the migration and guidance of newborn neurons rely on factors released by selective types of blood vessels. 

Weaknesses:

Some intermediate conclusions are weak and may be reinforced by additional experiments. 

We thank the reviewer for the thoughtful evaluation and constructive comments outlined in the “Recommendations for The Authors”. In response, we have incorporated additional data, revised relevant figures, and clarified explanations in the revised manuscript.

Reviewer #2 (Public review)

Summary: 

The authors establish a close spatial relationship between RMS neurons and blood vessels. They demonstrated that high blood flow was correlated with migratory speed. In vitro, they demonstrate that Ghrelin functions as a motogen that increases migratory speed through augmentation of actin cup formation. The authors proceed to demonstrate through the knockdown of the Ghrelin receptor that fewer RMS neurons reach the OB.

They show the opposite is true when the animal is fasted. 

Strengths: 

Compelling evidence of close association of RMS neurons with blood vessels (tissue clearing 3D), preferentially arterioles. Good use of 2-photon imaging to demonstrate migratory speed and its correlation with blood flow. In vitro analysis of Ghrelin administration to cultured RMS neurons, actin visualization, Ghsr1KD, is solid and compelling. 

We sincerely thank the reviewer for the encouraging comments and helpful suggestions. As noted, our original manuscript lacked sufficient in vivo evidence connecting blood flow with ghrelin signaling. To address this, we have added new data and revised the explanations throughout the manuscript as described below.

Weaknesses: 

(1) Novelty of findings attenuated due to prior work, especially Li et al., Experimental Neurology 2014. Here, the authors demonstrated that Ghrelin enhances migration in adultborn neurons in the SVZ and RMS. 

We agree with the reviewer that the idea that ghrelin enhances migration of new neurons is not entirely novel. The study by Li et al. (2014) provided critical insights that guided our investigation into ghrelin as a blood-derived factor promoting neuronal migration. However, our study expands on this by demonstrating that ghrelin directly stimulates migration via GHSR1a in cultured new neurons, and we further identified the cellular and cytoskeletal mechanisms involved. Specifically, we showed that ghrelin enhances somal translocation by activating actin dynamics at the rear of the cell soma. We have revised the Results and Discussion sections accordingly to emphasize these novel aspects as follows:

“A previous study demonstrated that the migration of V-SVZ-derived new neurons was attenuated in ghrelin knockout mice (Li et al., 2014). In our study, we found that the migration of cultured new neurons was enhanced by the application of ghrelin to the culture medium, and this effect was abolished by Ghsr1a knockdown (KD). These findings suggest that ghrelin directly stimulates neuronal migration through its receptor, GHSR1a, on new neurons. A previous study showed that GHSR1a is expressed in various regions of the brain (Zigman et al., 2006). In our experiments, new neuron-specific KD of Ghsr1a indicated that ghrelin signaling acts in a cell-autonomous manner to regulate neuronal migration.” (Discussion, page 13, lines 10–18)

“Furthermore, we identified the cellular and cytoskeletal mechanisms underlying this effect on migration. The results indicate that ghrelin enhances somal translocation during migration by activating actin cytoskeletal dynamics at the rear of the neuronal soma.” (Discussion, page 13, lines 24–26)

(2) The evidence for blood delivery of Ghrelin is not very convincing. Fluorescently-labeled Ghrelin appears to be found throughout the brain parenchyma, irrespective of the distance from vessels. It is also not clear from the data whether there is a link between increased blood flow and Ghrelin delivery. 

We agree that the correlation between blood flow and ghrelin transcytosis is not very convincing in our study. As the reviewer pointed out, Figure 3A gives the impression that fluorescent-labeled ghrelin is uniformly distributed throughout the brain parenchyma. However, high-magnification images newly added in Figure 3 show that some, but not all, vessels have particularly strong fluorescent signals in the parenchymal area adjacent to the abluminal side of vascular endothelial cells, visualized by CD31 immunostaining (Feng et al., 2004) (Figure 3A′, A′′). To quantify these observations, we defined two regions: Area I (perivascular area), within 10 μm of the abluminal surface of CD31-positive endothelium; and Area II (distant area), located 10–20 μm away (Figure 3E). Of note, Area I corresponds to the perivascular region where new neurons are frequently observed (Figure 1).

Importantly, we found strong ghrelin signals in vascular endothelial cells of endomucin-negative high-flow vessels (Figure 3C, D). This suggests that transcytosis of blood-derived ghrelin may occur more frequently in high-flow vessels due to increased endocytosis at the endothelium. To test this, we quantified signal gradients in the extra-vessel regions as fold changes (Area I / Area II), as illustrated in Figure 3E. The proportion of vessel segments with >1.5-fold increases was significantly higher in endomucin-negative vessels than in endomucin-positive ones (Figure 3F). Furthermore, vessels with >2-fold increases were observed exclusively in the endomucinnegative group (6.48% ± 1.18%). 

These data suggest that, in high-flow vessels, blood-derived ghrelin accumulates more in the immediate perivascular region than in areas further away. This supports the possibility that elevated blood flow delivers a larger amount of ghrelin to the vascular endothelium, enhancing its transcytosis into adjacent brain parenchyma. This mechanism may underlie the preferential migration of new neurons along perivascular regions with high blood flow, as shown in Figure 1.  We have incorporated this new data in Figure 3 and corresponding explanations into the Results, Figure legend and Methods

(3) The in vivo link between Ghsr1KD and migratory speed is not established. Given the strong work to open the study on blood flow and migratory speed and the in vitro evidence that migratory speed is augmented by Ghrelin, the paper would be much stronger with direct measurement of migration speed upon Ghsr1KD. Indeed, blood flow should also be measured in this experiment since it would address concerns in 2. If blood flow and ghrelin delivery are linked, one would expect that Ghsr1KD neurons would not exhibit increased migratory speed when associated with slow or fast blood flow vessels. 

In Figure 3, we showed that ghrelin transcytosis occurs preferentially in high-flow vessels, suggesting a role for ghrelin in mediating the effects of blood flow on neuronal migration. However, whether this dependence is solely attributable to ghrelin signaling remains unclear. 

To address this, we tested whether Ghsr1a-KD modifies the impact of reduced blood  flow on neuronal migration by combining Ghsr1a-KD with bilateral common carotid artery stenosis (BCAS), a chronic cerebral hypoperfusion model (Figure S9A). We found that BCAS decreased the percentage of Ghsr1a-KD new neurons reaching the OB, similar to the effect seen in control neurons (Figure S9B, see also Figure 2A–C). This suggests that blood flow influences neuronal migration even under Ghsr1a-KD conditions. 

Furthermore, we analyzed the distribution of Ghsr1a-KD neurons with respect to vessel flow characteristics. Even under Ghsr1a-KD, a higher proportion of new neurons were located in the area of endomucin-negative (high-flow) vessels compared with endomucin-positive (low-flow) vessels (Figure S9C), indicating that Ghsr1a-KD does not abolish the preferential association of migrating neurons with high flow vessels. These findings suggest that although ghrelin signaling contributes to blood flow-dependent migration, it is not the sole factor. Other blood-derived signals may also mediate this effect. We have included these new data in Figure S9 and updated the corresponding sections in the Results

Reviewer #1 (Recommendations for the authors) :

Major 

Page 6, Line 13. Please provide in the result section some explanation about how photothrombic clot is induced.  

We added the following explanation to the Results section to clarify the method used to induce photothrombotic clot formation.

“For clot formation, a restricted area of selected vessels was irradiated by a two-photon laser immediately after intravenous injection of rose bengal.” (Results, Page 7, lines 27–28)

Page 6, Line 18. The authors use the marmoset as an additional experimental model. Here, V-SVZ-derived newborn neurons migrate in other brain regions as compared to rodents. Please provide a clear rationale for moving from rodents to "common marmosets" as an experiment model. And why use marmosets only for this set of experiments? 

We clarified the rationale for using common marmosets in addition to mice as follows:

“Because blood vessel-guided neuronal migration in the adult brain is a conserved phenomenon across species (Kishimoto et al., 2011; Akter et al., 2021; Shvedov et al., 2024), we hypothesized that blood flow may also influence neuronal migration in other brain regions of primates. The neocortex, which supports higher-order brain functions and has undergone evolutionary expansion in primates, was selected as a target region. In common marmosets, but not in mice, V-SVZ-derived new neurons migrate toward the neocortex and ventral striatum (Akter et al., 2021) (Supplemental Movies S4 and S5).” (Results, Page 6, lines 19–25)

Figure 2B. The experimental setup is possibly problematic as the lentiviral tracing measurement does not take into consideration the rate of neurogenesis or newborn neuron survival. Can authors assess the rate of proliferation and survival in the VSVZ/RMS upon BCAS to decipher whether the reduced number of cells observed in the OB only results from migration changes? (comparable remark stands for Figure 5) 

To evaluate whether the reduction in the number of new neurons observed in the OB after BCAS (Figure 2B, C) is due solely to impaired migration, we assessed cell proliferation and survival in the V-SVZ and RMS. Specifically, we quantified the density of Ki67+ proliferating cells and cleaved caspase-3+ apoptotic cells in the sham and BCAS groups. BCAS significantly decreased cell proliferation and increased cell death in both the V-SVZ and RMS (Figure S4), suggesting that reduced neurogenesis and/or survival may contribute to the decreased neuronal distribution in the OB. 

Although we cannot exclude the possibility that changes in cell proliferation or survival contributed to this effect, our photothrombotic clot formation experiments are better suited to directly examine how acute reduction in blood flow affects neuronal migration. These experiments allowed us to measure the migration speed of new neurons shortly after inducing localized blood flow inhibition. We found that clot formation significantly reduced the migration speed of new neurons (Figure 2E, H), indicating that blood flow changes directly impair neuronal migration in the adult brain. 

We have included these new data in Figure S4 and updated the corresponding text in the Results, Discussion, Figure legend, and Methods as follows:

Figure 3. About ghrelin signaling. It is unclear whether its transcytosis occurs in endomucin-negative because of the high bloodstream flow. How can this be explained? What happens upon BCAS, is there still a close relation between ghrelin transcytosis, blood flow, and neuron migration? 

As correctly noted, our initial explanation and data did not provide sufficient evidence that higher blood flow delivers a larger amount of ghrelin into the brain parenchyma. We found that some vessels had particularly strong fluorescent signals in the parenchymal area adjacent to the abluminal surface of vascular endothelial cells, as visualized by CD31 immunostaining (Feng et al., 2004) (Figure 3A′, A′′). On the basis of our observation that strong fluorescent signals were detected in vascular endothelial cells of endomucin-negative (high-flow) vessels (Figure 3C, D), we hypothesized that ghrelin transcytosis may occur more frequently in high-flow vessels due to increased endocytosis at the vessel endothelium. 

To test this hypothesis, we quantified signal gradients in the extra-vessel regions by calculating fold changes in fluorescent intensity between two zones: Area I (0–10 μm from the abluminal surface of the endothelium) and Area II (10–20 μm away), as illustrated in Figure 3E. Area I corresponds to the perivascular region where new neurons are frequently found (Figure 1). We found that the proportion of vessel segments with >1.5-fold signal increase in Area I relative to Area II was significantly higher in endomucin-negative vessels than endomucin-positive ones (Figure 3F). Furthermore, vessel segments with >2-fold increases were observed exclusively in the endomucin-negative group (6.48% ± 1.18%). These results support the idea that higher blood flow increases the amount of ghrelin that reaches the luminal surface of vascular endothelial cells, thereby increasing the possibility of ghrelin transcytosis into the brain parenchyma.

We also examined whether blood flow inhibition–induced by BCAS or photothrombotic clot formation–affects the relationship between ghrelin transcytosis, blood flow, and neuronal migration. The above results suggest that blood flow reduction may decrease ghrelin transcytosis, thereby contributing to impaired neuronal migration. To further explore this, we analyzed the distribution of new neurons around high- versus low-flow vessels under BCAS conditions. In the BCAS group, we still observed a higher density of new neurons in the region of high-flow (endomucin-negative) vessels compared with in low-flow (endomucin-positive) ones (Figure S9C). This suggests that even under reduced blood flow, neuronal migration preferentially occurs near high-flow vessels. Taken together, these results suggest that ghrelin transcytosis, blood flow and neuronal migration are connected, and that this relationship persists under conditions of blood flow reduction.

Figure 4. Is ghrelin controlling both individual Dcx+ neuron migration as well as chain migration (cells moving more together)? This should be assessed and clarified. 

How is ghrelin controlling actin dynamics in newborn migrating neurons? Since somal translocation speed and somal stride length are both modulated by ghrelin, this factor may also control MT remodeling, could that be checked? 

We have revised the manuscript to better explain the role of ghrelin in both modes of neuronal migration–chain and individual. Initially, we demonstrated that ghrelin enhances the migration of new neurons in V-SVZ culture (Figure 4A, B), where these neurons migrate outward as chains, indicating that ghrelin facilitates chain migration. In subsequent in vitro experiments (Figure 4C–M), we showed that ghrelin also enhances the migration of individual neurons. To examine this in vivo, we injected Ghsr1a-KD and control lentiviruses into two different anatomical regions: the V-SVZ, where chain migration originates, and the OB core, where new neurons migrate individually. These experiments enabled us to assess the role of ghrelin signaling in each mode of migration independently. We found that ghrelin enhanced both chain migration in the RMS and individual migration in the OB. These results indicate that ghrelin signaling facilitates both forms of neuronal migration. We added the following text in the Results section:

“To assess the direct effect of ghrelin on neuronal migration, we applied recombinant ghrelin to V-SVZ cultures, in which new neurons emerge and migrate as chains (Figure 4A). Ghrelin significantly increased the migration distance of these neurons (Figure 4B), indicating enhanced chain migration. We then used super-resolution time-lapse imaging to examine individually migrating neurons with or without knockdown (KD) of growth hormone secretagogue receptor 1a (GHSR1a), a ghrelin receptor expressed in V-SVZ-derived new neurons (Li et al., 2014) (Figure 4C). Ghrelin enhanced the migration speed of control cells (lacZ-KD) cells, indicating that it also facilitates individual migration (Figure 4D).” (Results, Page 9, lines 5–12)

“Of the total labeled Dcx+ cells, the percentage of Dcx+ cells reaching the GL was significantly lower in the Ghsr1a-KD group than in the control group (Figure 5B, C), suggesting that ghrelin enhances individual radial migration of new neurons in the OB.” (Results, Page 10, lines 5–8) “These data indicate that ghrelin signaling facilitates both individual migration in the OB and chain migration in the RMS.” (Results, Page 10, lines 17–18)

We also added discussion on how ghrelin may regulate cytoskeletal dynamics in migrating neurons. Ghrelin signaling has been reported to control actin cytoskeletal remodeling in astrocytoma cells (Dixit et al., 2006), which led us to investigate similar effects in migrating neurons. Rac, a member of the Rho GTPase family, was shown to mediate this actin remodeling in astrocytoma migration, suggesting it may also be involved in ghrelin-induced actin cup formation in new neurons. Furthermore, because somal translocation depends not only on actin but also on microtubule dynamics (Kaneko et al., 2017), it is possible that ghrelin influences both systems. Supporting this idea, ghrelin signaling was shown to modulate microtubule behavior via SFK-dependent phosphorylation of α-tubulin (Slomiany and Slomiany, 2017). These findings suggest that ghrelin may enhance somal translocation through coordinated regulation of both the actin and microtubule systems. We added following text in the Results and Discussion sections:

“Ghrelin signaling has been reported to regulate actin cytoskeletal dynamics in astrocytoma cells (Dixit et al., 2006), which led us to examine whether a similar mechanism operates in migrating neurons.”(Results, Page 9, lines 23–25)

“Further studies are needed to elucidate how ghrelin promotes actin cup formation in migrating neurons. Given that Rac, a Rho family GTPase, mediates actin remodeling downstream of ghrelin in astrocytoma cells (Dixit et al., 2006), it is possible that Rac may also be involved in ghrelininduced cytoskeletal regulation in new neurons.” (Discussion, Page 13, lines 28–31)

“In addition to actin remodeling, ghrelin may regulate microtubule dynamics. Ghrelin signaling was shown to modulate microtubules via SFK-dependent phosphorylation of α-tubulin (Slomiany and Slomiany, 2017), raising the possibility that ghrelin promotes somal translocation of new neurons through coordinated regulation of both actin and microtubule networks (Kaneko et al., 2017).” (Discussion, Page 13, line 31–Page 14, line 2)

It would also be informative to provide immunolabeling of Ghsr1 in the V-SVZ / RMS/ OB to have a clear picture of the expression pattern of this receptor. Newborn neurons migrate along blood vessels, which are surrounded by astrocytes that have also been reported to express Ghsr1, thus could newborn neuron migration change may also arise from activation of Ghsr1 in their surrounding astrocytes? 

A previous study reported that GHSR1a is expressed in DCX+ new neurons in the RMS and OB, and in V-SVZ neural progenitor cells (Li et al., 2014). To visualize the spatial expression pattern of Ghsr1a, we performed RNAscope in situ hybridization because specific anti-GHSR1a antibodies suitable for immunohistochemistry were not available. Consistent with the previous report, we detected Ghsr1a mRNA in DCX+ new neurons in the VSVZ, RMS, and OB (Figure S5A), indicating that new neurons directly receive ghrelin signaling. 

Moreover, our KD experiments demonstrated that ghrelin enhanced the migration of new neurons in a cell-autonomous manner via GHSR1a (Figure 4, 5). Nevertheless, a recent study (Stark et al., 2024) showed that GHSR1a was expressed in various cell types, including glutamatergic and GABAergic neurons, suggesting that ghrelin may also exert non-cellautonomous effects on neuronal migration. Given the presence of diverse cell types, including neurons, microglia, pericytes, and astrocytes, along the migratory route, it remains possible that GHSR1a activation in these neighboring cells contributes to the overall regulation of neuronal migration. 

Figure 5. About the in vivo knockdown of Ghsr1a. The results section (page 9, line 3) mentioned that mice were either injected with one or the other construct but Figure 5 shows coincidence of GFP and dsRed positive cells. Were control and Ghsr1a shRNAs injected together into the same mouse? Could you quantify the number of cells in green (control), red (Ghsr1a KD), and yellow (both)? Won't they mostly be yellow? Have you tried injecting control and Ghsr1a separately? If yes, do you get the same result? Such analysis would be important to separate cell autonomous from noncell autonomous effects. 

To minimize variability in injection conditions, we initially coinjected control and Ghsr1a-KD lentiviruses into the same mice and analyzed their migration using a paired design. As the reviewer correctly noted, some cells were coinfected and expressed both EmGFP and DsRed (18.7% ± 2.86% of EmGFP+ cells and 10.8% ± 0.533% of DsRed+ cells). To ensure that this overlap did not affect our analysis, we excluded EmGFP+/DsRed+ double-positive cells and focused solely on EmGFP+/DsRed− (control) and EmGFP−/DsRed+ (Ghsr1a-KD) single-positive cells. 

We agree with the reviewer that coinjection could lead to reciprocal interactions between control and Ghsr1a-KD cells, potentially masking cell-autonomous effects. To address this, we performed an independent experiment in which control and Ghsr1a-KD lentiviruses were injected separately into different mice (Figure S7A), as suggested. Consistent with the results of the coinjection experiment, we found that the Ghsr1a-KD cells showed significantly reduced distribution in the GL compared with that in control cells (Figure S7B). Although we cannot exclude the possibility of a non-cell-autonomous effect of ghrelin, this result supports the conclusion that ghrelin signaling enhances neuronal migration in a cell-autonomous manner. 

Who is expressing Ghsr1a, newborn neurons, and or their progenitors? The production and survival of newborn V-ZVS cells should be assessed upon knockdown of the ghrelin receptor too. 

To determine whether the altered distribution of new neurons observed upon Ghsr1aKD is due to impaired migration rather than decreased cell production or survival, we examined the effects of Ghsr1a-KD on the proliferation and survival of new neurons and their progenitors, which express GHSR1a (Li et al., 2014). 

We compared the proportion of cleaved caspase-3+ cells and Ki67+ cells from the total labeled cells in the V-SVZ and RMS between the control and Ghsr1a-KD groups. There was no significant difference in the proportion of cleaved caspase-3+ cells between the groups (Control: 874 cells from 5 mice; Ghsr1a-KD: 678 cells from 7 mice), suggesting that ghrelin signaling does not affect the survival of new neurons and their progenitors. 

Similarly, the proportion of Ki67+ cells in the RMS did not differ significantly between the two groups (Figure S8), indicating that Ghsr1a-KD does not impair cell proliferation in the RMS. However, it remains technically difficult to evaluate whether Ghsr1a-KD affects proliferation in the VSVZ, because lentivirus injection into the VSVZ may interfere with GHSR1a expression not only in new neurons and neural progenitors, but also in other cell types known to express GHSR1a (Zigman et al., 2006). A previous study reported that ghrelin signaling promoted cell proliferation in the V-SVZ (Li et al., 2014), thus we cannot exclude the possibility that Ghsr1a-KD may affect V-SVZ proliferation.

To overcome this limitation, we assessed the effects of Ghsr1a-KD on neuronal migration using in vitro KD experiments (Figure 4C–J) and in vivo OB-core lentivirus injections (Figure 5A–C), both of which did not interfere with proliferation in the V-SVZ. These complementary approaches consistently demonstrated that Ghsr1a-KD reduces the migration speed of new neurons. 

“To determine whether the altered distribution of new neurons after Ghsr1a-KD is due to impaired migration rather than changes in cell production or survival, we assessed the effects of Ghsr1aKD on the proliferation and survival of new neurons and their progenitors, which express GHSR1a (Li et al., 2014). We quantified the proportion of cleaved caspase-3+ cells and Ki67+ cells from the total labeled cells in the V-SVZ and RMS in both control and Ghsr1a-KD groups. We found no significant difference in cleaved caspase-3+ cell proportions between the groups (Control: 874 cells from 5 mice; Ghsr1a-KD: 678 cells from 7 mice), suggesting that ghrelin signaling does not influence the survival of new neurons and their progenitors. Similarly, the percentage of Ki67+ cells in the RMS was similar between the two groups (Figure S8), indicating that Ghsr1a-KD does not impair cell proliferation in the RMS. However, technical limitations prevented a reliable evaluation of proliferation in the V-SVZ, as lentivirus injection into this region may interfere with GHSR1a expression in not only neural progenitors and new neurons, but also other GHSR1aexpressing cell types (Zigman et al., 2006). Although ghrelin signaling has been reported to promote cell proliferation in the V-SVZ (Li et al., 2014), our complementary in vitro KD experiments (Figure 4C–J) and in vivo OB-core lentivirus injections (Figure 5A–C), which did not affect the V-SVZ, consistently demonstrated that Ghsr1a-KD reduces neuronal migration. Taken together, our results suggest that blood-derived ghrelin enhances neuronal migration in the RMS and OB by stimulating actin cytoskeleton contraction in the cell soma, rather than by altering cell proliferation or survival.” (Results, Page 10, line 19–Page 11, line 4)

“rat anti-Ki67 (1:500, #14-5698-82, eBioscience); and rabbit anti-cleaved caspase-3 (1:200, #9661, Cell Signaling Technology)” (Methods, Page 48, lines 14–16)

How much is ghrelin/Ghsr1 signaling conserved in marmosets? 

How ghrelin signaling is conserved between mice and common marmosets is important to clarify. A previous study reported the existence of a ghrelin homolog in common marmoset, which shares high sequence similarity with that in mice (Takemi et al., 2016). Moreover, the GHSR1a homolog in the common marmoset (https://www.ncbi.nlm.nih.gov/protein/380748978) shares 95.36% amino acid identity with its mouse counterpart. These findings suggest that blood-derived ghrelin may similarly promote neuronal migration in the marmoset brain, as observed in mice. 

We have added the following text in the Discussion section:

“Our data showed that new neurons preferentially migrate along arteriole-side vessels rather than venule-side vessels in both mouse and common marmoset brains, suggesting that the mechanism of blood flow-dependent neuronal migration is conserved across rodent and primate species, as well as across brain regions. A previous study identified a ghrelin homolog in the common marmoset with high sequence similarity to the murine version (Takemi et al., 2016). In addition, the marmoset GHSR1a homolog shares 95.36% amino acid identity with that of the mouse (https://www.ncbi.nlm.nih.gov/protein/380748978). These findings suggest that bloodderived ghrelin promotes neuronal migration in the common marmoset brain in a manner similar to that in mice.” (Discussion, Page 15, lines 8–16)

Page 9. Starvation has been shown to boost ghrelin blood levels. What is the exact protocol used in this experiment and is this indeed increasing Ghrelin release from blood vessels in the V-SVZ? What about Ghsr1 expression level in newborn neurons? 

We have clarified the calorie restriction (CR) protocol used in our experiments. We adopted a 70% CR protocol, which was previously shown to enhance hippocampal neurogenesis when administered for 14 days (Hornsby et al., 2016). In our study, the daily food intake under ad libitum (AL) conditions was first measured, and CR mice were then fed 70% of that amount for 5 consecutive days (see Figure 5I and Figure S10A). 

To assess whether CR enhances ghrelin transcytosis into the brain parenchyma, we performed ELISA to quantify ghrelin levels in the OB and RMS. However, ghrelin concentrations were below the detection limit in both groups, precluding a direct comparison.

We also considered whether CR modulates the expression level of the ghrelin receptor GHSR1a. A recent study reported that fasting increased GHSR1a expression in the OB (Stark et al., 2024), raising the possibility that CR may exert a similar effect. To test this, we performed in situ hybridization and quantified Ghsr1a mRNA puncta in Dcx+ cells in the OB. No significant difference was found between the AL and CR groups (Figure S5B), suggesting that CR does not alter GHSR1a expression levels in new neurons. 

Although we cannot exclude the possibility that CR increases GHSR1a expression in other OB cell types, our combined CR and Ghsr1a-KD experiments strongly support a cellautonomous contribution of ghrelin signaling to the enhanced neuronal migration observed under CR conditions. Corresponding data and text have been added to Figure S5 and the Results, Discussion, and the Figure legend sections as follows:

Minor 

Page 4 

Line 19 In Supplemental movies 1 and 2, it is unclear where to see the GFP+ new neurons interact with BV. Can you add arrows as an indication for the readers? It will be better to add the anatomy term for orientation, caudal, or rostral in the video. (The same for Supplemental movies 3, 4, and 5).  

To clarify the regions of interest in Supplemental Movies 1 and 2, where neuron–vessel interactions in the RMS are highlighted, we added dotted lines indicating the RMS boundaries. In addition, we created a new movie (Supplemental Movie S1′) showing a high-magnification view of Supplemental Movie S1, in which arrows mark EGFP+ new neurons interacting with blood vessels. We also added orientation indicators (e.g., caudal and rostral) and arrows to highlight new neuron–vessel interactions in Supplemental Movies S1–S5. 

The following descriptions have been added to the Figure legends:

“Supplemental Movie S1′ 

High-magnification view extracted from Supplemental Movie S1. Arrows indicate EGFP+ cells interacting with blood vessels.” (Figure legend, Page 46, lines 6–8)

“Arrows indicate EGFP+ cells interacting with blood vessels.” (Figure legend, Supplemental Movie S3, Page 46, lines 16–17)

“Arrows indicate Dcx+ cells interacting with blood vessels.” (Figure legend, Supplemental Movies S4 and S5, Page 46, lines 21–22, 26–27)

Blood vessels are labeled in the Supplemental movies 2 and 3 by employing Flt1DsRed transgenic mice instead of RITC-Dex-GMA. However, Flt1-DsRed transgenic mice are not mentioned in the results section. 

We have now included an explanation regarding the use of Flt1-DsRed mice, in which vascular endothelial cells were labeled with DsRed.

“To visualize blood vessels, we also used Flt1-DsRed transgenic mice, in which vascular endothelial cells were specifically labeled with DsRed (Matsumoto et al., 2012). Using DcxEGFP/Flt1-DsRed double transgenic mice, we observed close spatial relationships between new neurons and blood vessels (Supplemental Movies S2 and S3).” (Results, Page 4, lines 22– 26)

Figure 5. Can you indicate (in the figure legend and the result section) the stage of the adult brain used for this experiment? 

We used 6- to 12-week-old adult male mice in all experiments in this study. To specify this, we have added the age of animals to both the Results and the relevant Figure legends as follows:

“Therefore, we first studied blood vessel-guided neuronal migration in the RMS and OB using three-dimensional imaging in 6- to 12-week-old adult mice, which enabled analysis of the in vivo spatial relationship between new neurons and blood vessels.” (Results, Page 4, lines 14–16)

“Figure 1 New neurons migrate along blood vessels with abundant flow in the adult brain.” (Figure legend, Page 25, line 4)

“(B, C) Three-dimensional reconstructed images of a new neuron (green) and blood vessels (red) in the rostral migratory stream (RMS) (B) and glomerular layer (GL) (C) of 6- to 12-weekold adult mice.” (Figure legend, Page 25, lines 6–8)

“(E) Transmission electron microscopy image of a new neuron (green) in close contact with a blood vessel (red) in the GL of a 6- to 12-week-old adult mouse.” (Figure legend, Page 26, lines 4–5)

“(F) Time-lapse images of a migrating neuron (indicated by asterisks) in the GL of a 6- to 12week-old Dcx-EGFP mouse.” (Figure legend, Page 26, lines 6–7)

“Figure 3 Ghrelin is delivered from the bloodstream to the RMS and OB in the adult brain (A) Representative images of the OB and cortex of a fluorescent ghrelin-infused mouse (6 to 12 weeks old).” (Figure legend, Page 30, lines 1–3)

“Lentivirus injection into the OB core (A) and the VSVZ (D) was performed in 6- to 12-week-old adult mice.” (Figure legend, Page 33, lines 3–4)

Reviewer #2 (Recommendations for author):

Major:

Ghsr1KD and blood flow 2-photon experiments to directly measure migratory speed. Could also do the same with fasting with or without Ghsr1KD.  

We thank the reviewer for the valuable suggestion to strengthen our study. As pointed out in the Public Review, we agree that direct in vivo measurement of neuronal migration speed under Ghsr1a-KD conditions is important to clarify the link between ghrelin signaling and blood flow. 

Two-photon imaging is the most suitable method for this purpose. Although we attempted two-photon imaging of Ghsr1a-KD new neurons, the number of virus-infected cells observed in vivo was too low to yield reliable data. Therefore, we chose an alternative strategy, combining Ghsr1a-KD with blood flow reduction using the BCAS model (Figure S9A), in which migration speed can be quantified based on the percentage of labeled cells reaching the OB. As stated in the Public Review response, BCAS significantly decreased the migration speed of Ghsr1a-KD new neurons (Figure S9B), indicating that Ghsr1a-KD does not abolish the influence of blood flow reduction. These findings suggest that ghrelin signaling is involved, but is not essential, for blood flow-dependent neuronal migration. 

As suggested by the reviewer, direct observation of migration dynamics (e.g., somal translocation, leading process extension, stationary and migratory phases) is needed, especially in calorie restriction experiments. Although our data indicate that ghrelin signaling is required for fasting-induced increases in migration speed of new neurons, calorie restriction could also change concentrations of other factors in blood (Bonnet et al., 2020; Wu et al., 2024; Alogaiel et al., 2025), which may independently affect behavior of migrating neurons. Given that ghrelin is not the sole factor contributing to blood flow-dependent neuronal migration, other circulating factors could affect behavior of migrating neurons in a different manner during fasting. In vivo twophoton imaging would be a powerful approach to determine whether fasting-induced neuronal migration is caused by upregulated somal translocation speed, which would further support a role for ghrelin in this process.

We have added the following text in the Discussion:

“Although our data indicate that ghrelin signaling is essential for fasting-induced acceleration of neuronal migration, calorie restriction may also alter the concentrations of other circulating factors (Bonnet et al., 2020; Wu et al., 2024; Alogaiel et al., 2025), which could independently influence the behavior of migrating neurons.” (Discussion, Page 14, lines 25–29)

Minor: 

(1) Show fluorescent Ghreliin in Figure 3 for all brain areas measured in Figure 1 (GL, EPL, GCL, and RMS) for direct comparison.  

To allow for direct comparison across brain regions, we added a new Supplemental figure showing the distribution of fluorescently labeled ghrelin in the OB, including the GL, EPL, GCL and RMS. This comprehensive view highlights ghrelin localization relative to vasculature and migrating neurons in the regions analyzed in Figure 1.

(1) Figure 1, panel I is presented in a confusing manner. High blood flow points to 0 degrees, low blood flow to 180 degrees. It implies (unintentionally, I am sure) that low blood flow results in migration away from OB. Maybe plot separately?

We agree that the original presentation of Figure 1I could be misinterpreted as referring to anatomical orientation (i.e., toward or away from the OB). To avoid confusion, we revised the figure to categorize new neuron–vessel interactions into four groups according to (1) the angle between the migration direction and vessel axis (small or large), and (2) whether the new neuron is migrating toward or away from the direction of higher blood flow. This new presentation avoids implying a fixed anatomical direction and better reflects the relationship between local blood flow and neuronal migration behavior. The revised figure is presented as Supplemental Figure S1.

Reviewer #1 (Public review):

Summary:

LRRK2 protein is familially linked to Parkinson's disease by the presence of several gene variants that all confer a gain-of-function effect on LRRK2 kinase activity.

The authors examine the effects of BDNF stimulation in immortalized neuron-like cells, cultured mouse primary neurons, hIPSC-derived neurons, and brain tissue from genetically modified mice. They examine a LRRK2 regulatory phosphorylation residue, LRRK2 binding relationships, and measures of synaptic structure and function.

Strengths:

The study addresses an important research question: how does a PD-linked protein interact with other proteins, and contribute to responses to a well-characterized neuronal signalling pathway involved in the regulation of synaptic function and cell health.

They employ a range of good models and techniques to fairly convincingly demonstrate that BDNF stimulation alters LRRK2 phosphorylation and binding to many proteins. IN this revised manuscript, aspects are well validated e.g., drebrin binding, but there is a disconnect between these findings and alterations to LRRK2 substrates. A convincing phosphoproteomic analysis of PD mutant Knock-in mouse brain is included. Overall the links between LRRK2, LRRK2 activity, and the changes to synaptic molecules, structures, and activity are intriguing.

Weaknesses:

The data sets remain disjointed, conclusions are sweeping, and not always in line with what the data is showing. Validation of 'omics' data is light. Some inconsistencies with the major conclusions are ignored. Several of the assays employed (western blotting especially) are underpowered, findings key to their interpretation are addressed in only one or other of the several models employed, and supporting observations are lacking.

Main Conclusions of Abstract:

(1) Increase in pLRRK2 Ser935 and pRAB after BDNF in SH-SY5Y & mouse neurons

Well supported, but only for pLRRK2 in neurons, why not pERK pAkt & pRab?

(2) Omics Proteome remodelling of LRRK2 interactome with BDNF & different in G2019S mouse neurons.

Supports that the phosphoproteome of G2019S is different. Drebrin interaction with LRRK2 very well supported. Link between drebrin and LRRK2 activity somewhat supported (pS935 site), but the consequence (non-specific pRab8) not supported, as there is no evidence of a change in LRRK2 substrate(s).

(3) Golgi 1 month LKO mouse altered dendritic spines, transient at 1m not older.

Supported but very small transient change in spines, disconnected to other results (e.g., drebrin).

(4) iPSC-derived neurons BDNF increases mEPSC frequency (transient at 70 not 50 or 90 days) in WT not KO "which appear to bypass this regulation through developmental compensation"

Weak, not clear what is being bypassed.

Main Conclusions Based on Old and New Figure / Data:

(1) Increase in pLRRK2 Ser935 and pRAB after BDNF in SH-SY5Y & mouse neurons

Well supported, but only for pLRRK2 in neurons, why not ERK Akt & Rab?

(2) BDNF promotes LRRK2 interaction with "post-synaptic actin cytoskeleton components"

Tone down, only one postsynaptic validated - drebrin strong BUT CONTRADICTORY; link between drebrin and LRRK2 activity (pS935 site) supported, consequence (non-specific pRab8) broken, no evidence of change in LRRK2 substrate.

(3) LRRK2 G2019S striatal phosphoproteome is different from WT.

It is different. Where is link to BDNF or Drebrin?

(4) BDNF signaling is impaired in Lrrk2 knockout neurons

TrkB changes seem higher in SHSY5Y. pAKT impaired, pERK not convincing. Primary neurons Akt slower but it and Erk mostly intact. MLi-2 did not block pAkt or pErk in WT or KO (higher in latter). Whatever is happening in KO, Mli-2 not really blocking effect in WT. If we are to assume that studying the KO was a means to understand LRRK2 function, the authors data should explain why we care if an effect is absent in LKO, if LRRK2 isn't doing the same job in WT?

BDNF increases synaptic puncta in WT not LKO (which start higher?). Is this BDNF increase blocked by LRRK2 inhibition?

(5) Postsynaptic structural changes in Lrrk2 knockout neurons

Golgi impregnation shows some very small spine changes at 1m. Not sustained over age. mRNA changes are very small (10% not even a fold... very weak and should be written as so). Derbrin levels reduced clearly at 1m, but probably also at 4 & 18. Underpowered, disconnected time course from the spine changes.

(6) An effect on "spontaneous electrical activity" at Div70

Weak. What is so special at 70 days that means we should be confident in the differences, or be satisfied that the other time points are legitimately ignored? These are 10-11 cells from 3 cultures assayed at 3 time points but only one is presented (rest in supplement). This should be a 2 (time) or 3 way (+culture RM) ANOVA. As it stands, in WT there is a little - no activity at 50 days, little to no at 70 days, and variable to lots or none at 90. BDNF did nothing at 50 or 90 but may have at 70. In KO low activity stable at 50 & 70, tanks at 90. BDNF would seem to have a similar effect on KO at 90 as WT at 70, but as there are only 7 cells it remains inconclusive. Thus the conclusion that BDNF signalling is broken in LKO is not well supported by the ephys data, nor is the BDNF effect in WT cells (even at the 70 day time point) shown to be susceptible to LRRK2 inhibition.

I didn't learn to much about race and ethnicity in my classes while I was in public school. They covered her Harlem renaissance but the really didn't talk much about slavery. It was predominantly overlooked.

Reviewer #3 (Public review):

Summary:

This manuscript by Mukherjee and colleagues extended earlier studies on the coordination of the SidC and SidE effector families on the generation of a unique ubiquitin layer on the surface of the vacuoles containing the bacterial pathogen Legionella pneumophila (LCV).

Strengths:

The main strength of the manuscript is the identification of the small GTPase Rab5 as a major "carrier" of these differently modified ubiquitin and ubiquitin chains, which was nicely quantified.

Weaknesses:

(1) The results are mostly descriptive, based on mechanistic studies from earlier works.

(2) The majority of the work was dedicated to the characterization of the unique ubiquitin layer on the LCV. One important question was ignored: what is the role of Rab5 in this process? Is the GTPase activity of Rab5 required for its ubiquitination by SidC and SidE? The authors should create a Rab5 KO cell line, complement the line with different mutants of Rab5, and examine their ubiquitination and association with the LCV.

(3) The finding that Rab5 is associated with the LCV supports the notion that the LCV has characteristics of endo- or/late endosomes. The positioning of the LCV in the endocytic pathway should be discussed in the context of earlier studies (e.g.,PMID: 38739652; PMID: 11067875; PMID: 11067875).

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1(Public review):

We deeply appreciate the reviewer comments on our manuscript. Following up the revisions, our manuscript has been improved thanks to their insightful remarks. We have proceeded with all the required changes.  

Weaknesses:

The authors have still not addressed the inconsistent/missing description for sample size, the appropriate number of * for each figure panel, and the statistical tests used.

Description of sample size, specific P value and statistical test used has been added it both in the main text, figures and figure legends.

The authors assign 5% oxygen as hypoxia. This is not the case as the in vivo environment is close to this value. 5% is normoxia. Clinical IVF/embryo culture occurs at 5% O2. Please adjust your narrative around this.

We define in our manuscript “normoxia” as the standard atmospheric oxygen levels in tissue culture incubators, which range from about 20–21% oxygen. Our definition of hypoxia is 5% concentration of oxygen, taking into consideration the standard levels of oxygen in the IVF clinics. Physiological oxygen in mouse varies from ~1.5% to 8% (Alva et al 2022). Considering that these levels of oxygen are the standard levels in tissue culture practices, a paragraph has been added to the discussion and materials and methods for further clarification   

Reviewer #2 (Public review):

Weakness:

Given that this is a study on the induction of aneuploidy, it would be meaningful to assess aneuploidy immediately after induction, and then again before implantation. This is also applicable to the competition experiments on page 7/8. What is shown is the competitiveness of treated cells. Because the publication centers around aneuploidy, inclusion of such data in the main figure at all relevant points would strengthen it. There is some evaluation of karyotypes only in the supplemental - why? Would be good not to rely on a single assay that the authors appear to not give much importance.

This is an excellent point. However, due to the stochasticity of the arising of aneuploidies when embryos are treated with AZ3146 and reversine (Bolton et al 2016), every treatment is likely to generate different levels of aneuploidy. Due to this, and to the technical limitations of generating single-cell genomic DNA sequencing at the blastocyst stage, we were unable to determine the karyotype of all cells after different conditions. Nevertheless, Regin et al 2024 (eLife) showed similar results on the overall transcriptome changes of different dosages of aneuploidy: high dosage embryos overexpress p53, like reversine-treated embryos; meanwhile, low dosage embryos overexpress the hypoxic pathway, including HIF1A, similar to embryos treated with AZ3146.  

Reviewer #1 (Recommendations for the authors):

Corrections required before final publishing:

Please ensure that the number of asterisks is in alignment with standard convention (* <0.05; ** <0.01; *** <0.001; **** <0.0001). If you want to describe an exact P -vale it should be presented as P = 0.0004. line 108 *** is <0.0004. line 263 * P<0.0044

Same issue appears in lines 697, 711, 722, 753, 685

Specific values have been added in the figures and modified in the text. 

Line 199: "...viable E9.5 embryos" missing "Figure S1D"

Modified in manuscript

Line 120: "...decidua" please add "Figure S1C"

Modified in manuscript

Line 126-127: Please add a description for the results (morula) in Fig 1D, e.g., It appears that YH2Ax persists from 8-cell to morula when treated with Reversine but not AZ3146"

At the morula stage, the levels of γH2A.X in reversine- and AZ3146-treated embryos are similar (Fig. 1E). However, at the blastocyst stage, high levels of γH2A.X are maintained in reversine-treated embryos and reduced in AZ3146-treated embryos, suggesting some level of DNA repair between the morula-to-blastocyst stages (Fig. S2A). In contrast, in hypoxia, the levels of γH2A.X are low in the three treatments at the morula stages, suggesting that DNA repair can be enhanced under hypoxic conditions. Similar results have been reported in somatic cells (Marti et al., 2021; Pietrzak et al., 2018).

Line 213: PARP1 levels were also similar under all conditions; but Fig3E, top right shows PARP1 was significantly lower with Reversine treatment; also please correct me if i am wrong, but does the phrase "all conditions" cross reference yH2AX and PARP1 between Fig 3 and Fig 1 to show the impact of hypoxia? Because from my understanding Fig 1 was done in 20% oxygen, but Fig 3 was done in 5% oxygen – hypoxia.

This is correct. Modification in the manuscript has been performed for clarification

Line 264: extra forward dash? "Reversine/AZ3146/ aggregation"

Modified in manuscript

Line 644: you don't have a control for IDF treatment, so how did you differentiate between impact of aneuploid drugs vs IDF treatment alone? Would the impact observed be due to compounding effect of aneuploidy drugs + IDF?

This is a great observation. We previously demonstrated that IDF-1174 treatments in embryos do not affect pre-implantation development (Fig. S3).

Line 681: change their behaviour is a vague statement. Be specific.

Modified in manuscript

Line 676 missing bracket "E)"

Modified in manuscript

Line 680: "...significantly on" should be "for"

Modified in manuscript

Line 682-685: "...hypoxia favours the survival of reversine-induced aneuploid cells." does it? the statement before this says in Rev/AZ chimeras, AZ blastomeres contribute similarly to reversine-blastomeres to the TE and PE but significantly increase contributions to the EPI.Wouldn't this mean hypoxia favours survival of AZ aneuploid cells in EPI?

In normoxic conditions, AZ3146 treated cells in Rev/AZ chimeras contributed mostly to the EPI and TE but not PE. In contrast, in normoxic conditions, Rev-treated cells contributed similarly to all the lineages. This result seems to be due to a better survival of Rev-treated cells under normoxic conditions (Fig. 4D-E)

Line 720: (b) shows blastocyst staining from what group? DMSO? Rev/AZ? Or are the 3 blastocysts shown here, 3 separate examples of Reversine-treated blastocysts? Would require labelling Fig S2B, and adding a short description in the corresponding figure legend

Figure (B) shows the expression pattern of PARP1 at the blastocyst stage. Modified in manuscript

Figure 2, Figure S3 and Figure S6: were these experiments performed at 5% or 20% O2, please add detail.

Modified in manuscript

Reviewer #2 (Recommendations for the authors):

Lines 45-46 understanding of reduction of aneuploidy should mention/discuss the paper of attrition/selection, of the kind by the Brivanlou lab for instance, or others. As well as allocation to specific lineages, including the authors' work.

A section in the discussion has been added in response to this recommendation. Comparison between models is debatable.

The response does not clarify whether other papers were cited instead, or the authors own work that has shown preferential allocation to TE.

Reviewer #2 (Public review):

Summary:

In this article, Assimopoulos et al. expand the FSL-XTRACT software to include new protocols for identifying cortical-subcortical tracts with diffusion MRI, with a focus on tracts connecting to the amygdala and striatum. They show that the amygdalofugal pathway and divisions of the striatal bundle/external capsule can be successfully reconstructed in both macaques and humans while preserving large-scale topographic features previously defined in tract tracing studies. The authors set out to create an automated subcortical tractography protocol, and they accomplish this for a subset of specific subcortical connections.

Strengths:

The main strength of the current study is the translation of established anatomical knowledge to a tractography protocol for delineating cortical-subcortical tracts that are difficult to reconstruct. Diffusion MRI-based tractography is highly prone to false positives; thus, constraining tractography outputs by known anatomical priors is important. The authors used existing tracing literature to create anatomical constraints for tracking specific cortical-subcortical connections and refined their protocol through an iterative process and in collaboration with multiple neuroanatomists. Key additional strengths include 1) the creation of a protocol that can be applied to both macaque and human data; 2) demonstration that the protocol can be applied to be high quality data (3 shells, > 250 directions, 1.25 mm isotropic, 55 minutes) and lower quality data (2 shells, 100 directions, 2 mm isotropic, 6.5 minutes); and 3) validation that the anatomy of cortical-subcortical tracts derived from the new method are more similar in monozygotic twins than in siblings and unrelated individuals.

Overall Appraisal:

This new method will accelerate research on anatomically validated cortical-subcortical white matter pathways. The work has utility for diffusion MRI researchers across fields.

Editors' note:

Both reviewers were satisfied with the responses to their feedback.

Reviewer #1 (Public review):

The manuscript by Zhang et al describes the use of a protein language model (pLM) to analyse disordered regions in proteins, with a focus on those that may be important in biological phase separation. While the paper is relatively easy to read overall, my main comment is that the authors could perhaps make it clearer which observations are new, and which support previous work using related approaches. Further, while the link to phase separation is interesting, it is not completely clear which data supports the statements made, and this could also be made clearer.

Major comments:

(1) With respect to putting the work in a better context of what has previously been done before, this is not to say that there is not new information in it, but what the authors do is somewhat closely related to work by others. I think it would be useful to make those links more directly. Some examples:

(1a) Alderson et al (reference 71) analysed in detail the conservation of IDRs (via pLDDT, which is itself related to conservation) to show, for example, that conserved residues fold upon binding. This analysis is very similar to the analysis used in the current study (using ESM2 as a different measure of conservation). Thus, the approach (pages 7-8) described as "This distinction allows us to classify disordered regions into two types: "flexible disordered" regions, which show high ESM2 scores and greater mutational tolerance, and "conserved disordered" regions, which display low ESM2 scores, indicating varying levels of mutational constraint despite a lack of stable folding." is fundamentally very similar to that used by Alderson et al. Thus, the result that "Given that low ESM2 scores generally reflect mutational constraint in folded proteins, the presence of region a among disordered residues suggests that certain disordered amino acids are evolutionarily conserved and likely functionally significant" is in some ways very similar to the results of that paper.

(1b) Dasmeh et al (https://doi.org/10.1093/genetics/iyab184), Lu et al (https://doi.org/10.1371/journal.pcbi.1010238) and Ho & Huang (https://doi.org/10.1002/pro.4317) analysed conservation in IDRs, including aromatic residues and their role in phase separation

(1c) A number of groups have performed proteomewide saturation scans using pLMs, including variants of the ESM family, including Meier (reference 89, but cited about something else) and Cagiada et al (https://doi.org/10.1101/2024.05.21.595203) that analysed variant effects in IDRs using a pLM. Thus, I think statements such as "their applicability to studying the fitness and evolutionary pressures on IDRs has yet to be established" should possibly be qualified.

(2) On page 4, the authors write, "The conserved residues are primarily located in regions associated with phase separation." These results are presented as a central part of the work, but it is not completely clear what the evidence is.

(3) It would be useful with an assessment of what controls the authors used to assess whether there are folded domains within their set of IDRs.

Reviewer #3 (Public review):

Summary:

This is a very nice and interesting paper to read about motif conservation in protein sequences and mainly in IDRs regions using the ESM2 language model. The topic of the paper is timely, with strong biological significance. The paper can be of great interest to the scientific community in the field of protein phase transitions and future applications using the ESM models. The ability of ESM2 to identify conserved motifs is crucial for disease prediction, as these regions may serve as potential drug targets. Therefore, I find these findings highly significant, and the authors strongly support them throughout the paper. The work motivates the scientific community towards further motif exploration related to diseases.

Strengths:

(1) Revealing conserved regions in IDRs by the ESM-2 language model.

(2) Identification of functionally significant residues within protein sequences, especially in IDRs.

(3) Findings supported by useful analyses.

Weaknesses:

(1) Lack of examples demonstrating the potential biological functions of these conserved regions

(2) Very limited discussion of potential future work and of limitations.

Reviewer #3 (Public Review):

Summary:

In this article, Barnett examines a pressing question regarding citing behavior of authors during the peer review process. In particular, the author studies the interaction between reviewers and authors, focusing on the odds of acceptance, and how this may be affected by whether or not the authors cited the reviewers' prior work, whether the reviewer requested such citations be added, and whether the authors complied/how that affected the reviewer decision-making.

Strengths:

The author uses a clever analytical design, examining four journals that use the same open peer review system, in which the identities of the authors and reviewers are both available and linkable to structured data. Categorical information about the approval is also available as structured data. This design allows a large scale investigation of this question.

Weaknesses:

My concerns pertain to the interpretability of the data as presented and the overly terse writing style.

Regarding interpretability, it is often unclear what subset of the data are being used both in the prose and figures. For example, the descriptive statistics show many more Version 1 articles than Version 2+. How are the data subset among the different possible methods?

Likewise, the methods indicate that a matching procedure was used comparing two reviewers for the same manuscript in order to control for potential confounds. However, the number of reviews is less than double the number of Version 1 articles, making it unclear which data were used in the final analysis. The methods also state that data were stratified by version. This raises a question about which articles/reviews were included in each of the analyses. I suggest spending more space describing how the data are subset and stratified. This should include any conditional subsetting as in the analysis on the 441 reviews where the reviewer was not cited in Version 1 but requested a citation for Version 2. Each of the figures and tables, as well as statistics provided in the text should provide this information, which would make this paper much more accessible to the reader. [Note from editor: Please see "Editorial feedback" for more on this]

Finally, I would caution against imputing motivations to the reviewers, despite the important findings provided here. This is because the data as presented suggest a more nuanced interpretation is warranted. First, the author observes similar patterns of accept/reject decisions whether the suggested citation is a citation to the reviewer or not (Figs 3 and 4). Second, much of the observed reviewer behavior disappears or has much lower effect sizes depending on whether "Accept with Reservations" is considered an Accept or a Reject. This is acknowledged in the results text, but largely left out of the discussion. The conditional analysis on the 441 reviews mentioned above does support a more cautious version of the conclusion drawn here, especially when considered alongside the specific comments left by reviewers that were mentioned in the results and information in Table S.3. However, I recommend toning the language down to match the strength of the data.

DOI: 10.21203/rs.3.rs-6864379/v1

Resource: RRID:Addgene_107738

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DOI: 10.21203/rs.3.rs-6864379/v1

Resource: RRID:Addgene_114472

Curator: @olekpark

SciCrunch record: RRID:Addgene_114472

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DOI: 10.21203/rs.3.rs-6864379/v1

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DOI: 10.21203/rs.3.rs-5724831/v1

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SciCrunch record: RRID:Addgene_178583

What is this?

DOI: 10.21203/rs.3.rs-5724831/v1

Resource: None

Curator: @scibot

SciCrunch record: RRID:Addgene_45560

What is this?

I like how they ended it. While we showed it helps we didn't find all the ways however in the future we will keep learning to make sure it fits everyone. Perfect!

This definitely is a topic that would draw bias.

I can agree with this I don't see a lot of bias in this study.

I love that through the whole study they are saying PA is the best and what but they still see while it is great but it doesn't fix everything. I love to see that in a study.

Wait wait wait. If we are the most physically active then why aren't we the "most happiest". Also very sad to see that we are the highest.

That is a strong point and one that I have never heard before but I guess I could see it I think.

So far their results don't look too bad. My question is with that 1% what happened there?

I like how the study isn't just all the same exercise. They picked so many different ones with all different levels difficulty.

So the data is random? Because how it sounds to me the data and study was random and they didn't pick people who won't make sure everything went good.

I liked what I read in this. They actually went to people in the field and didn't just be the person where they act like they know what they are talking about.

I do like how they added "seemed" because while they are trying to prove their point they still want to be correct and not give opinion.

I think anxiety might be different from depression and stress. Anxiety is our body fight or flight response so to me a little bit hard to stop when it is just in us.

What is their term of vigorous intensity?

First off we need to fix this. However I have a question if we are seeing a rise in it why are we not doing anything about it? What should we do to make it more less stressful, fun, etc.?

I would agree with this. I don't think everyone will face everyone but at some point we all hit that wall of those emotions which suck.

While I do agree with physical activity being good not just for students but everyone however my question with this already is for some physical activity work but for others it does not. Is it okay if some don't do it and do something else to help or is it to them only physical things?