Poetry is something that is a need so that people can have something outside of the real world.

I've never done this before, but will in the future if I have more info than the word limit

Facing is participation. Not doing a thing is also an action that contributes to racism. More of, it is implying the ability of critical thinking

this show how common the idea was

big culture trend of the 1990s

what does it mean?

I realized every parise does not work positively and some of them work negatively.

This is why I need to keep trying and do not give up.

Failures and mistakes are what shapes us as human beings. It teaches our brains in a way to know what and what not to do, it helps us develop and grow. Instead of being self-conscious about an error, we all should take it as a lesson for us to improve and become better not only as students, but as adults. These are skills and knowledge that will be with us for the rest of our lives.

Our brains are very powerful that's why a lot of people believe in manifestations. The more you think about something, it's more likely to take place because you are always subconsciously working towards what you want to happen.

When we know that intelligence can be improved/developed, that should encourage people to believe in themself and help people feel confident to learn and face challenges without fear.

Going to update this so we take the HS from the CN across all years to make things cleaner going forward. Need to then check results against GEM

There are a lot of words that we use that mean the same thing, some are long to make things sound fancy while others are abbreviated.

What could the wrong idea be??

I feel like thing could maybe have something to do with the fact I talked about in my video about how everyone has their own understanding of a poem. It is abandoned because it doesn't have true meaning.

Highlights inconsistency and racial bias in language

Even when no harm is meant by this term, "African native" still sounds degrading.

main purpose of the book

Africa is in the American subconscious despite limited knowledge. It appears in media, creating stereotypes, like tribes, famine, and wild animals, shaping perceptions based on assumptions, not facts.

This sentence is an example specifically of the typical media stereotype of Africa. It lines up with the most common movies that integrate Africa into them. The "hot, dangerous, and tribal" part of it.

This part of the text shows that many Americans have Africa as a subconscious idea in their head. This is mainly because of the things they've been told through media but also because of school not exactly teaching them completely. I believe that if schools would talk about the development of Africa, people wouldn't categorize Africa with the words used in the text. This is an example of many different popular shortcomings made about Africa. The only reason these terms are associated with Africa still is because people in America haven't took the time to learn about actually understanding Africa so how can it be taught? That's why it still persists even today.

What society/the news shows and tells us.

I did not know that Africa was this big. That is a very interesting fact.

This part of the text is a media stereotype because people get put an image of Africa into their mind where the only things that happen in Africa are bad, like how newspapers are about aids, genocide, Ebola, or civil war.

This is super interesting, I think that any poetry could be used to "change the world" I also have never thought of hymns as poems so that is a new perspective for me on that.

Why were poems written annoymously? Or do we just not know the authors because of how old they are?

In the mother's monologue about May's pacing, the mother brings up that she was stuck in the house when other girls her age were playing. I think that she had to take care of her mother from a young age, so she was always stuck in the house doing nothing but the routine of taking care of her and pacing. She has been trapped in the house and with her mother in body and mind. She knows exactly the age of her mother, but she asks her mother not knowing herself how old she is. Showing that she has forgotten to think of herself she only thinks of her mother.

In the play Footfalls there are a lot of patterns, for example when May is talking about what she's going to do for V she pauses after saying each action as if she is looking for reassurance that what she is saying is right. All of the actions have question marks, the only one that is not a question is "Again." All of the pulses are symbols of repetition. She is saying. Pausing. Doing. over and over again. This might be her reliving how she would take care of her when she was alive, stuck in the pattern that she lived from day to day.

I feel like thing is very important so you can check again if you missed anything!

In the play Footfalls there are a lot of patterns, for example when May is talking about what she's going to do for V she pauses after saying each action as if she is looking for reassurance that what she is saying is right. All of the actions have question marks, the only one that is not a question is "Again." All of the pulses are symbols of repetition. She is saying. Pausing. Doing. over and over again. This might be her reliving how she would take care of her when she was alive, stuck in the pattern that she lived from day to day.

In the mother's monologue about May's pacing, the mother brings up that she was stuck in the house when other girls her age were playing. I think that she had to take care of her mother from a young age, so she was always stuck in the house doing nothing but the routine of taking care of her and pacing. She has been trapped in the house and with her mother in body and mind. She knows exactly the age of her mother, but she asks her mother not knowing herself how old she is. Showing that she has forgotten to think of herself she only thinks of her mother

" Yes, some nights she does, in snatches. bows her poor head against the wall and snatches a little sleep. [Pause.] Still speak? Yes, some nights she does, when she fancies none can hear. " This specific section right here can be broke down to presence vs absence, strange of voice, and strand of time. All three work perfectly. When they say she sleeps in "snatches" it means her naps are not restful its like a continuous pattern of awake and then sleep which creates presence vs absence. Then when it says "bows her poor head against the wall" makes her seem tired and restless like she is consistently clinging on to life in a sense? or even a sense of being trapped? which this is a strand of time because this event feels repeated and it feels continuous, and it makes me feel like Shes hardly even there and it makes me think that she is suffering. Then when it says "Still speak? Yes, some nights she does, when she fancies none can hear." It shows she speaks to herself when nobody else is listening It makes her voice seem eerie or ghostly and it really makes me wonder if she is fading away, however still trying to cling to life. Her voice is like half present, shes halfway here, clinging for life.

the entire page 240 is a strand of voice. There is so many lines and phrases where they pause and continuously have the same tone. It says "Pause. No Louder." That makes me wonder how they consistently keep the same tone no matter what is being asked it is almost like it is one person talking it seems completely emotionless. It repeats the phrase "pause. No louder." approximately five times and it gives me/makes me feel a sense of eeriness. This also creates a perfect example of absence and presence because the presence is when they speak and then it feels so absent and so incomplete with a huge amount of pauses/

This shows a binary of presence vs absence because when it states "fade out on strip all in darkness" it becomes the feeling of absence. The feeling of presence is when she asks the question "will you never have done? Will you never have done revolving it all?" This creates a sinister vibe because now we all want to know what "it all" means it creates a strand of voice in a sense as well. This strand of voice can give the reader a guess on the tone of the question. It sounds like it starts off bold and then ends off with a quiet whisper as she fades away.

And to what extent is this beneficial to society? I can think of many online communities that encourage harm and the spread of false information. This past year I was disturbed to see a fully scanned and publicized journal from someone who had murdered children. People congregate around ideas that are damaging to others, especially online where they can dehumanize their victims and even like-minded users by escaping the context provided by real-life encounters. When I saw said journal I was confronted with the fact that people are essentially archiving these troubling movements. People are also able to spread racism, sexism and other forms of bigotry online, some of it being archived by individuals and some of it by programming. I am not promoting censorship, but dangerous ideas are spreading online, being stored, and accessed freely. How can archivists combat this sort of activity, while promoting their own ideals?

As they went out of their groups during seasonal festivals helps them learn new things and ways of doing things. This also helps them find their mates and learn from their mates and share them with their communities. I think this is very interesting as it helps them grow their communities and learn new ways.

Basic Cognitive process changes with age, as a child grows they start to get a better understanding on there surroundings and how things function as well as connecting facial expressions with emotions.

eLife Assessment

This paper discusses the cognitive implications of potential intentional burial, wall engraving creation, and fire as light source use behaviors by relatively small-brained Homo naledi hominins. The discussion presented in the paper is valuable theoretically in its healthy questioning of prior assumptions concerning the socio-biological constraints of hominin meaning-making behavior. The discussion also contributes practically given that these behaviors have been ascribed to Homo naledi in two associated papers. Still, the strength of evidence in this contribution relies on the validity of the conclusions from the two associated papers, which remain actively questioned. The ultimate assessment of this work will vary among individual readers depending on how they view this debate, but if the conclusions from the associated papers hold up, the conclusions in the current paper can be considered solid.

eLife Assessment

This manuscript introduces a useful protein-stability-based fitness model for simulating protein evolution and unifying non-neutral models of molecular evolution with phylogenetic models. The model is applied to five viral proteins that are of structural and functional importance. While the general modelling approach is solid, and effectively preserves folding stability, the evidence for the model's predictive power remains limited, since it shows little improvement over neutral models in predicting protein evolution. The work should be of interest to researchers developing theoretical models of molecular evolution.

Reviewer #1 (Public review):

Summary:

Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is guided by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which has struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death processes. Unfortunately, though, the model shows little improvement over neutral models in predicting protein sequence evolution, although it can predict protein stability better than models assuming neutral evolution. It appears that more work is needed to determine exactly what aspects of protein sequence evolution are predictable under such non-neutral phylogenetic models.

Major concerns:

(1) The authors have clarified the mapping between birth-death model parameters and fitness, but how fitness is modeled still appears somewhat problematic. The authors assume the death rate = 1 - birth rate. So a variant with a birth rate b = 1 would have a death rate d = 0 and so would be immortal and never die, which does not seem plausible. Also I'm not sure that this would "allow a constant global (birth-death) rate" as stated in line 172, as selection would still act to increase the population mean growth rate r = b - d. It seems more reasonable to assume that protein stability affects only either the birth or death rate and assume the other rate is constant, as in the Neher 2014 model.

(2) It is difficult to evaluate the predictive performance of protein sequence evolution. This is in part due to the fact that performance is compared in terms of percent divergence, which is difficult to compare across viral proteins and datasets. Some protein sequences would be expected to diverge more because they are evolving over longer time scales, under higher substitution rates or under weaker purifying selection. It might therefore help to normalize the divergence between predicted and observed sequences by the expected or empirically observed amount of divergence seen over the timescale of prediction.

(3) Predictability may also vary significantly across different sites in a protein. For example, mutations at many sites may have little impact on structural stability (in which case we would expect poor predictive performance) while even conservative changes at other sites may disrupt folding. I therefore feel that there remains much work to be done here in terms of figuring out where and when sequence evolution might be predictable under these types of models, and when sequence evolution might just be fundamentally unpredictable due to the high entropy of sequence space.

Reviewer #2 (Public review):

In this study, the authors aim to forecast the evolution of viral proteins by simulating sequence changes under a constraint of folding stability. The central idea is that proteins must retain a certain level of structural stability (quantified by folding free energy, ΔG) to remain functional, and that this constraint can shape and restrict the space of viable evolutionary trajectories. The authors integrate a birth-death population model with a structurally constrained substitution (SCS) model and apply this simulation framework to several viral proteins from HIV-1, SARS-CoV-2, and influenza.

The motivation to incorporate biophysical constraints into evolutionary models is scientifically sound, and the general approach aligns with a growing interest in bridging molecular evolution and structural biology. The authors focus on proteins where immune pressure is limited and stability is likely to be a dominant constraint, which is conceptually appropriate. The method generates sequence variants that preserve folding stability, suggesting that stability-based filtering may capture certain evolutionary patterns.

However, the study does not substantiate its central claim of forecasting. The model does not predict future sequences with measurable accuracy, nor does it reproduce observed evolutionary paths. Validation is limited to endpoint comparisons in a few datasets. While KL divergence is used to compare amino acid distributions, this analysis is only applied to a single protein (HIV-1 MA), and there is no assessment of mutation-level predictive accuracy or quantification of how well simulated sequences recapitulate real evolutionary paths. No comparison is made to real intermediate variants available from extensive viral sequencing datasets which gather thousands of sequences with detailed collection date annotation (SARS-CoV-2, Influenza, RSV).

The selection of proteins is narrow and the rationale for including or excluding specific proteins is not clearly justified.

The analyzed datasets are also under-characterized: we are not given insight into how variable the sequences are or how surprising the simulated sequences might be relative to natural diversity. Furthermore, the use of consensus sequences to represent timepoints is problematic, particularly in the context of viral evolution, where divergent subclades often coexist - a consensus sequence may not accurately reflect the underlying population structure.

The fitness function used in the main simulations is based on absolute ΔG and rewards increased stability without testing whether real evolutionary trajectories tend to maintain, increase, or reduce folding stability over time for the particular systems (proteins) that are studied. While a variant of the model does attempt to center selection around empirical ΔG values, this more biologically plausible version is underutilized and not well validated.

Ultimately, the model constrains sequence evolution to stability-compatible trajectories but does not forecast which of these trajectories are likely to occur. It is better understood as a filter of biophysically plausible outcomes than as a predictive tool. The distinction between constraint-based plausibility and sequence-level forecasting should be made clearer. Despite these limitations, the work may be of interest to researchers developing simulation frameworks or exploring the role of protein stability in viral evolution, and it raises interesting questions about how biophysical constraints shape sequence space over time.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is guided by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which has struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death processes. Unfortunately, though, the model shows little improvement over neutral models in predicting protein sequence evolution, although it can predict protein stability better than models assuming neutral evolution. It appears that more work is needed to determine exactly what aspects of protein sequence evolution are predictable under such non-neutral phylogenetic models. 

We thank the reviewer for the positive comments about our work. We agree that further work is needed in the field of substitution models of molecular evolution to enable more accurate predictions of specific amino acid sequences in evolutionary processes.

Major concerns: 

(1) The authors have clarified the mapping between birth-death model parameters and fitness, but how fitness is modeled still appears somewhat problematic. The authors assume the death rate = 1 - birth rate. So a variant with a birth rate b = 1 would have a death rate d = 0 and so would be immortal and never die, which does not seem plausible. Also I'm not sure that this would "allow a constant global (birth-death) rate" as stated in line 172, as selection would still act to increase the population mean growth rate r = b - d. It seems more reasonable to assume that protein stability affects only either the birth or death rate and assume the other rate is constant, as in the Neher 2014 model. 

The model proposed by Neher, et al. (2014), which incorporates a death rate (d) higher than 0 for any variant, was implemented and applied in the present method. In general, this model did not yield results different from those obtained using the model that assumes d = 1 – b, suggesting that this aspect may not be crucial for the study system. Next, the imposition of arbitrary death events based on an arbitrary death rate could be a point of concern. Regarding the original model, a variant with d = 0 can experience a decrease in fitness through the mutation process. In an evolutionary process, each variant is subject to mutation, and Markov models allow for the incorporation of mutations that decrease fitness (albeit with lower probability than beneficial ones, but they can still occur). All this information is included in the manuscript.

(2) It is difficult to evaluate the predictive performance of protein sequence evolution. This is in part due to the fact that performance is compared in terms of percent divergence, which is difficult to compare across viral proteins and datasets. Some protein sequences would be expected to diverge more because they are evolving over longer time scales, under higher substitution rates or under weaker purifying selection. It might therefore help to normalize the divergence between predicted and observed sequences by the expected or empirically observed amount of divergence seen over the timescale of prediction. 

AU: The study protein datasets showed different levels of sequence divergence over their evolutionary times, as indicated for each dataset in the manuscript. For some metrics, we evaluated the accuracy (or error) of the predictions through direct comparisons between real and predicted protein variants using percentages to facilitate interpretation: 0% indicates a perfect prediction (no error), while 100% indicates a completely incorrect prediction (total error). Regarding normalization of these evaluations, we respectfully disagree with the suggestion because diverse factors can affect (not only the substitution rate, but also the sample size, structural features of the protein that may affect stability when accommodating different sequences, among others) and this complicates defining a consistent and meaningful normalization criterion. Given that the manuscript provides detailed information for each dataset, we believe that the presentation of the prediction accuracy through direct comparisons between real and predicted protein variants, expressed as percentages of similarity, is the clearest way.

(3) Predictability may also vary significantly across different sites in a protein. For example, mutations at many sites may have little impact on structural stability (in which case we would expect poor predictive performance) while even conservative changes at other sites may disrupt folding. I therefore feel that there remains much work to be done here in terms of figuring out where and when sequence evolution might be predictable under these types of models, and when sequence evolution might just be fundamentally unpredictable due to the high entropy of sequence space. 

We agree with this reflection. Mutations can have different effects on folding stability, which are accounted for by the model presented in this study. However, accurately predicting the exact sequences of protein variants with similar stability remains difficult with current structurally constrained substitution models, and therefore, further work is needed in this regard. This aspect is indicated in the manuscript.

We want to thank the reviewer again for taking the time to revise our work and for the insightful and helpful comments.

Reviewer #2 (Public review): 

In this study, the authors aim to forecast the evolution of viral proteins by simulating sequence changes under a constraint of folding stability. The central idea is that proteins must retain a certain level of structural stability (quantified by folding free energy, ΔG) to remain functional, and that this constraint can shape and restrict the space of viable evolutionary trajectories. The authors integrate a birth-death population model with a structurally constrained substitution (SCS) model and apply this simulation framework to several viral proteins from HIV-1, SARS-CoV-2, and influenza.

The motivation to incorporate biophysical constraints into evolutionary models is scientifically sound, and the general approach aligns with a growing interest in bridging molecular evolution and structural biology. The authors focus on proteins where immune pressure is limited and stability is likely to be a dominant constraint, which is conceptually appropriate. The method generates sequence variants that preserve folding stability, suggesting that stability-based filtering may capture certain evolutionary patterns. 

Correct. We thank the reviewer for the positive comments about our study.

However, the study does not substantiate its central claim of forecasting. The model does not predict future sequences with measurable accuracy, nor does it reproduce observed evolutionary paths. Validation is limited to endpoint comparisons in a few datasets. While KL divergence is used to compare amino acid distributions, this analysis is only applied to a single protein (HIV-1 MA), and there is no assessment of mutation-level predictive accuracy or quantification of how well simulated sequences recapitulate real evolutionary paths. No comparison is made to real intermediate variants available from extensive viral sequencing datasets which gather thousands of sequences with detailed collection date annotation (SARS-CoV-2, Influenza, RSV). 

There are several points in this comment.

The presented method accurately predicts folding stability of forecasted variants, as shown through comparisons between real and predicted protein variants. However, as the reviewer correctly indicates, predicting the exact amino acid sequences remains challenging. This limitation is discussed in detail in the manuscript, where we also suggest that further improvements in substitution models of protein evolution are needed to better capture the evolutionary signatures of amino acid change at the sequence level, even between amino acids with similar physicochemical properties. Regarding the time points used for validation, the studied influenza NS1 dataset included two validation points. A key limitation in increasing the number of time points is the scarcity of datasets derived from monitoring protein evolution with sufficient molecular diversity between samples collected at consecutive time points (i.e., at least more than five polymorphic amino acid sites). 

As described in the manuscript, calculating Kullback-Leibler (KL) divergence requires more than one sequence per studied time point. However, most datasets in the literature include only a single sequence per time point, typically a consensus sequence derived from bulk population sequencing. Generating multiple sequences per time point is experimentally more demanding, often requiring advanced methods such as single-virus sequencing or amplification of sublineages in viral subpopulations, as was done for the first dataset used in the study (Arenas, et al. 2016), which enabled the calculation of KL divergence. The extent to which the simulated sequences resemble real evolution is evaluated in the method validation. As noted, intermediate time point validation was performed using the influenza NS1 protein dataset. Although, as the reviewer indicates, thousands of viral sequences are available, these are usually consensus sequences from bulk sequencing. Indeed, many viral variants mainly differ through synonymous mutations, where the number of accumulated nonsynonymous mutations is small. For example, from the original Wuhan strain to the Omicron variant, the SARS-CoV-2 proteins Mpro and PLpro accumulated only 10 and 22 amino acid changes, respectively.

Analyzing intermediate variants of concern (i.e., Gamma or Delta) would reduce this number affecting statistics. In addition, many available viral sequences are not consecutive in evolutionary terms (one dataset does not represent the direct origin of another dataset at a subsequent time point), which further limits their applicability in this study. There is little data from monitored protein evolution with consecutive samples. The most suitable studies usually involve in vitro virus evolution, but the data from these studies often show low genetic variability between samples collected at different time points. Finally, it is important to note that the presented method can only be applied to proteins with known 3D structures, as it relies on selection based on folding stability. Non-structural proteins cannot be analyzed using this approach. Future work could incorporate additional selection constraints, which may improve the accuracy of predictions. These considerations and limitations are indicated in the manuscript.

The selection of proteins is narrow and the rationale for including or excluding specific proteins is not clearly justified. 

The viral proteins included in the study were selected based on two main criteria, general interest and data availability. In particular, we included proteins from viruses that affect humans and for which data from monitored protein evolution, with sufficient molecular diversity between consecutive time points, is available. These aspects are indicated in the manuscript.

The analyzed datasets are also under-characterized: we are not given insight into how variable the sequences are or how surprising the simulated sequences might be relative to natural diversity. Furthermore, the use of consensus sequences to represent timepoints is problematic, particularly in the context of viral evolution, where divergent subclades often coexist - a consensus sequence may not accurately reflect the underlying population structure. 

The manuscript indicates the sequence identity among protein datasets of different time points, along with other technical details. Next, the evaluation based on comparisons between simulated and real sequences reflects how surprising the simulated sequences might be relative to natural diversity, considering that the real dataset is representative. We believe that the diverse study real datasets are useful to evaluate the accuracy of the method in predicting different molecular patterns. Regarding the use of consensus sequences, we agree that they provide an approximation. However, as previously indicated, most of the available data from monitored protein evolution consist of consensus sequences obtained through bulk sequencing. Additionally, analyzing every individual viral sequence within a viral population, which is typically large, would be ideal but computationally intractable.

The fitness function used in the main simulations is based on absolute ΔG and rewards increased stability without testing whether real evolutionary trajectories tend to maintain, increase, or reduce folding stability over time for the particular systems (proteins) that are studied. While a variant of the model does attempt to center selection around empirical ΔG values, this more biologically plausible version is underutilized and not well validated.

The applied fitness function, based on absolute ΔG, is well stablished in the field (Sella and Hirsh 2005; Goldstein 2013). The present study independently predicts ΔG for the real and simulated protein variants at each sampling point. This ΔG prediction accounts not only for negative design, informed by empirical data, but also for positive design based on the study data (Arenas, et al. 2013; Minning, et al. 2013), thereby enabling the detection of variation in folding stability among protein variants. These aspects are indicated in the manuscript. Therefore, in our view, the study provides a proper comparison of real and predicted evolutionary trajectories in terms of folding stability.

Ultimately, the model constrains sequence evolution to stability-compatible trajectories but does not forecast which of these trajectories are likely to occur. It is better understood as a filter of biophysically plausible outcomes than as a predictive tool. The distinction between constraint-based plausibility and sequence-level forecasting should be made clearer. Despite these limitations, the work may be of interest to researchers developing simulation frameworks or exploring the role of protein stability in viral evolution, and it raises interesting questions about how biophysical constraints shape sequence space over time. 

The presented method estimates the fitness of each protein variant, which can reflect the relative survival capacity of the variant. Therefore, despite the error due to evolutionary constraints not considered by the method, it indicates which variants are more likely to become fixed over time. In our view, the method does not merely filter plausible variants, rather, it generates predictions of variant survival through predicted fitness based on folding stability and simulations of protein evolution under structurally constrained substitution models integrated with birth-death population genetics approaches. The use of simulation-based approaches for prediction is well established in population genetics. For example, approaches such as approximate Bayesian computation (Beaumont, et al. 2002) rely on this strategy, and it has also been applied in other studies of forecasting evolution (e.g., Neher, et al. 2014). We believe that the distinction between forecasting folding stability and amino acid sequence is clearly shown in the manuscript, including the main text and the figures.

Reviewer #2 (Recommendations for the authors): 

I thank the authors for addressing the question about template switching, their clarification was helpful. However, the core concerns I raised remain unresolved: the claim that the method is useful for forecasting is not substantiated.  In order to support the paper's central claims or to prove its usefulness, several key improvements could be incorporated: 

(1) Systematic analysis of more proteins: 

The manuscript would be significantly strengthened by a systematic evaluation of model performance across a broader set of viral proteins, beyond the examples currently shown. Many human influenza and SARS-CoV-2 proteins have wellcharacterized structures or high-quality homology templates, making them suitable candidates. In the light of limited success of the method, presenting the model's behavior across a more comprehensive protein set, including those with varying structural constraints and immune pressures, would help assess generalizability and clarify the specific conditions under which the model is applicable. 

Following a comment from the reviewer in a previous revision of the study, we included the analysis of an influenza NS1 protein dataset that contains two evaluation time points. Next, to validate the prediction method, it is necessary to have monitored protein sequences collected at least at two consecutive time points, with sufficient divergence between them to capture evolutionary signatures that allow for proper evaluation. Additionally, many data involve sequences that are not consecutive in evolutionary terms (one dataset is not a direct ancestor of another dataset existing at a posterior time point), which disallows their applicability in this study. Little data from monitored protein evolution with trustable consecutive (ancestor-descendant) samples exist. The most suitable studies often involve in vitro virus evolution, but they usually show low genetic variability between samples collected at different time points. Although thousands of sequences are available for some viruses, they are usually consensus sequences from bulk sequencing and often show a low number of nonsynonymous mutations at the study protein-coding gene between time points. For example, from the original Wuhan strain and the Omicron variant, the SARS-CoV-2 proteins Mpro and PLpro accumulated only 10 and 22 amino acid changes, respectively. Analyzing intermediate variants of concern (i.e., Gamma or Delta) would reduce this number affecting statistics. Thus, in practice, we found scarcity of data derived from monitoring protein evolution, with trustable ancestor and corresponding descendant data at consecutive time points and with sufficient molecular diversity between them (i.e., at least more than five polymorphic amino acid sites). In all, we believe that the diverse viral protein datasets used in the present study, along with the multiple analyzed datasets collected from monitored HIV-1 populations present in different patients, provide a representative application of the method, since notice that similar patterns were generally generated from the analysis of the different datasets.

(2) Present clear data statistics: For each analyzed dataset, the authors should provide basic information about the number of unique sequences, levels of variability, and evolutionary divergence between start and end sequences. This would contextualize the forecasting task and clarify whether the simulations are non-trivial. In particular, it should be shown that the consensus sequence is indeed representative of the viral population at a given time point. In viral evolution we frequently observe co-circulation of subclades and the consensus sequence is then not representative. 

For each dataset analyzed, the manuscript provides the sequence identity between samples at the study time points (which also informs about sequence variability), sample sizes, representative protein structure, and other technical details. The study assumes that consensus sequences, typically generated by bulk sequencing, are representative of the viral population. Next, samples at different time points should involve ancestor-descendant relationships, which is a requirement and one of the limitations to find appropriate data for this study, as noted in our previous response.

(3) Explore other metrics for population level sequence comparison: 

In the light of possible existence of subclades, mentioned above, the currently used metrics for sequence comparison may underestimate performance of the simulations. It would be sufficient to see some overlap of simulated clades and and the observed clades. 

We found this to be a good idea. However, in practice, we believe that the criteria used to define subclades could introduce biases into the results. For some metrics, we evaluated the accuracy of the predictions through direct comparisons between all real and predicted protein variants, using percentages to facilitate interpretation. We believe that using subclades could potentially reduce the current prediction errors, but this would complicate the interpretation of the results, as they would be influenced by the subjective criteria used to define the subclades.

Currently, the manuscript presents a plausible filtering framework rather than a predictive model. Without these additional analyses, the main claims remain only partially supported. 

Please see our reply to the comment of the reviewer just before the section titled “Recommendations for the authors”.

Response to some rebuttal statements: 

(1) "Sequence comparisons based on the KL divergence require, at the studied time point, an observed distribution of amino acid frequencies among sites and an estimated distribution of amino acid frequencies among sites. In the study datasets, this is only the case for the HIV-1 MA dataset, which belongs to a previous study from one of us and collaborators where we obtained at least 20 independent sequences at each sampling point (Arenas, et al. 2016)" 

The available Influenza and SARS-CoV-2 data gathers isolates annotated with exact collection dates, providing reach datasets for such analysis. 

The available influenza and SARS-CoV-2 sequences are typically derived from bulk sequencing and, therefore, they are consensus sequences. As a result, they cannot be used to calculate KL divergence. Additionally, many of the indicated sequences from databases are not demonstrated to be consecutive in evolutionary terms (one dataset is not a direct ancestor of another dataset existing at a posterior time point), which disallows their applicability in this study. The most suitable studies often involve in vitro virus evolution, but they usually show low genetic variability between samples collected at different time points.

(2) "Regarding extending the analysis to other time points (other variants of concern), we kindly disagree because Omicron is the variant of concern with the highest genetic distance to the Wuhan variant, and a high genetic distance is  required to properly evaluate the prediction method." 

There have been many more variants of concern subsequent to Omicron which circulated in 2021. 

A key aspect is the accumulation of diversity in the study proteins across different time points. The SARS-CoV-2 proteins Mpro and PLpro accumulated only 10 and 22 amino acid changes from the original Wuhan variant to Omicron, respectively.

Analyzing intermediate variants of concern (e.g., Gamma or Delta) or those closely related to Omicron would reduce the number of accumulated mutations even further.   

We want to thank the reviewer again for taking the time to revise our work and for the insightful and helpful comments.

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

Reviewer #1 (Public review): 

Summary: 

Ferreiro et al. present a method to simulate protein sequence evolution under a birth-death model where sequence evolution is constrained by structural constraints on protein stability. The authors then use this model to explore the predictability of sequence evolution in several viral structural proteins. In principle, this work is of great interest to molecular evolution and phylodynamics, which have struggled to couple non-neutral models of sequence evolution to phylodynamic models like birth-death. Unfortunately, though, the model shows little improvement over neutral models in predicting protein evolution, and this ultimately appears to be due to fundamental conceptual problems with how fitness is modeled and linked to the phylodynamic birth-death model. 

AU: We thank the reviewer for the positive comments about our work.

Regarding predictive power, the study showed a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. Next, predicting the exact sequences was more challenging. In this revised version, where we added additional real data, we found that the accuracy of this prediction can vary among proteins (i.e., the SCS model was more accurate than the neutral model in predicting sequences of the influenza NS1 protein at different time points). Still, we consider that efforts are required in the field of substitution models of molecular evolution. For example, amino acids with similar physicochemical properties can result in predictions with appropriate folding stability while different specific sequence. The development of accurate substitution models of molecular evolution is an active area of research with ongoing progress, but further efforts are still needed. Next, forecasting the folding stability of future real proteins is fundamental for proper forecasting protein evolution, given the essential role of folding stability in protein function and its variety of applications. Regarding the conceptual concerns related to fitness modeling, we clarify them in detail in our responses to the specific comments below.

Major concerns:

(1) Fitness model: All lineages have the same growth rate r = b-d because the authors assume b+d=1. But under a birth-death model, the growth r is equivalent to fitness, so this is essentially assuming all lineages have the same absolute fitness since increases in reproductive fitness (b) will simply trade off with decreases in survival (d). Thus, even if the SCS model constrains sequence evolution, the birthdeath model does not really allow for non-neutral evolution such that mutations can feed back and alter the structure of the phylogeny. 

We thank the reviewer for this comment that aims to improve the realism of our model. In the model presented (but see later another model, derived from the proposal of the reviewer, that we have now implemented into the framework and applied it to the study data), the fitness predicted from a protein variant is used to obtain the corresponding birth rate of that variant. In this way, protein variants with high fitness have high birth rates leading to overall more birth events, while protein variants with low fitness have low birth rates resulting in overall more extinction events, which has biological meaning for the study system. The statement “All lineages have the same growth rate r = b-d” in our model is incorrect because, in our model, b and d can vary among lineages according to the fitness. For example, a lineage might have b=0.9, d=0.1, r=0.8, while another lineage could have b=0.6, d=0.4, r=0.2. Indeed, the statement “this is essentially assuming all lineages have the same absolute fitness” is incorrect. Clearly, assuming that all lineages have the same fitness would not make sense, in that situation the folding stability of the forecasted protein variants would be similar under any model, which is not the case as shown in the results. In our model, the fitness affects the reproductive success, where protein variants with a high fitness have higher birth rates leading to more birth events, while those with lower fitness have higher death rates leading to more extinction events. This parameterization is meaningful for protein evolution because the fitness of a protein variant can affect its survival (birth or extinction) without necessarily affecting its rate of evolution. While faster growth rate can sometimes be associated with higher fitness, a variant with high fitness does not necessarily accumulate substitutions at a faster rate. Regarding the phylogenetic structure, the model presented considers variable birth and death events across different lineages according to the fitness of the corresponding protein variants, and this affects the derived phylogeny (i.e., protein variants selected against can go extinct while others with high fitness can produce descendants). We are not sure about the meaning of the term “mutations can feed back” in the context of our system. Note that we use Markov models of evolution, which are well-stablished in the field (despite their limitations), and substitutions are fixed mutations, which still could be reverted later if selected by the substitution model (Yang 2006). Altogether, we find that the presented birth-death model is technically correct and appropriate for modeling our biological system. Its integration with structurally constrained substitution (SCS) models of protein evolution as Markov models follows general approaches of molecular evolution in population genetics (Yang 2006; Carvajal-Rodriguez 2010; Arenas 2012; Hoban, et al. 2012). We have now provided a more detailed description of the models in the manuscript.

Apart from these clarifications about the birth-death model used, we could understand the point of the reviewer and following the suggestion we have now incorporated an additional birth-death model that accounts for variable global birth-death rate among lineages. Specifically, we followed the model proposed by Neher et al (2014), where the death rate is considered as 1 and the birth rate is modeled as 1 + fitness. In this model, the global birth-death rate can vary among lineages. We implemented this model into the computer framework and applied it to the data used for the evaluation of the models. The results indicated that, in general, this model yields similar predictive accuracy compared to the previous birth-death model. Thus, accounting for variability in the global birth-death rate does not appear to play a major role in the studied systems of protein evolution. We have now presented this additional birth-death model and its results in the manuscript.

(2) Predictive performance: Similar performance in predicting amino acid frequencies is observed under both the SCS model and the neutral model. I suspect that this rather disappointing result owes to the fact that the absolute fitness of different viral variants could not actually change during the simulations (see comment #1). 

As indicated in our previous answer, our study shows a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. Next, predicting the exact sequences was more challenging, which was not surprising considering previous studies. In particular, inferring specific sequences is considerably challenging even for ancestral molecular reconstruction (Arenas, et al. 2017; Arenas and Bastolla 2020). Indeed, observed sequence diversity is much greater than observed structural diversity (Illergard, et al. 2009; Pascual-Garcia, et al. 2010), and substitutions between amino acids with similar physicochemical properties can yield modeled protein variants with more accurate folding stability, even when the exact amino acid sequences differ. As indicated, further work is demanded in the field of substitution models of molecular evolution. Next, in this revised version, where we included analyses of additional real datasets, we found that the accuracy of sequence prediction can vary among datasets. Notably, the analysis of an influenza NS1 protein dataset, with higher diversity than the other datasets studied, showed that the SCS model was more accurate than the neutral model in predicting sequences across different time points. Datasets with relatively high sequence diversity can contain more evolutionary information, which can improve prediction quality. In any case, as previously indicated, we believe that efforts are required in the field of substitution models of molecular evolution. Apart from that, forecasting the folding stability of future real proteins is an important advance in forecasting protein evolution, given the essential role of folding stability in protein function (Scheiblhofer, et al. 2017; Bloom and Neher 2023) and its variety of applications.

Next, also as indicated in our previous response, the birth-death model used in this study accounts for variation in fitness among lineages producing variable reproductive success. The additional birth-death model that we have now incorporated, which considers variation of the global birth-death rate among lineages, produced similar prediction accuracy, suggesting a limited role in protein evolution modeling. Molecular evolution parameters, particularly the substitution model, appear to be more critical in this regard. We have now included these aspects in the manuscript.

(3) Model assessment: It would be interesting to know how much the predictions were informed by the structurally constrained sequence evolution model versus the birth-death model. To explore this, the authors could consider three different models: 1) neutral, 2) SCS, and 3) SCS + BD. Simulations under the SCS model could be performed by simulating molecular evolution along just one hypothetical lineage. Seeing if the SCS + BD model improves over the SCS model alone would be another way of testing whether mutations could actually impact the evolutionary dynamics of lineages in the phylogeny. 

In the present study, we compared the neutral model + birth-death (BD) with the SCS model + BD. Markov substitution models Q are applied upon an evolutionary time (i.e., branch length, t) and this allows to determine the probability of substitution events during that time period [P(t) = exp (Qt)]. This approach is traditionally used in phylogenetics to model the incorporation of substitution events over time. Therefore, to compare the neutral and SCS models in terms of evolutionary inference, an evolutionary time is required, in this case it is provided by the birth-death process. Thus, the cases 1) and 2) cannot be compared without an underlined evolutionary history. Next, comparisons in terms of likelihood, and other aspects, between models that ignore the protein structure and the implemented SCS models are already available in previous studies based on coalescent simulations or given phylogenetic trees (Arenas, et al. 2013; Arenas, et al. 2015). There, SCS models outperformed models that ignore evolutionary constraints from the protein structure, and those findings are consistent with the results obtained in the present study where we explored the application of these models to forecasting protein evolution. We would like to emphasize that forecasting the folding stability of future real proteins is a significant finding, folding stability is fundamental to protein function and has a variety of applications. We have now indicated these aspects in the manuscript.

(4) Background fitness effects: The model ignores background genetic variation in fitness. I think this is particularly important as the fitness effects of mutations in any one protein may be overshadowed by the fitness effects of mutations elsewhere in the genome. The model also ignores background changes in fitness due to the environment, but I acknowledge that might be beyond the scope of the current work. 

AU: This comment made us realize that more information about the features of the implemented SCS models should be included in the manuscript. In particular, the implemented SCS models consider a negative design based on the observed residue contacts in nearly all proteins available in the Protein Data Bank (Arenas, et al. 2013; Arenas, et al. 2015). This data is distributed with the framework, and it can be updated to incorporate new structures (further details are provided in the distributed framework documentation and practical examples). Therefore, the prediction of folding stability is a combination of positive design (direct analysis of the target protein) and negative design (consideration of background proteins from a database to improve the predictions), thus incorporating background molecular diversity. We have now indicated this important aspect in the manuscript. Regarding the fitness caused by the environment, we agree with the reviewer. This is a challenge for any method aiming to forecast evolution, as future environmental shifts are inherently unpredictable and may affect the accuracy of the predictions. Although one might attempt to incorporate such effects into the model, doing so risks overparameterization, especially when the additional factors are uncertain or speculative. We have now mentioned this aspect in the manuscript.

(5) In contrast to the model explored here, recent work on multi-type birth-death processes has considered models where lineages have type-specific birth and/or death rates and therefore also type-specific growth rates and fitness (Stadler and Bonhoeffer, 2013; Kunhert et al., 2017; Barido-Sottani, 2023). Rasmussen & Stadler (eLife, 2019) even consider a multi-type birth-death model where the fitness effects of multiple mutations in a protein or viral genome collectively determine the overall fitness of a lineage. The key difference with this work presented here is that these models allow lineages to have different growth rates and fitness, so these models truly allow for non-neutral evolutionary dynamics. It would appear the authors might need to adopt a similar approach to successfully predict protein evolution. 

We agree with the reviewer that robust birth-death models have been developed applying statistics and, in many cases, the primary aim of those studies is the development and refinement of the model itself. Regarding the study by Rasmussen and Stadler 2019, it incorporates an external evaluation of mutation events where the used fitness is specific for the proteins investigated in that study, which may pose challenges for users interested in analyzing other proteins. In contrast, our study takes a different approach. We implement a fitness function that can be predicted and evaluated for any type of structural protein (Goldstein 2013), making it broadly applicable. Actually, in this revised version we added the analysis of additional data of another protein (influenza NS1 protein) with predictions at different time points. In addition, we provide a freely available and well-documented computational framework to facilitate its use. The primary aim of our study is not the development of novel or complex birthdeath models. Rather, we aim to explore the integration of a standard birth-death model with SCS models for the purpose of predicting protein evolution. In the context of protein evolution, substitution models are a critical factor (Liberles, et al. 2012; Wilke 2012; Bordner and Mittelmann 2013; Echave, et al. 2016; Arenas, et al. 2017; Echave and Wilke 2017), and the presented combination with a birth-death model constitutes a first approximation upon which next studies can build to better understand this evolutionary system. We have now indicated these considerations in the manuscript.

Reviewer #2 (Public review): 

Summary: 

In this study, "Forecasting protein evolution by integrating birth-death population models with structurally constrained substitution models", David Ferreiro and coauthors present a forward-in-time evolutionary simulation framework that integrates a birth-death population model with a fitness function based on protein folding stability. By incorporating structurally constrained substitution models and estimating fitness from ΔG values using homology-modeled structures, the authors aim to capture biophysically realistic evolutionary dynamics. The approach is implemented in a new version of their open-source software, ProteinEvolver2, and is applied to four viral proteins from HIV-1 and SARS-CoV-2. 

Overall, the study presents a compelling rationale for using folding stability as a constraint in evolutionary simulations and offers a novel framework and software to explore such dynamics. While the results are promising, particularly for predicting biophysical properties, the current analysis provides only partial evidence for true evolutionary forecasting, especially at the sequence level. The work offers a meaningful conceptual advance and a useful simulation tool, and sets the stage for more extensive validation in future studies.

We thank the reviewer for the positive comments on our study. Regarding the predictive power, the results showed good accuracy in predicting the folding stability of the forecasted protein variants. In this revised version, where we included analyses of additional real datasets, we found that the accuracy of sequence prediction can vary among datasets. Notably, the analysis of an influenza NS1 protein dataset, with higher diversity than the other datasets studied, showed that the SCS model was more accurate than the neutral model in predicting sequences across different time points. Datasets with relatively high sequence diversity can contain more evolutionary information, which can improve prediction quality. Still, we believe that further efforts are required in the field in improving the accuracy of substitution models of molecular evolution. Altogether, accurately forecasting the folding stability of future real proteins is fundamental for predicting their protein function and enabling a variety of applications. Also, we implemented the models into a freely available computer framework, with detailed documentation and a variety of practical examples.

Strengths: 

The results demonstrate that fitness constraints based on protein stability can prevent the emergence of unrealistic, destabilized variants - a limitation of traditional, neutral substitution models. In particular, the predicted folding stabilities of simulated protein variants closely match those observed in real variants, suggesting that the model captures relevant biophysical constraints. 

We agree with the reviewer and appreciate the consideration that forecasting the folding stability of future real proteins is a relevant finding. For instance, folding stability is fundamental for protein function and affects several other molecular properties.

Weaknesses: 

The predictive scope of the method remains limited. While the model effectively preserves folding stability, its ability to forecast specific sequence content is not well supported. 

Our study showed a good accuracy in predicting the real folding stability of forecasted protein variants under a selection model, but not under a neutral model. Predicting the exact sequences was more challenging, which was not surprising considering previous studies. In particular, inferring specific sequences is considerably challenging even for ancestral molecular reconstruction (Arenas, et al. 2017; Arenas and Bastolla 2020). Indeed, observed sequence diversity is much greater than observed structural diversity (Illergard, et al. 2009; Pascual-Garcia, et al. 2010), and substitutions between amino acids with similar physicochemical properties can yield modeled protein variants with more accurate folding stability, even when the exact amino acid sequences differ. As indicated, further work is demanded in the field of substitution models of molecular evolution. Next, in this revised version, where we included analyses of additional real datasets, we found that the accuracy of sequence prediction can vary among datasets. Notably, the analysis of an influenza NS1 protein dataset, with higher diversity than the other datasets studied, showed that the SCS model was more accurate than the neutral model in predicting sequences across different time points. Datasets with relatively high sequence diversity can contain more evolutionary information, which can improve prediction quality. In any case, as previously indicated, we believe that efforts are required in the field of substitution models of molecular evolution. Apart from that, forecasting the folding stability of future real proteins is an important advance in forecasting protein evolution, given the essential role of folding stability in protein function (Scheiblhofer, et al. 2017; Bloom and Neher 2023) and its variety of applications. We have now expanded these aspects in the manuscript.

Only one dataset (HIV-1 MA) is evaluated for sequence-level divergence using KL divergence; this analysis is absent for the other proteins. The authors use a consensus Omicron sequence as a representative endpoint for SARS-CoV-2, which overlooks the rich longitudinal sequence data available from GISAID. The use of just one consensus from a single time point is not fully justified, given the extensive temporal and geographical sampling available. Extending the analysis to include multiple timepoints, particularly for SARS-CoV-2, would strengthen the predictive claims. Similarly, applying the model to other well-sampled viral proteins, such as those from influenza or RSV, would broaden its relevance and test its generalizability. 

The evaluation of forecasting evolution using real datasets is complex due to several conceptual and practical aspects. In contrast to traditional phylogenetic reconstruction of past evolutionary events and ancestral sequences, forecasting evolution often begins with a variant that is evolved forward in time and requires a rough fitness landscape to select among possible future variants (Lässig, et al. 2017). Another concern for validating the method is the need to know the initial variant that gives rise to the corresponding future (forecasted) variants, and it is not always known. Thus, we investigated systems where the initial variant, or a close approximation, is known, such as scenarios of in vitro monitored evolution. In the case of SARS-CoV-2, the Wuhan variant is commonly used as the starting variant of the pandemic. Next, since forecasting evolution is highly dependent on the used model of evolution, unexpected external factors can be dramatic for the predictions. For this reason, systems with minimal external influences provide a more controlled context for evaluating forecasting evolution. For instance, scenarios of in vitro monitored virus evolution avoid some external factors such as host immune responses. Another important aspect is the availability of data at two (i.e., present and future) or more time points along the evolutionary trajectory, with sufficient genetic diversity between them to identify clear evolutionary signatures. Additionally, using consensus sequences can help mitigate effects from unfixed mutations, which should not be modeled by a substitution model of evolution. Altogether, not all datasets are appropriate to properly evaluate or apply forecasting evolution. These aspects are indicated in the manuscript. Sequence comparisons based on the KL divergence require, at the studied time point, an observed distribution of amino acid frequencies among sites and an estimated distribution of amino acid frequencies among sites. In the study datasets, this is only the case for the HIV-1 MA dataset, which belongs to a previous study from one of us and collaborators where we obtained at least 20 independent sequences at each sampling point (Arenas, et al. 2016). This aspect is now more clearly indicated in the manuscript. Regarding the Omicron datasets, we used 384 curated sequences of the Omicron variant of concern to construct the study data and we believe that it is a representative sample. The sequence used for the initial time point was the Wuhan variant (Wu, et al. 2020), which is commonly assumed to be the origin of the pandemic in SARS-CoV-2 studies. As previously indicated, the use of consensus sequences is convenient to avoid variants with unfixed mutations. Regarding extending the analysis to other time points (other variants of concern), we kindly disagree because Omicron is the variant of concern with the highest genetic distance to the Wuhan variant, and a high genetic distance is required to properly evaluate the prediction method. Actually, we noted that earlier variants of concern show a small number of fixed mutations in the study proteins, despite the availability of large numbers of sequences in databases such as GISAID. Additionally, we investigated the evolutionary trajectories of HIV-1 protease (PR) in 12 intra-host viral populations with predictions for up to four different time points. Apart from those aspects, following the proposal of the reviewer, we have now incorporated the analysis of an additional dataset of influenza NS1 protein (Bao, et al. 2008), with predictions for two different time points, to further assess the generalizability of the method. We have now included details of this influenza NS1 protein dataset and the predictions derived from it in the manuscript.

It would also be informative to include a retrospective analysis of the evolution of protein stability along known historical trajectories. This would allow the authors to assess whether folding stability is indeed preserved in real-world evolution, as assumed in their model.

Our present study does not aim to investigate the evolution of the folding stability over time, although it provides this information indirectly at the studied time points. Instead, the present study shows that the folding stability of the forecasted protein variants is similar to the folding stability of the corresponding real protein variants for diverse viral proteins, which provides an important evaluation of the prediction method. Next, the folding stability can indeed vary over time in both real and modeled evolutionary scenarios, and our present study is not in conflict with this. In that regard, which is not the aim of our present study, some previous phylogenetic-based studies have reported temporal fluctuations in folding stability for diverse protein data (Arenas, et al. 2017; Olabode, et al. 2017; Arenas and Bastolla 2020; Ferreiro, et al. 2022).

Finally, a discussion on the impact of structural templates - and whether the fixed template remains valid across divergent sequences - would be valuable. Addressing the possibility of structural remodeling or template switching during evolution would improve confidence in the model's applicability to more divergent evolutionary scenarios.

This is an important point. For the datasets that required homology modeling (in several cases it was not necessary because the sequence was present in a protein structure of the PDB), the structural templates were selected using SWISS-MODEL, and we applied the best-fitting template. We have now included in a supplementary table details about the fitting of the structural templates. Indeed, our proposal assumes that the protein structure is maintained over the studied evolutionary time, which can be generally reasonable for short timescales where the structure is conserved (Illergard, et al. 2009; Pascual-Garcia, et al. 2010). Over longer evolutionary timescales, structural changes may occur and, in such cases, modeling the evolution of the protein structure would be necessary. To our knowledge, modeling the evolution of the protein structure remains a challenging task that requires substantial methodological developments. Recent advances in artificial intelligence, particularly in protein structure prediction from sequence, may offer promising tools for addressing this challenge. However, we believe that evaluating such approaches in the context of structural evolution would be difficult, especially given the limited availability of real data with known evolutionary trajectories involving structural change. In any case, this is probably an important direction for future research. We have now included this discussion in the manuscript.

Reviewer #1 (Recommendations for the authors): 

(1) Abstract: "expectedly, the errors grew up in the prediction of the corresponding sequences" <- Not entirely clear what is meant by "errors grew up" or what the errors grew with.

This sentence refers to the accuracy of sequence prediction in comparison to that of folding stability prediction. We have now clarified this aspect in the manuscript.

(2) Lines 162-165: "Alternatively, if the fitness is determined based on the similarity in folding stability between the modeled variant and a real variant, the birth rate is assumed to be 1 minus the root mean square deviation (RMSD) in folding stability." <- What is the biological motivation for using the RMSD? It seems like a more stable variant would always have higher fitness, at least according to Equation 1.

RMSD is commonly used in molecular biology to compare proteins in terms of structural distance, folding stability, kinetics, and other properties. It offers advantages such as minimizing the influence of small deviations while amplifying larger differences, thereby enhancing the detection of remarkable molecular changes. Additionally, RMSD would facilitate the incorporation of other biophysical parameters, such as structural divergences from a wild-type variant or entropy, which could be informative for fitness in future versions of the method. We have now included this consideration in the manuscript.

(3) Lines 165-166: "In both cases, the death rate (d) is considered as 1-b to allow a constant global (birth-death) rate" <- This would give a constant R = b / (1-b) over the entire phylogenetic tree. For applications to pathogens like viruses with epidemic dynamics, this is extremely implausible. Is there any need to make such a restrictive assumption? 

Regarding technical considerations of the model, we refer to our answer to the first public review comment. Next, a constant global rate of evolution was observed in numerous genes and proteins of diverse organisms, including viruses (Gojobori, et al.1990; Leitner and Albert 1999; Shankarappa, et al. 1999; Liu, et al. 2004; Lu, et al. 2018; Zhou, et al. 2019). However, following the comment of the reviewer, and as we indicated in our answer to the first public review comment, we have now implemented and evaluated an additional birth-death model that allows for variation in the global birth-death rate among lineages. We have implemented this additional model in the framework and described it along with its results in the manuscript.

(4) Lines 187-188: "As a consequence, since b+d=1 at each node, tn is consistent across all nodes, according to (Harmon, 2019)." <- This would also imply that all lineages have a growth rate r = b - d, which under a birth-death model is equivalent to saying all lineages have the same fitness! 

We clarified this aspect in our answer to the first public review comment. In particular, in the model presented, protein variants with higher fitness have higher birth rates, leading to more birth events, while protein variants with lower fitness have lower birth rates leading to more extinction events, which presents biological meaning for the study system. In our model b and d can vary among lineages according to the corresponding fitness (i.e., a lineage may have b=0.9, d=0.1, r=0.8; while another one may have b=0.6, d=0.4, r=0.2). Since the reproductive success varies among lineages in our model, the statement “this is essentially assuming all lineages have the same absolute fitness” is incorrect, although it could be interpreted like that in certain models. Fitness affects reproductive success, but fitness and growth rate of evolution are different biological processes (despite a faster growth rate can sometimes be associated with higher fitness, a variant with a high fitness not necessarily has to accumulate substitutions at a higher rate). An example in molecular adaptation studies is the traditional nonsynonymous to synonymous substitution rates ratio (dN/dS), where dN/dS (that informs about selection derived from fitness) can be constant at different rates of evolution (dN and dS). In any case, we thank the reviewer for raising this point, which led us to incorporate an additional birth-death model and inspired some ideas.  Thus, following the comment of the reviewer and as indicated in the answer to the first public review comment, we have now implemented and evaluated an additional birthdeath model that allows for variation in the global birth-death rate among lineages. The results indicated that this model yields similar predictive accuracy compared to the previous birth-death model. We have now included these aspects, along with the results from the additional model, in the manuscript.

(5) Line 321-322: "For the case of neutral evolution, all protein variants equally fit and are allowed, leading to only birth events," <- Why would there only be birth events? Lineages can die regardless of their fitness. 

AU: In the neutral evolution model, all protein variants have the same fitness, resulting in a flat fitness landscape. Since variants are observed, we allowed birth events. However, it assumed the absence of death events as no information independent of fitness is available to support their inclusion and quantification, thereby avoiding the imposition of arbitrary death events based on an arbitrary death rate. We have now provided a justification of this assumption in the manuscript.

Reviewer #2 (Recommendations for the authors): 

(1) Clarify the purpose of the alternative fitness mode ("ΔG similarity to a target variant"): 

The manuscript briefly introduces an alternative fitness function based on the similarity of a simulated protein's folding stability to that of a real protein variant, but does not provide a clear motivation, usage scenario, or results derived from it. 

The presented model provides two approaches for deriving fitness from predicted folding stability. The simpler approach assumes that a more stable protein variant has higher fitness than a less stable one. The alternative approach assigns high fitness to protein variants whose stability closely matches observed stability, acknowledging that the real observed stability is derived from the real selection process, and this approach considers negative design by contrasting the prediction with real information. For the analyses of real data in this study, we used the second approach, guided by these considerations. We have now clarified this aspect in the manuscript.

(2) Report structural template quality and modeling confidence: 

Since folding stability (ΔG) estimates rely on structural models derived from homology templates, the accuracy of these predictions will be sensitive to the choice and quality of the template structure. I recommend that the authors report, for each protein modeled, the template's sequence identity, coverage, and modeling quality scores. This will help readers assess the confidence in the ΔG estimates and interpret how template quality might impact simulation outcomes. 

We agree with the reviewer and we have now included additional information in a supplementary table regarding sequence identity, modeling quality and coverage of the structural templates for the proteins that required homology modeling. The selection of templates was performed using the well-established framework SWISS-MODEL and the best-fitting template was chosen. Next, a large number of protein structures are available in the PDB for the study proteins, which favors the accuracy of the homology modeling. For some datasets, homology modeling was not required, as the modeled sequence was already present in an available protein structure. We have now included this information in the manuscript and in a supplementary table.

(3) Clarify whether structural remodeling occurs during simulation: 

It appears that folding stability (ΔG) for all simulated protein variants is computed by mapping them onto a single initial homology model, without remodeling the structure as sequences evolve. If correct, this should be clearly stated, as it assumes that the structural fold remains valid across all simulated variants. A discussion on the potential impact of structural drift would be welcome.

We agree with the reviewer. As indicated in our answer to a previous comment, our method assumes that the protein structure is maintained over the studied evolutionary time, which is generally acceptable for short timescales where the structure is conserved (Illergard, et al. 2009; Pascual-Garcia, et al. 2010). At longer timescales the protein structure could change, requiring the modeling of structural evolution over the evolutionary time. To our knowledge, modeling the evolution of the protein structure remains a challenging task that requires substantial methodological developments. Recent advances in artificial intelligence, particularly in protein structure prediction from sequence, can be promising tools for addressing this challenge. However, we believe that evaluating such approaches in the context of structural evolution would be difficult, especially given the limited availability of real datasets with known evolutionary trajectories involving structural change. In any case, this is probably an important direction for future research. We have now included this discussion in the manuscript.

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an effort or attempt that is made at the end of a series of failures to solve a problem, and is not expected to succeed:

the way in which something operates and the set of conditions that influences it

​a regular journey made by a judge to hear court cases in each of the courts of law in a particular area

Busbecq says that the Turkish soldier is much more disciplined and determined than the Christian soldier, and that a Turkish soldier will put up with anything while a christian soldier complains.

I thought that was the point of a teacher moving throughout the room? If they are being disruptive, wouldn't you want to move closer to the student so the behavior stops or does this mean that when a student has the floor and is speaking in front of the class or giving their input? Not necessarily being disruptive.

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Another thing to practice while taking this class

This is a good thing to keep in mind

These are the "Blessings for Obedience"

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high AST and normal ALT: RBCs have AST but not ALT inside them, so this represents more RBC breakdown as opposed to liver damage (both would be elevated).

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Descriptive and prescriptive grammar often clash instead of collaborating. Descriptive grammar explains how people actually speak/write; whereas, prescriptive grammar dictates how people should speak/write.

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The spacing should be the same as above/below (it is currently larger)

The spacing is larger than it should be (refer to note n)

The spacing is larger than it should be (refer to note n)

The spacing is larger than it should be (refer to note n)

The spacing is larger than it should be (refer to note n)

The spacing is larger than it should be (refer to note n)

The spacing is larger than it should be (refer to note n)

The spacing is larger than it should be (refer to note n)

The statement in this paragraph about commercials remains the same today. The ads distributed during the shows are often related to the type of program being broadcast. For example, if the show is for an adult audience, the ads will be aimed at promoting products for adults.

In this initial paragraph, Williams explains that while the categories of broadcasting shows are essential, developers of entertainment need to understand that the flow is most relevant in the distribution of the programs.

It’s true that once we start watching, it’s hard to stop. This happens with social media too, where feeds and algorithm keep us scrolling. It makes you wonder how much we’re actually choosing what to watch, and how much we’re just getting pulled along by a flow that’s designed to keep us glued to the screen.

Williams emphasizes that much of TV’s impact comes from viewers simply sitting and watching. Flow isn’t just a theory, it’s created by actual habits, showing how attention and cultural experience are shaped through continuous viewing.

Williams argues that television’s “real programme” isn’t any single show but the sequence itself. This changes how we think and view TV, since what matters isn’t just each show on its own, but how all the shows, ads, and promos flow together to create the overall experience.

I think that this statement is very important because now that we have recognized that it is a problem what are we doing to solve it? the answer is nothing. Content just keeps getting easier and shorter to consume and people keep consuming it

I think that this statement can now be applied with a modern lens and can speak to peoples current attention spans. Apps like Tik Tok and instagram have implemented ways of consuming hundreds of different types of content within just a few minutes of being on the app. I Truly Believe that cable television was the last time humans had a attention span that made it so they could sit through a whole broadcast on T.V

For some people, this unpredictable style of t.v has been something that people are fond of and keep coming back too. Fans of a series often try to search for and uncover outages from the show just because they crave more of that unpredictability that a certain show has. A stellar example of this would be the Comedy "The Office".

A claim of policy is the easiest claim to spot,

Cartoon Worlds We Would Love To Live In

Board & Card Games To Play On A Rainy Day

Ideas are useful to share.

Colloquial means it is informal, but in ordinary conversation.

Everybody creates creative work based on people's work. Nothing is really new.

Using a template is not plagiarism as long as the details are added my own words and proper credit has been given.

A picture of exaggeration. Audiences don't like when I insult the other side of the argument.

If no one disagrees, then I am stating the obvious. That is boring.

The common belief is arguing against everybody.

The page says that I don't have to argue against famous person. It can be anyone including myself.

The page says that I don't have to argue against famous person. It can be anyone including myself.

It is not necessarily disagreeing, but it is building upon argument.

The templates will help practice, but not automatically making a good writer.

Writing is like muscle memory.

How do you show that the shape of E is the same in the past? Do you just look at it, or is there some algorithm? Slide 17.

Clarify Rossby waves.

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Referee #2

Evidence, reproducibility and clarity

Summary:

This study demonstrates an improved integral gene drive (IGD) for use in Anopheles gambiae. Inserting the coding sequence for Cas9 in-frame with a germline-specific gene (nanos) improved the performance of this IGD relative to previously reported systems while reducing fitness costs. Integration of the gRNA cassette within a synthetic intron is an elegant solution to constraining the minimal elements of the IGD within a single insertion. The results of this study found that while the IGD can be used to propagate anti-malarial effectors (MM-CP) within a population, fitness costs and resistance alleles were higher than anticipated, potentially limiting the application of this particular IGD design without further optimisation.

The results comprehensively demonstrate the effective transmission and stability of the IGD over several generations, while also characterising the limitations of the system. Although I don't think the authors make any claims which are not supported by their results. It might be good to provide more of an explanation for how the performance of this IGD compares to the zpg IGD reported in Ellis et al 2022 for readers less familiar with the IGD literature.

The manuscript is overall very well written with clear results and methods. However, I found the descriptions referring to the effects of the maternal, paternal, and even grandmaternal inheritance hard to follow. The statistical analysis and replications are adequate as well.

Referee cross-commenting

I agree with the other reviewer's comments regarding the need to clarify a few points made in the overall well written manuscript.

Significance

Gene drives are the most promising genetic biocontrol method for controlling the spread of malaria. However, there are many technical challenges that have made the development of gene drives quite difficult. This study works to address one such challenge - constraining the expression of Cas9 to the germline by integrating it within an endogenous loci rather than using semi-synthetic promoters. While IGD have been demonstrated before, this study further improves on their performance while reducing off-target effects.

The manuscript is written for a highly specialized audience that is very familiar with the genetic biocontrol, and especially the gene-drive field of research.

My fields of research include genetic biocontrol and insect synthetic biology.

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Referee #1

Evidence, reproducibility and clarity

In this study, the authors develop a complete integral drive system in Anopheles gambiae malaria mosquitoes. This type of gene drive is interesting, with special advantages and disadvantages compared to more common designs. Here, the authors develop the Cas9 element and combine it with a previously developed antimalaria effector element. The new element performs very well in terms of drive efficiency, but it has unintended fitness costs, and a higher than desirable rate of functional resistance allele formation. Nevertheless, this study represents a very good step forward toward developing effective gene drives and is thus of high impact.

The format of the manuscript is a bit suboptimal for review. Please add line numbers next time for easy reference. It would also help to have spaces between paragraphs and to have figures (with legends) added to the text where they first appear.

It might be useful to add subsections to the results, just like in the methods section. It could even be expanded a bit with some specific parts from the discussion, through this is optional.

Abstract: The text says: "As a minimal genetic modification, nanosd does not induce widespread transcriptomic perturbations." However, it does seem to change things based on Figure 3c.

Page 2: "drive technologies for public health and pest control applications" needs a period afterward.

Page 2: "The fitness costs, homing efficiency, and resistance rate of the gene drive is" should be "The fitness costs, homing efficiency, and resistance rate of the gene drive are".

Page 2: "When they target important mosquito genes, gene drives are designed to ensure that the nuclease activity window (germline) does not overlap with that of the target gene (somatic)." is note quite correct. This is, of course, sensible for suppression drives, but it's not a necessary requirement for modification drives with rescue elements in many situations.

Page 2: "recessive somatic fitness cost phenotypes" is unclear. I think that you are trying to avoid the recessive fitness cost of null alleles becoming a dominant fitness cost from a gene drive allele (in drive-wild-type heterozygotes).

Page 2: "This optimization approach has had only limited success, and suboptimal performance is commonly attributed to not capturing all the regulatory elements specific to the germline gene's expression9,12". I don't think this is correct. There are several examples where a new promoter helped a lot. The zpg promoter in Anopheles gambiae allowed success at the dsx site in suppression cage studies (Kyrou et al 2018), and nanos gave big improvement to modification drives at the cardinal locus (Carballer et al 2020). In flies, several promoters were tested, and one allowed success in cage experiments (Du et al 2024). In Aedes, the shu promoter allowed for high drive performance (Anderson et al 2023), though this last one hasn't been tested in more difficult situations. I think you could certainly argue in the general case that not all promoters will work the way their transcriptome says, but there are many examples where they seem to be pretty good.

Page 2: "make it more likely that mutations that disrupt the drive components are selected against though loss of function of the host gene." I think that this needs a bit more explanation. You are referring to mutations in regulatory elements or frameshift mutations. This will make it more resistant to mutation. Also, these mutations would tend to have a minor effect expect perhaps in the cargo gene of a modification drive. By using a cargo gene in an integral drive, you could still keep it somewhat safer, but whether this is 1.2x or 10x safer is unclear.

Page 3: "they can incur severe unintended fitness costs". This is central to integral drives and this manuscript. It's worth elaborating on.

Page 3: "Regulatory elements from germline genes that have worked sub-optimally in traditional gene drive designs for the reasons outlined above may work well in an IDG design20." This is setting up the integral drive with nanos, but nanos DOES work well in traditional Anopheles gambiae gene drive designs. It is possible that you might end up with less somatic expression than Hammond et al 2020 (though the comparison is unclear due to batch effects in that study), but there is no direct comparison in this manuscript to that.

Page 3: "This suggests an impact of maternal deposition on drive efficiency only in female drive carriers." This is quite strange. Usually, I would expect to see an equal reduction in efficiency between male and female progeny. Could this be due to limited sample size? Random idea: It's also possible that almost all maternal deposition was mosaic and wouldn't be enough to direct affect drive conversion. However, it could cause enough of a fitness cost TOGETHER with new drive expression in females that perhaps only tissues with randomly low expression rates properly developed and led to progeny, reducing drive inheritance? Another possibility: Could the drive/resistance males have impaired fertility, so these ones are underrepresented in the batch cross? If nanos is needed in males and a single drive copy is not quite enough for good fertility or mating competitiveness, they may be underrepresented in your crosses. They might have worse fertility than drive homozygous males, which at least have two partially working copies of nanos rather than just one (in many cells, at least). Maybe check the testis for abnormal phenotypes?

Overall, it would be favorable if the drive allele was somewhere more fit than a nonfunctional resistance allele. This could already be achieved in this drive, but it doesn't get much mention.

Page 3: There should be a comma after, "Interestingly, while many of the observed mutations were predicted to abolish nanos expression" and "This could indicate that in these experiments".

Page 3 last sentence: Please improve the clarity.

Removing the EGFP is supposed to restore the fitness, and this was helpful in some previous integral drive constructs. This could get a bit more mention (it is possible that I missed this somewhere in the manuscript).

Page 4: The MM-CP line and it's association with the integral drive strategy could get a little more introduction. Maybe even a supplemental figure showing the general idea.

Page 5: "cassette is predicted to disrupt the CP function entirely (Fig. 5d)" also lacks a period.

Page 5: "The subsequent stabilization of the nanosd frequency and the lack of rapid loss suggests that any associated fitness cost is primarily recessive." This is not quite correct because by this point, drive/wild-type heterozygotes are rare, and this is where you'd find a potential dominant fitness cost. It should be correct in the end stages where it is a mix of drive and functional/nonfunctional resistance alleles (though the nonfunctional resistance alleles may cause greater fitness costs when together with a drive - see above).

Page 6: "Maternal deposition of Cas9, or Cas9;gRNA, into the zygote can lead to cutting at stages when homing is not favoured, and has been commonly observed for canonical Anopheles nanos drives9,10,35." Reference 35 (which is more suitable for referencing an example of nanos in other Anopheles) found some resistance alleles by deep sequencing, but the timing that they formed was unclear (it's not certain if it was maternal deposition). This study may be a more suitable reference: Carballar-Lejarazú R, Tushar T, Pham TB, James AA. Cas9-mediated maternal-effect and derived resistance alleles in a gene-drive strain of the African malaria vector mosquito, Anopheles gambiae. Genetics, 2022.

Page 8: "could further reduce the likelihood of resistance allele formation by increasing the frequency of HDR events." Multiple gRNAs would mostly help by reducing functional resistance allele formation, especially since drive conversion is already very high in Anopheles.

Page 8, last paragraph: This conclusion is perhaps a little optimistic considering the functional resistance alleles, which should get a little more attention in the summary or elsewhere in the discussion section.

Figure 1d: The vertical text saying "Non-WT" is confusing. The circles themselves show + and -. Also, "-" isn't necessarily a knockout allele, so I'm not sure if - is the best symbol for resistance.

Figure 2e: The vertical scale is not the most intuitive. Consider rearranging it to "Transition from larvae to pupae" starting at zero and going to 1 when all the larvae become pupae.

Figure 2e-f: For both of these, there are clear differences between males and females. Thus, when comparing drive homozygotes to wild-type, it would probably be better to have separate statistical comparisons for males and females.

Figure 3: Can any of these transcription results in individual genes potentially explain the observed fitness cost?

Figure 3b: The scale here also doesn't quite make sense. It's the fraction of underdeveloped ovaries, but the graph is also perhaps trying to show whether just 1-2 ovaries are present, or maybe how many ovaries are undeveloped, but then it would say "zero"? This should be clarified. Number of ovaries and how well-developed they are is separate (it can be put on the same graph, but needs to be more clear).

Figure 4f: The vertical axis should say "ONNV."

Figure 5c-d: These should be labeled as the most common resistance allele. Also, I'm not sure how relevant it is, but we also found an alternate start codon here: Hou S, Chen J, Feng R, Xu X, Liang N, Champer J. A homing rescue gene drive with multiplexed gRNAs reaches high frequency in cage populations but generates functional resistance. J Genet Genomics, 2024. Maybe this is a more common problem than one would expect?

Figure 5cd,S4,S5: They have a bit of a weird plot. Why not make four line graphs for each? Also, some alleles use the  symbol. + is wild-type, which is well understood, but - as resistance is not always clear, and seeing them together may confuse readers. Additionally, the fact that you have the most common resistance allele in Figure 5cd might mean that you know more about the genotype? If so, it would be best to separate wild-type and resistance alleles in whatever the final figure looks like.

Some supplemental raw data files would be useful if they were available, but the figures are through enough that this isn't essential.

Review by:

Jackson Champer, with major assistance from Ruobing Feng (essentially section B) and Jie Du

Referee cross-commenting

We don't have any cross-comments, other than supporting the idea of slightly more comparisons to the authors' previous construct.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

A key innovation of the nanosd gene drive is its integral gene drive (IGD) design, which inserts the drive cassette directly into the A. gambiae nanos gene, incorporating only the minimal components necessary for drive function. The drive achieves high transmission rates, without causing widespread disruption of gene expression or increasing susceptibility to malaria parasites, and imposes an acceptable fitness cost-primarily a reduction in female fecundity when homozygous. The strong performance of nanosd can be attributed to its design: Cas9 is expressed in the correct cells and timing to induce efficient homing, effectively hijacking the nanos gene's natural expression profile. However, despite the careful design aimed at preserving nanos function, the rescue was incomplete: homozygous female drive carriers exhibited a clear reduction in ovarian function.

In caged population trials, both the drive and a co-introduced anti-malaria effector gene reached high frequencies, even in the presence of emerging resistance alleles. Because the drive is inserted into an essential gene, nonfunctional resistance alleles are selected against and tend to be purged over time. Nonetheless, functional resistance remains a concern. The use of a single, though precisely positioned gRNA targeting the native nanos gene ATG site increases the likelihood of generating functional resistance alleles. Over the long term, if the drive imposes fitness costs, it may be outcompeted by such functional resistance alleles, potentially undermining the goal of sustained population modification.

Overall, this study represent a notable advance in Anopheles mosquito gene drive development and can be considered as high impact. - Place the work in the context of the existing literature (provide references, where appropriate).

Previous IGD efforts in Drosophila, mice and mosquitoes have demonstrated nearly super‐Mendelian inheritance but often at the expense of host fitness. For example, Nash et al. built an intronic‐gRNA Cas9 drive at the D. melanogaster rcd-1r locus that propagated efficiently through cage populations (Nash et al., 2022), and Gonzalez et al. reported that a Cas9 drive inserted at the germline zpg locus in Anopheles stephensi biased inheritance by ~99.8% (Gonzalez et al., 2025). However, these strong drives disrupted essential genes: in A. gambiae, inserting Cas9 into zpg produced efficient homing but rendered females largely sterile (Ellis et al., 2022). A similar germline Cas9 knock-in in Mus musculus enabled gene conversion in both sexes, albeit with only modest efficiency and potential fitness trade-offs (Weitzel et al., 2021). The current nanosd IGD is explicitly designed to overcome this limitation by selecting a more permissive gene target and using a minimal drive cassette, so as to preserve mosquito viability while maintaining robust drive efficiency, although still with reduced female drive homozygotes fertility.

This nanosd gene drive like previous homing drives in Anopheles, is capable of achieving a high level of inheritance bias. Although it uses the endogenous nanos regulatory elements, which have less leaky somatic expression compared to using vasa (Gantz et al., 2015; Hammond et al., 2016; Hammond et al., 2017) or zpg promoters(Hammond et al., 2021; Kyrou et al., 2018), to drive Cas9 expression and thereby reduces somatic expression-induced female sterility, the incomplete rescue of nanos function still leads to reduced female fertility in drive homozygotes. - State what audience might be interested in and influenced by the reported findings.

It's worth noting the broad audience that will find this work relevant. Gene drive developers and molecular geneticists will be impressed by the good drive performance and directly influenced by the design principles showcased here. The study's integral gene drive architecture that leverages the endogenous nanos regulatory elements, in-frame E2A peptide linkage for co-expression, and intronic insertion of gRNA and selectable markers addresses long-standing challenges in promoter leakage, somatic fitness costs, and resistance allele evolution. What's more, vector biologists and malaria researchers will be interested in the successful deployment of a gene drive in A. gambiae that actually carries a disease-blocking trait. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

We have worked on CRISPR gene drive development in both fruit flies and Anopheles mosquitoes and have experience with modeling their spread.

References

Ellis, D.A., Avraam, G., Hoermann, A., Wyer, C.A.S., Ong, Y.X., Christophides, G.K., and Windbichler, N. (2022). Testing non-autonomous antimalarial gene drive effectors using self-eliminating drivers in the African mosquito vector Anopheles gambiae. PLOS Genetics 18, e1010244-e1010244.

Gantz, V.M., Jasinskiene, N., Tatarenkova, O., Fazekas, A., Macias, V.M., Bier, E., and James, A.A. (2015). Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi. Proc Natl Acad Sci U S A 112, E6736-E6743.

Gonzalez, E., Anderson, M.A.E., Ang, J.X.D., Nevard, K., Shackleford, L., Larrosa-Godall, M., Leftwich, P.T., and Alphey, L. (2025). Optimization of SgRNA expression with RNA pol III regulatory elements in Anopheles stephensi. Scientific Reports 15, 13408.

Hammond, A., Galizi, R., Kyrou, K., Simoni, A., Siniscalchi, C., Katsanos, D., Gribble, M., Baker, D., Marois, E., Russell, S., et al. (2016). A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae. Nat Biotechnol 34, 78-83.

Hammond, A., Karlsson, X., Morianou, I., Kyrou, K., Beaghton, A., Gribble, M., Kranjc, N., Galizi, R., Burt, A., Crisanti, A., et al. (2021). Regulating the expression of gene drives is key to increasing their invasive potential and the mitigation of resistance. PLOS Genetics 17, e1009321-e1009321.

Hammond, A.M., Kyrou, K., Bruttini, M., North, A., Galizi, R., Karlsson, X., Kranjc, N., Carpi, F.M., D'Aurizio, R., Crisanti, A., et al. (2017). The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito. PLOS Genetics 13, e1007039-e1007039.

Kyrou, K., Hammond, A.M., Galizi, R., Kranjc, N., Burt, A., Beaghton, A.K., Nolan, T., and Crisanti, A. (2018). A CRISPR-Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nature Biotechnology 36, 1062-1066.

Nash, A., Capriotti, P., Hoermann, A., Papathanos, P.A., and Windbichler, N. (2022). Intronic gRNAs for the construction of minimal gene drive systems. Frontiers in Bioengineering and Biotechnology 0, 570-570. Weitzel, A.J., Grunwald, H.A., Ceri, W., Levina, R., Gantz, V.M., Hedrick, S.M., Bier, E., and Cooper, K.L. (2021). Meiotic Cas9 expression mediates gene conversion in the male and female mouse germline. Plos Biol 19, e3001478-e3001478.

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eLife Assessment

This work characterizes the function and localization of SLC4A1 variants associated with distal renal tubular acidosis in human patients. Cell culture and limited animal studies provide partial but incomplete support to the authors' claim that the variants disrupt normal protein degradative flux by alkalinizing the intracellular pH. The study is valuable in providing preliminary evidence for future exploration of the link between intracellular pH regulation by SLC4A1 and kidney cell function in vivo.

Reviewer #1 (Public review):

Summary:

This study is an evaluation of patient variants in the kidney isoform of AE1 linked to distal renal tubular acidosis. Drawing on observations in the mouse kidney, this study extends findings to autophagy pathways in a kidney epithelial cell line.

Strengths:

Experimental data are convincing and nicely done.

Weaknesses:

Some data are lacking or not explained clearly. Mutations are not consistently evaluated throughout the study, which makes it difficult to draw meaningful conclusions.

Reviewer #2 (Public review):

Context and significance:

Distal renal tubular acidosis (dRTA) can be caused by mutations in a Cl-/HCO3- exchanger (kAE1) encoded by the SLC4A1 gene. The precise mechanisms underlying the pathogenesis of the disease due to these mutations are unclear, but it is thought that loss of the renal intercalated cells (ICs) that express kAE1 and/or aberrant autophagy pathway function in the remaining ICs may contribute to the disease. Understanding how mutations in SLC4A1 affect cell physiology and cells within the kidney, a major goal of this study, is an important first step to unraveling the pathophysiology of this complex heritable kidney disease.

Summary:

The authors identify a number of new mutations in the SLC4A1 gene in patients with diagnosed dRTA that they use for heterologous experiments in vitro. They also use a dRTA mouse model with a different SLC4A1 mutation for experiments in mouse kidneys. Contrary to previous work that speculated dRTA was caused mainly by trafficking defects of kAE1, the authors observe that their new mutants (with the exception of Y413H, which they only use in Figure 1) traffic and localize at least partly to the basolateral membrane of polarized heterologous mIMCD3 cells, an immortalized murine collecting duct cell line. They go on to show that the remaining mutants induce abnormalities in the expression of autophagy markers and increased numbers of autophagosomes, along with an alkalinized intracellular pH. They also reported that cells expressing the mutated kAE1 had increased mitochondrial content coupled with lower rates of ATP synthesis. The authors also observed a partial rescue of the effects of kAE1 variants through artificially acidifying the intracellular pH. Taken together, this suggests a mechanism for dRTA independent of impaired kAE1 trafficking and dependent on intracellular pH changes that future studies should explore.

Strengths:

The authors corroborate their findings in cell culture with a well-characterized dRTA KI mouse and provide convincing quantification of their images from the in vitro and mouse experiments.

Weaknesses:

The data largely support the claims as stated, with some minor suggestions for improving the clarity of the work. Some of the mutants induce different strengths of effects on autophagy and the various assays than others, and it is not clear why this is from the present manuscript, given that they propose pHi and the unifying mechanism.

Reviewer #3 (Public review):

Summary:

The authors have identified novel dRTA causing SLC4A1 mutations and studied the resulting kAE1 proteins to determine how they cause dRTA. Based on a previous study on mice expressing the dRTA kAE1 R607H variant, the authors hypothesize that kAE1 variants cause an increase in intracellular pH, which disrupts autophagic and degradative flux pathways. The authors clone these new kAE1 variants and study their transport function and subcellular localization in mIMCD cells. The authors show increased abundance of LC3B II in mIMCD cells expressing some of the kAE1 variants, as well as reduced autophagic flux using eGFP-RFP-LC3. These data, as well as the abundance of autophagosomes, serve as the key evidence that these kAE1 mutants disrupt autophagy. Furthermore, the authors demonstrate that decreasing the intracellular pH abrogates the expression of LC3B II in mIMCD cells expressing mutant SLC4A1. Lastly, the authors argue that mitochondrial function, and specifically ATP synthesis, is suppressed in mIMCD cells expressing dRTA variants and that mitochondria are less abundant in AICs from the kidney of R607H kAE1 mice. While the manuscript does reveal some interesting new results about novel dRTA causing kAE1 mutations, the quality of the data to support the hypothesis that these mutations cause a reduction in autophagic flux can be improved. In particular, the precise method of how the western blots and the immunofluorescence data were quantified, with included controls, would enhance the quality of the data and offer more supportive evidence of the authors' conclusions.

Strengths:

The authors cloned novel dRTA causing kAE1 mutants into expression vectors to study the subcellular localization and transport properties of the variants. The immunofluorescence images are generally of high quality, and the authors do well to include multiple samples for all of their western blots.

Weaknesses:

Inconsistent results are reported for some of the variants. For example, R295H causes intracellular alkalinization but also has no effect on intracellular pH when measured by BCECF. The authors also appear to have performed these in vitro studies on mIMCD cells that were not polarized, and therefore, the localization of kAE1 to the basolateral membrane seems unlikely, based upon images included in the manuscript. Additionally, there is no in vivo work to demonstrate that these kAE1 variants alter intracellular pH, including the R607H mouse, which is available to the authors. The western blots are of varying quality, and it is often unclear which of the bands are being quantified. For example, LAMP1 is reported at 100kDa, the authors show three bands, and it is unclear which one(s) are used to quantify protein abundance. Strikingly, the authors report a nonsensical value for their quantification of LCRB II in Figure 2, where the ratio of LCRB II to total LCRB (I + II) is greater than one. The control experiments with starvation and bafilomyocin are not supportive and significantly reduce enthusiasm for the authors' findings regarding autophagy. There are labeling errors between the manuscript and the figures, which suggest a lack of vigilance in the drafting process.

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When going through the principles of clear directions we can see a list of things that seem useful. I believe that in the video we can see examples that would seem to translate in real life quite well.

When looking at the section labeled "register" it is interesting to see what truly goes into it. The difference between casual and urgent it quite clear. I would like to see the difference in urgent for a first year teacher and a highly expericned teacher.

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This shows how some people take technology and blames it for things that happens in society. I feel like the definition itself doesn't really target if it could be a good thing or bad thing. some people might feel like technological determinism is good because it helped Society evolve, while others might say its ruining society because its making humans more dependent on technology. Just like anything I feel like it has its pros and cons. Technogly comes from humans and evolution to make modern day things more easy to do.

maybe we can just plug in models of various scales and see how the performance project?

maybe each AI startup out there is a different wrapper, specific for each domain?

• This is a reference to Harriet Beecher Stowe's Uncle Tom's Cabin, which was a prominent abolitionist text. Here, Harris is suggesting that it was actually a kind portrait of slavery.

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يتكم الجزء اني بقدر اعمل متغيرات داخل Class تكون public واستدعيها في blade مثل ما يوضح في الصورة

اذا حذفت render function تلقائيا livewire رح تبحث علي اول view يتطابق اسمه مع اسم Control وتبعثو للواجهة

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It's interesting how these criticisms of mass society emerged right before postmodernism, which had a major focus on challenging universal truths and messages. I wonder if this is the research community's response to modernism. Does the film industry ever respond to what comes out of related research fields?

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eLife Assessment

This study presents the important finding that lysosomal damage triggers inflammatory signaling through ubiquitination and the TAB-TAK1-IKK-NF-kB axis. The data obtained from the unbiased transcriptomic and proteomic analyses are convincing and provide invaluable information to the field. Although further experiments will be required to clarify how TAB2/3 are recruited after various types of lysosome damage, this work will be of interest to researchers in the fields of organelle biology and inflammation.

Reviewer #1 (Public review):

Summary:

Lysosomal damage is commonly found in many diseases including normal aging and age-related disease. However, the transcriptional programs activated by lysosomal damage has not been thoroughly characterized. This study aims to investigate lysosome damage-induced major transcriptional responses and the underlying signaling basis. The authors have convincingly shown that lysosomal damage activates a ubiquitination-dependent signaling axis involving TAB, TAK1, and IKK, which culminate in the activation of NF-kB and subsequent transcriptional upregulation of pro-inflammatory genes and pro-survival genes. Overall, the major aims of this study are successfully achieved.

Strengths:

This study is well-conceived and strictly executed, leading to clear and well-supported conclusions. Through unbiased transcriptomics and proteomics screens, the authors identifies NF-kB as a major transcriptional program activated upon lysosome damage. TAK1 activation by lysosome damage-induced ubiquitination is found to be essential for NF-kB activation and MAP kinase signaling. The transcriptional and proteomic changes are shown to be largely driven by TAK1 signaling. Finally, the TAK1-IKK signaling is shown to provide resistance to apoptosis during lysosomal damage response. The main signaling axis of this pathway has been convincingly demonstrated.

Overall, this study identifies major transcriptional responses following lysosomal damage through unbiased approaches. It is important to consider the impact of these pathways in disease settings where lysosomal integrity is compromised.

Comments on revisions:

The authors have adequately addressed all previous comments. I have no further recommendations.

Reviewer #2 (Public review):

Summary:

Endo et al. investigate the novel role of ubiquitin response upon lysosomal damage in activating cellular signaling for cell survival. The authors provide a comprehensive transcriptome and proteome analysis of aging-related cells experiencing lysosomal damage, identifying transcription factors involved in transcriptome and proteome remodeling with a focus on the NF-κB signaling pathway. They further characterized the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis in controlling gene expression, inflammatory responses, and apoptotic processes.

Strengths:

In the aging-related model, the authors provide a comprehensive transcriptome and characterize the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis. Through compelling experiments and advanced tools, they elucidate its critical role in controlling gene expression, inflammatory responses, and apoptotic processes.

Weaknesses:

The study lacks deeper connections with previous research, particularly:

• The established role of TAB-TAK1 in AMPK activation during lysosomal damage

• The potential significance of TBK1 in NF-κB signaling pathways

Comments on revisions:

The authors have successfully addressed all the raised questions and the manuscript is now significantly improved.

Reviewer #3 (Public review):

Summary:

The response to lysosomal damage is a fast-moving and timely field. Besides repair and degradation pathways, increasing interest has been focusing on damaged-induced signaling. The authors conducted both transcriptomics and proteomics to characterize the cellular response to lysosomal damage. They identify a signaling pathway leading to activation of NFkappaB. Based on this and supported by Western blot and microscopy data, the authors nicely show that TAB2/3 and TAK1 are activated at damaged lysosomes and kick off the pathway to alter gene expression, which induces cytokines and protect from cell death. TAB2/3 activation is proposed to occur through K63 ubiquitin chain formation. Generally, this is a careful and well conducted study that nicely delineates the pathway under lysosomal stress. The "omics" data serves a valuable resource for the field. More work should be invested into how TAB2/3 are activated at the damaged lysosomes, also to increase novelty in light of previous reports.

Strengths:

Generally, this is a careful and well-conducted study that nicely delineates how the NFkB pathway is activated under lysosomal stress and modulates cell behavior. The "omics" data serves as a valuable resource for the field.

Weaknesses:

While activation of TAB2/3 by K63-linked Ub chains is convincing, more work needs to be done on how they are recruited by distinct damage types to probe relevance for different pathophysiological conditions."

Comments on revisions:

The authors have addressed much of my criticism. Specifically, they have put (with new experiments) the data on the TAB2/3-TAK1 pathway in perspective to the previously reported LUBAC-mediated activation of NFkB. They also addressed the question about the significance of K63-linked chains for TAB2/3 activation with new complementation experiments (a K63-specific NZF mutant failed to rescue).

The third point (types of damage as triggers) raises more questions, though. The authors find that, in contrast to LLOMe, GPN or DC661-induced damage does not activate TAK1 (consistent with lower damage levels). However, the authors still observe K63 ubiquitylation. This goes along with their finding that TAB2 is recruited in the absence of any ubiquitylation (blocked by TAK-243). It argues that TAB2 is recruited by an unknown cue (that may be damage-specific) and then activated by K63. The authors need to clarify whether TAB2 is or is not recruited in the GPN/DC661 conditions (in which K63 occurs, but TAK1 is not activated). The point about the effects of other damage types was also raised by reviewer #1 and should be solved. The fact that TAB2 is recruited independently of K63 should also be visualized in the model. The manuscript will then be an important contribution to the field.

Author response:

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

Reviewer #1 (Public review):

Summary:

Lysosomal damage is commonly found in many diseases including normal aging and age-related disease. However, the transcriptional programs activated by lysosomal damage have not been thoroughly characterized. This study aimed to investigate lysosome damage-induced major transcriptional responses and the underlying signaling basis. The authors have convincingly shown that lysosomal damage activates a ubiquitination-dependent signaling axis involving TAB, TAK1, and IKK, which culminates in the activation of NF-kB and subsequent transcriptional upregulation of pro-inflammatory genes and pro-survival genes. Overall, the major aims of this study were successfully achieved.

Strengths:

This study is well-conceived and strictly executed, leading to clear and well-supported conclusions. Through unbiased transcriptomics and proteomics screens, the authors identified NF-kB as a major transcriptional program activated upon lysosome damage. TAK1 activation by lysosome damage-induced ubiquitination was found to be essential for NF-kB activation and MAP kinase signaling. The transcriptional and proteomic changes were shown to be largely driven by TAK1 signaling. Finally, the TAK1-IKK signaling was shown to provide resistance to apoptosis during lysosomal damage response. The main signaling axis of this pathway was convincingly demonstrated.

Weaknesses:

One weakness was the claim of K63-linked ubiquitination in lysosomal damage-induced NF-kB activation. While it was clear that K63 ubiquitin chains were present on damaged lysosomes, no evidence was shown in the current study to demonstrate the specific requirement of K63 ubiquitin chains in the signaling axis being studied. Clarifying the roles of K63-linked versus other types of ubiquitin chains in lysosomal damage-induced NF-kB activation may improve the mechanistic insights and overall impact of this study.

Another weakness was that the main conclusions of this study were all dependent on an artificial lysosomal damage agent. It will be beneficial to confirm key findings in other contexts involving lysosomal damage.

We would like to thank Reviewer #1 for the positive and constructive comments on our study. For a main concern regarding the molecular mechanism by which TAB proteins are activated in response to lysosomal damage, we have added the experimental results to support that the lysosomal accumulation of K63 ubiquitin chains serves as a trigger to activate the TAB-TAK1 pathway. We also investigated and discussed the role of LUBAC-mediated M1 ubiquitin chains in NF-kB activation and the effects of other lysosomal-damaging compounds. Please see the response to “Reviewer #3 (Public review): Suggestions:”.

Reviewer #2 (Public review):

Summary:

Endo et al. investigate the novel role of ubiquitin response upon lysosomal damage in activating cellular signaling for cell survival. The authors provide a comprehensive transcriptome and proteome analysis of aging-related cells experiencing lysosomal damage, identifying transcription factors involved in transcriptome and proteome remodeling with a focus on the NF-κB signaling pathway. They further characterized the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis in controlling gene expression, inflammatory responses, and apoptotic processes.

Strengths:

In the aging-related model, the authors provide a comprehensive transcriptome and characterize the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis. Through compelling experiments and advanced tools, they elucidate its critical role in controlling gene expression, inflammatory responses, and apoptotic processes.

Weaknesses:

The study lacks deeper connections with previous research, particularly:

• The established role of TAB-TAK1 in AMPK activation during lysosomal damage

• The potential significance of TBK1 in NF-κB signaling pathways

We would like to thank Reviewer #2 for the helpful comments on our study. To achieve a more comprehensive understanding of the signaling pathways involved in the lysosomal damage response, we investigated additional related signal mediators, such as TBK1 and LUBAC. The citations related to AMPK have been incorporated.

Reviewer #3 (Public review):

Summary:

The response to lysosomal damage is a fast-moving and timely field. Besides repair and degradation pathways, increasing interest has been focusing on damaged-induced signaling. The authors conducted both transcriptomics and proteomics to characterize the cellular response to lysosomal damage. They identify a signaling pathway leading to activation of NFkappaB. Based on this and supported by Western blot and microscopy data, the authors nicely show that TAB2/3 and TAK1 are activated at damaged lysosomes and kick off the pathway to alter gene expression, which induces cytokines and protect from cell death. TAB2/3 activation is proposed to occur through K63 ubiquitin chain formation. Generally, this is a careful and well conducted study that nicely delineates the pathway under lysosomal stress. The "omics" data serves as a valuable resource for the field. More work should be invested into how TAB2/3 are activated at the damaged lysosomes, also to increase novelty in light of previous reports.

Strengths:

Generally, this is a careful and well-conducted study that nicely delineates the pathway under lysosomal stress. The "omics" data serves as a valuable resource for the field.

Weaknesses:

More work should be invested into how TAB2/3 are activated at the damaged lysosomes, also to increase novelty in light of previous reports. Moreover, different damage types should be tested to probe relevance for different pathophysiological conditions.

We would like to thank Reviewer #3 for the valuable comments on our study. We have added the experimental results to address two concerns raised by Reviewer #3. Please see the response to “Reviewer #3 (Public review): Suggestions:”.

Suggestions:

(1) A recent paper claims that NFkappaB is activated by Otulin/M1 chains upon lysosome damage through TBK1 (PMID: 39744815). In contrast, Endo et al. nicely show that ubiquitylation is needed (shown by TAK-243) for NFkB activation but only have correlative data to link it specifically to K63 chains. On page 15, line 11, the authors even argue a "potential" involvement of K63. This point should be better dealt with. Can the authors specifically block K63 formation? K63R overexpression or swapping would be one way. Is the K63 ligase ITCH involved (PMID: 38503285) or any other NEDD4-like ligase? This could be compared to LUBAC inhibition. Also, the point needs to be dealt with more controversially in the discussion as these are alternative claims (M1 vs K63, TAB vs TBK1).

It is well-characterized that the NZF domain of TAB proteins preferentially associates with K63-linked ubiquitin chains. Therefore, we performed the add-back experiment using siRNA-resistant TAB2 WT and mutants incapable of binding to K63-linked ubiquitin chains, dNZF and E685A, to elucidate the requirement of K63 ubiquitin chains for TAK1 activation. We investigated whether the add-back of TAB2 mutants rescues the activation of TAK1 in TAB2-depleted cells (Fig. 2E). TAB2 WT, but not dNZF and E685A, rescued TAK1 activation in response to LLOMe, suggesting that the specific interaction of TAB proteins and K63 ubiquitin chains is a key mechanism to activate TAK1. We also found that the treatment of an E1 inhibitor TAK-243 effectively prevented the lysosomal accumulation of K63 ubiquitin chains, but TAB2 was recruited to damaged lysosomes (Fig. S2B). This suggests that the recruitment of TAB proteins to damaged lysosomes is independent of the association with K63 ubiquitin chains. Collectively, it is postulated that TAB proteins require interaction with K63 ubiquitin chains for TAK1 activation, but not for recruitment to damaged lysosomes. We have added the sentences (p9, lines 7-20, and p10, lines 8-10).

Next, we confirmed that LUBAC functions are essential for NF-kB activation in the lysosomal damage response. RNF31/HOIP is a component of LUBAC that catalyzes M1 ubiquitination. The depletion of RNF31 showed no significant effects on TAK1 activation, but abolished IKK activation (Fig. S4G). It is well-characterized that LUBAC-mediated M1 ubiquitin chains recruit IKK subunits and transduce the signaling to downstream in the canonical pathway. We assume that K63 ubiquitin chains in damaged lysosomes initially activate TAB-TAK1 and trigger LUBAC-mediated M1 ubiquitination, and subsequently, M1 ubiquitination functions to recruit the IKK complex. Consequently, activated TAK1 phosphorylates IKK subunits in damaged lysosomes, leading to NF-kB activation. We also examined whether TBK1 is involved in the activation of NF-kB. TBK1 was phosphorylated upon LLOMe, and depletion of TAB and TAK1 resulted in a slight reduction of TBK1 phosphorylation (Fig. S4D, E). The treatment of a TBK1 inhibitor BX-795 exhibited no or little effects on TAK1 activation, but abolished phosphorylation of IKK and IkBa (Fig. S4F). These suggest that TBK1 is required for the activation of NF-kB. We have added the sentences (p13, line 13-p14, line 10).

As mentioned by Reviewer #3, it is important to identify the E3 ligase responsible for K63 ubiquitination in the lysosomal damage response. We have been aiming to identify such E3 ligase(s). However, depletions of ITCH and other E3 ligases that have been tested exhibited no or little effects on K63 ubiquitination and TAK1 activation.  We would like to explore E3 ligase(s) in future study.

(2) It would be interesting to know what the trigger is that induces the pathway. Lipid perturbation by LLOMe is a good model, but does activation also occur with GPN (osmotic swelling) or lipid peroxidation (oxidative stress) that may be more broadly relevant in a pathophysiological way? Moreover, what damage threshold is needed? Does loss of protons suffice? Can activation be induced with a Ca2+ agonist in the absence of damage?

To further clarify the initial trigger that induces TAB-TAK1 activation coupled with lysosomal damage, we examined other damage sources, GPN and DC661, which induce hyperosmotic stress and lipid peroxidation in lysosomes, respectively, thereby resulting in lysosomal membrane damage. Under our experimental conditions, the treatment of these compounds did not result in significant accumulation of Gal-3, indicating a reduced level of lysosomal membrane permeabilization compared with LLOMe (Fig. S2C, D), and no or little TAK1 activation was observed (Fig. S2E). TAB proteins require their association with K63 ubiquitin chains for TAK1 activation. It is therefore postulated that the severe lysosomal membrane permeabilization that triggers the formation and cytosolic exposure of K63 ubiquitin chains may be a determinant of TAB-TAK1 activation. In our future work, we would like to examine broad stimulation of lysosomal damage and further elucidate the initial mechanism of TAB-TAK1 activation. We have added the sentences (p9, line 21-p10, line 7).

(3) The authors nicely define JNK and p38 activation. This should be emphasized more, possibly also in the abstract, as it may contribute to the claim of increased survival fitness.

We further tested whether the inhibition of JNK affects the anti-apoptotic effect (Fig. S5B). The inhibition of JNK resulted in an increase in the cleaved caspase-3. This suggests that the anti-apoptotic action in the lysosomal damage response requires JNK as well as IKK. We have added the sentences in results to emphasize the pivotal role of stress-induced MAPKs (p15, lines 7-11).

Reviewer #1 (Recommendations for the authors):

(1) Although the ubiquitination-TAB-TAK1-IKK axis was previously characterized in other contexts, specific evidence supporting lysosomal recruitment of these components by ubiquitination during lysosome damage would be beneficial.

We found that the treatment of an E1 inhibitor TAK-243 abolished the lysosomal accumulation of K63 ubiquitin chains, but TAB2 and TAK1 were recruited to damaged lysosomes (Fig. S2B). This suggests that the recruitment of TAB proteins to damaged lysosomes is independent of the association with K63-linked ubiquitin chains. Next, we investigated whether the add-back of TAB2 mutants incapable of binding K63 ubiquitin chains rescues the activation of TAK1 in TAB2-depleted cells (Fig. 2E). K63 ubiquitin binding of TAB2 was essential for TAK1 activation in response to LLOMe. Taken together, it is suggested that TAB proteins require their interaction with K63 ubiquitin chains for TAK1 activation, but not for recruitment to damaged lysosomes. We have added the sentences (p9, lines 7-20, and p10, lines 8-10). Please also see the response to “Reviewer #3 (Public review): Suggestions:”.

(2) The activation of p38 and JNK by lysosomal damage does not fit well into the main conclusions of the paper, since IKK knockdown was sufficient to block cellular resistance to apoptosis (caspase cleavage in Fig. 5f). Are p38 and JNK also important for cell survival during lysosomal damage?

We found that the inhibition of JNK resulted in an increase in the cleaved caspase-3, suggesting that the anti-apoptotic action in the lysosomal damage response requires both IKK and JNK (Fig. S5B). We have added the sentences (p15, lines 7-11).

(3) Cell death tests are recommended to support the conclusions related to apoptosis.

As suggested by Reviewer #1, we performed the cell death assay using propidium iodide (PI) and confirmed that HeLa cells co-treated with LLOMe and TAK-243 or HS-276 exhibited increased cell death (Fig. 5E). This indicates a direct correlation between the degree of caspase-3 cleavage and cell death, possibly apoptosis.

(4) Page 8, line 19-21, gal3 is not exposed upon lysosomal damage. It is recruited from the cytosol by the exposed beta-galactoside-containing glycans on lysosomal membrane proteins.

We have corrected the corresponding sentence (p7, lines 17-20).

(5) Carefully checking grammar throughout the text is recommended. Below are a few examples:

a) Page 4, line 10, remove "that".

b) "K63 ubiquitin" shall be replaced with "K63 ubiquitination" or "K63 ubiquitin chains".

c) Page 8, line 9, "remain" should be "remains".

We have carefully checked the revised manuscript.

Reviewer #2 (Recommendations for the authors):

Despite the novelty and significance of these findings in advancing the field, several technical and experimental limitations require further clarification:

We have responded to each comment. Please see below.

The manuscript should introduce or discuss previous research showing that TAB-TAK1 facilitates AMPK activation during lysosomal damage and TAK1's increased association with damaged lysosomes (PMID: 31995728).

We have added the reference (PMID: 31995728) and the sentences (p17, lines 15-20).

Figure 2A: The differential LAMP1 staining intensity between control and LLOMe-treated cells needs explanation. The weaker LAMP1 signal in control and puncta changes, especially during 5-minute LLOMe treatment, require detailed clarification

We have added the explanation (p8, lines 17-21).

Recent literature (PMID: 34585663) reports TBK1 activation during lysosomal damage. The authors should investigate or discuss whether TBK1 potentially contributes to NF-κB signaling in this context.

We experimentally investigated whether TBK1 is involved in the TAB-TAK1 pathway. We confirmed that TBK1 was activated upon LLOMe (Fig. S4D). Depletions of TAB and TAK1 exhibited a modest decrease in TBK1 phosphorylation (Fig. S4E). The inhibition of TBK1 by BX-795 did not affect TAK1 activation, but abolished phosphorylation of IKK and IkBa (Fig. S4F). This suggests that TBK1 is required for NF-kB activation. We have added the reference (PMID: 34585663) and the sentences (p13, lines 13-21, p14, lines 8-10, and p18, lines 15-20).

The introduction of lysosomal damage response lacks comprehensive mechanistic information. For example, while ESCRT is discussed, other critical mechanisms such as lipid transfer and stress granule formation in lysosomal repair should be incorporated. Moreover, mTOR and AMPK signaling pathways undergo significant changes upon lysosomal damage.

We have added the sentences (p3, lines 16-18, and p3, line 21-p4, line 1).

The statement "lysosomal permeabilization causes the dissociation of mTORC1 from lysosomes" should explicitly reference PMID: 29625033.

We have added the suggested reference (PMID: 29625033, p4, line 19).

The claim that "The elimination of damaged lysosomes through lysophagy requires a period of more than half a day" needs a specific publication citation.

We have added the reference (PMID: 23921551) to claim the time-scale of lysosomal clearance (p4, line 21).

Figure 1G: The label "WO after 2h" lacks explanation in the figure legend and requires detailed interpretation.

To simplify the figures, we have deleted the label “WO after 2 h” (Fig. 1G, 3F, 5D, F-J, S4G, S5A). Instead, we have added the explanation in the figure legends (Fig. 1G).

Reviewer #3 (Recommendations for the authors):

(1) page 8, line 13: it is recommended to phrase colocalisation "at" damaged lysosomes rather than "in" damaged lysosomes as the resolution does not allow the claim of influx into lysosomes.

We have corrected the word (p8, line 17).

(2) page 11, line 22: why is "whereas" used to link two events driven by the same mechanism.

We have corrected the word (p13, line 8).

eLife Assessment

This important work describes the adaptation and evaluation of two red-shifted anion channelrhodopsins (RubyACRs) for optogenetic inhibition in Drosophila. The study provides convincing evidence for the effectiveness of RubyACRs in fly neurons, including electrophysiology, calcium imaging, and behavioral analysis. With minor revisions to address potential toxicity and compatibility with 2-photon imaging, this paper and the publicly available fly lines it describes will be resources that are of value to the neuroscience community.

Reviewer #1 (Public review):

Summary:

This study by Bushey et al., focuses on two newly released red-shifted anion-Channelrhodopsins (A1ACR and HfACR, referred as Ruby-ACRs) in Drosophila. Here, the authors use a combination of electrophysiology, calcium imaging, and behavioral analyses to demonstrate the advantages of Ruby-ACRs over previous optogenetic silencers like the green-shifted GtACR1 and the blue-shifted GtACR2: higher photocurrent, faster kinetics, and operating at a light spectrum range that prevents unwanted behavioral effects in the fly. The availability of these new red-shifted silencers constitutes a great addition to the Drosophila genetic toolkit.

Strengths:

(1) The authors generate both UAS and LexAop RubyACR reagents and test them in a variety of preparations (electrophysiological recordings, calcium imaging, different behavioral paradigms) that cover the breadth of the fly research environment.

(2) The optical stimulation parameters are carefully measured and characterized. Especially impressive is that they managed to titrate over both wavelength and intensity across their various assays. This provides a comprehensive dataset to the community.

(3) Tools are made available to the community through the stock center.

Weaknesses:

(1) The authors could better describe their construct and choice of parameters for the chosen construct. I am specifically wondering about the following points:

a) Why use that particular backbone (not the most commonly used one across recent literature (pJFRC7 is more common).

b) Why do the CsChrimson and GTACR1 have a Kir sequence in it, and why did the authors not put this in the RubyACRs? I would also prefer if authors don't refer to GtACR1 as GTACR-Kir in text (e.g., in line 72); instead, they should either refer to it as GtACR1 or GtACR1-kir-mVenus (based on the full genotype mentioned in their table at the end). Same for CsChrimson-kir. From what I understand, this is just a Kir trafficking sequence and not the entire Kir sequence, which can confuse the readers.

c) Finally, I would also encourage authors to deposit plasmids on Addgene.

(2) Figure 2 is interesting, but it is a bit unfortunate that there is a YFP baseline in most of the samples here (except Chrimson88; this should also be mentioned). I wonder how the YFP baseline impacts this data. Could the high intensity stimulation (red light) lead to bleaching of YFP or tdTomato that reduces the baseline in the green channel? All this also makes me wonder if authors tried tagging the RubyACRs with other fluorophores or non-fluorescent tags and how that impacted their functioning. Non-YFP-tagged versions would be more useful for applications involving GCaMP imaging.

(3) Another point for Figure 2: Since RubyACRs seem to have such a broad activation range, I wonder how much the imaging light (920nm) impacts the baseline in these experiments. If there were plots without the red light stimulation and just varying imaging light intensity, that could be useful to the research community.

(4) Also, for Figures 2C - D, in the methods authors indicate that the stimulation light intensities were progressively increased. Could this lead to desensitization of opsin? Wouldn't randomized intensities be a better way to do this? Perhaps it should be mentioned as a caveat.

(5) In Figure 3E the bottom middle panel Vglut-Gal4,GtACR1 shows a major increase in walking at light onset. This seems very different than all other conditions, and I could not find any discussion of this. It would help if some explanation were provided for this.

Reviewer #2 (Public review):

Summary:

Bushey et al. investigate the feasibility of using RubyACRs, specifically A1ACR1 and HfACR1 (described previously in (Govorunova et al., 2020)) as red-shifted inhibitory opsins in Drosophila melanogaster. The study employs a wide range of techniques to demonstrate successful neuronal inhibition. Electrophysiology experiments established that HfACR1 was most effective at hyperpolarizing cells, compared to A1ACR1 and GtACR1; both RubyACRs also appeared to be more effective than GtACR1 when the latter was actuated by green light. The authors further demonstrate successful neuronal inhibition using calcium imaging. RubyACRs were also shown to be useful in in vivo behavioral setups, specifically in spontaneous locomotion, associative learning, and courtship paradigms. In the courtship assay, in particular, the authors test multiple wavelengths of light at various light intensities, thus providing a rigorous analysis of the RubyACRs' efficacy under different light conditions.

Strengths:

The work provides the Drosophila field with a promising new tool. Red-shifted opsins are particularly advantageous in behavioral assays as red light penetrates the cuticle better than green or blue light, and provides less visual stimulation to the fly. It is also ideal for imaging as it allows for simultaneous optogenetic stimulation and GCamp imaging. A particular strength of the paper is the direct demonstration of RubyACR's capacity to inhibit neurons via electrophysiology and calcium imaging. Furthermore, inhibition effects in the three behavioral assays are strong and convincing. Given the apparent efficacy of RubyACRs and the advantages of a red-sensitive anion channelrhodopsin, this tool has great potential.

Weaknesses:

This work convincingly demonstrates the efficacy and potential utility of RubyACRs in Drosophila for imaging and behavior. However, the lethality/toxicity of RubyACRs is a relevant concern that should be addressed in-depth rather than glossed over, as it may pose a major obstacle to use. Discussing this issue in the present study will also help guide potential users and will set the stage for potential future efforts to ameliorate RubyACRs as optogenetic inhibitors.

Major concerns:

(1) Table 1 demonstrates high lethality in the RubyACRs compared to GtACR1. For example, in the MI04979-VGlut driver, GtACR1 expression resulted in 32.9% lethality, while HfACR1 expression resulted in 98.7% lethality. This lethality presents an obstacle to the potential adoption of this tool, and should be discussed in detail, rather than in passing. The authors might like to present "% lethality" rather than "% survived", as the former is more relevant when discussing the relative yield and health of flies that can be used in experiments.

(2) In Figure 3D, driver>opsin flies have lower locomotion during the baseline (i.e., dark) phase, compared to opsin-only controls or GtACR1 flies. For some comparisons, flies are walking around 10-fold slower. For example, in the case of VGlut-GAL4>HfACR1, test flies are walking at <1 mm/s, while "Empty" test flies are walking at ~10 mm/s. This suggests that, for these drivers, neuronal and/or network function is affected. It opens the possibility that the lethality and locomotor defects could be due to cell-autonomous toxicity. We ask the authors to provide a description of this effect in the Results and to discuss it in the Discussion. Relatedly, VGlut-GAL4>GtACR1 flies in red light exhibit a locomotion increase, but this data is not mentioned in the text. The use of differing scales for the Y-axes in these panels can be confusing when the reader is expected to compare velocity across different panels. It would be best if the y-axes were set to a single range, e.g., 0 to 12 mm/s.

(3) Lethality in broad drivers could result from cell-autonomous toxicity or neuronal dysfunction resulting from RubyACR expression. Ideally, the authors would address or even investigate the possible mechanisms of toxicity of the RubyACRs. Do cells and/or synapses expressing RubyACRs have normal morphology and function? For example, the authors could compare cell survival between flies with RubyACR expression and flies with a fluorescent protein with no opsin. The authors may also want to present lethality data for other, less broad drivers (such as MB320C, which was used for the associative memory assay) in order to demonstrate whether this problem is confined to broad drivers such as VGlut-GAL4, or if this is a problem with narrow drivers as well. If new experiments are not possible, these issues should at least be mentioned in the Discussion.

Minor concerns

(1) The specific method used for quantifying lethality is mentioned briefly in Table 1 but is not detailed in the Methods. The authors derive lethality by comparing to a sibling control group with either the opsin or the driver alone, but the opsin alone or driver alone may cause some lethality by themselves. We suggest the use of a viability assay, e.g. (Rockwell et al., 2019), which would give potential users a clearer picture of which developmental stage is most affected by opsin expression, as well as allow opsin-only, driver-only and experimental groups to be assessed separately (lethality would then be reported as the % of embryos that reach each stage of development, and eventually enclosure).

(2) For the calcium imaging analysis in Figure 2, the U-shaped curve observed for mean ΔF/F0 for A1ACR1 and HfACR1 may not be due to actual desensitization for the channels, as the authors suggest (lines 143-145), but may be due simply to a shifting baseline. The authors use the 5-s period preceding stimulation onset as F0, but in some cases (e.g., HfACR1 at 250 uW/mm2), calcium fluorescence rises above baseline and remains high post-stimulation (ΔF/F0 of +0.5, which we observe is the same magnitude as the ΔF/F0 of -0.5 observed during inhibition), thus affecting the ΔF/F0 for subsequent trials. The authors should discuss this incomplete recovery in the text, or (if available) use a static channel instead to provide a stable F0 for calculating ΔF/F0. Alternatively, if the authors wish to rigorously test the hypothesis that high light intensity indeed results in desensitization of these channels, they may consider using different flies for each light intensity or longer inter-stimulus intervals.

(3) For Figure 3C (Flybowl assay), the authors mention that "simply expressing the opsins decreased baseline locomotor activity compared to empty driver lines". However, the "Empty" controls in 3C appear to refer to opsin-only controls, not driver-only controls. The driver-only controls are not presented in the figure. The use of "empty" differs between the text and the figure, as the text refers to "empty" driver lines, while the figure uses "empty" to apparently refer to opsin-only controls. We recommend changing the terminology across all figures to be unambiguous, e.g., by using "opsin-only" or "driver-only" as opposed to the ambiguous "empty". In addition, the fact that opsin-only controls move less than driver-only controls may suggest some toxicity as a result of the opsin-only construct; this should be discussed further.

(4) Figures 4 and 5 lack the reporting of driver-only controls.

(5) Figures 3 and 4 lack positive controls; that is, the benchmarking of the efficacy of RubyACRs in their respective behavioral paradigms against a known inhibitor, e.g., GtACR1 with green light. To confirm that this GtACR1 transgene is functional, the authors could include GtACR1 with green light as a positive control for these two figures, as they have done for Figure 5-supplement 2 and 3.

(6) Several citations are missing. In their discussion, the authors highlight that shorter wavelengths of light are more attenuated by tissue (lines 278-281); this should be accompanied by the relevant citations (Inagaki et al., 2014). Similarly, the claim that behavioral experiments exhibit greater sensitivity to shorter wavelengths should be substantiated (lines 281-283).

References:

Govorunova EG, Sineshchekov OA, Li H, Wang Y, Brown LS, Spudich JL. 2020. RubyACRs, nonalgal anion channelrhodopsins with highly red-shifted absorption. Proc Natl Acad Sci U S A 117:22833-22840.

Inagaki HK, Jung Y, Hoopfer ED, Wong AM, Mishra N, Lin JY, Tsien RY, Anderson DJ. 2014. Optogenetic control of Drosophila using a red-shifted channelrhodopsin reveals experience-dependent influences on courtship. Nat Methods 11:325-332.

Rockwell AL, Beaver I, Hongay CF. 2019. A direct and simple method to assess Drosophila melanogaster's viability from embryo to adult. J Vis Exp e59996.

Reviewer #3 (Public review):

Summary:

This study by Bushey et al. adapts and evaluates two newly developed red-shifted optogenetic inhibitors, A1ACR1 and HfACR1, collectively referred to as RubyACRs, for neuronal silencing in Drosophila melanogaster. Traditional optogenetic inhibitors such as GtACR1 and GtACR2 are activated by green (~515 nm) and blue (~470 nm) light, respectively, which poses several limitations in Drosophila. Specifically, shorter-wavelength light suffers from reduced tissue penetration and increased absorption, and is visible to flies, potentially confounding behavioral assays, particularly those involving visual processing. In contrast, RubyACRs are activated by red light (~610-660 nm), which penetrates the cuticle more effectively and thus can be more potent in manipulating fly behavior. In the current manuscript, the authors first demonstrate that both A1ACR1 and HfACR1 can be robustly expressed in fly neurons and are properly trafficked to the plasma membrane. Upon red-light stimulation, both opsins produce strong and sustained hyperpolarization in larval motor neurons, outperforming GtACR1 in both magnitude and temporal dynamics. Next, using two-photon calcium imaging in the visual system, the authors further demonstrate that activation of RubyACRs significantly reduces GCaMP6s signal, indicating that they can reliably inhibit neuronal activity. Importantly, unlike reported in some mammalian studies, RubyACRs do not appear to trigger paradoxical depolarization at axon terminals in the fly visual system, as no evidence of aberrant depolarization is observed in motion-detecting Mi1 neurons.

In the second part of the manuscript, the authors characterize the effects of RubyACRs on fly behavior (walking, learning, and courtship song). Using the inhibition of genetically labelled neurons that regulate these behaviors, the authors demonstrate that stimulation of RubyACRs leads to potent suppression of locomotion, courtship song, or dopamine-dependent associative learning.

Strengths:

Altogether, the experiments conducted in this manuscript demonstrate that RubyACRs are powerful tools for optogenetic inhibition in Drosophila, with advantages in spectral compatibility, behavioral specificity, and potential applications in vivo two-photon calcium imaging.

Weaknesses:

The manuscript is strong, but it can be further improved with a few additional analyses and minor revisions. Especially, a more detailed evaluation of RubyACRs with two-photon excitation will help clarify to what extent these opsins can be simultaneously used together with green GECIs, such as GCaMPs.

Author response:

We thank the reviewers for their thoughtful and thorough consideration of the work. We appreciate the positive reception they give the work, and plan to address several of the comments with further experiments. To outline that work (and ensure that we are on the right track to addressing those concerns), we summarize the core concerns that prompt new experiments:

(1) Does the YFP tag on the ACRs interfere with simultaneous GCaMP imaging of RubyACR-expressing cells and could bleaching of the YFP complicate interpretation of the experiments here?

We will test whether 920 nm (2p) and 650 nm (1p) excitation cause YFP bleaching that interferes with interpretation of inhibitory calcium (i.e. GCaMP) signals. Because the YFP tag enhances opsin sensitivity, we prioritized these tagged RubyACRs for initial characterization. FLAG-tagged ACRs are in progress, but will take time to fully characterize. Considering that the RubyACR-EYFP versions work very well, and in many cases people will want the YFP tag, either for visualizing expression or to maximize sensitivity, we feel the current work is a valuable contribution on its own. Indeed several labs have already requested these lines.

(2) Are the ACRs activated by two-photon illumination?

We will examine GCaMP signals at increasing 2p intensities to determine whether imaging unintentionally activates RubyACRs, as well as whether 2p illumination could be used for intentional opsin activation.

(3) How toxic is the expression of these opsins?

We will update the quantification of toxicity in Table 1 to include all the drivers we used in this study. In fact the toxicity we observed was primarily with the vGlut driver, which was why that was the only information in the table. The other drivers we used did not appreciably reduce survival rate, but showing the one case where it did have a big effect left a strong and understandably inaccurate impression that toxicity was a big pitfall. We note that the widely used CSChrimson has similar % survival to the RubyACRs when expressed with these vGlut drivers.

We also plan to examine whether ACR expression leads to cell-autonomous perturbations. We will determine whether expression leads to some frequency of neuronal cell death, and we will evaluate whether any morphological effects occur.

We will also clarify in the Discussion that potential toxicity may be driver-specific (as it is here) and should be evaluated case-by-case by investigators using the tool.

(4) Use functional imaging to confirm inhibition of the neurons used only for behavioral experiments (pIP10 & PPL1-γ1pedc)

We will perform these imaging experiments. One caveat is that inhibition may not be readily detectable with GCaMP, as the resting calcium levels in pIP10 and PPL1-γ1pedc neurons may already be quite low. This differs from the non-spiking Mi1 neurons, where inhibition was clearly observed with GCaMP. For this reason, we consider the behavioral results stronger evidence of efficacy, but we agree that imaging could provide useful supporting evidence, recognizing that a negative result would be difficult to interpret.

(5) Confirm that the GtACR1 will inhibit locomotion in the flybowl when activated with green light, its spectral peak.

We will perform this benchmark experiment. Please note that our intention with this study was to find an effective red-light activated opto-inhibitor because these wavelengths are much less perturbing to behavior. In that respect, regardless of GtACR1’s performance with green light, the RubyACRs clearly provide important new tools for Drosophila behavioral neuroscience.

eLife Assessment

This manuscript is useful as it demonstrates that Rv2577, a Fe³⁺/Zn²⁺-dependent metallophosphatase, is secreted by Mycobacterium bovis BCG and localizes to the nucleus of mammalian cells, altering transcriptional and inflammatory responses. However, the study is incomplete as it lacks activity validation in macrophage cells and with virulent Mycobacterium tuberculosis strains. It is necessary to confirm Rv2577 secretion from a virulent strain and to clarify the direct or indirect role of MmpE in modulating host pathways, together with mechanistic insight into how MmpE influences lysosomal biogenesis and trafficking.

Reviewer #1 (Public review):

Summary:

Review of the manuscript titled " Mycobacterial Metallophosphatase MmpE acts as a nucleomodulin to regulate host gene expression and promotes intracellular survival".

The study provides an insightful characterization of the mycobacterial secreted effector protein MmpE, which translocates to the host nucleus and exhibits phosphatase activity. The study characterizes the nuclear localization signal sequences and residues critical for the phosphatase activity, both of which are required for intracellular survival.

Strengths:

(1) The study addresses the role of nucleomodulins, an understudied aspect in mycobacterial infections.

(2) The authors employ a combination of biochemical and computational analyses along with in vitro and in vivo validations to characterize the role of MmpE.

Weaknesses:

(1) While the study establishes that the phosphatase activity of MmpE operates independently of its NLS, there is a clear gap in understanding how this phosphatase activity supports mycobacterial infection. The investigation lacks experimental data on specific substrates of MmpE or pathways influenced by this virulence factor.

(2) The study does not explore whether the phosphatase activity of MmpE is dependent on the NLS within macrophages, which would provide critical insights into its biological relevance in host cells. Conducting experiments with double knockout/mutant strains and comparing their intracellular survival with single mutants could elucidate these dependencies and further validate the significance of MmpE's dual functions.

(3) The study does not provide direct experimental validation of the MmpE deletion on lysosomal trafficking of the bacteria.

(4) The role of MmpE as a mycobacterial effector would be more relevant using virulent mycobacterial strains such as H37Rv.

Reviewer #2 (Public review):

Summary:

In this paper, the authors have characterized Rv2577 as a Fe3+/Zn2+ -dependent metallophosphatase and a nucleomodulin protein. The authors have also identified His348 and Asn359 as critical residues for Fe3+ coordination. The authors show that the proteins encode for two nuclease localization signals. Using C-terminal Flag expression constructs, the authors have shown that the MmpE protein is secretory. The authors have prepared genetic deletion strains and show that MmpE is essential for intracellular survival of M. bovis BCG in THP-1 macrophages, RAW264.7 macrophages, and a mouse model of infection. The authors have also performed RNA-seq analysis to compare the transcriptional profiles of macrophages infected with wild-type and MmpE mutant strains. The relative levels of ~ 175 transcripts were altered in MmpE mutant-infected macrophages and the majority of these were associated with various immune and inflammatory signalling pathways. Using these deletion strains, the authors proposed that MmpE inhibits inflammatory gene expression by binding to the promoter region of a vitamin D receptor. The authors also showed that MmpE arrests phagosome maturation by regulating the expression of several lysosome-associated genes such as TFEB, LAMP1, LAMP2, etc. These findings reveal a sophisticated mechanism by which a bacterial effector protein manipulates gene transcription and promotes intracellular survival.

Strength:

The authors have used a combination of cell biology, microbiology, and transcriptomics to elucidate the mechanisms by which Rv2577 contributes to intracellular survival.

Weakness:

The authors should thoroughly check the mice data and show individual replicate values in bar graphs.

Reviewer #3 (Public review):

Summary:

In this manuscript titled "Mycobacterial Metallophosphatase MmpE Acts as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival", Chen et al describe biochemical characterisation, localisation and potential functions of the gene using a genetic approach in M. bovis BCG and perform macrophage and mice infections to understand the roles of this potentially secreted protein in the host cell nucleus. The findings demonstrate the role of a secreted phosphatase of M. bovis BCG in shaping the transcriptional profile of infected macrophages, potentially through nuclear localisation and direct binding to transcriptional start sites, thereby regulating the inflammatory response to infection.

Strengths:

The authors demonstrate using a transient transfection method that MmpE when expressed as a GFP-tagged protein in HEK293T cells, exhibits nuclear localisation. The authors identify two NLS motifs that together are required for nuclear localisation of the protein. A deletion of the gene in M. bovis BCG results in poorer survival compared to the wild-type parent strain, which is also killed by macrophages. Relative to the WT strain-infected macrophages, macrophages infected with the ∆mmpE strain exhibited differential gene expression. Overexpression of the gene in HEK293T led to occupancy of the transcription start site of several genes, including the Vitamin D Receptor. Expression of VDR in THP1 macrophages was lower in the case of ∆mmpE infection compared to WT infection. This data supports the utility of the overexpression system in identifying potential target loci of MmpE using the HEK293T transfection model. The authors also demonstrate that the protein is a phosphatase, and the phosphatase activity of the protein is partially required for bacterial survival but not for the regulation of the VDR gene expression.

Weaknesses:

(1) While the motifs can most certainly behave as NLSs, the overexpression of a mycobacterial protein in HEK293T cells can also result in artefacts of nuclear localisation. This is not unprecedented. Therefore, to prove that the protein is indeed secreted from BCG, and is able to elicit transcriptional changes during infection, I recommend that the authors (i) establish that the protein is indeed secreted into the host cell nucleus, and (ii) the NLS mutation prevents its localisation to the nucleus without disrupting its secretion.

Demonstration that the protein is secreted: Supplementary Figure 3 - Immunoblotting should be performed for a cytosolic protein, also to rule out detection of proteins from lysis of dead cells. Also, for detecting proteins in the secreted fraction, it would be better to use Sauton's media without detergent, and grow the cultures without agitation or with gentle agitation. The method used by the authors is not a recommended protocol for obtaining the secreted fraction of mycobacteria.

Demonstration that the protein localises to the host cell nucleus upon infection: Perform an infection followed by immunofluorescence to demonstrate that the endogenous protein of BCG can translocate to the host cell nucleus. This should be done for an NLS1-2 mutant expressing cell also.

(2) In the RNA-seq analysis, the directionality of change of each of the reported pathways is not apparent in the way the data have been presented. For example, are genes in the cytokine-cytokine receptor interaction or TNF signalling pathway expressed more, or less in the ∆mmpE strain?

(3) Several of these pathways are affected as a result of infection, while others are not induced by BCG infection. For example, BCG infection does not, on its own, produce changes in IL1β levels. As the authors did not compare the uninfected macrophages as a control, it is difficult to interpret whether ∆mmpE induced higher expression than the WT strain, or simply did not induce a gene while the WT strain suppressed expression of a gene. This is particularly important because the strain is attenuated. Does the attenuation have anything to do with the ability of the protein to induce lysosomal pathway genes? Does induction of this pathway lead to attenuation of the strain? Similarly, for pathways that seem to be downregulated in the ∆mmpE strain compared to the WT strain, these might have been induced upon infection with the WT strain but not sufficiently by the ∆mmpE strain due to its attenuation/ lower bacterial burden.

(4) CHIP-seq should be performed in THP1 macrophages, and not in HEK293T. Overexpression of a nuclear-localised protein in a non-relevant line is likely to lead to several transcriptional changes that do not inform us of the role of the gene as a transcriptional regulator during infection.

(5) I would not expect to see such large inflammatory reactions persisting 56 days post-infection with M. bovis BCG. Is this something peculiar for an intratracheal infection with 1x107 bacilli? For images of animal tissue, the authors should provide images of the entire lung lobe with the zoomed-in image indicated as an inset.

(6) For the qRT-PCR based validation, infections should be performed with the MmpE-complemented strain in the same experiments as those for the WT and ∆mmpE strain so that they can be on the same graph, in the main manuscript file. Supplementary Figure 4 has three complementary strains. Again, the absence of the uninfected, WT, and ∆mmpE infected condition makes interpretation of these data very difficult.

(7) The abstract mentions that MmpE represses the PI3K-Akt-mTOR pathway, which arrests phagosome maturation. There is not enough data in this manuscript in support of this claim. Supplementary Figure 5 does provide qRT-PCR validation of genes of this pathway, but the data do not indicate that higher expression of these pathways, whether by VDR repression or otherwise, is driving the growth restriction of the ∆mmpE strain.

(8) The relevance of the NLS and the phosphatase activity is not completely clear in the CFU assays and in the gene expression data. Firstly, there needs to be immunoblot data provided for the expression and secretion of the NLS-deficient and phosphatase mutants. Secondly, CFU data in Figure 3A, C, and E must consistently include both the WT and ∆mmpE strain.

Author response:

Reviewer #1 (Public review):

Summary:

Review of the manuscript titled " Mycobacterial Metallophosphatase MmpE acts as a nucleomodulin to regulate host gene expression and promotes intracellular survival".

The study provides an insightful characterization of the mycobacterial secreted effector protein MmpE, which translocates to the host nucleus and exhibits phosphatase activity. The study characterizes the nuclear localization signal sequences and residues critical for the phosphatase activity, both of which are required for intracellular survival.

Strengths:

(1) The study addresses the role of nucleomodulins, an understudied aspect in mycobacterial infections.

(2) The authors employ a combination of biochemical and computational analyses along with in vitro and in vivo validations to characterize the role of MmpE.

Weaknesses:

(1) While the study establishes that the phosphatase activity of MmpE operates independently of its NLS, there is a clear gap in understanding how this phosphatase activity supports mycobacterial infection. The investigation lacks experimental data on specific substrates of MmpE or pathways influenced by this virulence factor.

We thank the reviewer for this insightful comment and agree that identification of the substrate of MmpE is important to fully understand its role in mycobacterial infection.

MmpE is a putative purple acid phosphatase (PAP) and a member of the metallophosphoesterase (MPE) superfamily. Enzymes in this family are known for their catalytic promiscuity and broad substrate specificity, acting on phosphomonoesters, phosphodiesters, and phosphotriesters (Matange et al., Biochem J., 2015). In bacteria, several characterized MPEs have been shown to hydrolyze substrates such as cyclic nucleotides (e.g., cAMP) (Keppetipola et al., J Biol Chem, 2008; Shenoy et al., J Mol Biol, 2007), nucleotide derivatives (e.g., AMP, UDP-glucose) (Innokentev et al., mBio, 2025), and pyrophosphate-containing compounds (e.g., Ap4A, UDP-DAGn) (Matange et al., Biochem J., 2015). Although the binding motif of MmpE has been identified, determining its physiological substrates remains challenging due to the low abundance and instability of potential metabolites, as well as the limited sensitivity and coverage of current metabolomic technologies in mycobacteria.

(2) The study does not explore whether the phosphatase activity of MmpE is dependent on the NLS within macrophages, which would provide critical insights into its biological relevance in host cells. Conducting experiments with double knockout/mutant strains and comparing their intracellular survival with single mutants could elucidate these dependencies and further validate the significance of MmpE's dual functions.

We thank the reviewer for the comment. In our study, we demonstrate that both the nuclear localization and phosphatase activity of MmpE are required for full virulence (Figure 3D–E). Importantly, deletion of the NLS motifs did not impair MmpE’s phosphatase activity in vitro (Figure 2F), indicating that its enzymatic function is structurally independent of its nuclear localization. These findings suggest that MmpE functions as a bifunctional protein, with distinct and non-overlapping roles for its nuclear trafficking and phosphatase activity. We have expanded on this point in the Discussion section “MmpE Functions as a Bifunctional Protein with Nuclear Localization and Phosphatase Activity”.

(3) The study does not provide direct experimental validation of the MmpE deletion on lysosomal trafficking of the bacteria.

We thank the reviewer for the comment. The role of Rv2577/MmpE in phagosome maturation has been demonstrated in M. tuberculosis, where its deletion increases colocalization with lysosomal markers such as LAMP-2 and LAMP-3 (Forrellad et al., Front Microbiol, 2020). In our study, we found that mmpE deletion in M. bovis BCG led to upregulation of lysosomal genes, including TFEB, LAMP1, LAMP2, and v-ATPase subunits, compared to the wild-type strain. These results suggest that MmpE may regulate lysosomal trafficking by interfering with phagosome–lysosome fusion.

To further validate MmpE’s role in phagosome maturation, we will perform fluorescence colocalization assays in THP-1 macrophages infected with BCG/wt, ∆mmpE, complemented, and NLS-mutant strains. Co-staining with LAMP1 and LysoTracker will allow us to assess whether the ∆mmpE mutant is more efficiently trafficked to lysosomes.

(4) The role of MmpE as a mycobacterial effector would be more relevant using virulent mycobacterial strains such as H37Rv.

We thank the reviewer for the comment. Previously, the role of Rv2577/MmpE as a virulence factor has been demonstrated in M. tuberculosis CDC 1551, where its deletion significantly reduced bacterial replication in mouse lungs at 30 days post-infection (Forrellad et al., Front Microbiol, 2020). However, that study did not explore the underlying mechanism of MmpE function. In our work, we found that MmpE enhances M. bovis BCG survival in both macrophages (THP-1 and RAW264.7) and mice (Figure 2A-B, Figure 6A), consistent with its proposed role in virulence. To investigate the molecular mechanism by which MmpE promotes intracellular survival, we used M. bovis BCG as a biosafe surrogate and this model is widely accepted for studying mycobacterial pathogenesis (Wang et al., Nat Immunol, 2025; Wang et al., Nat Commun, 2017; Péan et al., Nat Commun, 2017).

Reviewer #2 (Public review):

Summary:

In this paper, the authors have characterized Rv2577 as a Fe3+/Zn2+ -dependent metallophosphatase and a nucleomodulin protein. The authors have also identified His348 and Asn359 as critical residues for Fe3+ coordination. The authors show that the proteins encode for two nuclease localization signals. Using C-terminal Flag expression constructs, the authors have shown that the MmpE protein is secretory. The authors have prepared genetic deletion strains and show that MmpE is essential for intracellular survival of M. bovis BCG in THP-1 macrophages, RAW264.7 macrophages, and a mouse model of infection. The authors have also performed RNA-seq analysis to compare the transcriptional profiles of macrophages infected with wild-type and MmpE mutant strains. The relative levels of ~ 175 transcripts were altered in MmpE mutant-infected macrophages and the majority of these were associated with various immune and inflammatory signalling pathways. Using these deletion strains, the authors proposed that MmpE inhibits inflammatory gene expression by binding to the promoter region of a vitamin D receptor. The authors also showed that MmpE arrests phagosome maturation by regulating the expression of several lysosome-associated genes such as TFEB, LAMP1, LAMP2, etc. These findings reveal a sophisticated mechanism by which a bacterial effector protein manipulates gene transcription and promotes intracellular survival.

Strength:

The authors have used a combination of cell biology, microbiology, and transcriptomics to elucidate the mechanisms by which Rv2577 contributes to intracellular survival.

Weakness:

The authors should thoroughly check the mice data and show individual replicate values in bar graphs.

We kindly appreciate the reviewer for the advice. We will update the relevant mice data in the revised manuscript.

Reviewer #3 (Public review):

Summary:

In this manuscript titled "Mycobacterial Metallophosphatase MmpE Acts as a Nucleomodulin to Regulate Host Gene Expression and Promote Intracellular Survival", Chen et al describe biochemical characterisation, localisation and potential functions of the gene using a genetic approach in M. bovis BCG and perform macrophage and mice infections to understand the roles of this potentially secreted protein in the host cell nucleus. The findings demonstrate the role of a secreted phosphatase of M. bovis BCG in shaping the transcriptional profile of infected macrophages, potentially through nuclear localisation and direct binding to transcriptional start sites, thereby regulating the inflammatory response to infection.

Strengths:

The authors demonstrate using a transient transfection method that MmpE when expressed as a GFP-tagged protein in HEK293T cells, exhibits nuclear localisation. The authors identify two NLS motifs that together are required for nuclear localisation of the protein. A deletion of the gene in M. bovis BCG results in poorer survival compared to the wild-type parent strain, which is also killed by macrophages. Relative to the WT strain-infected macrophages, macrophages infected with the ∆mmpE strain exhibited differential gene expression. Overexpression of the gene in HEK293T led to occupancy of the transcription start site of several genes, including the Vitamin D Receptor. Expression of VDR in THP1 macrophages was lower in the case of ∆mmpE infection compared to WT infection. This data supports the utility of the overexpression system in identifying potential target loci of MmpE using the HEK293T transfection model. The authors also demonstrate that the protein is a phosphatase, and the phosphatase activity of the protein is partially required for bacterial survival but not for the regulation of the VDR gene expression.

Weaknesses:

(1)   While the motifs can most certainly behave as NLSs, the overexpression of a mycobacterial protein in HEK293T cells can also result in artefacts of nuclear localisation. This is not unprecedented. Therefore, to prove that the protein is indeed secreted from BCG, and is able to elicit transcriptional changes during infection, I recommend that the authors (i) establish that the protein is indeed secreted into the host cell nucleus, and (ii) the NLS mutation prevents its localisation to the nucleus without disrupting its secretion.

We kindly appreciate the reviewer for the advice and will include the relevant experiments in the revised manuscript. The localization of WT MmpE and the NLS mutated MmpE will be tested in the BCG infected macrophages.

Demonstration that the protein is secreted: Supplementary Figure 3 - Immunoblotting should be performed for a cytosolic protein, also to rule out detection of proteins from lysis of dead cells. Also, for detecting proteins in the secreted fraction, it would be better to use Sauton's media without detergent, and grow the cultures without agitation or with gentle agitation. The method used by the authors is not a recommended protocol for obtaining the secreted fraction of mycobacteria.

We agree with the reviewer and we will further validate the secretion of MmpE using the tested protocol.

Demonstration that the protein localises to the host cell nucleus upon infection: Perform an infection followed by immunofluorescence to demonstrate that the endogenous protein of BCG can translocate to the host cell nucleus. This should be done for an NLS1-2 mutant expressing cell also.

We will add this experiment in the revised manuscript.

(2) In the RNA-seq analysis, the directionality of change of each of the reported pathways is not apparent in the way the data have been presented. For example, are genes in the cytokine-cytokine receptor interaction or TNF signalling pathway expressed more, or less in the ∆mmpE strain?

We thank the reviewer for pointing this out and fully agree that conventional KEGG pathway enrichment diagrams do not convey the directionality of individual gene expression changes within each pathway. While KEGG enrichment analysis identifies pathways that are statistically overrepresented among differentially expressed genes, it does not indicate whether individual genes within those pathways are upregulated or downregulated.

To address this, we re-analyzed the expression trends of DEGs within each significantly enriched KEGG pathway. The results show that key immune-related pathways, including cytokine–cytokine receptor interaction, TNF signaling, NF-κB signaling, and chemokine signaling, are collectively upregulated in THP-1 macrophages infected with ∆mmpE strain compared to those infected with the wild-type BCG strain. The full list of DEGs will be provided in the supplementary materials. The complete RNA-seq dataset has been deposited in the GEO database, and the accession number will be included in the revised manuscript.

(3) Several of these pathways are affected as a result of infection, while others are not induced by BCG infection. For example, BCG infection does not, on its own, produce changes in IL1β levels. As the author s did not compare the uninfected macrophages as a control, it is difficult to interpret whether ∆mmpE induced higher expression than the WT strain, or simply did not induce a gene while the WT strain suppressed expression of a gene. This is particularly important because the strain is attenuated. Does the attenuation have anything to do with the ability of the protein to induce lysosomal pathway genes? Does induction of this pathway lead to attenuation of the strain? Similarly, for pathways that seem to be downregulated in the ∆mmpE strain compared to the WT strain, these might have been induced upon infection with the WT strain but not sufficiently by the ∆mmpE strain due to its attenuation/ lower bacterial burden.

We thank the reviewer for the comment. We will update qRT-PCR data with the uninfected macrophages as a control in the revised manuscript.

Wild-type Mycobacterium bovis BCG strain still has the function of inhibiting phagosome maturation (Branzk et al., Nat Immunol, 2014; Weng et al., Nat Commun, 2022). Forrellad et al. previously identified Rv2577/MmpE as a virulence factor in M. tuberculosis and disruption of the MmpE gene impairs the ability of M. tuberculosis to arrest phagosome maturation (Forrellad et al., Front Microbiol, 2020). In our study, transcriptomic and qRTPCR data (Figures 4C and G, S4C) show that deletion of mmpE in M. bovis BCG leads to upregulation of lysosomal biogenesis and acidification genes, including TFEB, LAMP1, and vATPase. To further validate MmpE’s role in phagosome maturation, we will perform fluorescence colocalization assays in THP-1 macrophages infected with BCG/wt, ∆mmpE, complemented, and NLS-mutant strains. Co-staining with LAMP1 and LysoTracker will assess whether the ∆mmpE mutant is more efficiently trafficked to lysosomes.

Furthermore, CFU assays demonstrated that the ∆mmpE strain exhibits markedly reduced bacterial survival in both human THP-1 and murine RAW264.7 macrophages, as well as in mice, compared to the wild-type strain (Figures 4A and C, 6A). These findings suggest that the loss of MmpE compromises bacterial survival, likely due to enhanced lysosomal trafficking and acidification. This supports previous studies showing that increased lysosomal activity promotes mycobacterial clearance (Gutierrez et al., Cell, 2004; Pilli et al., Immunity, 2012).

(4) CHIP-seq should be performed in THP1 macrophages, and not in HEK293T. Overexpression of a nuclear-localised protein in a non-relevant line is likely to lead to several transcriptional changes that do not inform us of the role of the gene as a transcriptional regulator during infection.

We thank the reviewer for the comment. We performed ChIP-seq in HEK293T cells is based on the fact that this cell line is widely used in ChIP-based assays due to its high transfection efficiency, robust nuclear protein expression, and well-annotated genome (Lampe et al., Nat Biotechnol, 2024; Marasco et al., Cell, 2022). These features make HEK293T an ideal system for the initial identification of genome wide chromatin binding profiles of novel nuclear effectors such as MmpE.

Furthermore, we validated the major observations in THP-1 macrophages, including (i) RNAseq of THP-1 cells infected with either WT BCG or ∆mmpE strains revealed significant transcriptional changes in immune and lysosomal pathways (Figure 4A); (ii) Integrated analysis of CUT&Tag and RNA-seq data identified 298 genes in infected THP-1 cells that exhibited both MmpE binding and corresponding expression changes. Among these, VDR was validated as a direct transcriptional target of MmpE using EMSA and ChIP-PCR (Figures 5E-J, S5D-F). Notably, the signaling pathways associated with MmpE-bound genes, including PI3K-Akt-mTOR signaling and lysosomal function, substantially overlap with those transcriptionally modulated in infected THP-1 macrophages (Figures 4B-G, S4B-C, S5C-D), further supporting the biological relevance of the ChIP-seq data obtained from HEK293T cells.

(5) I would not expect to see such large inflammatory reactions persisting 56 days postinfection with M. bovis BCG. Is this something peculiar for an intratracheal infection with 1x107 bacilli? For images of animal tissue, the authors should provide images of the entire lung lobe with the zoomed-in image indicated as an inset.

We thank the reviewer for the comment. The lung inflammation peaked at days 21–28 and had clearly subsided by day 56 across all groups (Figure 6B), consistent with the expected resolution of immune responses to an attenuated strain like M. bovis BCG. This temporal pattern is in line with previous studies using intravenous or intratracheal BCG vaccination in mice and macaques, which also demonstrated robust early immune activation followed by resolution over time (Smith et al., Nat Microbiol, 2025; Darrah et al., Nature, 2020).

In this study, the infectious dose (1×10⁷ CFU intratracheally) was selected based on previous studies in which intratracheal delivery of 1×10⁷CFU produced consistent and measurable lung immune responses and pathology without causing overt illness or mortality (Xu et al., Sci Rep, 2017; Niroula et al., Sci Rep, 2025). We will provide whole-lung lobe images with zoomed-in insets in the revised manuscript.

(6) For the qRT-PCR based validation, infections should be performed with the MmpEcomplemented strain in the same experiments as those for the WT and ∆mmpE strain so that they can be on the same graph, in the main manuscript file. Supplementary Figure 4 has three complementary strains. Again, the absence of the uninfected, WT, and∆mmpE infected condition makes interpretation of these data very difficult.

We thank the reviewer for the comment. As suggested, we will conduct the qRT-PCR experiment including the uninfected, WT, ∆mmpE, Comp-MmpE, and the three complementary strains infecting THP-1 cells. The updated data will be provided in the revised manuscript.

(7) The abstract mentions that MmpE represses the PI3K-Akt-mTOR pathway, which arrests phagosome maturation. There is not enough data in this manuscript in support of this claim. Supplementary Figure 5 does provide qRT-PCR validation of genes of this pathway, but the data do not indicate that higher expression of these pathways, whether by VDR repression or otherwise, is driving the growth restriction of the ∆mmpE strain.

We thank the reviewer for the comment. The role of MmpE in phagosome maturation was previously characterized. Disruption of mmpE impairs the ability of M. tuberculosis to arrest lysosomal trafficking (Forrellad et al., Front Microbiol, 2020). In this study, we further found that MmpE suppresses the expression of key lysosomal genes, including TFEB, LAMP1, LAMP2, and ATPase subunits (Figure 4G), suggesting MmpE is involved in arresting phagosome maturation. As noted, the genes in the PI3K–Akt–mTOR pathway are upregulated in ∆mmpE-infected macrophages (Figure S5C).

To functionally validate this, we will conduct two complementary experimental approaches:

(i) Immunofluorescence assays: We will assess phagosome maturation and lysosomal fusion in THP-1 cells infected with BCG/wt, ∆mmpE, Comp-MmpE, and NLS mutant strains. Colocalization of intracellular bacteria with LAMP1 and LysoTracker will be quantified to determine whether the ∆mmpE strain is more efficiently trafficked to lysosomes.

(ii) CFU assays: We will perform CFU assays in THP-1 cells infected with BCG/wt or ∆mmpE in the presence or absence of PI3K-Akt-mTOR pathway inhibitors (e.g., Dactolisib), to assess whether activation of this pathway contributes to the intracellular growth restriction observed in the ∆mmpE strain.

(8) The relevance of the NLS and the phosphatase activity is not completely clear in the CFU assays and in the gene expression data. Firstly, there needs to be immunoblot data provided for the expression and secretion of the NLS-deficient and phosphatase mutants. Secondly, CFU data in Figure 3A, C, and E must consistently include both the WT and ∆mmpE strain.

We thank the reviewer for the comment. We will provide immunoblot data for the expression and secretion of the NLS-deficient and phosphatase mutants. Additionally, we will revise Figure 3A, 3C, and 3E to consistently include both the WT and ΔmmpE strains in the CFU assays.

Reference

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really it is:

The Nameless Book of Heaven

Myth Metaphor and Mystery

https://hyp.is/gN7YnB90EfCb6Ev1WYq1sQ/www.youtube.com/watch?v=CTSY0-r08o8

I agree with this statement and it just goes to show how flow depends on us, the users. Platforms like TikTok or YouTube only exist because we create content, watch, and interact, making us both the source and the audience, which blurs the line between entertainment and participation.

This shows that today’s flow isn’t just passive like TV. Social media targets us personally, demanding responses. It makes us part of the system, blurring boundaries between consumption and participation, like pressuring people to get the newest phone or app, which challenges how freely we choose where to focus our attention online.

And I believe that this will be true and long as watchable media is still viable. People will always crave more and the "Impluse to go on watching" will always live in humans as it is their nature. The construction of the flow theory in television is not only true in media but in all of life. Humans are constantly questioning and wanting answers which is why this model works so well.

I don't think that there is an ideological metaphor for the power of television. I think that any media consumption on a screen is very powerful, especially in the modern age we have seen it tear families and people apart.

[annotation example by profpandora]

What was life like for prehistoric humans? The image of adult males with spears for hunting was common, such as these:

1.https://en.wikipedia.org/wiki/Caveman#/media/File:Neanderthal_Flintworkers_(Knight,_1920).jpg

2.https://en.wikipedia.org/wiki/Neanderthal#/media/File:BlackTerror1636.jpg

The author, Ursula K. Le Guin sets out here to have readers question the (long-standing!) underlying cultural message of these common images.

We need to celebrate the words captured from our ancestors and borrowed from our descendents. -

Guangzhou seems to be a key node in maritime Silk Road trade. This shows China wasn’t only dependent on overland routes and also contradicts what I learnt in another class. China wasn't exactly 'landlocked' and it was connected to the world through maritime routes, just like India.

eLife Assessment

This important study applies a novel signal decomposition method to disentangle distinct signals contributing to the decision-making process, and provides convincing evidence for the operation of separate sensory encoding, attentional orienting, and ramping evidence accumulation signals. These findings are consistent with previous work, except for the absence of a motor component, which may relate to limitations of the analysis approach.

Reviewer #1 (Public review):

From my reading, this study aimed to achieve two things:

(1) A neurally-informed account of how Pieron's and Fechner's laws can apply in concert at distinct processing levels.

(2) A comprehensive map in time and space of all neural events intervening between stimulus and response in an immediately-reported perceptual decision.

I believe that the authors achieved the first point, mainly owing to a clever contrast comparison paradigm, but with good help also from a new topographic parsing algorithm they created. With this, they found that the time intervening between an early initial sensory evoked potential and an "N2" type process associated with launching the decision process varies inversely with contrast according to Pieron's law. Meanwhile, the interval from that second event up to a neural event peaking just before response increases with contrast, fitting Fechner's law, and a very nice finding is that a diffusion model whose drift rates are scaled by Fechner's law, fit to RT, predicts the observed proportion of correct responses very well. These are all strengths of the study.

The second, generally stated aim above is, in the opinion of this reviewer, unconvincing and ill-defined. Presumably, the full sequence of neural events is massively task-dependent, and surely it is more in number than just three. Even the sensory evoked potential typically observed for average ERPs, even for passive viewing, would include a series of 3 or more components - C1, P1, N1, etc. So are some events being missed? Perhaps the authors are identifying key events that impressively demarcate Pieron- and Fechner-adherent sections of the RT, but they might want to temper the claim that they are finding ALL events. In addition, the propensity for topographic parsing algorithms to potentially lump together distinct processes that partially co-evolve should be acknowledged.

To take a salient example, the last neural event seems to blend the centroparietal positivity with a more frontal midline negativity, some of which would capture the CNV and some motor-execution related components that are more tightly time-locked to, of course, the response. If the authors plotted the traditional single-electrode ERP at the frontal focus and centroparietal focus separately, they are likely to see very different dynamics and contrast- and SAT-dependency. What does this mean for the validity of the multivariate method? If two or more components are being lumped into one neural event, wouldn't it mean that properties of one (e.g., frontal burstiness at response) are being misattributed to the other (centroparietal signal that also peaks but less sharply at response)?

Also related to the method, why must the neural events all be 50 ms wide, and what happens if that is changed? Is it realistic that these neural events would be the same duration on every trial, even if their duration was a free parameter? This might be reasonable for sensory and motor components, but unlikely for cognitive.

In general, I wonder about the analytic advantage of the parsing method - the paradigm itself is so well-designed that the story may be clear from standard average event-related potential analysis, and this might sidestep the doubts around whether the algorithm is correctly parsing all neural events.

In particular, would the authors consider plotting CPP waveforms in the traditional way, across contrast levels? The elegant design is such that the C1 component (which has similar topography) will show up negative and early, giving way to the CPP, and these two components will show opposite amplitude variations (not just temporal intervals as is this paper's main focus), because the brighter the two gratings, the stronger the aggregate early sensory response but the weaker the decision evidence due to Fechner. I believe this would provide a simple, helpful corroborating analysis to back up the main functional interpretation in the paper.

The first component is picking up on the C1 component (which is negative for these stimulus locations), not a "P100". Please consult any visual evoked potential study (e.g., Luck, Hillyard, etc).

It is unexpected that this does not vary in latency with contrast - see, for example. Gebodh et al (2017, Brain Topography) - and there is little discussion of this. Could it be that nonlinear trends were not correctly tested for?

There is very little analysis or discussion of the second stage linked to attention orientation - what would the role of attention orientation be in this task? Is it spatial attention directed to the higher contrast grating (and if so, should it lateralise accordingly?), or is it more of an alerting function the authors have in mind here?

Reviewer #2 (Public review):

Summary:

The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with the predicted effects of the contrast manipulation. They identify 3 processes: stimulus encoding, attention orienting, and decision. These map onto classical event-related potentials. The decision-making component matched the CPP, and its properties varied with contrast and predicted decision-accuracy, while also exhibiting a burst not characteristic of evidence accumulation.

Strengths:

The design of the experiment is remarkable and offers crucial insights. The analysis techniques are beyond state-of-the-art, and the analyses are well motivated and offer clear insights.

Weaknesses:

It is not clear to me that the results confirm that there are only 3 processes, since e.g., motor preparation and execution were not captured. While the authors discuss this, this is a clear weakness of the approach, as other components may also have been missed. It is also unclear to what extent topographies map onto processes, since, e.g., different combinations of sources can lead to the same scalp topography.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors examine the processing stages involved in perceptual decision-making using a new approach to analysing EEG data, combined with a critical stimulus manipulation. This new EEG analysis method enables single-trial estimates of the timing and amplitude of transient changes in EEG time-series, recurrent across trials in a behavioural task. The authors find evidence for three events between stimulus onset and the response in a two-spatial-interval visual discrimination task. By analysing the timing and amplitude of these events in relation to behaviour and the stimulus manipulation, the authors interpret these events as related to separable processing stages for stimulus encoding, attention orientation, and decision (deliberation). This is largely consistent with previous findings from both event-related potentials (across trials) and single-trial estimates using decoding techniques and neural network approaches.

Strengths:

This work is not only important for the conceptual advance, but also in promoting this new analysis technique, which will likely prove useful in future research. For the broader picture, this work is an excellent example of the utility of neural measures for mental chronometry.

Weaknesses:

The manuscript would benefit from some conceptual clarifications, which are important for readers to understand this manuscript as a stand-alone work. This includes clearer definitions of Piéron's and Fechner's laws, and a fuller description of the EEG analysis technique. The manuscript, broadly, but the introduction especially, may be improved by clearly delineating the multiple aims of this project: examining the processes for decision-making, obtaining single-trial estimates of meaningful EEG-events, and whether central parietal positivity reflects ramping activity or steps averaged across trials. A fuller discussion of the limitations of the work, in particular, the absence of motor contributions to reaction time, would also be appreciated.

At times, the novelty of the work is perhaps overstated. Rather, readers may appreciate a more comprehensive discussion of the distinctions between the current work and previous techniques to gauge single-trial estimates of decision-related activity, as well as previous findings concerning distinct processing stages in decision-making. Moreover, a discussion of how the events described in this study might generalise to different decision-making tasks in different contexts (for example, in auditory perception, or even value-based decision-making) would also be appreciated.

DOI: 10.1101/gad.352272.124

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1101/2025.07.25.666882

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1101/2025.07.25.666841

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1038/s41598-025-17508-x

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

Curator: @AleksanderDrozdz

SciCrunch record: RRID:SCR_002953

What is this?

DOI: 10.3389/fnins.2025.1558227

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.neurobiolaging.2025.08.006

Resource: (IMSR Cat# JAX_000654,RRID:IMSR_JAX:000654)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:000654

What is this?

Anschauen um welche Effekte es hier genau geht

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2025-03064

Corresponding author(s): Massimo, Hilliard; Sean, Coakley

1. General Statements

We are grateful to the reviewers for taking time to review our manuscript and for providing such clear, insightful and actionable suggestions. The consensus between 4 independent reviewers that this story is of general interest to cell biologists, neurobiologists and clinical researchers is remarkable. In addition to our mechanistic insights into the regulation of GTPase activity, we think that the experimental systems we have developed will be of great value to study how GTPases their associated GAPs and GEFs function to maintain the nervous system, especially due to the demonstrated conservation of these molecules. We believe that our data provides a powerful and tractable model to study such molecules in a physiological context.

We agree with the reviewers' concerns and propose the following plan below to address them.

2. Description of the planned revisions

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

__Summary Stability of the PLM axon in C. elegans is maintained through interactions with the epidermis. Previous studies by this group found that loss of the tbc-10 Rab GTPase Activating Protein strongly enhanced the PLM axon break phenotype of unc-70/beta-spectrin mutants. TBC-10 is a GAP for RAB-35 and thus loss of rab-35 suppresses the tbc-10 phenotype. Of the two RAB-35 GEFs, loss of RME-4 partially suppressed the tbc-10 phenotype and FLCN-1 was not involved suggesting that there may be an additional GEF involved. Here Bonacossa-Pereira et al identify a point mutation in agef-1a (vd92) as a suppressor of tbc-10 PLM axon break phenotype (all experiments also have a dominant allele of unc-70) and confirm that point mutation is causative by replicating the mutation via genome editing (vd123). Rescue experiments demonstrate that AGEF-1a is required in the epidermis and not PLM as previous demonstrated with tbc-10 and unc-70. Rescue is dependent on a functional SEC7/GEF activity. AGEF-1a is a functional ortholog to human BIG2/ArfGEF2 as its expression fully rescues tbc-10. AGEF-1a functions upstream of RAB-35 as expression of activated RAB-35 can suppress loss of agef-1. AGEF-1a functions in parallel to RME-4 as the double has stronger suppression of tbc-10. AGEF-1a is an ARF GEF, however it functions independently of ARF-1.2 as loss of arf-1.2 does not suppress tbc-10. They demonstrate that AGEF-1a interacts with RAB-35 through colocalization experiments suggesting that AGEF-1a could directly activate RAB-35. Finally, they demonstrate that AGEF-1a regulates the localization of the LET-805 epidermal attached complex component as it restores localization in a tbc-10 mutant.

Major comments

The manuscript is well written and easy to understand.

The experiments are well done and controlled.

I enjoyed reading this paper. However...

Some of the claims are not supported by the data.__

__1) The claim that AGEF-1a directly interacts with RAB-35 was not demonstrated. The evidence provided to support a direct interaction are colocalization experiments in Figure 3. AGEF-1a does partially colocalize with RAB-35 in the epidermis. However, colocalization does not indicate a physical interaction direct or indirect. A simple fix would be to change the claim to that they partially colocalize. Optional, a physical interaction could be done with the split-GFP since they already have the AGEF-1 strain or they could perform co-IP experiments, though neither of those are proof of direct interactions.

__

We agree that the biochemical co-IP experiment could provide some answers, however, using a full length AGEF-1a would not only represent a significant technical challenge but will also not prove a direct interaction in a physiological context. To overcome this limitation, and to directly test their interaction in vivo, we propose to use a split-GFP approach as suggested by the reviewer. In this experiment, we will generate an endogenously tagged GFP1-10::rab-35 allele and combine it with the previously generated and available tagged agef-1a::GFP11x7. If AGEF-1 and RAB-35 closely interact, we should observe the reconstitution of full length GFP. It is possible that the endogenously tagged versions only provide a very weak GFP signal that will be difficult to detect. As an alternative approach, we will generate the same tagged molecules as overexpressed transgenes under epidermal-specific promoters (such as Pdpy-7). If the results are still negative, we agree to temper our claim that these molecules physically interact and rephrase the manuscript to reflect the new data.

• *

2) The claim that AGEF-1a facilitates RAB-35 activation is not supported. While it is likely that AGEF-1a facilitates RAB-35 activation based on the epistasis experiments as well as studies in mammalian cells there were no experiments to demonstrate that modulating AGEF-1a activity resulted in a change in RAB-35 activity. I would suggest tempering this claim to something along the line that the data are consistent with AGEF-1a regulating RAB-35 activity as shown in mammalian cells. An optional experiment would be to look at the colocalization of RAB-35 with a known effector in wild type and agef-1(vd92) with the expectation that there would be a higher level of colocalization in agef-1 mutants. Effector pull-down experiments or perhaps a cell based GEF assay could be used (PMID: 35196081).

We welcome this suggestion and acknowledge the limitations of these experiments. While we might be able to determine if AGEF-1 and RAB-35 physically interact in vivo with the experiments proposed above, screening for the relevant rab-35 effector in this context and/or doing effector pull-down/cell based GEF assays would be a significant technical challenge. We propose to temper our claim as suggested.

3) The claim that AGEF-1a functions independently of ARF-1.2 is not well supported. The fact that the ARF-1.2 mutant does not suppress tbc-10 suggests that ARF-1.2 may not be involved but does not eliminate the possibility that ARF-1.2 functions redundantly with ARF-5 or WARF-1/ARF-1.1. This can be resolved by toning down the claim. Alternatively, this can be tested by RNAi of arf-5 and warf-1 in tbc-10 and arf-1.2; tbc-10 mutants.

We agree that warf-1 and arf-5 could be functioning redundantly with arf-1.2. We have attempted to generate an AID::arf-5 allele to test the effect of cell-specific degradation, but homozygous AID::arf-5 animals were lethal. We have not yet examined warf-1. We believe the best way to test these two molecules is through RNAi knockdown, and we propose to do this experiment and adjust our interpretation and discussion according to the new data.

Minor comments

Figure 1C the CRISPR generated allele (vd123) is referred to as [S784L] and then in 1E vd92 is referred to as [S784L]. Perhaps it would be clearer if the allele name was used instead of the amino acid change.

We will reformat the manuscript to include the allele names instead of amino acid change.

Page 6 "We reasoned that if the S784L mutation we isolated causes a similar loss of the GTPase activation function, then SKIN::AGEF-1a[E608K] would not have the capacity to restore the rate of PLM axon breaks to background levels in agef-1[S784L]; tbc-10; vdSi2 animals." It was unclear to me whether you were testing if the S784L mutation could be disrupting a GEF independent function or might disrupt the nucleotide exchange activity as might be tested in a biochemical assay. There are many reasons this change could cause a loss of function phenotype (ie. Improper folding, mislocalization, etc.). The most clear explanation would be that you were testing if GEF function was required for rescue rather than testing if the S784L mutation disrupted GEF activity.

Indeed, this experiment reveals that reducing the activation of the AGEF-1 target phenocopies the effect of S784L and does not further enhance the effect of S784L. However, it does not answer if, specifically, the GEF function is affected by S784L. We propose to rewrite the quoted sentence as follows: "We asked whether the GEF function is required for axonal damage. If that is the case, then SKIN::AGEF-1a[E608K] overexpression should phenocopy the effect of AGEF-1a[S784L]."

• *

Page 13. It was unclear how testing if AGEF-1, RME-4, ARF-5 and RAB-35 form complexes in vivo (I assume you are suggesting colocalize based on figure 3 interpretation) would resolve how AGEF-1 was regulating RAB-35.

We apologize that our phrasing was not clear. We will rewrite this section to better reflect the following idea. Given literature data showing an allosteric interaction between RME-4/DENND1 and ARF-5/Arf5, and our own data showing that AGEF-1 regulates RAB-35, we believe these molecules could form a complex. Considering that we do not have data to support this notion, mostly due to the inability to test the effect of ARF-5, we will present this possibility in the discussion section.

__**Cross-commenting**

I agree with the comments made by the other reviewers and I stand by my own as well. I will echo that it is important to know the nature of their agef-1 allele.

Reviewer #1 (Significance (Required)):

Bonacossa-Pereira et al identify AGEF-1 as a regulator of axon integrity that functions in a pathway with RAB-35 in the epidermis is an exciting finding. As pointed out in the discussion, mutations in the human ortholog cause neurodevelopmental defects which leads to obvious characterization of BIG2/ArfGEF2 in neurons while this study indicates that this protein can have cell non-autonomous roles in regulating neurons. These findings could have important implications for understanding the etiology of these defects that would be of interest to neurobiologists and clinical researchers.

The finding of this paper would also be of interest to cell biologists and particularly those studying the roles of Rab and Arf GTPases in membrane trafficking, such as myself. The idea that AGEF-1 might function as a Rab35 GEF is provocative and would generate a lot of interest and skepticism from the field. However, there is no data to support that AGEF-1 would be a direct regulator of Rab35 over the previously demonstrated cross regulation of Rab35 by Arf GTPases. Therefore, it would be fine to speculate in the discussion a direct interaction, but I would refrain from suggesting this as a model and elsewhere in the manuscript.

__

Although we agree that current evidence is not sufficient to support the model where AGEF-1 is a direct regulator of RAB-35, our data points to the direction where there is an important genetic relationship between these molecules in a physiological context in a living animal, with a defined phenotype relevant to the nervous system maintenance. We think that the proposed revision experiments will provide a better understanding of how AGEF-1 functions with RAB-35 and we agree with the suggestion to rephrase our manuscript to reflect the limitations of our results.

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

This interesting manuscript reports the outcome of a fruitful C. elegans genetic screen with a complex but clever design. Through it, the authors identify AGEF-1 as a GEF that likely regulates the active state of the GTPase RAB-35 in the skin to protect touch receptor axons from mechanical breakage.

Major points: 1. Based on localization experiments, the authors claim "AGEF-1a interacts with RAB-35 in the epidermis" (Results heading) and state "these data demonstrate that AGEF-1a interacts with a subset of RAB-35 molecules in the epidermis." In general, localization studies cannot be used to conclude physical interaction (with some exceptions such as single-molecule kinetics). In this case, the data in my view do not even make a compelling argument for co-localization. There is a lot of AGEF-1 and RAB-35 signal everywhere and it may not be meaningful that the signals sometimes overlap. A more quantitative approach with controls would be needed to conclude meaningful co-localization. Importantly, this would still not demonstrate interaction.__

We thank the reviewer for the comment. Indeed, co-localization does prove a physical interaction, and we appreciate the concern about our imaging data not making a compelling argument. To address this notion, we plan to perform an experiment using a more robust, quantitative and physiologically relevant strategy. We will generate an endogenously tagged mScarlet3::rab-35 allele for precise endogenous localization. In addition, as a positive control, we will generate an endogenous rme-4::GFP11x7 allele to cell-specifically demonstrate the level of colocalization of RME-4 with mScarlet3::RAB-35 within the epidermis. To address the possible interaction between AGEF-1a and RAB-35 we will leverage a split-GFP approach to assess their interaction in vivo, in the context relevant to the phenotypes we observed (see reply to reviewer #1 point 1).

__2. The effect of the AGEF-1(S784L) mutation is not clear to me. Naively, as the S784L mutation lies in the auto-inhibitory domain, I would have expected AGEF-1 to become constitutively active, not inactive as the authors seem to suggest. Is the idea that it is constitutively auto-inhibited? The main evidence for a loss of function effect seems to be that a putative dominant negative mutation AGEF-1(E608K) does not further supress axon breakage when co-expressed in trans to AGEF(S784L), but in my view this only shows that, once the defect is suppressed, it cannot be suppressed any further. Defining the nature of the S784L allele is important. Some suggestions, although the authors may come up with different approaches: use of an inducible or cell-specific depletion system like AID/TIR1, Cre/lox, or FLP/FRT to circumvent the lethality of agef-1(0) and reveal what a true loss-of-function looks like; testing if deletion of the auto-inhibitory domain phenocopies S784L to test if this mutation impairs autoinhibition.

__

This is an very insightful comment. To address this point, we will follow the reviewer's suggestion and deplete AGEF-1 cell-specifically in the epidermis using the auxin-inducible degron system. Specifically, we will generate an agef-1::AID allele to degrade this molecule in a spatially and temporally controlled fashion, which will allow to circumvent the lethality of agef-1(0) and determine whether the S784L allele mimics the depletion of AGEF-1.

Although it would be interesting to further dissect the effect of this mutation on AGEF-1 activity, we believe that this falls outside of the scope of this manuscript. As an alternative, we propose to elaborate more in the discussion the implications of the possible roles for the S784L mutation to clarify our model of its function. Our data supports a model in which this mutation reduces AGEF-1 function leading to a reduction in the activity of its downstream target GTPases. It is possible that this is due to AGEF-1 becoming constitutively autoinhibited, or that this mutation affects the structure of the molecule in a way that it reduces its affinity towards its downstream effectors.

Minor points: 1. I am not able to see the "vesicle-like structures with a clear luminal space" or RAB-35 being "notably enriched at the membrane near the epidermal furrow" in Fig. 3. The "3D surface rendering" in Fig. 3e is grossly oversampled and should not be included.

We will rectify this section and include new super-resolved images using Airyscan confocal microscopy. We hope these will yield a better-quality representation of these concepts. __ 2. As the agef-1a isoform is specifically referenced throughout, please describe the different agef-1 isoforms somewhere to save readers from having to look this up.__

Yes, we will include a description of the isoforms. In C. elegans there are two: AGEF-1a which has been confirmed by cDNA and AGEF-1b which is predicted and partially confirmed by cDNA. The mutation we isolated exclusively affects AGEF-1a.

3. The authors include an interesting speculation in the Discussion: "Future investigations of BIG2-associated neurological disorders should consider... hyper-activity of BIG2 as a driver of neuropathology." If the authors have the tools to test the effect of hyperactive BIG2 in this system, it could be an exciting addition.

This is an exciting idea that we would like to keep in the Discussion. The biology of BIG2 activity regulation is a nascent field of research and we believe that to accurately generate and characterise a hyperactive BIG2 would be beyond the scope of this manuscript.

__ On a personal note, since GEFs act oppositely to GTPase Activating Proteins (GAPs), I had to stop and re-read carefully whenever the authors referred to a GEF "activating" a GTPase. I understand their meaning (i.e., putting the GTPase in its active GTP-bound state, not activating its GTPase function) but I wanted to point out this potential confusion in case there is a way to better define terms in the Introduction or change word choice. I realize this may be a standard jargon in the field.__

Indeed, this is confusing nomenclature and a difficult concept to deliver in an accurate and succinct manner. We propose to include a clearer, more didactic explanation of their function. In a simple explanation, GTPases perform cellular functions when bound to GTP. GAPs terminate GTPase activity by catalysing GTP hydrolysis, generating GDP. GEFs initiate GTPase activity by catalysing the release of GDP and allowing GTP binding.

__ Please check the correct nomenclature for CRISPR/Cas9.__

We will rectify where appropriate.

__6. p.7 "these molecules act in synergy", consider replacing with "redundantly".

__

We will rectify where appropriate.

__Reviewer #2 (Significance (Required)):

The significance of this story is to show that GEF-GTPases pairing can be highly context-dependent. Previous studies have identified GEFs that pair with RAB-35 and GTPases that pair with AGEF-1, but the authors find that these factors have at best a modest role in the context of skin-axon interactions. Instead, the authors suggest a novel GTPase-GEF pairing of RAB-35 with AGEF-1 and provide evidence that this relationship is conserved in the human homolog of AGEF-1. These results suggest that GTPase-GEF pairings depend not only on chemical affinity but also cellular context.

The main strength of the study is its clever genetics. For the screen, the authors looked for suppressors of a synthetic defect in axon integrity caused in part by elevated activity of RAB-35 due to loss of its GAP TBC-10. It is satisfying that this screen isolated a mutation in a GEF that in principle could counterbalance the loss of a GAP.

The main weakness of the study is the lack of direct evidence for an AGEF-1/RAB-35 interaction. While not necessary for publication, the inclusion of biochemical data to support the role of AGEF-1 as a GEF for RAB-35 and the effect of the S784L mutation on this activity would strongly elevate the study. The genetic data for this interaction are consistent with the model but not conclusive, and in my view the colocalization data are not compelling. Nevertheless this is a solid genetic story with a clever screen.__

__ __We appreciate the feedback and are grateful for the positive comments on the significance of our study. As explained in the significance section related to Reviewer 1, if we find evidence of a direct interaction between AGEF-1 and RAB-35 in the proposed new experiments, we will include it in the manuscript; alternatively, we will present it as a possibility in the discussion section, as suggested. We agree that a more nuanced understanding of the effect of the S784L is interesting and that our colocalization data can be improved, and we have proposed experiments to address these concerns.

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

This paper investigates the mechanism by which molecular pathways in the skin protect the processes of nerves that innervate them from damage. The authors previously showed that spectrin and the small GTPase RAB-35 act in the epidermis of C. elegans to protect mechanosensory axons from breaking. In this paper they used a suppression screen to identify another gene involved in this process, an ARF-GEF called AGEF-1. Partial loss-of-function mutations in agef-1 suppress the axon-breakage phenotype of spectrin mutations, and genetic experiments by the authors are consistent with the possibility that AGEF-1 could act directly as an exchange factor for RAB-35. Consistent with this model, they show that AGEF-1 and RAB-35 colocalise in the skin.

Major comments: The experiments in this paper are well-designed and well-controlled, and the interpretations of the results are all reasonable. On the other hand, I don't think the authors' hypothesis that AGEF-1 acts directly as an exchange factor for RAB-35, or that these two proteins directly interact, is definitively proven. This is not an issue of the authors overinterpreting their data--the paper is very carefully and thoughtfully written. However, the most interesting and counterintuitive finding--that an ARF-GEF could also be a RAB-GEF--might be strengthened with more experiments (for example, could they more directly show protein-protein interaction through co-IP or mass spec?).__

We thank the reviewer for the suggestion. We propose to further investigate the notion that AGEF-1a might be a direct interactor of RAB-35 using a split-GFP approach to assess whether these molecules closely interact, in vivo, in the physiological context that is relevant for the maintenance of the touch sensing neurons (please see reply to reviewer #1 major point 1 and reviewer #2 major point 1 for more details).

Minor comments: There are also two places where the fact that null mutations are lethal (for agef-1 and arf-5) prevented the authors from addressing the effect of agef-1 loss of function in the skin, and addressing whether ARF-5 could be an AGEF-1 target, respectively. In principle, they could have tried to make a CRISPR line in which these genes could be cell-specifically deleted in the skin (using a dpy-7-driven recombinase). I don't think either of these experiments are essential, but if it is feasible to make these lines it would tie up a couple of loose ends.

We agree to explore the roles of agef-1 and arf-5 loss-of-function. We propose to tissue-specifically degrade agef-1 using an auxin-inducible degradation strategy (please see reviewer #2 major point 2 reply for more details). For arf-5, we propose knocking-down its function using RNAi to overcome lethality (please see reviewer #1 major point 3 reply for more details).

__Reviewer #3 (Significance (Required)):

Overall I think this is an interesting paper on a topic of general interest. The most interesting finding is that an exchange factor for an ARF (a small GRPase involved in vesicle coating/uncoating) could also be an exchange factor for a RAB (a small GTPase involved in vesicle tethering). The evidence presented is suggestive and intriguing, though as noted above not completely definitive. In summary, I think it is an interesting paper in its current form, and anything it could do to more firmly establish a direct interaction between AGEF-1 and RAB-35 would increase its impact and importance.

__

We thank the reviewer for the positive evaluation of the significance of our study.

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

Summary: In this study Bonacossa-Pereira et al. identify AGEF-1a, an Arf-GEF, as a factor that functions in the epidermis through RAB-35 to regulate axonal integrity of the PLM mechanosensory neurons in C. elegans. Specifically, epidermal attachment sites are regulated by these genes form the epidermis and compromising these attachment sites results in axonal degeneration. The study provides some evidence that that RAB-35 and AGEF-1 at least partially colocalize in the skin. Finally, the authors provide evidence that the human orthologue BIG2 is capable of functionally replacing AGEF-1a in C. elegans. Overall, the experiments are well designed and the paper is clear and succinct. The conclusions are supported by the findings and provide an important extension of the author's findings a few back, when they identified the role of rab-35 in mediating the epidermal-neuronal attachment sites.

Major comments: 1. AGEF-1/BIG2 are known to regulate other GTPases such as ARF-5 or ARF-2. The authors exclude a non-redundant function for ARF-2, but are unable to establish a role for ARF-5 because of the lethality associated with the mutation. Alternative approaches, such as cell specific knock out or knock down experiment. In addition, studies to test potentially physical interaction such as pull-down assays, co-IP experiments and FRET could be used to test whether AGEF-can bind RAB-35 or ARF-5.__

We thank the reviewer for this suggestion. We propose addressing these concerns using a tissue-specific degradation for AGEF-1a (please see reviewer #1 major point 2 for details). To establish a role for ARF-5 we propose to do an RNAi mediated knock-down to overcome lethality (please see reviewer #1 major point 3 for details). Finally, we plan to use a split-GFP approach to test the physical interaction between agef-1a and rab-35 in vivo (please see reviewer #1 major point 1 for details)

__ Phenotypic readout has been limited to only axon breaks. It may be interesting to also test other aspects such as axonal deformities including swellings and vesiculation in other parts of the nervous system. Moreover, behavioral or functional experiments such as response to gentle touch or synaptic integrity could be informative.__

We have not observed any obvious touch receptor neurons axonal phenotypes other than axonal breaks in these mutants, and we will include a statement that reflects this concept. In relation to the behavior, we have not tested it as the results will be difficult to interpret for two reasons: first, the breaks are not always bilateral and one neuron is sufficient to provide mechanical response; second, the mixed identity of the PLM neurite allows it to retain some function despite being severed. However, if deemed essential, we will perform these experiments.

__ Overexpression constructs such as SKIN::RAB-35[Q69L], SKIN::BIG2, SKIN::AGEF-1a[E608K] in extrachromosomal transgenes could lead to non-physiological localization or effects. Single copy expression using MosSCI or CRISPR insertions are generally considered better approaches (other than endogenous reporters) to provide accurate insights at the physiological level. While the authors tacitly acknowledge this by conducting the experiments in a rab-35 mutant background and very low transgene concentration, at the very least this caveat regarding the localization should be discussed.__

This is an important remark, and we appreciate the comment. We acknowledge that experiments using extrachromosomal arrays have inherent caveats, especially for localization studies. To address the RAB-35 localization concern we plan to repeat the localization studies using an endogenously tagged RAB-35 using CRISPR to overcome the possible artifacts caused by extrachromosomal array driven expression (please see reviewer #1 point 1 for more details). For the cell-specific rescues or dominant-negative constructs expression, we believe that using extrachromosomal arrays is sufficient, since this allows us to compare genetically identical transgenic vs non-transgenic siblings of independent lines. Moreover, given these constructs are already driven by a tissue-specific promoter that is inherently stronger than their respective endogenous promoters, even a single-copy insertion would have the same caveats.

__4. The study does not address clearly whether AGEF-1a acts in parallel to spectrin or upstream/ downstream to it. Epistasis experiments could help to figure out the signaling pathway involved.

__

Indeed, this is a concept that we need to communicate more clearly. We have data showing that a mutation in agef-1 does not cause axonal damage on its own, and that it has no effect on the axonal damage caused by unc-70 dominant negative mutation alone. We only detect an effect of agef-1 when tbc-10 is mutated together with unc-70 (Fig. 1a of manuscript). Together, these data indicate that agef-1 functions upstream of rab-35, thus acting in parallel to unc-70 (see schematic below) to ensure the mechanical stability of neuron epidermal attachment. We plan to include this data and the following schematic as a supplement to better convey the idea and discuss the results appropriately.

__ The finding that BIG2 rescues the mutant defect is an important finding and rightfully finds its place in the abstract. I wonder whether a reference to the human diseases caused by loss of BIG2 in the abstract and introduction would not increase interest/impact for the study, rather than burying this potentially interesting connection in the discussion.

__

We appreciate the reviewer's comment, and welcome the suggestion. We propose to include relevant background about BIG2-related human diseases in the abstract and introduction as suggested and expand the discussion regarding BIG2 mutations.

__Minor comments:

1. Some explanation about how mutating the autoinhibitory domain could impact the catalytic activity of a GEF might be helpful.__

2. *

We acknowledge that this notion was not well communicated. We propose to elaborate more about why we think a mutation in the autoinhibitory domain might be affecting the GEF activity and we plan to do further experiments to dissect how this might be happening. Please see reviewer #2 major point 2 for a more detailed explanation.

__ The paper refers to rme-4(b1001) as a null allele while wormbase refers to the same as a missense allele. It would be more accurate to refer rme-4(b1001) as a strong loss of function or putative null.__

We agree and will refer to b1001 as a strong loss-of-function.

__ The paper does not clearly discuss limitations of the hypomorphic agef-1[S784L] and that the observed phenotypes in this hypomorph might underestimate the complete role of AGEF-1a.__

• *

We thank the reviewer for this suggestion. We propose to elaborate more on these limitations, especially considering the possible new results from the experiments suggested in reply to reviewer #2 major comment point 2.

__ In figure 1, where there really only one extrachromosomal transgenic line for some of the construct tested? __

• *

For the Pdpy-7::AGEF-1a lines we have scored 3 transgenic lines (data not included) and only one yielded a full rescue. For all extrachromosomal lines presented, we tested 3 independent transgenic lines. For brevity, we only included the result for the positive rescues (1 for BIG2 and 1 for AGEF-1a), except for the Pmec-4 lines, of which none rescued the phenotype (data included in Table S2). We will update Table S2 to include all the lines tested.

__ The concentrations of transgenes vary in different transgenes. Is there a rationale behind this? __

Yes, we have attempted multiple concentrations of injections for each transgene and there was some variability for each construct injected, thus we only included the ones where we observed an effect. As mentioned in point 4 above, we will update Table S2 to include details of all lines tested.

__ In Fig.1e: I may be useful to also show the "WT" phenotype, i.e. the strong defects to get a visual comparison for the degree of rescue. __

• *

We think this suggestion will help the readers. We will include this as a representative dashed line showing the WT phenotype.

__Reviewer #4 (Significance (Required)):

The study has identified AGEF-1a as a regulator of axonal maintenance, functioning to protect neurons against mechanical stress by acting through RAB-35. Additionally, this epidermal GEF, AGEF-1a is functionally conserved as its human orthologue BIG2 can replace AGEF-1a in C. elegans for axonal protection. Important points here are that the findings extend prior work by the authors of non-autonomous mechanism that regulates epidermal-neuronal attachment. In my humble opinion, the human disease connection, in particular with regard to the unexplained neuronal phenotypes in patients could be better developed in the manuscript. It may also increase impact/interest of a wonderful story that right now reads a bit 'wormy'.__

This is an important remark and we are grateful for the positive comments. The fact that human BIG2 is also conserved in C. elegans points to a fundamental role of this molecule in multicellular life, and it provides a tractable model to investigate the function of this molecule in a physiological context. We welcome the suggestion to elaborate more the connection with the unexplained neuronal phenotypes in patients and use a more accessible language to convey our findings to a wider audience.

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

N/A

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

__Reviewer #1 __

"...studies to test potentially physical interaction such as pull-down assays, co-IP experiments and FRET could be used to test whether AGEF-can bind RAB-35 or ARF-5."

While pull-down assays, co-IP and FRET would reveal whether AGEF-1a can form a complex with RAB-35, we believe that using a full length AGEF-1a would not only represent a significant technical challenge but will also not prove a direct interaction in a physiological context.

"...An optional experiment would be to look at the colocalization of RAB-35 with a known effector in wild type and agef-1(vd92) with the expectation that there would be a higher level of colocalization in agef-1 mutants. Effector pull-down experiments or perhaps a cell based GEF assay could be used (PMID: 35196081)."

We think that screening for the relevant rab-35 effector in this context and/or doing effector pull-down/cell based GEF assays would be a significant technical challenge. We propose to address this concern by tempering our claim as suggested by the reviewer.

"...It may be interesting to also test other aspects such as axonal deformities including swellings and vesiculation in other parts of the nervous system. Moreover, behavioral or functional experiments such as response to gentle touch or synaptic integrity could be informative."

As indicated above in major point 2 of reviewer 4, these are interesting ideas that might answer how the function of these neurons might be affected. However, in addition to the challenges indicated above, they will not provide further insights into how their integrity is maintained. We believe these will fall outside the scope of the manuscript, but if deemed essential we will perform behavioral analysis.

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Referee #4

Evidence, reproducibility and clarity

Summary:

In this study Bonacossa-Pereira et al. identify AGEF-1a, an Arf-GEF, as a factor that functions in the epidermis through RAB-35 to regulate axonal integrity of the PLM mechanosensory neurons in C. elegans. Specifically, epidermal attachment sites are regulated by these genes form the epidermis and compromising these attachment sites results in axonal degeneration. The study provides some evidence that that RAB-35 and AGEF-1 at least partially colocalize in the skin. Finally, the authors provide evidence that the human orthologue BIG2 is capable of functionally replacing AGEF-1a in C. elegans. Overall, the experiments are well designed and the paper is clear and succinct. The conclusions are supported by the findings and provide an important extension of the author's findings a few back, when they identified the role of rab-35 in mediating the epidermal-neuronal attachment sites.

Major comments:

1. AGEF-1/BIG2 are known to regulate other GTPases such as ARF-5 or ARF-2. The authors exclude a non-redundant function for ARF-2, but are unable to establish a role for ARF-5 because of the lethality associated with the mutation. Alternative approaches, such as cell specific knock out or knock down experiment. In addition, studies to test potentially physical interaction such as pull-down assays, co-IP experiments and FRET could be used to test whether AGEF-can bind RAB-35 or ARF-5.
2. Phenotypic readout has been limited to only axon breaks. It may be interesting to also test other aspects such as axonal deformities including swellings and vesiculation in other parts of the nervous system. Moreover, behavioral or functional experiments such as response to gentle touch orsynaptic integrity could be informative.
3. Overexpression constructs such as SKIN::RAB-35[Q69L], SKIN::BIG2, SKIN::AGEF-1a[E608K] in extrachromosomal transgenes could lead to non-physiological localization or effects. Single copy expression using MosSCI or CRISPR insertions are generally considered better approaches (other than endogenous reporters) to provide accurate insights at the physiological level. While the authors tacitly acknowledge this by conducting the experiments in a rab-35 mutant background and very low transgene concentration, at the very least this caveat regarding the localization should be discussed.
4. The study does not address clearly whether AGEF-1a acts in parallel to spectrin or upstream/ downstream to it. Epistasis experiments could help to figure out the signaling pathway involved.
5. The finding that BIG2 rescues the mutant defect is an important finding and rightfully finds its place in the abstract. I wonder whether a reference to the human diseases caused by loss of BIG2 in the abstract and introduction would not increase interest/impact for the study, rather than burying this potentially interesting connection in the discussion.

Minor comments:

1. Some explanation about how mutating the autoinhibitory domain could impact the catalytic activity of a GEF might be helpful.
2. The paper refers to rme-4(b1001) as a null allele while wormbase refers to the same as a missense allele. It would be more accurate to refer rme-4(b1001) as a strong loss of function or putative null.
3. The paper does not clearly discuss limitations of the hypomorphic agef-1[S784L] and that the observed phenotypes in this hypomorph might underestimate the complete role of AGEF-1a.
4. In figure 1, where there really only one extrachromosomal transgenic line for some of the construct tested?
5. The concentrations of transgenes vary in different transgenes. Is there a rationale behind this?
6. In Fig.1e: I may be useful to also show the "WT" phenotype, i.e. the strong defects to get a visual comparison for the degree of rescue.

Significance

The study has identified AGEF-1a as a regulator of axonal maintenance, functioning to protect neurons against mechanical stress by acting through RAB-35. Additionally, this epidermal GEF, AGEF-1a is functionally conserved as its human orthologue BIG2 can replace AGEF-1a in C. elegans for axonal protection. Important points here are that the findings extend prior work by the authors of non-autonomous mechanism that regulates epidermal-neuronal attachment. In my humble opinion, the human disease connection, in particular with regard to the unexplained neuronal phenotypes in patients could be better developed in the manuscript. It may also increase impact/interest of a wonderful story that right now reads a bit 'wormy'.

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Referee #3

Evidence, reproducibility and clarity

This paper investigates the mechanism by which molecular pathways in the skin protect the processes of nerves that innervate them from damage. The authors previously showed that spectrin and the small GTPase RAB-35 act in the epidermis of C. elegans to protect mechanosensory axons from breaking. In this paper they used a suppression screen to identify another gene involved in this process, an ARF-GEF called AGEF-1. Partial loss-of-function mutations in agef-1 suppress the axon-breakage phenotype of spectrin mutations, and genetic experiments by the authors are consistent with the possibility that AGEF-1 could act directly as an exchange factor for RAB-35. Consistent with this model, they show that AGEF-1 and RAB-35 colocalise in the skin.

Major comments: The experiments in this paper are well-designed and well-controlled, and the interpretations of the results are all reasonable. On the other hand, I don't think the authors' hypothesis that AGEF-1 acts directly as an exchange factor for RAB-35, or that these two proteins directly interact, is definitively proven. This is not an issue of the authors overinterpreting their data--the paper is very carefully and thoughtfully written. However, the most interesting and counterintuitive finding--that an ARF-GEF could also be a RAB-GEF--might be strengthened with more experiments (for example, could they more directly show protein-protein interaction through co-IP or mass spec?).

Minor comments: There are also two places where the fact that null mutations are lethal (for agef-1 and arf-5) prevented the authors from addressing the effect of agef-1 loss of function in the skin, and addressing whether ARF-5 could be an AGEF-1 target, respectively. In principle, they could have tried to make a CRISPR line in which these genes could be cell-specifically deleted in the skin (using a dpy-7-driven recombinase). I don't think either of these experiments are essential, but if it is feasible to make these lines it would tie up a couple of loose ends.

Significance

Overall I think this is an interesting paper on a topic of general interest. The most interesting finding is that an exchange factor for an ARF (a small GRPase involved in vesicle coating/uncoating) could also be an exchange factor for a RAB (a small GTPase involved in vesicle tethering). The evidence presented is suggestive and intriguing, though as noted above not completely definitive. In summary, I think it is an interesting paper in its current form, and anything it could do to more firmly establish a direct interaction between AGEF-1 and RAB-35 would increase its impact and importance.

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Referee #2

Evidence, reproducibility and clarity

This interesting manuscript reports the outcome of a fruitful C. elegans genetic screen with a complex but clever design. Through it, the authors identify AGEF-1 as a GEF that likely regulates the active state of the GTPase RAB-35 in the skin to protect touch receptor axons from mechanical breakage.

Major points:

1. Based on localization experiments, the authors claim "AGEF-1a interacts with RAB-35 in the epidermis" (Results heading) and state "these data demonstrate that AGEF-1a interacts with a subset of RAB-35 molecules in the epidermis." In general, localization studies cannot be used to conclude physical interaction (with some exceptions such as single-molecule kinetics). In this case, the data in my view do not even make a compelling argument for co-localization. There is a lot of AGEF-1 and RAB-35 signal everywhere and it may not be meaningful that the signals sometimes overlap. A more quantitative approach with controls would be needed to conclude meaningful co-localization. Importantly, this would still not demonstrate interaction.
2. The effect of the AGEF-1(S784L) mutation is not clear to me. Naively, as the S784L mutation lies in the auto-inhibitory domain, I would have expected AGEF-1 to become constitutively active, not inactive as the authors seem to suggest. Is the idea that it is constitutively auto-inhibited? The main evidence for a loss of function effect seems to be that a putative dominant negative mutation AGEF-1(E608K) does not further supress axon breakage when co-expressed in trans to AGEF(S784L), but in my view this only shows that, once the defect is suppressed, it cannot be suppressed any further. Defining the nature of the S784L allele is important. Some suggestions, although the authors may come up with different approaches: use of an inducible or cell-specific depletion system like AID/TIR1, Cre/lox, or FLP/FRT to circumvent the lethality of agef-1(0) and reveal what a true loss-of-function looks like; testing if deletion of the auto-inhibitory domain phenocopies S784L to test if this mutation impairs autoinhibition.

Minor points:

1. I am not able to see the "vesicle-like structures with a clear luminal space" or RAB-35 being "notably enriched at the membrane near the epidermal furrow" in Fig. 3. The "3D surface rendering" in Fig. 3e is grossly oversampled and should not be included.
2. As the agef-1a isoform is specifically referenced throughout, please describe the different agef-1 isoforms somewhere to save readers from having to look this up.
3. The authors include an interesting speculation in the Discussion: "Future investigations of BIG2-associated neurological disorders should consider... hyper-activity of BIG2 as a driver of neuropathology." If the authors have the tools to test the effect of hyperactive BIG2 in this system, it could be an exciting addition.
4. On a personal note, since GEFs act oppositely to GTPase Activating Proteins (GAPs), I had to stop and re-read carefully whenever the authors referred to a GEF "activating" a GTPase. I understand their meaning (i.e., putting the GTPase in its active GTP-bound state, not activating its GTPase function) but I wanted to point out this potential confusion in case there is a way to better define terms in the Introduction or change word choice. I realize this may be a standard jargon in the field.
5. Please check the correct nomenclature for CRISPR/Cas9.
6. p.7 "these molecules act in synergy", consider replacing with "redundantly".

Significance

The significance of this story is to show that GEF-GTPases pairing can be highly context-dependent. Previous studies have identified GEFs that pair with RAB-35 and GTPases that pair with AGEF-1, but the authors find that these factors have at best a modest role in the context of skin-axon interactions. Instead, the authors suggest a novel GTPase-GEF pairing of RAB-35 with AGEF-1 and provide evidence that this relationship is conserved in the human homolog of AGEF-1. These results suggest that GTPase-GEF pairings depend not only on chemical affinity but also cellular context.

The main strength of the study is its clever genetics. For the screen, the authors looked for suppressors of a synthetic defect in axon integrity caused in part by elevated activity of RAB-35 due to loss of its GAP TBC-10. It is satisfying that this screen isolated a mutation in a GEF that in principle could counterbalance the loss of a GAP.

The main weakness of the study is the lack of direct evidence for an AGEF-1/RAB-35 interaction. While not necessary for publication, the inclusion of biochemical data to support the role of AGEF-1 as a GEF for RAB-35 and the effect of the S784L mutation on this activity would strongly elevate the study. The genetic data for this interaction are consistent with the model but not conclusive, and in my view the colocalization data are not compelling. Nevertheless this is a solid genetic story with a clever screen.

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Referee #1

Evidence, reproducibility and clarity

Summary

Stability of the PLM axon in C. elegans is maintained through interactions with the epidermis. Previous studies by this group found that loss of the tbc-10 Rab GTPase Activating Protein strongly enhanced the PLM axon break phenotype of unc-70/beta-spectrin mutants. TBC-10 is a GAP for RAB-35 and thus loss of rab-35 suppresses the tbc-10 phenotype. Of the two RAB-35 GEFs, loss of RME-4 partially suppressed the tbc-10 phenotype and FLCN-1 was not involved suggesting that there may be an additional GEF involved. Here Bonacossa-Pereira et al identify a point mutation in agef-1a (vd92) as a suppressor of tbc-10 PLM axon break phenotype (all experiments also have a dominant allele of unc-70) and confirm that point mutation is causative by replicating the mutation via genome editing (vd123). Rescue experiments demonstrate that AGEF-1a is required in the epidermis and not PLM as previous demonstrated with tbc-10 and unc-70. Rescue is dependent on a functional SEC7/GEF activity. AGEF-1a is a functional ortholog to human BIG2/ArfGEF2 as its expression fully rescues tbc-10. AGEF-1a functions upstream of RAB-35 as expression of activated RAB-35 can suppress loss of agef-1. AGEF-1a functions in parallel to RME-4 as the double has stronger suppression of tbc-10. AGEF-1a is an ARF GEF, however it functions independently of ARF-1.2 as loss of arf-1.2 does not suppress tbc-10. They demonstrate that AGEF-1a interacts with RAB-35 through colocalization experiments suggesting that AGEF-1a could directly activate RAB-35. Finally, they demonstrate that AGEF-1a regulates the localization of the LET-805 epidermal attached complex component as it restores localization in a tbc-10 mutant.

Major comments

The manuscript is well written and easy to understand.

The experiments are well done and controlled.

I enjoyed reading this paper. However...

Some of the claims are not supported by the data.

1. The claim that AGEF-1a directly interacts with RAB-35 was not demonstrated. The evidence provided to support a direct interaction are colocalization experiments in Figure 3. AGEF-1a does partially colocalize with RAB-35 in the epidermis. However, colocalization does not indicate a physical interaction direct or indirect. A simple fix would be to change the claim to that they partially colocalize. Optional, a physical interaction could be done with the split-GFP since they already have the AGEF-1 strain or they could perform co-IP experiments, though neither of those are proof of direct interactions.
2. The claim that AGEF-1a facilitates RAB-35 activation is not supported. While it is likely that AGEF-1a facilitates RAB-35 activation based on the epistasis experiments as well as studies in mammalian cells there were no experiments to demonstrate that modulating AGEF-1a activity resulted in a change in RAB-35 activity. I would suggest tempering this claim to something along the line that the data are consistent with AGEF-1a regulating RAB-35 activity as shown in mammalian cells. An optional experiment would be to look at the colocalization of RAB-35 with a known effector in wild type and agef-1(vd92) with the expectation that there would be a higher level of colocalization in agef-1 mutants. Effector pull-down experiments or perhaps a cell based GEF assay could be used (PMID: 35196081).
3. The claim that AGEF-1a functions independently of ARF-1.2 is not well supported. The fact that the ARF-1.2 mutant does not suppress tbc-10 suggests that ARF-1.2 may not be involved but does not eliminate the possibility that ARF-1.2 functions redundantly with ARF-5 or WARF-1/ARF-1.1. This can be resolved by toning down the claim. Alternatively, this can be tested by RNAi of arf-5 and warf-1 in tbc-10 and arf-1.2; tbc-10 mutants.

Minor comments

Figure 1C the CRISPR generated allele (vd123) is referred to as [S784L] and then in 1E vd92 is referred to as [S784L]. Perhaps it would be clearer if the allele name was used instead of the amino acid change.

Page 6 "We reasoned that if the S784L mutation we isolated causes a similar loss of the GTPase activation function, then SKIN::AGEF-1a[E608K] would not have the capacity to restore the rate of PLM axon breaks to background levels in agef-1[S784L]; tbc-10; vdSi2 animals." It was unclear to me whether you were testing if the S784L mutation could be disrupting a GEF independent function or might disrupt the nucleotide exchange activity as might be tested in a biochemical assay. There are many reasons this change could cause a loss of function phenotype (ie. Improper folding, mislocalization, etc.). The most clear explanation would be that you were testing if GEF function was required for rescue rather than testing if the S784L mutation disrupted GEF activity.

Page 13. It was unclear how testing if AGEF-1, RME-4, ARF-5 and RAB-35 form complexes in vivo (I assume you are suggesting colocalize based on figure 3 interpretation) would resolve how AGEF-1 was regulating RAB-35.

Cross-commenting

I agree with the comments made by the other reviewers and I stand by my own as well. I will echo that it is important to know the nature of their agef-1 allele.

Significance

Bonacossa-Pereira et al identify AGEF-1 as a regulator of axon integrity that functions in a pathway with RAB-35 in the epidermis is an exciting finding. As pointed out in the discussion, mutations in the human ortholog cause neurodevelopmental defects which leads to obvious characterization of BIG2/ArfGEF2 in neurons while this study indicates that this protein can have cell non-autonomous roles in regulating neurons. These findings could have important implications for understanding the etiology of these defects that would be of interest to neurobiologists and clinical researchers.

The finding of this paper would also be of interest to cell biologists and particularly those studying the roles of Rab and Arf GTPases in membrane trafficking, such as myself. The idea that AGEF-1 might function as a Rab35 GEF is provocative and would generate a lot of interest and skepticism from the field. However, there is no data to support that AGEF-1 would be a direct regulator of Rab35 over the previously demonstrated cross regulation of Rab35 by Arf GTPases. Therefore, it would be fine to speculate in the discussion a direct interaction, but I would refrain from suggesting this as a model and elsewhere in the manuscript.

mirroring interferes with pergos.net login

important

This does not depend on i. You can do it outside of the for loop only once, instead of repeating the calculation 4000 times.

This is confusing. How are these probabilities? you mean parameters? but the parameters beta in the text were different: -2, 0, 2, 4.

Same as LP <- as.list(bp)? But why do you need the vector bp expressed as a list?

I never used it, but I understand that you can use the categorical family of {brms} to fit multinomial models.

In the frequentist world, the package {mclogit} seems to fit this kind of models, and also the function multinom() from package {nnet}.

Hi! nice post, thank you! This is an interesting topic on which I worked a bit in the past trying to implement multinomial models in INLA using this trick. But I got lost in the details and computational problems and never got around to complete it.

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Does this mean that we will open the article from the link and log into our Hypothesis account directly from the article link we are given?

Engage in a workout inspired by your favourite 2005 Muffin The Mule episode

Squeak and scurry around the room like a mouse

Get enough sleep each night

Fly around the room like a bird

This goes with #4.

This line right here goes with #5. I think tracking my happiness would affect me in a sense that I would get annoyed on how much I would have to keep inputting on a daily just to get a measurement on how I am feeling.

I think this make his argument stronger because he's challenging that can these apps that measure health off a metrics can make you feel happier.

This stood out to me the most cause it has so many levels of truth behind it.

Begin by putting

$$ \texttt{left} = \frac{0}{1}\quad\texttt{mediant} = \frac{1}{1}\quad\texttt{right}=\frac{1}{0}. $$

If you move left, keep \(\texttt{left}\), put \(\texttt{right}\) equal to \(\texttt{mediant}\) and compute new \(\texttt{mediant}\). If you move right keep the \(\texttt{right}\) etc.

This is was a turning point for the narrator. His confidence opposes with his unexpected fail.