This is the meaning of "Content Analysis"

Exigency: an urgent need or demand.

This is the most interesting part to me as the Kiwi Bots are programmed to create their own pathing around campus. It seems as though they should be able to determine when potholes develop; this would allow them to have a more concise route that avoids personal damage. I would relate this to how students cut paths through grass patches creating a more time-efficient route.

This is showing reader that they are continuously getting better.

use different word such as, (Intelligent or quick-witted)

even with this tech they always drive into me..

End of email should be one singular concluding email to wrap things up.

Always start the email by referring to them as their preferred name.

This is a good idea.

Use of direct address creates a personal, friendly tone

this statement expands on the personal tone the author creates transitioning from a friendly to an aggressive tone emphasizing the importance of the subject being talked about

Title the email regarding the topic/question you will be talking about, therefor the professor will know the point of the email.

Not proof reading is the biggest mistake, always proofread everything.

Email to professor should be <150 words

When writing an email to a professor, you should keep it short and fairly straight to the point

This is very helpful after the email that i will use with the professors

This is key as not to bore the professor or the person you're trying to email.

These are all very helpful excerpts from the email that I will use with my professors.

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

Does fear of failure push students to cheat more?

Praise can actually backfire, lowering resilience.

Intelligence praise = fear of mistakes, avoidance of challenge.

I sometimes pick the easy option just to not look bad.

different age or ethnictities seem don't change the result at all.

this show how common the idea was

Example of misplaced priorities that feeling good > actual learning. This shows how praise culture went too far.

big culture trend of the 1990s

what does it mean?

Notice how the strength is being shown within the writer’s words.

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?

Note pour Hugues: revoir cet état à la fin

Add link to note 6

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)

Shouldn't be in bold

Note to Hugues: Revoir à la fin s'assurer que le lien apporte à la note 13 (Actuellement la note sur les immo. indique note 14)

ever stuck in writing a email or not knowing what to do, this sample is a good way to start up your email.

college writing is not just for english class, it is a use for everything in this world to communicate on whatever your topic might be.

Another thing to practice while taking this class

This is a good thing to keep in mind

wrong, this still works in chrome and firefox

Poetry is important for a way of life, it isn't just a silly little reading to some people it is so much more than that.

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

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

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

You don't need to be a genius to understand what is happening in poetry, you can make it make sense however you feel it should make sense.

I feel like this shows that you can try to enjoy or understand anything you try to. I think it is important that he says that he was never interested in poetry until he kind of had to be.

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 think this is very important because every poem is what your mind thinks of it.

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

Crtical Reflection: How does media, school, and culture shape our ideas of Africa.

this shows how stereotypes form in American minds about Africa.

Suggests Africa is framed mainly through crisis and disaster in Western media.

Highlights how Africa is commonly viewed only through tourism or media rather then realities.

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.

didn`t find process on installing Installer Service on WIndows VMs, since v13 it requires deployer kit prepared and run on the Windows machine after that, Our job connects to this Installer using certificate and install persistent components

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.

same as for SQL

since v13 we support Linux server as LSS as well

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

Why do we leave them out? Do you never use them?

same as sap oracle and all other plugins - linux vbr v13 wont support compatibility with v12 plugin

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.

it is worth to make a not for all plugins: linux vbr v13 does not support backward compatibility with v12 plugins at all.

only windows vbr v13.0.1 will support compatibility with v12

same as for another plugins - not supported in 13

same as in Sap oracle lugin

mongo db should not be in sap oracle guide as well as in another plugin sections

veeam data cloud vault is currently not supported in v13. it is going to be supported only in 13.0.1

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.

now at Rutgers https://archives.libraries.rutgers.edu/repositories/11/resources/964

The reason a web site was created will help you understand the tone better and the purpose.

The advantage is that it's (usually) very up to date and can hold the most recent information whereas a printed material needs to be a bit older in order to be considered creditable. The disadvantage is that the publisher of a website could be anyone, and you need to check their expertese on the topic.

The benefit is that these are usually more credible because more editors look at it to determine its truth. However, that doesn't mean it would be best for our writing purposes, think about your audience vs their audience.

Skepticism is a useful tool in helping us shift thorough sources to find the reliable ones. We need this to help boost our writings because we need the right findings.

Adjusting your resume to each job application or opportunity is important to show your fit for a job. There isn't a "universal resume" that can be used for every circumstance even if all of your skills are the same.

I didn't know this. I will definitely try to gamify my resume now.

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

collocation

understood without being expressed directly

a style or a temporary mood

written document signed by a large number of people that asks somebody in a position of authority to do or change something

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

to approve something officially

to agree to something that you would not agree to before, after someone has persuaded you or threatened you

very emotional and energetic, but not sincere or without real meaning

a decision

so obvious that it can easily be seen or known, or (of a feeling) so strong that it seems as if it can be touched or physically felt

someone who has been chosen officially for a job or responsibility

having a lot of problems or difficulties

to try to make a bad situation better

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.

I guess layout would also depend on the size of the classroom that you have as well as how many kids you have in all of your classes. In my Pre-I, the only option that would have worked was the one option she had. I feel like the amount of students played heavily into her decision to have more of a traditional layout. Also, it allowed her to move up and down the aisle's. Which she did when the students were being a little rambunctious. It seemed to calm them down once she moved closer to the more talkative students. So an important factor that I need to keep in mind would be that I need to be able to move freely throughout the classroom. Does flexible seating work well in the high school?

I've been telling my husband that I need to save money for my future classroom decorations. He just keeps reassuring me that the school has most of the stuff I would need. I am curious as to what options the schools really have. Will I have to provide storage options, my desk, my chair etc? I have bought some things for the walls, but not sure if I should be on the lookout for other supplies.

Since I will be teaching history of some sort, I have been contemplating a split classroom where half of the students are facing the other half. Has anyone had experience with their classroom being laid out in this manner? It felt like a suitable option for debates and such. I have only had traditional classroom layouts with the teachers desk in the back and the students face the front.

too much lipid--> in liver disease and thalassemia, target cells can form

acute chest syndrome explains murmur and chest pain

These are the "Blessings for Obedience"

Nebula 75 audio dramas

FALSELY normal instead of LOW: G6PD-deficient red blood cells, specifically the older, more deficient ones, are destroyed during hemolysis, leaving behind younger, reticulocyte-rich cells with higher G6PD activity. A true G6PD deficiency is diagnosed using a genotyping test or retesting the patient after the hemolytic episode has resolved

Heinz bodies

TMP-SMX, fava bean, dapsone, primaquine, moth balls

heme is source of bilirubin, which is probably source of elevated unconjugated bilirubin.

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).

higher reticulocyte=hemolysis

need LDH, bilirubin, haptoglobin, blood smear, Coombs test (looking for antibodies stuck to RBC), DAT (direct= RBCs have antibiotics already attached), IAT (indirect=Ab in plasma)

summary: this case is a 62-year old male with a history of urinary tract trouble and anemic episode, presenting with dysuria, fever, CVA tenderness, with concern for urinary tract infection.

look for stones or prostatic obstruction

mechanical obstruction can lead to increased infection risk

Can make up if absent, in-class/group work

because we can do anytime, no absence leinency

Given in lab sections, 3 midterms, no final

Can do any time

great

great

十分な

dreaming hypothesis: dreams are like being mad because what is thought to be perceived is not real

(4 946)

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

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.

I use descriptive grammar in my everyday writing but i also try to be prescriptive as well.

Silly Things To Laugh About

Things We Dreamt Of Seeing As Kids

Simple, Wholesome Things To Be Happy About

Unusual Facts That Aren't True But Are Worth Being Happy For

Small Characters To Be Happy About

Cartoon Worlds We Would Love To Live In

Things To Celebrate Every Day

Everyday Moments To Be Happy About

Board & Card Games To Play On A Rainy Day

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.

A claim of policy is the easiest claim to spot,

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 person who criticize, objects to, or opposes something.

A text not yet published. When I am starting to write something, it helps to use template.

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?

Why monthly? Is monthly variability low for each snow station?

Clarify Rossby waves.

Celebrity Luxury Treatment To Be Happy About

Moments As Sweet As Candy

Red Carpet Luxuries To Be Happy About

Cosy Things To Do On A Snow Day

Cheesecake Recipes To Be Happy About

Cosy Comforts To Be Happy About

Ways To Bring The Outside In

Ways To Make Bedtime More Soothing & Relaxing

Imaginative Fantasies To Journal About

Feel Good Movies To Be Happy About

Creative Ways To Banish Boredom

Ways To Recover From A Long, Hard Sleep

Luxuries To Experience When You're In The Bath

Ways To Make Life Comfy

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Reply to the reviewers

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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.

What are Goodwill's competitive advantages?

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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.