Deactivate multi-factor authentication on your old device (the user isn't deactivating the device itself)

change the value of variable

Is there any way to get screenshots of these instructions from SAP? (There might not be).

It might be helpful to have a list of the everyday programs the user will need to install, along with links to their corresponding pages in the help portal.

The virtual desktop's toolbar?

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

Learn more at Review Commons

---

Reply to the reviewers

The work would have significant impact in the cilia community, if the conclusion is correct. This reviewer, however, has a concern about the authors concluding the presence/absence of TZ, based on only B9D1 and the H-shaped body among nine doublet microtubules. First, is it really established how the structure of Xenopus embryo TZ is? While Chlamydomonas is well known to have a H-shaped TZ, other species have different form inside the 9+0 doublet, or no feature (Comparison of TZ from various species in Dennis Diener https://doi.org/10.1016/B978-0-12-822508-0.00007-1). Fig.2B of this manuscript shows visible densities in the panel "Pre", but it does not look like an H-shape. The tomogram of TZ before deciliation seems clearer (but judging from wavy MTs and membrane in this tomogram, there could be unevenness of embedding and staining), while the tomogram after deciliation is thin and does not cover the entire width. Therefore it is not sure that absence of TZ can be concluded. If the author claims Xenopus embryo cilia have a H-shaped TZ, they have to provide multiple micrographs (ideally tomogram or serial section TEM to cover the entire TZ structure) and/or past literature on Xenopus embryo TZ. B9D1 is likely a membrane associated protein (according to their deciliation by detergent and mechanical force). This may mean B9D1 is located on or near the membrane, in vicinity to TZ, and thus binds to TZ after the main part of TZ is built. In this case, it is risky to judge presence of TZ based on B9D1. Also in this point, TEM imaging will be helpful to confirm the authors' conclusion.

RESPONSE: We appreciate the reviewer’s thoughtful comments on the loss of TZ upon deciliation and its absence during the initial regeneration period. The reviewer is right in their assessment that the TZ of Xenopus cilia has not been well defined before in any manuscript. We want the reviewer to consider that our goal was not to define the TZ in Xenopus but to study deciliation and how cilia regenerate in a vertebrate model system for the first time. We unexpectedly discovered that cilia are deciliated distal to the basal body at the plasma membrane, and the “H-shaped structure,” similar to TZ, was also removed and did not come back for first hour during regeneration. Given this surprising observation, we felt obliged to study and explain our results. To that end, we explored different resources (antibodies and markers of TZ) and different methods over 6 years trying to define TZ in Xenopus.

Our conclusion about the TZ structure came from multiple lines of evidence from our experiments and published literature, including the similarity in structure compared to other organisms and its physical location in the cilium. Specifically, 1) In a review of the basal bodies, Mitchell indirectly suggested that the electron-dense “H-shaped” structure could be a TZ in Xenopus. 2) The electron-dense “H” shaped structure in Chlamydomonas is similar, if not identical, to that shown in Xenopus cilia. 3) The physical location of TZ is always shown to be distal to the basal body and transition fibers (except in clubmoss Phylloglosum) while proximal to the central pair. The electron-dense “H-shaped” structure in Xenopus fulfills these criteria, suggesting that this structure is the TZ in Xenopus. 4) The TZ bonafide protein B9D1 is localized distal to Chibby, which labels the distal end of the basal body, suggesting that the TZ is localized distal to the basal body. Moreover, the loss of an “H-shaped” structure determined using TEM and tomograms corresponds to the loss of the B9D1 signal, further strengthening the conclusion that the H-shaped structure is the TZ.

We will include serial sectioning and imaging of multiple Xenopus cilia in control and 0hr (after deciliation) to address this reviewer's concerns further. Our preliminary data has suggested that the ciliary membrane is tightened around this electron-dense structure, similar to what has been shown before for other organisms like Chlamydomonas. and thus boosts our confidence that this structure likely corresponds to the TZ in Xenopus.

The reviewer has raised a concern that “the tomogram after deciliation is thin and does not cover the entire width. Therefore, it is not sure that absence of TZ can be concluded”. We note that even if the tomograms do not go through the entire cilium (supplementary videos 2 and 3), it does go through more than the center of cilium as seen by the presence of central pair microtubules and we can observe that the electron-dense “H-shaped” structure is not present in these cilia. Further, in the supplementary videos 5 and 6, even if the tomogram again only covers half of the cilia, we can see the presence of the structure, confirming that our tomograms can demonstrate the presence or absence of the H-shaped structure confidently. We have also provided TEM sections in addition to the tomograms to show the same result.

The Reviewer has commented that “B9D1 is located on or near the membrane, in vicinity to TZ, and thus binds to TZ after the main part of TZ is built”. This reviewer is correct in their assessment. This is why we argue that the presence or absence of B9D1 may be a good marker for understanding the presence or absence of TZ assembly.

TIMELINE: We are performing additional serial TEM in the control and deciliated (0hr.) embryos to address the reviewer’s concern. We will need 1 month to finish these experiments.

Their discussion about length/number of cilia and force generated by cilia is interesting, but in the context of this research, this reviewer is skeptical about its value. The calcium induced deciliation is not a physiological phenomenon, but an artificial event (please correct if I am wrong). The argument how length and number of cilia are regulated upon deciliation makes sense only in case deciliation happens regularly and the species must optimize themselves to survive. The argument about possible passway of protein transport to control ciliary number and length (Line408-) seems, although it is an interesting topic in general, not suitable in this manuscript. For this reviewer's view, it is relatively straightforward to interpret the result of cilia number/length under normal growth, without new protein expression (CHX), with protein degradation blocked. Cilia will extend when components are provided. Growth will slow down when it is exhausted. Existing cilia start degrading, when they lack proteins, which are necessary for turn-over. With the current experimental output, there is no point to describe redistribution of proteins.

RESPONSE: We appreciate the reviewer’s comment; however, we would like to argue that different methods of deciliation have been used in different model systems, such as Chlamydomonas, to study cilia regeneration. Although this reviewer may not find some of the experiments and conclusions appropriate for this manuscript, other research groups have found these results interesting. For example, reviewer 2 states, “To support their observations that cilia length is favored over cilia number under conditions of limiting ciliary precursor availability, the authors use a mathematical model that leads to the conclusion that force generation is optimized by increasing cilia length. This is a convincing conclusion and in agreement with other comparable modeling studies performed in the field.” We have already had great discussions about these results with many cilia researchers at multiple conferences. Therefore, we prefer to keep these experiments and results in the manuscript and let readers come to their own conclusions about their importance.

Minor points:

Line65: do they mean "selected few basal bodies"? – we have removed the word “select”

Line73: extracellular flow is not limited to developmental system. – we have altered the statement to add “growth, development and homeostasis”

Line124: alpha-tubulin signal and SEM image – we have added “and scanning electron microscopy (SEM)”

Line139: Could you define explicitly the two hypotheses? – Now, we have reworded the sentences to clarify the two hypotheses. “Therefore, we considered two hypotheses: First, Xenopus MCCs regenerate cilia or second, Xenopus depend on stem cell-based replacement of damaged MCCs.”

Line164: 10,31-33 are not suitable citation for the location of calcium induced deciliation in Chlamydomonas. cite Sanders and Salisbury JCB 108, 1751 – We have changed the citation.

Line181: Later -> latter – We have changed the text.

Line195: by mechanical shearing, B9D1 remained with cilia. They concluded that TZ stays with the axoneme by deciliation. How can they exclude the possibility that mechanical separation works differently from calcium shock? – We do not intend to claim that both calcium-based and mechanical ripping of cilia from cells adopt the same deciliation mechanism, and we have mentioned in line 193 that ‘we adopted an alternative approach of mechanical deciliation’. Using these two methods as complimentary to each other, our aim was to show that TZ is lost by both ciliation methods. For the calcium method, because the membrane is ripped with detergent, we show the loss of TZ by examining the MCCs devoid of cilia. In the mechanical deciliation protocol, since no detergent is involved, we can examine cilia that are likely to have intact membranes and thus maintain a B9D1 signal.

Line214: 1.33uM -> 1.33um - We have made these changes to the text.

__RESPONSE: __All the minor points in the manuscript are addressed.

Overall, the results are well presented and allow strong conclusions to be drawn. The results are based on both immunofluorescence studies and EM analysis. To support their observations that cilia length is favored over cilia number under conditions of limiting ciliary precursor availability, the authors use a mathematical model that leads to the conclusion that force generation is optimized by increasing cilia length. This is a convincing conclusion, and in agreement with other comparable modeling studies performed in the field. It would be fascinating to be able to measure the flow parameters at the cell surface during cilia regeneration to see whether this regeneration actually leads to an increase in the overall flow or force generated by the cilia. But as the authors explain, this is probably a difficult experiment to carry out and appears to be optional in the context of this study.

__RESPONSE: __We thank the reviewer for recognizing and stating that “the results are well presented and allow strong conclusions to be drawn”. We also want to sincerely thank the reviewer for understanding the technical difficulties in performing these experiments.

The authors are apparently only able to detect a single TZ protein, B9D1, to follow the fate of the TZ during the deciliation and reciliation process. In some ways, this provides an incomplete demonstration that all the TZ is indeed removed during deciliation, although this is supported by EM observations. It also provides a limited understanding of the time course of TZ re-formation during reciliation. Given the limitations of antibody availability, could it be possible to express tagged proteins in the animal cap system to track more TZ proteins? In particular, would it be possible to track for example Cby and NPHP proteins. What is the behavior of Cep290? This would greatly reinforce the conclusions on the molecular reorganisation of the TZ after deciliation and during cilia regeneration.

__RESPONSE: __We appreciate this reviewer’s brilliant questions on understanding the time course of TZ re-formation during reciliation. When we started this project and observed that TZ was lost upon deciliation in our preliminary TEM experiment, our first goal was to confirm this outstanding result. Thus, we did more TEMs and EM tomography, used bonafide TZ protein B9D1 to label the structure, and observed its loss upon deciliation. Taken together, we feel highly confident that TZ is lost upon deciliation. To address this reviewer’s concerns, we will performing additional serial TEMs to confirm the loss of TZ after deciliation.

Our next goal was to understand what the reviewer has mentioned, the TZ assembly time course. We started with TEMs at different time points and again saw a surprising result: TZ assembly was delayed compared to cilia axoneme. We were driven by this question of understanding how cilia “put together” the complex structure of TZ structurally and molecularly using EM and fluorescence data. We first attempted a few antibodies, including B9D1, CEP290, MKS5, and NPHP4, to localize to the TZ in the Xenopus cilia. Despite our efforts with different fixation strategies, only B9D1 appeared to localize to the TZ, whereas others did not give any signal or localized at the basal body. Next, we tried localizing TMEM216, TMEM67, and NPHP4 using fluorescent tags, but we again found the same result: they localized to the basal body but not at the TZ. We are perplexed by this result and are pursuing the reasons behind them. However, these experiments are out of the scope of this paper. We want to note that we have used Chibby in our experiments and that it is not lost upon deciliation (Fig S1). This is because Chibby is a distal transition fiber protein (distal end of basal body) and does not extend up to the transition zone.

TIMELINE: To address the reviewer's concern, we are performing additional serial TEM in the control and deciliated (0hr.) embryos. We will attempt to localize CEP290-GFP, requiring approximately 1 month to finish the experiment. However, we would like to note that we cannot guarantee that this experiment will work, as similar experiments with other TZ markers have failed before.

Minor comments

1. Figure 4: The images are poorly defined, and it is difficult to distinguish individual basal bodies and cilia. Therefore, it is not clear how the authors can confidently quantify the number of basal bodies in each condition to construct the graph at the bottom of the figure. In addition, it would be interesting to label the basal body with a centriolar marker to better define it. - Figure 4 labels the Transition Zone protein B9D1 and cilia marker acetylated tubulin and not basal bodies. The graph represents the number of cells with the presence or absence of elongated B9d1 signal.

2. Figure 5: not clear why the graph on the lower right does not include the control at 3 and 6 hrs? Is it because the number is too high and difficult to quantify? – Yes, the reviewer is right. Cilia become too long and too many to quantify their number reliably.

3. References: I would like to draw the authors' attention to studies of deciliation in Paramecia that could be cited in the introduction or discussion of the conservation of this pathway through evolution. – We have added multiple references to paramecia throughout the manuscript. Specifically, we mention that deciliation and regeneration in unicellular models like paramecia have added to our understanding of ciliogenesis. Line 102 “While it is important to remember that regeneration of cilia may not be identical to de novo assembly, cilia regeneration studies in Chlamydomonas reinhardtii, Paramecium and Tetrahymena etc., have provided significant insights into ciliogenesis, g., cargo transport, the presence of precursor pool, regulation of ciliary gene expression.18,23–26”. Further, we also added the reference to paramecia in results, line164 “Next, we determined the location where the deciliation treatment severed cilia. Unicellular models such as Chlamydomonas, Paramecium and Tetrahymena lose cilia distal to the TZ and below the central pair (CP) microtubules33.”. We also add discussion on the importance of TZ in paramecia, line 203 “Interestingly in Paramecium also a unicellular multiciliated cell, displays constant shedding of cilia when TZ proteins are depleted.25”. These statements have been supported by the following studies that are now cited in the manuscript: Machemer and Ogura 1979 Journal of Cell Physiology (10.1113/jphysiol.1979.sp012990) and Gogenddeau et al., Plos Biology (10.1371/journal.pbio.3000640).

RESPONSE: All the minor points in the manuscript are addressed.

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

The manuscript by Rao et al. focuses on determining the mechanism of cilia regeneration using Xenopus mucociliary epithelium. The authors employ a simple yet powerful approach to trigger deciliation of multiciliated cells, enabling them to study the mechanism of cilia regeneration. This research has a significant impact on the field of cilia biology and enhances our understanding of ciliopathies. Through detailed cell biological methodologies, the authors obtained intriguing results, including the finding that deciliation removes the transition zone and that cilia repair precedes the transition zone assembly. Additionally, the authors demonstrate that IFT proteins involved in cilia construction concentrate at selected basal bodies. Although there are open questions that the authors also highlight, this manuscript provides solid, pioneering insights into the process of cilia regeneration in vivo.

Significance

The manuscript characterizes the mechanism of cilia regeneration, providing new insights into processes that could be harnessed to restore ciliary function in patients suffering from chronic respiratory diseases.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

Summary

This manuscript investigates how cilia regenerate in multi-ciliated cells. The authors have exploited an original multi-ciliated cell system derived from the Xenopus embryonic cap and use chemical and mechanical deciliation to understand the different steps of cilia regeneration. In this model, they show that cilia are excised just above the BB and below the ciliary transition zone. Their results indicate that during ciliary regeneration, axoneme reassembly precedes TZ formation and that ciliary reassembly relies on de novo protein synthesis. In the context of limited protein synthesis, cells regenerate fewer cilia, but of almost the same size as control cells, suggesting the existence of a cell control system to maximise force generation. Mathematical modelling of the forces exerted by defined numbers of cilia of different lengths supports this hypothesis.

Major comments

Overall, the results are well presented and allow strong conclusions to be drawn. The results are based on both immunofluorescence studies and EM analysis. To support their observations that cilia length is favored over cilia number under conditions of limiting ciliary precursor availability, the authors use a mathematical model that leads to the conclusion that force generation is optimized by increasing cilia length. This is a convincing conclusion, and in agreement with other comparable modeling studies performed in the field. It would be fascinating to be able to measure the flow parameters at the cell surface during cilia regeneration to see whether this regeneration actually leads to an increase in the overall flow or force generated by the cilia. But as the authors explain, this is probably a difficult experiment to carry out and appears to be optional in the context of this study.

The authors are apparently only able to detect a single TZ protein, B9D1, to follow the fate of the TZ during the deciliation and reciliation process. In some ways, this provides an incomplete demonstration that all the TZ is indeed removed during deciliation, although this is supported by EM observations. It also provides a limited understanding of the time course of TZ re-formation during reciliation. Given the limitations of antibody availability, could it be possible to express tagged proteins in the animal cap system to track more TZ proteins? In particular, would it be possible to track for example Cby and NPHP proteins. What is the behavior of Cep290? This would greatly reinforce the conclusions on the molecular reorganisation of the TZ after deciliation and during cilia regeneration.

Minor comments

Figure 4: The images are poorly defined and it is difficult to distinguish individual basal bodies and cilia. It is therefore not clear how the authors can confidently quantify the number of basal bodies in each condition to construct the graph at the bottom of the figure. In addition, it would be interesting to label the basal body with a centriolar marker to better define the basal body.

Figure 5: not clear why the graph on the lower right does not include the control at 3 and 6 hrs? Is it because the number is too high and difficult to quantify?

References: I would like to draw the authors' attention to studies of deciliation in Paramecia that could be cited in the introduction or discussion of the conservation of this pathway through evolution.

Significance

The mechanisms of deciliation and re-ciliation have mostly been studied in protozoa (Chlamydomonas, Paramecia) or in primary ciliated cell cultures. Only a few studies have described deciliation in multiciliated cells, such as sea urchins, or physiological deciliation in the oviduct. The Xenopus deciliation system described here has already been used to determine the dynamics of IFT proteins during ciliogenesis or to define the ciliary proteome. In this study, the authors go one step further by describing more precisely which part of the cilium is shed upon induction of deciliation and the dynamics of the recruitment of the Tip and of the TZ proteins.

This study provides a completely new perspective on the deciliation process:

1. the authors show that, contrary to what is generally accepted from protozoan studies, the deciliation process, in Xenopus multiciliated cells, expels the TZ, leaving only the basal body in the cell;
2. While ciliogenesis is described in various models to begin with the formation of the TZ, in this Xenopus system the TZ maturates after the onset of axonemal elongation, calling into question the precise function of the TZ in axonemal elongation. The observations could be further strengthened by analyzing more TZ proteins to better understand the time course of events involved in the deciliation-reciliation program.

The protocol used to deciliate Xenopus multiciliated cells has been described in previous manuscripts. Its use here reveals striking differences in the deciliation-reconciliation pathways from what is known in the field. It provides new conceptual perspectives for researchers working on the basic mechanisms of ciliogenesis. Note that, as a geneticist and specialist in ciliogenesis using various model organisms, I am not fully competent to critically evaluate the mathematical models developed in this study.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

In this manuscript entitled "Machanisms of cilia regeneration in Xenopus multiciliated epithelium in vivo", the authors mostly focus on the question, whether TZ (transition zone of cilia) plays an essential role for ciliogenesis during cilia regeneration in multiciliated cells. They used Xenopus embryo as a system to examine this question. While cilia regeneration has been actively studies in unicellular green algae, Chlamydomonas reinhardtii, the mechanism of cilia regeneration is not known yet. Their approach is to investigate cells after deciliation by calcium shock, based on a TZ protein B9D1, as well as ultrastructure observation using conventional electron microscopy.

The authors observed loss of signal from B9D1 and H-shaped objects, which is typical for TZ, upon deciliation induced by calcium and also during the following re-growth of cilia. Based on these experiments they concluded that TZ formation is not necessary for cilia regeneration in multiciliated cells, differently from Chlamydomonas. They further conducted experiments to pursue source of component proteins for re-generation. They compared CHX-treated cells (lacking new protein production) and CHX/MG132 (reduced protein degradation) treated cells to find how the massive amount of protein components upon re-ciliation for multiple cilia will be supplied and regulated. This reviewer found the results of the experiments clearly presented and conducted properly.

The work would have significant impact in the cilia community, if the conclusion is correct. This reviewer, however, has a concern about the authors concluding the presence/absence of TZ, based on only B9D1 and the H-shaped body among nine doublet microtubules. First, is it really established how the structure of Xenopus embryo TZ is? While Chlamydomonas is well known to have a H-shaped TZ, other species have different form inside the 9+0 doublet, or no feature (Comparison of TZ from various species in Dennis Diener https://doi.org/10.1016/B978-0-12-822508-0.00007-1). Fig.2B of this manuscript shows visible densities in the panel "Pre", but it does not look like an H-shape. The tomogram of TZ before deciliation seems clearer (but judging from wavy MTs and membrane in this tomogram, there could be unevenness of embedding and staining), while the tomogram after deciliation is thin and does not cover the entire width. Therefore it is not sure that absence of TZ can be concluded. If the author claims Xenopus embryo cilia have a H-shaped TZ, they have to provide multiple micrographs (ideally tomogram or serial section TEM to cover the entire TZ structure) and/or past literature on Xenopus embryo TZ. B9D1 is likely a membrane associated protein (according to their deciliation by detergent and mechanical force). This may mean B9D1 is located on or near the membrane, in vicinity to TZ, and thus binds to TZ after the main part of TZ is built. In this case, it is risky to judge presence of TZ based on B9D1. Also in this point, TEM imaging will be helpful to confirm the authors' conclusion.

Their discussion about length/number of cilia and force generated by cilia is interesting, but in the context of this research, this reviewer is skeptical about its value. The calcium induced deciliation is not a physiological phenomena, but an artificial event (please correct if I am wrong). The argument how length and number of cilia are regulated upon deciliation makes sense only in case deciliation happens regularly and the species must optimize themselves to survive. The argument about possible passway of protein transport to control ciliary number and length (Line408-) seems, although it is an interesting topic in general, not suitable in this manuscript. For this reviewer's view, it is relatively straightforward to interpret the result of cilia number/length under normal growth, without new protein expression (CHX), with protein degradation blocked. Cilia will extend when components are provided. Growth will slow down when it is exhausted. Existing cilia start degrading, when they lack proteins, which are necessary for turn-over. With the current experimental output, there is no point to describe redistribution of proteins.

Minor points:

Line65: do they mean "selected few basal bodies"?

Line73: extracellular flow is not limited to developmental system.

Line124: alpha-tubulin signal and SEM image

Line139: Could you define explicitly the two hypotheses?

Line164: 10,31-33 are not suitable citation for the location of calcium induced deciliation in Chlamydomonas. cite Sanders and Salisbury JCB 108, 1751

Line181: Later -> latter

Line195: by mechanical shearing, B9D1 remained with cilia. They concluded that TZ stays with the axoneme by deciliation. How can they exclude the possibility that mechanical separation works differently from calcium shock?

Line214: 1.33uM -> 1.33um

Significance

The work would have significant impact in the cilia community, if the conclusion is correct. Their discussion about length/number of cilia and force generated by cilia is interesting, but in the context of this research, this reviewer is skeptical about its value.

Would it be correct to say "you will need to repeat these steps every time you reset your Office365 password?"

This could also make for a good entry in the troubleshooting field. What happens if the user doesn’t re-configure these settings after changing their password?)

Is there a reason this isn't in the "before you begin" section?

This might be a good place to explain the "comment" function in Plunet that lets users explain any discrepancies in their hours.

We cannot explain our own actions faithfully.

We're going to understand and be able to explain the actions of a super intelligence?

To me, this is the biggest issue. In the face of deception, behavior tests fail.

• variable names only starts with alphabetic character
• keywords or reserved words cannot be used as the name of variable

easy to use

'=' means set up variable

represents either 0 or 1

creating a vairable

represent names by characters

represent true or false

non digit number. i.e.) 6.3, 60293.93032

represent integer

reference to an object of a class

primitive types of variable

saving through memory of computer

進化ゲーム理論は均衡に到達する過程に興味がある

（無作為に選ばれた）新たな対戦相手

生物学的解釈の包含： 社会的解釈（個人の戦略変更）は、生物学的解釈（死亡と誕生）を包含できます。つまり、個体の「戦略の変更」を「その個体の死亡と新しい戦略を持つ個体の誕生」と見なすことができます。

test

my first annotation.

Author response:

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

(1) Though we cannot survey all mutants, our observation that 774 genetically diverse adaptive mutants converge at the level of phenotype is important. It adds to growing evidence (see PMID33263280, PMID37437111, PMID22282810, PMID25806684) that the genetic basis of adaptation is not as diverse as the phenotypic basis. This convergence could make evolution more predictable.

(2) Previous fitness competitions using this specific barcode system have been run for greater than 25 generations (PMID33263280, PMID27594428, PMID37861305, PMID27594428). We measure fitness per cycle, rather than per generation, so our fitness advantages are comparable to those in the aforementioned studies, including Venkataram and Dunn et al. (PMID27594428).

(3) Our results remain the same upon removing the ~150 lineages with the noisiest fitness inferences, including those the reviewer mentions (see Figure S7).

(4) We agree that there are likely more than the 6 clusters that we validated with follow-up studies (see Discussion). The important point is that we see a great deal of convergence in the behavior of diverse adaptive mutants.

(5) The growth curves requested by the reviewer were included in our original manuscript; several more were added in the revision (see Figures 5D, 5E, 7D, S11B, S11C).

---

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

Public Reviews.

Reviewer #1 (Public Review): 

Summary: 

In their manuscript, Schmidlin, Apodaca, et al try to answer fundamental questions about the evolution of new phenotypes and the trade-offs associated with this process. As a model, they use yeast resistance to two drugs, fluconazole and radicicol. They use barcoded libraries of isogenic yeasts to evolve thousands of strains in 12 different environments. They then measure the fitness of evolved strains in all environments and use these measurements to examine patterns in fitness trade-offs. They identify only six major clusters corresponding to different trade-off profiles, suggesting the vast genotypic landscape of evolved mutants translates to a highly constrained phenotypic space. They sequence over a hundred evolved strains and find that mutations in the same gene can result in different phenotypic profiles.  

Overall, the authors deploy innovative methods to scale up experimental evolution experiments, and in many aspects of their approach tried to minimize experimental variation. 

We thank the reviewer for this positive assessment of our work. We are happy that the reviewer noted what we feel is a unique strength of our approach: we scaled up experimental evolution by using DNA barcodes and by exploring 12 related selection pressures.  Despite this scaling up, we still see phenotypic convergence among the 744 adaptive mutants we study. 

Weaknesses: 

(1) One of the objectives of the authors is to characterize the extent of phenotypic diversity in terms of resistance trade-offs between fluconazole and radicicol. To minimize noise in the measurement of relative fitness, the authors only included strains with at least 500 barcode counts across all time points in all 12 experimental conditions, resulting in a set of 774 lineages passing this threshold. This corresponds to a very small fraction of the starting set of ~21 000 lineages that were combined after experimental evolution for fitness measurements. 

This is a misunderstanding that we clarified in this revision. Our starting set did not include 21,000 adaptive lineages. The total number of unique adaptive lineages in this starting set is much lower than 21,000 for two reasons. 

First, ~21,000 represents the number of single colonies we isolated in total from our evolution experiments. Many of these isolates possess the same barcode, meaning they are duplicates. Second, and perhaps more importantly, most evolved lineages do not acquire adaptive mutations, meaning that many of the 21,000 isolates are genetically identical to their ancestor. In our revised manuscript, we explicitly stated that these 21,000 isolated lineages do not all represent unique, adaptive lineages. We changed the word “lineages” to “isolates” where relevant in Figure 2 and the accompanying legend. And we have added the following sentence to the figure 2 legend (line 212), “These ~21,000 isolates do not represent as many unique, adaptive lineages because many either have the same barcode or do not possess adaptive mutations.”

More broadly speaking, several previous studies have demonstrated that diverse genetic mutations converge at the level of phenotype and have suggested that this convergence makes adaptation more predictable (PMID33263280, PMID37437111, PMID22282810, PMID25806684). Most of these studies survey fewer than 774 mutants. Further, our study captures mutants that are overlooked in previous studies, such as those that emerge across subtly different selection pressures (e.g., 4 𝜇g/ml vs. 8 𝜇g/ml flu) and those that are undetectable in evolutions lacking DNA barcodes. Thus, while our experimental design misses some mutants (see next comment), it captures many others. Thus, we feel that “our work – showing that 774 mutants fall into a much smaller number of groups” is important because it “contributes to growing literature suggesting that the phenotypic basis of adaptation is not as diverse as the genetic basis (lines 176 - 178).”

As the authors briefly remark, this will bias their datasets for lineages with high fitness in all 12 environments, as all these strains must be fit enough to maintain a high abundance. 

We now devote 19 lines of text to discussing this bias (on lines 160 - 162, 278-284, and in more detail on 758 - 767).

We walk through an example of a class of mutants that our study misses. One lines 759 - 763, we say, “our study is underpowered to detect adaptive lineages that have low fitness in any of the 12 environments. This is bound to exclude large numbers of adaptive mutants. For example, previous work has shown some FLU resistant mutants have strong tradeoffs in RAD (Cowen and Lindquist 2005). Perhaps we are unable to detect these mutants because their barcodes are at too low a frequency in RAD environments, thus they are excluded from our collection of 774.”

In our revised version, we added more text earlier in the manuscript that explicitly discusses this bias. Lines 278 – 283 now read, “The 774 lineages we focus on are biased towards those that are reproducibly adaptive in multiple environments we study. This is because lineages that have low fitness in a particular environment are rarely observed >500 times in that environment (Figure S4). By requiring lineages to have high-coverage fitness measurements in all 12 conditions, we may be excluding adaptive mutants that have severe tradeoffs in one or more environments, consequently blinding ourselves to mutants that act via unique underlying mechanisms.”

Note that while we “miss” some classes of mutants, we “catch” other classes that may have been missed in previous studies of convergence. For example, we observe a unique class of FLU-resistant mutants that primarily emerged in evolution experiments that lack FLU (Figure 3). Thus, we think that the unique design of our study, surveying 12 environments, allows us to make a novel contribution to the study of phenotypic convergence.

One of the main observations of the authors is phenotypic space is constrained to a few clusters of roughly similar relative fitness patterns, giving hope that such clusters could be enumerated and considered to design antimicrobial treatment strategies. However, by excluding all lineages that fit in only one or a few environments, they conceal much of the diversity that might exist in terms of trade-offs and set up an inclusion threshold that might present only a small fraction of phenotypic space with characteristics consistent with generalist resistance mechanisms or broadly increased fitness. This has important implications regarding the general conclusions of the authors regarding the evolution of trade-offs. 

We agree and discussed exactly the reviewer’s point about our inclusion threshold in the 19 lines of text mentioned previously (lines 160 - 162, 278-284, and 758 - 767). To add to this discussion, and avoid the misunderstanding the reviewer mentions, we added the following strongly-worded sentence to the end of the paragraph on lines 749 – 767 in our revised manuscript: “This could complicate (or even make impossible) endeavors to design antimicrobial treatment strategies that thwart resistance”. 

More generally speaking, we set up our study around Figure 1, which depicts a treatment strategy that works best if there exists but a single type of adaptive mutant. Despite our inclusion threshold, we find there are at least 6 types of mutants. This diminishes hopes of designing simple multidrug strategies like Figure 1. Our goal is to present a tempered and nuanced discussion of whether and how to move forward with designing multidrug strategies, given our observations. On one hand, we point out how the phenotypic convergence we observe is promising. But on the other hand, we also point out how there may be less convergence than meets the eye for various reasons including the inclusion threshold the reviewer mentions (lines 749 - 767).

We have made several minor edits to the text with the goal of providing a more balanced discussion of both sides. For example, we added the words, “may yet” to the following sentences on lines 32 – 36 of the abstract: “These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance.”

(2) Most large-scale pooled competition assays using barcodes are usually stopped after ~25 to avoid noise due to the emergence of secondary mutations. 

The rate at which new mutations enter a population is driven by various factors such as the mutation rate and population size, so choosing an arbitrary threshold like 25 generations is difficult. 

We conducted our fitness competition following previous work using the Levy/Blundell yeast barcode system, in which the number of generations reported varies from 32 to 40 (PMID33263280, PMID27594428, PMID37861305, see PMID27594428 for detailed calculation of the fraction of lineages biased by secondary mutations in this system). 

The authors measure fitness across ~40 generations, which is almost the same number of generations as in the evolution experiment. This raises the possibility of secondary mutations biasing abundance values, which would not have been detected by the whole genome sequencing as it was performed before the competition assay. 

Previous work has demonstrated that in this evolution platform, most mutations occur during the transformation that introduces the DNA barcodes (Levy et al. 2015). In other words, these mutations are already present and do not accumulate during the 40 generations of evolution. Therefore, the observation that we collect a genetically diverse pool of adaptive mutants after 40 generations of evolution is not evidence that 40 generations is enough time for secondary mutations to bias abundance values.

We have added the following sentence to the main text to highlight this issue (lines 247 - 249): “This happens because the barcoding process is slightly mutagenic, thus there is less need to wait for DNA replication errors to introduce mutations (Levy et al. 2015; Venkataram et al. 2016).”

We also elaborate on this in the method section entitled, “Performing barcoded fitness competition experiments,” where we added a full paragraph to clarify this issue (lines 972 - 980).

(3) The approach used by the authors to identify and visualize clusters of phenotypes among lineages does not seem to consider the uncertainty in the measurement of their relative fitness. As can be seen from Figure S4, the inter-replicate difference in measured fitness can often be quite large. From these graphs, it is also possible to see that some of the fitness measurements do not correlate linearly (ex.: Med Flu, Hi Rad Low Flu), meaning that taking the average of both replicates might not be the best approach.  Because the clustering approach used does not seem to take this variability into account, it becomes difficult to evaluate the strength of the clustering, especially because the UMAP projection does not include any representation of uncertainty around the position of lineages. This might paint a misleading picture where clusters appear well separate and well defined but are in fact much fuzzier, which would impact the conclusion that the phenotypic space is constricted. 

Our noisiest fitness measurements correspond to barcodes that are the least abundant and thus suffer the most from stochastic sampling noise. These are also the barcodes that introduce the nonlinearity the reviewer mentions. We removed these from our dataset by increasing our coverage threshold from 500 reads to 5,000 reads. The clusters did not collapse, which suggests that they were not capturing this noise (Figure S7B).

More importantly, we devoted 4 figures and 200 lines of text to demonstrating that the clusters we identified capture biologically meaningful differences between mutants (and not noise). We have modified the main text to point readers to figures 5 through 8 earlier, such that it is more apparent that the clustering analysis is just the first piece of our data demonstrating convergence at the level of phenotype.

(4) The authors make the decision to use UMAP and a gaussian mixed model to cluster and represent the different fitness landscapes of their lineages of interest. Their approach has many caveats. First, compared to PCA, the axis does not provide any information about the actual dissimilarities between clusters. Using PCA would have allowed a better understanding of the amount of variance explained by components that separate clusters, as well as more interpretable components. 

The components derived from PCA are often not interpretable. It’s not obvious that each one, or even the first one, will represent an intuitive phenotype, like resistance to fluconazole.  Moreover, we see many non-linearities in our data. For example, fitness in a double drug environment is not predicted by adding up fitness in the relevant single drug environments. Also, there are mutants that have high fitness when fluconazole is absent or abundant, but low fitness when mild concentrations are present. These types of nonlinearities can make the axes in PCA very difficult to interpret, plus these nonlinearities can be missed by PCA, thus we prefer other clustering methods. 

Still, we agree that confirming our clusters are robust to different clustering methods is helpful. We have included PCA in the revised manuscript, plotting PC1 vs PC2 as Figure S9 with points colored according to the cluster assignment in figure 4 (i.e. using a gaussian mixture model). It appears the clusters are largely preserved.

Second, the advantages of dimensional reduction are not clear. In the competition experiment, 11/12 conditions (all but the no drug, no DMSO conditions) can be mapped to only three dimensions: concentration of fluconazole, concentration of radicicol, and relative fitness. Each lineage would have its own fitness landscape as defined by the plane formed by relative fitness values in this space, which can then be examined and compared between lineages. 

We worry that the idea stems from apriori notions of what the important dimensions should be. The biology of our system is unfortunately not intuitive. For example, it seems like this idea would miss important nonlinearities such as our observation that low fluconazole behaves more like a novel selection pressure than a dialed down version of high fluconazole. 

Third, the choice of 7 clusters as the cutoff for the multiple Gaussian model is not well explained. Based on Figure S6A, BIC starts leveling off at 6 clusters, not 7, and going to 8 clusters would provide the same reduction as going from 6 to 7. This choice also appears arbitrary in Figure S6B, where BIC levels off at 9 clusters when only highly abundant lineages are considered. 

We agree. We did not rely on the results of BIC alone to make final decisions about how many clusters to include. Another factor we considered were follow-up genotyping and phenotyping studies that confirm biologically meaningful differences between the mutants in each cluster (Figures 5 – 8). We now state this explicitly. Here is the modified paragraph where we describe how we chose a model with 7 clusters, from lines 436 – 446 of the revised manuscript:

“Beyond the obvious divide between the top and bottom clusters of mutants on the UMAP, we used a gaussian mixture model (GMM) (Fraley and Raftery, 2003) to identify clusters. A common problem in this type of analysis is the risk of dividing the data into clusters based on variation that represents measurement noise rather than reproducible differences between mutants (Mirkin, 2011; Zhao et al., 2008). One way we avoided this was by using a GMM quality control metric (BIC score) to establish how splitting out additional clusters affected model performance (Figure S6). Another factor we considered were follow-up genotyping and phenotyping studies that demonstrate biologically meaningful differences between mutants in different clusters (Figures 5 – 8). Using this information, we identified seven clusters of distinct mutants, including one pertaining to the control strains, and six others pertaining to presumed different classes of adaptive mutant (Figure 4D). It is possible that there exist additional clusters, beyond those we are able to tease apart in this study.”

This directly contradicts the statement in the main text that clusters are robust to noise, as more a stringent inclusion threshold appears to increase and not decrease the optimal number of clusters. Additional criteria to BIC could have been used to help choose the optimal number of clusters or even if mixed Gaussian modeling is appropriate for this dataset. 

We are under the following impression: If our clustering method was overfitting, i.e. capturing noise, the optimal number of clusters should decrease when we eliminate noise. It increased. In other words, the observation that our clusters did not collapse (i.e.

merge) when we removed noise suggests these clusters were not capturing noise. 

Most importantly, our validation experiments, described below, provide additional evidence that our clusters capture meaningful differences between mutants (and not noise).  

(5) Large-scale barcode sequencing assays can often be noisy and are generally validated using growth curves or competition assays. 

Some types of bar-seq methods, in particular those that look at fold change across two time points, are noisier than others that look at how frequency changes across multiple timepoints (PMID30391162). Here, we use the less noisy method. We also reduce noise by using a stricter coverage threshold than previous work (e.g., PMID33263280), and by excluding batch effects by performing all experiments simultaneously, since we found this to be effective in our previous work (PMID37237236). 

Perhaps also relevant is that the main assay we use to measure fitness has been previously validated (PMID27594428) and no subsequent study using this assay validates using the methods suggested above (see PMID37861305, PMID33263280, PMID31611676, PMID29429618, PMID37192196, PMID34465770, PMID33493203). Similarly, bar-seq has been used, without the suggested validation, to demonstrate that the way some mutant’s fitness changes across environments is different from other mutants (PMID33263280, PMID37861305, PMID31611676, PMID33493203, PMID34596043). This is the same thing that we use bar-seq to demonstrate. 

For all of these reasons above, we are hesitant to confirm bar-seq itself as a valid way to infer fitness. It seems this is already accepted as a standard in our field. However, please see below.

Having these types of results would help support the accuracy of the main assay in the manuscript and thus better support the claims of the authors. 

While we don’t agree that fitness measurements obtained from this bar-seq assay generally require validation, we do agree that it is important to validate whether the mutants in each of our 6 clusters indeed are different from one another in meaningful ways.

Our manuscript has 4 figures (5 - 8) and over 200 lines of text dedicated to validating whether our clusters capture reproducible and biologically meaningful differences between mutants. In the revised manuscript, we added additional validation experiments, such that three figures (Figures 5, 7 and S11) now involve growth curves, as the reviewer requested. 

Below, we walk through the different types of validation experiments that are present in our manuscript, including those that were added in this revision.

(1) Mutants from different clusters have different growth curves: In our original manuscript, we measured growth curves corresponding to a fitness tradeoff that we thought was surprising. Mutants in clusters 4 and 5 both have fitness advantages in single drug conditions. While mutants from cluster 4 also are advantageous in the relevant double drug conditions, mutants from cluster 5 are not! We validated these different behaviors by studying growth curves for a mutant from each cluster (Figures 7 and S11), finding that mutants from different clusters have different growth curves. In the revised manuscript, we added growth curves for 6 additional mutants (3 from cluster 1 and 3 from cluster 3), demonstrating that only the cluster 1 mutants have a tradeoff in high concentrations of fluconazole (see Figure 5D & 5E). In sum, this work demonstrates that mutants from different clusters have predictable differences in their growth phenotypes.

(2) Mutants from different clusters have different evolutionary origins: In our original manuscript, we came up with a novel way to ask whether the clusters capture different types of adaptive mutants. We asked whether the mutants in each cluster originate from different evolution experiments. They often do (see pie charts in Figures 5, 6, 7, 8). In the revised manuscript, we extended this analysis to include mutants from cluster 1. Cluster 1 is defined by high fitness in low fluconazole that declines with increasing fluconazole. In our revised manuscript, we show that cluster 1 lineages were overwhelmingly sampled from evolutions conducted in our lowest concentration of fluconazole (see pie chart in new Figure 5A). No other cluster’s evolutionary history shows this pattern (compare to pie charts in figures 6, 7, and 8).

**These pie charts also provide independent confirmation supporting the fitness tradeoffs observed for each cluster in figure 4E. For example, mutants in cluster 5 appear to have a tradeoff in a particular double drug condition (HRLF), and the pie charts confirm that they rarely originate from that evolution condition. This differs from cluster 4 mutants, which do not have a fitness tradeoff in HRLF, and are more likely to originate from that environment (see purple pie slice in figure 7). Additional cases where results of evolution experiments (pie charts) confirm observed fitness tradeoffs are discussed in the manuscript on lines 320 – 326, 594 – 598, 681 – 685.

(3) Mutants from each cluster often fall into different genes: We sequenced many of these mutants and show that mutants in the same gene are often found in the same cluster. For example, all 3 IRA1 mutants are in cluster 6 (Fig 8), both GPB2 mutants are in cluster 4 (Figs 7 & 8), and 35/36 PDR mutants are in either cluster 2 or 3 (Figs 5 & 6). 

(4) Mutants from each cluster have behaviors previously observed in the literature: We compared our sequencing results to the literature and found congruence. For example, PDR mutants are known to provide a fitness benefit in fluconazole and are found in clusters that have high fitness in fluconazole (lines 485 - 491). Previous work suggests that some mutations to PDR have different tradeoffs than others, which corresponds to our finding that PDR mutants fall into two separate clusters (lines 610 - 612). IRA1 mutants were previously observed to have high fitness in our “no drug” condition and are found in the cluster that has the highest fitness in the “no drug” condition (lines 691 - 696). Previous work even confirms the unusual fitness tradeoff we observe where IRA1 and other cluster 6 mutants have low fitness only in low concentrations of fluconazole (lines 702 - 704).

(5) Mutants largely remain in their clusters when we use alternate clustering methods:  In our original manuscript, we performed various different re-clustering and/or normalization approaches on our data (Fig 6, S5, S7, S8, S10). The clusters of mutants that we observe in figure 4 do not change substantially when we re-cluster the data. In our revised manuscript, we added another clustering method: principal component analysis (PCA) (Fig S9).  Again, we found that our clusters are largely preserved.

While these experiments demonstrate meaningful differences between the mutants in each cluster, important questions remain. For example, a long-standing question in biology centers on the extent to which every mutation has unique phenotypic effects versus the extent to which scientists can predict the effects of some mutations from other similar mutations. Additional studies on the clusters of mutants discovered here will be useful in deepening our understanding of this topic and more generally of the degree of pleiotropy in the genotype-phenotype map.

Reviewer #2 (Public Review): 

Summary: 

Schmidlin & Apodaca et al. aim to distinguish mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains. They barcoded a collection of fluconazole-resistant isolates and evolved them in different environments with a view to having relevance for evolutionary theory, medicine, and genotypephenotype mapping. 

Strengths: 

There are multiple strengths to this paper, the first of which is pointing out how much work has gone into it; the quality of the experiments (the thought process, the data, the figures) is excellent. Here, the authors seek to induce mutations in multiple environments, which is a really large-scale task. I particularly like the attention paid to isolates with are resistant to low concentrations of FLU. So often these are overlooked in favour of those conferring MIC values >64/128 etc. What was seen is different genotype and fitness profiles. I think there's a wealth of information here that will actually be of interest to more than just the fields mentioned (evolutionary medicine/theory). 

We are grateful for this positive review. This was indeed a lot of work! We are happy that the reviewer noted what we feel is a unique strength of our manuscript: that we survey adaptive isolates across multiple environments, including low drug concentrations.  

Weaknesses: 

Not picking up low fitness lineages - which the authors discuss and provide a rationale as to why. I can completely see how this has occurred during this research, and whilst it is a shame I do not think this takes away from the findings of this paper. Maybe in the next one! 

We thank the reviewer for these words of encouragement and will work towards catching more low fitness lineages in our next project.

In the abstract the authors focus on 'tradeoffs' yet in the discussion they say the purpose of the study is to see how many different mechanisms of FLU resistance may exist (lines 679-680), followed up by "We distinguish mutants that likely act via different mechanisms by identifying those with different fitness tradeoffs across 12 environments". Whilst I do see their point, and this is entirely feasible, I would like a bit more explanation around this (perhaps in the intro) to help lay-readers make this jump. The remainder of my comments on 'weaknesses' are relatively fixable, I think: 

We have expanded the introduction, in particular lines 129 – 157 of the revised manuscript, to walk readers through the connection between fitness tradeoffs and molecular mechanisms. For example, here is one relevant section of new text from lines 131 - 136: “The intuition here is as follows. If two groups of drug resistant mutants have different fitness tradeoffs, it could mean that they provide resistance through different underlying mechanisms. Alternatively, both could provide drug resistance via the same mechanism, but some mutations might also affect fitness via additional mechanisms (i.e. they might have unique “side-effects” at the molecular level) resulting in unique fitness tradeoffs in some environments.”

In the introduction I struggle to see how this body of research fits in with the current literature, as the literature cited is a hodge-podge of bacterial and fungal evolution studies, which are very different! So example, the authors state "previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms" (lines 129-131) and then cite three papers, only one of which is a fungal research output. However, the next sentence focuses solely on literature from fungal research. Citing bacterial work as a foundation is fine, but as you're using yeast for this I think tailoring the introduction more to what is and isn't known in fungi would be more appropriate. It would also be great to then circle back around and mention monotherapy vs combination drug therapy for fungal infections as a rationale for this study. The study seems to be focused on FLU-resistant mutants, which is the first-line drug of choice, but many (yeast) infections have acquired resistance to this and combination therapy is the norm. 

We ourselves are broadly interested in the structure of the genotype-phenotype-fitness map (PMID33263280, PMID32804946). For example, we are interested in whether diverse mutations converge at the level of phenotype and fitness. Figure 1A depicts a scenario with a lot of convergence in that all adaptive mutations have the same fitness tradeoffs.

The reason we cite papers from yeast, as well as bacteria and cancer, is that we believe general conclusions about the structure of the genotype-phenotype-fitness map apply broadly. For example, the sentence the reviewer highlights, “previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms” is a general observation about the way genotype maps to fitness. So, we cited papers from across the tree of life to support this sentence.  And in the next sentence, where we cite 3 papers focusing solely on fungal research, we cite them because they are studies about the complexity of this map. Their conclusions, in theory, should also apply broadly, beyond yeast.

On the other hand, because we study drug resistant mutations, we hope that our dataset and observations are of use to scientists studying the evolution of resistance. We use our introduction to explain how the structure of the genotype-phenotype-fitness map might influence whether a multidrug strategy is successful (Figure 1).

We are hesitant to rework our introduction to focus more specifically on fungal infections as this is not our primary area of expertise.

Methods: Line 769 - which yeast? I haven't even seen mention of which species is being used in this study; different yeast employ different mechanisms of adaptation for resistance, so could greatly impact the results seen. This could help with some background context if the species is mentioned (although I assume S. cerevisiae). 

In the revised manuscript, we have edited several lines (line 95, 186, 822) to state the organism this work was done with is Saccharomyces cerevisiae. 

In which case, should aneuploidy be considered as a mechanism? This is mentioned briefly on line 556, but with all the sequencing data acquired this could be checked quickly? 

We like this idea and we are working on it, but it is not straightforward. The reviewer is correct in that we can use the sequencing data that we already have. But calling aneuploidy with certainty is tough because its signal can be masked by noise. In other words, some regions of the genome may be sequenced more than others by chance.

Given this is not straightforward, at least not for us, this analysis will likely have to wait for a subsequent paper. 

I think the authors could be bolder and try and link this to other (pathogenic) yeasts. What are the implications of this work on say, Candida infections? 

Perhaps because our background lies in general study of the genotype-phenotype map, we are hesitant about making bold assertions about how our work might apply to pathogenic yeasts. We are hopeful that our work will serve as a stepping-stone such that scientists from that community can perhaps make (and test) such statements.   

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

I found the ideas and the questions asked in this manuscript to be interesting and ambitious. The setup of the evolution and fitness competition experiments was well poised to answer them, but the analysis of the data is not currently enough to properly support the claims made. I would suggest revising the analysis to address the weaknesses raised in the public review and if possible, adding some more experimental validations. As you already have genome sequencing data showing the causal mutation for many mutants across the different clusters, it should be possible for you to reconstruct some of the strains and test validate their phenotypes and cluster identity. 

Yes, this is possible. We added more validation experiments (see figure 5). We already had quite a few validation experiments (figures 5 - 8 and lines 479 - 718), but we did not clearly highlight the significance of these analyses in our original manuscript. Therefore, we modified the text in our revised manuscript in various places to do so. For example, we now make clearer that we jointly use BIC scores as well as validation experiments to decide how many clusters to describe (lines 436 - 446). We also make clearer that our clustering analysis is only the first step towards identifying groups of mutants with similar tradeoffs by using words and phrases like, “we start by” (line 411) and “preliminarily” (line 448) when discussing the clustering analysis.  We also point readers to all the figures describing our validation experiments earlier (line 443), and list these experiments out in the discussion (lines 738 - 741).

Also, please deposit your genome sequencing data in a public database (I am not sure I saw it mentioned anywhere). 

We have updated line 1088 of the methods section to include this sentence: “Whole genome sequences were deposited in GenBank under SRA reference PRJNA1023288.”

Reviewer #2 (Recommendations For The Authors):

I don't think the figures or experiments can be improved upon, they are excellent. There are a few times I feel things are written in a rather confusing way and could be explained better, but also I feel there are places the authors jump from one thing to another really quickly and the reader (who might not be an expert in this area) will struggle to keep up. For example: 

Explaining what RAD is - it is introduced in the methods, but what it is, is not really explained. 

Since the introduction is already very long, we chose not to explain radicicol’s mechanism of action here. Instead, we bring this up later on lines 614 – 621 when it becomes relevant.

More generally, in response to this advice and that from reviewer 1, we also added text to various places in the manuscript to help explain our work more clearly. In particular, we clarified the significance of our validation experiments and various important methodological details (see above). We also better explained the connection between fitness tradeoffs and mechanisms (see above) and added more details about the potential use cases of our approach (lines 142 – 150).

The abstract states "some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, and some mutants to the same gene have different tradeoffs than others". Firstly, this sentence is a bit confusing to read but if I've read it as intended, then is it really surprising? It's difficult for organisms (bacteria and fungi) to develop multiple beneficial mutations conferring drug resistance on the same background, hence why combination antifungal drug therapy is often used to treat infections. 

This is a place where brevity got in the way of clarity. We added a bit of text to make clear why we were surprised. Specifically, we were surprised because not all mutants behave the same. Some resist single drugs AND their combination. Some resist single drugs but not their combination. The sentence in the abstract now reads, “For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others.”

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

Impossible d'avoir le code css sur Figma. On peut deviner le font-family, font-style, font-weight, font-size, mais aucune idée pour trouver le line height...

eLife assessment

This valuable research identifies Smim32 as a new genetic marker for the claustrum and generates transgenic mouse lines aimed at enhancing specificity when studying this brain region. However, the evidence supporting the increased specificity of this marker and its associated transgenic lines is inadequate, as Smim32's specificity to the claustrum is limited. Nevertheless, this work will be of interest to researchers studying the molecular organization of the claustrum.

Math camp was Cathy Davidson's happiest educational experience in her childhood. Loved theoretical maths in grade school too and also she majored in philosophy of mathematics.

Shared experience by author

The New education: redesigning higher education

Related to Higher Education

There is a disturbing new trend happening in Latin America, specifically in Colombia, and that is:

0:07 Men being attracted by extremely beautiful, sexy Colombian and Latin American women and then being drugged, robbed, killed, overdosed, kidnapped, all the crimes that you can possibly think of.

0:21 And a lot of men come to Latin America and thinking that, well, women are just gonna throw themselves at me. And to a certain extent, it is easier than dating in the United States and Germany and all these western countries, and it is better. Women are more beautiful, women are more feminine. But you have to be extremely careful. You have to know where you are.

0:40 Recently, I've seen story after story after story, and this is an alarming trend because these criminals are getting harder. They're getting harsher on their crimes, they're getting more sadistic, and they're also planning a lot better.

0:53 One recent case that I heard of was a German guy who went to Colombia, and on his second day, one girl that he met invited him to cook some food at home, cook some delicious Colombian food, and he said, why not? I'm probably not gonna bang this girl, but I'm gonna go home to her so she can cook me some food. She slipped scopolamine. a drug. into a drink, gave it to him, and then a few hours later, he woke up with his money gone, everything stolen, and even his crypto was stolen.

1:28 They figured out how to get access to his crypto. They stole $15,000, which if you're watching this, you're a wealthy individual, you might think, well, 15K, whatever. But if you have 15 million in your crypto account or your Binance account or in your bank account, they will figure out how to steal it from you.

1:42 Scopolamine is a drug that basically makes you into a little slave, into a little servant, and you'll do whatever the attacker wants. They tell you, go tell the security and tell them that I'm your friend. That literally happens in Colombia.

1:57 People get drugged, and then they go to their Airbnb, to their hotel, and they tell the security, that's my friend. Let them come with me. And they come with you, and they steal absolutely everything because it's called the devil's breath.

2:08 It's a drug that essentially turns you into a zombie, and it has bans all over Latin America, and a lot of police forces in Latin America are trying to ban this drug. They're trying to control the amount of it that is produced, imported, and they have very strict penalties. If you traffic it, if you sell it, you go to prison for a long time.

2:26 But in these countries, these women, they figured out how to get as much money as possible from these expats, from these tourists. You might meet them on Tinder, you might meet an absolutely beautiful girl, and she invites you to some coffee shop. And then after the coffee shop, you think, yeah, this is going so well. She says, let's go cook some food. Let's go meet some of my friends.

2:46 Or in one case that I saw, she invited him with her private driver. She said, oh, I have a private driver. He can take us really nice places. It's not safe here. And the private driver was the guy's kidnapper, ended up kidnapping the guy.

3:00 And there was also another story of a very famous Minnesota man, Asian man, but American, Asian-American. He went to Colombia. He met an absolutely beautiful girl. He showed her off to his family. This Asian American was then kidnapped by this girl, but not immediately, not on the first date. Multiple dates later, after he came back for a second time to Colombia. He met her first, went back home, told everybody about his beautiful girlfriend, came back to Colombia, and then he was kidnapped. The kidnappers asked for $2,000, which is ridiculous, and then they killed him anyway.

3:42 And this can happen to you in Mexico, in Colombia, Brazil. You have to know where you're going. If you're in Mexico, don't go out past eight, 9:00 PM in bad areas. If you're coming to a place like Tulum, stay in the absolute safest areas. Don't think, ah, I know what I'm doing. Ah, they're exaggerating the crime.

4:00 And especially if you're come to Latin America for dating or if you're using dating apps, if it's too good to be true, it literally is.

4:09 If you meet a girl on Tinder and she has bikini pictures all over her profile, if you see that she's pushing hard to meet you, if you see that she's pushing hard to either go to your place or to go to her place. If she wants you to go to her place, it's a red flag anywhere in the world. Even in Russia.

4:24 I heard of a story from a friend of mine. The guy was invited by the girl to her house, and he thought, oh, I'm getting laid with an extremely hot rushing girl. He went to her place. He got his kidney removed. He woke up the next morning, whoa, disoriented without his kidney.

4:38 Well, of course, a girl isn't going to invite you to your place in a random country, especially if she doesn't speak your language. Most girls are not that slutty. That's probably not gonna happen to you, unless you're some footballer or a famous person.

4:51 So you have to keep an eye out for red flags, and you always have to keep in mind that people will try to take advantage of you, especially if you don't speak the language.

5:00 I speak native Spanish. I don't wear my Rolex. I don't wear expensive clothes when I'm going out in Mexico, in Colombia and Argentina. You just don't show off. You speak the language or you try to. If you're going to Brazil, learn Portuguese, because you are a target, especially if you're tall, white.

5:20 I've seen many tall Americans, white as hell, white as paper, and they walk through Mexico like it's their front yard. You are a target. Don't wear expensive jewelry. Here in Tulum, Rolexes get stolen all the time. I was reading through many articles of gun robberies and overall armed violence, and they were all because a person had an extremely expensive something, either a camera, and I'm filming this in an area where they're actually building new buildings. There's security all over the place. It's called Selvazama, absolutely beautiful. They're gonna, well, they're gonna chop up all these trees and they're gonna build new buildings. It's quite safe here. It's actually the best. One of the safest place in Mexico, I would say.

5:56 But if you're going through a rough area, especially if it's at night, you don't wanna have a Rolex. You don't want to have Gucci shoes. I'm wearing my New balance, my little shorts from Zara.

6:08 You have to know where you are. It's not Dubai. It's not Miami. You have to always be aware, and if it's too good to be true, it probably is when dating these Latino women.

6:18 Now, my full game plan. I was born and raised in Puerto Rico, one of the least safest places in the world. I've spent a lot of time in Colombia, in Mexico, in Dominican Republic, many places where people get robbed, they get stabbed, they get lost, they get kidnapped.

6:31 My game plan, one, as I said, do not show off in any way. You think, oh, a Rolex is gonna get me laid. No, it's gonna get you kidnapped. Do not show off. Do not wear expensive clothes. Wear Zara, H&M, even if you're a billionaire, just wear the cheapest clothes possible while looking nice. You don't wanna look like a homeless person, but if you're going through a rough area, it's better to look like a homeless person, so that the attacker thinks: This person is more poor than I am. Why am I gonna attack them?

6:53 Second of all, if you have an expensive phone, like an iPhone, try to not use it that much outside, to be honest, or get a copy. For example, you could buy a second phone, like an iPhone 10 or an iPhone 11, and then you have your iPhone 15 at home. You use the other iPhone for just going outside, Google Maps, WhatsApp, get a local WhatsApp number.

7:10 Again, speak the language. If you speak the language with a Colombian girl, she thinks twice about kidnapping you, about taking you somewhere. The taxi drivers will think twice about robbing you or putting a gun to your face because you speak the language. You know yourself around.

7:25 And also make friends in these places. If you go to Mexico, know local people so that you can always keep them updated if everything is okay.

7:32 If you're going to a place like I went to, Tijuana, very dangerous city in Mexico, next to the border, or in the border or at the border, with the United States, you wanna have people that you let them know every few hours how you are, or every day how you are.

7:46 Hey, I'm doing great. I'm here in Tijuana. I stayed the night. Everything is fine, everything is fine. You just let them know that everything is fine. This is how Latino people keep themselves updated to see if everything is fine. My family's like that, hey, are you alive? Everything fine?

7:59 We're not in a war zone, but this is how Latino culture is, because we know that shit happens. We know that people get kidnapped. We know that people get stabbed, so you wanna get adjusted to that culture.

8:07 And overall, know the different areas of the city. Stay in the absolute best, safest possible area every time you go somewhere. Don't try to save a buck. Don't try to stay in the area with the most people with the highest chance of getting laid. And don't do anything stupid.

8:21 Don't go to some cabaret. I see it on Reddit all the time, on the Mexico groups and on the the different Latin American groups, that people go to cabarets, erotic massage centers. They go to different nightclubs that they shouldn't be going to, and they get stabbed. They get robbed, they get kidnapped. You want to be vigilant.

8:35 It's a great place. Latin America is beautiful, it's free. There's investment opportunities. There's land to buy, there's low taxes, there's beautiful women. It's one of the best areas of the world, but you have to know where you are.

Închidere balcoane

Acord asociație pentru modficiări constructive

cool

https://www.theguardian.com/environment/2021/jan/25/global-ice-loss-accelerating-at-record-rate-study-finds

https://www.theguardian.com/environment/2018/apr/13/avoid-at-all-costs-gulf-streams-record-weakening-prompts-warnings-global-warming

https://www.theguardian.com/environment/2019/jun/18/arctic-permafrost-canada-science-climate-crisis

https://www.theguardian.com/environment/2022/aug/29/major-sea-level-rise-caused-by-melting-of-greenland-ice-cap-is-now-inevitable-27cm-climate

Neue Studie zu den Tipping Points mit dramatischen Ergebnissen

so you took a slow kill bullet from the chemical warfare industry (big pharma)<br /> and now youre dying a slow death.<br /> hmm ... thats the price of being "normal" : P

But: HOW were those works made "authoritative" in the first place?

https://www.theguardian.com/world/article/2024/jul/30/india-landslides-kerala-death-toll-latest-updates-wayanad

eLife assessment

This study presents valuable new insights into a HIV-associated nephropathy (HIVAN) kidney phenotype in the Tg26 transgenic mouse model, and delineates the kidney cell types that express HIV genes and are injured in these HIV-transgenic mice. A series of compelling experiments demonstrated that PKR inhibition can ameliorate HIVAN with reversal of mitochondrial dysfunction (mainly confined to endothelial cells), a prominent feature shared in other kidney diseases. The data support that inhibition of PKR and mitochondrial dysfunction has potential clinical significance for HIVAN.

Reviewer #1 (Public Review):

Summary:

HIV associated nephropathy (HIVAN) is a rapidly progressing form of kidney disease that manifests secondary to untreated HIV infection and is predominantly seen in individuals of African descent. Tg26 mice carrying an HIV transgene lacking gag and pol exhibit high levels of albuminuria and rapid decline in renal function that recapitulates many features of HIVAN in humans. HIVAN is seen predominantly in individuals carrying two copies of missense variants in the APOL1 gene, and the authors have previously shown that APOL1 risk variant mRNA induces activity of the double strand RNA sensor kinase PKR. Because of the tight association between the APOL1 risk genotype and HIVAN, the authors hypothesized that PKR activation may mediate the renal injury in Tg26 mice, and tested this hypothesis by treating mice with a commonly used PKR inhibitory compound called C16. Treatment with C16 substantially attenuated renal damage in the Tg26 model as measured by urinary albumin/creatinine ratio, urinary NGAL/creatinine ratio and improvement in histology. The authors then performed bulk and single-nucleus RNAseq on kidneys from mice from different treatment groups to identify pathways and patterns of cell injury associated with HIV transgene expression as well as to determine the mechanistic basis for the effect of C16 treatment. They show that proximal tubule nuclei from Tg26 mice appear to have more mitochondrial transcripts which was reversed by C16 treatment and suggest that this may provide evidence of mitochondrial dysfunction in this model. They explore this hypothesis by showing there is a decrease in the expression of nuclear encoded genes and proteins involved in oxidative phosphorylation as well as a decrease in respiratory capacity via functional assessment of respiration in tubule and glomerular preparations from these mouse kidneys. All of these changes were reversed by C16 treatment. The authors propose the existence of a novel injured proximal tubule cell-type characterized by the leak of mitochondrial transcripts into the nucleus (PT-Mito). Analysis of HIV transgene expression showed high level expression in podocytes, consistent with the pronounced albuminuria that characterizes this model and HIVAN, but transcripts were also detected in tubular and endothelial cells. Because of the absence of mitochondrial transcripts in the podocytes, the authors speculate that glomerular mitochondrial dysfunction in this model is driven by damage to glomerular endothelial cells.

Strengths:

The strengths of this study include the comprehensive transcriptional analysis of the Tg26 model, including an evaluation of HIV transgene expression, which has not been previously reported. This data highlights that HIV transcripts are expressed in a subset of podocytes, consistent with the highly proteinuric disease seen in mouse and humans. However, transcripts were also seen in other tubular cells, notably intercalated cells, principal cells and injured proximal tubule cells. Though the podocyte expression makes sense, the relevance of the tubular expression to human disease is still an open question.

The data in support of mitochondrial dysfunction are also robust and rely on combined evidence from downregulation of transcripts involved in oxidative phosphorylation, decreases in complex I and II as determined by immunoblot, and assessments of respiratory capacity in tubular and glomerular preparations. These data are largely consistent with other preclinical renal injury model reported in the literature as well as previous, less thorough assessments in the Tg26 model.

Comments on latest version:

The authors have revised the manuscript to acknowledge the potential limitations of the C16 tool compound used and have performed some additional analyses that suggest the PT-Mito population can be identified in samples from KPMP. The authors added some control images for the in situ hybridizations, which are helpful, though they don't get to the core issue of limited resolution to determine whether mitochondrial RNA is present in the nuclei of injured PT cells. Some additional work has been done to show that C16 treatment results in a decrease in phospho-PKR, a readout of PKR inhibition. These changes strengthen the manuscript by providing some evidence for the translatability of the PT-mito cluster to humans and some evidence for on-target activity for C16. It would be helpful if the authors could quantify the numbers of cells in IHC with nuclear transcripts as well as pointing out some specific examples in the images provided, as comparator data for the snRNAseq studies in which 3-6% of cortex cells had evidence of nuclear mitochondrial transcripts.

Reviewer #2 (Public Review):

Summary:

Numerous studies by the authors and other groups have demonstrated an important role for HIV gene expression kidney cells in promoting progressive chronic kidney disease, especially HIV associated nephropathy. The authors had previously demonstrated a role for protein kinase R (PKR) in a non-HIV transgenic model of kidney disease (Okamoto, Commun Bio, 2021). In this study, the authors used innovative techniques including bulk and single nuclear RNAseq to demonstrate that mice expressing a replication-incompetent HIV transgene have prominent dysregulation of mitochondrial gene expression and activation of PKR and that treatment of these mice with a small molecule PKR inhibitor ameliorated the kidney disease phenotype in HIV-transgenic mice. They also identified STAT3 as a key upstream regulator of kidney injury in this model, which is consistent with previously published studies. Other important advances include identifying the kidney cell types that express the HIV transgene and have dysregulation of cellular pathways.

Strengths:

Major strengths of the study include the use of a wide variety of state-of-the-art molecular techniques to generate important new data on the pathogenesis of kidney injury in this commonly used model of kidney disease and the identification of PKR as a potential druggable target for the treatment of HIV-induced kidney disease. The authors also identify a potential novel cell type within the kidney characterized by high expression of mitochondrial genes.

Weaknesses:

Though the HIV-transgenic model used in these studies results in a phenotype that is very similar to HIV-associated nephropathy in humans, the model has several limitations that may prevent direct translation to human disease, including the fact that mice lack several genetic factors that are important contributors to HIV and kidney pathogenesis in humans. Additional studies are therefore needed to confirm these findings in human kidney disease.

Author response:

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

Responses to recommendations for the authors: 

Reviewer #1 (Recommendations For The Authors):

The manuscript would be strengthened with the following key revisions mostly having to do with image quality: 

(1) It is very difficult in Figure 4B to see which nuclei actually have evidence of mitochondrial transcripts. It might be helpful to provide arrows to specific cells and also to provide some estimate of the percentage of cells with nuclear mt-transcripts as measured by ISH compared to the 3-6% of cortex cell estimate seen in the snRNAseq analysis. 

As suggested, now we have added arrows to help readers to see the signals in nuclei. The detection threshold of ISH and single-nucleus RNA-seq should be different, and therefore, measuring estimates of PT-Mito by ISH would not be reliable.

(2) The phospho-PKR images provided as evidence of C16 activity (Supplemental Figure 1) are too dim to be very useful. Could brighter images be provided? 

We have now adjusted the LUTs of images in Supplemental Figure 1.

eLife assessment

Chang et al. have investigated the catalytic mechanism of I-PpoI nuclease, a one-metal-ion dependent nuclease, by time-resolved X-ray crystallography using soaking of crystals with metal ions under different pH conditions. This convincing study revealed that I-PpoI catalyzes the reaction process through a single divalent cation. The study uncovers important details of the roles of the metal ion and the active site histidine in catalysis.

Reviewer #1 (Public Review):

This study is convincing because they performed time-resolved X-ray crystallography under different pH conditions using active/inactive metal ions and PpoI mutants, as with the activity measurements in solution in conventional enzymatic studies. Although the reaction mechanism is simple and maybe a little predictable, the strength of this study is that they were able to validate that PpoI catalyzes DNA hydrolysis through "a single divalent cation" because time-resolved X-ray study often observes transient metal ions which are important for catalysis but are not predictable in previous studies with static structures such as enzyme-substrate analog-metal ion complexes. The discussion of this study is well supported by their data. This study visualized the catalytic process and mutational effects on catalysis, providing a new insight into the catalytic mechanism of I-PpoI through a single divalent cation. The authors found that His98, a candidate of proton acceptor in the previous experiments, also affects the Mg2+ binding for catalysis without the direct interaction between His98 and the Mg2+ ion, suggesting that "Without a proper proton acceptor, the metal ion may be prone for dissociation without the reaction proceeding, and thus stable Mg2+ binding was not observed in crystallo without His98". In the future, this interesting feature observed in I-PpoI should be investigated by biochemical, structural and computational analyses using other one metal-ion dependent nucleases.

Reviewer #2 (Public Review):

Summary:

Most polymerases and nucleases use two or three divalent metal ions in their catalytic functions. The family of His-Me nucleases, however, use only one divalent metal ion, along with a conserved histidine, to catalyze DNA hydrolysis. The mechanism has been studied previously but, according to the authors, it remained unclear. By use of time resolved X-ray crystallography, this work convincingly demonstrated that only one M2+ ion is involved in the catalysis of the His-Me I-PpoI 19 nuclease, and proposed concerted functions of the metal and the histidine.

Strengths:

This work performs mechanistic studies, including the number and roles of metal ion, pH dependence, and activation mechanism, all by structural analyses, coupled with some kinetics and mutagenesis. Overall, it is a highly rigorous work. This approach was first developed in Science (2016) for a DNA polymerase, in which Yang Cao was the first author. It has subsequently been applied to just 5 to 10 enzymes by different labs, mainly to clarify two versus three metal ion mechanisms. The present study is the first one to demonstrate a single metal ion mechanism by this approach.<br /> Furthermore, on the basis of the quantitative correlation between the fraction of metal ion binding and the formation of product, as well as the pH dependence, and the data from site specific mutants, the authors concluded that the functions of Mg2+ and His are a concerted process. A detailed mechanism is proposed in Figure 6.<br /> Even though there are no major surprises in the results and conclusions, the time-resolved structural approach and the overall quality of the results represent a significant step forward for the Me-His family of nucleases. In addition, since the mechanism is unique among different classes of nucleases and polymerases, the work should be of interest to readers in DNA enzymology, or even mechanistic enzymology in general.

Weaknesses:

Two relatively minor issues are raised here for consideration by the authors:

p. 4, last para, lines 1-2: "we next visualized the entire reaction process by soaking I-PpoI crystals in buffer....". This is a little over-stated. The structures being observed are not reaction intermediates. They are mixtures of substrates and products in the enzyme-bound state. The progress of the reaction is limited by the progress of soaking of the metal ion. Crystallography is just been used as a tool to monitor the reaction (and provide structural information about the product). It would be more accurate to say that "we next monitored the reaction progress by soaking...."

p. 5, beginning of the section. The authors on one hand emphasized the quantitative correlation between Mg ion density and the product density. On the other hand, they raised the uncertainty in the quantitation of Mg2+ density versus Na+ density, thus they repeated the study with Mn2+ which has distinct anomalous signals. This is a very good approach. However, still no metal ion density is shown in the key figure 2A. It will be clearer to show the progress of metal ion density in a figure (in addition to just plots), whether it is Mg or Mn.

Revised version: The authors have properly revised the paper in response to both questions raised in the weakness section. The first issue is an important clarification for others working on similar approaches also. For the second issue, the metal ion density is nicely shown in Fig. S4 now.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

This study is convincing because they performed time-resolved X-ray crystallography under different pH conditions using active/inactive metal ions and PpoI mutants, as with the activity measurements in solution in conventional enzymatic studies. Although the reaction mechanism is simple and may be a little predictable, the strength of this study is that they were able to validate that PpoI catalyzes DNA hydrolysis through "a single divalent cation" because time-resolved X-ray study often observes transient metal ions which are important for catalysis but are not predictable in previous studies with static structures such as enzyme-substrate analog-metal ion complexes. The discussion of this study is well supported by their data. This study visualized the catalytic process and mutational effects on catalysis, providing new insight into the catalytic mechanism of I-PpoI through a single divalent cation. The authors found that His98, a candidate of proton acceptor in the previous experiments, also affects the Mg2+ binding for catalysis without the direct interaction between His98 and the Mg2+ ion, suggesting that "Without a proper proton acceptor, the metal ion may be prone for dissociation without the reaction proceeding, and thus stable Mg2+ binding was not observed in crystallo without His98". In future, this interesting feature observed in I-PpoI should be investigated by biochemical, structural, and computational analyses using other metal-ion dependent nucleases. 

We appreciate the reviewer for the positive assessment as well as all the comments and suggestions.

Reviewer #2 (Public Review): 

Summary: 

Most polymerases and nucleases use two or three divalent metal ions in their catalytic functions. The family of His-Me nucleases, however, use only one divalent metal ion, along with a conserved histidine, to catalyze DNA hydrolysis. The mechanism has been studied previously but, according to the authors, it remained unclear. By use of a time resolved X-ray crystallography, this work convincingly demonstrated that only one M2+ ion is involved in the catalysis of the His-Me I-PpoI 19 nuclease, and proposed concerted functions of the metal and the histidine. 

Strengths: 

This work performs mechanistic studies, including the number and roles of metal ion, pH dependence, and activation mechanism, all by structural analyses, coupled with some kinetics and mutagenesis. Overall, it is a highly rigorous work. This approach was first developed in Science (2016) for a DNA polymerase, in which Yang Cao was the first author. It has subsequently been applied to just 5 to 10 enzymes by different labs, mainly to clarify two versus three metal ion mechanisms. The present study is the first one to demonstrate a single metal ion mechanism by this approach. 

Furthermore, on the basis of the quantitative correlation between the fraction of metal ion binding and the formation of product, as well as the pH dependence, and the data from site-specific mutants, the authors concluded that the functions of Mg2+ and His are a concerted process. A detailed mechanism is proposed in Figure 6. 

Even though there are no major surprises in the results and conclusions, the time-resolved structural approach and the overall quality of the results represent a significant step forward for the Me-His family of nucleases. In addition, since the mechanism is unique among different classes of nucleases and polymerases, the work should be of interest to readers in DNA enzymology, or even mechanistic enzymology in general. 

Thank you very much for your comments and suggestions.

Weaknesses: 

Two relatively minor issues are raised here for consideration: 

p. 4, last para, lines 1-2: "we next visualized the entire reaction process by soaking I-PpoI crystals in buffer....". This is a little over-stated. The structures being observed are not reaction intermediates. They are mixtures of substrates and products in the enzyme-bound state. The progress of the reaction is limited by the progress of the soaking of the metal ion. Crystallography has just been used as a tool to monitor the reaction (and provide structural information about the product). It would be more accurate to say that "we next monitored the reaction progress by soaking....". 

We appreciate the clarification regarding the description of our experimental approach. We agree that our structures do not represent reaction intermediates but rather mixtures of substrate and product states within the enzyme-bound environment. We have revised the text accordingly to more accurately reflect our methodology.

p. 5, the beginning of the section. The authors on one hand emphasized the quantitative correlation between Mg ion density and the product density. On the other hand, they raised the uncertainty in the quantitation of Mg2+ density versus Na+ density, thus they repeated the study with Mn2+ which has distinct anomalous signals. This is a very good approach. However, there is still no metal ion density shown in the key Figure 2A. It will be clearer to show the progress of metal ion density in a figure (in addition to just plots), whether it is Mg or Mn. 

Thank you for your insightful comments. We recognize the importance of visualizing metal ion density alongside product density data. To address this, we included in Figure S4 to present Mg2+/Mn2+ and product densities concurrently.

Reviewer #1 (Recommendations For The Authors): 

(1) Figure 6. I understand that pre-reaction state (left panel) and Metal-binding state (two middle panels) are in equilibrium. But can we state that the Metal-binding state (two middle panels) and the product state (right panel) are in equilibrium and connected by two arrows? 

Thank you for your comments. We agree that the DNA hydrolysis reaction process may not be reversible within I-Ppo1 active site. To clarify, we removed the backward arrows between the metal-binding state and product state. In addition, we thank the reviewer for giving a name for the middle state and think it would be better to label the middle state. We added the metal-binding state label in the revised Figure 6 and also added “on the other hand, optimal alignment of a deprotonated water and Mg2+ within the active site, labeled as metal-binding state, leads to irreversible bond breakage (Fig. 6a)” within the text.

(2) The section on DNA hydrolysis assay (Materials and Methods) is not well described. In this section, the authors should summarize the methods for the experiments in Figure 4 AC, Figure 5BC, Figure S3C, Figure S4EF, and Figure S6AB. The authors presented some graphs for the reactions. For clarity, the author should state in the legends which experiments the results are from (in crystallo or in solution). Please check and modify them. 

Thank you for the suggestion. We have added four paragraphs to detail the experimental procedures for experiments in these figures. In addition, we have checked all of the figure legends and labeled them as “in crystallo or in solution.” To clarify, we also added “in crystallo” or “solution” in the corresponding panels.

(3) The authors showed the anomalous signals of Mn2+ and Tl+. The authors should mention which wavelength of X-rays was used in the data collections to calculate the anomalous signals. 

Thank you for the suggestion. We have included the wavelength of the X-ray in the figure legends that include anomalous maps, which were all determined at an X-ray wavelength of 0.9765 Å.

(4) The full names of "His-Me" and "HNH" are necessary for a wide range of readers. 

Thank you for the suggestion. We have included the full nomenclature for His-Me (histidine-metal) nucleases and HNH (histidine-asparagine-histidine) nuclease.

(5) The authors should add the side chain of Arg61 in Figure 1E because it is mentioned in the main text. 

Thank you for the suggestion. We have added Arg61 to Figure 1E.

(6) Figure 5D. For clarity, the electron densities should cover the Na+ ion. The same request applies to WatN in Figure S3B.

Thank you for catching this detail. We have added the electron density for the Na+ ion in Figure 5D and WatN in Figure S3B.

(7) At line 269 on page 8, what is "previous H98A I-PpoI structure with Mn2+"? Is the structure 1CYQ? If so, it is a complex with Mg2+. 

Thank you for catching this detail. We have edited the text to “previous H98A I-PpoI structure with Mg2+.”

(8) At line 294 on page 9, "and substrate alignment or rotation in MutT (66)." I think "alignment of the substrate and nucleophilic water" is preferred rather than "substrate alignment or rotation". 

Thank you for the suggestion. We have edited the text to “alignment of the substrate and nucleophilic water.”

(9) At line 305 on page 9, "Second, (58, 69-71) single metal ion binding is strictly correlated with product formation in all conditions, at different pH and with different mutants (Figure 3a and Supplementary Figure 4a-c) (58)". The references should be cited in the correct positions. 

Thank you for catching this typo. We have removed the references.

(10) At line 347 on page 10, "Grown in a buffer that contained (50 g/L glucose, 200 g/L α-lactose, 10% glycerol) for 24 hrs." Is this sentence correct? 

Thank you for catching this detail. We have corrected the sentence.

(11) At line 395 on page 11, "The His98Ala I-PpoI crystals of first transferred and incubated in a pre-reaction buffer containing 0.1M MES (pH 6.0), 0.2 M NaCl, 1 mM MgCl2 or MnCl2, and 20% (w/v) PEG3350 for 30 min." In the experiments using this mutant, does a pre-reaction buffer contain MgCl2 or MnCl2? 

Thank you for bringing this to our attention. We have performed two sets of experiments: 1) metal ion soaking in 1 mM Mn2+, which is performed similarly as WT and does not have Mn2+ in the pre-reaction buffer; 2) imidazole soaking, 1 mM Mn2+ was included in the pre-reaction buffer. We reasoned that the Mn2+ will not bind or promote reaction with His98Ala I-PpoI, but pre-incubation may help populate Mn2+ within the lattice for better imidazole binding. However, neither Mn2+ nor imidazole were observed. We have added experimental details for both experiments with His98Ala I-PpoI.

(12) In the figure legends of Figure 1, is the Fo-Fc omit map shown in yellow not in green? Please remove (F) in the legends. 

We have changed the Fo-Fc map to be shown in violet. We have also removed (f) from the figure legends.

(13) I found descriptions of "MgCl". Please modify them to "MgCl2". 

Thank you for catching these details. We have modified all “MgCl” to “MgCl2.”

(14) References 72 and 73 are duplicated. 

We have removed the duplicated reference.

Reviewer #2 (Recommendations For The Authors): 

p. 9, first paragraph, last three lines: "Thus, we suspect that the metal ion may play a crucial role in the chemistry step to stabilize the transition state and reduce the electronegative buildup of DNA, similar to the third metal ion in DNA polymerases and RNaseH." This point is significant but the statement seems a little uncertain. You are saying that the single metal plays the role of two metals in polymerase, in both the ground state and the transition state. I believe the sentence can be stronger and more explicit. 

Thank you for raising this point. We suspect the single metal ion in I-PpoI is different from the A-site or B-site metal ion in DNA polymerases and RNaseH, but similar to the third metal ion in DNA polymerases and nucleases. As we stated in the text,

(1) the metal ion in I-PpoI is not required for substrate alignment. The water molecule and substrate can be observed in place even in the presence of the metal ion. In contrast, the A-site or B-site metal ion in DNA polymerases and RNaseH are required for aligning the substrates.

(2) Moreover, the appearance of the metal ion is strictly correlated with product formation, similar as the third metal ion in DNA polymerase and RNaseH.

To emphasize our point, we have revised the sentence as

“Thus, similar to the third metal ion in DNA polymerases and RNaseH, the metal ion in I-PpoI is not required for substrate alignment but is essential for catalysis. We suspect that the single metal ion helps stabilize the transition state and reduce the electronegative buildup of DNA, thereby promoting DNA hydrolysis.”

Minor typos: 

p. 2, line 4 from bottom: due to the relatively low resolution... 

Thank you for catching this. We have edited the text to “due to the relatively low resolution.”

Figure 4F: What is represented by the pink color? 

The structures are color-coded as 320 s at pH 6 (violet), 160 s at pH 7 (yellow), and 20 s at pH 8 (green). We have included the color information in figure legend and make the labeling clearer in the panel.

p. 9, first paragraph, last line: ...similar to the third... 

Thank you for catching this. We have edited the text.

eLife assessment

The study answers the important question of whether the conformational dynamics of proteins are slaved by the motion of solvent water or are intrinsic to the polypeptide. The results from neutron scattering experiments, involving isotopic labelling, carried out on a set of four structurally different proteins are convincing, showing that protein motions are not coupled to the solvent. A strength of this work is the study of a set of proteins using spectroscopy covering a range of resolutions. The work is of broad interest to researchers in the fields of protein biophysics and biochemistry.

Reviewer #1 (Public Review):

Zheng et al. study the 'glass' transitions that occurs in proteins at ca. 200K using neutron diffraction and differential isotopic labeling (hydrogen/deuterium) of the protein and solvent. To overcome limitations in previous studies, this work is conducted in parallel with 4 proteins (myoglobin, cytochrome P450, lysozyme and green fluorescent protein) and experiments were performed at a range of instrument time resolutions (1ns - 10ps). The author's data looks compelling, and suggests that transitions in the protein and solvent behavior are not coupled and contrary to some previous reports, the apparent water transition temperature is a 'resolution effect'; i.e. instrument response is limited. This is likely to be important in the field, as a reassessment of solvent 'slaving' and the role of the hydration shell on protein dynamics should be reassessed in light of these findings.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "Decoupling of the Onset of Anharmonicity between a Protein and Its Surface Water around 200 K" by Zheng et al. presents a neutron scattering study trying to elucidate if at the dynamical transition temperature water and protein motions are coupled. The origin of the dynamical transition temperature has been debated for decades, specifically its relation to hydration.

The study is rather well conducted, with a lot of effort to acquire the perdeuterated proteins, and some results are interesting.

Author response:

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

eLife assessment

The study answers the important question of whether the conformational dynamics of proteins are slaved by the motion of solvent water or are intrinsic to the polypeptide. The results from neutron scattering experiments, involving isotopic labelling, carried out on a set of four structurally different proteins are convincing, showing that protein motions are not coupled to the solvent. A strength of this work is the study of a set of proteins using spectroscopy covering a range of resolutions. A minor weakness is the limited description of computational methods and analysis of data. The work is of broad interest to researchers in the fields of protein biophysics and biochemistry.

We thank the editors and reviewers for the positive and encouraging comments.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zheng et al. study the 'glass' transitions that occurs in proteins at ca. 200K using neutron diffraction and differential isotopic labeling (hydrogen/deuterium) of the protein and solvent. To overcome limitations in previous studies, this work is conducted in parallel with 4 proteins (myoglobin, cytochrome P450, lysozyme and green fluorescent protein) and experiments were performed at a range of instrument time resolutions (1ns - 10ps). The author's data looks compelling, and suggests that transitions in the protein and solvent behavior are not coupled and contrary to some previous reports, the apparent water transition temperature is a 'resolution effect'; i.e. instrument response limited. This is likely to be important in the field, as a reassessment of solvent 'slaving' and the role of the hydration shell on protein dynamics should be reassessed in light of these findings.

Strengths:

The use of multiple proteins and instruments with a rate of energy resolution/ timescales.

We thank the reviewer for highlighting our key findings.

Weaknesses:

The paper could be organised to better allow the comparison of the complete dataset collected. The extent of hydration clearly influences the protein transition temperature. The authors suggest that "water can be considered here as lubricant or plasticizer which facilitates the motion of the biomolecule." This may be the case, but the extent of hydration may also alter the protein structure.

Following the reviewer’s suggestion, we studied the secondary structure content and tertiary structure of CYP protein at different hydration levels (h = 0.2 and 0.4) through molecular dynamics simulation. As shown in Table S2 and Fig. S6, the extent of hydration does not alter the protein secondary structure content and overall packing. Thus, this result also suggests that water molecules have more influence on protein dynamics than on protein structure. We added the above results in the revised SI.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "Decoupling of the Onset of Anharmonicity between a Protein and Its Surface Water around 200 K" by Zheng et al. presents a neutron scattering study trying to elucidate if at the dynamical transition temperature water and protein motions are coupled. The origin of the dynamical transition temperature is highly debated since decades and specifically its relation to hydration.

Strengths:

The study is rather well conducted, with a lot of efforts to acquire the perdeuterated proteins, and some results are interesting.

We thank the reviewer for highlighting our key findings.

Weaknesses:

The MD data presented appears to be missing description of the methods used.

If these data support the authors claim that different levels of hydration do not affect the protein structure, careful analysis of the MD simulation data should be presented that show the systems are properly equilibrated under each condition. Additionally, methods are needed to describe the MD parameters and methods used, and for how long the simulations were run.

We have now added the methods of MD simulation into the revised SI.

“The initial structure of protein cytochrome P450 (CYP) for simulations was taken from PDB crystal structure (2ZAX). Two protein monomers were filled in a cubic box. 1013 and 2025 water molecules were inserted into the box randomly to reach a mass ratio of 0.2 and 0.4 gram water/1 gram protein, respectively, which mimics the experimental condition. Then 34 sodium counter ions were added to keep the system neutral in charge. The CHARMM 27 force field in the GROMACS package was used for CYP, whereas the TIP4P/Ew model was chosen for water. The simulations were carried out at a broad range of temperatures from 360 K to 100 K, with a step of 5 K. At each temperature, after the 5000 steps energy-minimization procedure, a 10 ns NVT is conducted. After that, a 30 ns NPT simulation was carried out at 1 atm with the proper periodic boundary condition. As shown in Fig. S7, 30 ns is sufficient to equilibrate the system. The temperature and pressure of the system is controlled by the velocity rescaling method and the method by Parrinello and Rahman, respectively. All bonds of water in all the simulations were constrained with the LINCS algorithm to maintain their equilibration length. In all the simulations, the system was propagated using the leap-frog integration algorithm with a time step of 2 fs. The electrostatic interactions were calculated using the Particle Mesh Ewalds (PME) method. A non-bond pair-list cutoff of 1 nm was used and the pair-list was updated every 20 fs. All MD simulations were performed using GROMACS 4.5.1 software packages.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Response to author's changes:

See public review: The MD data presented appears to be missing description of the methods used.

If these data support the authors claim that different levels of hydration do not affect the protein structure, careful analysis of the MD simulation data should be presented that show the systems are properly equilibrated under each condition. Additionally, methods are needed to describe the MD parameters and methods used, and for how long the simulations were run.

We have now added the methods of MD simulation into the revised SI. Please see Reply 5.

Reviewer #2 (Recommendations For The Authors):

The authors answered my questions and substantially improved the manuscript.

We thank the reviewer for the encouraging comments .

eLife assessment

Zhu, et al. present convincing data that details the function of the infertile crescent gene (ifc) in fly development with implications on human neurodegenerative disease. The authors unveil interesting and novel phenotypes of ifc loss-of-function in glia. The experiments are well planned and executed, and the data support the conclusions. These important findings have theoretical and practical implications beyond a single subfield and the methods are in line with current state-of-the-art.

Reviewer #1 (Public Review):<br /> Summary:

Zhu et al., investigate the cellular defects in glia as a result of loss in DEGS1/ifc encoding the dihydroceramide desaturase. Using the strength of Drosophila and its vast genetic toolkit, they find that DEGS1/ifc is mainly expressed in glia and its loss leads to profound neurodegeneration. This supports a role for DEGS1 in the developing larval brain as it safeguards proper CNS development. Loss of DEGS1/ifc leads to dihydroceramide accumulation in the CNS and induces alteration in the morphology of glial subtypes and a reduction in glial number. Cortex and ensheathing glia appeared swollen and accumulated internal membranes. Astrocyte-glia on the other hand displayed small cell bodies, reduced membrane extension and disrupted organization in the dorsal ventral nerve cord. They also found that DEGS1/ifc localizes primarily to the ER. Interestingly, the authors observed that loss of DEGS1/ifc drives ER expansion and reduced TGs and lipid droplet numbers. No effect on PC and PE and a slight increase in PS.

The conclusions of this paper are well supported by the data. The study could be further strengthened by a few additional controls and/or analyses.

Strengths:

This is an interesting study that provides new insight into the role of ceramide metabolism in neurodegeneration.

The strength of the paper is the generation of LOF lines, the insertion of transgenes and the use of the UAS-GAL4/GAL80 system to assess the cell-autonomous effect of DEGS1/ifc loss in neurons and different glial subtypes during CNS development.

The imaging, immunofluorescence staining and EM of the larval brain and the use of the optical lobe and the nerve cord as a readout are very robust and nicely done.

Drosophila is a difficult model to perform core biochemistry and lipidomics but the authors used the whole larvae and CNS to uncover global changes in mRNA levels related to lipogenesis and the unfolded protein responses as well as specific lipid alterations upon DEGS1/ifc loss.

Weaknesses:

The authors performed lipidomics and RTqPCR on whole larvae and larval CNS from which it is impossible to define the cell type-specific effects. Ideally, this could be further supported by performing single cell RNAseq on larval brains to tease apart the cell-type specific effect of DEGS1/ifc loss.

It's clear from the data that the accumulation of dihydroceramide in the ER triggers ER expansion but it remains unclear how or why this happens. Additionally, the authors assume that, because of the reduction in LD numbers, that the source of fatty acids comes from the LDs. But there is no data testing this directly.

The authors performed a beautiful EMS screen identifying several LOF alleles in ifc. However, the authors decided to only use KO/ifcJS3. The paper could be strengthened if the authors could replicate some of the key findings in additional fly lines.

The authors use M{3xP3-RFP.attP}ZH-51D transgene as a general glial marker. However, it would be advised to show the % overlap between the glial marker and the RFP since a lot of cells are green positive but not perse RFP positive and vice versa.

The authors indicate that other 3xP3 RFP and GFP transgenes at other genomic locations also label most glia in the CNE. Do they have a preferential overlap with the different glial subtypes?

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Zhu et al. describes phenotypes associated with the loss of the gene ifc using a Drosophila model. The authors suggest their findings are relevant to understanding the molecular underpinnings of a neurodegenerative disorder, HLD-18, which is caused by mutations in the human ortholog of ifc, DEGS1.<br /> The work begins with the authors describing the role for ifc during fly larval brain development, demonstrating its function in regulating developmental timing, brain size, and ventral nerve cord elongation. Further mechanistic examination revealed that loss of ifc leads to depleted cellular ceramide levels as well as dihydroceramide accumulation, eventually causing defects in ER morphology and function. Importantly, the authors showed that ifc is predominantly expressed in glia and is critical for maintaining appropriate glial cell numbers and morphology. Many of the key phenotypes caused by the loss of fly ifc can be rescued by overexpression of human DEGS1 in glia, demonstrating the conserved nature of these proteins as well as the pathways they regulate. Interestingly, the authors discovered that the loss of lipid droplet formation in ifc mutant larvae within the cortex glia, presumably driving the deficits in glial wrapping around axons and subsequent neurodegeneration, potentially shedding light on mechanisms of HLD-18 and related disorders.

Strengths:<br /> Overall, the manuscript is thorough in its analysis of ifc function and mechanism. The data images are high quality, the experiments are well controlled, and the writing is clear.

Weaknesses:<br /> (1) The authors clearly demonstrated a reduction in number of glia in the larval brains of ifc mutant flies. What remains unclear is whether ifc loss leads to glial apoptosis or a failure for glia to proliferate during development. The authors should distinguish between these two hypotheses using apoptotic markers and cell proliferation markers in glia.

(2) It is surprising that human DEGS1 expression in glia rescues the noted phenotypes despite the different preference for sphingoid backbone between flies and mammals. Though human DEGS1 rescued the glial phenotypes described, can animal lethality be rescued by glial expression of human DEGS1? Are there longer-term effects of loss of ifc that cannot be compensated by the overexpression of human DEGS1 in glia (age-dependent neurodegeneration, etc.)?

(3) The mechanistic link between the loss of ifc and lipid droplet defects is missing. How do defects in ceramide metabolism alter triglyceride utilization and storage? While the author's argument that the loss of lipid droplets in larval glia will lead to defects in neuronal ensheathment, a discussion of how this is linked to ceramides needs to be added.

(4) On page 10, the authors use the words "strong" and "weak" to describe where ifc is expressed. Since the use of T2A-GAL4 alleles in examining gene expression is unable to delineate the amount of gene expression from a locus, the terms "broad" and "sparse" labeling (or similar terms) should be used instead.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.<br /> Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.

Strengths:<br /> This manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions.

Weaknesses:<br /> I didn't find any obvious weakness.

Author response:

'We thank the reviewers for their helpful comments and criticisms of our manuscript and are pleased by the overall positive nature of the comments. For the eLife Version of Record, we plan to carry out the following experiments to address reviewer comments:

- We will use genetic approaches (e.g., driving p35 in glia to block apoptosis) and molecular markers, such as phospho-Histone H3, to assess whether reduced glial proliferation or increased glial apoptosis contributes to reduced glial cell number.

- We will assess the ability of glial-specific expression of the Drosophila or Human ifc/DEGS1 transgenes to rescue the ifc lethal phenotype to adulthood.

- We will replicate key phenotypic findings with additional ifc alleles.

- We will enhance our characterization of 3xP3 RFP transgenes with respect to glial subtypes both for the insert we used in our study and at least one independent insert.

- We will edit the text of the manuscript to clarify additional points raised by the reviewers.

Once we complete the above approaches, we will modify our manuscript accordingly and submit a full response to the reviews to eLife along with the revised manuscript,'

eLife assessment

This study presents a useful modification of a standard model of genetic drift by incorporating variance in offspring numbers, claiming to address several paradoxes in molecular evolution. It is unfortunate that the study fails to engage prior literature that has extensively examined the impact of variance in offspring number, implying that some of the paradoxes presented might be resolved within existing frameworks. In addition, while the modified model yields intriguing theoretical predictions, the simulations and empirical analyses are incomplete to support the authors' claims.

Reviewer #1 (Public Review):

Summary:

The authors present a theoretical treatment of what they term the "Wright-Fisher-Haldane" model, a claimed modification of the standard model of genetic drift that accounts for variability in offspring number, and argue that it resolves a number of paradoxes in molecular evolution. Ultimately, I found this manuscript quite strange. The notion of effective population size as inversely related to the variance in offspring number is well known in the literature, and not exclusive to Haldane's branching process treatment. However, I found the authors' point about variance in offspring changing over the course of, e.g. exponential growth fairly interesting, and I'm not sure I'd seen that pointed out before. Nonetheless, I don't think the authors' modeling, simulations, or empirical data analysis are sufficient to justify their claims.

Weaknesses:

I have several outstanding issues. First of all, the authors really do not engage with the literature regarding different notions of an effective population. Most strikingly, the authors don't talk about Cannings models at all, which are a broad class of models with non-Poisson offspring distributions that nonetheless converge to the standard Wright-Fisher diffusion under many circumstances, and to "jumpy" diffusions/coalescents otherwise (see e.g. Mohle 1998, Sagitov (2003), Der et al (2011), etc.). Moreover, there is extensive literature on effective population sizes in populations whose sizes vary with time, such as Sano et al (2004) and Sjodin et al (2005). Of course in many cases here the discussion is under neutrality, but it seems like the authors really need to engage with this literature more.

The most interesting part of the manuscript, I think, is the discussion of the Density Dependent Haldane model (DDH). However, I feel like I did not fully understand some of the derivation presented in this section, which might be my own fault. For instance, I can't tell if Equation 5 is a result or an assumption - when I attempted a naive derivation of Equation 5, I obtained E(K_t) = 1 + r/c*(c-n)*dt. It's unclear where the parameter z comes from, for example. Similarly, is equation 6 a derivation or an assumption? Finally, I'm not 100% sure how to interpret equation 7. I that a variance effective size at time t? Is it possible to obtain something like a coalescent Ne or an expected number of segregating sites or something from this?

Similarly, I don't understand their simulations. I expected that the authors would do individual-based simulations under a stochastic model of logistic growth, and show that you naturally get variance in offspring number that changes over time. But it seems that they simply used their equations 5 and 6 to fix those values. Moreover, I don't understand how they enforce population regulation in their simulations---is N_t random and determined by the (independent) draws from K_t for each individual? In that case, there's no "interaction" between individuals (except abstractly, since logistic growth arises from a model that assumes interactions between individuals). This seems problematic for their model, which is essentially motivated by the fact that early during logistic growth, there are basically no interactions, and later there are, which increases variance in reproduction. But their simulations assume no interactions throughout!

The authors also attempt to show that changing variance in reproductive success occurs naturally during exponential growth using a yeast experiment. However, the authors are not counting the offspring of individual yeast during growth (which I'm sure is quite hard). Instead, they use an equation that estimates the variance in offspring number based on the observed population size, as shown in the section "Estimation of V(K) and E(K) in yeast cells". This is fairly clever, however, I am not sure it is right, because the authors neglect covariance in offspring between individuals. My attempt at this derivation assumes that I_t | I_{t-1} = \sum_{I=1}^{I_{t-1}} K_{i,t-1} where K_{i,t-1} is the number of offspring of individual i at time t-1. Then, for example, E(V(I_t | I_{t-1})) = E(V(\sum_{i=1}^{I_{t-1}} K_{i,t-1})) = E(I_{t-1})V(K_{t-1}) + E(I_{k-1}(I_{k-1}-1))*Cov(K_{i,t-1},K_{j,t-1}). The authors have the first term, but not the second, and I'm not sure the second can be neglected (in fact, I believe it's the second term that's actually important, as early on during growth there is very little covariance because resources aren't constrained, but at carrying capacity, an individual having offspring means that another individuals has to have fewer offspring - this is the whole notion of exchangeability, also neglected in this manuscript). As such, I don't believe that their analysis of the empirical data supports their claim.

Thus, while I think there are some interesting ideas in this manuscript, I believe it has some fundamental issues: first, it fails to engage thoroughly with the literature on a very important topic that has been studied extensively. Second, I do not believe their simulations are appropriate to show what they want to show. And finally, I don't think their empirical analysis shows what they want to show.

References:

Möhle M. Robustness results for the coalescent. Journal of Applied Probability. 1998;35(2):438-447. doi:10.1239/jap/1032192859

Sagitov S. Convergence to the coalescent with simultaneous multiple mergers. Journal of Applied Probability. 2003;40(4):839-854. doi:10.1239/jap/1067436085

Der, Ricky, Charles L. Epstein, and Joshua B. Plotkin. "Generalized population models and the nature of genetic drift." Theoretical population biology 80.2 (2011): 80-99

Sano, Akinori, Akinobu Shimizu, and Masaru Iizuka. "Coalescent process with fluctuating population size and its effective size." Theoretical population biology 65.1 (2004): 39-48

Sjodin, P., et al. "On the meaning and existence of an effective population size." Genetics 169.2 (2005): 1061-1070

Reviewer #2 (Public Review):

Summary:

This theoretical paper examines genetic drift in scenarios deviating from the standard Wright-Fisher model. The authors discuss Haldane's branching process model, highlighting that the variance in reproductive success equates to genetic drift. By integrating the Wright-Fisher model with the Haldane model, the authors derive theoretical results that resolve paradoxes related to effective population size.

Strengths:

The most significant and compelling result from this paper is perhaps that the probability of fixing a new beneficial mutation is 2s/V(K). This is an intriguing and potentially generalizable discovery that could be applied to many different study systems.

The authors also made a lot of effort to connect theory with various real-world examples, such as genetic diversity in sex chromosomes and reproductive variance across different species.

Weaknesses:

One way to define effective population size is by the inverse of the coalescent rate. This is where the geometric mean of Ne comes from. If Ne is defined this way, many of the paradoxes mentioned seem to resolve naturally. If we take this approach, one could easily show that a large N population can still have a low coalescent rate depending on the reproduction model. However, the authors did not discuss Ne in light of the coalescent theory. This is surprising given that Eldon and Wakeley's 2006 paper is cited in the introduction, and the multiple mergers coalescent was introduced to explain the discrepancy between census size and effective population size, superspreaders, and reproduction variance - that said, there is no explicit discussion or introduction of the multiple mergers coalescent.

The Wright-Fisher model is often treated as a special case of the Cannings 1974 model, which incorporates the variance in reproductive success. This model should be discussed. It is unclear to me whether the results here have to be explained by the newly introduced WFH model, or could have been explained by the existing Cannings model.

The abstract makes it difficult to discern the main focus of the paper. It spends most of the space introducing "paradoxes".

The standard Wright-Fisher model makes several assumptions, including hermaphroditism, non-overlapping generations, random mating, and no selection. It will be more helpful to clarify which assumptions are being violated in each tested scenario, as V(K) is often not the only assumption being violated. For example, the logistic growth model assumes no cell death at the exponential growth phase, so it also violates the assumption about non-overlapping generations.

The theory and data regarding sex chromosomes do not align. The fact that \hat{alpha'} can be negative does not make sense. The authors claim that a negative \hat{alpha'} is equivalent to infinity, but why is that? It is also unclear how theta is defined. It seems to me that one should take the first principle approach e.g., define theta as pairwise genetic diversity, and start with deriving the expected pair-wise coalescence time under the MMC model, rather than starting with assuming theta = 4Neu. Overall, the theory in this section is not well supported by the data, and the explanation is insufficient.

Reviewer #3 (Public Review):

Summary:

Ruan and colleagues consider a branching process model (in their terminology the "Haldane model") and the most basic Wright-Fisher model. They convincingly show that offspring distributions are usually non-Poissonian (as opposed to what's assumed in the Wright-Fisher model), and can depend on short-term ecological dynamics (e.g., variance in offspring number may be smaller during exponential growth). The authors discuss branching processes and the Wright-Fisher model in the context of 3 "paradoxes": (1) how Ne depends on N might depend on population dynamics; (2) how Ne is different on the X chromosome, the Y chromosome, and the autosomes, and these differences do match the expectations base on simple counts of the number of chromosomes in the populations; (3) how genetic drift interacts with selection. The authors provide some theoretical explanations for the role of variance in the offspring distribution in each of these three paradoxes. They also perform some experiments to directly measure the variance in offspring number, as well as perform some analyses of published data.

Strengths:

(1) The theoretical results are well-described and easy to follow.

(2) The analyses of different variances in offspring number (both experimentally and analyzing public data) are convincing that non-Poissonian offspring distributions are the norm.

(3) The point that this variance can change as the population size (or population dynamics) change is also very interesting and important to keep in mind.

(4) I enjoyed the Density-Dependent Haldane model. It was a nice example of the decoupling of census size and effective size.

Weaknesses:

(1) I am not convinced that these types of effects cannot just be absorbed into some time-varying Ne and still be well-modeled by the Wright-Fisher process.

(2) Along these lines, there is well-established literature showing that a broad class of processes (a large subset of Cannings' Exchangeable Models) converge to the Wright-Fisher diffusion, even those with non-Poissonian offspring distributions (e.g., Mohle and Sagitov 2001). E.g., equation (4) in Mohle and Sagitov 2001 shows that in such cases the "coalescent Ne" should be (N-1) / Var(K), essentially matching equation (3) in the present paper.

(3) Beyond this, I would imagine that branching processes with heavy-tailed offspring distributions could result in deviations that are not well captured by the authors' WFH model. In this case, the processes are known to converge (backward-in-time) to Lambda or Xi coalescents (e.g., Eldon and Wakely 2006 or again in Mohle and Sagitov 2001 and subsequent papers), which have well-defined forward-in-time processes.

(4) These results that Ne in the Wright-Fisher process might not be related to N in any straightforward (or even one-to-one) way are well-known (e.g., Neher and Hallatschek 2012; Spence, Kamm, and Song 2016; Matuszewski, Hildebrandt, Achaz, and Jensen 2018; Rice, Novembre, and Desai 2018; the work of Lounès Chikhi on how Ne can be affected by population structure; etc...)

(5) I was also missing some discussion of the relationship between the branching process and the Wright-Fisher model (or more generally Cannings' Exchangeable Models) when conditioning on the total population size. In particular, if the offspring distribution is Poisson, then conditioned on the total population size, the branching process is identical to the Wright-Fisher model.

(6) In the discussion, it is claimed that the last glacial maximum could have caused the bottleneck observed in human populations currently residing outside of Africa. Compelling evidence has been amassed that this bottleneck is due to serial founder events associated with the out-of-Africa migration (see e.g., Henn, Cavalli-Sforza, and Feldman 2012 for an older review - subsequent work has only strengthened this view). For me, a more compelling example of changes in carrying capacity would be the advent of agriculture ~11kya and other more recent technological advances.

Smith Corona Typewriter Ribbon Vibrator Bind, Lift Sticking Up, Forming/Adjusting Process by [[Phoenix Typewriter]]

Smaller engines do the baseband energy while the biggest engine is designed for reacting quickly to needs.

Idle engine (maintenance). Some measures are following the outside temperature trend, particularly heatwaves are visible. However, some other curves seems to be stable and regulated. It could be that the cooling system is shared among the engines. More specific, it could be that the water flow is the same for all machines. It would be interesting to observe, whether the operation of other engines affects temperature variation in this plot.

u/Affectionate-Dog8414 has started a small repair shop in their garage. Denver, CO

• Overview of Research History and Commercial Development:

  • The research group's work extends over 60 years, difficult to condense into a short talk.
  • "Processes of commercial product development" are well-known, but research's purpose is less understood.

• Importance of Research and Key Innovations:

  • Research is vital for foundational innovations; examples include text on screens, interactive text, pointing devices, copy-paste functions, menus, and scroll bars.
  • Early pioneers like Ivan Sutherland and Doug Engelbart in the 60s, and Xerox PARC's Smalltalk in the 70s, introduced groundbreaking concepts in computing.

• Challenges in Research and Development:

  • High costs and limited computing power in early decades delayed commercialization of research.
  • Innovations often took decades to reach commercial viability due to Moore's Law and decreasing hardware costs.

• Examples of Fundamental Research Leading to Industry Transformation:

  • Machine learning, neural networks, and the Internet's development were rooted in research labs.
  • "Neural networks were invented in the 40s by neuroscientists" and later led to modern AI advancements.

• Impact and Future of Research Funding:

  • Public funding in the 60s enabled long-term ambitious projects; today, such projects lack sufficient funding.
  • The absence of funding today could hinder future innovation and technological progress.

• Concept of Bootstrapping Research Environments:

  • Bootstrapping research focuses on creating innovative environments to enhance research effectiveness.
  • Doug Engelbart’s lab aimed to invent tools to improve the lab's own productivity, leading to user interface innovations.

• Research Methods and Dynamic Land:

  • The research group Dynamicland uses space to show context and enable spatial manipulation of ideas.
  • Their work includes creating expansive spatial interfaces beyond traditional screens, using posters and physical objects for programming and interaction.

• Examples of Dynamicland’s Projects:

  • Real Talk: a system where physical objects are programmed and manipulated by hand, fostering visible and tangible computing environments.
  • Dynamicland as a community space where diverse residents collaboratively create and innovate in a shared environment.

• Vision for the Future of Computing:

  • Advocates for computing as ubiquitous infrastructure, accessible and modifiable by everyone, akin to reading and writing.
  • Emphasizes creating environments where people can work together interactively and understand complex systems holistically.

• Final Thoughts:

  • The ultimate goal is for humanity to leverage computation to understand and solve complex problems, with a vision for a future where computing is an integral and accessible part of everyday life for all.

Relevant quotes: - "Processes of commercial product development" are well-known. - "Neural networks were invented in the 40s by neuroscientists." - "Public funding in the 60s enabled long-term ambitious projects." - "Dynamicland uses space to show context and enable spatial manipulation of ideas." - "The ultimate goal is for humanity to leverage computation to understand and solve complex problems."

for - search - google - high resolution addressing of disaggregated text corpus mapped to graph - search results of interest - high resolution addressing of disaggregated text corpus mapped to graph

search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

to - search results of interest - high resolution addressing of disaggregated text corpus mapped to graph - A New Method for Graph-Based Representation of Text in - The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1- ... - https://hyp.is/H9UAbk46Ee-PT_vokcnTqA/www.mdpi.com/2076-3417/10/12/4081 - Encoding Text Information with Graph Convolutional Networks - According to our understanding, this is the first personality recognition study to model the entire user text information corpus as a heterogeneous graph and ... - https://hyp.is/H9UAbk46Ee-PT_vokcnTqA/www.mdpi.com/2076-3417/10/12/4081

for - from - search - google - high resolution addressing of disaggregated text corpus mapped to graph

from - search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://hyp.is/ch_J9k43Ee-lGzfOapoCvQ/www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

is this correct? it can be derived from the two wind components?

for - further research - common cliques in graphs - question - relevance to disaggregating text corpus into sub-sentence graph nodes?

for - from - search - google - high resolution addressing of disaggregated text corpus mapped to graph

from - search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://hyp.is/ch_J9k43Ee-lGzfOapoCvQ/www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

I didn't know this was a myth, I've seen so many of these where celebs would take, or where it would be used in an investigation. But I understand why it would be a myth since I would also consider myself one of those people who would fall anxious when answering questions. https://tenor.com/view/the-simpsons-lie-detector-yes-x-files-mulder-and-scully-gif-20366565

He misses the point of wisdom. Wisdom is about mindset and uplifting each other, to care and empathize... It's not about objective correctness; truth or false, this is science... Nor is it about the correctness of living life, that is ethics and morality...

Wisdom is thus about mindset and empathy.

Hestia - NGINX fastCGI

NGINX wordpress, 优化速度

We know your sense of adventure knows no limits. The sturdy build and lightweight setup of the Getaway Pod XTD will take you to even the remote destinations on your wishlist.

Nothing in the Getaway Pod XTD has been left to chance. With solar panels to keep you powered up, an ensuite shower and toilet tent for convenience and a wrap-around awning to rival the best of them, this 4wd camping trailer means business.

Every detail has is designed to maximise the space, experience and creature comforts. From reverse cycle aircon to the luxury kitchen or from the king size bed with dual doors to the generous 870 litre storage space.

Getaway Pod was started by two Aussie cousins who searched for an adventure solution that would perfectly blend their desire for creature comforts and extreme adventures.

The Getaway Pod is the compact lightweight 4wd camping trailer that ticked all their boxes.

STURDY DESIGN. CLEVER FEATURES. 100% LUXURY.

WE BELIEVE YOU CAN HAVE IT ALL

replace with: compact & lightweight teardrop 4wd camping trailer

Remove this because it was moved to the above the fold banner.

Replace this with: Ready to explore the lighter, simpler & easier way to travel?

THE 4WD CAMPING TRAILER FOR EFFORTLESS LUXURIOUS HOLIDAYS. ANYTIME, ANYWHERE.

要么调整负载，要么调整电压

NBdca

Wasdwadwa

When someone wants to tell you how they are feeling they will tell you with their face. There are many emotions you can tell people by the way your face looks. This is called facial expression. So when your face looks angry it will tell other people who see it that you are angry.

DOI: 10.1016/j.celrep.2021.108892

Resource: RRID:Addgene_12259

Curator: @Naa003

SciCrunch record: RRID:Addgene_12259

---

What is this?

DOI: 10.1016/j.celrep.2021.108875

Resource: (DSHB Cat# 4F3 anti-discs large, RRID:AB_528203)

Curator: @Naa003

SciCrunch record: RRID:AB_528203

---

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (IZSLER Cat# BS CL 129, RRID:CVCL_0045)

Curator: @evieth

SciCrunch record: RRID:CVCL_0045

---

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (BCRJ Cat# 0125, RRID:CVCL_0367)

Curator: @evieth

SciCrunch record: RRID:CVCL_0367

---

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (DSMZ Cat# ACC-625, RRID:CVCL_1966)

Curator: @evieth

SciCrunch record: RRID:CVCL_1966

---

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (DSMZ Cat# ACC-83, RRID:CVCL_0244)

Curator: @evieth

SciCrunch record: RRID:CVCL_0244

---

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (RRID:CVCL_8277)

Curator: @scibot

SciCrunch record: RRID:CVCL_8277

---

What is this?

DOI: 10.3390/toxics12020102

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @anisehay

SciCrunch record: RRID:SCR_006457

---

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (BCRC Cat# 60005, RRID:CVCL_0030)

Curator: @vtello

SciCrunch record: RRID:CVCL_0030

---

What is this?

DOI: 10.1016/j.ccell.2024.06.012

Resource: CVCL_2186

Curator: @vtello

SciCrunch record: RRID:CVCL_2186

---

What is this?

DOI: 10.1016/j.celrep.2024.114530

Resource: (ATCC Cat# CRL-2700, RRID:CVCL_G654)

Curator: @vtello

SciCrunch record: RRID:CVCL_G654

---

What is this?

DOI: 10.1016/j.celrep.2024.114530

Resource: (ATCC Cat# CRL-11268, RRID:CVCL_1926)

Curator: @vtello

SciCrunch record: RRID:CVCL_1926

---

What is this?

DOI: 10.1016/j.celrep.2024.114530

Resource: (RRID:CVCL_D615)

Curator: @vtello

SciCrunch record: RRID:CVCL_D615

---

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-150721-5

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-150721-5

---

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-980202-1296

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-980202-1296

---

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-190806-1

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-190806-1

---

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-141030-2

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-141030-2

---

What is this?

DOI: 10.14336/ad.2024.0224

Resource: (Santa Cruz Biotechnology Cat# sc-31984, RRID:AB_2167511)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2167511

---

What is this?

DOI: 10.1016/j.immuni.2024.07.002

Resource: (RCB Cat# RCB1189, RRID:CVCL_0006)

Curator: @vtello

SciCrunch record: RRID:CVCL_0006

---

What is this?

DOI: 10.1002/ijc.35098

Resource: (ICLC Cat# HTL06001, RRID:CVCL_1511)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1511

---

What is this?

DOI: 10.1101/2024.07.21.604463

Resource: (ATCC Cat# CRL-4001, RRID:CVCL_6573)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_6573

---

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (Millipore Cat# 06-863, RRID:AB_310268)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_310268

---

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (Cell Signaling Technology Cat# 9198, RRID:AB_2561044)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2561044

---

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (Cell Signaling Technology Cat# 3879, RRID:AB_2255011)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2255011

---

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (BCRC Cat# 60250, RRID:CVCL_0553)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0553

---

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (ECACC Cat# 86082104, RRID:CVCL_0332)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0332

---

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (ATCC Cat# CRM-HTB-26, RRID:CVCL_0062)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0062

---

What is this?

DOI: 10.1139/cjm-2024-0014

Resource: SCR_003070

Curator: @dhovakimyan1

SciCrunch record: RRID: SCR_003070

---

What is this?

DOI: 10.1101/2024.07.24.604995

Resource: pClamp (RRID:SCR_011323)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_011323

---

What is this?

DOI: 10.1101/2024.07.19.604274

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

---

What is this?

DOI: 10.1128/msphere.00254-24

Resource: (KCB Cat# KCB 2010167, RRID:CVCL_0358)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0358

---

What is this?

DOI: 10.1101/2024.07.24.604946

Resource: (IMSR Cat# JAX_002810,RRID:IMSR_JAX:002810)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:002810

---

What is this?

DOI: 10.1101/2024.07.24.604946

Resource: (IMSR Cat# JAX_017320,RRID:IMSR_JAX:017320)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:017320

---

What is this?

DOI: 10.1101/2024.07.24.604946

Resource: (IMSR Cat# JAX_005628,RRID:IMSR_JAX:005628)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:005628

---

What is this?

DOI: 10.1101/2024.07.25.605169

Resource: Addgene_86244

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_86244

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_91794

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_91794

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_91792

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_91792

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_116374

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_116374

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_12259

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_12259

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_12260

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_12260

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_1864

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_1864

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_90007

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_90007

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_90005

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_90005

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_90006

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_90006

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_31355

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31355

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_31354

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31354

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_31353

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31353

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_32886

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_32886

---

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_31352

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31352

---

What is this?

DOI: 10.1186/s13048-024-01479-0

Resource: (ATCC Cat# HTB-161, RRID:CVCL_0465)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0465

---

What is this?