https://web.archive.org/web/20240208185222/https://www.nature.com/articles/d41586-024-00349-5

Paper by author Lizzie Wolkovich refused because of inaccurate suspicion of ChatGPT usage. Another cut to the peer review system? She had her GitHub writing receipts. Intriguing. Makes me think about blogging in Obs while having a private blogging repo that tracks changes. n:: use github while writing for [[Reverse Turing menszijn bewijs vaker nodig 20230505100459]] purposes.

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

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Dear Editor,

Herewith we submit our fully revised peer-reviewed preprint that had been reviewed by Review Commons. We thank the Review Commons team and reviewers for thoroughly commenting on our preprint and providing very useful additional points for consideration and discussion.

You will find - the revised manuscript (third preprint version uploaded on biorxiv)<br /> - two reviewer letters (through Review Commons), - our rebuttal letter<br /> - a revised manuscript version with highlighted changes.

Our manuscript reports that an active form of FIT, an essential transcription factor for root iron acquisition in plants, forms dynamic nuclear condensates in response to a blue light stimulus.<br /> A hallmark of our work is the thorough investigation of the nature of the FIT nuclear bodies in plant cells, that we were able to characterize as highly dynamic condensates in which active FIT homo- and heteromeric protein complexes can accumulate preferentially. Through co-localization with nuclear body markers, we found that these FIT condensates are related to speckles, which are a sub-type of nuclear bodies connected with splicing activities. This suggests that FIT condensates are linked with post-transcriptional regulation mechanisms.

The reviewers highlight that an “impressive set of microscopic techniques” has been combined to study in a unique manner the characteristics and functionalities of FIT nuclear bodies in living plant cells. We show that FIT nuclear bodies can be formed in roots of Arabidopsis thaliana. The microscopic imaging techniques we used to characterize the nature and functionalities of FIT nuclear bodies in plant cells have several constraints related to sensitivity and a required strength of fluorescent protein signal. For technical reasons to be able to apply qualitative and quantitative imaging techniques, we conducted the investigation of FIT condensates in Nicotiana benthamiana, a classical and widely used plant protein expression system.

As stated in the reviews, the connection between plant nutrition and nuclear bodies is an “unprecedented” new mode of regulation. The significance of our work is underlined by the fact that we report a “very precise cellular and molecular mechanism in nutrition” that is as yet “still largely unexplored in this context”. Therefore, our study “sheds light on the functional role of this membrane-less compartment and will be appreciated by a large audience.”

We propose that condensate formation is a mechanism that may steer iron nutrition responses by providing a link between iron and light signaling. For sessile plants, it is absolutely essential that environmental signals are sensed and integrated with developmental and physiological programs so that plants can rapidly adjust to a changing environment and potential stress situations. Since iron is a micronutrient that may be toxic when present in excess, e.g. through catalyzing oxidative stress, plants strictly control the acquisition and allocation of iron. Hence, FIT nuclear bodies may be regulatory hubs that integrate at the sub-nuclear level environmental signaling inputs in the control of micronutrient uptake, possibly connected with splicing.

Our work lays the ground for future studies that can address the proof of concept in more detailed manner in plants exposed to varying environmental conditions to reveal the interconnection of environmental and nutritional signaling.

We prepared a revised preprint in which we address all reviewer comments. Please find our revision and our detailed response to all reviewer comments.

With these changes, we hope that our peer-reviewed preprint can receive a positive vote,

We are looking forward to your response,

Sincerely

Petra Bauer and Ksenia Trofimov on behalf of all authors

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

In this paper entitled " FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) accumulates in homo- and heterodimeric complexes in dynamic and inducible nuclear condensates associated with speckle components", Trofimov and colleagues describe for the first time the function of FIT in nuclear bodies. By an impressive set of microscopies technics they assess FIT localization in nuclear bodies and its dynamics. Finally, they reveal their importance in controlling iron deficiency pathway. The manuscript is well written and fully understandable. Nonetheless, at it stands the manuscript present some weakness by the lack of quantification for co-localization and absence controls making hard to follow authors claim. Moreover, to substantially improve the manuscript the authors need to provide more proof of concepts in A. thaliana as all the nice molecular and cellular mechanism is only provided in N. bentamiana. Finally, some key conclusions in the paper are not fully supported by the data.<br /> Please see below:

Main comments:

1) For colocalization analysis, the author should provide semi-quantitative data counting the number of times by eyes they observed no, partial or full co-localization and indicate on how many nucleus they used.

Authors:

We have added the information in the Materials and Method section, lines 731-734:

In total, 3-4 differently aged leaves of 2 plants were infiltrated and used for imaging. One infiltrated leaf with homogenous presence of one or two fluorescence proteins was selected, depending on the aim of the experiment, and ca. 30 cells were observed. Images are taken from 3-4 cells, one representative image is shown.

In all analyzed cases, except in the case of colocalization of FIT and PIF4 fusion proteins, the ca. 30 cells had the same localization and/or colocalization patterns. This information has also been added in the figure legends. Each experiment was repeated at least 2-3 times, or as indicated in the figure legend.

2) Do semi-quantitative co-localization analysis by eyes, on FIT NB with known NB makers in the A. thaliana root. For now, all the nicely described molecular mechanism is shown in N. benthamiana which makes this story a bit weak since all the iron transcriptional machinery is localized in the root to activate IRT1.

Authors:

The described approach has been very optimal, and we were able to screen co-localizing marker proteins in FIT NBs in N. benthamiana to better identify the nature of FIT NBs. This has been successful as we were able to associate FIT NBs with speckles. The N. benthamiana system allowed optimal microscopic observation of fluorescence proteins and quantification of FIT NB characteristics in contrast to the root hair zone of Arabidopsis where Fe uptake takes place. FIT is expressed at a low level in roots and also in leaves, whereby fluorescence protein expression levels are insufficient for the here-presented microscopic studies. The tobacco infiltration system is also well established to study FIT-bHLH039 protein interaction and nuclear body markers. We discuss this point in the discussion, see line 489-500.

3) The authors need to provide data clearly showing that the blue light induce NB in A. thaliana and N. benthamiana.

Authors:

For tobacco, see Figure 1B (t = 0, 5 min) and Supplemental Movies S1. For Arabidopsis, please see Figure 1A (t = 0, 90 and 120 min) and Supplemental Figure S1A. We provide an additional image of pFIT:cFIT-GFP Arabidopsis control plants, showing that NB formation is not detected in plants that were grown in white light and not exposed to blue light before inspection (Supplemental Figure S1B). We state, that upon blue light exposure, plants had FIT NBs in at least 3-10 nuclei of 20 examined nuclei in the root epidermis in the root hair zone (in three independent experiments with three independent plants). White-light-treated plants showed no NB formation unless an additional exposure to blue light was provided (in three independent experiments, three independent plants per experiment and with 15 examined nuclei per plant).

4) Direct conclusion in the manuscript:

• Line 170: At this point of the paper the author cannot claim that the formation of FIT condensates in the nucleus is due to the light as it might be indirectly linked to cell death induced by photodamaging the cell using a 488 lasers for several minutes. This is true especially with the ELYRA PS which has strong lasers made for super resolution and that Cell death is now liked to iron homeostasis. The same experiment might be done using a spinning disc or if the authors present the data of the blue light experiment mentioned above this assumption might be discarded. Alternatively, the author can use PI staining to assess cell viability after several minutes under 488nm laser.

Authors:

As stated in our response to comment 3, we have included now a white light control to show that FIT NB formation is not occurring under the normal white light conditions. Since the formation of FIT NBs is a dynamic and reversible process (Figure 1A), it indicates that the cells are still viable, and that cell death is not the reason for FIT NB formation.

• Line 273: I don't agree with the first part of the authors conclusion, saying that "wild-type FIT had better capacities to localize to NBs than mutant FITmSS271AA, presumably due its IDRSer271/272 at the C-terminus. This is not supported by the data. In order to make such a claim the author need to compare the FA of FIT WT with FITmSS271AA by statistical analysis. Nonetheless, the value seems to be identical on the graphs. The main differences that I observed here are, 1) NP value for FITmSS271AA seems to be lower compared to FIT-WT, suggesting that the Serine might be important to regulate protein homedimerization partitioning between the NP and the NB. 2) To me, something very interesting that the author did not mention is the way the FA of FITmSS271AA in the NB and NP is behaving with high variability. The FA of those is widely spread ranging from 0.30 to 0.13 compared to the FIT-WT. To me it seems that according to the results that the Serine 271/272 are required to stabilize FIT homodimerization. This would not only explain the delay to form the condensate but also the decreased number and size observed for FITmSS271AA compared to FIT-WT. As the homodimerization occurs with high variability in FITmSS271AA, there is less chance that the protein will meet therefore decreasing the time to homodimerize and form/aggregate NB.

Authors:

We fully agree. We meant to describe this result it in a similar way and thank you for help in formulating this point even better. Rephrasing might make it better clear that the IDRSer271/272 is important for a proper NB localization, lines 272-278:

“Also, the FA values did not differ between NBs and NP for the mutant protein and did not show a clear separation in homodimerizing/non-dimerizing regions (Figure 3D) as seen for FIT-GFP (Figure 3C). Both NB and NP regions showed that homodimers occurred very variably in FITmSS271AA-GFP.

In summary, wild-type FIT could be partitioned properly between NBs and NP compared to FITmSS271AA mutant and rather form homodimers, presumably due its IDRSer271/272 at the C-terminus.”

• Line 301: According to my previous comment (line 273), here it seems that the Serine 271/272 are required only for proper partitioning of the heterodimer FIT/BHLH039 between the NP and NB but not for the stability of the heterodimer formation. However, it might be great if the author would count the number of BHLH039 condensates in both version FITmSS271AA and FIT-WT. To my opinion, they would observe less BHLH039 condensate because the homodimer of FITmSS271AA is less likely to occur because of instability.

Authors:

bHLH039 alone localizes primarily to the cytoplasm and not the nucleus, and the presence of FIT is crucial for bHLH039 nuclear localization (Trofimov et al., 2019). Moreover, bHLH039 interaction with FIT depends on SS271AA (Gratz et al., 2019). We therefore did not consider this experiment for the manuscript and did not acquire such data, as we did not expect to achieve major new information.

5) To wrap up the story about the requirements of NB in mediating iron acquisition under different light regimes, provide data for IRT1/FRO2 expression levels in fit background complemented with FITmSS271AA plants. I know that this experiment is particularly lengthy, but it would provide much more to this nice story.

Authors:

Data for expression of IRT1 and FRO2 in FITmSS271AA/fit-3 transgenic Arabidopsis plants are provided in Gratz et al. (2019). To address the comment, we did here a NEW experiment. We provide gene expression data on FIT, BHLH039, IRT1 and FRO2 splicing variants (previously reported intron retention) to explore the possibility of differential splicing alterations under blue light (NEW Supplemental Figure S6 and S7, lines 454-466). Very interestingly, this experiment confirms that blue light affects gene expression differently from white light in the short-term NB-inducing condition and that blue light can enhance the expression of Fe deficiency genes despite of the short 1.5 to 2 h treatment. Another interesting aspect was that the published intron retention was also detected. A significant difference in intron retention depending on iron supply versus deficiency and blue/white light was not observed, as the pattern of expression of transcripts with respective intron retentions sites was the same as the one of total transcripts mostly spliced.

Minor comments

In general, I would suggest the author to avoid abbreviation, it gets really confusing especially with small abbreviation as NB, NP, PB, FA.

Authors:

We would like to keep the used abbreviations as they are utilized very often in our work and, in our eyes, facilitate the understanding.

Line 106: What does IDR mean?

Authors:

Explanation of the abbreviation was added to the text, lines 105-108:

“Intrinsically disordered regions (IDRs) are flexible protein regions that allow conformational changes, and thus various interactions, leading to the required multivalency of a protein for condensate formation (Tarczewska and Greb-Markiewicz, 2019; Emenecker et al., 2020).”

Line 163-164: provide data or cite a figure properly for blue light induction.

Authors:

We have removed this statement from the description, as we provide a white light control now, lines 157-158:

“When whole seedlings were exposed to 488 nm laser light for several minutes, FIT became re-localized at the subnuclear level.”

Line 188: Provide Figure ref.

Authors:

Figure reference was added to the text, lines 184-185:

“As in Arabidopsis, FIT-GFP localized initially in uniform manner to the entire nucleus (t=0) of N. benthamiana leaf epidermis cells (Figure 1B).”

Line 194: the conclusion is too strong. The authors conclude that the condensate they observed are NB based on the fact the same procedure to induce NB has been used in other study which is not convincing. Co-localization analysis with NB markers need to be done to support such a claim. At this step of the study, the author may want to talk about condensate in the nucleus which might correspond to NB. Please do so for the following paragraph in the manuscript until colocalization analysis has not been provided. Alternatively provide the co-localization analysis at this step in the paper.

Authors:

We agree. We changed the text in two positions.

Lines 176-178__: “__Since we had previously established a reliable plant cell assay for studying FIT functionality, we adapted it to study the characteristics of the prospective FIT NBs (Gratz et al., 2019, 2020; Trofimov et al., 2019).”

Lines 192-193: “__We deduced that the spots of FIT-GFP signal were indeed very likely NBs (for this reason hereafter termed FIT NBs).”

Line 214: In order to assess the photo bleaching due to the FRAP experiment the quantification of the "recovery" needs to be provided in an unbleached area. This might explain why FIT recover up to 80% in the condensate. Moreover, the author conclude that the recovery is high however it's tricky to assess since no comparison is made with a negative/positive control.

Authors:

In the FRAP analysis, an unbleached area is taken into account and used for normalization.

We reformulated the description of Figure 1F, lines 212-214:

“According to relative fluorescence intensity the fluorescence signal recovered rapidly within FIT NBs (Figure 1F), and the calculated mobile fraction of the NB protein was on average 80% (Figure 1G).”

Line 220-227: The conclusion it's too strong as I mentioned previously the author cannot claim that the condensate are NBs at this step of the study. They observed nuclear condensates that behave like NB when looking at the way to induce them, their shape, and the recovery. And please include a control.

Authors:

Please see the reformulated sentences and our response above.

Lines 176-178: “Since we had previously established a reliable plant cell assay for studying FIT functionality, we adapted it to study the characteristics of the prospective FIT NBs (Gratz et al., 2019, 2020; Trofimov et al., 2019).”

Lines 192-193: “__We deduced that the spots of FIT-GFP signal were indeed very likely NBs (for this reason hereafter termed FIT NBs).”

Line 239: It's unappropriated to give the conclusion before the evidence.

Authors:

Thank you. We removed the conclusion.

Line 240: Figure 2A, provide images of FIT-G at 15min in order to compare. And the quantification needs to be provided at 5 minutes and 15 minutes for both FIT-G WT and FIT-mSS271AA-G counting the number of condensates in the nucleus. Especially because the rest of the study is depending on these time points.

Authors:

This information is provided in the Supplemental Movie S1C.

Line 241: the author say that the formation of condensate starts after 5 minutes (line 190) here (line 241) the author claim that it starts after 1 minutes. Please clarify.

Authors:

In line 190 we described that FIT NB formation occurs after the excitation and is fully visible after 5 min. In line 241 we stated that the formation starts in the first minutes after excitation, which describes the same time frame. We rephrased the respective sentences.

Lines 185-188: “A short duration of 1 min 488 nm laser light excitation induced the formation of FIT-GFP signals in discrete spots inside the nucleus, which became fully visible after only five minutes (t=5; Figure 1B and Supplemental Movie S1A).”

Lines 239-242: “While FIT-GFP NB formation started in the first minutes after excitation and was fully present after 5 min (Supplemental Movie S1A), FITmSS271AA-GFP NB formation occurred earliest 10 min after excitation and was fully visible after 15 min (Supplemental Movie S1C).”

Line 254: Not sure what the authors claim "not only for interaction but also for FIT NB formation ". To me, the IDR is predicted to be perturbed by modeling when the serines are mutated therefore the IDR might be important to form condensates in the nucleus. Please clarify.

Authors:

The formation of nuclear bodies is slow for FITmSS271AA as seen in Figure 2. Previously, we showed that FITmSS271AA homodimerizes less (Gratz et al., 2019.) Therefore, the said IDR is important for both processes, NB formation and homodimerization. We have added this information to make the point clear, lines 253-255:

“This underlined the significance of the Ser271/272 site, not only for interaction (Gratz et al., 2019) but also for FIT NB formation (Figure 2).”

Line 255: It's not clear why the author test if the FIT homodimerization is preferentially associated with condensate in the nucleus.

Authors:

We test this because both homo- and heterodimerization of bHLH TFs are generally important for the activity of TFs, and we unraveled the connection between protein interaction and NB formation. We state this in lines 228-232.

Line 269-272: It's not clear to what the authors are referring to.

Authors:

We are describing the homodimeric behavior of FIT and FITmSS271AA assessed by homo-FRET measurements that are introduced in the previous paragraph, lines 256-268.

Line 309: This colocalization part should be presented before line 194.

Authors:

We find it convincing to first examine and characterize the process underlying FIT NB formation, then studying a possible function of NBs. The colocalization analysis is part of a functional analysis of NBs. We thank the reviewer for the hint that colocalization also confirms that indeed the nuclear FIT spots are NBs. We will take this point and discuss it, lines 516-522:

“Additionally, the partial and full colocalization of FIT NBs with various previously reported NB markers confirm that FIT indeed accumulates in and forms NBs. Since several of NB body markers are also behaving in a dynamic manner, this corroborates the formation of dynamic FIT NBs affected by environmental signals.”

“In conclusion, the properties of liquid condensation and colocalization with NB markers, along with the findings that it occurred irrespective of the fluorescence protein tag preferentially with wild-type FIT, allowed us to coin the term of ‘FIT NBs’.”

Line 328: add the ref to figure, please.

Authors:

Figure reference was added to the text, lines 330-332:

“The second type (type II) of NB markers were partially colocalized with FIT-GFP. This included the speckle components ARGININE/SERINE-RICH45-mRFP (SR45) and the serine/arginine-rich matrix protein SRm102-mRFP (Figure 5).”

Line 334: It seems that the size of the SR45 has an anormal very large diameter between 4 and 6 µm. In general a speckle measure about 2-3µm in diameter. Can the author make sure that this structure is not due to overexpression in N. benthamiana or make sure to not oversaturate the image.

Authors:

Thank you for this hint. Indeed, there are reports that SR45 is a dynamic component inside cells. It can redistribute depending on environmental conditions and associate into larger speckles depending on the nuclear activity status (Ali et al., 2003). We include this reference and refer to it in the discussion, lines 557-564:

“Interestingly, typical FIT NB formation did not occur in the presence of PB markers, indicating that they must have had a strong effect on recruiting FIT. This is interesting because the partially colocalizing SR45, PIF3 and PIF4 are also dynamic NB components. Active transcription processes and environmental stimuli affect the sizes and numbers of SR45 speckles and PB (Ali et al., 2003; Legris et al., 2016; Meyer, 2020). This may indicate that, similarly, environmental signals might have affected the colocalization with FIT and resulting NB structures in our experiments. Another factor of interference might also be the level of expression.”

Line 335: It seems that the colocalization is partial only partial after induction of NB. The FIT NB colocalize around SR45. But it's hard to tell because the images are saturated therefore creating some false overlapping region.

Authors:

The localization of FIT with SR45 is partial and occurs only after FIT has undergone condensation, see lines 335-338.

Line 344-345: It's unappropriated to give the conclusion before the evidence.

Authors:

We explain at an earlier paragraph that we will show three different types of colocalization and introduce the respective colocalization types within separate paragraphs accordingly, see lines 314-321.

Line 353: increase the contrast in the image of t=5 for UAP56H2 since it's hard to assess the colocalization.

Authors:

This is done as noted in the figure legend of Figure 6.

Line 381-382: "In general" does not sound scientific avoid this kind of wording and describe precisely your findings.

Authors:

We rephrased the sentence, line 387-388:

Localization of single expressed PIF3-mCherry remained unchanged at t=0 and t=15 (Supplemental Figure S5A).

Line 384-385: Provide the data and the reference to the figure.

Authors:

We apologize for the misunderstanding and rephrased the sentence, line 389-391:

After 488 nm excitation, FIT-GFP accumulated and finally colocalized with the large PIF3-mCherry PB at t=15, while the typical FIT NBs did not appear (Figure 7A)

Line 386: The structure in which FIT-G is present in the Figure 7A t=15 is not alike the once already observed along the paper. This could be explained by over-expression in N. benthamiana. Please explain.

Authors:

Thank you for the hint. We discuss this in the discussion part, see lines 555-568.

Line 393: Explain and provide data why the morphology of PIF4/FIT NB do not correspond to the normal morphology.

Authors:

Thank you for the valuable hints. Several reasons may account for this and we provide explanations in the discussion, see lines 555-568.

Line 396-398: It seems also from the data that co-expression of PIF4 of PIF3 will affect the portioning of FIT between the NP and the NB.

Authors:

We can assume that residual nucleoplasm is depleted from protein during NB formation. This is likely true for all assessed colocalization experiments. We discuss this in lines 492-494.

The discussion is particularly lengthy it might be great to reduce the size and focus on the main findings.

Authors:

We shortened the discussion.

Referees cross-commenting

All good for me, I think that the comments/suggestions from Reviewer #2 are valid and fair. If they are addressed they will improve considerably the manuscript.

Reviewer #1 (Significance):

This manuscript is describing an unprecedent very precise cellular and molecular mechanism in nutrition throughout a large set of microscopies technics. Formation of nuclear bodies and their role are still largely unexplored in this context. Therefore, this study sheds light on the functional role of this membrane less compartment and will be appreciated by a large audience. However, the fine characterization is only made using transient expression in N. Bentamiana and only few proofs of concept are provided in A. thaliana stable line.

Reviewer #2 (Evidence, reproducibility and clarity):

The manuscript of Trofimov et al shows that FIT undergoes light-induced, reversible condensation and localizes to nuclear bodies (NBs), likely via liquid-liquid phase separation and light conditions plays important role in activity of FIT. Overall, manuscript is well written, authors have done a great job by doing many detailed and in-depth experiments to support their findings and conclusions.

However, I have a number of questions/comments regarding the data presented and there are still some issues that authors should take into account.

Major points/comments:

1) Authors only focused on blue light conditions. Is there any specific reason for selecting only blue light and not others (red light or far red)?

Authors:

There are two main reasons: First, in a preliminary study (not shown) blue light resulted in the formation of the highest numbers of NBs. Second, iron reductase activity assays and gene expression analysis under different light conditions showed a promoting effect under blue light, but not red light or dark red light (Figure 9). This indicated to us, that blue light might activate FIT, and that active FIT may be related to FIT NBs.

2) Fig. 3C and D: as GFP and GFP-GFP constructs are used as a reference, why not taking the measurements for them at two different time points for example t=0 and t=5 0r t=15???

Authors:

Free GFP and GFP-GFP dimers are standard controls for homo-FRET that serve to delimit the range for the measurements.

3) Line 27-271: Acc to the figure 3d, for the Fluorescence anisotropy measurement of NBs appears to be less. Please explain.

Authors:

FA in NBs with FITmSS271AA is variable and the value is lower than that of whole nucleus but not significantly different compared with that in nucleoplasm. We describe the results of Figure 3D in lines 272-275.

4) Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.

Authors:

Neither for FIT/bHLH039 nor the FITmSS271AA/bHLH039 pair, there is a significant decrease in the fluorescence lifetime values between t=0 and t=5/15. FIT-G is a control to delimit the range. The interesting experiment is to compare the protein pairs of interest between the different nuclear locations at t=5/15.

5) Line 300-301: In Figure 4D and 4E. Fluorescence lifetime of G measurement at t=0 seems very similar for both FIT-G as well as FITmSS but if we look at the values of t=0 for FIT-G+bhlh039 it is greater than 2.5 and for FITmSS271AA-G+bhlh039 it is less which suggests more heterodimeric complexes to be formed in FITmSS271AA-G+bhlh039. Similar pattern is observed for NBs and NPs, according to the figure 4d and E.

Therefore, heterodimeric complexes accumulated more in case of FITmSS271AA-G+bhlh039 as compared to FIT-G+bhlh039 (if we compare measurement values of Fluorescence lifetime of G of FITmSS271AA-G+bhlh039 with FIT-G+bhlh039).

Please comment and elaborate about this further.

Authors:

These conclusions are not valid as the experiments cannot be conducted in parallel. Since the experiments had to be performed on different days due to the duration of measurements including new calibrations of the system, we cannot compare the absolute fluorescence lifetimes between the two sets.

6) Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.

Authors:

Please see our response to your comment 4).

7) Line 439-400: As iron uptake genes (FRO2 and IRT1) are more induced in WT under blue light conditions and FRO2 is less induced in case of red-light conditions. So, what happens to Fe content of WT grown under blue light or red light as compared to WT grown under white light. Perls/PerlsDAb staining of WT roots under different light conditions will add more information to this.

Authors:

We focused on the relatively short-term effects of blue light on signaling of nuclear events that could be related to FIT activity directly, particularly gene expression and iron reductase activity as consequence of FRO2 expression. These are both rapid changes that occur in the roots and can be measured. We suspect that iron re-localization and Fe uptake also occur, however, in our experience differences in metal contents will not be directly significant when applying the standard methods like ICP-MS or PERLs staining.

Minor comments:

Line 75-76: Rephrase the sentence

Authors:

We rephrased the sentence, lines 73-74:

“As sessile organisms, plants adjust to an ever-changing environment and acclimate rapidly. They also control the amount of micronutrients they take up.”

Line 119: Rephrase the sentence

Authors:

We rephrased the sentence, line 118-119:

“Various NBs are found. Plants and animals share several of them, e.g. the nucleolus, Cajal bodies, and speckles.”

Line 235-236: rephrase the sentence

Authors:

We rephrased the sentence, line 232-234:

“In the work of Gratz et al. (2019), the hosphor-mimicking FITmS272E protein did not show significant changes in its behavior compared to wild-type FIT.”

Line 444: Correct the sentence “Fe deficiency versus sufficiency”

Authors:

We corrected that, line 449-451:

“In both, the far-red light and darkness situations, FIT was induced under iron deficiency versus sufficiency, while on the other side, BHLH039, FRO2 and IRT1 were not induced at all in these light conditions (Figure 9I-P).”

Referees cross-commenting

I agree with R1 suggestions/comments and i think manuscript quality will be much better if authors carry out the experiments suggested by R1. I believe this will also strengthen their conclusions.

Reviewer #2 (Significance):

Overall, manuscript is well written, authors have done a nice job by doing several key experiments to support their findings and conclusions. However, the results and manuscript can be improved further by addressing some question raised here. This study is interesting for basic scientists which unravels the crosstalk of light signaling in nutrient signaling pathways.

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

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Referee #2

Evidence, reproducibility and clarity

_The manuscript of Trofimov et al shows that FIT undergoes light-induced, reversible condensation and localizes to nuclear bodies (NBs), likely via liquid-liquid phase separation and light conditions plays important role in activity of FIT. Overall, manuscript is well written, authors have done a great job by doing many detailed and in-depth experiments to support their findings and conclusions.<br /> However, I have a number of questions/comments regarding the data presented and there are still some issues that authors should take into account.

Major points/comments:

1. Authors only focused on blue light conditions. Is there any specific reason for selecting only blue light and not others (red light or far red)?
2. Fig. 3C and D: as GFP and GFP-GFP constructs are used as a reference, why not taking the measurements for them at two different time points for example t=0 and t=5 0r t=15???
3. Line 27-271: Acc to the figure 3d, for the Fluorescence anisotropy measurement of NBs appears to be less. Please explain.
4. Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.
5. Line 300-301: In Figure 4D and 4E. Fluorescence lifetime of G measurement at t=0 seems very similar for both FIT-G as well as FITmSS but if we look at the values of t=0 for FIT-G+bhlh039 it is greater than 2.5 and for FITmSS271AA-G+bhlh039 it is less which suggests more heterodimeric complexes to be formed in FITmSS271AA-G+bhlh039. Similar pattern is observed for NBs and NPs, according to the figure 4d and E.<br /> Therefore, heterodimeric complexes accumulated more in case of FITmSS271AA-G+bhlh039 as compared to FIT-G+bhlh039 (if we compare measurement values of Fluorescence lifetime of G of FITmSS271AA-G+bhlh039 with FIT-G+bhlh039).<br /> Please comment and elaborate about this further.
6. Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.
7. Line 439-400: As iron uptake genes (FRO2 and IRT1) are more induced in WT under blue light conditions and FRO2 is less induced in case of red-light conditions. So, what happens to Fe content of WT grown under blue light or red light as compared to WT grown under white light. Perls/PerlsDAb staining of WT roots under different light conditions will add more information to this.

Minor comments:

Line 75-76: Rephrase the sentence

Line 119: Rephrase the sentence

Line 235-236: rephrase the sentence

Line 444: Correct the sentence "Fe deficiency versus sufficiency"

Referees cross-commenting

I agree with R1 suggestions/comments and i think manuscript quality will be much better if authors carry out the experiments suggested by R1. I believe this will also strengthen their conclusions.

Significance

Overall, manuscript is well written, authors have done a nice job by doing several key experiments to support their findings and conclusions. However, the results and manuscript can be improved further by addressing some question raised here. This study is interesting for basic scientists which unravels the crosstalk of light signaling in nutrient signaling pathways.

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

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Referee #1

Evidence, reproducibility and clarity

In this paper entitled " FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) accumulates in homo- and heterodimeric complexes in dynamic and inducible nuclear condensates associated with speckle components", Trofimov and colleagues describe for the first time the function of FIT in nuclear bodies. By an impressive set of microscopies technics they assess FIT localization in nuclear bodies and its dynamics. Finally, they reveal their importance in controlling iron deficiency pathway. The manuscript is well written and fully understandable. Nonetheless, at it stands the manuscript present some weakness by the lack of quantification for co-localization and absence controls making hard to follow authors claim. Moreover, to substantially improve the manuscript the authors need to provide more proof of concepts in A. thaliana as all the nice molecular and cellular mechanism is only provided in N. bentamiana. Finally, some key conclusions in the paper are not fully supported by the data.<br /> Please see below:

Main comments:

1. For colocalization analysis, the author should provide semi-quantitative data counting the number of times by eyes they observed no, partial or full co-localization and indicate on how many nucleus they used.
2. Do semi-quantitative co-localization analysis by eyes, on FIT NB with known NB makers in the A. thaliana root. For now, all the nicely described molecular mechanism is shown in N. benthamiana which makes this story a bit weak since all the iron transcriptional machinery is localized in the root to activate IRT1.
3. The authors need to provide data clearly showing that the blue light induce NB in A. thaliana and N. benthamiana.
4. Direct conclusion in the manuscript:
5. Line 170: At this point of the paper the author cannot claim that the formation of FIT condensates in the nucleus is due to the light as it might be indirectly linked to cell death induced by photodamaging the cell using a 488 lasers for several minutes. This is true especially with the ELYRA PS which has strong lasers made for super resolution and that Cell death is now liked to iron homeostasis. The same experiment might be done using a spinning disc or if the authors present the data of the blue light experiment mentioned above this assumption might be discarded. Alternatively, the author can use PI staining to assess cell viability after several minutes under 488nm laser.
6. Line 273: I don't agree with the first part of the authors conclusion, saying that "wild-type FIT had better capacities to localize to NBs than mutant FITmSS271AA, presumably due its IDRSer271/272 at the C-terminus. This is not supported by the data. In order to make such a claim the author need to compare the FA of FIT WT with FITmSS271AA by statistical analysis. Nonetheless, the value seems to be identical on the graphs. The main differences that I observed here are, 1) NP value for FITmSS271AA seems to be lower compared to FIT-WT, suggesting that the Serine might be important to regulate protein homedimerization partitioning between the NP and the NB. 2) To me, something very interesting that the author did not mention is the way the FA of FITmSS271AA in the NB and NP is behaving with high variability. The FA of those is widely spread ranging from 0.30 to 0.13 compared to the FIT-WT. To me it seems that according to the results that the Serine 271/272 are required to stabilize FIT homodimerization. This would not only explain the delay to form the condensate but also the decreased number and size observed for FITmSS271AA compared to FIT-WT. As the homodimerization occurs with high variability in FITmSS271AA, there is less chance that the protein will meet therefore decreasing the time to homodimerize and form/aggregate NB.
7. Line 301: According to my previous comment (line 273), here it seems that the Serine 271/272 are required only for proper partitioning of the heterodimer FIT/BHLH039 between the NP and NB but not for the stability of the heterodimer formation. However, it might be great if the author would count the number of BHLH039 condensates in both version FITmSS271AA and FIT-WT. To my opinion, they would observe less BHLH039 condensate because the homodimer of FITmSS271AA is less likely to occur because of instability.
8. To wrap up the story about the requirements of NB in mediating iron acquisition under different light regimes, provide data for IRT1/FRO2 expression levels in fit background complemented with FITmSS271AA plants. I know that this experiment is particularly lengthy, but it would provide much more to this nice story.

Minor comments

In general, I would suggest the author to avoid abbreviation, it gets really confusing especially with small abbreviation as NB, NP, PB, FA.

Line 106: What does IDR mean?

Line 163-164: provide data or cite a figure properly for blue light induction.

Line 188: Provide Figure ref.

Line 194: the conclusion is too strong. The authors conclude that the condensate they observed are NB based on the fact the same procedure to induce NB has been used in other study which is not convincing. Co-localization analysis with NB markers need to be done to support such a claim. At this step of the study, the author may want to talk about condensate in the nucleus which might correspond to NB. Please do so for the following paragraph in the manuscript until colocalization analysis has not been provided. Alternatively provide the co-localization analysis at this step in the paper.

Line 214: In order to assess the photo bleaching due to the FRAP experiment the quantification of the "recovery" needs to be provided in an unbleached area. This might explain why FIT recover up to 80% in the condensate. Moreover, the author conclude that the recovery is high however it's tricky to assess since no comparison is made with a negative/positive control.

Line 220-227: The conclusion it's too strong as I mentioned previously the author cannot claim that the condensate are NBs at this step of the study. They observed nuclear condensates that behave like NB when looking at the way to induce them, their shape, and the recovery. And please include a control.

Line 239: It's unappropriated to give the conclusion before the evidence.

Line 240: Figure 2A, provide images of FIT-G at 15min in order to compare. And the quantification needs to be provided at 5 minutes and 15 minutes for both FIT-G WT and FIT-mSS271AA-G counting the number of condensates in the nucleus. Especially because the rest of the study is depending on these time points.

Line 241: the author say that the formation of condensate starts after 5 minutes (line 190) here (line 241) the author claim that it starts after 1 minutes. Please clarify.

Line 254: Not sure what the authors claim "not only for interaction but also for FIT NB formation ". To me, the IDR is predicted to be perturbed by modeling when the serines are mutated therefore the IDR might be important to form condensates in the nucleus. Please clarify.

Line 255: It's not clear why the author test if the FIT homodimerization is preferentially associated with condensate in the nucleus.

Line 269-272: It's not clear to what the authors are referring to.

Line 309: This colocalization part should be presented before line 194.

Line 328: add the ref to figure, please.

Line 334: It seems that the size of the SR45 has an anormal very large diameter between 4 and 6 µm. In general a speckle measure about 2-3µm in diameter. Can the author make sure that this structure is not due to overexpression in N. benthamiana or make sure to not oversaturate the image

Line 335: It seems that the colocalization is partial only partial after induction of NB. The FIT NB colocalize around SR45. But it's hard to tell because the images are saturated therefore creating some false overlapping region.

Line 344-345: It's unappropriated to give the conclusion before the evidence.

Line 353: increase the contrast in the image of t=5 for UAP56H2 since it's hard to assess the colocalization.

Line 381-382: "In general" does not sound scientific avoid this kind of wording and describe precisely your findings.

Line 384-385: Provide the data and the reference to the figure.

Line 386: The structure in which FIT-G is present in the Figure 7A t=15 is not alike the once already observed along the paper. This could be explained by over-expression in N. benthamiana. Please explain.

Line 393: Explain and provide data why the morphology of PIF4/FIT NB do not correspond to the normal morphology.

Line 396-398: It seems also from the data that co-expression of PIF4 of PIF3 will affect the portioning of FIT between the NP and the NB.

The discussion is particularly lengthy it might be great to reduce the size and focus on the main findings.

Referees cross-commenting

All good for me, I think that the comments/suggestions from Reviewer #2 are valid and fair. If they are addressed they will improve considerably the manuscript.

Significance

This manuscript is describing an unprecedent very precise cellular and molecular mechanism in nutrition throughout a large set of microscopies technics. Formation of nuclear bodies and their role are still largely unexplored in this context. Therefore, this study sheds light on the functional role of this membrane less compartment and will be appreciated by a large audience. However, the fine characterization is only made using transient expression in N. Bentamiana and only few proofs of concept are provided in A. thaliana stable line.

Reviewer #1 (Public Review):

This is an excellent paper with extensive data and important results. The authors present convincing data that resurgent sodium current from Nav1.8 and Nav1.9 channels is mediated, at least in part, by FHF proteins. They show that expression of FHF4A, and to a lesser extent, FHF1A, FHF2A, and FHF3A, can induce resurgent Na current in Nav1.8 channels expressed in ND7/23 cells and Nav1.9/beta1/beta2 channels expressed in HEK cells. They further show that shRNA knock-down of FHF4 can substantially reduce (though not eliminate) native Nav1.8-mediated resurgent current in DRG neurons, that this reduces repetitive firing of the neurons, and that repetitive firing can be restored by a peptide derived from the N-terminal of FHF4A. An additional very interesting result is the identification of an inner-pore residue that differs between Nav1.9 and Nav1.5 and when mutated in Nav1.5 almost eliminates the ability of the Navβ4 peptide to produce resurgent current without affecting resurgent current induced by FHF2A, suggesting different intrapore binding sites for the Navβ4 peptide and FHFs. Altogether, the results in the paper make a major contribution to understanding the molecular events involved in generating resurgent current in Nav1.8 and Nav1.9 channels. The paper contains an impressive amount of data building on an equally impressive foundation of techniques developed in previous work and the results are clear and convincing.

There are some aspects of the presentation that could be improved.

Line 74 "...and show for the first time that INaR can be reconstituted in heterologous systems by coexpressing full-length A-type FHFs with VGSC α-subunits."

It seems debatable whether the expression of Nav1.8 in ND7/23 cells constitutes truly "heterologous" expression. After all, ND7/23 cells are an immortalized DRG cell line. At the least, the authors need to explain why ND7/23 cells were used for the Nav1.8 expression and to acknowledge that ND7/23 cells may express proteins in addition to the transfected Nav1.8 and FHF that could be important for the generation of resurgent current. Did they ever attempt to express Nav1.8 and FHF4A in HEK or CHO cells? There should also be a reference to the literature (I suppose Lee et al PLoS One, 14:e0221156y, 2019) showing that ND7/23 cells do not express endogenous Nav1.8 currents.

Reviewer #1 (Public Review):

Neural stem cells express cascades of transcription factors that are important for generating the diversity of neurons in the brain of flies and mammals. In flies, nothing is known about whether the transcription factor cascades are build from direct gene regulation, e.g. factor A binding to enhancers in gene B to activate its expression. Here, Xin and Ray show that one temporal factor, Slp1/2, is regulated transcriptionally via two molecularly defined enhancers that directly bind two other transcription factors in the cascade as well as integrating Notch signaling. This is a major step forward for the field, and provides a model for subsequent studies on other temporal transcription factor cascades.

Reviewer #2 (Public Review):

This manuscript describes an algorithm of estimating real time effective reproductive number R_e (t). This algorithm combines several methods in a reasonable way: deconvolution of time series of reported case into time series of infection, a Poisson model for generation of infections, and block-bootstrap of residuals to assess uncertainty. Each component is not necessarily novel, but the performance of this algorithm has been validated using comprehensive simulation studies. The algorithm was applied to COVID-19 surveillance data in selected countries across continents, revealing a great deal of heterogeneity in the association of R_e (t) with nonpharmaceutical interventions. Overall, the conclusions seem reliable.

I have several moderate critiques and suggestions:

1. For a statistical point of view, it seems much more natural to integrate the infection generation process and the delay from infection to reporting, possibly with reporting errors, into the same model, with which you will avoid combing the bootstrap and the credible intervals in a somewhat awkward way. I understand you can take advantage of EpiEstim package, but the likelihood is very simple and easy to program up. Nevertheless, I'm not strongly against the current paradigm.

2. Is there a strong reason to believe the residuals are autocorrelated? The block sampling with block size 10 seems arbitrary. The authors fitted a ARIMA model to the residuals for some countries, how good was the fitting? If the block size doesn't matter, then probably the stronger but simpler assumption of independent residuals may not compromise much the estimation of R_e (t).

3. I don't see the necessity of using segmented R_e (t) instead of a smooth curve in the simulation studies. The inferential performance, especially the coverage of the CI's, is much less satisfactory when a segment has a steep slope. The authors may consider constructing splines based on the segments or using basis functions directly.

4. The authors smoothed the log-transformed observed incidences to come up with the residuals. For Poisson data, a variance-stabilized transformation is taking the square root, not the logarithm. In addition, as you already have bootstrap estimates, why not using quantiles directly for CIs but instead using a normal approximation (asymptotic)? When incidence is low, the normal approximation may be much less satisfactory. Also, when using normal approximation for CI, it's much safer to calculate standard deviation and construct CI at the log-scale, i.e., log(θ ̂^*(t)), and then exponentiate back.

5. The stringency index is a convenient metric for intervention intensity. However, it doesn't reflect actual compliance as the authors admitted. Another likely more pertinent metric is human movement (could be multiple movement indices). Human movement indices may not be available in all countries, but they are available in some, e.g., the US, and first wave in China. In some states of the US, it was clear that human movement decreased substantially even before initiation of lockdown. Lack of human movement metrics most likely has contributed to the difficulty in the interpretation of Figure 4.

Reviewer #1 (Public Review):

This paper presents an approach to estimate Rt for 170 countries. While it is an impressive amount of work, I think the pipeline is similar to many currently available frameworks. The paper claims the following novelties over current framework, but more efforts are needed to be done to make it convincing.

1) Obtain stable estimate from multiple types of data:<br /> It turns out the stable estimates are just repeatedly use the same approaches to different time series (Figure 3A middle). From the wording I think there should be some methods to combine these time series to have a single estimate of Rt. Overall, the Rt and the time series of infection should be unique. It would be suboptimal, for example if there are big difference in the results from death time series and reported cases time series, which one should I trust?

2) Adequate representation of uncertainty:<br /> This is the result in Figure 2, suggesting the CI from EpiEstim is too narrow. This would be expected given that EpiEstim assumed the input infection time series is observed and fixed. It would be expected that the proposed approach would provide wider CI and hence the proportion covered would be more. However, I think to validate the wider CI is the correct one, simulation studies are required. I think the most related one would be Figure 1B. The results suggested that the approach works when the Rt is not rapid changing. However, I have concern on the methods for simulation (details below).

3) Real-time of the Rt<br /> There is no simulation about the real-time property of the Rt. The most related one is still Figure 1B. However, looks at the right-tail of the figure (the real-time performance), the proportion covered the true value is decreasing and more efforts are needed to support the framework can be accurate in real-time. For example, how is the real-time performance when Rt is increasing, or Rt decrease sharply due to lock down?

4) simulation methods to estimate Rt<br /> Both 2) and 3) needs simulation to support the results, and hence the simulation approach would be critical. The first part based on Poisson distribution to generate an infection time series, which is OK. However, the issue is the secondary part about how the authors obtained the time series for death/hospitalization/reported cases. To me, after generating the infection time series, based on the delay distribution from infection to death/hospitalization/reported, we could obtain those time series. I am not clear and sure if the authors approach is correct by using smoothing and fitting ARIMA to get those time series.

Minor comments:<br /> 1) What is near real-time mean? The estimates of Rt are delay for a few days like other approach?<br /> 2) For the results in Table 1, I think if there are some results suggesting that other approaches (like EpiEstim) perform worse than the proposed approach, it would be better to illustrate the value of the proposed approach.<br /> 3) I think more discussions are needed for the similarity and differences for current approach. For example, Abbott et al (https://wellcomeopenresearch.org/articles/5-112) used a similar pipeline.<br /> 4) Figure S11 is about accounting for known imports. While if the local cases are dominate and hence imported cases would not have a big impact on estimates of Rt. The impact of imported cases on estimates of Rt could be complicated, as suggested that in Tsang et al. (https://pubmed.ncbi.nlm.nih.gov/34086944/). In addition to assume imported cases and 'exported' cases could be canceled, it is also assumed that the imported cases had similar transmissibility to the local cases, which may not be true if there is border control.

Reviewer #2 (Public Review):

The authors state the role of TNF-α in PCOS is mainly driven by mTOR pathway, validated that level of TNF-α is contributed mainly by Bcells. Authors further showed the molecular mechanism involved in Bcells when exposed to metformin drug. Overall, the study is well written, and the finding is important for a logical design of immunotherapeutic strategy for PCOS.

The current study holds the key in better understanding the molecular mechanism induced in Bcells by metformin. The signaling molecule(s) from the current study may hold promising target for further validation at preclinical and clinical level.

The current hypothesis is strongly supported and validated with strong experimental designs. The key question has been addressed with multiple experiments. Overall, the manuscript is well designed and concluded.

Reviewer #1 (Public Review):

Strengths of the manuscript<br /> 1. Comparative human and mouse studies<br /> 3. Studies carried are methodical demonstrating-<br /> a) Increased production of TNF-α in B cells in women with PCOS.<br /> b) Inhibition of TNF-α by Metformin which is done through inhibition of phosphorylation of mTOR<br /> c) Metformin induces mitochondrial remodeling in pathological B cells<br /> d) Metformin reduces glucose uptake in pathological B cells<br /> e) Inhibition of mTOR resulting in the inhibitory effect on the expression of TNF-α in pathological B cells, decreased the MMP, ROS levels and glucose uptake in pathological B cells

Weaknesses<br /> 1. Better final culmination of data possible final diagram/schematic of pathway leading to TNF-α elevations in PCOS B cells, downstream effects and impact of Metformin.

Reviewer #3 (Public Review):

Leonard et al. investigated the consequences of elp1 deletion in a mouse model of Familial Dysautonomia (FD). They used a previously published wnt-conditional knock-out mouse model that eliminates elp1 from the neural crest lineage, and they focused on the development of the trigeminal ganglia. FD patients do exhibit facial pain and temperature sensation reduction that are likely linked to trigeminal nerve defects. The authors showed elp1 expression in trigeminal ganglia and that the development of the trigeminal ganglia is affected in this FD mouse. Specifically, they showed that the TrkA-expressing nociceptors in the trigeminal ganglia is particularly affected. Additionally, they showed for the first time that TrkA neurons in the trigeminal ganglia are primarily neural crest derived, while TrkB and C neurons are placode derived.

The conclusions of this paper are supported by the presented data.

Strengths: The scientific question/gap in knowledge is clear and the approaches and data support the conclusions made. The data is well organized and clearly presented and lead to new insight into the two questions about the role of elp1 in trigeminal neuron development and into the composition of trigeminal ganglia and the cell’s origin. The authors present a thorough analysis of this mouse model with respect to their question.

Weaknesses: The study is very descriptive. The vast majority of data is imaging and morphology based. Alternative, additional techniques could strengthen the results and conclusions.

Reviewer #2 (Public Review):

In this study, Leonard et al. investigate the role of Elp1 gene deletion in the neural crest during gangliogenesis of the trigeminal ganglion and how its absence affects target innervation. They characterize the expression of Elp1 and show that it is expressed in differentiating neurons that commences with the placode-derived neurons. They show that conditional knockout of Elp1 in the neural crest component does not affect ganglion size during the onset of gangliogenesis, but defects arise during subsequent development as the nerves grow towards their targets. Defects in Elp1 CKO mice include apoptosis of TrKA-positive neurons and their loss in the target sites. Finally, they show that only TrkA-positive neurons are affected in the Elp1 CKO mice. Overall, the results from this study are informative and contribute to our understanding of Familial Dysautonomia. Although the authors use the TrKA/B/C staining to quantify some of their data, they should have taken advantage of these specific markers in addition to the position identity of neural crest and placode-derived neurons in the ganglia to strengthen their observations of specific knockdout of Elp1 in neural crest-derived neurons as well as the targeting defects. The authors would need to clarify several statements in the manuscript and quantify most of their data.

1. In characterizing Elp1 expression during gangliogenesis, the authors use several markers to identify the placode and neural crest-derived neurons in regions where they appear to overlap. However, these neurons are segregated in the proximal (neural crest) and distal (placode) regions of the ganglion, which in addition to the markers they used would make a stronger point. The authors could also use similar location of neurons in addition to differential expression of TrKA/TrKB to confirm the absence of Elp1 in the neural crest-derived neuron as opposed to the placode-derived neurons of the CKO mice, and also to show that neuron apoptosis occurs in only the distal region of the trigeminal ganglion. Furthermore, the authors could use this differential expression of TrKA and TrKB to show the specific loss of TrKA neurons in the target sites of the Elp1 CKO mice.<br /> 2. The authors state that two litters were examined per experiment, but do not provide the numbers of knockout and wild-type mice used for each experiment. In addition, quantification of the data such as the thickness of the central nerve root between control and Elp1 CKO mice would make the authors' claim stronger.<br /> 3. The authors conclude that nerve defects caused by loss of Elp1 could be based on the target region. What is the expression pattern of NGF in the target tissues where the nerve bundles appear to be disformed in Elp1 CKO? Does the NGF expression overlap with other neurotrophic factors in this region?<br /> 4. The authors suggest that in Elp1 CKO mice, most TrKA neurons do not express Elp1. Since this was knocked out in NC-derived neurons, these results would be much stronger of they showed that Elp1 expression is maintained in the placode (TrkB) neurons under these conditions.<br /> 5. Are there specific targets innervated by neural crest- or placode-derived neurons? Given the defects observed in Elp1 CKO, it would be good to know what happens to the placode-derived neurons (TrKB neurons) that should be functioning normally.

Reviewer #1 (Public Review):

Mutations in Elp1 is a known genetic cause of Familial Dysautonomia (FD), and many deficits in the trunk peripheral nervous system and autonomic nervous system have been described using a Wnt1-Cre driven conditional knock-out (CKO) line of Elp1. Because patients also have loss of pain and temperature perception in the craniofacial region, the authors studied the trigeminal ganglion and its nerves, which innervate the face and oropharynx, in Elp1 CKO embryos during the period of trigeminal axon outgrowth. It was of interest to compare the previous findings from the trunk sensory/autonomic system, which is derived entirely from neural crest progenitors, to their findings in the trigeminal system, which has a dual origin from neural crest and cranial placodes. The authors present data indicating that Elp1 CKO specifically affects the initial axon outgrowth and maintenance of peripheral innervation of neural crest-derived, TrkA-expressing neurons.

Major strengths and weaknesses of methods and results:<br /> • The authors have used an appropriate Cre line to delete Elp1, a known causative gene of FD, in the majority of neural crest progenitors of the trigeminal ganglion (Vg). However, the authors would need to consider reports that there also are Wnt1-negative neural crest cells residing in the mouse embryonic Vg, i.e., Wnt1-Cre is not expressed in every neural crest cell and therefore there will be neural crest derived cell in the Vg of their embryos in which ELp1 has not been deleted.<br /> • The images are of very high quality and mostly are convincing. However, some figures should include high magnification of the cells. For example, in Figure 1-supplemental, there needs to be a high mag image of pax3+ cells to show that there is no Elp1 in the neural crest derived neurons or glia. At low mag, the data support the claim, but high mag would be much more convincing. Same comment with regards to Sox10+ cells in Figure 1P, and double-labeled cells in Figure 7-supplement and Figure 8.<br /> • Figure 3 shows nicely that the Vg appears to form normally in Elp1 CKO embryos, but in several places in the results and the discussion, the language implies that the authors analyzed neural crest migration, which they did not. This has been elegantly done in frog - as the authors cite for Elp3 - and in a mutant mouse using a Sox10-Venus reporter line (Karpinski et al., 2021 Dis Models Mech). Either the authors should do these types of experiments to directly address migration, or they should modify their language to state that they presume the migration pattern is normal. Similarly, the authors state in the section (p10, para2, line 7) that the cells are "appropriately distributed throughout the forming ganglion". This is a rather vague statement with no description of the data that support the conclusion other than eyeballing the picture. Others (e.g., Karpinski et al 2021) have shown that one needs to perform precise neighbor-relationships analyses to detect differences between genotypes in the diverse Vg.<br /> • It would be helpful to the reader if the composition of the "control" embryos were explained in the results/figure legends, and not just in the methods. Also, include the number of litters examined in each case and do the images come from siblings? What is the variance between litters?<br /> • The images in Figures 4, 5 and 6 are exceptional, and convincingly show that there are different axon trajectories between wild type and mutants. But disappointingly, with the exception of the Scholl analysis in Figure 5, there virtually no quantitation of the phenotypes. The descriptions (e.g., p11, para1) are descriptive and vague. Also, are the types and the levels of abnormal axon trajectories shown in each figure found in every embryo analyzed (N= only 2)? The claim that the central root in Figure 4 is smaller in Elp1 CKO, needs to be actually measured. Also, it would be useful to compare their "bulk" axon data to more recent reports using cutting edge approaches to label single Vg axons.<br /> • The rationale for studying a sensory ganglion that derives from two different progenitor populations is excellent because it allows the authors to compare their results to the many findings have already been described for the trunk sensory system, which is derived entirely from the neural crest. Many of the findings for Vg are the same as what was found for trunk sensory ganglia, so impact is rather low. It could be increased significantly if other cranial ganglia were investigated for comparison. As examples: VIIIg is derived exclusively from cranial placode progenitors; the facial nerve has one ganglion that is neural crest derived and one that is placode derived.<br /> • The authors very nicely show that the Elp1 CKO causes a decrease specifically in TrkA expression in axons and a loss of TrkA Vg neurons. This corroborates previous data for sympathetic and trunk sensory neurons. I do feel that the description in the text of whether Trk expression segregates with sensory modality and/or with neural crest versus placode origin is missing some references, for example the apparent specific effect of the 22Q11 deletion on neural crest derived, TrkA+ Vg neurons. As state above, it also would be useful to discern neural-crest derived Vg neurons by a genetic lineage tracer such as Wnt1-gfp.

Reviewer #2 (Public Review):

This paper questions the processes that lead to T cells being sustained throughout life despite the involution of the thymus with age. As the temporal dynamics of individual cells and their offspring cannot be readily traced in vivo, a combination of new experimentation and reassessment of published data with hypothesis-based mathematical model fitting is used to assess the merits of alternative possibilities.

The first experimental methodologies used for primary data is a cohort of congenic busulfan chimeric mice who underwent bone marrow transplants between 7 and 25 weeks of age. With distinct CD45 variants on the host and donor cells, measurements at distinct time points reveal relative information on the proportion of original and donor cells.

The considered interpretation of the first section indicates the general issue that care must be taken when naively fitting mathematical models as the marginally better fitting model to the CD8+ data proves to be inconsistent with data shown elsewhere. The age-dependent loss model, which well explains the CD4+ data and is a good explanation of the CD8+ data, is then further explored.

The mathematical model is used to make out-of-sample predictions based on a data set published by another lab (Houston et al.PNAS 2011) measuring the ratio of recent thymic emigrants to mature naive cells, through Rag2 GFP+ expression. It is demonstrated that the age-dependent loss model predicted the trends, but not the quantitative values, in both the CD4 and CD8 ratios, while the age-dependent division model does not. Although one would not necessarily expect perfect quantitative alignment across such distinct systems, one notices that the quantitative fit is not perfect. While no suggested explanation is provided by the authors, it's clear why the age-dependent division model is rejected.

The second of two experimental methodologies is a Rag GFP+ Ki67 RFP+ reporter mouse to enable tracking of recent thymic emigrants in neonates to enable mathematical models to extract quantitative information on kinetics, dependent on model assumptions. For CD4+ cells, the model based on adult mice makes notably accurate predictions.

To resolve a conundrum in the early time point CD8+ data, externally published data (Reynaldi et al., PNAS 2019) is reexamined. At this stage, the mathematical model becomes more complex and so inferences are a little more speculative, but - within the natural caveats that come with those - the inference would be that naive CD8 T cells rarely divide and increase their capacity to survive with cell age, but to explain the early time point data it is suggested that generated within the first few weeks of life are lost at a higher baseline rate than those in adults.

The discussion element of the paper recapitulates and contextualises earlier inferences, neatly summarising the findings. The Supplementary Information is extensive and clear and should enable reconstruction of the work by others. While further experimental data may support or challenge the inferences made in the present paper, the clear line of reasoning, sophisticated set of considered data, and analysis tools are convincing and the piece would form a worthy addition to the literature.

Reviewer #1 (Public Review):

The paper uses a very clever combination of datasets and mathematical models to distinguish how host age, cell age, and cell numbers influence the proliferation and loss rates of naive T cells in mice throughout their life. The paper demonstrates very nicely how easily one can draw false conclusions based on cross-sectional data on T-cell turnover. For example, the observation that naive T cells in young mice have higher levels of Ki67 expression is generally interpreted as evidence for homeostatic regulation of cell numbers, which are relatively low at a young age. This paper shows that in fact, these high Ki67 levels are more likely unrelated to cell numbers and instead reflect the different dynamics (or even just different inherited Ki67 expression levels) of cells that have recently migrated from the thymus. The analyses show that a basic model in which naive T cells gradually attain extended lifespans in a cell-intrinsic manner provides a good description of the data obtained from clean laboratory mice.

It remains unclear how these data should be translated to the human situation, as naive T-cell maintenance in mice and humans differ fundamentally. While in mice, the vast majority of new naive T cells are made by the thymus, and naive T cells hardly undergo cell division without differentiation to memory cells, in humans the situation is completely different. Naive T cells in human adults are extremely long-lived and the cells that are being renewed are made by peripheral T-cell division, not by output from the thymus. It, therefore, remains questionable whether the insights obtained in this paper directly translate to the human situation. Related to this, it should be noted that all findings described here were made in clean laboratory mice. It remains unknown to what extent the described mechanisms may differ in a more natural setting in which mice are regularly exposed to foreign antigens.

Reviewer #3 (Public Review):

It is known that stress-induced plant volatiles can be perceived by neighboring plants, but the underlying mechanism is largely unknown. The authors of this manuscript attempted to identify receptors that interact with isoprenoids, the most abundant plant stress-induced volatile organic compounds (VOCs). They established a framework that allowed them to screen for plant odorant-binding proteins (OBPs) through all available databases. Comparing plant protein sequences with a large group of animal OBP sequences and expanding the investigation to previously known OBPs turned out to be fruitful. Molecular simulation is a powerful screening technique to study the interaction of the potential plant OBPs with selected isoprenoids. The finding that plant OBPs may bind different VOCs in the same binding site is interesting.

The in silico selection of the plant OBP candidates and ligand docking experiments provide a useful tool to understand signals that underpin plant-plant interactions as well as how plants respond to those cues. However, the conclusions made by the authors may be too premature:

Isoprenoids are both constitutive and stress-induced. The authors did not address, through such a docking study, how plants distinguish VOCs associated with impending stresses, particularly when the OBPs could generally interact with multiple VOCs. One may also wonder how many other types of VOCs exist that are stress-responsive, and what their receptors are.

The BLAST of 432 OBPs did not find the JA receptor, suggesting that the JA receptor sequence is not closely related to that of the OBPs that insects use to recognize plant volatiles in order to locate suitable host plants. Therefore, identification of OBPs based on sequence similarity may miss those plant proteins that possess OBP structure and function but differ in primary sequences with existing OBPs.

Docking studies can serve as a lead for potential OBP-VOC interaction, but in silico data alone is insufficient to conclude the role of the putative OBPs. Functional evidence is necessary to demonstrate their interaction with VOCs because such interaction indeed affects plant response.

Reviewer #2 (Public Review):

The Authors started from the consideration that volatiles emitted by plants may serve as communication media to other plants. Hence, the 'receiver' plant needs a way to bind these molecules and initiate the transduction cascade, that is a dedicated protein that is able to bind volatiles because it has a binding groove able to accomodate the ligand. The Authors searched the available databases for putative proteins that can serve this goal, by similarity to already known proteins from plants and animals. Then, by molecular docking known plant volatiles, the Authors demonstrate that the identified proteins have the predicted structural features that allow ligand binding.

The main strength of the paper resides in the idea and in the wide search for similarities among proteins pertaining to different kingdoms. The main weakness of this work resides in the fact that it is entirely in silico, without providing any actual data from real proteins. However, it is well known that in silico simulations may be only suggestive of the actual behavior of a protein.<br /> The Authors reached their goal of identifying various proteins with putative binding abilities, however the lack of any experimental data should be made clear since the beginning of the manuscript, in the title and abstract. With this caveat, the information provided in this paper may be a useful starting point for experimentally testing of the hypotheses.

Reviewer #1 (Public Review):

The chemical sensing mechanisms of plants are largely unknown. The authors hypothesise that plant chemical receptors may be transporter proteins or odorant binding proteins. The authors carried out an "in silico" analysis to investigate whether there are analogous proteins in plants to odorant binding proteins found in animals and insects. A search through protein databases found 5 possible sequences or partial sequences and these were used for further screening using BLAST software for screening comparison. The search for OBPs in plants based on literature evidence and sequence similarities to known OBPs from animal organisms or database annotations , produced a list of plant proteins, or protein families, with potential OBP activity. Molecular docking simulation experiments, to identify candidate plant OBPs were carried out identifying the ligand binding sites and calculated binding energies together with binding constants. This data mining activity has produced some interesting data but also raises several questions. The majority of binding constants tabulated were in the hundreds of micromolar to millimolar concentrations which raises the question of what concentrations of volatile chemicals are plants able to detect. Most odorant binding proteins found in insects and animals have binding constants in the low micromolar range for the target analytes. So if these putative plant odorant binding proteins do have a role in chemical sensing further practical experiments are needed and these need to be discussed. The data are not enough to indicate that these putative proteins identified have relevant functions in chemical sensing in plants.

Reviewer #3 (Public Review):

There has been considerable recent interest in understanding the high degree of diversity observed in microbial communities. From a theoretical perspective, this has led to a resurgence of interest in resource-competition models. Several recent papers have studied the effects of trade-offs on total enzyme budgets within these models. An interesting observation is that with exact trade-offs, communities can self-organize to a state with an arbitrarily large number of species coexisting. One assumption of these models is that the total "cost" of enzymes is a linear function. The current work relaxes this assumption, and shows that this state of arbitrarily high coexistence relies on the linearity of the cost function.

Strengths: This study is rigorous, clearly presented, and the conclusions are mathematically sound. The authors analyze both chemostat and serial dilution systems.

Weaknesses: The results are qualitatively as expected from previous studies of the role of inexact trade-offs, and are more limited. The nonlinear trade-offs explored here are essentially equivalent to the unequal enzyme budgets explored in prior work. Indeed, these nonlinearities can be directly mapped to unequal budgets: for example, a nonlinearity that favors expression of a single enzyme is directly equivalent to a larger enzyme budget for species that produce only a single enzyme. Previous studies showed that unequal enzyme budgets lead to a loss of diversity, as is found in this work. Moreover, these prior studies found that even if trade-offs are not exact, the slow loss of diversity due to inexact trade-offs can be offset by invasion of new strategies and can therefore still lead to a large number of coexisting species.

The likely impact of this work on the field is modest, given that those who are already experts in the field will recognize that nonlinear trade-offs are equivalent to unequal enzyme budgets. Moreover, the current study does not actually provide any specific support for nonlinear trade-offs other than a few remarks in the Introduction.

Reviewer #2 (Public Review):

This paper deals with the diversification of metabolic strategies in an evolving population. The authors consider a consumer-resource model under different metabolic trade-offs (sublinear, linear, and superlinear). They show that the linear case is a marginal scenario that corresponds to high diversity as a consequence of neutral evolution. Both the sub- and superlinear cases lead to the coexistence of fewer species than resources, as expected by the competitive exclusion principle.

The manuscript is well written and easy to follow. The derivation using adaptive dynamics is interesting and the results are robust. I am mainly concerned by the premises of the work.

l 70 "This is a very interesting finding because it violates the competitive exclusion principle". This is not strictly correct (and I think this is a central point). To violate the competitive exclusion principle, more species than resources should *stably* coexist. The stability requirement is essential. Otherwise, the principle can be easily falsified by a neutral model: in presence of even 1 single resource, an arbitrary number of ecologically equivalent species coexist (neutrally). Neutral coexistence is, as well known, structurally unstable: arbitrary small differences (which break the ecological equivalence) drive many species to extinction (restoring the bound on diversity given by the number of resources).

In the model by Posfai et al. (2017) coexistence is in fact only neutral. There is a manifold of fixed points and stability is marginal (several eigenvalues of the community matrix are equal to zero, e.g. see https://arxiv.org/pdf/2002.04358 ). The fixed point of their dynamics (abundance of different species) depends on the initial conditions. The high levels of diversity are, as a consequence, structurally unstable. This can be shown in multiple ways: introducing an (arbitrarily small) species variability in the trade-off (E depends on species identity), introducing variability in the dilution rate d (appearing in eq 3), or, as done in the paper, by altering the functional form of the trade-off.

This is a central point. It explains why many species are observed in the model by Posfai et al. And it explains why the result is extremely (infinitely) sensitive to the parameterization. These ecological considerations are mirrored in the fact that for gamma = 1 the evolutionary dynamics are neutral.

Reviewer #1 (Public Review):

Previous theoretical work argued that among species that compete for resources, physiological tradeoff (e.g. consuming more of food 1 leads to consuming less of food 2) can give rise to species diversity that greatly exceeds the number of resources, even in a well-mixed environment not conducive for species diversity. If previous work were to be general, it would be exciting because this offers a clue to the puzzle scientists have been trying to solve for a long time: what supports high species diversity? Caetano et al show that the finding from previous work only holds under a very restrictive condition (tradeoff function being linear). When that condition is violated (which can frequently occur in biology), we end up with either a single generalist species, or specialists each specializing on a single resource. Thus, in general, the total number of species cannot be larger than the total number of limiting resources in a well-mixed environment, as posited by the competitive exclusion principle. In short, we are back at where we were.

Reviewer #3 (Public Review):

This manuscript clearly shows that, in Neurospora, ACF chromatin remodeler represses some of H3K27-methylated genes in an H3K27me-dependent fashion. This is the first report to demonstrate ISWI class remodeler functions through H3K27me. The main weakness of the manuscript is the modest impact on mechanistic understanding of how ACF or H3K27me functions. Also, the authors' model needs additional support to firmly establish the causality between ACF-dependent nucleosome repositioning and transcriptional repression.

Reviewer #2 (Public Review):

In this paper Wiles et al. show that mutations in the iswi and acf genes, which encode components of a nucleosome remodeling complex, lead to expression of a subset of H3K27me-repressed genes. The strengths of the paper include the detailed genomic analysis supporting the statements that Iswi and Acf regulate a subset of H3K27me3-repressed genes. Data showing that the +1 nucleosome shifts 50bp in H3K27me-genes upregulated in the iswi mutant is also very strong. There is strong data documenting the proteins that Iswi interacts with in N. crassa. The data showing the nucleosome shift in the acf mutant is not as strong. The summary figure is highly speculative because there is no data for discrete localization of Acf. Another piece of data that is lacking is what happens to H3K36me in iswi and acf mutants. Knowing this is important because a similar set of genes seem to be derepressed in an ash1 mutant as in the acf and iswi mutants, although the level of depression in ash1 is not as great as in iswi mutants. The summary diagram shows loss of H3K36me as a separate mechanism than loss of the ACF complex. We don't know that since there was no analysis of H3K36me in iswi or acf mutants. Still, the major findings of the paper are important.

Reviewer #1 (Public Review):

Polycomb repression of heterochromatic genes differs in different organisms but has been most widely studied in Drosophila. Neurospora lacks components of Drosophila polycomb repression complexes.

Using a powerful forward genetic screen the authors found that components of the ACF complex were required to maintain repression of H3K27 methylated heterochromatic genes in Neurospora.

ACF binds widely to chromatin across the genome and is not restricted to heterochromatic genes. This indicates that it also functions outside of heterochromatin. Its interaction with heterochromatin is affected somewhat by the loss of H3K27 methylation.

ACF appears to be necessary to position the +1 nucleosome over the promoter of H3K27 methylated heterochromatic genes.

Reviewer #3 (Public Review):

This study shows learning-induced compartmentalized plasticity in Kenyon cells (KCs) of the mushroom body gamma lobe using a genetically encoded acetylcholine sensor. The authors made several major discoveries that the plasticity is bidirectional and depends on memory valence. Interestingly, the compartments along the axon terminals of the KCs undergo spatial 'gradient' of plasticity. Knock-down of the genes that differentially regulate intracellular calcium induced distinct effects on learning-induced plasticity. These results explain, at least partly, that learning-induced plasticity at KC-MBON synapses takes place on the presynaptic side. The experiments and analysis are overall thorough, and conclusions are generally supported by the results. In the following, I list some flaws to be addressed that concern necessary controls, more careful interpretation of the statistics and results, and the choice of the cac RNAi strain.

Reviewer #2 (Public Review):

Stahl et al. investigate presynaptic Kenyon cell plasticity in the context of appetitive and aversive memories, using a genetically-encoded acetylcholine receptor-based sensor as primary read-out (instead of calcium indicators used in several previous studies). Therefore, the authors investigate one step downstream of the classically conducted calcium (or cAMP, etc.) imaging experiments - the level of neurotransmitter release. Likewise, this is one step upstream of the other widely-used readout of the postsynaptic MBON activity (or dopaminergic neurons). The authors investigate contributions of CS+ and/or CS- plasticity throughout the compartments of the gamma lobe. All in all, this manuscript confirms several previous studies that investigated individual compartment plasticity (often plasticity is measured at the level of the MBONs and therefore in single compartments), and taken together with other recent publications, this study can be seen as a valuable compendium (as it addresses appetitive and aversive short-term memory protocols in the context of several compartments). As the authors address an important step between presynaptic and postsynaptic calcium transients, their work actually confirms that conclusions deduced from changed calcium transients correlate to neurotransmitter release, as expected. This study also addresses differential effects on the CS+ and the CS-, which is important, as previous studies often concentrated (partially for technical reasons) on either the CS+ specifically, or the relative changes of CS+ to CS- only. The authors further investigate the role of the active zone-localized voltage gated calcium channel cacophony in memory-related plasticity - this part needs to be strengthened by additional controls.

Reviewer #1 (Public Review):

The paper aims to understand if and how axonal compartmentalization functions in the MB 𝝲 lobe in the context of olfactory learning. Further the authors focus on these process and find distinct molecular mechanisms underlying appetitive and aversive learning (cac and IP3). Lastly the authors focus on how this compartmentalization influences the downstream MBON function.

The paper is written well and straightforward in its approach and implications. The neuroanatomy, experimental details are clearly presented and I would rate this paper very high on a readability scale. The imaging and behavioral approaches are appropriate for the research question and address the limitations from a technical perspective. Results are presented well, but authors don't dwell on the results much before transitioning into another part of the question they seek to ask. This undermines the complexity of the MB circuits and an effort should be made to address dynamics of other lobes that underlie these behaviors. One issue is that even though the experiments work well together in supporting the results they fail to incorporate more complex dynamics of other lobes or consider the EM connectivity which might complicate the simplistic interpretations. The role of dopamine in this compartmental logic has not been directly addressed which potentially plays into this circuit.

This is impactful work as it addresses the pre-synaptic dynamics of MB with a focus on ACh release across compartments and how they differ between two forms of learning. The use of Grab Ach is a big highlight of the paper as this question could not have been asked without this tool. The authors also address the regulation of Ca2+ responses in the compartments that result in altered responses in valence-coding MBONs. Its also interesting to see the switch in 𝝲2 and 𝝲3 dynamics between appetitive and aversive learning. Lastly the match of Ca2+ responses in MBONs with the Ach compartmentalization highlights the behavioral relevance of axonal compartmentalization.

Reviewer #3 (Public Review):

Adefuin et al use multiphoton imaging of M/T cell responses to investigate whether neuronal representations of binary mixtures can be explained as a sum of the components. The current view in the field (built largely from studies in anesthetized animals), is that mixture summation is non-linear and increases with the degree in glomerular response overlap elicited by the components. The authors reproduce these results and ask whether the same phenomenon is observed in the awake state, in particular when the animals are engaged in an odor discrimination task. Unlike in the anesthetized state, the authors find that mixture representations are linear in the awake brain. They use a series of systematic behavioral paradigms to show that the observed linearity in the awake state (compared to anesthetized) is not dependent on task engagement (reward is given randomly, post-odor) or stimulus relevance (reward is given before odor). While the experiments are well done and the data is presented clearly, I have several major concerns about the interpretation of their results.

1) Given the data the authors present, it is unclear if one can conclude that the olfactory system is more or less linear in the awake state compared to the anaesthetised one. What seems to change most across the awake vs. anesthetized state is the response amplitude. Responses appear to be ~3x smaller in the awake mice. In the anesthetized state, non-linearity seems most apparent for large response amplitudes (>5 dF/F) with mixture responses being sub-linear, most likely due to saturation effects. The authors themselves do an analysis in Figure 6 - supplement 1 to show that most of the observed non-linearity in the anesthetized animals can be explained away after accounting for amplitude normalisation. The authors use this analysis to comment that the level of linearity is the same across all the three awake states, but the same figure shows that it is in fact the same even for the anaesthetized state.

To put it differently, it is indeed true from the authors data that the OB response gain is significantly lower in the awake state, but it is unclear if the summation is more linear if measured at similar response amplitude regimes in both awake and anaesthetised mice.

2) The authors argue that keeping response amplitudes small in the awake brain prevents sub-linear summation and therefore may lead to better mixture decomposition. They do a decoding analysis in anaesthetised mice to show that linear mixture representations (instead of using observed sub-linear representations) make odor classification easier. However, I find this analysis uninformative and misleading. It is no surprise that the decoders trained on single odor representations should perform better (or equivalent) when using linear sums as input instead of observed sub-linear representations. The authors use this observation to suggest that this mechanism aids discrimination ability in the awake state. However, given that even the single odor responses are much weaker and noisier in the awake state, it is likely that even the single odor discrimination ability is poorer in the awake state. By the same logic, mixture decomposition might be also much poorer in the awake brain than the anesthetized brain, even though summation is more linear, just because responses are weaker and noisier. In my opinion, the authors should compare decoding accuracy across awake vs. anesthetized responses if they want to assert that linearisation of responses in the awake brain leads to easier decomposition. Because otherwise, while linearisation in principle can aid decomposition, at least in the form that the authors observe here, it may come at a high cost on signal-to-noise ratio which would undo the gain that linearity provides, in principle, for discrimination.

3) At a more philosophical level, to this Reviewer, it is unclear if anesthesia vs. awake state difference in response should constitute the main focus of the manuscript. The authors explore summation properties under four different brain states, one of which is anaesthesia (also least behaviorally relevant). In three out of four states, they observe that summation is linear. In the fourth (anaesthesia), they observe that summation is sub-linear, but this happens at much larger response amplitude regimes compared to the three awake states sampled, presumably due to saturation. To me, it seems that the Authors here show that mixture summation in the OB, is largely independent of brain state since it is unaffected by whether the animal is task engaged or motivated etc.

4) It is unclear how to interpret the dendritic imaging comparison. First, the dendritic signal is pooled across many cells. If any of the cells that are being pooled shows sub-linearity, the pooled population response will look sub-linear, albeit less so than at the single cell level. Second, again like for the anesthetized vs. awake comparison, there is a discrepancy in response amplitudes - dendritic responses are ~2x stronger than the somatic responses and sub-linear summation would be more apparent as one approaches the saturation regime. Third, dendritic responses pool both mitral and tufted, while the somatic data the authors present is predominantly from tufted cells.

Reviewer #2 (Public Review):

This study addresses how complex stimuli are represented in neural responses. This is particularly relevant to olfaction because the vast majority of stimuli are complex mixtures that perceptually, are not easy to decompose into parts. Nonetheless, the ability to discern a relevant odor from background odors is essential. This process is easier when neural responses to mixtures reflect the linear sum of the responses to the individual components. The main conclusion of this study is that the linearity of olfactory bulb responses to two-component mixtures increases awake versus anesthetized states. The authors provide some evidence to support this claim. However, this could be better quantified and there is a temporal aspect of linearization that is not addressed. Perhaps the most interesting aspect of the study is the difference in linearity between the dendrites and the somata of the mitral/tufted cells. But a statistical analysis of this finding was not evident. Overall a mechanistic or functional approach to understanding these findings is lacking. The differences linearity between the anesthetized and awake are simply explained by response saturation anesthetized animals. There are hints at mechanism by which linearity is supported in the OB with comparisons between soma and dendrite but these are not well developed. There is a model that addresses the functional significance of linearity but this is only supplemental and not well described.

Reviewer #1 (Public Review):

Adefuin and colleagues examined the interaction between components of binary odor mixtures in odor responses in mice. The authors used two-photon calcium imaging from the soma and apical dendrites of mitral/tufted cells in the olfactory bulb. Odor responses were measured in various conditions: under anesthesia (ketamine/xylazine), while well-trained mice were engaged in an odor discrimination task, or disengaged. The authors first show that mixture components interacted sublinearly in a large fraction of mitral/tufted cells (46%; Fig. 6D) consistent with previous studies. However, when odor responses were measured in awake animals, very few mitral/tufted cells showed sublinear responses at soma (8-9%; Fig. 6D). Interestingly, sublinear interaction was evident in apical dendrites of mitral/tufted cells (45%). Whether mixture components are represented linearly or not in the olfactory system is an important question, related to the animal's ability to identify or segment mixture components. Somewhat contrary to previous studies, this study demonstrate largely linear interactions. Furthermore, this study compares various behavioral conditions. These results are important and of interest to those who study sensory systems. I have a few concerns regarding data analysis.

1. Non-linear interactions are detected by the activity showing a deviation from linearity greater than 2 standard deviations. Using this criterion, non-linear interactions might decrease if the trial-by-trial activity becomes more variable. This is concerning because the activity might be less variable in the anesthetized condition, and the reduction in sublinear interactions in awake conditions may be due to a general increase in response variability during awake. Can the authors exclude the possibility that the decrease in sublinear interactions is merely due to an increase in response variability in the awake conditions. This issue also applies to the comparison between apical dendrites versus soma; are the signals in apical dendrite less variable (maybe due to some averaging across dendrites from multiple cells; see the following point 5)?

2. Related to the above issue, it would be useful to analyze the difference between conditions using different metrics to fully understand what really are different between conditions. The scatter plots shown in various figures do not show drastic differences between awake and anesthetized conditions, as might be indicated by the percent of sublinear responses. It would be useful to characterize the magnitude of sublinear/supralinear effects. For example, one can calculate a fractional change in the mean response. Does this measure show consistent difference between awake and anesthetized conditions?

Reviewer #3 (Public Review):

Mechanical stress has been emerging as the key factor for activating pro-metastatic features, and authors hypothesize confined migration results in anoikis (death in suspension) and increased invasiveness.

By clever repurposing of transwell membranes, authors have generated a confined migration assay (CM), in which cells that have crossed the 3 micron-pore membrane are collected, cultured and further analyzed. The intensity of the CM-related response was shown to increase with number of CM rounds, and the response decays and reverts back 5 days after the CM event.

The resistance to anoikis achieved by CM was not conferred by compressive stress, or migration without constriction through 8-micron pores, and it was further demonstrated to rely on NFkB activation and IAP regulation at post-transcriptional level.<br /> Using RNASeq, authors next show that while transcription is globally inhibited, resulting in lower nuclear stiffness, components of cell adhesion and regulation of NK-cell cytotoxicity were upregulated. Both of these functions were elegantly and succinctly confirmed by time-lapse measurements of cell velocities and immunofluorescence.

Finally, in vivo experiments confirmed metastasis was increased in tail-vein injected cells post-CM.

Reviewer #2 (Public Review):

The authors present a detailed account of the anti-apoptotic characteristics of breast cancer cells that have undergone confined migration, specifically demonstrating an upregulation of cIAP1, cIAP2 and XIAP that promotes anoikis resistance. These cells were also more migratory and potentially displayed a resistance to immune surveillance.

On the whole the paper is technically well performed, although the conclusion that confined migration causes these effects needs further work to be validated. A key question is whether confined migration has caused these changes to occur within individual cells, or whether the assay design results in selection of cells with these characteristics.

For example, it may not be a surprise that selecting cells on the basis of their ability to migrate through a membrane would result in cells with an increased random migratory capacity? Therefore it may also be possible that these innately migratory cells have different expression patterns that include an upregulation of anti-apoptotic proteins? Therefore, there is a need to determine whether these cells were present in the population prior to confined migration, or whether these characteristics were acquired during the process.

Reviewer #1 (Public Review):

This manuscript by Fanfone et al., describes the use an in vitro model of confined migration through transwells via a serum gradient, to study how mechanically challenged breast cancer cells acquire IAP-mediated resistance against anoikis. They demonstrate that these mechanically challenged breast cancer cells survive low-attachment culture to form spheroids. Additionally, the authors demonstrate that after confined migration NFkB is activated but potentially dispensable, as when it is blocked, it does not impact anoikis or spheroid formation. Also, mechanically challenged breast cancer cells demonstrate enhanced motility. Finally, the authors also demonstrate in vivo how mechanically challenged breast cancer cells were more metastatic competent to successfully colonize the lungs. This is an impressive manuscript that reveals how developing an in vitro model of confined migration provides much needed insight as to how microenvironmental constriction can in fact aid cancer cells in acquiring a pro-survival and enhanced migratory phenotype to successfully metastasize.

Reviewer #3 (Public Review):

In this MS, the authors first reproduce with some additional details, in particular using various superresolution approaches, our knowledge of the peri-synaptic localization of clathrin labelled endocytic zones (EZ) and determine that peri-synaptic EZ are dynamically distinct from shaft clathrin-coated structures, being more stable. They then perform an extensive characterization of the localization of a set of endocytic accessory proteins and show that a large fraction localizes to the perisynapse. These include HIP1R, ꞵ2-adaptin, Dyn2, CPG2, Eps15, and Itsn1L. In contrast, a subset of other endocytic proteins (PICALM, Epsn2, Amph and FCHO1) were less enriched at EZ. With time lapse video-microscopy, they analyze the residence of these various endocytic proteins next to the PSD and find that they exhibit a range of behavior, from short lived proteins such as FCHO1 to proteins more stably associated to the PSD.

Next, using two-color single molecule localization microscopy, the authors study the colocalization of these various proteins with respect to clathrin in EZ, and find that endocytic proteins have distinct spatial organization relative to the clathrin structure marking the EZ. They find that ꞵ2-adaptin, Eps15, and Itsn1L were often distributed in smaller patches around and sometimes within the EZ. HIP1R showed a more homogenous distribution and often colocalized with the EZ. Dyn2 showed an overall more homogenous distribution, similar to HIP1R.

Finally, they touch upon the mechanism of EZ localization next to the PSD. They first recapitulate the central role of Shank by finding that Shank-KD also reduced the association of the PSD with other endocytic proteins in addition to clathrin. They then use a pharmacological approach to suggest that EZ positioning is not related to actin dynamics, and overexpressed an AP2 mutant that unable to interact with PIP2 to suggest that interaction with the membrane is not necessary for EZ positioning.

Altogether this is a carefully performed study, but the knowledge acquired remains relatively incremental to our previous understanding of EZ positioning.

The identification of proteins of the endocytic machinery localized at EZ in not surprising and lacks mechanical insight. The study of the mechanism of EZ positioning next to the PSD also lacks additional insight compared to our previous knowledge of the implication of Shank. Finally, this study does not address either the mechanism of EZ assembly nor its function, nor its regulation, which are to my sense the important questions to answer regarding EZs.

Reviewer #2 (Public Review):

Catsburg et al attack an interesting topic with a combination of advanced molecular and imaging approaches. The endocytic zone in neuronal spines congregates the endocytic protein clathrin near synapses, and has been demonstrated in several studies to be of functional importance for regulating synaptic transmission by influencing the endocytosis and recycling of glutamate receptors. Basic characterization of this "zone" has been provided by these authors and others, but a more complete description of its character or components has been essentially missing in the field.

The work first provides new descriptive characterization of clathrin structures in spines vs dendrites using an excellent knock-in approach they recently developed. These results are straightforward but important validation of earlier findings here using the CRISPR approach, and also will be useful baseline information with which to examine changes in the zone for instance during various forms of neuronal plasticity. Then, taking what seems like a tip from the work in other cells as to the organization of proteins at sites of clathrin-mediated endocytosis, they survey the abundance and stability vs transience of a large set of relevant proteins. This is fundamental information necessary to discern the nature and role of the zone.

The authors then test two mechanisms that might hold the zone or its proteins in place, connections to the PSD protein Shank or to the abundant spine actin cytoskeleton. Experiments to manipulate these two connections do not demonstrate unraveling of the entire system, but instead show a fairly remarkable and specific loss of different proteins. Most notably (to this reader), after Shank knockdown, beta2 adaptin (part of the AP2 complex that was originally used to define the zone in EM) is lost, but Dyn2 (a cytosolic and dynamically recruited part of the machinery during CME) is retained. These and the related results provide new insight to the complex mechanisms that must be at work governing the assembly and dynamics of this specialized domain in spines.

Overall, the work is systematically and rigorously conducted, and the results are nicely presented with a minor few exceptions. The knock-in application will help establish this general approach as the new standard in the field. Indeed, the overexpression approach used for the protein survey by contrast with the elegant knock-ins feels slightly disappointing, indicating how important it is that the field move as quickly as possible to utilize knock-ins where possible. The overall conclusions about the components, dynamics, and mechanisms of the endocytic zone are stated carefully, and add greatly to understanding of this structure.

Reviewer #1 (Public Review):

The authors characterize the molecular organization of the endocytic zone (EZ) known to associate PSD with clathrin coats using advanced imaging approaches, including high-resolution STED imaging and time-lapse live-cell imaging. They also test 12 well-known endocytic proteins to see whether and how they colocalize with EZ and find that a subset of them show strong and long-lived colocalizations, whereas others show weak and transient colocalizations, with EZ by advanced imaging and cell biological disruptions. They find Shank is a key component of the postsynaptic density that coordinates the targeting and localization of the new EZ components. This is a careful and comprehensive analysis of the molecular composition and dynamics of known and additional EZ components using high-resolution and time-lapse imaging. The results are largely convincing and conclusive and provide insights into how synaptic membrane proteins and receptors are trafficked in and out of synapses to regulated synaptic plasticity, which would have significant impacts on broad fields of molecular and cellular neuroscience.

Reviewer #2 (Public Review):

In the submitted paper, the authors first show that activity of the CCC1 promoter is ubiquitous. They further analyze the phenotype of the mutant in the root and show a root cell elongation defect in epidermal cells as well as in root hairs. The ccc1 mutants also lack the collet root hairs and show trichoblast-atrichoblast cell fate identity defects in the primary root. The authors perform a set of elegant experiments where they show that, surprisingly, the ccc1 plants are resistant to hyperosmotic environment. The ccc1 cells show delayed plasmolysis, ccc1 seeds show better germination, and ccc1 root hair elongation is recovered in hyper-osmotic media. Interestingly, the absence of collet root hairs was also recovered in hyper-osmotic environment, even though it is not clear whether this was caused by 'reparation' of collet hair elongation or collet hair cell fate specification. The phenotypic analysis is carefully performed and the results are unexpected and intriguing.

The authors further show that in root trichoblasts, GFP-CCC1 localizes to the TGN/EE compartment, and that in this tissue, the fusion protein recovers the root hair elongation of the mutant. Further, the authors focus on the subcellular phenotypes of the endomembrane system performance in the ccc1 mutant background. It is shown that PIP2 aquaporin internalized less in the ccc1 than in control, which hints to that endocytosis is reduced in the ccc1 cells. An alternative explanation however could be that the mutant is more osmotically tolerant also on the subcellular level. To test the endomembrane trafficking rate, PIN2 aggregation and recovery from BFA bodies is performed, as well as quantifications of FM4-64 uptake, and the authors conclude that the mutant has generic endomembrane trafficking defects.

The authors hypothesize that the endomembrane defects might stem from a disturbance in TGN/EE luminal pH caused by an ion imbalance in the ccc1 cells. Therefore, they measured the luminal pH of TGN/EE using a genetically encoded phluorin and demonstrate a more alkaline pH values in the ccc1 mutants. Finally, the authors show that during hyperosmotic stress, the TGN/EE pH rises in the control plants, suggesting that this pH rise is functionally connected to the stress response. The second part of the manuscript that focuses on subcellular phenotypes uses advanced live-cell imaging tool and successfully measures pH in minute volumes of TGN/EE compartments. In addition, the specificity of the phenotype is demonstrated by careful analysis of vacuolar and cytoplasmic pH. These well performed experiments indeed point to the function of CCC1 in ion control in TGN/EE.

Weaknesses of the manuscript:

The functionality of the GFP-CCC1 fusion is questionable as it was impossible to obtain transgenic lines that would express GFP-CCC1 under the control of the native promoter, not allowing full complementation of the ccc1 phenotype. This hints to a possible dominant-negative effect of this particular protein fusion. The authors therefore express GFP-CCC1 using a trichoblast-specific promoter and show that the root hair elongation phenotype is complemented, demonstrating some functionality of this construct. Moreover, the root hair length data in the ccc1-1 mutants shown in figure 2D and 3C differ, which to some extent weakens the important conclusion that the GFP-CCC1 is functional at least in this cell type. Functionality of this construct is a crucial aspect for the manuscript. The possible dominant-negative effect of the construct weakens the conclusion about the subcellular protein localization, which in turn weakens the main conclusion of the paper - that CCC1 by regulating ion fluxes in the TGN/EE allows proper endomembrane functionality. The subcellular localization of CCC1 should be demonstrated without any doubts, as it was previously localized to the PM and endomembranes in pollen tubes (Domingos 2019). If CCC1 localized at the PM, alternative explanations of the phenotypes of the nature of the mutant phenotype that would include regulation ion fluxes across the PM would appear more probable than the TGN/EE hypothesis.

Quantification of endomembrane trafficking represents another important argument in the proposed hypothesis. The section that demonstrates the reduction in exo- and endocytosis is however not utterly convincing. It has been shown that a major contribution to the BFA body PIN2 pool originates from de-novo synthesis of the protein (Jasik et al, PMID:27506239). In the figure 6A, it is apparent that the BFA washout leads to disappearance of BFA bodies in the ccc1 mutant, but the level of PM fluorescence was decreased, leading to an apparent 'minimal recovery' of the cytoplasm:PM ratio. In case of endocytosis, the experiment combining FM4-64 uptake with BFA is hard to interpret as endocytosis visualization, because TGN/EE aggregation might be disturbed in the ccc1, as the authors suggest. A more detailed endomembrane trafficking then simple cytoplasm/PM ratios of signal could be performed to address what is happening with trafficking in this interesting mutant.

In summary:

The manuscript is clearly written, the logic of the text is comprehensible, the data seems robust and well presented. The manuscript attempts to explain the organ- and cellular scale phenotype of the root growth and root hair elongation by the subcellular defect in the TGN/EE luminal pH via defective endomembrane trafficking. The functional connection between the organellar and cellular phenotype and the function of CCC1 is however still somewhat preliminary.

Reviewer #1 (Public Review):

In their manuscript "Plant Trans-Golgi Network/Early Endosome pH regulation requires Cation Chloride Cotransporter (CCC1)" the authors sought out to understand the importance of the cation chloride co-transporter CCC1 on plant function and intracellular ion homeostasis. The authors provide new data showing that CCC1 functions at the TGN/EE where it regulates ion homeostasis. Plants lacking CCC1 show a disruption to normal endomembrane trafficking, leading to defects in root hair cell elongation and patterning. Interestingly the authors show that the cell elongation defects can be rescued by supplementing the plants with an external osmolyte such as mannitol. Through the characterisation of CCC1 in A. thaliana, this paper shows that cation/anion transporters are essential in maintaining fine control over endosomal pH, in addition to previously characterised endosomal proton/cation transporters such as NHX5, NHX6, and CLCd.

The paper is well written, and the experimental design is generally well thought out. The data mostly supports the authors conclusions, however there are some areas where changes are necessary to improve the clarity and completeness of the experimental work.

1) The co-localisation experiment of CCC1 with VHA-a1 (TGN/EE marker) shows that they highly overlap, however, there are clear regions where the CCC1 and VHA-a1 marker do not co-localise, suggesting CCC1 has a broader localisation pattern which is also alluded to in the text.

It is important to clearly determine which endomembrane compartments CCC1 localises to as this has large implications in interpretation of data regarding where the endomembrane trafficking defects originate from (eg: TGN/EE dysfunction, or other organelles, such as the Golgi and MVB/LE), and for comparisons with other intracellular transporters such as NHX5 and NHX6 (which have broader localisation at the Golgi, TGN/EE, and MVB/LE). A more detailed localisation approach by also assessing the co-localisation of CCC1 with Golgi and MVB/LE markers is necessary.

2) The authors identify defects in cell elongation in ccc1-1 root epidermal cells, as well as defects in the formation of collet hairs. It is not clear whether the defects in collet hair formation is due to defects in cell elongation, or in root hair cell identity as root hair cell identity is disrupted in ccc mutants. Since under control conditions some ccc mutants do not form collet hair cells at all this would suggest that the hair cell identity is also disrupted, rather than just elongation. However, the root hair length quantification experiment does show very clear cell elongation defects in ccc1 mutants. The two phenotypes should be differentiated more clearly in the text.

3) Figure 6 describes experiments designed to assess whether ccc1 mutants have defects in endo- and/or exocytosis. The authors assess endocytosis using an FM4-64 uptake experiment where they conclude that ccc1 mutants have defects in endocytosis. However, the data from the 10-minute time point (which is usually used to measure endocytosis) shows no difference between wild-type and mutant lines. There are clear differences in FM4-64 uptake to the BFA bodies after 60 minutes (Golgi+TGN) which instead suggests ccc1 mutants primarily have defects in post-Golgi trafficking, rather than endocytosis.<br /> The authors should also assess whether secretion/recycling of PIP2;1 and PIN2-GFP is altered by quantifying the signal at the plasma membrane, and potentially by performing FRAP assays of PIP2;1 or PIN2-GFP at the plasma membrane. The authors could also assess whether ccc1 mutants have general defects in secretion by visualisation of sec-RFP in ccc1 mutants. These experiments (in addition to the co-localisation experiments suggested above) would provide much stronger evidence to determine the exact source of trafficking defects.

4) The calibration curves from Fig. 7 are missing

5) The control image of PRP3::H2B in wild type seedlings is missing

Reviewer #3 (Public Review):

Layer 5 neurons of the entorhinal cortex are thought to play a key role in memory consolidation because they receive inputs from the hippocampus and send axons to large parts of the neocortex (Rosov et al., 2020; Witter et al., 2017). Six years ago, it was shown that Layer 5 neurons of the entorhinal cortex can in fact be divided into 2 populations (Layer 5a and 5b) with very different axonal profiles (Surmeli et al., 2015). Neurons in Layer 5a, but not those in layer 5b, project to the neocortex. Based on these findings, Layer 5a neurons appear perfectly suited to influence neocortical areas and contribute to memory consolidation.

The current manuscript by McClure et al. reveals an important new twist to our understanding of this Layer 5 output pathway: a large proportion of layer 5a neurons that project to the telencephalon (including the neocortex) also provide excitatory inputs to the hippocampus. The idea that neurons in the deeper layers of the entorhinal cortex send axons to the hippocampus is not new (Köhler, 1985; Witter and Amaral, 1991), but, until now, it was not clear if the population of neurons projecting to the neocortex was also sending axons to the hippocampus.

McClure and colleagues make use of two modern tracing strategies to test whether Layer 5a neurons projecting to the telencephalon also project to the hippocampus. First, they inject a retrograde AAV expressing Cre-recombinase in extra-hippocampal areas targetted by Layer 5a neurons. A second AAV expressing a reporter protein in a Cre-dependent manner was also injected in the deep layer of the medial entorhinal cortex. The Cre-dependent reporter protein was observed in the CA1 areas. The second approach used is MAPseq. A MAPseq barcode RNA virus library was injected into the deep layers of the medial entorhinal cortex. With this technique, the majority of infected neurons are expected to express a unique RNA sequence, which will be present both in the cell body the axon terminals. The entorhinal cortex, hippocampus, and other telencephalic structures were then processed to identify the bar codes present in the different target areas of Layer 5a neurons. The majority of barcodes found in the telencephalic structures were also found in the hippocampus, suggesting that a very large proportion of Layer 5a neurons projecting to the hippocampus also project to the hippocampus.

The manuscript has a clear message and the conclusions are generally well supported by the data presented. Two complementary methods are used to show that MEC deep layer neurons projecting to the telencephalon also project to the hippocampus. The reported proportion of Layer 5a neurons having projections towards the telencephalon and the hippocampus is very high, suggesting that this is an important feature defining this cell population.

One limitation of this work is that the functional role of the axon collaterals in the hippocampus is not explored in detail. The main target cells in the CA1 areas have however been identified. In addition, the authors describe a mouse line in which Cre-recombinase in the entorhinal cortex is limited to Layer 5a neurons. These mice will surely prove useful in future studies investigating the role of Layer 5a neurons.

Reviewer #2 (Public Review):

In this study, the authors have characterized a novel projection from layer 5a of entorhinal cortex to CA1 of hippocampus. Overall the data are convincing that 5a cells project to CA1. The optogenetic experiments provide strong evidence that the projection to CA1 is glutamatergic and targets pyramidal cells and several classes of interneurons. The authors present some evidence that the same 5a cells send axon collatorals to retrosplenial cortex + CA1 and nucleus accumbens + CA1 - however these data could be strengthened by making it more clear whether only 5a cells were targeted within the MEC. This reviewer does not have the expertise to comment on the MAPseq experiments.

The data characterize the projection well - but lack any kind of functional insight. Without this, it is difficult to place these findings in context. For example, are the projections to different interneuron subtypes different? Are there conditions when input to CA1 would be strong or weak - in other words - what are the dynamics of the layer 5MEC to CA1 pathway? Does silencing this projection effect behavior?

In summary - these data nicely characterize a novel projection from layer 5a of MEC to CA1, but leave open questions as to the function of this projection.

Reviewer #1 (Public Review):

This manuscript characterizes where deep layers of the medial entorhinal cortex (layer 5a and layer 5b) project to in the brain. Using a variety of circuit mapping techniques (cell-type specific anatomical tracing, high-throughput RNA sequencing based projection mapping and optogenetics aided circuit mapping), the authors find that the same neurons in the layer 5a of the medial entorhinal cortex send projections to both the telencephalon and the hippocampus. They also find that the projections target hippocampal pyramidal cells and interneurons and has a unique topography. While these findings are interesting and suggest that deep layers of the entorhinal cortex may coordinate hippocampal-cortical interactions in memory processing, but this is just speculation based on the anatomical connections.

Reviewer #2 (Public Review):

In this manuscript, the authors investigated the role of PEN1 and its close homolog SYP122 in pre- and post-invasive immunity reflected by the formation of papilla and haustorial encasement at the point of the attempted/sucessful penetration from non-adapted powdery and other filamentous pathogens. The authors collected genetic, cytological and cell biological data to show that these two syntaxins are required for the formation of the induced callosic defense structures. They further suggested that the two syntaxins function redundantly and differently. PEN1 may possess two different functions: the first is to mediate rapid delivery of papilla materials to powdery mildew penetration site, which is unique and not shared by SYP122, and the second is to work along with SYP122 to enable formation of a more general papilla/encasement in response to filamentous pathogens, and the latter is evolutionarily conserved as SYP12a from Marchantia can compensate the loss of SYP122.

Reviewer #1 (Public Review):

In this study the authors show that PEN1 and its close paralog SYP122 are required for pre-invasive (papillae formation) and invasive immunity (encasement of haustoria) against non-adapted powdery mildew fungal infection. Importantly, papillae formation on plants challenged with C. destructivum and P. infestans also required PEN1 and SYP122, suggesting that defense structures determined by the PEN1 and SYP122 operate against diverse filamentous pathogens. In addition, the authors provide strong evidence that the function specified by PEN1/SYP122 also exist in M. polymorpha, indicating an ancient evolution of the pre-invasive/invasive structures. Overall, the study makes a strong case that PEN1 and SYP122 play a crucial role in the biogenesis of ancient defense structures.

Reviewer #2 (Public Review):

This study examines the effects of mechanical loading on the bones of two transgenic mouse models of connexin 43 overexpression, one mutant which impairs both gap junction intercellular communication (GJIC) and hemichannel activity (130-136) and another that supports only enhanced hemichannel activity but not GJIC (R76W). The authors conclude that hemichannels but not GJIC facilitate the effects of mechanical loading on bone via the secretion of PGE2 through the hemichannels.

While provocative, the data fall short of being convincing of the interpretation.

A major concern is the statistical approaches used to evaluate data. The conclusions obligate that each group of animals (WT, R76W and 130-136 mice with or without loading) be compared to each other to determine differences in their ability to mount a response of bone to a mechanical load. The correct statistical test is a two way ANOVA when there are multiple variables (genotype and load). However, multiple t-tests are used to support major conclusions. Since primary data was supplied by the authors in the supplement, we checked this using statistical software. Many of the statistical analyses do not hold up when run through the appropriate statistical test. Thus, the primary findings reported are not supported.

Two additional significant weaknesses affect the potential quality and impact of this study.

1. No convincing evidence is presented that the phenotype was rescued by PGE2. In Figure 8 and the corresponding supplement, vehicle treated and PGE2 treated unloaded controls are not shown and are critical to the appropriate interpretation of the experiment. Meaningful bone parameters including bone area and cortical thickness are not affected by the PGE2. Trabecular bone was completely unaffected by PGE2 or even the M1 antibody. Also, a one-way ANOVA is the incorrect measure with which to assess these changes. There are many variables in these mice: treatment with or without M1 antibody, loading or unloading (although not included) and treatment with or without PGE2. These are not accounted for with the statistical models used to assess the data.

2. No convincing evidence that PGE2 secretion through connexin 43 hemichannels is shown. Instead, Figure 4C shows that a protein (COX2) responsible for producing PGE2 is reduced in the cells that produce PGE2 in the D130-136 mice. Several papers have shown that connexin 43 regulates ptgs2 and could affect PGE2 abundance independent of the ability to pass through connexin 43 hemichannels and others show that PGE2 also regulates connexin 43 abundance and gap junctional communication.

Reviewer #1 (Public Review):

I'm not sure why the authors are not seeing Evans Blue dye entry into the osteocytes of loaded bone from D130-136 transgenic mice. The Augusta, GA very nicely (and it has since been repeated) that osteocyte membrane disruptions occur with much milder loading (e.g., treadmill running) and allow in EB dye. These membrane tears have nothing to do with channel or hemichannel activity. So it is very hard to understand why the D130-136 mice would be spared from membrane tears that should allow copious amounts of EB into the cells. Do certain mutations in connexin prevent membrane tears?

If the R76W mutation enhances hemichannel function, and the conclusions of the paper are correct that the hemichannels are controlling the response to loading, then why were the R76W mutants not more responsive than WT to mechanical loading?<br /> Fig 3: How is it justified to say that the D130-D136 mice had increased bone formation response to loading on the periosteum when the relative change between loaded and nonloaded look to be about the same in all three genotypes? Are the authors not adjusting for the higher or lower control leg bone formation measurements?

Reviewer #3 (Public Review):

This work provides a detailed map of mono-synaptic connections between the BA and the vH. Particularly valuable, they use CRACM to identify the inhibitory and excitatory mono-synaptic connections. They extend their investigation of mono-synaptic connections to the vH output neurons (BA, NAc, and mPFC projecting neurons). Then they build an integrate-and-fire network model, constrained by their experimental data. Finally, they test the model's prediction at the behavioral level.

Overall, this work is carefully designed and nicely executed. Although, I am not qualified in evaluating their modeling, I liked their approach. It is a well-rounded experimental design, where they use their own set of data to construct a model with predictive power that later put in test. That is, they bridged the gap between slice electrophysiology and behavior with circuit modeling.

However, some of the main claims require more experimental evidence. This includes increasing the power for the behavioral experiments, evaluating a potential contribution of topographical bias within the BA, verifying that high concentration of the calcium chelator is not having unintended consequences, and a more thorough validation of the effect of SalB on action potentials.

Reviewer #2 (Public Review):

The work described in this manuscript very elegantly explores the functional connectivity of BA projections to vHPC neurons, using an original and skilful combination of optogenetics and retrograde tracing approaches, as well as the potential role of these projections in goal-directed behavior. Overall the manuscript benefits from extensive studies on the functional connectivity between BA and vHPC, and contributes important novel information to the field including solid evidence for the long postulated long-range inhibitory efferents from the BA.

My enthusiasm for the work is, however, diminished by the preliminary nature of the behavioural experiments and the lack of cogent experimental evidence for some of the claims, including the validity of the ratio used to compare the strength of the functional BA-inputs to different populations of vHPC principal neurons shown in Fig 3 and Fig 4.

Reviewer #1 (Public Review):

A strength of the manuscript is that it includes data obtained with a variety of complementary approaches, such as CRACM, electrophysiology, behavior and modeling that all contribute toward a comprehensive definition of BA-VH connectivity.

However, there are weaknesses that make the claims raised not as direct as they should. Furthermore, the data submitted does contains some important gaps. Specifically: CRACM should significantly improve, a better characterization of the "novel" BA long-range GABAergic neurons should be provided, the identification of the VH cellular targets (CA1, CA3 pyramidal cells?) should be provided too, VH pyramidal cells with multiple projection areas should be investigated.

Reviewer #3 (Public Review):

Fernandez-Leon et al. investigate the role of the pre-limbic area (PL) in regulating approach avoidance behavior in situations of learned motivational conflict where animals experience both cues that predict an aversive outcome as well as cues that signal the availability of food. This region has been implicated in threat responding and food seeking separately but has not previously been examined in situations of conflict. The authors employ an individual differences approach, subdividing animals based on their food seeking behavior in the presence of conflicting cues that signal food availability and footshock and use a combination of in vivo recordings and optogenetic manipulations to identify a role for specific cell types in the PL in regulating risky behaviors in aversive contexts. This manuscript adds to the growing literature on neural mechanisms of processing approach-avoidance conflict.

This work has many strengths. Examining approach and avoidance in a conflict paradigm, rather than separately, provides a more ethological study of the neural basis of these behaviors as, beyond the confines of a laboratory, action selection commonly occurs in the face of multiple competing cues. Subdividing animals into 'pressers' and 'non-pressers' based on individual differences in engagement in food seeking behavior is an excellent strategy to gain insight into the behavioral function of these cells. Recognizing that not engaging in food seeking does not necessarily reflect failure to complete the task but rather a bias toward avoidance behavior is insightful and important. The authors suggest a number of interesting and potentially important differences in PL neural activity between pressers and non-pressers. For example, pressers (i.e. rats that continue to seek food in the presence of an aversive cue) have both more food-cue responsive neurons and greater magnitude of excitatory and inhibitory responses to food-cues, a difference that is sustained when food-cues are presented in the presence of an aversive cue. Pressers and non-pressers also had marked differences in oscillatory frequency, an intriguing finding that warrants further investigation. Optogenetic experiments nicely establish causality with precise temporal resolution.

The design of the behavioral paradigm somewhat limits the ability the ability to draw certain conclusions. During testing, food-cues were presented discretely while the shock-cue was constant preventing direct comparison of responding to appetitive and aversive cues that would have been highly interesting. Furthermore, during the test session, reward cues are always presented first followed by the addition of the shock-cue. This, and the extended shock-cue presentation under extinction conditions makes it difficult to entirely rule out alternative interpretations for differences between pressers and non-pressers, for example, more rapid extinction of fear memory in pressers than non-pressers. Beyond this, the lack of direct statistical comparison of neural activity in pressers and non-pressers undermines the strength of the central conclusions of this paper.

The authors hypothesize that stimulating glutamatergic PL neurons decreases signal to noise ratio between cells that are active during food seeking and those that are not, thus resulting in a decrease in food-seeking. This is interesting and plausible proposal to be further explored in future research.

Reviewer #2 (Public Review):

This manuscript includes a series of studies to assess the role of prelimbic neurons in mediating behavior during an approach-avoidance conflict task. The authors used a novel task to assess the ability of rats to remember cues previously associated with either reward (food) or threat (footshocks) to make a behavioral decision. In doing so, they uncovered two behavioral phenotypes: "Pressers", who continued to press a lever for food during conflict; and "Non-Pressers", who exhibited a suppression of food-seeking behavior in face of conflict. A combination of optogenetics and single-unit recordings were used to assess the neural mechanisms underlying this individual variability in reward-seeking behavior during conflict. The authors report that increased risk-taking behavior in "Pressers" is associated with reward-cue-elicited responses in the prelimbic cortex and reduced spontaneous activity in prelimbic glutamatergic neurons during conflict. Further, activation of prelimbic glutamatergic, but not GABAergic, neurons attenuated reward-seeking responses selectively in "Pressers"; and inhibition of prelimbic glutamatergic neurons increased reward-approach behavior and decreased freezing behavior during conflict in "Non-Pressers".

These experiments were well-designed, the methods were appropriate to address the questions at hand, and the manuscript is well-written. The ethologically-relevant approach-avoidance task is novel and will be of interest to the field. In particular, the ability to capture distinct behavioral phenotypes and individual differences using this test will allow further investigation of the neural determinants of reward-seeking and threat-avoiding behavior during conflict.

As currently presented, there are some concerns regarding the statistical analyses and whether they support all of the authors' claims. As the individual differences component of the manuscript is particularly novel and of interest, it is a bit concerning that these analyses include a sample size of 25 "Pressers" and 7 "Non-Pressers". In relation, it is not clear that the neural responses of these two behavioral phenotypes were ever directly compared. For example, in Figure 2 and Supplementary Figure 2, the area under the curve for neural responses during reward and conflict are presented independently for the two phenotypes and direct comparisons to assess group differences and/or interactions are not apparent. Similarly, it is not clear why data only from "Non-Pressers" is shown in Figure 7, as the methods suggest that both "Pressers" and "Non-Pressers" were used for this experiment. Further, in general, it is difficult to deduce which statistical analyses support the claims made in the manuscript text, as the analyses are only presented in the Figure legends and in Supplemental Table 1 and don't always seem congruent.

Reviewer #1 (Public Review):

The paper by Fernandez-Leon examined the role of PL glutamate and GABAergic neurons during a conflict-based behavioural task. The task consisted of lever press during an audio-visual compound in the presence of an aversively conditioned odour. The behavioural data indicated that two cohort of animals were generated - pressers and non-pressers. Pressers continued to press the lever (reward-seek) in the presence of the aversively conditioned odour (albeit to a lesser degree) whereas the non-pressers ceased pressing. Single unit recordings revealed a reduction in the number of food-cue responsive neurons under conflict (compared to no conflict). Different subsets of PL neurons were shown to signal freezing, avoidance and risk-assessment during conflict. The data show reduced spontaneous activity in PL glutamatergic neurons when animals lever press under conflict. Activation of these neurons using ChR2 under the control of the CaMKII promotor attenuated food-seeking behaviour in a neutral context in pressers. Inhibiting the same neurons in non-pressers reduced defensive behaviours often seen to cues conditioned with shock and increased food-based conditioned behaviours.

The strengths of the paper are numerous and include the novel behavioural design that pits reward up against aversion. Examining the distinct conflict phenotypes throughout the paper was also excellent. The integration of single-cell recordings, LFPs, and optogenetics were considerable strengths allowing to dissect the glutamatergic vs GABAergic microcircuits in the PL during this behaviour. The discussion of the results in the context of the existing literature was excellent.

Despite the clear strengths of the paper, some weaknesses exist. A closer examination of the single units is warranted. The claim that putative classification of PL neurons into glutamatergic and GABAergic based on waveform and spike timing given the optotagging results seems premature. The optotagging analysis needs additional data including an eYFP control to show what, if any, effect light stimulation alone has on neural responses. Some consideration of whether the two behavioural phenotypes are due to differences under conflict or due to perception is also needed.

Reviewer #2 (Public Review):

The present MS describes an effort to create a general mathematical model of synaptic neurotransmission. The authors invested great efforts to create a complex model of the presynaptic mechanisms, but their approach of the postsynaptic mechanisms is way oversimplified. The authors claim that their model is consistent with lots of in vivo and in vitro experimental data, but this night be true for a small subselection of experimental papers (they cite 7 experimental papers regularly in the MS!). The authors also indicate that their modeling has a realistic foundation, namely they can relate some parameters in their equations to molecules/molecular mechanisms. One example is the parameter N, which they claim indicate the number of SNARE complexes requires for fusion. The reviewer finds it rather misleading because it alludes that there is a parameter for complexin, Rim1, Rim-BP, Munc13-1 etc... The equations clearly cannot formulate and reflect diversity due to different isoforms of even the above mentioned key presynaptic molecules.

Reviewer #1 (Public Review):

Wang and Dudko derive analytical equations for one special case of a model of Ca-dependent vesicle fusion, in the attempt to find a "general theory" of synaptic transmission. They use a model with 2 kinetically distinct fast and slow pools (equation 1).

Critique

1) Overall, the analytical approach applied here remains limited to the quite arbitrarily chosen 2-pool model. Thus, while the authors are able to re-capitulate the kinetics of transmitter release under a series of defined intracellular Ca-concentration steps, [Ca]i (see Fig. 2B; data from Woelfel et al. 2007 J. Neuroscience), this is nevertheless not surprising because the data by Woelfel et al. was originally also fit with a 2-pool model. More importantly, the 2-pool model is valid for describing release kinetics at high [Ca]i, but it cannot account for other important phenomena of synaptic transmission like e.g. spontaneous and asynchronous release which happen at lower [Ca]i, with different Ca cooperativity (Lou et al., 2005). Along the same lines, the derivations of the equations by Wang and Dudko are not valid in the range of low [Ca]i below about 1 micromolar (see "private recommendations" for details). This, however, limits the applicability of the model to AP-driven transmitter release, and it shows that based on one specific arbitrarily chosen model (here: the 2-pool model), one cannot claim to build a realistic and full "theory" for synaptic transmission.

2) In their derivations, Wang and Dudko collapse the intracellular Ca-concentration [Ca]i, a parameter directly quantified in the several original experiments that went into Fig. 2A, into a dimensionless relative [Ca]i "c" (see equation 7). Similarly, the release rates are collapsed into a dimensionless quantity. With these normalizations, Ca-dependent transmitter release measured in several preparations seems to fall onto a single theoretical prediction (Fig. 2A). The deeper meaning behind the equalization of the data was unclear, except a demonstration that the data from these different experiments can in general be described with a two-pool model, which is at the core of the dimensionless equations. One issue might be that many of the original data sets used here derive from the same preparation (the calyx of Held), and therefore the previous data might not scatter strongly between studies. This could be clarified by the authors by also plotting the data from all studies on the non-normalized [Ca]i axis for comparison. Furthermore, it would be useful to include data from other preparations, like the inner hair cells (Beutner et al. 2001 Neuron; their Fig. 3) which likely have a lower Ca-sensitivity, i.e. are right-shifted as compared to the calyx (see discussion in Woelfel & Schneggenburger 2003 J. Neuroscience). Thus, it is unclear why normalization of [Ca]i to "c" should be an advantage, because differences in the intracellular Ca sensitivity of vesicle fusion exist between synapses (see above), and likely represent important physiological differences between secretory systems.

3) Finally, the authors use their model to derive the number of SNARE proteins necessary for vesicle fusion, and they arrive at the quite strong conclusion that N = 2 SNAREs are required. Nevertheless, this estimate doesn't fit with the number of n = 4-5 Ca2+ ions which the original studies of Fig. 2A consistently found. The Ca-sensitivity at the calyx of Held, and the steepness of the release rate versus [Ca]i relation is determined by Ca-binding to Synatotagmin-2 (the specific Ca sensor isoform found at the calyx synapse), as has been determined in molecular studies at the calyx synapse (see Sun et al. 2007 Nature; Kochubey & Schneggenburger 2011 Neuron). Furthermore, in other secretory cells, the number of SNARE proteins has been estimated to be {greater than or equal to} 3 (Mohrmann et al., Science 2010).

Taken together, the derivation of the analytical equations for the kinetic scheme of a 2-pool model is mathematically interesting, and the scholarly derived equations are trustworthy. Nevertheless, the derived analytical model in fact captures only a specific stage of synaptic transmission focusing on Ca-dependent fusion of vesicles from two pools at [Ca]i >1 microM. Other important processes and mechanistic components (e.g. spontaneous, asynchronous release, Ca-dependent pool replenishment, postsynaptic factors) are either over-simplified or remained out of the scope of the theory. Therefore, the paper is far from providing a general "theory for synaptic transmission", as the title promises.

Reviewer #2 (Public Review):

In their work Schavemaker & Lynch aim to understand the reason for the structural differences between the bacterial, archaeal and eukaryotic flagella. Using structural data of three model organisms, which represent three domains of life, the authors have determined the energy costs of building and operating flagella. They further expand their analysis to additional 196 species, using morphological data from the literature about their flagella and cell volume. To evaluate cost-effectiveness of the flagella, the authors analyzed their effect on swimming speed, nutrient uptake, and cell growth. The reason for the structural differences between the different types of flagella remained an enigma for decades. In their work, the authors offer a fascinating and convincing approach to explain the reason for these differences. Energetic considerations appear to be critical for the evolution and maintenance of flagella. Interestingly, the data also indicate flagellar proteins experience different evolutionary forces due to differences in the energetic costs caused by the protein copy-number. The conclusions of this paper are mostly well supported by data. Yet, a few key structural elements are missing in the calculated construction cost of the eukaryotic flagella. This may affect the authors' estimations and conclusions.

Reviewer #1 (Public Review):

The authors use available structural biology data to compute the energetic cost to build and maintain the activity of flagella in a broad range of unicellular swimming organisms, including bacteria, archaea and eukaryotes. From this energy balance, they try to decipher what advantages the different types of flagellum can provide in terms of motility, feeding and growth. This eventually brings new insights into why bacteria, archaea and eukaryotes have evolved with different types of flagella.

Strengths:

The main strength of this study relies on the collection of the data set from three types of unicellular swimming organisms - bacteria, archaea and eukaryotes - for about 200 species. Interestingly, selected species span a large phase space in terms of numbers of flagella/cilia, flagellum length, cell volume... This allows robust analysis and interpretation of the data.

The method for establishing the energy balance of the construction of complex protein structures seems to be robust. For example, the result obtained by this method to compute the energy cost of E. Coli flagellum is of the same order of magnitude as previously reported values estimated by other methods. This method could be used for other cellular functions for which it is otherwise difficult to estimate the energy cost either experimentally or theoretically.

Weaknesses:

The conclusion on the lack of an evolutionary advantage for small cells to swim to find food rather than waiting for food to diffuse is not particularly new. Indeed, Purcell in his famous 1977 paper "life at low Reynolds number" reached the same conclusion by using simple scaling arguments to estimate the trade-off between swimming to find more food and the swimming energy cost.

Although the method does have strengths in principle, the weakness of the paper is that the main conclusions are not discussed enough or put in perspective with regards to the initial aims of the paper: better understand why three different types of flagellum exist. In particular, the fact that "there is no detectable difference in the cost-effectiveness of generating swimming speed between eukaryotic and prokaryotic flagella" is not really discussed. One of the major characteristics of eukaryotes is that they have evolved into more complex multicellular forms of life where multiciliated cells are ubiquitous and support more diverse physiological functions (transport, washing surface...) than swimming. So maybe an evolutionary advantage of eukaryotic flagella over prokaryotic flagella should be discussed in that context.

Overall the data presented in this study support the conclusions of the paper, which are not overclaimed.

Reviewer #2 (Public Review):

This study reports a "deep mutational scan" of the Mpro gene of SARS-CoV-2. The authors describe a complex new experimental system, together with all necessary controls to validate results. They focus in particular on mutations that disrupt function, with an eye toward guiding evolution-proof antiviral compounds. I find no serious weaknesses in the work or the manuscript. Rigorous connections to functional implications for other coronaviruses are also presented, and this work will be of broad interest to virologists working in this area as well as to a broader audience of evolutionary geneticists.

Reviewer #1 (Public Review):

Along with vaccines and non-pharmaceutical interventions, antivirals are essential tools in the ongoing SARS-CoV-2 pandemic. The viral main protease (Mpro) has several features making it an excellent candidate: it plays an essential role in replication and infection, is conserved in coronaviruses, and lacks human homologs. No inhibitors have been approved, but several are in clinical trials. As a result, the potential for resistance mutations to arise is largely unknown. In this manuscript, Flynn et al. use Deep Mutational Scanning (DMS) in three yeast-based systems to investigate the sensitivity of Mpro function to single-site mutations as an indicator of this potential.

The major strength of this work is the discovery of protein regions which are intolerant of substitutions. Medicinal chemistry efforts focusing on these regions may provide candidates which are more robust towards resistance mutations, as functional constraints may not allow alterations to modify ligand binding in those locations.

An additional strength of this paper is the novelty of combining multiple independent readouts of protein function (in this case the three yeast screens). DMS often uses a single measure of fitness to infer protein function. If this is organismal growth, the readout is a very coarse-grained phenotype which can be difficult to interpret. Having an additional specific measurement of protein function provides more specific insight into what is driving fitness changes. This is unusual in DMS studies and comparisons between these distinct additional functional readouts make this study of particular interest for DMS practitioners. While the consistency between the methods is analyzed in some detail, there is potential to mine interesting data from the highest magnitude and most significant outliers (if they exist), especially those that differ between the live/die screen and the two fluorescent-based screens. The live/die screen may report on changes in substrate specificity or gains of catalytic function differently than the two fluorescent-based screens.

One limitation of this work is with benchmarking the functional scores. It is unclear how the fitness scores would map to overall viral fitness. The authors observe a bimodal distribution of fitness scores in all screens, with almost all mutations having either WT-like or non-functional, with very few intermediate phenotypes. This is perhaps to be expected for a highly-adapted protein lying near the top of a fitness landscape, but if some amount of the null fitness scores are viable, this might undermine some of the overall conclusions.

Overall, this is a careful and impressive work. It will likely be useful to guide anti-viral design and will also interest the DMS field.

Major points:

1. The "collateral" fitness effects of mutations on proteins, i.e. those which change overall fitness by means other than directly altering the specific function, are also important sources of abundance changes in DMS experiments [https://www.pnas.org/doi/10.1073/pnas.1918680117]. In this instance, given that expression of Mpro is toxic at high levels, it seems plausible that the fluorescence-based fitness measurements could be influenced by these effects. In these experiments, libraries are first grown for many generations, and in some instances are also expanded after selection before sequencing. The experimental design of the FACS experiments limits the impact of these collateral effects, but these may significantly shape the pre-selection library abundances. Given that the competitive growth experiment involves a T=0 sequencing sample, would it be possible to analyze this pre-selection variant distribution for potential bias?<br /> 2. The comparisons with the clinically observed mutants are impressive. They note that "only nine having a score below that of the WT distribution". Are these seen with other mutations? As the authors note, their study is confined to single-site mutations and does not directly consider epistatic effects. Are there potential second-site mutations that could explain these 9 outliers?<br /> 3. We have some additional questions about the data analysis. In all three experiments, variant proportions are mapped to [0,1], where average stop codon proportions are set as 0 and WT codons as 1. The use of this mapping should not influence the results, but it obscures the overall power. That is, the magnitudes of the changes in abundance underlying, e.g., an apparent loss of function mutation are unclear. The details of the unnormalized abundance changes should be included to improve reproducibility as well as interpretability. We also wonder whether the growth experiments should be fit to selection coefficients as well as normalized fitness scores.

4. Finally, we would request that the analysis scripts be made available. Given the current lack of standardization for DMS experiments and the difficulty with these types of analysis, this is both necessary for reproducibility and as a benefit for the community.

Reviewer #2 (Public Review):

Activin isoforms, ActA, ActB, ActC, ActAC, ActE, have important roles regulating metabolism, and as well as endocrine function and cell differentiation in animals, and thus understanding how the isoforms function and how they have diverged from one another to carry out their unique roles in vivo is important.

In previous studies, the receptor binding and signaling properties of ActA and ActB, as well as two other activin class ligands, GDF8 and GDF11, have been fairly thoroughly characterized with respect to their signaling activity and receptor binding properties (and the underlying structural basis for this). In contrast, ActC, which is expressed at high levels in both the liver and in mature adipocytes, remains poorly characterized, with conflicting reports regarding its signaling activity and antagonistic activity against other activin isoforms.

In this study, the authors employ purified activins, and purified activin class type I and type II receptors (Alk4, Alk5, and Alk7 and ActRII and ActRIIB, respectively), together with cell-based assays and SPR binding studies to characterize the receptor binding and signaling properties of ActC and ActAC, relative to the much more well-characterized ActA and ActB.

Consistent results obtained using orthogonal approaches convincingly demonstrate that the inhibin betaC monomer (in the context of a dimeric activin) does not bind Alk4, but it does bind Alk7 weakly and it also binds ActRII and ActRIIB weakly (with a preference for ActRII over ActRIIB). In this light, ActC is clearly unique compared to ActA and ActB in that it has markedly reduced type II receptor binding affinity, but it does uniquely bind Alk7. Importantly, the combination of ActRII/ActRIIB together with over expressed Alk7 in HEK293 is shown to confer ActC with robust signaling activity. It is also shown that ActAC can bind Alk7 and signal, though because of the inhibin bC subunit (with lower type II receptor binding affinity and a preference for weakly binding Alk7 over Alk4 and Alk5) its signaling activity is reduced relative to ActA. In short, the authors conclude from this data that the competition previously reported for ActC against ActA and ActB likely arises not from binding competition (akin to the way inhibin A antagonizes alctivins), but by promoting the formation of ActAC, which is a less potent signaling ligand than ActA.

In this study, the authors also use the tools described above to interrogate the effects of known activin antagonists, follistatin (specifically Fst288 and FstL3) and inhibin A, and show that while ActC is sensitive to antagonism by InhA, it is not antagonized by Fst288 or FstL3.

One very important strength of the studies reported here is their reliance on highly purified activins and activin class receptor extracellular domains, which allow the authors to characterize in detail the binding properties and in turn relate this to signaling activity in their well-controlled HEK-293 CAGA-Luc reporter cell line. Overall, these results shed significant new light on the underlying and unique behavior of ActC (and ActAC) compared to ActA and ActB and are highly robust.

In order to relate the results described above to role of ActC in vivo, the authors show that while ActC is not capable of signaling in primary non-differentiated adipocytes isolated from mice where Alk7 expression is limited, but they are capable of signaling once the adipocytes mature and differentiate and express Alk7 at a much higher level.

Finally, to understand the underlying molecular basis for ActC receptor binding properties, the authors noted that one of the conserved Ala residues in the core of the type II receptor binding interface was substituted with glutamine; substitution of this in the context of ActC conferred enhanced type II receptor binding affinity, consistent with the idea that glutamine at this conserved position sterically impairs type II receptor binding.

Overall, the results presented strongly support the author's claims and clearly demonstrate that ActC is unique with respect to both type I and type II receptor, as well as follistatin binding, compared to ActA and ActB. In terms of the larger picture, these differences likely engender ActC with the ability to regulate activin signaling in a manner that is largely independent of the more widely expressed ActA and ActB.

Reviewer #1 (Public Review):

The Activin isoform ActC is expressed at high levels in liver and adipose tissue where it plays important physiological roles. However, little is known about ActC signaling mechanisms. Here the authors use purified ActC and purified receptors to demonstrate that ActC interacts with ALK7. An important test of the authors' hypothesis that ALK7 mediates the effect of ActC is the demonstration that pre-adipocytes from ALK7-impaired fail to respond to ActC, but responsiveness is restored by differentiation associated with increased ALK7 expression. Collectively, these data provide strong evidence that ALK7 mediates the actions of ActC. This represents a significant advance in knowledge.

Reviewer #2 (Public Review):

Studies on improving mitochondrial function are clinically significant as many diseases and overall cellular metabolism is linked to mitochondrial health. C-terminal amidated synthetic tetrapeptides with an alternative cation and aromatic residues are being shown to improve mitochondrial health. These peptides can accumulate on the mitochondrial membrane and appear to improve electron transfer efficiency with a concomitant increase in ATP production and reduction in ROS production. The lead peptide SS-31( arg-Dmt-Lys-Phe-NH2 ) has been shown to improve mitochondrial health in disease conditions such as hypoxia, ischemia, and aging-related disorders. The unusual Dmt (2,6 dimethyltyrosine) at the second position of the tetrapeptide is shown to have a characteristic free radical quenching property. The peptide SS-31 appears to be a promising target for various mitochondrial disorders.

In this study, Mitchell et al. tested the three best tetrapeptides with alternative basic/aromatic amino acid sequence setting SS-31 as a benchmark to understand the importance of side-chain and type of aromatic side chains role in the structure and binding to the membrane using extensive NMR, biophysical and MD simulation studies either in solution and in the membrane-bound state. Among the tested SS-20, SPN4 and SPN10 show similar or better features than known SS-31 peptides. The study depicts the importance of polar groups on aromatic side chains. In particular SPN10 decreases, the enthalpy of membrane interactions and ability reduce membrane surface potential. All the tested peptides were shown to be targeted to mitochondria, increasing the viability and ATP content in cell lines. In particular, SPN10 appears to have a more significant impact on the recovery from stress by improving mitochondrial health. These studies, in my view, would be important for the development of therapeutics based on SPN10.<br /> Broadly, the experimental design and the results support their conclusions. More discussion on how do these peptides improve mitochondrial health may be included.<br /> The idea of tetrapeptides binding to liposomes and calculating lipid to peptide binding ratios is quite good. However, it will make more sense to show their binding to the isolated mitochondria and compare the binding ratios of tetrapeptides to mitochondria.

Reviewer #1 (Public Review):

Wayne Mitchell et al. report the study aimed to determine the structural features of cationic hydrophobic tetrapeptides in their cytoprotective efficacy. Detailed structural characterization of the peptides "free" in solution and bound to membranes is followed by their comparison in protecting cells from starvation-induced stress and the loss of viability. Overall, there are important and detailed observations regarding the peptide-membrane interactions, while their relevance to cytoprotection mechanisms in not demonstrated.

Strengths:<br /> i. The authors performed and described detailed experimental and computational analyses of the structure of the peptides in solutions and in model membranes. The data obtained provide important insights into the nature of the interaction between the peptides and model membranes.<br /> ii. The use of isothermal titration calorimetry allowed the authors to investigate the equilibria of binding of the tetrapeptides to model membranes containing cardiolipin.<br /> iii. The observation of the reverse-turn conformation for all peptides except SS-31, when bound to membranes is interesting, even though its significance in the cytoprotection mechanisms is not clear.<br /> iv. An intriguing conclusion from the structural studies is the potential to improve membrane binding of the peptides by preparing the cyclized forms.<br /> Weaknesses:<br /> i. The manuscript mostly focuses on the mechanisms of interaction of the peptides with membranes and this is a strength of the paper. There is no clear link between hose data and cytoprotection. My recommendation is to modify the title to better reflect the major focus and strength of the paper.<br /> ii. Quantitative structure activity relationship requires a correlation between quantities describing structural feature(s) of the compound and its efficiency/potency. It is not clear what structural feature of the tetrapeptide is most influential of its potency and what efficiency/potency parameter would best reflect its action.<br /> iii. Determination of the structural effects on the compound function requires modulation of a single structural property and assessment of its effect on the compound's activity. Therefore, SS-31 may be compared to SPN4, as the only change is the replacement of Dmt residue with tyrosine. SS-20 may be compared with SPN10, as both phenyl rings are replaced by indole rings. However, comparison between SS-31 and SS-20 introduces more variables (replacement of Dmt by phenylalanine, and change in amino acid order in the peptide), which makes the interpretation difficult. It is not clear why testing the role of amino acid order was not performed without changing the identities of aromatic amino acids.<br /> iv. The cellular uptake of the peptides was determined only for selected biotinylated peptides (SS-31 and SPN10) and only in a qualitative manner (immunostaining, to confirm the presence of the peptides in cells). Comparison of the uptake requires quantitative analyses for all four peptides.<br /> v. Cell protection experiments are lacking an important control - non-stressed cells. Without such control, it is difficult to interpret the observed cytoprotection data.

Reviewer #3 (Public Review):

This is an original study to explore the role of Septin-7, a cytoskeleton protein, in skeletal muscle physiology. The authors produced a unique mouse model with Septin-7 conditional knockdown specifically in skeletal muscle, which allowed them to examine the structure and function changes of skeletal muscle in response to the reduced protein expression level of Septin-7 in vivo and ex vivo at different development stages without the influence of other body parts with reduced Septin-7 expression. The study on the cellular model, C2C12 myoblast/myotubes with knockdown of Septin-7 expression, provided additional evidence of the importance of this cytoskeleton protein in regulating myoblast proliferation and differentiation. Majority of the data are supportive to the major claim in this manuscript. However, additional key experiments and data analysis are needed to provide more mechanistic characterization of Septin-7 in muscle physiology.

Major Concerns:

1. The Septin-7 knockdown mouse model, the EM and IHC techniques are all established in the research group. It is a surprise to see that authors missed the opportunity to characterize the morphological changes in the T-tubule network, triad structure, the distribution of Ca release units (i.e., IHC of DHPR and RyR), and its co-localization with other key cytoskeletal proteins (i.e. actin) etc., in the muscle section or isolated muscle fibers.

2. The authors only studied one time point following the Tomaxifen treatment (4-month old with 3-month treatment). Based on Fig 2D, a significant body weight reduction was achieved after one month of the Tamoxifen treatment (at the age of 7 weeks), indicating a potential reduced muscle development at this age. Mice are considered fully matured at the age of 2 months. It will be more informative if the muscle samples and the in vivo and in vitro muscle activity are analyzed at this time point (7 or 8-week old), which should provide a direct answer if the knockdown of Septin-7 affects the muscle development. Additionally, a time dependent correlation of the level of Septin-7 knockdown with muscle function/morphology analysis should better define the role of Septin-7 in muscle development and function.

3. Although the expression level of Septin-7 reduced during muscle development (Fig 1C), but its expression is still evident at the age of 4 months (Fig 1C and Fig S1F), indicating a potential role of Septin-7 in maintaining normal muscle function. It is important to examine whether the Tomaxifen treatment started after the muscle maturation at the age of 2-month old would affect the muscle structure and function. Particularly, these type of KD mice will be critical to answer if the KD will affect the regeneration rate following the muscle injury. The outcome will further test or support their claim of the essential roles of Septin-7 in muscle regeneration.

4. Regarding the impact of Septin-7 on differentiation, it could be problematic if the images with the resolution shown in Figure S4A-C were used for fusion index calculation. If those are just zoomed in representative images and the authors used other lower resolution, global view images for quantification, those images are needed to be shown. The authors may also need to elaborate on why they stained Desmin instead of MYH for quantification of the fusion index of myotubes (page 27). Desmin also marks mesenchymal cells. If Septin-7 is truly affecting differentiation, a decrease of MYH expression can be readily detected by IHC or WB. Additionally, Septin-7 may also affect the migration or fusion of myoblasts instead of differentiation. The observation of altered cell morphology and filopodia/lamellipodia formation (Figure 3C) in Septin7-KD cells before differentiation also implies a potential role of Septin-7 in migration. This possibility should be at least discussed.

5. The image shown in Figure 5F does not support the pooled data showed in Figure 5C. The size of mitochondria is remarkably lager in Cre+ muscle (Fig 5E and 5F). The morphology of mitochondria in Cre+ muscle are apparently normal (Fig 5F), while the mitochondrial DNA content are drastically reduced (Figure 5H), which is an important discovery and deserved to be further confirmed by WB and/or qPCR for critical mitochondrial proteins (i.e. MTCOX, COXV, etc.).

6. Figure 2 H & I: It is unclear whether the muscle force was normalized to the individual muscle weight.

7. The IHC results in Figure 6B are confusing. There are no centrally located nuclei in the Pax7 alone image of Figure 6B but abundant in the Pax7 + H&E image. The brown color of DAB and the purple color of hematoxylin are hard to be distinguished.

Reviewer #2 (Public Review):

This is a comprehensive work describing for the first time the location and importance of the cytoskeletal protein Septin-7 in skeletal muscle. The authors, using a Septin-7 conditional knockdown mouse model, the C2C12 cell line, and enzymatically isolated adult muscle fibers, explore the normal location of this protein in muscle fibers, the morphological alterations in conditioned knockdown conditions, the developmental alterations, and the functional alterations in terms of force production.<br /> The global picture that emerges shows Septin-7 as a fundamental brick in both muscle construction, development, and regeneration; all this leads to reinforcing the basically structural nature of this protein role.

Reviewer #1 (Public Review):

Here the authors aimed to gain insight into the role of Septin-7 in skeletal muscle biology using a novel and powerful mouse model of inducible muscle specific septin-7 deletion. They combine this with CRISPR/Cas9 and shRNA mediated manipulation of Septin-7 in C2C12 cells in vitro to explore its role in muscle progenitor morphology and proliferation. There are a variety of interesting observations, with clear phenotypes induced by the Septin-7 manipulation, including effects on body weight, muscle force production, mitochondrial morphology, and cell proliferation. However each area is somewhat superficially examined, and certain conclusions require additional validation for robust support. Additionally, mechanistic insight into Septin 7's role is limited. Therefore, while the phenotypes are likely of intrigue to both the muscle and septin community, to significantly advance the field will require additional experimentation.

Specifically, it is currently difficult to distinguish between developmental and adult roles of Septin-7. The authors induce tamoxifen-mediated deletion at 1 month of age and examine muscle structure/function only at 4 months. By not studying early time points, it is difficult to determine whether particular phenotypes are directly due to Septin deletion or a secondary consequence of muscle atrophy and/or a decline in body weight. Further, by not inducing deletion at a later time point (i.e. after 2 months when muscle is generally matured), it is difficult to assess whether septin-7 plays a role in maintaining structure and function of mature muscle, or if its primary role is in muscle development.

Further, the conclusion that septin-7 has an essential role in regeneration (seemingly based on expression increasing after injury) is unsupported and requires further experimentation where injury and regeneration is triggered in the absence of Septin-7 to establish a causative role.

Finally, there are intriguing observations in mitochondrial and myofiber organization and mitochondrial content; however further interrogation into additional relevant metrics of each, and at different time points of Septin-7 deletion, are needed to better understand these phenotypes and gain insight into Septin-7's role in their regulation.

Reviewer #3 (Public Review):

In this manuscript, a range of techniques are used to define transcriptomic diversity amongst EECs and to explore functional differences that result from activation of select EEC populations. A key aspect of the study is the development of an intersectional approach that allows EECs to be selectively manipulated using a chemogenetic strategy. The authors use this approach to infer that activation of various subsets of EECs can reduce food consumption in hungry mice but differentially modulate associative learning, with activation of Tac1-Cre targeted enterochromaffin cells providing a strong aversive signal but activation of CCK-Cre targeted EECs appetitive learning. Moreover, inhibitors, nerve transection and toxin treatment suggest distinct mechanisms and cellular substrates for these effects.

Some aspects of the study are compelling including the use of Vil-Flp as a means to restrict recombination to the EECs and the taste aversion caused by activating all EECs or Tac1-lineage EECs during conditioning was profound and quite surprising. But the complex temporal effects of EEC activation on feeding and variation shown by controls make some of the results on this far less compelling and this aspect of the study should be toned down or reassessed. There were also some aspects of the data presentation that could be tightened to make the manuscript more accessible to the reader.

Reviewer #2 (Public Review):

Animals ingest food to replenish energy, a process that begins with ingestion, digestion in the stomach and small intestine, absorption of nutrients by blood capillaries innervating the intestine, and circulation of nutrients to body tissues that utilize or store energy. The entire process of realizing the metabolic consequences of ingestion occurs over minutes to hours, a timescale that is not conducive for guiding moment-to-moment behavioral actions during food ingestion, such as food choices. Moreover, because of the delayed metabolic consequences of ingestion, it is important to predict the caloric load to cease food intake and avoid internal damage caused by overconsumption. How are these predictive signals generated?

In this manuscript, Bai et al investigate whether enteroendocrine cells (EECs) are involved in relaying nutritive signals to the brain to influence food preference learning and satiety. Notably, EECs in the proximal intestine are known to express chemoreceptors that could sense chemical properties of food that has already been partially digested by the stomach. In turn, it is well-established that EECs release neurotransmitters that can activate peripheral sensory nerve endings, which relay gut-related signals to the brain where they can become integrated with taste. Bai et al make several critical advancements in 1) developing tools for systematic investigation of EEC function, 2) providing some of the first direct evidence showing that activation of EECs suppresses food intake and 3) delineating mechanisms by which EECs modulate feeding behavior.

Strengths<br /> •The authors supplement existing single-cell RNA sequencing of the proximal intestine to identify a population of EECs that had not been previously described via sequencing and unsupervised clustering (Cck/Tph1).<br /> •Development of Villin-Flp mice, which can be bred with Cre driver lines to selectively manipulate EECs.<br /> •Several subpopulations (Fev, Tac1, Cck, Gcg) suppress food intake when selectively activated using DREADDs.<br /> •Suppression of food intake by Tac1 EECs requires 5-HT3R activation, whereas suppression of food intake via Cck EECs requires CCKA-R.<br /> •Activation of Tac1 EECs can produce a conditioned taste aversion, whereas activation of Cck EECs can condition a flavor preference.<br /> •Both TAC1R and 5-HT3R activation are required for Tac1 EEC activation to condition a taste aversion. This is interesting because TAC1R was not required for suppression of food intake.<br /> •Blockade of CCKAR abolishes the ability of Cck EECs to condition a flavor preference. This is consistent with published literature regarding CCK and appetition.<br /> •Conditioned taste aversion induced by activation of Tac1 EECs is partially attenuated by ablating TRPV1 spinal sensory afferents.<br /> •Conditioned flavor preference induced by activation of Cck EECs is entirely abolished by vagotomy.

Weaknesses<br /> •This is a tidy, well-controlled study without any glaring issues.

Reviewer #1 (Public Review):

Post-ingestion signals provide important information about the nutritional value of food and warnings of potential toxicity. In the gut, there are a sparse group of cells inside the epithelium, called Enteroendocrine cells (EECs) that serve as detectors of mechanical and chemical stimuli. EECs can be chemosensory, mechanosensory or both. Upon stimulation, they release signaling molecules (e.g. transmitters, peptides and hormones) that communicate directly with afferents in the gut or as circulating factors. Previous work has shown EECs are functionally heterogenous and their signaling promote satiety, malaise, and associative learning. This study uses single cell sequencing to provide details of EEC transcriptomic diversity. Bai and colleagues use the transcriptomic information to devise intersectional genetic strategies to manipulate the function of different collections of EECs. For the most part, they focus on two groups that co-express the gene Vilin and either CCK or Tac1. Generally, chemogenetic stimulation of most types of EECs slows down food consumption, but the authors contend for different reasons. They show that mice learn to prefer flavors when paired with CCK+/Villin+ cell stimulation whereas mice learn to avoid flavors paired with Tac1+/Vilin+ cell stimulation. Bai and colleagues go on to use pharmacological interventions to investigate some of the signaling pathways potentially involved and coarse ablation/transection manipulations to implicate distinct neural pathways.

Generally, this is an interesting and potentially useful study. The transcriptomic data, particularly when combined with the previous work from the Clevers' lab and others, helps better define different kinds of EECs. The Vilin-Flp strain appears to be a very useful tool for intersectional studies of cells in the digestive system. The perturbation of feeding during chemogenetic stimulation is clear, particularly the large effect seen when CNO is given to mice expressing hM3Dq in Tac1+/Vilin+ cells. Of course, the model that aversive signaling occurs via 5-HT signaling between Tac1+ cells and nociceptive DRG neurons is consistent with recent work from the Julius lab and others. Furthermore, CCK signaling is known to promote satiety associated with palatable and nutritious foods and so the function Bai and colleagues assign these EECs makes sense. I'm less sure about the specificity of the genetic strategy. It is of course difficult to survey the entire body to know that just EECs are being manipulated. Equally, one wonders if EECs expressing Tac1 or CCK are equivalent in the various specialized compartments of the digestive tract. Additionally, the large variation across controls is somewhat surprising, given feeding assays like these in hungry mice tend to be very robust and reproducible across strains. In some cases, the difference across strains seems larger than the manipulations.

Reviewer #2 (Public Review):

This is an engaging and exciting manuscript that provides new insights into the co-evolution of phage lambda and its E. coli bacterial hosts along with genetic mechanisms and fitness measurements. The manuscript is well written, its figures are clearly presented, it makes a number of novel contributions to evolutionary mechanisms and continues to build upon a simple yet informative microbial model system. The experimental designs were well crafted and the conclusions drawn were supported by the experimental and computational data. While I have no major criticisms of the work being presented, the paper could be improved by increasing its accessibility. There is an inherent assumption that the reader has an understanding of the prior evolutionary work performed on the phage lambda model system. Some simple additions and changes would help increase readership and accessibility of the work.

Reviewer #1 (Public Review):

The authors aimed to test the hypothesis that coevolution between species deforms fitness landscapes in such a way that it facilitates the evolution of new functions. They approach this question by building upon a bacteriophage λ and E. coli model for which the authors have published papers of high impact previously. Coevolving λ and E. coli engage in an evolutionary arms race: E. coli evolves resistance to λ, which is quickly followed by λ evolving the ability to reinfect E. coli. λ evolves reinfection ability through mutations in its binding protein "J," which allows it to use a transporter that is secondary to the primary route λ uses to kill its host. Such evolutionary novelty, coupled with the genetic tractability of this system, makes it a great model to answer the questions presented.

Major strengths of this work include the authors' use of a high throughput gene-editing phenotyping technique (MAGE-Seq), which allowed them to generate a compendium of 580 mutants containing different combinations of mutations in the J protein. The competition experiments performed with these mutants produced fitness landscapes of high dimensionality, which were sufficient to address the question at hand. The data generated by the simulations are revealing; however, the paper would benefit from a visual representation showing key attributes of the simulations, including the parameter space explored.

The goal of this paper was to show that coevolution facilitates evolutionary innovation. The authors do show evidence of this. However, when "reconstructing coevolution in an experimental population," the authors use only one population from Meyer et al. 2012 to make this point. The authors do not explain why they chose this population for the analysis, which is somewhat misleading.

Lastly, the suggestion made at the end of the manuscript that this research has the potential to prevent future pandemics is too tangential. Indeed, the authors share my opinion in their admission that "... it [these data] is [are] difficult to extrapolate ... to understanding pandemics".

In sum, this work takes an original approach to examining how the topology of fitness landscapes changes with interacting species and how the interactions contribute to evolutionary innovations. The foundation the authors have laid in theory and practice should receive positive reception from the scientific community.

Reviewer #3 (Public Review):

Redman et al. describe a novel approach for long-term cellular and sub-cellular resolution functional and structural imaging of the transverse hippocampal circuit in mice. The authors discuss their procedure for implanting a glass microperiscope and show data that clearly support their ability to simultaneously record from neurons within the DG, CA3, and CA1 subregions of the hippocampus. They offer optical characterization demonstrating sufficient resolution to image at the cellular and subcellular level, which is further supported by experimental data characterizing changes in morphology of CA1 apical dendritic spines. Finally, neurons are recorded from as mice engage in navigation behavior, allowing authors to characterize spatial properties of hippocampal cells and relate findings to prior work in the field.

The ability to image from multiple hippocampal subregions simultaneously is a great technical achievement, sure to advance study of the hippocampal circuit. In particular, this approach will likely have tremendous application for addressing the question of how neural representations dynamically change across the hippocampal subfields during initial encoding of novel contexts or later during retrieval of familiar. While the feasibility and utility of this preparation is supported by the data, further characterization of recorded cells will aid the comparison of data collected using this imaging approach to data previously collected with other methodologies.

1. Further measures could be taken to more thoroughly evaluate the impact of the implant on cell health. While authors evaluate glial markers, it is not obvious how long after implant these measurements were taken. Additionally, authors could characterize cell responses of neurons recorded proximal to and more distal to their implant to further evaluate implant effect on cell health.

2. More in-depth analysis of place cells will aid the comparison of data collected using this novel approach to previously published data. For instance, trial-by-trial data and clearer descriptions of inclusion criteria will allow readers a more detailed understanding of observed place cells.

Reviewer #2 (Public Review):

Strengths

The Hippocampus is a key brain region for episodic and spatial memory. The major Hippocampal subregions: Dentate Gyrus (DG), CA3, and CA1 have predominantly been investigated independently due to technical limitations that only allow one subregion to be recorded from at a time. In this paper the authors developed a new method that allows DG, CA3, and CA1 to be imaged simultaneously in the same mouse during behavior with a 2-photon microscope. This method will allow investigation of the interactions between Hippocampal subregions during memory processes - a critical yet unexplored area of Hippocampal research. This method therefore provides a new tool that will help provide insight into the complex functions of the Hippocampus during behavior.

This method also provides high resolution optical access to deep dendritic structures that have been out of reach with existing methods. The authors demonstrate they can measure the structure of single spines on distal apical dendrites of CA1 cells. They track populations of spines and quantify spine changes, spines loss, and spine appearance. Spine turnover is thought to be a key process in how the Hippocampus encodes and consolidates memories, and this method provides a means to quantify spine dynamics over very long time periods (months) and can be used to study spine dynamics in CA3 and DG.

Weaknesses

This method requires the implantation of a relatively large glass microperiscope that cuts through part of the Septal end of the Hippocampus. This is a necessary step to image transversally and observe all the major subregions simultaneously. This is an unfortunate limitation as it damages the very circuits being investigated. The authors attempt to address this by measuring the functional properties of Hippocampal cells, such as their place field features, and claim they are similar to those measured with other methods that do not damage the Hippocampus. However, it is very likely the implant-induced damage is affecting the imaged cells in some way, so caution should be taken when using this method. The authors are very aware of this and briefly discuss the issue. In addition, the authors observe damaged adjacent to face of the glass microperiscope that extends to ~300 um from the face. This area should therefore be avoided when imaging the Hippocampus through the microperiscope.

Reviewer #1 (Public Review):

Redman and colleagues employed microprisms and two-photon optical imaging to track separately the structure of dorsal CA1 pyramidal neurons or the activity patterns of dorsal Dentate Gyrus, CA3, CA2 and CA1 pyramidal neurons, longitudinally in live mice. First, they carried out a characterization of the optical properties of their system. Second, they performed an example tracking of dendritic spines in the apical aspect of dorsal CA1 pyramidal neurons. Finally, they characterized differences in spatial coding along the tri-synaptic pathway, in the same animals. The main focus of the manuscript is technological and the authors show interesting data to support their technique, which I believe will be of relevance to neuroscientists interested in the hippocampal formation.

Strengths.<br /> While using microprisms to achieve a "side" view of neurons in specific brain areas is not new per se [see Chia et al., J. Neurophysiol. (2009), Andermann et al., Neuron (2013), Low et al., PNAS (2014) etc.] the authors were able to visualize activity of a large neuronal circuit such as the hippocampal tri-synaptic pathway - for the first time - in the same animal exploring an environment. This is not only a technical feat but it opens new scientific avenues to study how information is transformed at different stages within the hippocampus, as such I think this will be of broad interest for people in the field. In addition, the authors demonstrated imaging of dendritic spines in the apical aspect of pyramidal neurons but limited to dorsal CA1 due to the labelling density of the transgenic mouse line they decided to use. Despite the fact that imaging apical dendritic spines in dorsal CA1 has been shown earlier [see Schmid et al., Neuron (2016) and Ulivi et al., JoVE (2019)], the use of the micro periscope greatly increases the flexibility of these sort of experiments by enabling tracking of large portion (both apically and basally) of the dendritic arbors of dorsal CA1 pyramidal neurons.

Weaknesses.<br /> While the data are sufficient to demonstrate the technique, the conceptual advance of the paper is very narrow. The findings on spatial coding differences in different hippocampal subregions - namely a non-uniform distribution of spatial information in the different hippocampal subregions - do not add new knowledge but largely confirm the literature. The results on the dynamics of apical dendritic spines of pyramidal neurons in dorsal CA1 seem to confirm previous work, but the interpretation of these results differs fundamentally. In fact both papers cited by the authors (Attardo et al., and Pfeiffer et al.,) come to the conclusion that dendritic spines on basal dendrites of CA1 pyramidal neurons are highly unstable, at least by comparison to other neocortical areas. The authors seem to ignore this discrepancy. However, this discrepancy has importance also to the characterization of the technique the authors developed. In fact, the optical resolution of the system strongly affects the ability to resolve neighboring spines - especially at the high density of dorsal CA1 - and thus it has a direct effect on the measures of synaptic stability [Attardo et al., Nature, (2015)]. The authors duly report lateral and axial resolutions for their micro periscopes and both are lower than the ones of Attardo and Pfeiffer, thus the authors should consider the effects of this difference on the interpretation of their data.

Reviewer #2 (Public Review):

Johnson and Desai have developed a yeast experimental-evolution system where they can insert barcoded disruptive mutations into the genome and measure their individual effect on fitness. In 2019 they published a study in Science where they did this in a set of yeast variants derived by crossing two highly diverged yeast. They found a pattern that they termed "increasing cost epistasis": insertion mutations tended to have more deleterious effects on higher fitness backgrounds. The term "increasing cost epistasis" was coined to play off the converse pattern commonly observed in experimental evolution of "diminishing returns epistasis" wherein beneficial mutations tend to have smaller effects on more fit backgrounds. Another way to think about fitness effects is in terms of robustness: when mutations tend to have little effect on phenotype, the system is said to be robust. Thus, when increasing costs epistasis is observed, it suggests that higher fitness backgrounds are less robust.

In this paper, Johnson and Desai use this same barcoded-insertions system in yeast, but here the backgrounds receiving insertions are adapting populations. More specifically, they took 6 replicate populations that evolved for 8-10k generations and inserted a panel of 91 mutations at 6 timepoints along the evolutions. They then did this entire experiment in two different environments: one in rich media at permissive temperature (YPD 30) and one in a defined media at high temperature (SC 37). Importantly, the mutations accumulating in a population over time here are driven by selection-and thus the patterns of epistasis observed here are probably more relevant to "real" evolution than the backgrounds from the 2019 paper. The overarching question, then, is whether similar patterns of epistasis is found in these long-term adaptations and across conditions as was previously observed.

The first major finding in this work is that at YPD 30 (where the yeast are presumably "happy"), the mean fitness effect does decline in most (but not all) populations as they adapt. Since the population is becoming more fit over time (relative to a constant reference type), this is consistent with the previously observed pattern of increasing cost epistasis. The strength of the effect is, however, weaker than in that previous work. The authors speculate that this may reflect the fact that far few mutations are involved here than in the previous study-giving far fewer opportunities for (mostly negative) epistatic effects. I find this explanation likely correct, although speculative.

The second major, and far more surprising, result is that in the other condition (SC 37), the insertion mutations mutations do not show a consistent trend: mean fitness effect of the insertion mutations does not change as adaptation proceeds. This is despite the fact that fitness increases in these population over time just as it did in the YPD populations. Toward the end of the paper, the authors speculate as to why this is the case. They argue that in the YPD 30 environment, selection is mainly on pure growth rate. They suggest that the growth rate depends on different components such as DNA synthesis, production of translation machinery, and cell wall synthesis. Critically, these components are non-redundant and can't "fill in" for each other. So, for example, rapid DNA synthesis is of little value if cell-wall synthesis is slow. As adaptation fixes mutations that increase the function of one of these growth components, they shift the "control coefficient" to other components. This, they argue, may be the major explanation behind increasing cost epistasis. I find the logic of their argument compelling and potentially providing great insight into developing a richer view of epistasis. Future experiments will be needed to test how well the hypothesis holds up. They then flip the argument around and suggest that in the SC 37 environment, the targets of selection are fundamentally different from those in growth-centric YPD 30 conditions. Instead, they argue, there is likely more redundancy in the components that mutations are affecting. I again find their arguments compelling.

After establishing these observed patterns for mean effects, they examine individual mutations and look at the relationship of fitness effects as a function of background fitness. The upshot of this analysis is that there are more negative correlations than positive ones (especially in the YPD 30 conditions), but also that there is a lot of variation: there are many mutations that show no correlation and a small number with a positive correlation. This casts substantial doubt on the simplistic view that for the vast majority of mutations, fitness itself causes mutations to have greater costs. As a minor point of criticism, a lot of statistical test are being done here and there is no attempt to address the issue of multiple testing. I would like the authors to address this. I say minor because I don't think the overarching patterns are being affected by a few false positive tests.

From here the authors turn to using a formal modeling to understand epistasis better. For each mutation, they fit the fitness data to three models: fitness-mediated model = fitness effects are explained by background fitness, idiosyncratic model = fitness effects can change at any point in an evolution when a new mutation fixes, and full model = fitness effects depend on both fitness and idiosyncratic effects.

My major criticism of the work lies here: the authors don't explain how the models work carefully and thoroughly, leaving the reader to question downstream conclusions. Typically, when models are nested (as the fitness-mediated and idiosyncratic models appear to be nested within the full model), the full model will, by definition, fit the data better than the nested models. But that is not the case here: for many mutations the idiosyncratic model explains more of the variance than the full model (e.g. Figure 3A). (Note, the fitness-mediated model never fits better than the full model). Further, when dealing with nested models in general, one should ask whether the more complex model fits the data enough better to justify accepting it over simpler model(s). There are clearly details and constraints in the models used here (and likely in the fitting process) that matter, but these are not discussed in any detail. Another frustrating part of the model fitting is that each model is fit to each mutation individually, but there is no attempt to justify this approach over one where each model is expected to explain all mutations. I'm not saying I think the authors have chosen a poor strategy in what they have done, but they have made a set of decisions about how to model the problem that carries consequences for interpretation, and they don't justify or discuss those decisions. I think this needs to be added to the paper. I think this should include both a high level, philosophy-of-our-approach section and, probably elsewhere, the details.

The reason this matters is because the authors move on to use the fitted models and the estimated coefficients from the models in discussing and interpreting the structure of epistasis. For example, they say "We find that the fitness model often explains a large amount of variance, in agreement with our earlier analysis, but the idiosyncratic model and the full model usually offer more explanatory power." Looking at Figure 3A, this certainly appears to be the case, yet that type of statement is squarely in the domain of model comparison/selection-but as explained above, this issue is not addressed. Relatedly, the authors go on to argue that "Positive and negative coefficients in the idiosyncratic model represent positive and negative epistasis between mutations that fix during evolution and our insertion mutations." I'm left wondering whether the details of the model fitting process matter. I am left asking how the idiosyncratic model would perform on data that arose, for example, under the fitness-mediated model? Or how it would perform on data originating under the full model? Is it true that when data arises under a different model (say the full model) but is fit under the idiosyncratic model, negative coefficients always represent negative epistasis and positive coefficients will always represent positive epistasis and that model misspecification does not introduce any bias? Another thing I am left wondering about concerns the number of observed coefficients in the idiosyncratic model: if one mutation shows similar effects across backgrounds, it might generate one coefficient during model fitting, while another mutation that has different effects on different backgrounds could give rise to several coefficients-is there some type of weighting that addresses the fact that individual mutations can generate different numbers of coefficients? One can imagine bias arising here if this isn't treated carefully.

One of the main conclusion that the authors reach is that the pattern of increasing cost epistasis (observed previously and here in the YPD 30 environment) may not arise from the effect of background fitness itself, but instead arise because epistatic effects tend to be negative-and the more interactions there are (with mutations accumulating over time), the more they tend to have a negative cumulative effect. I find it very likely that the authors have this major conclusion correct. By contrast, they find that at SC 37, the distribution of fitness effects is less negatively skewed-with a considerable number of coefficients estimated to be > 0. They close with a really interesting discussion exploring how these patterns likely arise from underlying biology of the cell, metabolic flux, redundancy, and selection for loss-of-function vs gain-of-function. I find a lot of this interesting and insightful. But because some of their conclusions rest squarely on the modeling, I encourage the authors to be more thorough and convincing in how they execute this aspect of the work.

Reviewer #1 (Public Review):

The 2019, Johnson et al., Science study (referred to as "2019 study" or "prior study" in the rest of the comments) measured mutational robustness in F1 segregants derived from a yeast cross between a laboratory and a wine strain, which differ at >35,000 loci. To realize this, the authors developed a pipeline 1) to create the same set of transposon insertion mutations in each yeast strain via transformation; and 2) to measure the fitness effects of these specific insertion mutations.

In this manuscript, the authors applied the same pipeline to laboratory evolved yeast strains that differ in only tens or hundreds of loci and thus are much less divergent than those used in the prior study. Both studies aim to characterize how the fitness of the sets of insertion mutations (mostly deleterious) vary depending on the existing mutations (mostly beneficial) in those yeast strains. However, the current manuscript, especially when compared to the prior study, suffers from several major weaknesses.

First, only 91 genes out of >6,000 genes in the yeast genome are perturbed in the manuscript. The small set of disruption mutations is unlikely to faithfully capture the pattern of epistasis in the selected clones. By comparison, >1,000 insertion mutations were evaluated in the 2019 study. Because the majority of the >1,000 tested mutations were neutral, the authors focused on 91 insertions that had significant fitness effects. The same 91 insertion mutations are used in the current study. However, as evident in both studies, epistasis plays an important role in how insertion mutations interact with different genetic backgrounds. Considering the vastly different genetic backgrounds between clones used in the prior and current studies, the insertion mutations of interest in the current study is unlikely to be the same as those in the prior study. The large-scale genetic insertion used in the prior study is suggested to be conducted in the current study.

Second, the statistical power in the current manuscript is insufficient to support the conclusions. Fitness errors were not considered when several main conclusions were drawn (fitness errors on the y-axis of Figure 1B are not available; fitness errors on the x-axis of Figure 2 are not available). The current conclusions are invalid without knowing the magnitude of fitness error. Fitness of each clone should be measured in at least two replicates in order to infer errors of fitness measurements. Additionally, the authors isolated two clones from the same timepoint of each population and treated them as biological replicates based on the fitness correlation between the two clones. However, this practice can be problematic because the extent of fitness correlation varies across populations and it is less likely to capture the patterns of epistasis when clones are isolated from more heterogeneous populations. Similarly, the authors could avoid this bias by measuring the fitness of each clone in multiple replicates and treat the two clones from the same timepoint/population separately.

Reviewer #3 (Public Review):

Genetically engineered mouse (GEM) models containing either recombinases (Cre and Flp) or drug inducible platforms (such as tetracycline inducible system) have been widely used for conditional and inducible control of gene inactivation. These systems achieve control of gene expression at the genetic level and these strategies achieve inactivation of the target protein after a considerable amount of time (usually several hours to days). However, there are not many examples of tools to achieve the same at the protein level and to quickly inactivate proteins. In this manuscript "Rapid and specific degradation of endogenous proteins in mouse models using 2 auxin-inducible degrons", the authors demonstrate the application of a plant-based chemical degradation system called auxin-inducible degron (AID) to inactivate target proteins, as quickly as 1 hour.

The degron system, by far, has been used in cell culture models. Its limitations such as leakiness and the requirement of higher doses of the substrate [indole-3-acetic acid (IAA)] and the toxicity associated with such high doses had diminished the enthusiasm of researchers in developing GEM models to test the system. The pioneering inventors of the degron system (Masato T. Kanemaki's group) recently made improvements to the AID, termed AID2, which overcame these limitations, and thus they could demonstrate the application of the degron mediated degradation of target proteins by generating GEM models. Very recently, another group (preprint Abuhansem et al 2021; now published in Developmental Cell, April 2022) successfully generated degron GEM models. These studies are set as initial examples of successful use of the degron system in mouse models.

Although not novel, the authors in this manuscript elegantly show the utility of the degron system. The authors developed degron GEM models for two proteins condensin I and condensin II and performed a series of experiments using primary lymphocytes isolated from the models and by using the animal models themselves. The experiments are well designed, the data agree with the interpretations and the conclusions are logical.

Reviewer #2 (Public Review):

The authors of this study have successfully generated a sophisticated novel system to alter the degradation of specific proteins using a heterologous system derived from plants. They used the auxin-inducible degron (AID) approach by constructing and validating, in a robust stepwise manner, the different compounds for this ternary system to operate. First, by tagging the selected proteins (NCAPH and NCAPH2, two compounds of condensin chromosomal complexes) with AID and a fluorescent reporter protein, using CRISPR tools. Second, by creating a single copy-inserted transgene expressing TIR1, the E3 ligase substrate receptor, targeted into the Rosa26 locus and under the control of the CAG promoter. And third, by exposing cells or animals to the ligand, the plant hormone auxin, and studying the kinetics and dosage of all three compounds. The authors took good care to document that the indicated genetic alterations did not significantly interfere with normal physiology. Although some overt phenotypes were reported (reduced size and fertility) they correctly concluded their system was not fundamentally impairing endogenous cellular processes. Next, upon recreating the double mutant mice, they went in a stepwise manner, to explore the efficacy of their approach first in several primary cell types and, later, directly in vivo, both in adult stages and during embryo development. Their findings convincingly demonstrate their ability to target the selected proteins for degradation and the associated alterations (such as mitotic arrest) that were not universally found, suggesting organ- and cell-specific limitations yet to be defined, possibly related to the capacity of TIR1 to function in all cell types or to the actual arrival of auxins to all cellular destinations. Their experimental system is used to document that some cell types appear to be dependent on these condensin complexes to complete their cell cycles whereas other cells appear to complete the mitotic cycle in their absence, suggesting additional mechanisms of mitotic control to be further studied. Even with the limitations expressed by the authors themselves, this novel approach to assessing protein function appears to be highly powerful and might be applied to a variety of biological questions, now directly targeting proteins, beyond previous studies whose rationale was limited by the inducible and conditional gene expression, where DNA (and not proteins) was targeted hoping to foresee phenotypes associated with subsequent protein synthesis alteration. In summary, a technically brilliant study, well controlled, sincerely declaring the already known limitations that will have a significant impact on molecular and developmental studies in cells and in animals.

Reviewer #1 (Public Review):

In this paper, Macdonald et al describe an original method for the inactivation of gene function using auxin-inducible degrons. They validate their approach in mice in this proof of concept on two genes: Ncaph and Ncaph2. The paper shows the efficiency of knockdown at the protein level both in mice and in different primary cells. It also shows the rapidity of degradation of the targeted protein.

An immediate parallel comes between this technology and the cre and creERT2 spatiotemporal approach which has revolutionized the study of gene function in a cell/tissue type, in adults (bypassing embryonic lethality) and also allows to dissect more finely the different functions of pleiotropic genes. By acting at the protein level, this auxin-inducible degrons approach opens new solutions complementary to the cre/loxP system to achieve these three objectives.

As with any new tool, there are at least two main questions to answer regarding the use of auxin-inducible degrons:<br /> • Is this technology effective, in this case, does it efficiently reduce target protein expression?<br /> • What are the biases of this new technology?

Concerning the use of the cre/loxP system, many years and many articles have been necessary to better master the technology and especially to better understand its biases and how to bypass them. It is therefore unrealistic to expect this article to completely answer the two questions above. The term proof of concept seems thus interesting to put forward in this article. Indeed, this paper is above all a proof of the effectiveness of this system on two genes. Regarding the description of the biases, it provides elements that suggest that some drawbacks, similar to those of the creERT2 system, could be expected. In the future, additional papers will be required to better characterize the tool and understand for which target gene this approach is efficient and specific.

Strengths:<br /> • This paper is a proof of concept of the efficiency of this approach for inactivation at the protein level.<br /> • It induces a rapid target protein degradation which allows the immediate study of a phenotype.<br /> • The methods of validation of the auxin-inducible degrons approach are convincing.<br /> • The results are very complementary to the paper of Yesbolatova et al. and thus open technological opportunities for the use of this system in cell lines and animal models, particularly the mouse.<br /> • The whole text is very precise and well detailed (including materials and methods and supplementary figures) facilitating reuse by other scientists.<br /> • The proteome data are available in the PRIDE repository, highlighting the authors' commitment to FAIR principles.

Weaknesses:<br /> • Potential biases and how to work around them should be discussed further in this paper and will require additional studies in the future.<br /> • The level of generalizability of this approach will require further studies.<br /> • This approach only addresses the study of protein-coding genes and will not work to study the non-coding genome.

Reviewer #3 (Public Review):

The primary strength of this study is in establishing the N999S heterozygous mouse as a useful model system for debilitating paroxysmal non-kinesigenic dyskinesia (PKND), with or without epilepsy. This outcome was hard-won following a comprehensive analysis of biophysical, neurophysiological, and behavioral tests. Ultimately the convincing evidence was demonstrated through a clever application of a stress-related behavioral test (quite in alignment with triggers in patients) to elicit the hypo-motility associated with PKND. Like patients who exhibit variable penetrance, even highly inbred mice exhibit much variability, and uncovering a robust phenotype took a nuanced approach and perseverance.

To reach this point, several experiments provided mechanistic insights into the mutant channel behavior. First, whole-cell patch clamp experiments revealed shifts in the G-V consistent with gain-of-function behavior previously characterized using the N999S and D434G mutants expressed heterologously. Novel observations of H444Q revealed a loss-of-function (LOF) behavior with the G-V shifted to positive potentials but to a lesser degree. These electrophysiological phenotypes establish the rank of predicted severity as N999S>D434G>H444Q.

This prediction was tested in brain slices of heterozygous animals where the mutant channels would be normally spliced and associate with WT subunits and other components such as beta subunits. The investigators evaluated BK currents by patch clamp from hippocampal neurons where BK channels are known to play key functional roles. Both N999S and D434G showed the predicted increase in current magnitude, though interestingly the differences between them apparent in heterologous expression were lost in the native setting. Curiously, no differences in BK current magnitude were observed in neurons of heterozygotes carrying the putatively LOF mutation H444Q.

In terms of seizure susceptibility, D434G mutants different from WT and less severe than N999S mutants with respect to time to evoked seizure, although differences in "EEG power" were not statistically significant between D434G and WT. These observations support the conclusion that D434G represents an intermediate disease phenotype.

The behavioral studies were the most effective in revealing differences among the variants and in defining GOF N999S heterozygotes as a compelling animal model for PKND and providing evidence that the LOF mutation conferred the opposite effect of hyperkinetic mobility. The findings provide the new insight that KCNMA is the target of heritable, monogenic disease, a conclusion that was previously not forthcoming because known human mutations have arisen de novo. The dyskinetic phenotypes in response to stress induction are wholly consistent with patient symptoms.

With respect to rigor and reproducibility, it is commendable that the investigators were blinded to genotype during data collection and analysis. Moreover, the study provides an important confirmation of previous findings from another lab regarding the cellular phenotype of the N999S mutant. WT controls were compared to transgenic littermates within individual transgenic lines. In some cases, the sample sizes were rather low (see below), but otherwise the study seems rigorous.

The strengths of the manuscript far outweighed the weaknesses. The experiments interpreted to suggest a gene dosage effect with D434G were not compelling to this reviewer and might be better documented in the supplement with the conclusion that further work is required.

The consequences of the altered BK current levels were assessed on the voltage dependence of firing frequency in the hippocampal neurons, but it was not very clear how increased BK current would enhance neuronal excitability. Also, how might it lead to the PKND phenotype? A paragraph even speculating on these mechanistic links in the Discussion would be welcome.

Reviewer #2 (Public Review):

The manuscript by Park et al focuses on cellular and behavioral effects of BK channel mutations in mouse models. This work is important, as it establishes that BK channel mutations linked to human neurological disease can, on their own, cause similar pathology in mice, and it also begins to provide neurological bases for the associated behavioral deficits (in terms of possible effects on action potential waveforms), and specifically, the N999S/WT mouse may serve as a model for understanding the neuronal correlates of paroxysmal dyskinesia (PNKD3) linked to this mutation in humans. Briefly, the authors find that BK currents and AP firing rates were increased in hippocampal dentate gyrus (DG) neurons in N999S/WT and D434/WT mice, with no changes observed in H444Q/WT mice. In behavioral assays, N999S/WT and D434/D434 mice became immobile after stress, whereas H444Q/H444Q mice became hyperkinetic.

The manuscript is well written and well organized, and the experimental data are of high quality.

I have some comments pertaining to possible interpretations of the mutant data, specifically on the potential impact of the neuronal BK-beta4 subunit on the mutation effects. This can lead to alterations in channel activity that may be different in neurons that express the beta-4 subunit (like DG neurons) vs. non-beta4 expressing neurons (like midbrain dopaminergic neurons). It may ultimately be important to build on this work by assessing activity in different neurons in the brain.

Reviewer #1 (Public Review):

Park et al investigated the association of three different mutations of the KCNMA1 gene (expressing the potassium channel BK) with paroxysmal nonkinesigenic dyskinesia (PNKD3). To this end they use electrophysiology in heterologous expression systems, neuronal cultures of dentate granule cells, and behavioural tests in transgenic mice harbouring the specific mutations. They find that two mutations (N999S and D434G) result in gain-of-function properties, associated to larger BK currents and increased action potential firing. Conversely, another mutation (H444Q) renders loss-of-function characteristics, which are correlated with non-significant changes in ionic currents or excitability. Behavioural tests were conducted with hetero- or homozygous transgenic mice harbouring the BK mutations. Results reveal significantly decreased seizure threshold and higher immobility after stress for gain-of-function, but not loss-of-function mutants. The latter showed hyperkinetic behaviour. The data provide relevant evidence linking gain-of-function defects on BK function to the disease phenotype, and provide a novel mouse model for PNKD disease. The conclusions of this paper are mostly supported by data. Some aspects need to be revised and clarified.

Reviewer #3 (Public Review):

The manuscript by Langmuller, Champer and colleagues reports a set of experiments and models investigating the fitness effects of transgenes in Drosophila melanogaster carrying CRISPR components to determine how useful such transgenes may be for population control. This study benefits from well-designed transgene constructs that allow the investigators to distinguish the effects of on-target and off-target Cas9 endonuclease activity, and a sophisticated maximum likelihood modeling framework that allows estimation of the fitness effects of the transgene constructs. The manuscript's major shortcoming is the absence of statistical analysis of the allele frequency data and some potentially unrealistic assumptions that went into the model.

My first recommendation is that a statistical analysis of the allele frequency data should be included in the manuscript, rather than inferring patterns solely from visual inspection of the data. Specifically, the manuscript claims that (lines 176-180): "We found Cas9_gRNAs to be the only construct that systematically decreased in frequency across all replicate cages (Figure 2). Interestingly, the allele frequency change was not consistent with fixed direct fitness costs. Instead, the construct frequency "bottomed out" in most replicates, and this occurred more quickly when the starting frequency was higher (Figure 2)." These conclusions regarding allele frequency changes should be supported by statistical analyses. What is the uncertainty surrounding the allele frequency estimates? Some indication of this uncertainty (such as error bars) could be added to Figure 2. Which of the trajectories in Figure 2 show a statistically significant change in allele frequency over the course of the experiment? Is the *increase* in the frequency of the no-Cas9_no-gRNA replicates significant? What support is there for the claim that the allele frequency changes "bottomed out"? Does a non-linear model fit these data significantly better than a linear trend? What is the evidence that allele frequency decreases slowed earlier "when the starting frequency was higher"? What is the evidence that "replicates 3 and 4 ... had very different frequency dynamics"? While they started at different frequencies, the slope of those two trajectories could be statistically indistinguishable. What is the authors' interpretation of the Cas9_gRNAs replicates 6 & 7 whose trajectories did not decrease?

My second recommendation involves the assumptions that went into the maximum likelihood modeling. In particular, it strikes me as unrealistic to assume that 1) the genome contains only a single off-target site that is entirely responsible for the decrease in fitness due to Cas9 activity; and 2) that the rate of off-target mutation is as high as it is assumed to be ("In individuals that carry a construct, all uncut off-target alleles are assumed to be cut in the germline, which are then passed on to offspring that could suffer negative fitness consequences."). Regarding point 1), isn't a more realistic scenario that there are multiple off-target sites, each with a potentially different fitness consequence resulting from Cas9-induced mutations? If so, doesn't the likelihood that all off-target sites have been cut depend on the number of such sites, as multiple off-target sites should reduce the mutation rate at any single site. This possibility also suggests that there may be multiple loci with potentially deleterious Cas9-induced alleles segregating within the experimental populations. Regarding point 2), even assuming only a few potential off-target sites per genome, it seems like the rate of off-target cutting would have to be unrealistically high to approach mutating all off-target sites in the population. The conversion efficiency of the constructs used here is reported as ~80% and 60% in females and males, respectively; it seems likely that the rate of Cas9 mutation at off-target sites is lower than this efficiency for the target site. These assumptions should be justified or relaxed before claiming that mutational saturation of off-target sites is responsible for a decreasing fitness loss over the course of the experiments (after confirming that there is statistical support for the claim that the allele frequency trajectories bottom out).

My third suggestion involves the correspondence between the results of the likelihood modeling and the phenotypic assays. The best fit model inferred a viability loss of 26% and no detectable effects on female choice (or male attractiveness) or fecundity. In contrast, the phenotypic assays inferred no detectable effect on viability, but a 50% reduction in male attractiveness and 25% reduction in female fecundity. I think that the authors' conclusion that "[t]hese assays broadly confirmed our previous findings" needs some context or explanation as to how these numerically discrepant findings are broadly confirming, beyond the speculation that the discrepancy in viability may be due to rearing in vials vs. population cages.

My fourth suggestion involves the comparison between the Cas9_gRNAs and Cas9HF1_gRNAs transgenes. The inference that off-target cuts are the major source of fitness loss for the Cas9_gRNAs construct relies heavily on the observation that there was no decrease in allele frequency for the two Cas9HF1_gRNAs replicates. It therefore seems critical to be confident in this observation, and to rule out alternative explanations as much as possible. For example, did the authors confirm that the Cas9HF1_gRNAs construct has on-target Cas9 activity levels as high as the Cas9_gRNAs construct? Although I am not certain about this (see comments in the next paragraph on this point), I think the transgene constructs used to estimate drive conversion rates are different from the constructs used for the population cage experiments; if this is correct, I think it would be helpful to provide the on-target mutation rates for the actual constructs used in the population cages.

Relatedly, I was confused about the portion of the manuscript that reports the drive conversion efficiency. The manuscript states, "As a proof-of-principle that Cas9HF1 is indeed a feasible alternative, we designed a homing drive that is identical to a previous drive (45), except that it uses Cas9HF1 instead of standard Cas9. This drive targets an artificial EGFP target locus with a single gRNA (see Methods)." Given that the rate of drive conversion was estimated by the loss of GFP, these homing drive constructs must be different from the constructs used in the population cage experiments, as those constructs targeted a site on chromosome 3L which does not contain GFP. I could not find a description of these homing constructs in the Methods - while a reader might be able to puzzle this out by reading reference #45, I think it would be helpful to explicitly describe these details in this manuscript.

Reviewer #2 (Public Review):

This paper reports a set of Drosophila population cage experiments aimed at quantifying fitness effects associated with the expression of Cas9 gene drive constructs in the absence of homing. The study attempts to deconvolve fitness effects due to the presence of the active nuclease at a genomic location from those that arise from off-target effects elsewhere in the genome: an important issue when considering gene drive strategies in the wild. To distinguish effects due to cleavage at the target site from activity elsewhere in the genome, a construct where Cas9 was replaced with a high fidelity nuclease (Cas9HF1) was employed. The experimental design compares the active nuclease-gRNA constructs targeting a site on another chromosome with no gRNA and reporter only controls, all inserted in the same locus. The Cas9 construct was assayed in 7 replicates with Cas9HF1 and controls assessed as duplicates with cages running for between 8 and 19 generations.

There is a lack of clarity in terms of the cage set up design, the description in the supplementary methods could clarify if all the replicates came from a single founder and the difference in set-ups that necessitated ignoring some 1st generations.

The main finding reported from this part of the work is that with the control populations the frequency of the construct remained fairly constant across the generations, but the active nuclease tended to decline. I am somewhat confused by some of the claims here. First, the the authors report a "bottoming out" effect where construct frequency declines then levels off: I am not entirely convinced that Figure 2 shows this. For example, comparing replicates 4 and 5 (8 and 16 generations respectively), it looks to me that there is a steady decline at the same rate with no evidence for a plateau. Perhaps replicates 2 and 3 show "some" evidence of levelling. In addition, replicates 4, 5, 6 and 7 have similar construct starting frequencies (particularly 5 and 7, which are only a few % different) yet the former show a steady decline whereas the latter maintain the construct at a steady level. This does not appear to be consistent with the authors explanation of higher off-target effects in populations carrying high frequencies of the construct. It would be helpful if the authors could more clearly explain the trajectories presented in Figure 2.

Utilising the allele frequencies obtained from the cages, 2 locus ML models were applied with the construct insertion site and an idealised off target site. They argue, correctly in my view, that fitness effects can be attributed to off target activity and not cleavage at the 3L target since the Cas9HF1 construct shows no substantive effect. In the models they assume that the presence of Cas9 in the germline (or maternally contributed) will invariably lead to cleavage at the idealised site. The model indicates that the construct insertion per se has no direct fitness costs but that off-target effects may have fitness consequences of approximately 30%, and seek to support this conclusion with simulations. I found this section difficult to follow but I feel that the conclusions are supported.

Direct phenotypic assays with the active Cas9 nuclease were performed, looking at viability, mating preference and fecundity. Relegating these data to the supplements is not useful. While significant effects are attributed to the Cas9-gRNA construct, the authors cannot rule out a DsRed effect and it is a shame they did not assay at least one of the control constructs. In addition, in their modelling they assume that Cas9 activity will always cleave but see no evidence for this in the heterozygote viability assay. Whether this is due to the difference in rearing conditions that the authors claim is debatable.

Finally, since the initial cage experiments suggest that the Cas9HF1 enzyme reduces off-target effects they assay this enzyme in a model homing drive, indicating that this enzyme performs as well as the regular Cas9. Again, relegation of these data to supplementary datasets is unhelpful and it would improve the manuscript if these results could be simply summarised in a figure.

Taken together, I think this is a useful study but is presented in a way that is at times impenetrable to the non expert. More clarity in presenting the cage and modelling data, as well as promotion of figures from supplementary material to the main manuscript would considerably aid the non expert and provide greater confidence in the interpretations. If these issue could be clarified I feel the work provides a useful addition to the gene drive field and will help those thinking about developing such strategies, particularly relevant are the findings related to the Cas9HF1 enzyme.

Reviewer #1 (Public Review):

The goal of the work was to test for direct and indirect fitness costs associated with specific types of constructs that could be used for gene drive. The authors conclude that there are no direct fitness costs associated with the presence and expression of either Cas9 or the guide RNAs but that the Cas9 is causing off-target cuts that result in loss of fitness. They also conclude that a newer form of CAS doesn't cause these off-target cuts. While the goal of this study is important, there are many caveats associated with the work as reported, and these limit interpretation of the results, Many of the caveats are pointed out in the discussion.

I am specifically concerned by the fact that from what I read, a company made the transgenic lines and that there was only one transgenic line per treatment. Unless the fly line used for the insertion was completely homozygous for the chromosome where the insertion was made, the lines could have differed in fitness, due to somewhat deleterious reccessives captured in one G1 but not another. This cost could have persisted for a number of generations after the crosses were made, especially in the high frequency "releases". This may not have been a real problem, but without any replication it is difficult to know.

My concern is reinforced by the fact that the no-Cas9, no-gRNA line goes up in frequency for the first 5 generations and then becomes stable in frequency. The loss of the fitness advantage is consistent with a fitness effect partially linked to the insertion site in that one cross but not others.

It is important to note that the starting points are cages with separate vials of the control and experimental strain. Even a small difference in development time of the two strains in the first generation could lead to an excess of homozygotes in the next generation.

I am also concerned by the fact that the main conclusion is that the decline in frequency in the Cas9-gRNA line is due to off-target cuts, but there was no sequencing to back up that conclusion. In the discussion, this problem is mentioned but dismissed. I don't see how it can be dismissed when this is a major conclusion that remains based on very indirect evidence.

When releasing homing gene drives, the initial frequency of the transgenic line is very low, and as in the Garrood et al paper cited, it is possible for the gene drive to outpace the non-target cutting. The modeling does not address what the impact of the presumed fitness costs in this experiment would be for a replacement/suppression drive released at low frequency.

Reviewer #3 (Public Review):

Patient with DMD can have severe arrhythmias. The authors explored the expression of cardiac ion channels and modelled arrhythmogenic behavior in stem cell derived cardiomyocytes from DMD patients. Of particular focus was the Nav1.5-Kir2.1 channelosome. Of three samples (two male, one female) all had reduced sodium current (Ina) and two of three had reduced potassium current (Ik1). In the one stem cell line attempted, transfecting alpha1-syntrophin rescued channel expression and cellular electrophysiological profile.

The work successfully identifies, for three DMD patients, reduction in two cardiac ion channels previously known to cluster in a channelosome, with particular emphasis on reduced Ina current. The rescue by alpha1-syntrophin supports the mechanism of the putative arrhythmogenic ion channel deficiencies and provides a potential therapeutic pathway. The study is limited by a small samples size of three patients and is confined to the traditional limitations of stem cell derived cardiomyocytes which have limitations relative to mature adult cardiomyocytes or in vivo studies. However, despite these inherent limitations, the findings provide important mechanistic details on DMD arrhythmogenesis and provide a crucial lead for investigators interested in developed therapeutic solutions for a devastatingly lethal disease.

Reviewer #2 (Public Review):

In the manuscript "SNTA1 Gene Rescues Ion Channel Function in iPSC-CMs from Muscular Dystrophy Patients with Cardiomyopathy and Arrhythmias", authors used DMD donor iPSC-CM to demonstrate electrophysiological dysfunction, specifically pinpointed to the dislocated Nav1.5-Kir2.1 complex, which can be rescued by scaffolding protein a-syntrophin (SNTA1). This model illustrates a very straightforward mechanism of DMD associate cardiomyopathy and offers a feasible/potential treatment. The iPSC in vitro differentiated CM often present with concerns of not resembling rod-shape mature myocytes. This manuscript did an elegant assay by using matrigel-coated PDMS system, yielding morphologically matured myocytes. Beautiful work.

Reviewer #1 (Public Review):

The strengths of the paper lie in its novelty in relating cardiac to skeletal muscle phenotypes in muscle dystrophic disease. It addresses a well known but poorly understood clinical finding implicating the associated skeletal muscle SNTA deficiency with a NaV1.5-Kir2.1 and consequent cardiac arrhythmic phenotype.

The study was based on iPSCs from a restricted number of clinical patients. Future studies will need to follow these up using genetically modified animals to determine phenotypes and the whole organ and animal levels.

This is a well written paper on work of high and interesting quality.

Reviewer #2 (Public Review):

The paper by Khamle et al shows that CHI3L1 augments SARS-COV2 pseudovirus uptake in cells and that blocking CHI3L1 partially reduces uptake but the effect is not as efficient as some mAbs or soluble ACE2. A major limitation of the work is all of the data are based solely on experiments with pseudovirus. To be impactful, work would need to be performed with live virus assays as well as in vivo with either K18 mice or hamster models.

Reviewer #1 (Public Review):

In this manuscript, Kamle and colleagues report that inhibition of host constitutively-expressed chitinase 3-like-1 (CHI3L1) increased epithelial expression of ACE2 and SPP, resulting in epithelial cell viral uptake of pseudoviruses that express the alpha, beta, gamma, delta or omicron S proteins. They further show that antagonism of CHI3L1 using anti-CHI3L1 or kasugamycin inhibits epithelial cell infection by the pseudoviruses with ancestral, alpha, beta, gamma S protein mutations. The in vitro data has relevance to SARS-CoV-2 pathogenesis and potentially has therapeutic implications in that the anti-CHI3L1 antibody and/or kasugamycin might be a treatment for this pandemic virus. These in vitro data are novel and the results are clear and convincing.

The most important challenge with this manuscript is whether these in vitro findings translate into inhibiting SARS-CoV-2 variants in vivo. Are the effects of anti-CHI3L1 or kasugamycin great enough to change the course of the disease? Given the limitations of the mouse model of human ACE2 expression, determining how effective this strategy is in disease pathogenesis is difficult to discern. Without in vivo results, the importance of the data in this manuscript is unknown and this is a significant limitation that should be certainly noted in the discussion and possibly the abstract.

Reviewer #3 (Public Review):

The authors undertake a detailed investigation focused on how the abundance of the sole Cav2 Ca2+ channel Cac in Drosophila is regulated at active zones (AZs) using the larval neuromuscular junction (NMJ) as a model system. The larval NMJ is a particularly powerful system to address this question, and the authors have taken full advantage of the unique approaches available. Specifically, using endogenously tagged Cac alleles and transgenes combined with cell biological, electrophysiological, and imaging approaches, the authors make several key findings. Most notably, the authors generated endogenously tagged, photoconvertible Cac alleles that enable the quantification of Cac turnover at specific AZs over time and during synaptic development. They find that the abundance of Cac vs the AZ scaffold BRP is independently regulated, that Cac does not appear to intermix between different AZs, and that Cac levels are buffered, where the alpha-2 delta subunit promotes new Cac delivery to AZs and removal of existing Cac without changing the apparent overall abundance.

Previous studies in Drosophila and in other systems have revealed considerable insight into Ca2+ channel regulation at AZs. For example, it is well established that the alpha-2 delta subunit is required for Cac trafficking to AZs, and in rodents, that Cav2 channels are not necessary for AZ or synapse assembly. There is, therefore, a question about how significant the new findings reported here are to the field. There is also a significant issue with the interpretation of the requirement of Cac in AZ assembly, where the authors have not demonstrated to what extent Cac and Ca2+ influx is eliminated in their conditional knockout in motor neurons. However, few studies have directly focused on how Cav2 channels are regulated and turned over at AZs, particularly during development and in baseline states. Most importantly, the photoconvertible Cac alleles and related imaging experiments reported in this study demonstrate significant new insights about the yin and yang of Ca2+ channel delivery and turnover at AZs, at a resolution and rigor rarely achieved. Furthermore, this study also provides an excellent foundation to unlock how Cav2 channel regulation at AZs is modified by such processes as neuronal activity and synaptic plasticity. Thus, in my opinion, this work will be of significant interest and importance to the field.

Reviewer #2 (Public Review):

This manuscript represents a highly significant contribution to our understanding of synapse assembly and VGCC regulation. The authors generated a new Flpstop allele of Cac enabling them to assess the phenotypic consequences of loss of Cac at L3 NMJs for the first time. These data convincingly demonstrate that Cac is not required for either AZ nucleation or accumulation of cytomatrix proteins. Through a series of elegant experiments, they demonstrate that, unlike Brp, Cac levels are buffered at NMJ synapses. They then turn their attention to the auxiliary Ca2+ channel subunit a2d and show that it is limiting for Cac accumulation at AZs. Finally, they generate an endogenously tagged Cac-Maple enabling them to estimate Cac turnover and to argue that a2d is rate-limiting for Cac recycling. Strengths of this paper include the importance of studying how AZ levels of VGCCs are regulated, the rigorous and logically presented experiments, and the exciting findings regarding the role of a2d in Cac delivery and turnover. Weaknesses are minor. (1) Regarding the novelty of the Cac findings, VGCCs have been shown to be dispensable for synapse assembly in mammalian neurons (Held et al., 2020). However, this is the first report to carefully approach this question in Drosophila, and the concurrence of the results suggests that it likely reflects an evolutionarily conserved design principle of presynaptic terminals. (2) The authors argue that a2d is rate-limiting for Cac delivery to AZs. This conclusion is based on Figures 6 and 7, where they show defects in delivery/turnover in homozygous and heterozygous mutant a2d NMJs. Whether a2d overexpression is sufficient to drive delivery/turnover was not investigated. Overall, this is an impressive paper that will have a lasting impact on the field of cellular and molecular neuroscience.

Reviewer #1 (Public Review):

In this study, the authors set out to investigate mechanisms regulating voltage-gated calcium channel abundance at synaptic active zones. As action-potential induced calcium influx into presynaptic terminals drives neurotransmitter release and because the number of participating calcium channels has a major influence on this signaling, insights into principles regulating calcium channel abundance are highly relevant for our understanding of mechanisms controlling neural excitability. Despite the pivotal relevance, relatively little is known about how this abundance is controlled.

The study here provides some novel insights into this regulation during synapse development and upon genetic challenges in Drosophila melanogaster. The Drosophila neuromuscular junction is one of the few model synapses where channel levels, delivery, and turnover can be investigated using light microscopy. Previous studies have relied on the over-expression of a GFP-tagged obligatory subunit (Cacophony, Cac) of the synaptic voltage-gated channel and concluded on a consecutive arrival of calcium channels and active zone scaffolding proteins such as Bruchpilot (BRP)(Fouquet et al., 2009). The prevailing view is that BRP and other AZ scaffolds (such as BRP) then stabilize the channels at the AZs (Kittel et al., 2006; Liu et al., 2011). The current study provides further insights into this and uses genetically more advanced endogenously GFP-tagged Cac (one developed by the O'Gilles lab, one developed for this study) to evaluate the relation between Cac and BRP. Additional, well-suited genetic tools were applied to study these questions: Cac gene expression was knocked out in individual cells to study its loss while avoiding lethality, and development was delayed to study its adaptation during synaptic maturation in greater detail. In vivo imaging experiments (photobleaching and conversion) used to investigate Cac´s local, synaptic turnover and (for the first time) stability.

While the authors challenge the view that Cac is required for BRP AZ incorporation and that BRP abundance limits Cac levels, the study also lends support to conclusions obtained in previous studies after Cac overexpression as they show that this overexpression does not result in excessive over-population at synaptic AZs, an important finding in the light of previous studies. In fact, a major and unexpected finding is that the local abundance of Cac may be particularly well regulated and buffered against changes of gene expression to a much larger extent than the AZ scaffolding protein BRP. A remarkable finding is the high stability of these proteins at the synapse (days) and that this stability can be further increased to maintain local synaptic levels if protein synthesis or -delivery are impaired. Overall, the study provides some novel, unexpected and important insights regarding stable calcium channel abundance at synapses. However, the current manuscript may be further improved because despite the wealth of high-quality data, some of the measurements and comparisons are indirect and some alternative interpretations possible.

Reviewer #3 (Public Review):

Given the large variation in and high heritability of hippocampus volume in the population, taking out known variation in the healthy population is a nice way of reducing heterogeneity, and a step forward towards using normative models in clinical practice. The dataset the nomograms are based on is large enough to do so even when stratified by polygenic scores for hippocampal volume, and these provide interesting information on the role of genetics in hippocampus volume.

There are however several concerns regarding the applicability of the models to the ADNI dataset. First, the lack of overlap in the age range between the dataset the model is trained on and the application to subjects that are outside that age range is questionable. The authors prefer Gaussian process regression (GPR) over a sliding window-based approach using the argument that the former allows for predictions in a larger age range but extrapolation beyond the reach of the data is usually not valid. The claim that Supplementary Figure 6 shows accurate extension beyond these limits is in my opinion not justified. If anything, we can be rather certain that the extensive growth of the hippocampus up to age 48 is not realistic (see e.g. Dima et al., 2022). Second, the drop in mean 'percentile' difference between high and low polygenic scoring individuals that if one uses genetically adjusted nomograms seems nice, but this difference is currently just a number and the reader cannot see whether this difference is significant, or clinically relevant.

Reviewer #2 (Public Review):

There is much to be commended about the goal of integrating genetic risk into normative model estimates in order to improve diagnostic utility. Choosing a well-validated biomarker and genetic risk profile and a well-powered combination of datasets are further strengths of the present work. The authors chose a well-validated and robust analytical approach for generating normative models in Gaussian Process Regression. My general assessment is that this is a solid piece of scientific research that took a specific hypothesis and evaluated it well. More broadly they provide an interesting model for how one can integrate genetics into a normative modelling imaging framework.

As this paper could potentially serve as such a role-model function there may be some elements of the methodology and the results that could be further expanded upon in the main manuscript. Some extended evaluation of potential technical sources of variation could be included, but principally I think the integration of weights into GPR directly could be discussed more in-depth alongside the evaluation of in which scenario's this may be appropriate to do. The authors could also speculate on whether a similar methodology is applicable in other contexts or for other combinations of data types.

Reviewer #1 (Public Review):

In this paper, the authors estimate growth curves ('nomograms') for hippocampal volume (HV) using Gaussian process regression applied to UK Biobank data and evaluate the influence of polygenic scores for HV on the estimated centile curves. By taking this into account, the centile scores are shifted up or down accordingly. The authors then apply this to the ADNI cohort and show that subjects with dementia mostly lie in the lower centiles, but this does not improve the prediction of transition from mild cognitive impairment to dementia.

This paper is reasonably well written and the finding that centile curves for different phenotypes are sensitive to genetic features will be of interest to many in the field, albeit perhaps somewhat unsurprising given the polygenic score evaluated here is for the same phenotype under investigation (i.e. HV). I think using centiles derived from nomograms/normative models for precisely assessing both current staging and progression of neurological disorders is a highly promising direction. Regarding this manuscript, I have a few comments about the methodology and interpretation of results, which I will outline below.

- My most significant concern is that It appears that the assumption of Gaussian residuals is violated by the HV phenotypes that the authors fit their GP to. For example, in figure 2, the distribution is clearly skewed and the lower centiles in particular are poorly fit to the data. First, please provide additional metrics to assess the fit and calibration of these models quantitatively (the latter can be done e.g. via Q-Q plots). Second, I think if the authors wish to make precise inferences about the centile distribution for the reference model, then the deviation from Gaussianity ought to be accommodated in some manner. There are several options for this, including different noise models (e.g. Gamma, inverse Gamma, SHASH, etc), variable transformation, or quantile regression. One option that could be useful in the context of Gaussian process regression is the use of likelihood warping (see e.g. Fraza et al 2021 Neuroimage and references therein) which was originally developed for GP models. I would recommend the authors pursue one of these routes and provide metrics to properly gauge the fit.

- Related to the above, it is likely that the selection of subjects with high/low polygenic scores for HV changes the shape of the distribution. It is currently impossible to assess this because no data points are shown in these cases. Please also add this information, along with comparable quantitative metrics to those for the models above.

- How did the authors handle site effects? There appears to be no adjustment for the fact that the ADNI data are acquired from different sites that were not used during the estimation of the normative models. I would expect to see this dealt with properly (e.g. via fixed or random effects included in the modelling) or at the very least a convincing demonstration that site effects are not clearly biasing the results.

- How do the authors interpret the finding that the relationship between the polygenic scores and HV is different in the cohorts they consider (i.e. bimodal in UKB and unimodal in ADNI)? Does this call into question the appropriateness of the subsampled model for the clinical cohort?

- Perhaps the authors can comment on (or better, evaluate) how this genetic shift could be accommodated in normative models (e.g. the possibility of including polygenic risk scores as predictor variables in the normative model). This would remove the need for post hoc adjustment and would allow more precise control over the adjustment than just taking the upper/lower xxx % of the PGS distribution as is done in the current manuscript.

- Related to my point above, it is perhaps unsurprising that the polygenic score for the HV phenotype influences the centile distribution. I think the paper would benefit considerably by also evaluating other polygenic scores (e.g. APOE4 as in some of the prior cited references). it would be interesting to compare the magnitude and shape differences for these adjustments. The authors can consider this an optional suggestion.

Reviewer #3 (Public Review):

When a genome suffers toxic DNA damage such as double-strand breaks (DSBs), the cell sets up detection and repair systems to ensure cell survival. In the last decade, the mobility of both the damaged chromatin and the rest of the genome has emerged as a means of signaling the damage. However, the role of this mobility in the repair process between the damaged sequence and a homologous copy by homologous recombination (HR) remains poorly understood. The authors question the order and the relationship between homologous repair events, namely resection at the break site, recruitment of the recombination machinery, chromosome mobility, and the encounter between damaged sequence and homologous copy (pairing) in order to determine the impact that chromosome mobility may have in HR.

The strength of this study is that it uses a eukaryote model, budding yeast, where the genetics of DSB repair is extremely well known and where chromosome mobility has been discovered (among others by the team). Yeast cells are naturally diploid or haploid, this study uses diploids allowing for homologous recombination under physiological conditions. In addition, this study tracks DSB repair events over a short time frame, allowing a closer look at the step at which chromosome mobility occurs. The study of resection at the DSB as a regulator of chromosome mobility is thus quite appropriate.

In order to strengthen the conclusions of this paper, we suggest adding some controls and details to allow adequate matching of results to conclusions. In particular, to be able to control for the delay in the mobility of the ∆mre11 mutant at 4h, the mobility of the wild-type strain at the same time is essential. Similarly, if resection induces an increase in mobility that favors pairing, after pairing, mobility should return to normal: it would be good to show this.<br /> The use of a haploid strain is interesting because it has a slower resection. Analysis of mobility in the haploid strain would probably confirm the relationship between resection and mobility.

The community already uses the methods described here. The impact of this study will be to advance the understanding of the regulation of chromosome mobility when DSB happen, an important and innovative parameter for the repair of damage.

Reviewer #2 (Public Review):

In "temporal coordination between chromosomal mobility and homologous recombination", Joseph and colleagues investigated the timing of repair events after inducing a single Double-Strand Break (DSB) in yeast diploid cells. In particular, the authors focus on the events of resection, chromatin mobility, homologue chromosomes pairing, and repair by gene conversion, and demonstrated how a delay in the early event of resection due to the deletion of the MRX component MRE11 corresponds to a delay in all the other steps. Interestingly, they also showed that, when the proper timing of resection is restored in mre11D cells by the overexpression of DNA2, also the timing of DSB mobility, pairing, and repair is restored.

The temporal correlation between DSB induction, DNA resection, chromosome pairing, and repair is well supported by the data. In addition, data showing the delay in processing, mobility, and repair in mre11D cells are clear and well presented, and the suppression by DNA2 overexpression is also well characterized. However, the author's claim that chromatin mobility is required for DSBs repair by promoting pairing is not completely demonstrated.

Reviewer #1 (Public Review):

In this manuscript, the authors build on prior work from their group to investigate the mechanisms of DNA double-strand break (DSB) mobility and its consequences for homology-directed repair in budding yeast. The authors observe that the kinetics of the initial processing of the DSB (end resection, modeled by cells lacking Mre11) predicts the timing of subsequent events, including the DSB-induced increase in mobility, loading of RPA and formation of the nucleoprotein filament, and homolog pairing leading to gene conversion (GC) in this diploid model system. Over-expression of the nuclease Dna2 but not Exo1 rescues all molecular defects tied to resection and GC arising from loss of Mre11 in a manner that surprisingly is not reflected in cell growth after irradiation. Taken together, the authors argue that these data reinforce the notion that increased mobility of DSB downstream of resection defines the kinetics of GC, a hypothesis that has remained somewhat in question.

In general, the data are of high quality, the data are clearly presented (including useful cartoons in most figures) and the manuscript is well-written. The data support the authors' conclusions. Some challenges in dissecting the causality of increased DSB mobility remain as tools to separate the function of factors that influence DSB mobility versus those that are required for other events in homology search (e.g. nucleosome remodeling, Rad51 loading) are still difficult to untangle, even despite the efforts in this study. The impact that this work will have on the field is somewhat challenging to assess as most of the observations recapitulate those made previously for other resection modulators or repair factors including Rad51 (by this group) and Sae2 (also investigated by the Longhese group). Based on this, one might conclude that the requirement for resection in driving enhanced mobility of the DSB is well established. However, as the authors point out, there has been some evidence put forward that seemingly contradicts this model. A more detailed discussion of the differences in the experimental details employed by this current and prior studies is warranted and would help the reader and field weigh the evidence.

Reviewer #3 (Public Review):

The manuscript by Barr et al., investigates the molecular phenotype, regulation by type 2 immunity, and function, of ectopic tuft cells that appear in the lungs of mice recovering from infection by the mouse-adapted PR8 strain of influenza A virus. They use reporter mice and either bulk or single cell RNA sequencing to reveal the molecular heterogeneity among tuft cells present in lungs of mice 43 days after PR8 infection. Lineage tracing using a Krt5-CreER driver line was used to demonstrate the basal cell origin of ectopic tuft cells and mice harboring homozygous null alleles for either Pou2f3, Trpm5, IL4Ra or IL25, were evaluated to determine roles for tuft cells and type 2 immunity in regulation of dysplastic epithelial remodeling. Their data confirm that ectopic tuft cells are derived from dysplastic Krt5-expressing cells that appear following PR8 infection, that pre-existing tuft cells play no role in basal cell dysplasia, and that ectopic tuft cells derived from dysplastic basal cells play no role in lung remodeling. Furthermore, they show that neither type 2 cytokines nor IL25, an upstream regulator of type 2 immune responses, play roles in regulating the pulmonary response to PR8 infection. Finally, they show that tuft cells are also induced in the lungs of bleomycin-injured mice and that the presence of tuft cells in alveolar regions of PR8-infected mice does not influence the inability of dysplastic basal cells to assume alveolar epithelial cell fates. The manuscript is well written and experiments were performed with rigorous experimental design and data of high quality. However, even though findings have potential importance and could be of interest, results seem preliminary and lack a strong rationale.

Major concerns:<br /> 1. Studies of tuft cells in the gut and their response to type 2 immunity, which were the basis for this line of investigation into ectopic tuft cells in the PR8-infected lung, have shown that tuft cells are part of a feed-forward loop leading to tuft cell expansion and enhanced type 2 immune responses including increased abundance of goblet cells. Since ectopic pulmonary tuft cells are derived from dysplastic basal cells after PR8 infection, rather than the reverse, this is clearly not the case in lungs of PR8 infected mice. Furthermore, since tuft cells are derived from hyperplastic basal cells in lungs of PR8-infected mice, it would seem unlikely that they impact the extent of basal cell hyperplasia.<br /> 2. Tuft cell expansion following parasitic infection of the gut and associated type 2 inflammation, and basal cell differentiation into tuft cells leading to their increased abundance following lung injury, are distinct processes and likely to be regulated through distinct mechanisms. As such, the rationale for investigating the roles of type 2 cytokines in the regulation of tuft cell appearance is rather weak. In the absence of data demonstrating how basal to tuft cell differentiation is regulated, this component of the study seems preliminary.

Reviewer #2 (Public Review):

The authors study distal tuft cells that are induced by influenza and bleomycin. These cells are of unknown function. In this paper, the authors find that:

1. Tuft cells originate from p63+ distal cells in virally-induced dysplastic regions of the lung as evidenced by lineage tracing.<br /> 2. Single-cell sequencing reveals heterogeneity of tuft cells reminiscence of the murine tracheal tuft cells and supports a p63+ cell origin.<br /> 3. The tuft cell induction is independent of the IL-25 and IL-4Ra pathway. Since type 2 inflammation has been associated with tuft cell induction in the intestine, this suggests different biology for the distal pulmonary tuft cells, although the inflammation-associated biology of the corresponding tracheal tuft cells has not been established.<br /> 4. Tuft cell-deficient mice do not develop abnormalities of alveolar regeneration following influenza. Similarly, mucous metaplasia, which is associated with type 2 inflammation, was unchanged in the tuft cell-deficient mice.<br /> 5. Of note, the paper presents important negative results, but the function of tuft cells remains enigmatic.

Reviewer #1 (Public Review):

In this manuscript, Barr and colleagues report some novel and surprising results in regards to the development and role of tuft cells during influenza-induced lung injury. The authors demonstrate beautifully how unlike in the intestine lung tuft cells do not require Il-25, Il-4Ra, or Trmp5 but do require Pou2f3. Interestingly, loss of tuft cells in Pou2f3 null mice did not affect basal cell or goblet cell differentiation in basal cell pods, raising the question as to what they are really doing there.

Reviewer #3 (Public Review):

This is a very extensive study (numbers of neurons recorded, tasks, thorough analyses). The paper is well written and the logic of the analyses is clearly explained. Only in some places, my impression was that some analyses might have been lengthy or redundant (for example, Figure 4). Several interesting new findings were: the presence of choice probabilities in V3A with a larger sample of neurons, the noise correlations, and in particular the analysis of the pre-saccadic activity and the comparison between V3A and CIP.

One potential weakness of this study is that a large part of the results is entirely expected. We know for a long time that V3A provides the input to CIP (the Nakamura study used the term 'LIP' but covered CIP as well). Therefore, most findings in this study are what one would expect when comparing an earlier area to a higher area in the dorsal stream hierarchy (receptive fields, latencies, tolerance to distance, choice probabilities). On the other hand, there are not that many studies comparing two mid-level areas for which the connectivity is known. In the future, studies like this one will certainly contribute to building better models of the visual cortex in general and of the dorsal stream in particular.

Another limitation of this study is the use of choice probabilities as such. As the authors themselves point out, the interpretation of CPs is difficult since they probably result from a combination of factors. Moreover, the discrepancy between the Elmore study and this study shows how easy it is to come to the wrong conclusions. It is noteworthy that the choice-related activity in V3A grows markedly 200-1000 ms after stimulus onset, but is absent in the first interval (in which the animals most likely decide on the stimulus), consistent with a feedback signal from higher areas if one wants to investigate the relation between neural activity and behavioral performance, it is far more powerful to use causal perturbation techniques. Finally, it is inherent in this type of follow-up study that a large part consists of the re-analysis of data previously acquired in CIP.

Reviewer #2 (Public Review):

In this study, Doudlah et al test how neurons from two interconnected areas of the posterior parietal cortex respond during a task in which animals have to judge the tilt of a 3D stimulus and plan a saccade toward a direction related to their percept. This study is an extension of a study from the same group published in elife in 2020 (Chang et al. 2020). Here, they compare the results of this previous experiment, in which they recorded the activity of neurons from the Caudal IntraParietal area (area CIP), to new results acquired in cortical area V3a. The working hypothesis is that V3a represents low-level visual features while CIP integrates this information and transforms it into a decision signal related to the animals' choice. First, they show that both areas respond to the same task in a very similar manner. Neurons from both areas show strong selectivity to different features of the stimuli, including viewing distance, and 3D orientation. Interestingly, they show that CIP neurons encoded the 3D pose of the stimuli and animals' choice related to the orientation of these stimuli more strongly than V3a neurons. This suggests hierarchical processing of visual information, with feedforward transformation of sensory information into choice-related signals from V3a to CIP. In addition, they show that both areas show similar perisaccadic activity during a visually guided saccade.

Strengths<br /> The hypothesis proposed by the authors is clear and well presented. Results clarify the role of V3a in the processing of depth by the visual cortex. It also shows that V3a's role probably extends the extraction of visual features.<br /> In general, data and analyses are clear and make sense altogether. Comparing how neurons from different areas are of high interest in order to understand how information is processed along the cortical hierarchy and allow them to put results from the parietal cortex in perspective with sensory processing in the visual cortex.

Weakness<br /> I have few concerns about this article. My main concern is related to the analysis related to saccadic-related activity in V3a and CIP. Since monkeys perform a visually guided saccade, it is difficult to interpret neuronal activity during the saccade as perisaccadic. In this task, monkeys fixate for 1300 ms. Then, a target stimulus appears and they have to saccade toward it in less than 500 ms. They interpret variations of activity as perisaccadic. Such a task design does not allow us to dissociate between saccadic or visual responses. I am therefore not fully convinced by the last section of the analysis about the hierarchical processing of presaccadic activity.

Reviewer #1 (Public Review):

This manuscript by Doudlah and colleagues is a comparison of CIP and V3a in monkeys during the performance of perceptual decisions about 3D visual stimuli. Monkeys performed a 3D visual tilt discrimination task in which they reported the tilt of a 3D stimulus by making a saccade to the correct saccade target corresponding to that direction of tilt. These two areas span the parietal-occipital junction and are both relatively infrequently studied compared to more commonly studied occipital and parietal visual and visuomotor cortical areas. The comparison is particularly novel as these areas have not been directly compared in past work during such decisions. A major strength of the study is the comparison of activity in these two brain areas recorded from the same animals performing the same task (2 of the 3 V3A monkeys were the same as the 2 CIP monkeys). The study gives novel insight into the relationship and relative hierarchical relationship between the two areas in visual feature processing and higher-order 3D spatial representations, with CIP showing higher-order spatial representations and more choice-correlated responses. Furthermore, the study finds a greater modulation of V3A activity by extraretinal factors, suggesting that V3A be characterized more as "association" than "visual cortex"

Major findings of the study:

1) 3D pose representations are more prominent in CIP than in V3A, but V3A shows that 3D orientation is more strongly and specifically encoded in V3A.

2) Choice-correlated activity was stronger in CIP.

3) Presaccadic activity was observed in both areas prior to the choice, with earlier onset in V3a. The CIP presaccadic activity is consistent with the temporal integration of presaccadic activity in V3a.

Overall the study gives insight into the functioning and relationship between these dorsal stream areas and has the potential for high impact by virtue of explicating the roles of these less explored visual and parietal cortical areas in visual feature processing and saccadic choice.

Reviewer #3 (Public Review):

Punishment is a key form of learning and behavior change, yet its core behavioural and brain mechanisms remain poorly understood and certainly less well understood than reward learning. This manuscript by Jacobs et al from the Moghaddam laboratory uses dual fibre photometry for calcium transients to make an important advance in understanding how punishment is learned by studying how punishment changes action and punisher coding in the PFC and VTA of rats. This work builds on the elegant single unit work from this group reported previously. The authors use a single action, probabilistic task whereby rats are first trained to nosepoke for sugar pellets on an FR1, with a 5 sec DS signalling reinforcement. Then, in blocks of 30 trials each, the nosepoke is punished on a probabilistic contingency of 0%, 6%, 10%. The authors used dual fibre photometry to concurrently record calcium transients in "dmPFC" and VTA, with a focus on transients related to action emission and punisher as well as reward delivery.

There are quite a few key findings here: 1) action transients in dmPFC change across punishment from modest inhibitory transients in 0% risk to no change (i.e possible loss of inhibitory transient in PFC) or modest positive transients (in VTA) as risk increased from 6-10%; 2) comparison with past single-unit data suggested similarity between photometry and single unit measures for the action but not DS; 3) there was no change in punisher transients in these regions; 4) diazepam which had modest behavioral effects to alleviate punishment had no effects on PFC transient to the action or punisher but did reveal peri-action ramping-like transients in VTA; 5) diazepam increased correlated activity between VTA and PFC at 0% and 6% risk

Overall, I enjoyed reading this manuscript and I learned much from it. The work builds neatly and clearly on the past work of this group in this task, providing new information on how punishment shapes action coding in the prefrontal cortex and VTA, how it shapes correlated activity between these regions, and how benzodiazepines may affect these to achieve their anxiolytic effects. The critical conclusions are that these regions are important for action, but not punisher, encoding, and that peri-action ramping in VTA neurons and VTA-PFC correlated activity contribute to the anxiolytic effects of benzodiazepines in this task.

Comments

1. I think it is worth drawing the distinction between punishment (i.e. learning and performance) versus the punisher (footshock). For example, the title (and across the manuscript) refers to "punishment coding" to mean transients to the punisher itself. I would suggest using "punisher" when referring to the outcome used (footshock) or its associated transients and "punishment" when referring to learning. So, learning punishment involves changes in action but not punisher encoding in these regions.

2. "dmPFC". Different researchers mean different things by this term. Would it be possible to state exactly where the fibres were instead (e.g., Laubach et al., eNeuro, 2018)?

3. I did struggle to understand the functional significance of the PFC transients. I am convinced they are real and robust because we see precisely the same in our own unpublished work. But, I am still puzzled as to what a loss of an 'inhibitory' transient around the punished action in PFC means? This is not really addressed but it is the main effect of punishment on action coding in the PFC and I think some readers would appreciate the author's interpretation of this.

4. Related to 3, it was also not clear why these PFC transients differed only at 6% risk and not also 10% risk. Again, I think this is worth discussing.

5. Re: analyses. I thought these were generally well done. There are two questions one might be interested in. The first is whether the transients are different from 0%. The second is whether transients differ across sessions. The figures do a good job at answering the second question (which to me is the most important question) by using coloured bars above transients to show when session differences are present as assessed by a robust analysis. However, I do think some readers would also appreciate knowing whether and when transients themselves were significantly < or > 0%. Perhaps these figures could be presented as supplementary data.

6. The comparison with previously published single-unit data was very interesting. Here I was persuaded that these correlations were meaningful because of the difference between these correlations for cue and action. I am not suggesting the authors do the following, I only offer it for their consideration in future work. Kriegeskorte has developed ways of assessing dissimilarity in different data types from the same behavioural designs that could prove very helpful and persuasive here (e.g., Front. Syst. Neurosci., 24 November 2008; https://doi.org/10.3389/neuro.06.004.2008).

7. The authors comment on the overgeneralisation of punishment learning. That is, in session 1 there is a broad suppression of behavior by punishment that was not obviously present in the remaining sessions. I am not sure overgeneralisation is the best term because this implies punishment learning generalised. More likely is that Pavlovian fear was present in session 1 to generally suppress nosepoking and this fear was reduced in the remaining sessions as the instrumental punishment contingency was learned. Bolles made this point some years ago and it may be worth citing Bolles et al. Learning and Motivation Volume 11, Issue 1, February 1980, Pages 78-96, on this point.

Reviewer #2 (Public Review):

The authors combined fiber photometry measurement calcium transients in dorsomedial prefrontal cortex and ventral tegmental area with diazepam treatment in a task assessing risk behavior in rats. They observed risk-related changes in calcium transients around action and reward that were altered by diazepam. Further, diazepam worked to synchronize action and reward-related transients between the two regions. The strengths and weaknesses of the authors' manuscript are addressed below:

• Strengths<br /> 1. The rationale for studying these two regions is clear and in support, both show clear changes in calcium transients during the risk-assessment task.<br /> 2. Comparing fluorescence correlation in the two regions during saline and diazepam treatment was clever and striking.

• Weaknesses<br /> 1. I had difficulty following the ANOVA results for Figure 1. I assume ANOVA was performed with factors of session and block. However, a single F statistic is reported. I do not know what this is referring to. It would be more appropriate to either perform repeated measures ANOVA with session and block as factors for each dependent variable or even better, multiple analyses of variance for the three dependent measures concurrently. Then report the univariate ANOVA results for each dependent measure. The graphs in Figure 1 (C-E) suggest a main effect of block, but as reported, I cannot tell if this is the case. Further, why was sex not included as an ANOVA factor?<br /> 2. The authors describe session 1 as characterized by 'overgeneralization' due to increased reward latencies. I do not follow this logic. Generalization typically refers to a situation in which a response to one action or cue extends to a second, similar action or cue. In the authors' design, there is only one cue and one action. I do not see how generalization is relevant here.<br /> 3. The authors consistently report dmPFC and VTA 'neural activity'. The authors did not record neural activity. The authors recorded changes in fluorescence due to calcium influx into neurons. Even if these changes have similar properties to neural activity measured with single-unit recording, the authors did not record neural activity in this manuscript.<br /> 4. Comparing the patterns in Figures 2 and 3, it appears that dmPFC change in fluorescence was similar in non-shocked and shock trials up until shock delivery. However, the VTA patterns differ. No cue differences were observed for sessions 1-3 on shock trials (Figure 3A), yet differences were observed on non-shocked trials (Figure 2F). Further, changes in fluorescence between sessions 1 and 2/3 appeared to emerge just as foot shock would have been delivered. A split should be evident in Figure 3B - but it is not. Were these differences caused by sampling issues due to foot shock trials being rarer?<br /> 5. Similar to Figure 1, I could not follow the ANOVA results for the effects of diazepam treatment on trials completed, action latency and reward latency (Figure 4). Related, from what session do the bar plot data in Figure 4B come from? Is it the average of the 6% and 10% blocks? I cannot tell.<br /> 6. For the diazepam experiment, did all rats receive saline and diazepam injections in separate sessions? If so, were these sessions counterbalanced? And further, how did the session numbers relate to sessions 1-3 of the first study? All of these details are extremely relevant to interpreting the results and comparing them to the first study, as session # appeared to be an important factor. For example - the decrease in dmPFC fluorescence to reward during the No-Risk block appeared to better match the fluorescent pattern seen in sessions 1 and 2 of the first experiment. In which case, the saline vs. diazepam difference was due to saline rats not showing the expected pattern of fluorescence.<br /> 7. The authors seem convinced that fiber photometry is a surrogate for neural activity. Although significant correlation coefficients are found during action and reward, these values hover around 0.6 for the dmPFC and 0.75 for the VTA. Further, no correlations are observed for cue periods. A strength of the calcium imaging approach is that it permits the monitoring of specific neural populations. This would have been very valuable for the VTA, in which dopamine and GABA neurons must show very different patterns of activity. Opting for fiber photometry and then using a pan-neuronal approach fails to leverage the strength of the approach.

Reviewer #1 (Public Review):

In the current study, the authors used photometry to record bulk calcium signal from dmPFC and VTA in awake behaving rats performing a "punishment risk task". In this task, the rats responded on a lever for reward after a 5s cue on an FR1 schedule across three 30-trial blocks in which the risk of shock on lever press increased from 0% to 6% and then 10%. Rats were trained on the food task then recordings were made across the initial 3 sessions of training with shock. The authors show that trials completed and action latencies changed across blocks consistent with an increasing effect of shock on the behavior, and also changed across the sessions in a way that suggested some sort of learning related to punishment. Against this backdrop, they found that bulk calcium signal changed - generally increasing - with risk across blocks and also across sessions. This effect looks particularly prominent at high risk (10%) the time of reward in both areas and also to the cue and action in VTA. Pre-session administration of diazepam normalized some of the performance measures in the shock blocks and this was associated with reduction of the bulk signal in both areas where prior increases were seen (my interpretation of comparison of figures 2 and 5). Interestingly signal at the time of the few shocks was not markedly different between blocks and was not heavily impacted by diazepam. Authors interpret the results as supporting a role for both areas in foraging in the face of risky outcomes and further suggest that the plasticity (and effects of diazepam) are not related directly to punishment but instead reflect changes in the peri-action period as they term it.

Overall I liked the paper. It follows nicely on prior work and presents a straightforward and interesting experiment, using a validated anxiolytic in the context of their task to test what components of the neural response are related to this emotional component. The results are quite interesting I think, particularly since in the 10% block where significant increases in activity seem to evolve with learning and be reversed by diazepam. That said, I have a couple of concerns to consider.

Probably the biggest overall issue is that it is unclear what is being learned specifically. There is no probe test at the end to dissociate the direct impact of shock from its learned impact. And the blocks are not signaled in some other way. And though there seems to be some evidence that the shock effects get more pronounced with a session, it is not clear if the rats are really learning to associate specific shock risks with the particular trials. Indeed with so few sessions and so few actual shocks, this seems really unlikely, especially since without an independent cue, the shock and its frequency is the cue for the block switch. It seems especially unlikely that there is a strong dichotomy in the rats model of the environment between 6% and 10% blocks. This may be quite relevant for understanding foraging under risk. But I think it means some of the language in the paper about contingencies and the like should be avoided.

The second issue I had was that I had some trouble lining up the claims in the results with what appeared to be meaningful differences in the figures. Just looking at it, it seems to me that VTA shows higher activities at higher shocks, particularly at the time of reward but also when comparing safe vs risky anyway for the cue and action periods. DmPFC shows a similar pattern in the reward period. This is interesting and would be consistent with sort of a contrast effect perhaps. But these results are not described at all like this. The focus is on the action period only and on ramping? I don't really see ramping. And at the top of para 3 in the discussion, it says "Anxiogenic contingencies also did not influence the phasic response to reward...". But fig 3 seems to show clearly different reward responses? The characterization of the change is particularly important since to me it looks like the diazepam essentially normalizes these features of the response. This makes sense to me, but if those are not the features of the response that are highlighted, then the diazepam data is harder to understand.

In any event, I think with a few changes in terminology and perhaps how the data is described this looks like a valuable and important result for those interested in this sort of complex task/model.

Reviewer #2 (Public Review):

The authors show that Purkinje cells (PCs) may inhibit a subset of nearby molecular layer interneurons (MLIs); this connection is never reciprocal, i.e. PC activity can regulate MLI activity and thus the modulation of other PCs, but is not triggering a delayed modulation of its own activity. Modeling and in vivo recordings demonstrate that this MLI recruitment - as well as plasticity at parallel fiber inputs onto these PCs - is required for the temporal relationships that enable spike pauses in PCs and related conditioned responses. The work is very informative and will advance the field. Minor concerns that should be addressed are whether the model allows for an assessment of how many MLIs need to be modulated to explain the pause in PC firing and what the cause for the early phase in the PC pause is before MLI spike rates are modulated. In addition, the model presented here includes bidirectional parallel fiber-PC plasticity, but it is not described what the specific roles of LTD and LTP might be, and whether indeed there is a role for LTP at all.

Reviewer 1# (Public Review):

Purkinje cells (PCs) in the cerebellum extend axonal collaterals along the PC layer and within the molecular layer. Previous anatomical studies have shown the existence of these tracts and recently, the existence of functional synapses from PCs to PCs, molecular layer interneurons (MLIs), and other cell types was demonstrated by Witter et al., (Neuron, 2016) using optogenetics. In this manuscript, Halverson et al., first characterize the PC to MLI synapse properties in the slice using optogenetics and dual patch recordings. They then use computer simulations to predict the role of these connections in eyelid conditioning and test these predictions using in vivo recordings in rabbits. Authors claim that PCs fire before their target MLIs and that their activity is anticorrelated. They further suggest that the special class of MLIs receiving inhibitory input from PCs might serve to synchronize PCs during eyelid conditioning.

Major comments:

1. The manuscript is quite long with 9 main figure panels and 6 supplementary figures. The flow of the results is not smooth. While the first 4 figures are nicely done in terms of their results and organization, the same cannot be said about the rest of the figures. In fact, it would make sense to split the manuscript in two, one describing the synaptic properties and circuit mapping of the PC-PC-MLI circuit and the other describing their role in eyelid conditioning. As it stands, this manuscript is a tough read and difficult to get through. Further, the authors have not connected the initial slice physiology with the later in vivo work to argue for their presence in the same paper. For example, the quantal content measurement, the short-term plasticity, the mobilization rate measurement, etc do not figure in the latter half of the manuscript at all. I strongly suggest carving figures 1-4 out into a separate manuscript.

2. Authors conclude that eyelid PCs and eyelid PC-MLIs are inversely correlated and that PCs precede PC-MLIs during CRs and therefore could drive their activity. Both of these points are insufficiently justified by their analysis. First, it is not clear how eyelid PCs are identified - I'm assuming this is based on negative correlation with CRs just like positively correlated MLIs are assigned as eyelid PC-MLIs. If this is how PCs and PC-MLIs are identified, then the inverse correlation between the two cell types results from this definition itself. And, their activity pattern during CRs, illustrated in many figure panels is hardly surprising.

Second, to show that PCs fire ahead of PC-MLIs, the authors calculate the difference in fractional change in spike rate before and after the start of the CR (PC-MLI). Their reasoning is that if the bulk of firing rate change happened before the start of CR for PCs, but at the start or later for PC-MLIs, then this value will be positive, else it will be negative. The distribution of these values was shifted to the positive side leading them to conclude that PCs fire ahead of PC-MLIs. However, this is a huge logical jump. The sign of (PC-MLI) is dependent on the depth of modulation in each cell type as well and does not necessarily indicate relative timing. In any case, such caveats have not been ruled out in their analysis. This analysis to establish timing is unconvincing. Would it not be better to look at the timing of the spike modulation start directly rather than the round-about method they are using?

3. Many figure panels make the same point and appear redundant. For example, that PCs and PC-MLIs are inversely correlated with each other in vivo during CRs is shown in Figure 7, figure 8a, S2, S4, and S5. Of course, in each case, the data are sorted differently (according to ISI, CR initiation, cumulative distributions, etc.,) but surely, the point regarding inverse relationship can be conveyed more concisely?

4. Several details are missing in the methods section even though parts of it may have been published before. For instance, how are CRs calculated in the simulation? Methods state that 'The averaged and smoothed activity of the eight deep nucleus neurons was used to represent the output of the simulation and the predicted "eyelid response" of the simulation'. It is not clear what the nature of this transform is and if any calibration factors were used. How comparable are the simulated CRs in kinetics and amplitude to experimental CRs?

Reviewer #3 (Public Review):

Ganley et al have investigated the anatomical organisation and function of descending serotoninergic (5-HT) pathways that originate in the medulla and project to the spinal cord. It has long been known that descending 5-HT pathways had powerful modulatory effects. However, previous studies have shown that this system can either facilitate or inhibit pain, although relatively little was known about the underlying mechanisms. The authors initially confirm previous findings that 5-HT neurons in the lateral pargigantocellularis (LPGi) nucleus project to the superficial laminae of the dorsal horn, while those in the nucleus raphe magnus (NRM) innervate more ventral regions, including the deep dorsal horn. In the course of this work, they made an apparently serendipitous discovery, that retrograde tracing from the spinal cord with the AAV2Retro serotype preferentially captures projections from LPGi, while largely sparing those from the NRM. They then exploit this finding, together with selective targeting of NRM by means of small midline injections into the medulla, to examine the effects of chemogenetically activating either the NRM or LPGi components of the descending serotoninergic system. Their behavioural results show a clear and extremely interesting distinction: activating the LPGi 5-HT cells decreases responses to hot or cold stimuli (with no apparent effect on those to mechanical stimuli), while activating the NRM 5-HT cells increases mechanical sensitivity, with no effect on responses to thermal stimuli. This suggests that both sensory modality affected, and the polarity of the effect, depend on the source of the 5-HT cells and that these are mediated through different regions of the dorsal horn.

In the course of the study, the authors also provide interesting insights into the vagaries of two widely used viral tracing techniques: monosynaptic rabies tracing and the use of AAV2Retro for retrograde tracing. For example, they show that medullary 5-HT neurons are capable of being directly infected with rabies virus, but are not labelled through transsynaptic transport, probably reflecting the lack of synapses formed by many of these cells. Their findings show the importance of caution when interpreting negative results with these techniques.

The article is generally well written and illustrated. The work has been conducted carefully, and the interpretation is suitably cautious.

Reviewer #2 (Public Review):

This paper will be of interest to scientists within the fields of sensory processing and neuromodulation, as it deepens our understanding of descending serotonergic neuron subsystems and their differential innervation of dorsoventral spinal cord regions and thus likely differential roles in sensory tasks. Sophisticated viral strategies coupled with transgenic approaches are deployed and exploited in novel ways, which will also be of interest. However, in its current form, the paper would benefit from additional controls, more complete data presentation and analyses, and broader inclusion of the literature and its implications for interpreting the present work.

Strengths:<br /> The identification of a functional subdivision within the medullary serotonergic raphe as relates to sensitivity to peripheral thermal versus mechanical stimuli adds to the ongoing discussion on pain control circuitry. By uncovering and appreciating a differential susceptibility to AAV2retro infection, the authors identified a novel means by which to classify descending, spinal-cord-projecting serotonergic neurons. The analyses of various viral methods for the study of serotonergic neurons also add to ongoing discussions in the field.

Weaknesses:<br /> The interpretation of some experiments is not fully supported by the data presented. Additional data is needed, genetic tools require validation for the intended use, and in some cases control experiments need expansion (as relates to the use of DREADDs). As well, the manuscript narrative might be better served if some figures/panels were presented as supplemental, and if the context and discussion included a broader swath of the work published in this area.

Reviewer #1 (Public Review):

The authors thoroughly investigated four different standard methods for circuit tracing, and show that these methods differentially label lateral versus midline hindbrain serotonergic neurons and their processes. The authors then made use of these differences to individually target the two neuronal populations. In particular, the lateral paragigantocellularis (LPGi) were successfully labeled with an intersectional methodology owing to their distinctive susceptibility to AAV2retro transduction. While the differences are useful in this study, the authors do not address the biological underpinnings of these differences in labeling. Instead, the initial component of the study describes these labeling differences.

Based on these identified differences in labeling, the authors demonstrate that medial and lateral hindbrain serotonergic neuron axon projections innervate different regions of the spinal cord. Interestingly, nucleus raphe magnus (NRM) neurons project to most of the spinal cord but are absent from the superficial lamina. Considering the superficial lamina corresponds to the site of C-fiber and A-LTRM dorsal root ganglion sensory neuron innervation, it implies that serotonergic neurons in the NRM may not regulate pain circuits directly involving these nociceptors. Indeed, the authors demonstrate that activation of NRM neurons increases mechanical sensitivity. In contrast, LPGi axons were projected to the superficial lamina, and activation of these neurons decreased thermal sensitivity. This study does not elucidate how descending serotonergic projections are involved in the modulation of these pain circuits; i.e. through synaptic transmission with primary afferents or interneurons. Moreover, it is not clear whether these projections are different between lamina I and lamina II, or whether these vary along the rostrocaudal axis. Since distinct spinal cord lamina is innervated differentially by specific subtypes of peripheral neurons - the authors could consider co-labeling descending neuronal projections with known laminar and DRG subtype markers.

To address the functional implications of the innervation patterns seen, the authors investigated the involvement of lateral and medial hindbrain neurons in thermal and mechanical pain circuits using three behavioral assays: Hargreaves, Cold plantar, and von Frey. This is the major point of the study - namely that the lateral paragigantocellularis serotonergic neurons modulate thermal sensation, while the medial serotonergic neurons modulate responses to mechanical stimuli. This provides a good start to understanding the functional differences. To fully understand the role of hindbrain serotonergic neurons in descending pain modulation, these studies should be expanded to include chemical, pruriceptive, proprioceptive, and high and low-threshold mechanical stimuli, as well as chronic pain.

It is also clear that a shortcoming of this study is that the authors did not study sex differences although such differences are well-documented in serotonin signaling and pain perception.

Reviewer #3 (Public Review):

Sokoya, Parolek et al. set out to investigate the pathobiochemistry of osteoporosis with calvarial doughnut lesions (OP-CDL), a bone disorder caused by mutations in the gene encoding for the lipid synthesis enzyme sphingomyelin synthase 2 (SMS2). The authors revealed that two of the most severe mutations lie in a region responsible for the export of the protein from the ER to the Golgi and the plasma membrane (PM). Using cells expressing such catalytically active, but mislocalized SMS2 variants as models and later confirming their findings in fibroblasts derived from patients, the authors characterized the effects of producing sphingomyelin (SM) at the wrong organelle. Both by technically impressive organellar lipidomics and by the use of genetically encoded biosensors for SM, the authors convincingly demonstrated that SMS2 variants accumulate large amounts of SM at the ER. Furthermore, this imbalance in an otherwise tightly controlled subcellular lipid distribution also affected the levels of other lipids (some expected, such as the direct substrates PC and Cer as well as its product DAG, and some unexpected, such as the signaling lipid ceramide-1-phosphate Cer1P) at the ER, but also at the PM. The authors focussed on the subcellular and intrabilayer distributions of SM as well as of cholesterol, two lipids with high affinity for each other, and similar contributions to biophysical properties of the membrane, such as membrane order. SM accumulation in the ER increased ER membrane order as expected, however, this was not accompanied by cholesterol accumulation in the ER. On the other hand, the resulting decrease of SM levels on the outer PM leaflet decreased membrane order and made the cells more susceptible to lipid extracting agents such as methyl-β-cyclodextrin, implying that while cholesterol levels at the ER are well controlled independently of SM, the PM-pool of cholesterol responds to changes in SM levels.

Strengths:<br /> Overall, this work is an important description of the effects of breaking lipid gradients. It is of high technical quality and makes use of state-of-the-art methods. The first part of the manuscript lays a solid foundation, detailing the mislocalization phenotype and the accompanying lipidome changes, which in itself will be a great resource for the community.<br /> It is astonishing that changes in the subcellular localization of one lipid can lead to such drastic changes in a collective membrane property in other organelles of the secretory pathway. An increase in ER membrane order, as well as a decrease in PM order, was convincingly shown using environmentally-sensitive reporters. This is important to consider, given that the distinct functions of subcellular organelle rely on different membrane properties (e.g. the barrier function of the PM requires high membrane order whereas the biosynthetic tasks of the ER require loose packing).<br /> The techniques employed were applied in different cells (engineered HeLa cells, osteosarcoma cells, patient-derived fibroblasts) and show the appropriateness of the used models to investigate processes relevant to the disease.

Weaknesses:<br /> The second part of the manuscript currently lacks coherence, especially regarding the connection between SM and Chol gradients. A clear hypothesis of how the respective (mis)localizations arise, would help with a better understanding of the lipid landscape in this disease.<br /> The use of cytosolic Equinatoxin to investigate SM localization has limitations, mainly that in the SMS2-mutant expressing cells it localizes to yet unidentified punctae in the cell. Given that the nature of these punctae could not be elucidated, artefacts stemming from the probe cannot be excluded. Overall, this hinders interpretation. Most importantly, the central claim - SM production in the ER leads to the breakdown of SM asymmetry in the PM and mislocalization of SM to the inner leaflet - could not be directly addressed.<br /> The authors focused on the interesting connection between SM and cholesterol. They showed that overall cholesterol levels do not change (neither in whole cells nor in the ER or PM isolates) in the SMS2-mutant cell lines. However, the cholesterol-binding protein D4H is strongly localized to endolysosomes in the disease model. This observation was not discussed, nor independently confirmed. In addition, the use of cholesterol-extracting molecules resulted in higher sensitivity of SMS2-mutant cells. In how far this is due to cholesterol or SM mislocalization or changes in their respective levels was not sufficiently explored and makes an overall assessment difficult.

Reviewer #2 (Public Review):

The authors have identified an ER export signal in sphingomyelin synthase (SMS) - this is defective in SMS mutants associated with a certain bone disease. Consequently, disease mutants of the enzyme remain in the ER, producing sphingomyelin locally instead of at the Golgi apparatus. This results in dysregulation of the sphingomyelin gradient through the secretory pathway, loss of the canonical asymmetric distribution of this lipid (localization exclusively to the exoplasmic side of the cell membrane) and causes a pronounced, secondary outcome: a change in cholesterol organization at the plasma membrane. These changes may explain the bone disease phenotype. More generally, the authors reveal new cell biology and introduce new tools to perturb lipid gradients in cells.

This is a very interesting, technically excellent paper that offers new insights but stretches some of its conclusions.

The key results are:<br /> 1. Bone disease-linked SMS2 variants under study are ER-localized whereas their wild-type counterparts are located in the Golgi apparatus;<br /> 2. SM levels in the ER are high (but surprisingly unaffected at the PM) in SMS-knockout cells expressing disease mutants, and, as a consequence of unspecific lipid scrambling activity in this organelle, SM is distributed "symmetrically" across the bilayer (in the wild-type situation, SM is locked into the exoplasmic/lumenal leaflet in the Golgi apparatus and plasma membrane);<br /> 3. Cholesterol levels at the PM are the same in SMS-knockout cells expressing disease mutants versus wild-type cells, but the cholesterol is organized differently in the former as evinced by detection with a biosensor and susceptibility of the cells to cyclodextrin treatment. This is consistent with data obtained with membrane fluidity probes.

Reviewer #1 (Public Review):

Sphingomyelin (SM) is an abundant lipid of the plasma membrane and is synthesized from ceramide and PC by two enzymes, SMS1 and SMS2, with the former being in the Golgi and the latter in the plasma membrane. Mutations in SMS2 have been found to underly a rare genetic disorder of bone formation, and previous work from the authors has shown that in two cases this arises from autosomal dominant missense mutations in the N-terminal cytoplasmic tail of SMS2 (I62S and M64R). Their previous study reported that these mutations cause SMS2 to accumulate in the ER instead of the plasma membrane, with cells from the patients containing elevated levels of SM, suggesting that the lipid is being synthesized by the SMS2 in the ER.

This new study extends this work by using both organelle fractionation and in vivo imaging to confirm that these mutations in SMS2 do indeed result in substantially elevated levels of SM in the ER. They also find changes in phospholipid desaturation in the ER which suggests a compensatory adjustment, but interestingly, levels of cholesterol in the ER do not appear to increase despite the known affinity of SM for cholesterol. SM is normally only found on the outer leaflet of membranes and so is not exposed to the cytoplasm. The authors use an in vivo probe for SM to argue that in the mutant cells SM is being present on the inner leaflet of both the ER and plasma membrane, and with a concomitant reduction in cholesterol levels, and lipid order, in the outer leaflet of the plasma membrane. These findings lead them to comment on the robustness of the intramembrane system to a major perturbation of lipid distribution and to speculate that these changes in lipid distribution may contribute to the bone deposition defects in the patients.

Overall the data is clearly presented, well-controlled, and quantified. The cell biology of lipids in general, and SM in particular, is an interesting and important topic. The first part of the paper demonstrating the elevated levels of SM in the ER is very convincing with both fractionation and in vivo imaging showing clear changes in the mutants. The second part, using in vivo imaging with an SM reporter to show the appearance of SM in the cytosolic face of membranes in the mutant cells, is less convincing, and although there are clear differences, the authors admit that the nature of the difference is perplexing. Indeed there seems little direct evidence at present to support the conclusion that SM is being translocated to the cytoplasmic face of the ER. Thus, significant further work is required to support the conclusions on exposure of SM to the cytosolic face of membranes.

Reviewer #3 (Public Review):

This is a well-performed study by an experienced group that has identified AKAP12 as a scaffolding partner of HSP47 in normal HSCs that blocks HSP47's collagen chaperoning activity and its interaction with UPR signals in HSCs. Furthermore, site-specific phosphorylation of AKAP12 inhibits its interaction with HSP47. This induces HSP47's collagen chaperoning activity, collagen production, and HSP47's interaction with UPR signaling proteins upon pro-fibrogenic stimulation. Blocking AKAP12 phospho-modification inhibits HSC activation as well as overall liver injury possibly via modulation of the ER stress response and inhibition of ER stress-linked inflammatory signals.

The paper fills an important gap in understanding the interaction of AKAP12 and HSP47.

Reviewer #2 (Public Review):

Liver fibrosis is a pathological process that accompanies chronic liver diseases and causes significant morbidity and mortality worldwide. Thus, the understanding of the mechanisms involved in this process with the goal to develop effective treatments is of high significance. In this manuscript, the authors identify AKAP12 as a player in the activation of hepatic stellate cells (HSCs), the main liver cell type involves in the production of extracellular matrix and inflammatory mediators during the process of liver fibrosis. Using a mouse model of liver fibrosis (CCl4 treatment) and in-vitro experiments with isolated HSC, they show that phosphorylation of AKAP12 mediated by PKCalpha diminishes AKAP12 scaffolding activity towards HSP47, a chaperone of collagen in the ER. AKAP12 phosphorylation is increased in activated HSCs and induces HSP47 chaperoning activity with enhanced production of collagen but also ER stress with production of inflammatory mediators. Interestingly, by using CRISPR-AVV6 vectors for gene editing to specifically target AKAP12's activation phospho-sites (both deletion and mutation of the sites) in HSCs they were able to effectively suppress the liver injury and fibrotic response in mice treated with CCl4. The therapeutic effect was both mediated by a decrease in collagen production and also by modulation of the ER-stress and the mediated inflammatory response both in HSCs and in hepatocytes. The study would have been strengthened by using other alternative experimental models of liver fibrosis (i.e DDC, MCD diet) to broaden the significance of the mechanism. Although the study is interesting and reports a novel mechanism for drug design to potentially target the process of liver fibrosis, several issues related to data presentation call for questions on the rigor of the study.

Reviewer #1 (Public Review):

The manuscript by Ramani et al. investigates the involvement of targeting A-kinase anchoring protein 12 (AKAP12), a protein that is described to exhibit a plethora of functions, for its regulation in liver injury and wound healing. Using molecular biology and gene editing techniques, the researchers delineate AKAP12's interaction with a chaperone, heat-shock protein 47, to understand the pathophysiology of hepatic cells. They conclude this protein's communications as a potential therapeutic target.

The authors build on their previous observation that AKAP12 phosphorylation impacts hepatic stellate cell activation, a process that may lead to liver fibrosis. In this manuscript, a link is described, heat-shock protein 47, a chaperone that is inhibited by AKAP12's phosphorylation state and therefore is unable to mature collagen. The authors go through many motions to address their hypothesis (including tetrachloro carbon treatment, western blotting, microscopy, Crispr/Cas9 gene editing, proteomic profiling, etc.), which is a strength of the study. Since I am not an expert in liver (patho)physiology, I can only comment on the technical aspect of this manuscript, which is sound but would benefit from an improved presentation of the data. An impact on society can be envisioned, if the protein interactions can be pharmacologically addressed, for instance with a small molecule screen.

Reviewer #2 (Public Review):

This work aims to fill an important theoretical gap regarding the role of potential threats to the self in altruistic / prosocial helping. Much of our prevailing knowledge about the motivations for prosocial behavior focuses on the distress of the conspecific-in-need. Leveraging animal research, the authors hypothesize that defensive neural circuitry may aid prosocial helping under threat. Further building on prior work detailing responses along the threat imminence continuum, the authors hypothesize that cognitive fear circuits would respond to more distal threats whereas reactive fear circuits would respond to imminent threats. In addition to examining helping behavior under conditions of threat to self, the authors included representational similarity analyses (RSA) to examine how overlapping representations of self and other distress related to helping behavior. The potential to challenge existing empathy accounts of prosocial helping is intriguing and worth interrogating.

Strengths:<br /> The theoretical basis for this work is sound and the authors attempt to answer a question of broad interest.

The technical approach (acknowledging certain methodological limitations) is appropriate to answer the questions the authors aim to investigate. The authors combine univariate and multivariate analyses, which provides a more fulsome explanation for the phenomena in question than any of these approaches alone.

The inclusion of threat imminence and threat value increase the contribution of this work to understanding how helping decisions vary as a function of threat features.

The authors preregistered the study analyses and hypotheses.

Weaknesses:<br /> Although this study has strengths in principle, the weaknesses of this work result in an inability to support the conclusions the authors attempt to make.

One of the stated goals of this work is to determine how the neural representations of self threat and other's distress are associated with helping behavior. To dissociate representations of threat and other's distress within the defensive circuitry would require the authors to show that the neural representations of threat are separable in voxel space from the neural representations of other's distress. The authors do not explicitly show that the two kinds of neural representations are dissociable or that neural representations are sufficiently stable within conditions to be used in an RSA. One can imagine that representational drift across trials could lead to high variance of neural representations within a single condition, leading to low similarity scores within that condition and therefore deflated second-order similarity scores.

To show that the neural representations of self threat and other's distress are dissociable, the authors would need to first determine the distributions of these two kinds of representations in conditions where subjects are faced only with a threat to themselves or only with the distress of another. This could be done, for example, by continuing to image while subjects rated self threat and other's distress and extracting the distribution of voxel patterns that are correlated with these ratings; then, methods from signal detection theory could be used to determine if the two distributions are separable. The authors assume that when the threat and conspecific in need are presented simultaneously the neural representations in these contexts will be a linear combination of the separate representations for self threat and other's distress. Evidence from other domains suggests that may be unlikely; for example, in olfaction, sensory representations of mixtures of two odors are rarely a linear combination of the individual sensory representations of each odor presented separately. These conceptual issues with the representational similarity analysis hinder the interpretation of the RSA performed here and make it difficult to accept the authors' interpretation that "neural representations of threat promoted helping'.

In the section "Greater engagement of reactive fear circuits led to helping", the authors pool imaging data from trials on which a "no help" decision was made with imaging data from safe trials. A decision under threat to help a conspecific and an arbitrary decision with no consequences for either the subject or conspecific should involve different neural mechanisms, so there is no clear justification for pooling the data from these two conditions. Pooling data from these two conditions makes it impossible to determine whether the results of the ANOVA provide sufficient evidence for their conclusion that "greater engagement of reactive fear circuits led to helping'.

The authors explicitly instructed participants that they "would have a pre-set number of times they could help on each run, and thus they should try to balance, per run, the number of times they helped and not helped". These instructions undermine most of the study conclusions. The verbal instruction to balance helping and non-helping behavior introduces an auxiliary constraint into the task, likely inducing metacognitive processes in participants via which they monitor their behavior to ensure that they satisfy the constraint. It is unclear how such metacognitive processes would alter neural activity during the task, making it difficult to distinguish whether the observed BOLD signal is involved in decision making or is also influenced by metacognition. The data indicate that most subjects heeded these instructions: Figure 2B illustrates that most subjects maintained differences of less than 10% in helping vs. non-helping behavior and approximately half maintained differences of less than 5%, suggesting that most subjects attempted to balance helping and non-helping decisions.

A few other methodological limitations are worth noting: insufficient motion correction, improper spatial smoothing for RSA analyses, and lack of power analysis.

Reviewer #1 (Public Review):

The authors strive to study the relationship of self-directed defensive responses and altruistic behaviors. The latter is often studied in terms of economic gain and losses, where helping others typically result in a monetary loss (or similar utility loss) to oneself. However, in reality, lending a helping hand is sometimes paired with more ecologically relevant threats. This paper introduces such threats by adapting and developing upon the paradigm used in Vieira et al. (2020), and further implements the threats on the threat imminence continuum, where the concept of "reactive fear" and "cognitive fear" circuits become helpful in characterizing the individual's self-directed defensive responses.

The paper asks two main questions: 1. how are defensive neural circuits differentially involved in helping others along the threat imminence continuum; 2. whether the neural representation of threat to others, or the neural representation of threat to self, underlie the behavior of helping others. The paper answers the first question using a conjunction of univariate ANOVA and multivariate searchlight, and the latter through a representational similarity analysis (RSA).

Strengths:

Conceptually, this paper taps into the dynamics between altruistic behaviors and defensive responses to simulated ecological threat in humans, which is substantially relevant but to date a rare breed. This innovation helps advance the understanding in the intersection. it also builds strongly upon previous human/animal research, where the "cognitive fear" and "reactive fear" circuits on the threat imminence continuum have been established.

Methodologically, the authors use a rich set of analysis, both univariate and multivariate, calibrated for answering specific questions. They provide good justifications for these analysis. For example, the presence of both the threat (potential shock) and the ditress felt by others (the video) could be viewed as a confound. The paper used it as a feature, and utilized RSA to differentiate neural representations for threat to self and threat to others within the defensive circuit.

Overall, the main claims in the paper are well supported by the data and analysis.

Reviewer #2 (Public Review):

The authors present a 3-year population study of M. tuberculosis transmission in Valencia, Spain.<br /> They set out to assess the epidemiology and transmission dynamics of M. tuberculosis in their region, and evaluate how valid SNP thresholds, defined in other settings, might be in Valencia, or indeed elsewhere.

The authors achieve dense sampling (77% of all culture positive cases). They show how local transmission accounts for much of the local case load, and nicely demonstrate with a ROC curve that the 11.5 SNPs achieves optimal sensitivity and specificity when used as a threshold for contact tracing. The authors also use time scaled phylogenetic reconstruction of historical transmission events to show that local strains have been circulating for over 150 years.

The authors draw 4 conclusions, namely that transmission can still be an important factor even in a low burden setting; that much transmission occurs in the community rather than in the household; that generous SNP threshold capture transmission links that are often no longer relevant to contact tracing effort; and that where strains are endemic, a continuum of relatedness (SNP distance) can be observed. They conclude, correctly, that the public health response needs to be informed by knowledge of the local problem, and that epidemiological patterns can be quite different even across similar social-economic settings with similar incidence of disease. These conclusions are important and should encourage others to investigate their localities in such detail rather than drawing inferences from the findings of ostensibly similar settings.

It is however also important to be clear on the distinction between the use of SNP threshold for directing contact investigations and the use of SNP thresholds for understanding the transmission dynamics in a population over time (e.g. the behaviour of an endemic strain). There may be a role for SNP thresholds when directing contact tracing, although this remains controversial, whereas the authors correctly show that threshold are far less useful for understanding the longer term behaviour of local strains.

Reviewer #1 (Public Review):

This paper aims to look at 4 main things:<br /> 1. The correlation between a country's/region's TB burden and the level of local transmission<br /> 2. The relevance of proposed SNP cut-offs for defining transmission clusters in different settings<br /> 3. The link between genetic clusters of Mycobacterium tuberculosis isolates and transmission clusters proposed by contact tracing methods<br /> 4. The effect of historical local transmission on current day epidemiological dynamics

Overall, the paper achieves many of these goals to different extents, with strong support for the first three aims. However, some difficulties with modelling past transmission dynamics leaves the last aim, and indeed that contained within the title, much less well supported.

1. The correlation between a country's/region's TB burden and the level of local transmission<br /> One of the primary findings of the paper is that low burden does not mean low local transmission. This is what is often purported, based primarily on work in the UK, which the authors nicely show has very different dynamics than Valencia. There is strong evidence presented here that the same public health actions cannot be used to eliminate TB in all low burden settings, although this could be better outlined in the discussion.

2. The relevance of proposed SNP cut-offs for defining transmission clusters in different settings<br /> The 12 SNP cut-off is used almost universally to define recent transmission of M. tuberculosis, even though it was only originally demonstrated in a low burden, low transmission setting and then subsequently linked to timespans in a small MDR-TB dataset in a high burden country. The demonstration that the 12 SNP threshold means very different things in different settings is well presented and is a necessary point to make. The results outlined here add well to previous suggestions on this point but is likely the strongest evidence for setting-specific cut-offs, or abolition of cut-offs completely, that has been published to date.

3. The link between genetic clusters of Mycobacterium tuberculosis isolates and transmission clusters proposed by contact tracing methods<br /> This finding is well supported by the sensitivity/specificity and accuracy measures. The correlations between SNP cut-off and epidemiological link are in line with several previous publications so are to be expected. The fact that transmission is also likely occurring more in the community than the household has been reported by some in high burden settings but not well known in low burden settings, which this work clearly shows, but could be better highlighted in the discussion.

4. The effect of historical local transmission on current day epidemiological dynamics<br /> This section is perhaps where the paper is either most confusing or least supported. While it is very likely that the current population structure is heavily influenced by past transmission dynamics, the approach to this question is not easily understood. The Bayesian analysis is primarily well done with adequate prior settings and MCMC parameters, although the apparent use of a SNP alignment without ascertainment bias correction has a strong chance to produce inaccuracies in the time tree. The historical estimation of transmission events over 150 years implicitly has many assumptions, such as transmission burden being the same in each country and local transmission excluding export and subsequent re-import, making it difficult to understand and extrapolate the findings with any certainty.

Reviewer #3 (Public Review):

In this manuscript Liu and colleagues describe an essential and specific function of CFAP61 in sperm flagella formation and function in human and mouse. CFAP61 (or its homolog in other organisms) was previously shown by several groups to be part of the Calmodulin and radial Spoke associated Complex (CSC), which is located at the base of radial spokes (RS) and interacts with the Dynein Regulatory Complex (DRC) and inner dynein arm of motile cilia/flagella. Here, the authors first re-investigate exomes from a previously described cohort of patients with a multiple morphological abnormalities of the flagella (MMAF) phenotype, and identify one patient with a homozygous intronic variant in CFAP61 that they show induces skipping of CFAP61 exon 2. Next, they generate a Cfap61 mutant mouse, and using various approaches they convincingly demonstrate that loss of CFAP61 severely affects spermatogenesis whereas other ciliated tissues such as trachea appear normal. Consistently, Cfap61 mutant males are infertile but do not display additional phenotypes indicative of ciliary dysfunction. The authors also present immunoprecipitation data suggesting that CFAP61 and its Chlamydomonas homolog, FAP61, interact with a range of axonemal proteins, including known components of the CSC but also other RS components, as well as some axonemal dynein arm and IFT proteins.

The overall quality of the data is good and the manuscript is nicely presented. Most of the phenotypes described for the Cfap61 mutant mice are reminiscent of those previously reported for another Cfap61 mutant mouse model (Huang et al., 2020, Science Bulletin vol. 65 p. 854-864). Furthermore, while the biochemical data largely confirms previously published work done in model organisms like Chlamydomonas and Tetrahymena, additional work will be needed to fully understand the precise mechanism by which CFAP61 interacts with various axonemal components to regulate sperm flagella formation and function.

Reviewer #2 (Public Review):

Through analysis of exome sequencing of a patients cohort affected by infertility, Liu et al discovered a patient whose genome contains a missense mutation (C143+5G>A) in CFAP61, the human homologue of FAP61 radial spoke protein in Chlamydomonas. The objective of the authors is to confirm that the mutation in CFAP61 causes the infertility phenotype at the organism level and to detail defects in sperm flagella at the molecular level caused by the lack of CFAP61.

The authors first confirm that the mutation found in the patient changes a splice site, which in turn leads to exon skipping of CFAP61 using a minigene assay. Subsequently, they show that sperm flagella from the patient and obtained from a Cfap61 -/- CRISPR-Cas9 KO model show multiple morphological abnormalities of the sperm flagella (MMFA phenotype). This observations are consistent with previous analysis of a Cfap61 -/- mouse KO model also generated by CRISPR-Cas9 by Huang et al in Science Bulletin article published previously. In particular they show that CFAP61 is critical for the late stages of Radial Spoke assembly in elongated spermatids and spermatozoa and contribute to flagella stability, while it does not appear to impact round spermatids organization.

The manuscript is overall interesting, well executed and the interpretation is largely consistent with the data. Most of the results are centered on the characterization of sperm flagella phenotype in the mouse Cfap61 -/- KO, which largely mirrors published work.

A well executed systematic analysis of protein-protein interactions in pull down assays with human and Chlamydomonas Radial spoke proteins suggest that CFAP61 is an elongated protein part of the Calmodulin and Spoke associated complex (CSC).

Analysis of airway multiciliated cells from Cfap61 -/- mice show that CFAP61 plays an important role only in flagella, but not in airway motile formation or function.

Reviewer #1 (Public Review):

This work reported an MMAF patient carrying CFAP61 splicing variant. The author then make a Cfap61-knockout mouse using CRISPR/CAS9 to examine the function of Cfap61 in vivo. Beyond the phenotype of MMAF, the author tried to study the mechanism. They found that CFAP61 can interact with many RS proteins and other proteins relating to the sperm flagellum in mice. These results together with IF results in several markers in CSC, RS and other proteins in sperm flagella suggested the failure of flagella axoneme assembly in Cfap61-/- mice.<br /> Since the function of Cfap61 in mice has been reported before, some results in the first half has been uncovered. While the second half of the mechanism research is complete and the data support the conclusions, but there are still some concerns.

1. To conclude that CFAP61 is required for sperm flagellum formation for human, more evidence from human experiments would be needed. The human protein interaction and IF experiments in human smear slides could be useful to support the claim.

2. IF results in Cfap61+/+ and Cfap61-/- for mature spermatozoa using anti-CFAP61 antibody would be needed.

3. According to previous reports and the in vitro results from this paper, CFAP61 should have a strong interaction with CFAP91 (MAATS1)/ CFAP251 (WDR66) since they are all CSC members. It is unclear why the IP-MS results of anti-CFAP61 antibody show neither of these two proteins (Figure 6-figure supplement 1).

4. According to the diagram given by the authors, CFAP61 is located in the RS stalk and neck part, but it's an inference from IP results in vitro. A more direct evidence for this would make the paper stronger.

5. It is a little confusing that CFAP61 can also interact with IFT proteins. It would have been helpful if the authors could provide more evidence that the IFT retention is directly caused by deletion of CFAP61 but not a consequence.

Reviewer #3 (Public Review):

The study by Petrov and colleagues examined whether rare cancer drivers can be examined in a network context. For this purpose, the authors develop a new computational tool that is based on two "channels" (MutSet and PathReg) to provide evidence on whether a gene might reflect a driver gene. Based on these channels, they evaluate ten large cancer cohorts and assess the overlap of their results with established cancer genes or datasets that are enriched for cancer genes. Based on this comparison, they find a strong enrichment for known cancer genes.

In my opinion, the study addresses an important point. Indeed, many discovery algorithms have been based on mutational recurrence. While these strategies robustly identify the most frequently mutated cancer genes, they yield diminishing returns for rare driver genes so that several magnitudes of large datasets would be required for identification of rare driver genes. Therefore, network-based identification of rare driver genes could be a useful criterion to identify rare driver genes, for instance, based on their interaction with canonical drivers. If could have an important impact on diagnostics and therapeutic decision making.

While this idea is intriguing, it is not entirely novel. For more than a decade, mutation data in TCGA have been viewed in networks and many previous studies have tried to identify driver genes based on networks. I think a critical point would be to compare the authors' methods against these previous approaches and to demonstrate that it overcomes the limitations that previous studies reported in this field. Also, it was unclear to me whether the authors were able to achieve their goal to identify genes based on network contexts - i.e., is there a new class of driver genes that can be identified based on their approach that could not be understood based on previous studies? Alternatively, could this method/strategies be expanded to predict other phenotypes than driver genes.

In sum, the study provides a very interesting approach to the discovery of rare driver genes. The authors have invested a lot of work to perform many technical validation analyses of their approach.

Reviewer #2 (Public Review):

Petrov et al present NEAdriver, a network-based method aimed at the identification of mutational (point mutations and copy number variants) driver genes across tumors. This is a timely subject, which constitutes one of the main aims of cancer genomics.

I have two main lines of criticism to the paper. The first concerns the algorithm itself, which I feel is not thoroughly explained and described. The second concern is results which I find insufficiently described and incompletely validated from my point of view.

My main concern about the algorithm is that as far as I can tell, the authors don't correct for the background mutation rate of genes in the calculation of the MutSet. This may result in identifying false positive driver genes with abnormally high nuber of mutations across cohorts due to known covariates of the mutation rate, such as the replication time or the level of transcription. Correctly estimating the background mutation rate of genes across tumors is a key tenet of methods that search for signals of positive selection in genes. For further explanation on this subject see https://doi.org/10.1038/nature12213, https://doi.org/10.1016/j.cell.2017.09.042. This may explain why known highly mutated non driver genes like TTN, RYR1 and others appear as significant recurrently across different cohorts. This issue is key for any method that uses mutation data to identify driver genes and must be addressed by the authors.

Reviewer #1 (Public Review):

Here, Petrov and Alexeyenko tackle one of the main questions in cancer genomics: Given that there are tumours that do not have any known driver mutations, how can we find novel, undiscovered cancer genes? For this purpose, they develop a method to identify these drivers that is not based on mutational frequency (which is commonly used) and which instead relies on functional networks. The method seems interesting and potentially useful, however, at the moment it is hard to follow the details as it is written very technically and, to this reader, is confusing at times. Additionally, it seems that there are some important details missing in the explanation of the methodology, as well as efforts to validate the results. In my opinion this can be a valuable addition to the literature, but needs to be more clearly explained and at least a few novel genes validated experimentally if possible (I am not saying the authors need to perform these experiments, it could be from data of other papers, but where these genes were found to play a relevant role).

Reviewer #1 (Public Review):

In the present manuscript, Schrecker et al. provide a series of structural views of a eukaryotic (budding yeast) clamp loader RFC bound to ATPgS, a client clamp, and substrate DNA. Cryo-electron microscopy is used to image the clamp loader complexes, revealing several different conformational intermediates. In vitro clamp loading, ATPase and replication assays are then employed to test some of the findings resulting from the structural analysis.

The paper is generally well-written and organized in a straightforward manner. The strengths of the work include the high detail (resolution) of the structural models and the multiple conformational intermediates that are observed, which together are used to develop a sensible mechanistic model for clamp loader function that builds on prior work in this system. New findings include establishing a role for the BRCT domain of RFC1 in binding DNA, showing how DNA is distorted by clamp loader binding, and showing how the clamp loader can associate with both primer/template and nicked DNA substrates. These insights are well supported by the data shown in the manuscript. The structural data are also used to propose a refinement of a model for clamp loader function in which, contrary to prior published findings, ATP hydrolysis is not required for loaded clamps to close around a duplex DNA. This conclusion is sensible given the structural data, although experimental evidence that the nucleotide substrate used was not hydrolyzed while preparing the sample for imaging by EM is not shown and the article should be revised to provide this evidence.

Reviewer #2 (Public Review):

Schrecker, Castaneda and colleagues present cryo-EM structures of RFC-PCNA bound to 3'ss/dsDNA junction or nicked DNA stabilized by slowly hydrolyzable ATP analogue, ATPyS. They discover that PCNA can adopt an open form that is planar, different from previous models for the loading a sliding clamp. The authors also report a structure with closed PCNA, supporting the notion that closure of the sliding clamp does not require ATP hydrolysis. The structures explain how DNA can be threaded laterally through a gap in the PCNA trimer, as this process is supported by partial melting of the DNA prior to insertion. The authors also visualise and assign a function to the N-terminal domain in the Rfc1 subunit of the clamp loader, which they find modulates PCNA loading at the replication forks, in turn required for processive synthesis and ligation of Okazaki fragments.

This work is extremely well done, with several structures with resolutions better than 3Å, which a significant achievement given the dynamic nature of the PCNA ring loading process. To investigate the role of the N-terminal domain of Rfc1 in PCNA loading, the authors use in vitro reconstitution of the entire DNA replication reaction, which is a powerful method to identify specific defects in Okazaki fragment synthesis and ligation.

Important issues

1. Figure 3B,D,F. I would find them much more informative if the authors showed the overlay between atomic model and cryo-EM density in the main figure. If the figure becomes too busy, the authors could decide to just add additional panels with the overlay as well as the atomic models alone. I do not think that showing segmented density for the DNA alone, as done is Figure 6C is sufficient. Also including the density for e.g. residues Trp638 and Phe582 seems important.

2. Cryo-EM samples preparation included substoichiometric RPA, which has been shown to promote DNA loading of PCNA by RFC. Would the authors expect a subset of PCNA-RFC-DNA particles to contain RPA as well? The glycerol gradient gel indicates that, at least in fraction 5, a complex might exist. If the authors think that the particles analyzed cannot contain RPA, it would be useful to mention this.

3. Published kinetic data indicate that ATP hydrolysis occurs before clamp closure. To incorporate this notion in their model, the authors suggest that ATP hydrolysis might promote PCNA closure by disrupting the planar RFC:PCNA interaction surface and hence the dynamic interaction of PCNA with Rfc2 and -5 in the open state. In addition, ATP hydrolysis promotes RFC disengagement from PCNA-DNA by reverting from a planar to an out-of-plane state. This model appears reasonable and nicely combines published data with the new findings reported by the authors. However, the model is oversimplified in Figure 6, where the only depicted effect of ATP hydrolysis is RFC release. Perhaps the authors could use the figure caption to acknowledge that ATP hydrolysis likely still has a role in facilitating PCNA closure.

4. Can the authors explain what steps should be taken to describe PCNA loading by RFC in conditions where ATP hydrolysis is permitted? How would such experiments further inform the molecular mechanism for the loading of the PCNA clamp?

Reviewer #3 (Public Review):

In this report, Schrecker et al. use cryo-EM to examine structures of ternary complexes containing the S. cerevisiae clamp loader (RFC), sliding clamp (PCNA), and two different DNA molecules, a DNA molecule with a ss/ds DNA junction with a 3' recessed end (3' ss/ds DNA) and a nicked DNA molecule. These are the first structural data for RFC containing the full-length Rfc1 subunit, and these structures along with biochemical assays demonstrate that Rfc1 interacts with dsDNA to increase the efficiency of clamp loading, particularly on the lagging strand. In order for clamp loaders to accomplish the mechanical task of binding clamps, opening clamps, chaperoning clamps to the appropriate DNA sites, and releasing the clamps onto DNA, multiple clamp loader complexes and conformational states must exist. To date, we have a limited structural view of these complexes and conformational states that is based on visualization of a handful of structures from several different organisms. Importantly in this work, the authors were able to capture three different RFC-PCNA-DNA complexes with proteins from the same organism. And this series of structures provides key insight into the structural basis by which RFC-PCNA initially binds DNA and passes ds DNA through the opening of PCNA into the central chamber of the clamp loader. The data show that RFC opens PCNA wide enough to allow dsDNA to pass through the opening which differs from models based on structures from other organisms and molecular dynamics simulations. Unexpectedly, PCNA is open in a planar configuration in a geometry that resembles a horseshoe rather than opening out-of-plane in a spiral configuration. Another intriguing surprise is that RFC melts several base pairs at the primer 3' end, but it is not yet clear how this may contribute to DNA binding or specificity for 3' ss/ds DNA. One potential caveat to these studies is that ATPgammaS was substituted for ATP to block hydrolysis and trap intermediate complexes. It is possible that either RFC conformations or the relative populations of different conformational states are influenced by the bound nucleotide. Overall, this is an important study that answers many questions about the mechanism of clamp loading and also raises some intriguing new questions to stimulate further studies.

Reviewer #1 (Public Review): 

Alexej Schatz and York Winter wrote "LabNet," a C++ tool to control Raspberry Pi (raspi) GPIO (General Purpose Input-Output) and other hardware using a network messaging protocol using protobuf. The authors were primarily concerned with performance, specifically low execution latencies, as well as extensibility to a variety of hardware. LabNet's network architecture is asymmetrical and treats one or many raspis as servers that can receive control signals from one or more clients. Servers operate as (approximately) stateless "agents" that execute instructions received in message boxes using a single thread or pool of threads. The authors describe several examples of basic functionality like time to write and read GPIO state to characterize the performance of the system, the code for which is available in a linked GitHub repository. 

The described performance of LabNet is impressive, with near- or sub-millisecond latency across the several tests when conducted over a LAN TCP/IP connection. The demonstrated ability to interact with the server from three programming languages (C++, C#, and Python) also would be quite useful for a tool that intends to be as general-purpose as this one. The design decisions that led to the use of protobuf and SObjectizer seem sound and supportive of the primary performance goal. 

As far as I'm concerned, the authors accomplished their goals and give a convincing demonstration in their performance tests. 

The authors compare LabNet to: 

- Whisker ( https://web.archive.org/web/20200222133946/http://egret.psychol.cam.ac.uk/whisker/index.shtml ), an aging proprietary experimental package typically sold along with purpose-built hardware; <br /> - pyControl and Bpod, both of which are open-source software frameworks for performing behavioral experiments using a specific combination of microcontrollers and an ecosystem of extension parts; <br /> - Autopilot, a software framework for performing behavioral experiments on the raspberry pi as well as modular development of hardware controllers and other common experimental components. 

Each of these packages has a different enough scope and accompanying differences in design priorities that I think are worth noting to give context to the niche LabNet fills. For example, pyControl and Autopilot emphasize ease of use, pyControl and Bpod are built around state machines for controlling experiments, etc. All have some facility for designing and performing experiments themselves and are thus a bit "higher level" than LabNet, which is intended more as a GPIO and hardware control system specifically. I think LabNet is more aptly compared to something like pigpio (https://web.archive.org/web/20220130033233/https://abyz.me.uk/rpi/pigpio/) which is also a low-level GPIO control library with network control capabilities. In that respect LabNet fills at least two needs that aren't well-served by existing tools: first, it provides a means to extend the server with additional commands that can be exposed to multiple programming languages. Second, that lets users control additional hardware and implement custom logic aside from simple on/off commands (for example, the ability to output sound) - this would be particularly useful as a way of controlling HATs and other devices. LabNet's agent-based concurrency architecture also seems like it will allow the number of simultaneously controlled devices to scale well. LabNet's network-first design positions it well for behavioral experiments that are often better served by a swarm of networked computers rather than a single controlling computer. 

The largest point of improvement that I expect will unfold over this project's development lifecycle will be its documentation. LabNet has no documentation to speak of, outside a brief description of the build process for a relatively voluminous body of code (~27k lines) with relatively few comments. There is no established norm as to what stage in a scientific software package's development a paper should be written, so I take the lack of documentation at this stage as just a sign that this project is young. The primary barrier for the broader landscape of scientific software is less that of availability of technically proficient packages, but the ease with which they can be adopted and used by people outside the development team. The ability of downstream researchers to use and extend the library to suit their needs will depend on future documentation. For example, at the moment the Python adapter to the client and server is present in the examples folder but relatively un-annotated, so it might be challenging to adapt to differing needs at the moment (https://github.com/WinterLab-Berlin/LabNet/blob/34e71c6827d2feef9b65d037ee5f2e8ca227db39/examples/python/perf_test/LabNetClient_pb2.py and https://github.com/WinterLab-Berlin/LabNet/blob/34e71c6827d2feef9b65d037ee5f2e8ca227db39/examples/python/perf_test/LabNetServer_pb2.py ). Documentation for projects like this that aim to serve as the basis from which to build experimental infrastructure can be quite challenging, as it often needs to spread beyond the package itself to more general concerns like how to use Raspberry Pis, how to set them up to be available over a network, and so on, so I look forward to seeing the authors meet that challenge. 

I would like to thank the authors for their work and thank them for bringing us a fast way to control experimental hardware over the network.

Reviewer #2 (Public Review): 

The manuscript introduces LabNet as a network-based platform for the control of hardware in Neuroscience. The authors recognize and attempt to address two fundamental problems in constructing systems neuroscience experiments: on one hand the importance of precise timing measurements of behavior; on the other hand, the need for flexibility in experimental design. These two goals are often at great odds with each other. Precise timing is more easily achieved when using fewer, dedicated homogeneous devices such as embedded microcontrollers. Flexibility can be found in the diversity of devices and programming languages available for commercial personal computers, but this often comes at the cost of a non-real-time operating system, where timing can be much harder to predict accurately. There is also a limitation on the number of devices which can be simultaneously controlled by a single processor, which can be an impediment for high-throughput behavior studies where the ability to run dozens of experiments in parallel is desirable. 

LabNet proposes to address this tension by focusing on the design of a pure hardware control and instrumentation layer implemented on top of the Raspberry Pi family of microprocessors. The idea is to keep coordination of experimental hardware in a central computer, but keep time-critical components at the edge, each node running the same control software in a Raspberry Pi to provide precise timing guarantees. Flexibility would be provided by the ability to connect an arbitrary number of nodes to the central computer using a unified message-passing protocol by which the computer can receive events and send commands to each node. 

The authors propose the use of the C++ programming language and the actor-model as a unifying framework for implementing individual nodes and present a series of benchmarks comparing their system against other established hardware control platforms. 

The idea of keeping time-critical components at the edge, and the use of network communication protocols, and in particular message-passing systems such as the actor-model, to scale up experimental control is reasonable. These principles have undoubtedly been very successful in enabling the creation of massively distributed systems such as web applications connecting millions of devices to each other every second. 

The Design section then introduces the actor model, the C++ library SObjectivizer used to implement it, and the binary message protocol used for transmission of data across nodes. As currently written, however, this section seems overly technical and hard to grasp for readers who might be interested in experimental neuroscience, but who lack the expertise to understand all mentioned functional constructs and required expertise in the C++ language. Several concepts are mentioned only in passing and without introductory references for the non-expert reader. The level of detail also seems to distract from conveying a more meaningful understanding of the remaining trade-offs involved between network communication, latency, synchronization, and bandwidth. 

The essence of the actor-model could probably be captured more succinctly, and more time spent discussing some of these critical decisions underlying LabNet's design principles. For example, although each Raspberry Pi device runs a LabNet server, the current implementation allows only one client connection per node. This might be surprising for some readers as it excludes a large number of possible network topologies, and the reason presented for the design decision as currently detailed is hard to understand without further clarification. 

The main method for evaluating the performance of LabNet is a series of performance tests in the Raspberry Pi comparing clients written in C++, C# and Python, followed by a series of benchmarks comparing LabNet against other established hardware control platforms. While these are undoubtfully useful, especially the latter, the use of benchmarking methods as described in the paper should be carefully revisited, as there are a number of possible confounding factors. 

For example, in the performance tests comparing clients written in C++, C# and Python, the Python implementation is running synchronously and directly on top of the low-level interface with system sockets, while the C++ and C# versions use complex, concurrent frameworks designed for resilience and scalability. This difference alone could easily skew the Python results in the simplistic benchmarks presented in the paper, which can leave the reader skeptical about all the comparisons with Python in Figure 3. Similarly, the complex nature of available frameworks also raises questions about the comparison between C# and C++. I don't think it is fair to say that Figure 3 is really comparing languages, as much as specific frameworks. In general, comparing the performance of languages themselves for any task, especially compiled languages, is a very difficult topic that I would generally avoid, especially when targeting a more general, non-technical audience. 

The second set of benchmarks comparing LabNet to other established hardware control platforms is much more interesting, but it doesn't currently seem to allow a fair assessment of the different systems. Specifically, from the authors' description of the benchmarking procedure, it doesn't seem like the same task was used to generate the different latency numbers presented, and the values seem to have been mostly extracted from each of the platform's published results. This unfortunately reduces the value of the benchmarks in the sense that it is unclear what conditions are really being compared. For example, while the numbers for pyControl and Bpod seem to be reporting the activation of simple digital input and output lines, the latency presented for Autopilot uses as reference the start of a sound waveform on a stereo headphone jack. Audio DSP requires specialized hardware in the Pi which is likely to intrinsically introduce higher latency versus simply toggling a digital line, so it is not clear whether these scenarios are really comparable. Similarly, the numbers for Whisker and Bpod being presented without any variance make it hard to interpret the results. 

One of the stated aims of LabNet was to provide a system where implementing new functionality extensions would be as simple as possible. This is another aspect of experimental neuroscience that is under active discussion and where more contributions are very much needed. Surprisingly, this topic receives very little attention in the paper itself. It is not clear whether the actor model is by itself supposed to make the implementation of new functionality easier, but if this is the case, this is not obvious from the way the design and evaluation sections are currently written, especially given the choice of language being C++. 

One of the reasons behind the choice of Python for other hardware platforms such as pyControl and Autopilot is the growing familiarity and prevalence of Python within the neuroscience research community, which might assist researchers in implementing new functionality. Other open-hardware projects in neuroscience allowing for community extensions in C++ such as Open-Ephys have informally expressed the difficulty of the C++ language as a point of friction. I feel that the aim of "ease of extensibility" should merit much more discussion in any future revision of the paper. 

Indeed, they only mention in passing that user extensibility is in the conclusion where it is stated that it is not currently possible to modify LabNet without directly modifying and recompiling the entire code base. A software plug-in system is suggested, and indeed this would be extremely beneficial in achieving the second stated aim. 

Finally, a set of example experimental applications would have been extremely useful to ground the design of LabNet in practical terms, in addition to the example listings. Even in diagrammatic form, describing how specific experiments have been powered by LabNet would give readers a better sense of the kind of designs that might be currently more appropriate for this platform. For example, video is being increasingly used in behavioral experiments, and Raspberry Pi drivers are available for several camera models, but this important aspect is not mentioned at all in the discussion, so readers interested in video would not know from reading this paper whether LabNet would be appropriate for their goals. 

As the manuscript currently stands, I don't feel the authors have achieved their second stated aim, and I am unfortunately not fully convinced that the experimental results are adequate to demonstrate the achievement of the first aim. I fully agree, however, that a robust, high-performance and flexible hardware layer for combining neuroscience instruments is desperately needed, and so I do expect that a more thorough treatment of the methods developed in LabNet will in the future have a very positive impact on the field.

Reviewer #1 (Public Review): 

In this manuscript, the authors test their previously proposed model (also presented in Figure 1A) that ImuB interacts with the DnaN DNA polymerase III β clamp to recruit DnaE2. The previously identified mutasome components ImuA', ImuB, and DnaE2 and essential for DNA-damage induced mutagenesis. Although the exact function of ImuA' and ImuB is unknown, ImuB has long before been proposed to interact with DnaN via an interaction domain within ImuB that has already been identified. Since the experiments herein test and validate a well-establish model, the results are somewhat expected. However, all models should be tested experimentally, making this an important confirmation. The manuscript nicely makes use of both in vivo and in vitro approaches and the data is convincing for the most part, although the inability of the fusion proteins used in this study to complement the knockout strains during exposure to the DNA damaging agent MMC does raise an important limitation of the tools used herein. The major concern is the limited new biological insight gained from the study.

Reviewer #2 (Public Review): 

The imuABC genes from Caulobacter crescentus were first described almost 20 years ago, yet very little biochemical analysis regarding their mechanism of action has been published. It is now appreciated that these imuABC genes are present in a large number of clinically important bacterial pathogens, underscoring their potential importance to adaptation and antibiotic resistance. The imuA gene in M. tuberculosis is somewhat diverged from imuA in C. crescentus (the first described ImuABC), and is thus named imuA' to reflect this. The goal of the work described in this report was to gain further insights into the function of the M. tuberculosis ImuA', ImuB, and ImuC proteins in DNA damage-induced mutagenesis. The authors used fluorescent fusions of the M. tuberculosis imuA'BC gene products to demonstrate that ImuA', ImuB, and ImuC each colocalized with the beta sliding clamp protein in live cells. The beta clamp helps to organize replication and repair proteins on the DNA at replication forks; thus their colocalization implies functional complexes. These in vivo results were correlated with biochemical results in which they used size exclusion chromatography to measure interactions between the purified beta sliding clamp protein and the ImuB protein or ImuA'-ImuB protein complex. These biochemical results support earlier published results describing these interactions using yeast-two-hybrid (Warner et al., 2010, PNAS). Importantly, the authors also demonstrate that both a mutant ImuB containing a disruption to its beta clamp binding motif, as well as griselimycin (GSR), an antimicrobial that binds the beta clamp at the same site required for its interaction with the clamp binding motif, both impaired ImuB-beta clamp interactions in vitro, and resulted in the loss of their colocalization in vivo. A potentially powerful conclusion of this work is that adjuvant therapies such as GSR may inhibit mutagenesis, limiting M. tuberculosis drug resistance, facilitating the ability of traditional antimicrobial therapies to treat the infection. While the work has several strengths, there are also some shortcomings. 

Strengths: 

1) The authors describe fluorescent fusions of the M. tuberculosis ImuA', ImuB, and ImuC proteins that support ImuABC function in UV-induced mutagenesis. Using these fusions, they demonstrate colocalization of the different Imu proteins and the beta clamp in live cells following treatment with MMC. 

2) This is the first report of purified ImuA' and ImuB, which allowed the authors to biochemically test for their interactions with each other, as well as interaction of ImuB with the beta clamp, both of which were previously reported based on results of yeast-two-hybrid experiments (Warner et al., 2010, PNAS). This, together with the live cell imaging work, goes a long way towards testing and refining the model for ImuA'BC 'mutasome' function first proposed in 2010 (Warner et al., PNAS). 

3) The authors provide in vitro results suggesting GSR, in addition to its known role in blocking replication, may also inhibit mutagenesis in M. tuberculosis. This is an exciting possibility, and provides support for the view that anti-evolution therapies may be possible. 

Shortcomings: 

1) Colocalization studies were performed using ImuA' and ImuB fluorescent fusions that failed to complement MMS sensitivity. Given their in vivo inactivity of these fusions, what does their colocalization with the beta clamp actually mean? Since these same fusions supported UV mutagenesis, it seems UV may be a superior means of analyzing colocalization of these fluorescently tagged proteins. 

2) The methods used to analyze the colocalization is not explained for non-experts. A more complete description of how colocalization was established along the z-axis is needed. Likewise, a more thorough discussion of exactly what the foci consist of is needed. 

3) ImuC could not be purified for in vitro analysis. This is unfortunate since it is thought to harbor the polymerase activity involved in mutagenesis. While not the fault of the authors, this significantly limits the scope of the in vitro work regarding ImuA'BC function in mutagenesis. 

4) The in vitro analysis of ImuA'-ImuB, ImuB-beta clamp, and ImuA'-ImuB-beta clamp interactions lack quantitative descriptions, and a complete analysis of the possible interactions among these proteins was not explored, limiting our understanding of their possible function in mutagenesis.

Reviewer #3 (Public Review): 

In this study, Gessner et al., characterize the localization dynamics of the mycobacterial mutasome complex, comprising ImuA', ImuB and DnaE2, in order to understand the molecular composition and mechanism of action of this complex in living cells. For this, they construct semi-functional fluorescent fusion constructs of ImuB (as well as ImuA' and DnaE2) in M. smegmatis, a non-pathogenic model system used to study several pathways present in M. tuberculosis. They find that ImuB localizes with the beta-clamp upon damage exposure. They further show that the clamp binding motif in ImuB is essential for its localization as well as in vitro beta-clamp interaction. Finally, they treat cells with the beta-clamp targeting antibiotic griselimycin and find that it abrogates ImuB interaction with the clamp. They suggest that ImuB localization (or its disruption) can serve as a reliable proxy to screen for mutasome-inhibiting antibacterial drugs. 

Strengths:

The in vivo dynamics of the ImuA'-ImuB-DnaE2 complex is not well-studied, when compared with its E. coli counterpart UmuDC-RecA. In this direction, the authors generate a set of tools that can be utilized to understand the molecular mechanism of action of this complex in detail. The distinct localization dynamics of ImuA' and ImuB is of particular interest, given the several unresolved questions associated with ImuA' function in induced mutagenesis. 

Limitations:

The strength of the manuscript lies in the imaging data, which is rich with information with regards to the dynamics of localizations over time for clamp, ImuB and DnaE2, under damage. In the current form, the authors do not utlize this to provide insights into any "real-time" dynamics of the mutasome. The results with regards to the clamp interaction are well-done, but largely confirmatory. Importantly, it remains unclear whether the localization read-out is reliably indicative of mutasome function, given the discrepancy in performance of the constructs in UV vs MMC-induced damage. The authors primarily rely on MMC for all their in vivo read-outs and the tags do not perform as wild type in this damage condition. This needs to be resolved.

Reviewer #1 (Public Review):

Streptococcus pyogenes expresses coiled-coil M proteins that interact with host proteins to promote virulence. One example is M protein association with antimicrobial peptide LL-37. However, since the coiled-coil M proteins lack canonical protein-protein interaction sites, it remained unclear how these interactions occurred. In this manuscript, the authors solved a crystal structure of a complex of LL-37 with the M protein M87. The M87 coiled coil unfurled to exposed its hydrophobic core to interact with LL-37. The authors then aim to show that this mechanism contributed to LL-37 resistance of S. pyogenes. These studies have provided important new information regarding the mechanism of interaction between coiled coil proteins and the alpha helical LL-37. Overall, this is an interesting and convincing manuscript. The only major concern relates to the conclusions made based on the E to R substitutions when alanine substitutions yielded no effect on the interactions. The interpretation would be that the electrostatic interaction with the Lysine in LL-37 is not important for the association of M87 with LL-37. The E85R mutant could be causing repulsion effects that are dominant negative. These and associated caveats raise concern that some data may be over-interpreted or requires further analysis.

Reviewer #2 (Public Review):

This is an excellent paper on an original and exciting discovery from the Ghosh group. The paper is also nicely and concisely written and in a style that will be accessible to a wide audience.

In short, the paper shows that the dimeric coiled coil of a bacterial surface (M) protein can open up to trap a human antimicrobial peptide by forming a new 2:1 heteromeric 3-helix bundle. This is a really neat discovery, which has clear implications for understanding how M proteins protect bacteria against attack by the hosts, and it could also inspire protein designers and synthetic biologists to design mimetic systems along these lines.

Reviewer #1 (Public Review): 

In this article Farrell et al. leverage existing datasets which measure frailty longitudinally in mice and humans to model 'robustness' (the ability to resist damage) and 'resilience' (the ability to recover from damage), their dynamics across age, and their relative contributions to overall frailty and mortality. The concept of separating damage/robustness from recovery/resilience is valid and has many important applications including better assessment and prediction of effective intervention strategies. I also appreciate the authors' sophisticated attempts to effectively model longitudinal data, which is a challenge in the field. The use of human and mouse data is another strength of the study, and it is quite interesting to see overlapping trends between the two species. 

While I find the rationale sound and appreciate the approach taken at a high level, there are a few key considerations of the specific data used which are lacking. The authors conceptualize resilience based on studies which primarily use short time scales and dynamic objective measures (ex. complete blood cell counts in Pyrkov et al.) often in conjunction with an acute stress stimulus. For example, they heavily cite Ukraintseva et al. who define resilience as "the ability to quickly and completely recover after deviation from normal physiological state or damage caused by a stressor or an adverse health event." 

Given these definitions, the human data used seem to fit within this framework, but we should carefully consider the mouse data. The mouse frailty index is a very useful tool for efficiently measuring the organismal state in large cohorts. A tradeoff for quickly measuring a broad range of health domains is that the individual measurements are low resolution (categorical) and involve inherent subjectivity (which may be considered part of the measurement error). Some transitions in individual components are due to random measurement error and I believe this is especially likely with decreases (or 'resilience' transitions). 

The reason I think the resilience transitions are subject to high measurement error is that I am skeptical as to whether many of the deficits in the mouse index are reversible under normal physiologic conditions. For example, it is exceptionally unlikely for a palpable/visible tumor to resolve in an aged mouse over the time scales studied here, thus any reversal that was observed is very likely due to random measurement error. Other components which I have doubts about reversibility are alopecia, loss of fur color, loss of whiskers, tumors, kyphosis, hearing loss, cataracts, corneal capacity, vision loss, rectal prolapse, genital prolapse. 

In summary, I applaud the authors' efforts in generating complex models to better understand longitudinal aging data. This is an important area that needs further development. I appreciate their conceptualization of resilience and robustness and think this framework has an important place in aging research. I also appreciate their cross-species approach. However, the authors may have over-conceptualized and made some assumptions about the mouse data which may not be valid. It will be important to assess the results with careful consideration of the time scales of the underlying biology and the resolution and measurement error inherent to these tools.

Reviewer #2 (Public Review): 

This study uses repeated measurements of the frailty index (FI), composed of multiple binary parameters. It is posited that newly detected changes in the number of these parameters represent damage and that the parameters that have previously been detected but are not detected currently represent damage repair. Statistical treatment then follows, deriving resilience and robustness and their changes over time. This is an interesting idea. Strengths of the study include analyses across species (mice and humans), including multiple datasets in mice. 

What are the elements of FI that increase at each period of life, and what are those that decrease? For example, humped phenotype or alopecia are more likely to appear in old mice and are essentially irreversible, whereas weight loss due to infection may be more common in young mice and is reversible. Therefore, the choice of health deficits would affect the model and, for example, may artificially lead to a decreased value of what the authors call damage repair. 

More generally, information on the frailty index lacks sufficient details. I doubt that this method has sufficient accuracy to draw conclusions from as little as 32 female mice (21 + 11 animals in datasets 1 and 2) and 63 males (13 + 6 + 44 animals in datasets 1, 2 and 3). Also, only 25 enalapril-treated mice of each sec were analyzed, and only 17 exercised mice (11 females and 6 males). The number of human participants is large, but the total follow-up period is not shown, and the subjects were assessed based on 23 parameters only. 

A key assumption in this work is that increased FI is equivalent to the rise in damage. However, the relationship between changes in FI and damage is unknown. One can imagine a situation when damage increases, but protection also increases. In this case, fitness may increase, decrease or remain unchanged. What is the basis for calling an increased number of health deficits damage? Is there a more reliable method to measure damage that could support the authors' claims?

Reviewer #3 (Public Review): 

In this work, the authors aimed at investigating two related components of aging-related processes of health deficits accumulation in mice and humans: the processes of damage (representing the robustness of an organism) and repair (corresponding to resilience), and at determining how different interventions (the angiotensin-converting enzyme inhibitor enalapril and voluntary exercise) in mice and a representative measure of socio-economic status (household wealth) in humans affect the rates of damage and repair. Two key elements in this study allowed the authors to achieve their goals: 1) the use of relevant data containing repeated measurements of health deficits from which they were able to compute the cumulative indices of health deficits in mice and humans and which are also necessary to evaluate the processes of damage and repair; 2) the methodological approach that allowed them to formulate the concepts of damage and repair, model them and estimate from the available data. This methodological framework coupled with the data resulted in important findings about the contribution of the age-related decline in robustness and resilience in health deficits accumulation with age and the differential impact of interventions on the processes of damage and repair. This provides important insights into these key components of the process of aging and this research should be of interest to both lab researchers who plan experimental studies with laboratory animals to study potential mechanisms and interventions affecting health deficits accumulation as well as researchers working with human longitudinal studies who can apply this approach to further investigate the impact of different factors on robustness and resilience and their contribution to the overall health deterioration, onset of diseases and, eventually, death. 

The key strength of this work is a rigorous analytic approach that includes joint modeling of longitudinal measurements of health deficits and mortality (in mice). This approach avoids biased inference which would be observed if longitudinal data were analyzed alone, ignoring attrition due to mortality. Another strength is a comprehensive analysis of both laboratory animal data that allows exploring the impact of different interventions on the processes of damage and repair and human data that allows investigating disparities in these processes in individuals with different socioeconomic backgrounds (represented by household wealth). 

One weakness (which is commonplace for human studies) is self-reported data on health deficits in humans which makes it difficult to compare with lab data where deficits are assessed objectively by lab researchers. The subjective nature of health deficits measurements complicates the interpretation of findings, especially about repairs of deficits. In addition, it is not clear whether the availability/absence of caregivers at different exams/interviews factors into the answers on difficulty/not difficulty with specific activities constituting health deficits and, respectively, into their change over time reflected in damage/repair estimates.

Reviewer #1 (Public Review): 

Accumulating evidence indicates RNA N6-adenosine methylation as an important post-transcriptional RNA modification in the regulation of gene expression, organ development, and disease development. However, the role of m6A mRNA methylation in cardiomyocyte proliferation and heart regeneration in normal development and in heart injury is not known. The authors first identified increased m6A mRNA levels during heart regeneration following injury to the neonatal heart, in association with selectively up-regulated expression of Mettl3, the methyltransferase catalyzing RNA N6-adenosine methylation, a finding suggesting a potential role of m6A in heart regeneration. Using cardiomyocyte cell lines, primary cardiomyocytes, and neonatal heart regeneration models, the authors next showed that down-regulation of Mettl3 markedly increased cardiomyocyte proliferation and heart regeneration in association with expected down-regulation of m6A mRNA levels in cardiomyocytes. This effect was selective in the injured neonatal heart. Together, the data indicate an important role of Mettl3 in the regulation of cardiomyocyte proliferation and heart regeneration. The quality of the data is high and the conclusions are convincing. 

Next, the authors assessed the role of Mettl3 in heart regeneration and tissue repair at non-regenerative stages in postnatal mouse models and even in adult mice. Down-regulation of Mettl3 expression improved heart regeneration and tissue repair, in association with cardiomyocyte proliferation and improved cardiac functions. The role of Mettl3 was also assessed using Mettl3 overexpression and largely opposite effects relative to the effects of down-regulation of Mettl3 expression were detected. The authors attributed the effect of down-regulated Mettl3 expression to its impact on the regulation of Fgf16 expression. This is supported by the finding that Mettl3 down-regulation was associated with decreased Fgf16 mRNA m6A methylation and increased Fgf16 mRNA levels. Finally, the authors assessed the role of Fgf16 in heart regeneration by introducing the expression of wild type vs mutant Fgf16, with the latter having m6A consensus sequence deleted. The mutant Fgf16 increased heart regeneration in neonatal heart injury models, with increased cardiomyocyte proliferation and improved cardiac function. Overall, the authors have identified a novel mechanism for regulating cardiomyocyte proliferation and heart regeneration during heart injury. There is an impressive amount of rigorous data. It is a significant contribution to bring all these mechanisms together in the context of cardiomyocyte proliferation and heart regeneration. However, in some cases, the claims are probably overstated based on the data shown. Some of the findings are inconsistent with the interpretations. There are places where additional evidence is required in order to justify the claims.

Reviewer #2 (Public Review): 

The manuscript identified m6A RNA methylation (via increased m6A writer, Mettl3 expression) as a critical regulator of cardiomyocyte proliferation during the initial regenerative window that was proposed earlier in the mouse heart. Although these processes are developmentally induced, the results of the manuscript show Mettl3-dependent m6A RNA modifications as a negative regulator of cardiomyocyte proliferation and cardiac regeneration. The authors have comprehensively profiled Mettl3 expression and Mettl3-dependent m6A regulation during cardiac regeneration using a variety of in vivo models (both neonatal heart development and post-MI injury) as well as using in vitro primary cardiomyocytes to identify Fgf16 as a key downstream mRNA transcript for m6A RNA modification by Mettl3 to further show that m6A-dependent cytoplasmic decay of Fgf16 mRNA in a Ythdf2-dependent pathway as the key underlying mechanism regulating cardiac regeneration in these models. Overall, a well-thought-out study that reports exciting new data that shows suppression of a developmentally induced phenomenon as a therapeutic option for inducing cardiac regeneration. 

Strengths of the manuscript:

The manuscript investigates an important topic relevant to cardiac regeneration, which carries great clinical significance given that the cardiomyocyte turnover, as well as the processes for replacement of cardiomyocyte loss following MI injury, are limited. Therefore, any major discoveries to enhance the regenerative ability of the heart is critical to treating cardiac disease and this manuscript underscores a previously unrecognized mechanism (m6A modification) as a critical regulator of cardiomyocyte proliferation and cardiac regeneration. 

The manuscript's major finding is the identification of developmental induction for Mettl3/m6A within the cardiac regenerative window (p1-p7). Another critical finding is that modulation of Mettl3 proves to be a negative regulator of cardiomyocyte proliferation and cardiac regeneration. 

In the therapeutic setting, using post-MI mouse models, the manuscript further shows that targeting Mettl3 can enhance cardiac regeneration by specific effects on cardiomyocyte proliferation than on non-cardiomyocytes. 

At the molecular level, the authors have carefully looked at Mettl3/m6a pathways in cardiomyocytes and non-cardiomyocyte proportions of the heart to show that Mettl3 is a critical mediator of cardiomyocyte hypertrophy as well as their proliferation in non-cardiomyocytes thus strengthening their conclusion that m6A process is being regulated in cardiomyocytes thus having a direct impact on cardiac regeneration. 

Furthermore, the manuscript characterizes the downstream mRNA targets of Mettl3-mediated m6A modifications and found that Fgf16 mRNA is a critical target for m6A modification by Mettl3, which in turn leads to m6A-dependent degradation for Fgf16 via YTHDF2 pathway. 

Taken together, the manuscript provides critical evidence demonstrating Mettl3-dependent m6A pathways regulating cardiomyocyte proliferation and heart regeneration. 

Weaknesses of the manuscript:

It would be more appreciated if the manuscript can provide more insights and rationale by discussing why a developmentally induced Mettl3/m6A phenomenon (i.e. induced during normal developmental stages as well as post apical resection injury) would turn out to be a negative regulator of cardiac regeneration, than having a positive impact on cardiac regeneration.

Reviewer #1 (Public Review): 

The authors characterized the expression of DDR2 in the developing craniofacial skeleton. The authors showed that Ddr2-deficient mice exhibited defects in craniofacial bones including impaired calvarial growth and frontal suture formation, cranial base hypoplasia due to aberrant chondrogenesis, and delayed ossification at growth plate synchondroses. The histological studies are well done. However, the studies as shown in this manuscript do not provide cellular and molecular mechanisms beyond what is already known, particularly beyond what the authors have already published in a similar study in Bone Research (Mohamed et al., 2022 Feb 9;10(1):11). With the same Cre lines and analytic approaches, the authors already showed in the Bone Research paper that Ddr2 in the Gli1+ cells is required for chondrocyte proliferation and polarity in growth plate development and osteoblast differentiation. Cartilage development and bone formation occur in both long bones and craniofacial skeleton, the authors showed similar functions of Ddr2 in similar skeletal tissues, although the location is different. One new point in this manuscript might be: the authors indicated that loss of Ddr2 led to ectopic chondrocyte hypertrophic (Fig. 7I). But what the data actually showed was delayed chondrocyte hypertrophy and abnormal location of the delayed hypertrophic chondrocytes, which could be well caused by abnormal chondrocyte polarity. This interesting defect was superficially described with no mechanistic investigation at cellular or molecular level.

Reviewer #2 (Public Review): 

DDR2 is a collagen-binding receptor that is required for proper skull development. Ddr2 loss-of-function in humans is associated with the developmental disease spondylo-meta-epiphyseal dysplasia (SMED). Here, the authors aim to elucidate the role of DDR2 in skull development. In this work, the role of DDR2 in skull and face development is studied in mice, which exhibit SMED-like symptoms in the absence of Ddr2. Histological studies showed that Ddr2 knockout disrupts organization and proper differentiation within progenitor-rich regions of the skull from which bone growth occurs. Histology and lineage tracing studies revealed that DDR-expressing cells in/around these zones 1) generally also express the proliferation regulator Gli1, and 2) eventually contribute to osteogenic and chondrogenic lineages. Cell-type specific knockout studies were used to show that DDR2 has a development-specific role: knockout of Ddr2 in Gli+ cells re-capitulated the developmental abnormalities observed in global Ddr2 knockout mice; knockout in chondrocytes partially recapitulated developmental abnormalities, and osteoblast-specific knockout mice were indistinguishable from their wild-type littermates. This work also catalogues the locations of Ddr2 positive cells and their lineages at various stages of development. Additionally, the anatomical effects of loss of DDR2 function on skull and face development are thoroughly described in global and cell-type specific knockouts. 

This work is a vital and stimulating contribution to the scientific literature. The authors' claims and conclusions are well supported by the evidence they present. 

The scientific approach is sound and the conclusions important. However, a limitation of the work's discussion is a lack of attention paid to the specific biophysical mechanism that DDR2 is playing during development. The discussion of the positioning of the golgi is nice, but a lack of golgi polarity is likely a downstream effect of processes occurring within the cell adhesion and mechanotransduction machinery. Perhaps, like integrins, DDR2 is a mechanosensor that the cell needs to properly sense local collagen orientation, polarize, and secrete properly-organized COL2. It would be beneficial to put up some guideposts that will facilitate engagement from the molecular biophysics/mechanobiology community.

Reviewer #3 (Public Review): 

From this work, the authors investigated a number of parameters in order to profoundly understand and demonstrate the vital role of ongoing interaction between components of extracellular matrix and particular stem cells to induce normal Craniofacial development. Thus, there was a focus on the genetic manipulation (knockout) impact of molecules behind the above-mentioned interaction, and on determining how such modification would be reflected on skull bone morphogenesis. 

Strengths and Weaknesses:

• Using different animals' backgrounds in the same experiment might impact work outcomes.

• Better to have (ethical approval) at the beginning of the material and methods in separate paragraphs.

• It is great that the authors precisely explain all the measurements.

• Supplementary file to have details of used antibodies might be required.

• All methods have been written in academic and clear ways.

• It is nice that there is a conclusion sentence by end of the results paragraph, which made it easy for readers to fully remember and understand.

• It is possible to see a reduction in proliferative chondrocyte, with no change in apoptosis rate?

• Results are supposed to be compatible.

• Very nice and representative images from the immunofluorescence protocol.

• Using different techniques to confirm observations is clearly manifested in methods and results.

It is clear that the author has used different methods and techniques in order to meet his work's objectives. Importantly, there was more than one procedure to confirm observations that are related to one or more than one aim. 

Although determining to what extent the outcomes of this work could be applied to community need might require a subspecialist physician's opinion, it seems that observations of the present study are likely to require a series of further investigations in order to take it to the level of human users. Notably, identification of molecules and pathways behind skull development abnormalities would open a door to early diagnosis reasons for such deformities, thus mitigating future abnormalities either by developing new prevention methods or discovering unique medications.

Reviewer #1 (Public Review):

The MS by Mehta et al, reports the first full-length structure of a polytopic membrane adenylyl cyclase from M. tuberculosis (Rv1625c/Cya). The authors used detergent-purified full-length Cya mixed with a stabilizing nanobody and determined the structure using cryo-EM yielding a 3D reconstruction at 3.8 Å resolution. Full-length Cya (443aa) consists of an N-terminal domain followed by 6 transmembrane (TM) helices, and a helical domain (HD) that connects to the catalytic domain. The full-length protein was active as a dimer and not affected by the presence of the nanobody. In contrast, a soluble form consisting of only HD-cat was not active. The structure of the full-length Cya shows a complex with three molecules of the nanobody, two bound symmetrically to the cytosolic domains and a third one bound asymmetrically to the extracellular surface. The density map covered residues 41-428 of the full-length Cya. The overall topology of each monomer in the Cya dimer corresponded to approximately half of the only known structure of the full-length mammalian membrane-bound AC9, previously solved by the same group. The overall conservation among the bacterial and mammalian cyclases is remarkable. The structure unmasked novel pockets (some conserved with AC9), allowing the authors to suggest a function for the TM bundle as an organizer of the HD for efficient communication to the catalytic domain, and more speculative, a role for the TM, via one of the identified pockets, to act as a putative receptor for yet unidentified ligands able to transduce information via the HD towards enzyme activation.

The overall conclusions of this manuscript are well supported by the data, and although some are speculative, they provide a predictive framework for future studies aiming at biochemical validation.

Reviewer #2 (Public Review):

Mehta et al report a cryo-EM structure of membrane-bound adenylyl cyclase from Mycobacterium tuberculosis. The structure is a dimer, each subunit consisting of a 6TM membrane domain, a helical domain, and a cytoplasmic adenyl cyclase domain. The overall shape of the dimer is similar to the monomeric (12 TM) mammalian adenylyl cyclase AC9, although there is a swap in the position of the helices that link the TM domain to the helical domain.

An intriguing question is what role the TM domains play in membrane-bound adenylyl cyclases. The authors speculate that they may provide binding sites for ligands and describe 4 different pockets (2 extracellular, 2 cytoplasmic). One extracellular pocket contains the density of a metal ion. Mutation of this pocket reduces the activity of the cytoplasmic catalytic domain and induces conformational changes in the whole cytoplasmic region. This provides some evidence for changes in the TM domain being propagated to control cytoplasmic catalytic activity.

In summary, the work is an important structure of bacterial membrane-bound adenylyl cyclase. The description of potential binding pockets is interesting in the step towards understanding the role of the membrane domain although as yet the lack of a clear physiological ligand makes this role still an open question.

Reviewer #1 (Public Review):

In this online randomized clinical trial, Sudharsanan et al. used the nudge design/approach to examine the association between framing the risk of the Covid vaccine and the willingness of the participants in getting the vaccine. This is a timely and important study.

The results indicate that adding a descriptive risk label by the numerical side effect and providing a comparison to mortality due to motor vehicle accidents increased willingness to get the Covid vaccine by 3% and 2.4%, respectively, and independently.

The study is designed properly, and the manuscript is well written. The findings are important from a policy perspective. Using nudges is an easy and inexpensive way to pursue people toward better decision makings in public health.

Authors should explain in more detail the methods used to minimize bias. Second, probably the same nudge does not produce homogenous results among different population cohorts. One thing that can strengthen future work is to conduct cluster analysis to examine different nudging mechanisms among different subpopulations.

Reviewer #2 (Public Review):

With participants recruited from the United States and the United Kingdom, Sudharsanan et al. conducted an online randomized controlled trial, which examined three framing strategies of the side effects of a hypothetical COVID-19 vaccine: adding a qualitative risk label next to the numerical risk, adding a comparison group (along with which comparison group is most effective), and for those with comparison groups, framing the comparison in relative terms. Two outcomes were considered in the study: the self-reported willingness to take the vaccine and the perceived safety of the vaccine. Analysis showed that adding a qualitative label and a comparison to motor-vehicle mortality significantly increased the propensity for vaccination, whereas adding a comparison to COVID-19 mortality or framing the comparison in relative terms did not have a significant effect on the willingness of vaccination. These findings add useful evidence to the literature on the behavioral incentives of vaccination uptake.

The study is well designed and implemented, with the data being carefully collected and analyzed. However, as indicated by the authors, some evidence is nonsignificant or counterintuitive, suggesting a retrospective need to increase the sample size for more convincing and granular results.

Self-reported willingness to get vaccinated somehow depends upon the regional situation of the pandemic, which is partially reflected by the incidence and mortality among the local population. This factor of potential importance was not accounted for in the study, especially given that participants are recruited from two different countries with different governmental mandates.

Reviewer #3 (Public Review):

The authors were trying to examine if different framing strategies and types of presentation affect an individual's willingness to take vaccines. The paper has several strengths that should be noted. First, they have used a large randomized control trial composed of 8988 participants of adults ages 18 and older conducted in the United States and the United Kingdom. They have found a significant increase in vaccine willingness (about 3%) when a low-risk descriptive risk label is attached to numerical side effects, parallelly when a comparison group is added vaccine willingness also increases by 2.4%. They found these effects to be additive and increase further when combined together (to about 6.1%). Furthermore, they analyzed whether framing strategies varied with respect to age, sex, and country and they found no significant differences. The authors have addressed their aims quite competently and the results also point out that framing strategies are linked to vaccine hesitancy. Overall, this paper does not have too many weaknesses. One area of concern was the loss of 3000 participants for their study which would have been really great for a secondary sub-analysis. Although in the limitation the authors mentioned that the racial composition of the participants varied from the national estimates, it should also be noted that the age composition of participants was also a lot younger than the general population. To sum it up, the authors did a fantastic job of incorporating a novel idea that can be repeated in other countries as well.

Reviewer #1 (Public Review):

This paper asks how hawkmoths stabilize their head during externally-imposed body rolls. It finds that when body rolls are fast or there is little light, moths use their antennae to stabilize their head. The authors use convincing manipulations to show the necessity of the antennae for these behaviors and for stable free flight. This finding expands beyond similar studies in dipteran flies, showing that mechanical sensing of rotation can be performed by Johnston's organ in the antennae in moths, while similar functions are performed by the halteres in dipteran flies.

Reviewer #2 (Public Review):

This manuscript from Chaterjee and colleagues examines head-stabilization reflexes in the hawkmoth. Using light level manipulations and surgical manipulations of the antennae, they show that hawkmoths combine visual and mechanosensory feedback in order to stabilize the head over a wide range of temporal frequencies. Similar to other systems studied, such as flight stablization and antennal positioning, they find that visual feedback makes a stronger contribution at low frequencies while mechanosensory feedback contributes at higher frequencies. Finally they show that loss of head movement during flight contributes to flight instability, suggesting that an inability to stabilize the head might contribute to the effects of antennal manipulation on flight stability. Overall this is a nicely done study with clear findings that support a general principle of multisensory integration for feedback control. One way in which the manuscript could be strengthened is by explicitly modeling the feedback controller shown in Figure 5, and quantitatively comparing results obtained from this model to experimental results. In addition, it might be helpful to further quantify flight trajectories in head-stabilized moths.

Reviewer #3 (Public Review):

Payel Chatterjee et al. investigated how compensatory head movements in the nocturnal hawkmoth Daphnis nerii are controlled. This was done by subjecting tethered moths to open-loop body rotations under different light conditions, while simultaneously measuring their ability to main head angle in the presence or absence (achieved by antennal ablation) of antennal mechanosensory feedback. They find that head stabilization is mediated primarily by visual feedback during roll movements at lower frequencies, while antennal mechanosensory feedback is required when roll occurs at higher frequencies or under dark conditions. The findings add to our understanding of how non-dipteran insects (that lack halteres) stabilize their heads. Compensatory head-movements are essential for stabilizing the visual field on the retina, reducing motion blur and supporting visual self-motion estimation. These are all important parameters to control flight and allow for fast manoeuvres in air. The conclusions of the paper are well supported by the data.

Reviewer #1 (Public Review):

Wosniack et al. perform the analysis of larval trajectories from behavioral experiments and build a phenomenological model and efficiently combine the two to dissect behavioral strategies that Drosophila larvae use during foraging. The paper touches upon several factors that influence foraging: from food quality and distribution to genetic polymorphism and finally the contribution of sensory cues. While the first two are well explored and characterized in the paper, the contribution of different sensory modalities is less investigated. They study how homogeneous food substrates or food distributed in patches influence foraging strategies. They find a modular organization of behavioral strategies that is dependent of food characteristics: food quality modulates crawling speed, turning and pausing while increases in the time spent inside the patches are the result of biasing turning towards the patch center when the larvae are at the food-no food interface. Furthermore, using anosmic animals they determine that olfaction is differentially involved in the foraging decisions depending on the type of food substrates that the larvae are exploring. Finally, they perform this analysis in rover and sitter larvae to determine the effect of the foraging gene polymorphism on these behaviors and show that its expression (where sitter larvae are slower, turn less and pause more compared to rover larvae) is dependent on the food distribution. They propose that larvae adapt the extent of their exploration to the quality of food. This detailed analysis of elements that constitute behavioral strategies sets the basis for identifying genes involved in foraging and the neural substrates of the different behavioral modules and ultimately understanding the neural circuit mechanisms involved.

The paper efficiently combines analysis of larval trajectories from experiments with computational modeling and identifies the behavioral elements that contribute to foraging. The authors show that olfaction has an important role when foraging on yeast substrates but not on sugar-rich substrates using anosmic larvae. They propose that taste could contribute more on sugar and apple juice substates however they do not test this hypothesis. Did the authors try or consider testing the Gr43a mutant on these substrates? Determining to which extent taste contributes to the different strategies completes the picture of how sensory cues contribute to foraging decisions that the authors started to address by tackling the contribution of olfaction to foraging on the different substrates. Also on patchy substrates, is the border completely smooth or could the larvae also sense the border as a rough edge? Could other modalities be involved?

In Figure 3C the crawling speed is lower in yeast and apple juice experiments both inside and outside of patches (and in both rovers and sitters) compared to sucrose experiments. Do the authors have an explanation for this? Also, as they note, surprisingly the turn bias persisted when the larvae exited the patches. Are these two related? Do larvae turn more frequently?

The authors describe and discuss handedness in larval turning. While this in itself is an interesting characterisation, it does not appear to be thoroughly addressed in the context of its influence on foraging behavior. The authors conclude that the presence of patches induces turning bias that overrides handedness. It would be interesting to determine whether there are differences in turn size and/or reorientation frequency depending if the larvae are turning on the preferred side versus the non-preferred side.

During different types of taxes, the larvae modulate crawling speed, duration, turn rate, size and direction to avoid unfavourable conditions and approach unfavourable conditions. This is true across different types of sensory gradients. Some of these strategies are also described in this paper. The authors make a link between behaviour on patch-no patch interface and taxis behaviour. It would be interesting to further develop the comparison between the behavioural elements described here and those in navigational strategies in sensory gradients. The commonalities and possible modular organisation of both could point to an existence of neural circuits for the different behavioural modules that are recruited differentially dependent on the sensory context, motivation state, or a combination of both (and based on different types of sensory information).

Reviewer #2 (Public Review):

The study by Wosniack et al. investigates the impact of polymorphism on effective foraging behavior in patchy environments. The paper combines behavioral tracking data and phenomenological modelling to effectively describe and understand the navigational strategies underlying patch foraging in Drosophila larvae.

A major strength of the work is the use and integration of the model that accompanies the experimental findings and is refined with evidence from experiments throughout the paper. A key result is that the genetic differences between rover/sitter larvae only manifest in patchy environments and are effectively hidden when larvae are exposed to homogeneous environments.

This is a well-written and clear manuscript that effectively uses relatively simple techniques of behavioral tracking to quantify larval navigation (and patch residency, albeit this is not connected explicitly to optimal foraging in the text.)

One aspect where the work could be strengthened is by highlighting where the differences in patch residence times between the model and data might arise. Especially in the anosmic animals, this removes one sensory aspect and one might feasibly expect the trajectory models to match better with the measured data. If this is not the case, it would be helpful for the readers to discuss the differences more explicitly.

The modeling approach taken here is generalizable to a number of model- and non-model species which can be tracked, e.g., nematodes, where similar models have been used to describe navigation albeit not in the context of sparse patches.

Reviewer #3 (Public Review):

The authors of the paper study foraging strategy in crawling Drosophila larvae. They utilize single-larva tracking in isotropic and patchy food nutrition environments, detailed quantitative analysis of the animals' behavioral states and transitions, and a random-walk-style Monte Carlo simulation setting. They investigate how specific components of behavior are modulated for the animal to locate suitable food resources.

Strengths:

* The main results of the paper, laying out how crawling speed, turn/pause rates, and turn direction bias work together cause larvae to find the food they need are interesting, nicely presented, and important for ultimately understanding how foraging really works in detail, here at the behavioral level, and somewhere down the road at the circuit and/or molecular levels too.<br /> * Comparing rovers and sitters throughout the experimental parts of the paper was a really nice idea, with interesting results, and it is well motivated in the introduction.<br /> * The handedness of individuals is a nice finding as well, I think the first time this has been published for larval Drosophila.<br /> * Simulations that use empirical results as probability distributions make for a nice environment for testing ideas about larva behavior.<br /> * Creating the patchy food environments was a great idea, as it puts the larva behavior in a more realistic setting, but still controlled enough to be analyzed clearly.

Weaknesses:

* For an animal that tends to have a very high variance in its behavior, the number of larvae used in each experiment seems pretty low to me. As a result, some of the secondary claims are perhaps not as well supported when they rely on "not significant" statistical test results.<br /> * The introduction is generally good, but could perhaps better motivate why fly larva foraging should be of interest to a more general audience.<br /> * The execution of the simulations seems reasonable, but perhaps don't add a lot to this particular paper, especially given how much of the manuscript they take up.

Overall, the primary results of the paper do achieve the stated goals and set the stage nicely for further studies into the underlying mechanisms of foraging in larvae.

For those studying foraging, especially in flies/larvae but probably other animals as well, this should be an important paper that highlights the utility of individual animal tracking with high resolution, analyzing specific components of behavior, and creating simulation environments as playgrounds for investigating the impact of those components.

Reviewer #1 (Public Review):

The synthesis and metabolism of sphingolipid (SL) are involved in wide range of biological processes. In the present study, the authors investigate the role of SPTLC1, one of the essential subunits of serine palmitoyl transferase complex, in both physiological and pathophysiological angiogenesis, via using inducible endothelial-specific SPTLC1 knockout mice. They found SPTLC1 deficiency in ECs inhibited retinal angiogenesis along with reducing several SL metabolites in plasma, red blood cells, and peripheral organs. In addition, the authors found SPTLC1 EC-KO mice are resistant to APAP-induced liver injury. Overall, the in vivo findings in the present study are of potential interest and the authors have given clear evidence that endothelial SPTLC1 is critical to retinal angiogenesis. However, the underlying mechanisms are completely lacking in the present study. Most of the evidence provided is circumstantial, associative, and indirect. To be specific,

1. The authors found endothelial SPTLC1 is important to both angiogenesis and the plasma lipid profile. However, the authors did not present the data to demonstrate the relationship between them. The in vivo findings about the phenotype and the plasma lipid profile might be true and unrelated. It would be important to know whether supplementing the reduced lipid induced by SPTLC1 KO could rescue the angiogenesis related phenotype in mice, or, whether the alternative way to inhibit the SL synthesis could mimic the phenotype of KO mice.

2. A major issue is that the present study did not reveal is a real downstream target. It is possible that VEGF signaling might be impaired by SPTLC1 knockout as discussed by the authors. However, the authors did not demonstrate this point with data. Including both in vivo and in vitro data to evaluate the effects of SPTLC1 deficiency on VEGF signaling might further strengthen the hypothesis. Besides, with in vitro experiments, the authors might further find the critical metabolite(s) involved in VEGF signaling and angiogenesis.

Reviewer #2 (Public Review):

Andrew Kuo et al. investigated the role of endothelial de novo sphingolipids (SL) synthesis using endothelial cell specific SPTLC1 knockout (ECKO) mice. They showed that these mice exhibited low concentration of various SL species in not only ECs but also RBC, circulation, and other non-EC tissues. They also showed that ECKO mice exhibited impaired angiogenesis in normal and oxygen-induced retinopathy models, consistent with the decrease of endothelial proliferation and tip cell formation. They finally revealed that these mice were resistant to acetaminophen-induced acute liver injury in early phase. The experiments were well-designed, and the results were clear and convincing. The authors concluded that endothelial cells were the major source of SL in circulation and various organs (liver and lung) other than retina (and probably brain). The weakness of the current version of the manuscript is that the authors did not elucidate the mechanisms underlying the observed phenomena.<br /> 1) The authors showed impaired angiogenesis in ECKO mice using neonatal retina model. Based on the fact that this phenotype was similar to that in endothelial VEGFR2 deficient mice, they suggested that VEGF responsiveness is altered in ECKO mice. Although this hypothesis is plausible, the authors would need to prove it by evaluating VEGFR signaling (VEGFR phosphorylation, Akt activation etc.) in ECKO mice.<br /> 2) The acetaminophen-induced liver injury was reduced in ECKO mice in early phase. However, it is still unclear whether SL production itself affects liver injury. The authors discussed the possibility that gene deficiency increases unconsumed serine resulting in GSH increase, but it is essentially independent to SL. If possible, it would be good if the authors could investigate the effect of SL administration on the liver injury progression.<br /> 3) This paper showed the impaired cell proliferation in Sptlc1 KO EC mice, and discussed it. Authors described that this phenotype was similar to that of Nos3 KO mice, but its inconsistency with Sptlc2 ECKO adult mice was only justified by a word "isoform-selective function". Authors could quantify eNOS expressions in Sptlc1 KO mice, compared results and then discuss this matter.

Public Review:

This paper reports on the development of a mechanistic model of bone remodeling that accounts for key regulatory factors of the remodeling process which control bone cell numbers. The model is used to simulate osteoporosis and a variety of combined drug treatments. A number of drug treatments were implemented in a pseudo pharmacokinetic fashion. The model was first calibrated on a large number of experimental data sets. Subsequently the model was tested/validated using complementary experimental data sets. Simulation results show that the model is able to predict a significant number of experimental data sets. In a further step, a variety of different combined drug therapies were tested in order to identify an optimum combination. The authors concluded that this computational modeling framework has great potential for future use in order to optimize combined dosing regimen.

The paper is very well written and the methods, and results are clearly described. Also, the authors provided all the source codes for their simulation results to be reproducible. The mathematical model was well described and the accompanying figure helped identifying action of different regulatory mechanisms and drug actions.

Some weaknesses of the paper are the following:

1) Formulation of the equation for BMD: was simply assumed to be the product of bone density and mineral content. Particularly, the latter function is formulated in a very phenomenological way. There are more rigorous, materials science based, ways to formulate bone mineralization.

2) Pharmacokinetic (PK) formulations of drugs: the representation of drug concentration for the different drugs is a simplification. Generally, PK models need to be used to provide values of drug concentrations in the bone compartment which interact with the remodeling process. The bisphosphonate PK model might be more complex due to the absorption of the drug into the bone matrix and dissolution of the drug during bone resorption.

3) The discussion section was rather unconventional, as no links/comparisons with existing literature were made. However, given that the essential computational modeling results are on combined drug treatments that have not been tested experimentally nor with other computational simulations, this is ok.

The authors demonstrated that use of a mechanistic bone remodeling model combined with different drug actions allows to explore optimal treatment regimens including combined drug therapies for osteoporosis. The results clearly showed that some drug combinations lead to significantly higher BMD gains than others.

Reviewer #1 (Public Review):

The authors comprehensively assess the measurement properties (reliability, criterion/predictive validity) of behavioral and neural measures of fear acquisition/extinction, with a focus on longitudinal reliability and consequences of analytic and processing choices via a multiverse approach. In a longitudinal design (6-mo interval), the authors collected fear acquisition and extinction measures (SCR, ratings, fMRI) at two time points in a relatively larger sample for this type of work. Most notably, test-retest reliability, which is identified as a key component in individual-difference and clinical translation work, was generally low, whereas internal consistency was generally high. Group-level (averaged) reliability and cross-phase prediction (i.e., criterion validity) were generally good. Most measurement indices varied as a function of modality, processing, or statistical decisions. This work is framed within a larger discussion of the role of measurement properties in individual difference work and clinical translation and will serve as an important building block towards improvement in both these areas.

The conclusions of this work are largely supported by the data and methodological approach, and this is a good benchmark for the field. However, some aspects could be clearer or streamlined, and some analytic choices are relative weaknesses.

Strengths:

The overall approach is excellent and represents the vanguard of open science practices (preregistration, all materials freely available, documentation of analysis deviations, multiverse analyses, etc.). Relatedly, this comprehensive approach reveals how different analytic choices/researcher degrees of freedom can have sometimes drastic effects on fundamental measurement properties. I think this underlines what I view as the key contribution of this manuscript: empirically highlighting the need for the fear conditioning field to pay more attention to measurement properties.

Going beyond standard associative measures of reliability (ICCs) is an important contribution of this work, as they allow the authors to comment on nuances of individual-difference reliability that are not possible with the coarser ICCs. In turn, this facilitates researchers in making more informed decisions regarding the design of fear conditioning tasks to assess individual differences.

The fMRI results are a particular strength, as fMRI continues to be a common fear conditioning index, yet its measurement properties within these studies are critically understudied. The choice to use standard ICCs in conjunction with similarity approaches is particularly fruitful here, as in conjunction with overlap metrics we now have a much better appraisal of the different components of reliability in fMRI data - and potential explanations for differences between behavioral and fMRI reliabilities.

Weaknesses:

The authors structure their effort around the premise that reliability is essential in conducting solid individual-differences science, which I agree with wholeheartedly. However, I think the authors rely on relatively arbitrary cut-offs for classifying reliability as good/poor/etc to an extent that is not warranted, particularly in the context of the Discussion, and it takes away from the impact of this effort. As the authors point out, these categorical cut-offs are more guidelines than strict rules, yet the manuscript is structured around the premise that individual-level reliability is problematically poor. Many cut-off recommendations are based on psychometric work on trait self-report measures that usually assume fewer determinants/sources of error than would be seen in neuroscience experiments, which in turn allows for larger ceilings for effect sizes and reliability. The current manuscript does not address this issue and what meaningful (as opposed to good) fear conditioning reliability is when moving away from the categorical cut-offs. In other words, is it possible that the authors actually observed "good" reliability in the context of fear conditioning work, and that this reliability is lower than other types of paradigms is just inherent to the construct being studied?

The internal consistency (cross-sectional reliability) calculation used is not well-justified, and potentially needs additional parameters. It is not clear why the authors deviate from the internal consistency calculation described in Parson, Kruijt, and Fox et al., 2019, especially given that these procedures are used for other metrics elsewhere in the manuscript.

In fMRI analyses, the authors use an ROI approach based on prior studies of fear acquisition and extinction. The majority of the most consistently identified regions (as seen in meta-analyses, Fullana et al., 2016, 2018) are analyzed. However, it is not clear why other regions are omitted, particularly given meta-analytic evidence. Striatal regions and the thalamus are the most notable omissions. Further, a weakness is that functional ROIs in this study were based on peak coordinates from a handful of prior studies, instead of meta-analytically identified coordinates. As such, I do not think the authors present the strongest foundation for making conclusions about the reliability of fear conditioning fMRI Data.

Reviewer #2 (Public Review):

The manuscript describes a large set of statistical analyses on fear conditioning data from 107 participants (N=71 at two time points six months apart). The analyses comprise approaches to determine the reliability and predictability of conditioned fear responses: skin conductance, ratings, and fMRI data.

The approach is thorough, with a range of analysis approaches, including within- and between-subjects similarity, the individual-level overlap of fMRI results, intraclass correlation coefficients, and cross-sectional reliability. It is important to determine these values so that researchers can discard incorrect assumptions, such as the belief that threat responses at baseline can be predictive of treatment responses in patient populations.

The poor reliability identified by several of these approaches is likely to be of great importance to this large, translational field. A positive result was good reliability at the group level for fear learning, but not extinction.

Reviewer #1 (Public Review):

By performing a genome-wide association study that exploits a previously developed probabilistic model to discriminate between 'true' severe malaria and invasive bacterial infection, Gilchrist et al. identify a variant in the gene BIRC6 that increases the risk of invasive bacterial disease across a diverse range of bacterial pathogens in Kenyan children. Although discovered in a fairly modest sample size, the association at rs183868412 replicates in an independent sample with a fairly large effect size (OR=2.77, 95% CI 1.49-5.12). It, therefore, seems likely that the association is a true positive and that the variant has a fairly large effect on risk of invasive bacterial infection.

The risk variant, rs183868412:T, is present at frequencies of only around 1-3% in African populations (Table 5), and is absent from non-African samples. This makes investigation of potential mechanisms by which the variant exerts its influence on invasive bacterial disease difficult. Leveraging a study of 100 European ancestry and 100 African ancestry samples, the authors find evidence that rs183868412:T affects the splicing of BIRC6 in stimulated monocytes and that this may explain the association signal they found. However, due to the fact that rs183868412:T is at relatively low frequency in Africans and not present in European samples, it is hard to be completely confident in this analysis since it must hinge on only a handful of carriers of the rs183868412:T allele.

The paper gives an example of how a probabilistic model for phenotypic classification can be used via weighting to increase power to discover genetic variants with effects on a specific (sub)phenotype. Further investigation of the discovered variant may be useful for understanding the biology of general susceptibility to invasive bacterial disease.

Reviewer #2 (Public Review):

This is a thoughtful paper that infers likely bacteremia cases probabilistically amp amongst putative severe malaria cases and then uses them in a GWAS. Based on this approach it identifies a biologically interesting risk allele for bacteremia, which is found at low frequencies in African population which seems to increase risk uniformly amongst a variety of age groups and bacterial diseases.

Reviewer #3 (Public Review):

This manuscript describes host genetic data of several cohorts of Kenyan children with culture proven bacteremia, severe malaria, and controls, and the association with bacteraemia. We know that many children with severe malaria actually have a bacterial co-infection. Because it is difficult to get the numbers needed for such GWAS studies, the authors plus up their numbers by lumping together bacteremia and severe malaria cases - the latter in a weighted manner for the continuum of malaria and bacteraemia. In the next step they validate their findings in a new cohort of 434 bacteraemia cases and present functional studies in monocytes. The methods used are interesting and the data are valid. Findings are important. I am not an expert in statistics, so I cannot judge the statistical methods in detail, but they seemed to be valid.

I have a few major points.

1. Overview of cohorts - overview. A graphical overview of cohort could be helpful for the reader- including groups, comparisons, and time periods of collection.<br /> 2. Overview of cohorts - phenotypes. The datasets used have been published previously with clinical phenotypes in more detail. Would it be possible to include a supplementary table providing these clinical phenotypes per group? In how many patients in the severe malaria group cultures were performed?<br /> 3. The potential impact of the prevalence of Pf HRP2 gene deletions on the analysis is probably limited because the cohort was collected in the period 1995-2008; this should be mentioned.<br /> 4. BIRC6 is identified as risk factor for invasive bacterial infection. BIRC6 (or BRUCE) is rightfully discussed by the authors in detail. BIRC6/BRUCE indeed is a ubiquitin conjugating E2 enzyme and a well-established anti-apoptosis regulator. Interestingly, we identified UBE2U to be associated with outcome in invasive pneumococcal disease (Lees et al Nature Comm 2019). The author may well find a link here.<br /> 5. The discussion could a presented a bit more balanced. 2/3 is now used to discuss the potential role of BIRC 6- this could be condensed while limitations of the study should also be discussed.

Reviewer #1 (Public Review):

The authors examined using various optical live-imaging techniques the beat properties and coordination of motile cilia across the whole surface of the zebrafish nose embryo. As far as I know, this is a level of detail that had never been explored. This is an important "model organism" to understand vertebrates, and motile cilia and their carpets are a fascinating system, with aspects that one expects to be conserved across species.

The experimental data is really impressive to me. I think there is in fact a wealth of data, and the analysis of it here is just one part of what can be extracted. The theory and the specific question are also posed clearly, and are a strength. I do wonder why the experiments did not include visualization of the fluid flow, on the same fish - this is actually a much easier experiment I think, and would have given an important other spatial map to relate to the cilia dynamics.

The theoretical analysis backing the data allows the authors to discuss on what length scale cilia are strictly synchronized to each other, versus the (longer) scale over which there is an element of coordinated dynamics. This is an important conceptual point that is discussed here very clearly.

I think the authors have made a clear point on the degree of coherence in the in-vivo system, and on the consequences on longer range coordination and fluid transport.

Motile cilia are one of the most fascinating and conserved structures across eukaryotes, and motile cilia carpets deliver critical and poorly understood physiological fluid transport. Here the authors present a new level of detail in-vivo zebrafish embryo nose data on the coordination of cilia, and discuss it in the context of what is known from other systems and from basic physical models. I think this paper will have strong impact on the specific field of motile cilia, and is generally an important result in development. Both physicists and biologists will use this and build on it.

I do wonder about the title: "Local synchronization of cilia and tissue-scale cilia alignment are sufficient for global metachronal waves". This is indeed what they find, but this title will not be very clear to many biologists. I would recommend naming the species. In fact I think a better title would focus on having discovered that there is a certain behavior in zebrafish... it sounds less "general" but represents the work better.

Reviewer #2 (Public Review):

The strength of this work is the quality and quantitative nature of the experimental data. Despite the complexity of the zebrafish olfactory pit architecture, the analysis resolution achieved is remarkable. The data reveals for the first time that cilia beating is heterogeneous and patchy, yet still becomes ordered across the entire tissue.

The main weakness is the disconnect between the theoretical contribution and the experiments. There is some overinterpretation here - the authors should revise their conclusions accordingly. The main output of the theory is a comparison between pumping direction, rates and efficiencies for different wave and lattice parameters. The theory does not yet explain how global metachronism arises from local synchrony, what sets the wave direction etc... but only the implications of this once it does.

A key (and unexpected) finding is the asymmetry between the left and right nose in terms of metachronal wave direction (detectable due to the quality and rigor of the analysis). The manuscript leaves open how this can arise, since the model does not help explain why the wave direction can differ, despite cilia being oriented similarly.

I think the present title does not reflect the key findings presented in the paper and should be changed - see specific recommendations below. This is fundamentally a detailed study and mapping of cilia coordination in the zebrafish nose - the organism name should really appear in the title, there is no need to over-emphasize the implications for metachronal coordination in general. I suspect these conclusions may be specific to this organism, and so the overall message may not apply to other ciliated tissues.

Reviewer #1 (Public Review):

Staphylococcus epidermidis is a commensal that colonizes corneocytes of humans and other mammals. Colonization is crucial for many aspects of health including the development of our immune system and protection against invading pathogens causing skin and soft tissue infections including Staphylococcus aureus and Streptococcus pyogenes. However, certain strains of S. epidermidis, especially those that colonize hospitalized patients, have the ability to bind implantable foreign bodies (e.g. hip/knee/shoulder implants, catheters) and subsequently form biofilms. Biofilms are difficult to treat with antimicrobial agents, and, in many cases, the only method of treatment is to remove the device causing significant morbidity.

Embp is a very long protein encoded by most strains of S. epidermidis that extends from the cell surface. It has been previously documented that Embp binds to the serum binding protein fibronectin, a glycoprotein that coats implanted foreign devices. Data from this manuscript documents that Embp does not bind to Fibronectin in its soluble form. However, once Fibronectin binds to a surface such as foreign bodies ( called the fibrillated form) other epitopes are exposed to which Embp binds. There are three major strengths of this manuscript. First, using elegant genetic techniques as well as advanced atomic force spectroscopy experiments, the investigators found that Embp binds only to fibrillated Fibronectin and not soluble Fibronectin. Second, they found that the large number of repeats associated with Embp function is similar to velcro where the strength of each interaction (between F- or FG- repeat and Fibronectin) is small but the additive nature of the interaction is significant. Lastly, they found using flow experiments that Embp functions in an environment with high shear stress similar to blood. This is a very exciting result and has a significant impact on the field, especially since other adhesins presumably facilitated adherence to Fibronectin in a low shear environment.

In summation, the results suggest that new biomaterials can be designed to inhibit the formation of fibrillated fibrinogen. Indeed, studies in this manuscript document that Fibronectin primarily is found as a globular form when attached to compounds such as poly ethyl acrylate.

Reviewer #2 (Public Review):

The objective of this study was to use atomic force microscopy to analyse the strength of the binding interaction between the fibronectin binding repeats of Embp and the fibrillary form of the host protein and to study bacterial adhesions under shear-stress.

Strengths:

-Using a surrogate host to study interactions between truncates of Embp with variable numbers of Fn binding repeats<br /> -Being able to attach globular Fn to a surface so that it either remains in the globular state or unfolds to reveal the buried binding domain<br /> -Studying bacterial attachment to immobilized Fn under flow conditions mimicking the blood stream<br /> -Applying atomic force microscopy to study the strength of binding between a single cell expressing Embp or a single molecule of Embp attached to the cantilever and surface-immobilized Fn allowing measurement of the strength of binding of individual and multiple Fn binding repeats

Weaknesses:

-The application of AFM is not discussed in the context of the extensive studies by the Dufrene group who have published many papers on the binding of staphylococcal adhesins to immobilized ligands.<br /> -The AFM analysis lacks detail, please add more to enable replication<br /> -The Dufrene group showed that some interactions are promoted by shear stress under flow conditions (for example SdrG and ClfA binding to fibrinogen). In these cases the force need to separate molecules is very strong and equivalent to that needed to break a covalent bond. It is not clear how the Embp-Fn bond responds to shear stress<br /> -There is no discussion or comparison of Fn binding by the well characterized FnBPs of S.aureus (used as a control in Figure 1) which bind to the type I repeats in the Fn N-terminus<br /> The authors have achieved their objectives and have advanced the state of knowledge about the interaction in an incremental fashion. It does not provide any major new insights. They have confirmed that Embp only binds the fibrillar form of Fn and they have shown that the strength of the interaction is proportional to the number of binding repeats in Embp truncates. This study confirms the value of force microscopy to studying bacterial adhesin-ligand interactions

Reviewer #1 (Public Review):

The exact role of Gdown1 has remained something of an enigma. At the biochemical level, Gdown1 can be strongly inhibitory to both preinitiation complex assembly and transcript elongation. However, there are now many examples, including those shown here, where the loss of Gdown1 can have very little effect. Other work has shown that Gdown1 is typically cytoplasmic, focusing attention on the mechanisms that keep Gdown1 out of the nucleus. Here the authors conclusively demonstrate that Gdown1 has two independent protein domains that drive nuclear export and a separate domain that normally anchors Gdown1 on the cytoplasmic face of the nucleus. When these localization domains are mutated and Gdown1 has routine nuclear access transcription is inhibited, apparently in part because of reduced Pol II levels. This in turn compromises cell viability. Cell viability can also be reduced by exposure to arsenite. In that case, Gdown1 gains nuclear access and reduces the negative effect, presumably because temporary transcriptional shutdown is protective in that case.

Reviewer #2 (Public Review):

Zhu et al. studied GDOWN1, a known RNA Polymerase II transcriptional regulator, and specifically investigated regulation of its localization. Using fluorescent microscopy and pull-down assays, the authors determined the multi-valent nature by which GDOWN1 constitutively remains in the cytoplasm. They found three non-redundant regulator domains within GDOWN1 that ensure GDOWN1 remains in the cytoplasm and only localizes into the nucleus upon stress. Specifically, the authors found two independent domains that directly bind to exportin 1, which ensures the proper export out of the nucleus. Further, they find a third domain that directly interacts with the Nuclear Pore Complex. These three domains regulate nuclear import/export. Additionally, they demonstrate that GDOWN1 inhibits transcriptional activity upon nuclear localization. Their data suggests GDOWN1 is a transcriptional regulator that regulates transcription during cell-stress.

Overall, this is a solid paper, the data supports the authors' claims, and only a few points require clarification.

1. Throughout the paper, the authors use a BiFC assay to monitor direct interactions between GDOWN1 and other transcription factors in the cell. While this assay works well for their experiments, we are unsure why GDOWN1 appears to interact with every protein found in the cytoplasm. This is particularly concerning when we look at GDOWN1 interacting with itself (Figure 1D), as GDOWN1 is not known to self-oligomerize. The authors should provide a negative control that GDOWN1 does not non-specifically interact with any cytoplasm-localized protein.<br /> 2. Additionally, every GDOWN1 truncation tested was able to interact with NELF-E. We are unsure why each truncation tested (given that they tested multiple non-overlapping GDOWN1 regions) can interact with NELF-E. Do the authors believe that NELF-E directly interacts with every tested GDOWN1 construct? We believe that demonstration of BiFC specificity is critical for the conclusions drawn in the manuscript.<br /> 3. The authors note that the NES1 site is not as strong as the NES2 site at regulating exportin 1-dependent nuclear export. However, they suggest this is because mutating the NES2 site is more likely to disrupt the CAS site nearby. We ask the authors to expand on this concept. Do they have direct evidence that NES2 disrupts CAS activity (such as regulating its association with the nuclear pore complex)?<br /> 4. The authors show the critical role of the NES1, NES2, and CAS sites for the localization and function of GDOWN1. Have the authors checked post-translational modification databases to check if any of the identified sites could be post-translationally modified and thereby regulated? Elucidation of the mechanism by which GDOWN1 localization is regulated is of broad interest to the transcription community.

Reviewer #1 (Public Review):

This is an important paper that will be of broad interest. As the authors point out there has been a lot of work in the genetics of complex traits in the last decade-plus, but very few animal studies have actually validated candidate causative variants in a rigorous manner. The human and mouse literature is especially troublesome, so it will be up fly people to do the heavy lifting, although they have not done much yet. Lots of validation efforts exist, but they tend to be correlative and prone to various artifacts. Here the authors attempt to create fly strains that differ at only a single nucleotide, and show that this change has a measurable impact on fecundity (but not some other traits people have posited this nucleotide could impact). The work is rather trailblazing in this regard.

Overall the RNAi knockdowns only add a little, and the work's real contribution is going to be the gene replacement experiment, and the DGRP crossing experiment. I thought there was a little more work that could be done to make the DGRP crossing experiment more valuable. I think it important to include heterozygous genotypes (is the variant recessive or additive?) and to quantify the contribution of the manipulated variant to total variation. Similarly, I thought the supplement could have described the replacement strategy a little better. It is unclear what the background is in which the replacements are measured? Is it isogenic (that is stated but never shown) ... what is the evidence? Are there other parts of the genome segregating ... can one totally exclude segregating variation as an explanation for the results. I guess this is perhaps an omission and not an error, but a little more validation of the backgrounds is required. Finally, I would have liked to have seen some speculation as to how this particular SNP mechanistically impacts female fecundity.

I hope these concerns are mere speed-bumps for a paper that is otherwise strong. In some cases what was done just has to be explained a little better. In other cases a small amount of sequencing or a few more crosses would greatly strengthen the paper.

Reviewer #2 (Public Review):

The authors aim to link a SNP to variation in a complex phenotype. This paper builds on considerable prior research on Eip75B and uses three distinct functional genetic methods to measure the phenotypic effects of allelic variation. The work is thorough and, when combined with prior work identifying variation in this locus, represents an unusually complete look at the link between a specific genetic variant and its pleiotropic phenotypic effects.

The major strength of this work is the depth to which variation at this specific locus is investigated. Conducting three separate experiments, each with independent line-level replication within them, is admirable. Together, these data provide a convincing demonstration that Eip75B has effects on egg production. The authors clearly demonstrate that a single SNP can have effects on a complex phenotype, albeit not the primary phenotype Eip75B had previously been associated with (lifespan).<br /> This finding is important because it provides validation that GWAS and evolution experiments are capable of locating single nucleotide variants with demonstrable effects on complex traits.

A limitation of this work is that the authors do not provide much discussion of the implications of their findings. By necessity, their experiments focus primarily on one of many SNPs in a gene where prior work has detected many different candidate SNPs associated with phenotypic variation in complex traits. It would be good to learn whether future work should systematically functionally characterize all variants, whether this is a worthwhile pursuit for complex traits, and what should be done about those traits that are (nearly) omnigenic. I suspect the findings of this paper will be influential and it could be fruitful for the authors to provide additional perspective for readers seeking to build on it.

Reviewer #1 (Public Review):

Ma et al take a novel approach to an important problem of host cell susceptibility to HIV. They tackle an understudied area of glycan effects on HIV infection using a new method they developed called CyTOF-lec. This method allows single cell detection of infected cells when using a reporter virus for infection. Importantly, the authors go to considerable trouble to use biologically relevant systems, including a transmitted virus and tonsil, endometrial and peripheral T cells. They find that cells expressing higher levels of fucose and sialic acid are more likely to be infected with to HIV than those with low levels. The studies presented here suggest, although didn't fully resolve, that sialic acid itself may be important for infection in CD4, CCR5 positive cells, although they can't really rule out that sialic acid is simply a biomarker for other cell features, such as activation state and entry receptor levels, which are known to impact susceptibility to HIV. Nonetheless, the findings point to glycans as a biomarker and potential determinant for HIV cells susceptibility and open the door to new avenues for studies of the interplay between cell surface glycans and viral infections.

Reviewer #2 (Public Review):

In this manuscript by Ma et al., the authors develop a mass cytometry that includes 5 heavy metal conjugated lectins. After some validation of this panel, the authors use the panel to analyze human PBMCs, tonsils and endometrial CD4 T cells before and after infection with an HIV virus with HSA reporter tag. They found that HIV infection was associated with higher levels of staining with 4 out of 5 lectins (sialic acid and fucose binders). Using the PP-SLIDE algorithm they previously developed, and they predicted that HIV preferentially infected higher cells with higher lectin binding and led to an increase in staining after infection. To validate this hypothesis, sorting of high vs. low lectin staining cells was performed to show that cells with higher lectin staining also had higher rates of HIV infection. They also used sialidase to reduce sialic acid levels and showed that it reduced HIV infection in PBMCs from two different donors. In addition to the development, validation and demonstration of mass cytometry lectin staining, the finding that glycosylation can influence HIV infectivity is novel and could open up new avenues for investigation. I think this work will be generally useful to the mass cytometry and HIV communities.

Reviewer #1 (Public Review):

Lurie et al. investigate the effects of stimulation applied to the lateral temporal lobe on the ipsilateral hippocampus with respect to endogenous hippocampal theta phase. They find that the magnitude of evoked hippocampal potentials correlated with hippocampal theta phase at the time of stimulation. The experiments are novel and could be valuable for showing how to use cortical stimulation to modulate the hippocampus. However, I did not find the paper to be suitable in its current form because of the high variance in hipp-cortical stimulation latency between subjects, which is not properly measured. This and other concerns make the findings challenging to interpret.

1 - Problems with the analysis of stimulation latency<br /> The data in this paper show a variable latency in signal propagation from stimulation sites to hippocampal recording electrodes. In an attempt to measure this latency, the authors examine the theta phase offset between each pair of stimulation and recording electrodes (Figure 9). They interpret their results as showing a consistent 90-degree phase offset. However, their data do not support this interpretation because in fact their measurements show a bimodal distribution of phase differences with peaks at 0 and 180 degrees. It is not valid to interpret the circular mean of a bimodal distribution because the result is not well defined. Further, individual electrodes do not show a mean difference of 90 degrees.

Because the results do not reliably support the claim of a consistent 90 phase difference between the hippocampus and cortex, it is a substantial problem for the paper, given the importance of hippocampal-cortical timing in their interpretation. In particular, the authors should reconsider how they frame their results in relation to the Siegle and Wilson work and others.

2 - Problems with the figures<br /> Some figures in the paper were hard to interpret and I felt it would benefit readers for many to be combined. The results from Figures 3 through 7 would be helpful to see side by side, as they show various investigations of the same data. In Figure 4, it would be helpful to see both plots from (a) on the same axis, as is in (b). I did not find that the accuracy estimation paper in Figure 2 was important to include in the main paper. It would be better suited for the supplement, in my view, unless I am missing something.

Reviewer #2 (Public Review):

Lurie and colleagues attempted to assess whether electrical inputs to the human hippocampus are affected by theta phase. Building off a long history of rodent and computational research showing that hippocampal theta phase affects a multitude of hippocampal processes, including evoked excitatory potentials, the authors examined this effect in a group of human epilepsy patients. Each patient had recording wires in the hippocampus and stimulating electrodes in the lateral temporal cortex. Subjects were given stimulating pulses at random and then the evoked hippocampal potentials were compared according to which theta phase stimulation occurred on. After accounting for conduction delays between the lateral temporal stim site and the hippocampal recording site, they found that evoked potentials delivered on the failing phase of hippocampal theta had larger evoked responses for both an early (~70-110ms) and late (~120-200ms) components. Similar protocols were tried for evoked potentials in both the amygdala and orbitofrontal cortex, and only the only theta phase-dependent effect was found for early components in amygdalar evoked potentials. The current work is consistent with a large body of rodent research and also adds interesting new wrinkles to how we should consider oscillatory linked neural interactions.

The data set is large (8 subjects with hundreds of stimulation events each) and is well analyzed. The approach to theta phase estimation is well thought out and consistent with past efforts. The combination of oscillatory synchrony offset based phase lag estimation with stimulation provides a new window in which we should conceptualize neural interactions. While conduction delays are obviously well known, most rodent experiments studying evoked activity rely on internal hippocampal stimulation, so no phase lag. In contrast, most studies of area-to-area theta based communication rely upon hippocampal theta phase and fail to consider any possible conduction delays, which could significantly alter the impact of phase-locked activity in distant areas. This is an important point that neural network simulations need to carefully consider and the current publication provides a blueprint for how to conceptualize (and quantify) these effects.

Reviewer #3 (Public Review):

The authors are interested in understanding how increased input to the hippocampus from entorhinal cortex at particular phases of the theta rhythm alters hippocampal function. Rodent studies have shown memory enhancement from phase specific stimulation of the hippocampus. The authors examine hippocampal responses to intracranial electrical stimulation of the lateral temporal lobe in patients with medically refractory epilepsy. Specifically, they examine the amplitude of the resulting evoked potentials relative to stimulation at different phases of the hippocampal LFP between 3-8 Hz. They are not stimulating entorhinal cortex, but the electrodes they stimulate were determined based on their ability to evoke hippocampal responses in the LFP. They hypothesize that this in-network stimulation should modulate hippocampal responses. The authors find that hippocampal responses are indeed enhanced, but in an opposite pattern to what they expected. Responses are larger when hippocampus stimulation occurs at the peak of the LFP, which they suggest is likely due to a ~90 degree phase difference between the lateral temporal input region and the hippocampus. The effects are small but significant and would be bolstered by 1) controlling for the timing at which stimulation was delivered, 2) showing data for every subject, 3) controlling for re-referencing scheme, and 4) showing results in a richer frequency and phase context. With adequate controls and supportive data, these results will provide an important contribution to understanding how stimulation in concert with the brain's natural rhythms can be used to more effectively modulate brain activity, which is an important step towards understanding how such precise modulation of brain activity can be used to alter memory, perception, and behavior.

Reviewer #1 (Public Review):

The manuscript entitled "Spatial modeling reveals nuclear 1 phosphorylation and 2 subcellular shuttling of YAP upon drug-induced liver injury" by Wehling et al. sought to provide increased resolution for the dynamic regulation of the HIPPO/Yap signaling pathway in hepatocytes. The authors employed a multi-tier approach utilizing computational/machine learning as well as classical biochemical and molecular biology techniques both in vitro and in vivo. The authors demonstrate the nuclear phosphorylation of YAP as a critical regulator of subcellular localization. Furthermore, the authors provide a mechanism by which drug-induced liver injury activates a cascade of ROS/Akt to culminate on YAP phosphorylation to direct its activation/inactivation in vivo. This work extends our current understanding of the HIPPO/Yap pathway and provides novel signaling pathways that converge on Yap activation during pathophysiological conditions. The claims are well-supported by a variety of experimental conditions.

Reviewer #2 (Public Review):

The study used two different model versions to distinguish two competing hypotheses concerning the mechanisms of YAP and TAZ phosphorylation and their regularization of nuclear localization. In the first (canonical) model, YAP and TAZ are phosphorylated in the cytosol, whereas in the second (alternative) model YAP and TAZ are phosphorylated in the nucleus. By comparing the model predictions to spatially resolved data, the authors could convincingly reject the first model and show that the second model explains the observed data better. The authors conclude that YAP and TAZ cytoplasmic-nuclear shuttling is regulated by phosphorylation in the nucleus and this conclusion is well supported by their data. Albeit not modeled, the authors also show this phosphorylation depends on ROS and AKT signaling.

Strengths: The study is a very nice example of systems biology and how modelling can be used to make different assumptions explicit to test different hypotheses. A key strength is the mathematical modelling with partial differential equations and the use of spatially resolved data. Spatial features of the data were compared to the models and used to distinguish two different, competing hypotheses. The results reveal a new mechanism of YAP and TAZ phosphorylation in the nucleus. Uncertain parameters in the model were estimated and predictions arising from the model were validated using multiple experimental techniques, increasing the confidence of the findings. In vivo significance was validated in a mouse model.

Weaknesses: As is to be expected, the parameters in the model were not identifiable leading to large variability in the estimates. However, this is typical for these types of systems biology models. Another potential weakness is that different diffusion rates were assumed for the canonical and alternative models, and the significance of having different diffusion rates for two different model versions remains unclear. To implement the different hypotheses, two extreme model versions were analyzed (the alternative model had no phosphorylation in the cytosol, only the unphosphorylated form of the YAP/TAZ is shuttled into the nucleus and only the phosphorylated YAP/TAZ is exported). The reality is most likely less discrete and somewhere in between with some phosphorylation/dephosphorylation occurring in both compartments and nuclear shuttling occurring for both the unphosphorylated and phosphorylated forms. The time-dependency of the model simulations was not analyzed, and the nature of the observed biphasic time-dependent APAP response remains elusive. It would be interesting to see how the model can explain the time course of the APAP stimulation experiment.

Reviewer #3 (Public Review):

In this manuscript, Wehling and colleagues discovered an important role of nuclear phosphorylation in the Hippo pathway that regulates the cellular localization of YAP and TAZ in the nucleus and cytoplasm. Using PDE modeling and imaging assays, they showed that the difference in shuttling dynamics between YAP and TAZ can be accounted for by a single parameter determining the nuclear phosphorylation capability. The authors further demonstrated that in a drug-induced liver injury model, YAP shuttling is also regulated by nuclear phosphorylation, which may be mediated by the induction of reactive oxygen species and subsequently AKT. The work has added the cell nucleus as a critical compartment in the regulation of the Hippo pathway, which challenged and extended the conventional model. The conclusions are mostly well supported by the experimental evidence, although some improvements can be made in data presentation.

Overall, the work is of general interest to the field of developmental and regenerative biology. It will benefit a broader audience if the role of nuclear phosphorylation can be further linked to tissue damage and the regeneration process in future studies.

Reviewer #1 (Public Review):

Recent studies in rodents suggest the existence of conditioned responses (CRs) to fearful stimuli beyond the classically measured freezing behavior. Here, Trott and colleagues acknowledge that if valid, these results represent an important theoretical issue for prior research using conditioning paradigms. To assess the validity of these prior conclusions, the authors first replicated these behavioral results using similar experimental conditions. Next, they conducted a series of important controls to assess whether these alternative CRs (i.e., darting, flight) are indeed the result of associative processes. In contrast to this model, the authors found that darting and flight where largely the result of nonassociative processes. In general, the experimental design and their accompanying results largely support the authors conclusions. Precisely, they support a model in which what authors refer to as Peak Activity Ratio (PAR) responses are the result from nonassociative processes akin to potentiated startle (or sensitization). As such, in my view, the paper is already quite compelling. It is an excellent study that offers an important contribution to the field. Still, the addition of direct statistical comparisons of data presented in Fig. 2 and S2 and a few clarification statements should aid in the interpretation of the data and the study itself.

Reviewer #2 (Public Review):

Trott et al examine associative and nonassociative influences on freezing and flight-like responses (locomotion/darting) during and after Pavlovian fear conditioning in mice. Initial experiments use a paradigm popularized recently by Fadok et al 2017 - where a serial conditioned stimulus (SCS) precedes a shock US (tone->noise->shock). They replicate the main result: freezing develops primarily to the tone and activity bursts/darting develop to noise. Control groups demonstrate that noise-elicited flight does not depend on embedding in a compound SCS or pairing noise with shock in the training phase. Velocity measurements during early shock-free tests indicate that vigorous movements occur mostly at noise onset and are bigger when the noise is novel. Since the tone is not necessary for noise-elicited flight, all subsequent experiments focus on reactions to noise after treatments in the same context to probe the contribution of associative vs non-associative processes directly. In brief, they find the strongest flight-like reactions when the noise is novel at test and there is a history of shock in the chamber. Habituating the noise or pairing it with shock both blunt subsequent flight reactions to noise onset. Interestingly, less vigorous movements later in the noise cue were potentiated by noise shock pairings (relative to shock-only or unpaired controls), suggesting that a component of the response is associative. Other data suggest that noise behaves as a weak US (disrupts ongoing freezing, repeated presentations support low levels of freezing & darting). The authors conclude that apparent conditioned flight responses are primarily due to non-associative pseudoconditioning/dishabituation of alpha responses to a sudden stimulus change - akin to fear-potentiated startle (FPS). This is an important addition to a recent literature that has generally assumed that active responses after conditioning are associative. The velocity data are especially clear and support the FPS analogy. The conclusions regarding the SCS-flight paradigm are justified by the current data but are partially inconsistent with findings recently published by another lab (Totty et al 2021). It is also unclear if the non-associative processes identified in the present studies apply to conditioned darting studies using a pure tone.

Specific questions/concerns:

1. The section relating SCS-flight reactions to alpha responses and fear potentiated startle (FPS) is interesting and potentially important. However, parts of this narrative are unclear. First, FPS has a strong associative component but the flight reaction studied here apparently does not. Second, pairing a tone with shock increases startle reactions preceded by a tone. Here, pairing noise with shock suppresses alpha reactions. Is there evidence that pairing startle-eliciting noises with shock reduces typical startle reactions? Is the issue here that SCS-flight studies are designed poorly to demonstrate the phenomenon (pairing the whole SCS compound with shock vs pairing just the tone with shock then testing noise reactions after a tone)? Lastly, an important experiment by Totty et al (Fig 5) is not discussed. They show that SCS presentations fail to elicit flight reactions in a threatening context (previously paired with unsignaled shocks) unless they were paired with shock in an earlier phase (different context). This seems inconsistent with the FPS interpretation of SCS-flight, since the threatening context should have increased alpha reactions to the novel noise. Along with other control experiments, it also suggests that associative processes related to SCS-shock pairings make a strong contribution to flight. Perhaps there is something unique about compound stimuli paired with shock that cannot be addressed with the simple noise-shock control experiments reported here? This should be discussed in the manuscript.

2. Sex differences have been reported for darting behavior in a Pavlovian paradigm using a single tone CS, but have not been observed in studies using a tone-noise SCS. The combined analyses here (lines 424-429) also finds no sex differences for mice conditioned with a single noise CS. However, the original reports identified only a subset of females that showed prominent darting and stronger shock reactions. Is there any evidence for this Darter vs. Non-darter classification in your dataset? Either way it would be helpful to add graphs illustrating the sex difference analysis that include data points for individuals, at least in supplemental.

3. Lines 432-439: This concluding paragraph is a missed opportunity for a more nuanced discussion of "active vs. passive" defense and perhaps different categories of "flight". The papers cited do not suggest that rats freeze because no other response is available (thought the Blanchards may have said this elsewhere). All the studies investigate CRs in situations where both freezing and locomotor movements are possible. Although it is true that freezing is not the absence of a response, it is the absence of movement. The distinction between movement/ambulation and immobility in threatening situations is important for describing brain circuits of defense and necessary to explain transitions to flight, escape, active avoidance, and even "choosing locations to freeze" by moving down threat gradients. Similar passive vs active terminology goes back at least to Konorski (1967), though "stationary" may be more appropriate than "passive" (Sigmundi, 1987). Related:<br /> -Line 66: "but not activity bursts", Line 77: "Gruene et al suggest that freezing and darting were competing CRs to the same level of threat". Please clarify the Predatory Imminence Theory views on this. If conditioned rats move to the safest spot to freeze (de Oca et al, 2007), is this not an activity burst? Does the velocity of the movement matter? How do these movements relate to the startle-like responses seen at CS onset vs. the more sustained activity reported here for paired groups? de Oca 2007 describes conditioned flight to a familiar enclosure and freezing as compatible post-encounter responses to the same threat, but flight and freezing cannot occur at the same time and must be competitive.

4. The notion that noise is a (weak) US requires further discussion. Specifically, how do you define a US? And are these properties necessary for the argument that apparent conditioned flight/darting reactions are non-associative startle-like reactions? Freezing goes up when rats experience noise alone trials, but this does not appear to be a result of context conditioning (no BL freezing on day 2 of training). Further, there appears to be no summation once the context is paired with shock (freezing during habituated noise; Exp 4). Noise-elicited freezing appears to sensitize in phase 1 but at the same time darting responses habituate. This pattern is unlike what one might expect for even a very weak shock. One reason this seems important: the paper begins by explaining the challenge posed by the SCS-flight paradigm and the conditioned darting paradigm. However, the studies presented here focus on noise-elicited behavior and imply that similar phenomena occur in the conditioned darting paradigm. The conditioned darting studies all use a pure tone that may not be characterized as a US. Tone-elicited behavior isn't discussed much in the manuscript, but tone-elicited darts in Experiment 2 (pseudoconditioning control) appear lower than those elicited after tone-shock pairings in Experiment 1. So it remains unclear if conditioned darting results from non-associative processes, especially if the tone does not act as a weak US.

5. Baseline data for Darts is missing throughout and should be added to all trial-by-trial graphs. This is important since all phases occurred in same chambers and baseline fear levels could drive darting before stimuli are presented.

6. Line 133: "noise was never paired with shock". This is an important point -- but the white noise stimulus contains the tone frequency, and this was paired with shock in the previous phase.

Reviewer #3 (Public Review):

In this manuscript, Trott et al report a series of elegant experiments designed to parse associative and non-associative influences on escape-like locomotor responses elicited by conditioned stimuli. The work is timely, and the experiments are well designed to assess the contribution of novelty, surprise, and learning to behaviors such as darting and flight. The results paint a complicated picture, the complexities of which need better exploration in the discussion.

The authors' do indeed demonstrate that increased locomotion in response to a white noise can be driven by non-associative factors. This seems particularly true in the 1st and 2nd experiments, in which a novel/surprising serial compound stimulus or a white noise alone triggered a switch from freezing to locomotion more effectively than any associative factor. The picture becomes more complex with experiment 3, however, which compared paired and unpaired groups, as well as a variety of other controls. Contrary to what one might predict on the basis of the first two experiments, the paired group showed less freezing and more darts than the unpaired group (a straightforward interpretation of experiments 1 & 2 would suggest that paired and unpaired should be equal across all measures). Further, the pattern of locomotion driven by non-associative novelty (shock-only group) is very similar to the pattern observed in the unpaired group - a strong spike at the onset of the white noise (alpha response) that returns to near baseline levels by the end of the stimulus. In contrast, the paired group showed a more enduring, multi-peak pattern of elevated locomotion that filled the white noise. The 4th experiment revealed that a locomotor pattern dominated by alpha responses (ie similar to unpaired and shock-only groups in experiment 2) is indeed attenuated by habituation, and thus can be considered non-associative. However, habituation does not seem to have a strong effect on the white noise-filling pattern of locomotion observed in the paired group. Indeed, the descriptions of experiments 3 & 4 in the results section do acknowledge that paired groups produce a particular pattern of locomotor activity/darting that differs from non-associative groups. The discussion, however, does not address this point. Though the paper provides good evidence for the role of non-associative factors in flight-like behavior, it does not totally refute a role for associative factors, as well. Associative factors may not be necessary to produce darting and increased locomotion, but in certain cases they seem sufficient to do so.