Reviewer #3 (Public Review): 

Georgiadou, et al. combined public human transcriptomic datasets with new mouse model experiments to determine if high-throughput gene expression comparisons provide higher resolution, compared to clinical phenotypes, for matching five different mouse models of malaria to human malarial syndromes. A notable strength of the analysis is that the authors have attempted to correct for differential abundance of immune-cell population subsets in their differential expression (DE) calculations. This is a perennial bias in peripheral-blood transcriptomic studies. However, a potential concern is that the affect of this correction was not directly compared to uncorrected models and/or direct modeling of these cell proportions to the disease outcomes in each experiment. This is relevant because the cell populations for the mouse experiments were assessed via flow cytometry, whereas they were inferred from the transcriptomics data in the human studies. It would also be useful to see a comparison of proportions measured by flow with those inferred from transcriptomics on the same samples. 

The authors have done a commendable job of using independent public datasets for validation and to achieve their aim of performing an unbiased investigation of the similarities and differences of mouse models to human malarial disease using comparative transcriptomics. As presented, the results do not convincingly identify which mouse models are best suited to use to study specific human malaria phenotypes. On the other hand, the span of the mouse models with one another compared to human in the PCA plots is consistent with the claim that a strength of mouse models is that they reflect the broad diversity of human disease. 

Readers should be aware that early vs. late timepoints were used for DE calculations in the mouse studies to recapitulate uncomplicated vs. severe disease in humans. Early timepoints from animals that go on to develop severe disease may already show some transcriptomic patterns of SM, which could thus be lost in this comparison.

Reviewer #1 (Public Review): 

The authors extend their previous work on Capicua (Cic), which focused on peripheral T cell differentiation and function, and now focus on T cell development by making use a Vav-cre Cic-f/f mouse model to conditionally delete Cic in all hematopoietic cells. Their findings show that in the absence of Cic there is an apparent block at the TCR-beta selection checkpoint and during negative and positive selection of CD4+CD8+ (double-positive, DP) thymocytes. Additionally, they show that the TCR repertoire of regulatory T (Treg) cells is altered in Cic deficient mice. Transcriptomic analysis point to the underlying mechanism for these defects in TCR mediated outcomes as being due to a de-repression of several genes responsible for inhibiting ERK/MAPK signaling, as well as other related signaling outcomes. The authors further examine the roles of DUSP4/6 and Spry4 as critical players in Cic-deficient thymocytes. Overall, the work is clearly presented and makes use of several TCR-transgenic mouse models to further delineate the defects in T cell selection.

Reviewer #2 (Public Review): 

In this work, Kim et al. investigate the impact of transcriptional repressor CIC deletion on thymocyte development. They use a CIC flox/flox mouse crossed with different Cre transgenic models expressing the Cre enzyme at various stages of T cell development and show that early CIC deletion impacts both DN and DP stages of development. They continue their study by demonstrating the impact of CIC on TCR signaling in DP thymocytes and showed by RNAseq that CIC deletion de-repressed several genes involved in TCR signal regulation. Among them, SPRY4 and DUSP6 are known to regulate calcium flux and Erk signaling, respectively. Interestingly, Spry4/CIC double knock out mice display a partially rescued phenotype. 

The scientific approach of this paper is coherent and while the role of CIC in thymocyte selection has already been partially described by the authors in a previous study, a specific study of the CIC-deletion impact on T cell development as in this report could be useful. The techniques and mouse models used in the study are relevant and the authors answer the question they raised.

Reviewer #3 (Public Review): 

Previous studies have shown that T-cell specific capicua (CIC) deficiencies lead to lymphoproliferative autoimmune-like phenotypes. The current manuscript addresses the role of CIC in the regulation of positive and negative selection of the T cell receptor (TCR) repertoire as dysregulation of thymic selection may contribute to this autoimmune pathology. For this, the authors make use of mice in which CIC has been conditionally deleted in hematopoietic progenitor cells, prior to T cell commitment and thymic selection processes. 

While there are no overt differences in the numbers of mature CD4 and CD8 single positive thymocytes in adult Cicf/f;Vav1-Cre mice harboring a polyclonal T cell receptor repertoire, stronger evidence of defects in both positive and negative selection in the absence of CIC are unmasked with the use of TCR transgenic models. Complementary read-outs of TCR signaling and TCR signal strength suggest that immature CD4+CD8+ double positive (DP) thymocytes are refractory to TCR stimulation. This is likely due to the modulation of regulators of TCR signaling that are normally repressed by CIC; indeed, gene expression analysis of DP thymocytes from Cicf/f;Vav1-Cre and control mice identify differential expression of a number of putative CIC target genes that have been described in other cell types and systems and include Spry4, a suppressor of Ras-independent ERK activation. The authors report that over-expression of Spry4 in thymocytes is sufficient to inhibit ERK phosphorylation and suppression of calcium signaling in response to TCR stimulation, and that an Spry4-deficiency on the Cicf/f;Vav1-Cre background partially rescues defects in T cell development in this model. 

As a transcriptional repressor with known targets that act as negative regulators of the RTK/MAPK signaling pathways, major players in the signaling cascade downstream of the T cell receptor, it is perhaps not surprising that CIC may regulate thymic selection, processes that rely on finely tuned TCR signals, and the data presented are largely in line with this hypothesis. Ultimately, whether the extent of the skewing of the T cell repertoire due to differences in thymic selection is responsible for the autoimmune phenotypes in the CIC knock-out mice will need to be determined.

Reviewer #1 (Public Review): 

The authors present a compelling investigation into the sensory-motor integration of echolocating toothed whales during prey capture. The strengths of this article reside on the use of data from both wild and captive animals of different species of toothed whales. This enables generalization of the findings and conclusions which strengthens the claims. 

I have no major issues with this work. It is my opinion that a more thorough discussion on prey tracking behaviors and the evolutionary arms-race would situate the findings in a broader context and improve the manuscript. 

I specifically liked the clever analyses presented in Figure 3 B-C for the wild animals. Though beyond the scope of this manuscript, it would be interesting to repeat the experiment with the captive animals tracking a target that moves repeatedly (not isolated trials). In this same line, future studies including physiological methods (ABRs and EMGs to track muscle responses) could provide further insight into the processing times for the pathway proposed by the authors. <br> The manuscript is concise and clear. The methods are described appropriately.

Reviewer #1 (Public Review):

Nakayama and colleagues report a unique screening concept utilizing conserved mechanisms between zebrafish gastrulation and cancer metastasis for identification of potential anti-metastatic drugs. They screen 1280 FDA-approved drugs using the gastrulation as a marker, and identify Pizotifen as an epiboly interrupting drug. Then they find that pharmacologic and genetic inhibition of HTR2C, a target of Pizotifen, suppresses metastatic progression in a zebrafish and mouse model through inhibition of epithelial to mesenchymal transition (EMT) via Wnt-signaling.

Their work is of interest and has the potential to appeal to a broad audience. However, additional experiments are needed to further substantiate their concept that human cancer metastasis mimic/recapitulate zebrafish gastrulation in terms of conserved mechanism, as well as to confirm the validity of their screening method regarding to the effects of global toxicity.

Major concerns:

The first major concern I have is the appropriateness to think the gastrulation as a parameter/index of cancer metastasis. While they cherry-picked some genes that they are known to be involved in both gastrulation and cancer metastasis, more broad analysis should probably be necessary to conclude so. For examples, the authors can analyze comprehensive RNA-seq data set to see if the pathways/networks are similar between gastrulation (zebrafish embryo development data set) and cancer metastasis (benign/primary tumors vs metastasis tumors in TCGA).

The second concern is about the Pizotifen's effects on cancer metastasis. Since the Pizotifen suppresses gastrulation, it might have some harmful effect on the organogenesis/development of day2 embryos that they used in zebrafish transplantation model. And if so, cancer metastasis can be suppressed indirectly. The authors could examine if Pizotifen could have some side effects on day2 embryos. The drug also has some cell viability suppressive effects in vivo so as the pics in Fig.2D looks like, and it would be good if this possibility was excluded.

Finally, the mechanistic parts would need more confirmation and rescue experiments. Transplanted cells can be sorted after the treatment and the expression changes of EMT markers can be examined to see if the phenomenon happens in vivo as well. All main results can be rescued to see if the effect of Pizotifen against EMT happens through HTR2C-Wnt axis.

Reviewer #2 (Public Review):

The manuscript by Nakayama describes the use of a small molecule based screen in zebrafish embryos to find compounds that selectively impact metastasis in cancer. This screen is based on the strong premise that compounds that selectively delay or prevent gastrulation where cells are extensively migrating and undergoing convergence and extension movements in zebrafish embryos are similar to the mechanisms co-opted by malignant cells during metastasis. Screening 1280 FDA approved drug from the Prestwick library identified a total of 132 hits which delayed or disrupted epiboly. After eliminating toxic compounds and drugs that severely disrupted gastrulation, 5.2% of the drugs delayed epiboly. 20 of the compounds identified decreased cancer cell migration, and one compound, Pitzotifen, was shown to inhibit cell invasion and metastasis in cell based and in vivo xenograft based assays in both zebrafish and mice. Pitzotifen is an antagonist for the serotonin receptor 2C (HTR2C) which the authors show is a driver of ZEB1 mediated epithelial to mesenchymal transition (EMT) through regulation of Wnt/ beta-catenin signaling.

Strengths

1. The in vivo screening method in zebrafish is able to identify compounds that selectively effect gastrulation and consequently in cell lines have a pronounced effects on cell migration and invasion and Pizotifen also disrupts metastasis in both a zebrafish and murine xenograft assays. The assays especially in the zebrafish model arerong and convincing. This study further supports current findings that show that cancer cells co-opt normal developmental programs to spread and metastasize.

2. A clear logical progression of experiments is provided to demonstrate that Pizotifen through regulation of Wnt-signaling can ablate EMT mediated by ZEB1.

3. Pizotifen is an inhibitor of serotonin receptor HTR2C which can regulate EMT, metastases and ZEB1 expression.

Weaknesses

1. The significance of the genes/ proteins identified in the screen are not well articulated. Did the screen identify genes that are correlated with poor outcome and or metastasis in cancer. Second, the selection of human cancer cell lines to validate the findings are not clear. Is HTR2C and other genes identified relevant only to the subset of cancers that the cell lines represent or is it a common pan-cancer effect. Analyses of TCGA or other data sets to show relevance of HTR2C gene expression and survival and or metastasis would greatly strengthen the relevance of the hits and the rationale for picking the cell lines to study.

2. Some of the data provided does not support the conclusions drawn. For example, in figure 3 C, the luciferase images clearly show that Pizotifen treated mice have smaller tumors while the data in 3 A and B do not show this data. The measurements of tumor volume appear to be taken too early to actually be reproducible/representative.

3. It is not clear if HTR2C selectively only inhibits ZEB1 mediated metastases or are its effects more general i.e. its expression also effects other EMT regulators for example SNAIL, SLUG and or TWIST expression.

Reviewer #3 (Public Review):

Nakayama et al. applied the molecular similarities between metastatic cell movement and cell movement in gastrulation to conduct a screening using FDA-approved drugs. Using a zebrafish model involving epiboly gastrulation as a marker, in vitro human cancer cell lines, xenotransplantation, and mouse models, they find that Pizotifen, an antagonist for serotonin receptor 2C (HTR2C), has anti-metastatic effects. The authors further show that HTR2C overexpression is sufficient to induce metastasis. They further link this finding to the wnt pathway, suggesting that the decrease of ß-catenin is involved in the loss of metastatic properties. Overall, the data is interesting and strong. However, the link to the wnt pathway is weak without sufficient experimental evidence.

Reviewer #1 (Public Review): 

This is a very important study for the field, as the involvement of S1 in motor planning has never been described. The paradigm is very elegant, the methods are rigorous and the manuscript is clearly written. However, there are some concerns about the interpretation of the data that could be addressed. 

• The authors claim that planning and execution patterns are scaled version of each other, and that overt movement during planning is prevented by global deactivation. This is an interesting perspective, however the presented data are not fully convincing to support this claim: 

(1) the PCM analysis shows that correlation models ranging from 0.4 to 1 perform similarly to the best correlation model. This correlation range is wide and suggests that the correspondence between execution/planning patterns is only partial. <br> (2) in Fig.4 A-B, the distance between execution/planning patterns is much larger than the distance between fingers. How can such a big difference be explained if planning/execution correspond to scaled versions of the same finger-specific patterns? If the scaling is causing this difference, then different normalization steps of the patterns should have very specific effects on the observed results: 1) removing the mean value for each voxel (separately for execution and planning conditions) should nullify the scaling and the planning/execution patterns should perfectly align in a finger-specific way; 2) removing the mean pattern (separately for each finger conditions) should effectively disturb the finger-specific alignment shown in Fig.4C. <br> These analyses would corroborate the authors' conclusion. 

• A conceptual concern is related to the task used by the authors. During the planning phase, as a baseline task, participants are asked to maintain a low and constant force for all the fingers. This condition is not trivial and can even be considered a motor task itself. Therefore, the planning/execution of the baseline task might interfere with the planning/execution of the finger press task. Even more controversial, the design of the motor task might be capturing transitions between different motor tasks (force on all finger towards single-finger press) rather than pure planning/execution of a single task. The authors claim that the baseline task was used to control for involuntary movements, however, EMG recordings could have similarly controlled for this aspect, without any confounds. 

• In Fig.2F, the authors show no-planning related information in high-order areas (PMd, aSPL), while such information is found in M1 and S1. This null result from premotor and parietal areas is rather surprising, considering current literature, largely cited by the authors, pointing to high-order motor or parietal areas involved in action planning.

Reviewer #2 (Public Review): 

The present investigation aimed at exploring the role of primary somatosensory cortex (S1) in the representation of single finger movements during motor planning and the relationship of this representation with the one present during movement execution. 

The authors conducted a high-field (7 Tesla) fMRI study focusing their analysis on the contralateral S1 and on the primary motor cortex (M1). Participants had to perform a delayed execution task, in which they were instructed to perform specific finger movements after a variable delay. After the delay, a go/no-go cue would instruct the participant either to perform or not perform the cued movement. 

Univariate analysis showed a widespread deactivation within M1 and S1 during planning. Nevertheless, multivariate pattern analysis (MVPA) showed that information about the upcoming finger movements was present both in M1 and S1 during the planning phase. The informative patterns of upcoming finger movements during the preparatory delay were highly correlated to the representation of finger movements during the execution phase of the task. 

A control analysis excluded the possibility that the main results of the study might be caused by subtle differences in the forces exerted by the fingers during the planning phase. Indeed, the applied forces were comparable across fingers and they didn't predict neural activation. 

The interpretations of these findings suggested a possible role of preparatory signals in S1 which might be to contribute to motor control either through predicting upcoming changes in sensory processing or through a direct effect on the spinal cord. 

The present investigation is well-conducted and the analyses are solid and clear. The authors adopted a controlled experimental paradigm and design. In details, the authors adopted a delayed paradigm to dissociate the planning from the execution phase of the task and conducted the analysis only on the no-go trials avoiding any possible contamination with motor execution activity. Moreover, they conducted a further analysis to exclude that preparatory state of the participants in terms of the force exerted on the response apparatus. All these specific choices in how to implement the present study underline the expertise in the field of the authors. 

With respect to the general aim of the study, I think the authors showed convincing evidence that S1 represents specific information about upcoming finger movements. Moreover, the additional control analyses further clarified the specific role of the S1, excluding possible confounding factors which might provide alternative interpretations to the present findings. The conclusions drawn in the manuscript are clearly supported by the results and in line with recent findings on the contribution of S1 to motor planning. To conclude, the present work provided novel insights on the contribution of the somatosensory cortex during motor control by adopting state-of-the-art analytical approaches.

Reviewer #3 (Public Review): 

I found the manuscript to be well written and the study very interesting. There are, however, some analytical concerns that in part arise because of a lack of clarity in describing the analyses. 

1) Some details regarding the methods used and results in the figures were missing or difficult to understand based on the brief description in the Methods section or figure legend. 

2) I think the manuscript would benefit from a more balanced description on the role of S1. As the authors state, S1 is traditionally thought to process afferent tactile and proprioceptive input. However, in the past years, S1 has been shown to be somatopically activated during touch observation, attempted movements in the absence of afferent tactile inputs, and through attentional shifts (Kikkert et al., 2021; Kuehn et al., 2014; Puckett et al., 2017; Wesselink et al., 2019). Furthermore, S1 is heavily interconnected with M1, so perhaps if such activity patterns are present in M1, they could also be expected in S1? 

3) Related to the previous comment: If attentional shifts on fingers can activate S1 somatotopically, could this potentially explain the results? Perhaps the participants were attending to the fingers that were cued to be moved and this would have led to the observed activity patterns. I don't think the data of the current study allows the authors to tease apart these potential contributions. It is likely that both processes contribute simultaneously. 

4) The authors repeatedly interpret the absences of significant differences as indicating that the tested entities are the same. This cannot be concluded based on results of frequentist statistical testing. If the authors would like to make such claims, then they I think they should include Bayesian analysis to investigate the level of support for the null hypothesis.

Reviewer #1 (Public Review): 

The manuscript is well-written and easy to follow. The authors are thorough in their characterization, shown both through the text itself and the figures. Most of the comments relate to the narrative structure itself and are merely suggestions. Overall, this work represents an important resource for the community and especially to people working on the role of the SEZ in feeding and motor behaviors. 

Specific comments and suggestions: 

• The authors give a very nice overview of the SEZ and the split-Gal4 technique. However, they spend much less time discussing the rationale behind using the cell body numbers within subesophageal neuromeres. This to me assumes two extremely different kinds of readers, one relatively new to Drosophila research and the other relatively well-versed. Since this technique is crucial to the approach used throughout the manuscript and significant in the authors labeling about 1/3 of the region, I would suggest the authors to give a brief summary and justification as to why they decided to use this neuromere labeling technique, and spend more time in the discussion (perhaps in the paragraphs between lines 352-386) talking about the pros and cons of this technique (is it expected to label fewer than 50% of the neurons? How may this complement the EM and FAFB dataset, and what are the advantages and disadvantages using the technique employed here?). 

Related suggestions: <br> o Line 81: elaborate on deutocerebral contributions <br> o Lines 84-85: along similar lines, Hox gene drivers 

• Figure 9: having a color legend in the figure itself will facilitate understanding of this figure. I think it would be nice to have visual examples of interneurons, projection neurons, and so forth. Perhaps when the authors first describe neurons in Group 1, instead of marking "first half of the group" (line 210) the authors can explicitly name the neuron types (peep, doublescoop, etc.) 

• In the polarity section of the discussion, it would be interesting to have additional remarks relating to how to determine whether these identified neurons are thought to be ascending and why. Since one of the authors has previously characterized some ANs, perhaps comparisons to this work would be helpful to readers new to this region of the brain. 

• The parallel structures used in characterizing Groups 1 through 6 are very useful. However, I think that when the authors relate each group to previous works, this might fit better in the Discussion section.

Reviewer #2 (Public Review): 

The authors aimed to generate a novel set of split-Gal4 drivers to access neurons located within the SEZ region of the adult fruit fly brain. This paper achieved its goals - (i) it generated a novel collection of SEZ specific Gal4 drivers and (ii) provided the framework for scientists to generate additional SEZ specific split Gal4 drivers. Figures 2-8 were beautiful and very compelling, a strength of the manuscript. I especially liked the regional specificity of the different SEZ cell types. A weakness was the heavy jargon used, what felt like an incomplete explanation of how the individual cell types were determined, and no "raw" data to back up the polarity quantifications. Regardless, the authors achieved their aims and their results have many exciting future implications for Drosophila behavioralists.

Reviewer #3 (Public Review): 

Sterne et al generates 138 sparse gal-4 lines that target different types of cells in the subesophageal zone (SEZ) of Drosophila adults. Using bioinformatic tools, the different cells types are clustered into six domains based on neuron morphology. These include presumptive sensory, motor and interneurons, and in selected cases provide tools to target neurons that were identified previously by stochastic methods. The main strength of the paper is providing tools for the Drosophila community interested in SEZ and behavioral processes related to this region of the fly nervous system. It is doing a great service. What this paper does not provide are functional and synaptic information of these lines, and the coverage is estimated to be about 30%. The tools provided here will nicely complement EM connectivity analysis of the adult SEZ.

Reviewer #1 (Public Review):

The authors used NMR spectroscopy and kinetic aggregation assays to investigate how heat-shock protein HSPB1 and two J-proteins (Hsp40), DNAJB1 and DNAJA2, interact with tau and prevent its aggregation. All three chaperones prevent or reduce tau aggregation, but interestingly the authors convincingly demonstrate that they interact with distinct tau species. NMR data (Fig. 1) show that the same two regions within tau are being recognized by HSPB1 and DNAJA2, which seem to bind to the monomeric tau, but DNAJB1 does not appear to bind tau (or rather it binds very weakly). The authors followed up on this interesting observation to show that DNAJB1 does reduce tau aggregation by interacting with a soluble oligomeric tau species. Some interesting observations about distinct specificities of the substrate-binding domains in the two J-proteins are also reported. Overall, this is beautiful work.

Reviewer #2 (Public Review):

This is a carefully performed study that combines a range of biophysical experiments to study molecular and mechanistic aspects of the roles of the tau aggregation suppressors HSPB1 and two Hsp40 family members, DNAJA2 and DNAJB1. The differential roles and mechanism involved are dissected and rationalized by the involvement of distinct substrate binding domains CTDI and CTDII in the two Hsp40 proteins. The findings provide novel insight into mechanistic features of the modulation of tau aggregation by heat shock proteins using a combination of in vitro experiments and will form the basis for further studies and validation in a cellular context.

Reviewer #3 (Public Review):

This paper addresses important questions about how two chaperones, DNAJA2 and DNAJB1, interfere with tau aggregation. The description of their interaction mechanisms and their interaction regions is both novel and interesting to the field of chaperone and tau aggregation. The use of NMR and kinetic analysis is compelling to obtain useful information. This information will be useful to understand the importance and the mode of action of the chaperones in a biological context.

In general, no attention is paid to how aggregation is triggered. There is no mention of heparin in the results and the quantity of heparin used to trigger aggregation is not written in the method. This is a crucial aspect that is relevant to what aggregation pathway is modeled in vitro, in particular in the light of recent results showing that heparin-induced fibrils are different from brain-extracted fibrils (Fichou et al. Chem Comm 2018, Zhang et al., elife 2019).

My major concern in this paper is about the assumption that tau aggregation-prone conformers were generated. The conjecture that an excess of heparin increases the population of aggregation-prone conformers is not justified. On the contrary, excess of heparin was shown to form off-pathway oligomers (thus not aggregation-prone) (Ramachandra & Udgaonkar JBC 2011) that do not exhibit exposed PHF6(*) (Fichou et al, Frontiers in neurosciences 2019) expected to be the signature of aggregation-prone conformers (Eschmann et al. Scientific reports 2017; Chen et al. Nat. Comm 2019). If more aggregation conformations were present, they would aggregate more easily, and not be stable as it happens when heparin concentration is increased. Thus, I don't believe that the interpretation that the chaperone interfere with aggregation-prone conformers is justified by the data on heparin-tau complex. In general heparin-tau complex should not be use a proxy for aggregation-prone conformations in the different result sections and in the discussion.

The data on P301L/S mutant is more convincing. However, the deduction that DNAJB1 binds P301L/S better because of the exposure of PHF6 is plausible but purely hypothetical (and it should be described as is). I'm in particular concerned with the facts that (i) the PHF6-exposed conformers are likely to represent a small population in the P301L/S mutants and (ii) PHF6* is not exposed in heparin-tau complexes (Fichou et al, Frontiers in neurosciences 2019) and yet they bind DNAJB1.

Reviewer #1 (Public Review):

This work uses a continuous culture system with simplified soil microbial communities to investigate how diversity-disturbance relationships (DDRs) change with different disturbance "intensities" (here, defined as mortality rate or dilution rate in a continuous system) and "frequencies" (here, defined as the number of dilution events that occur per day to achieve the desired mortality rate). Understanding the mechanisms that support different DDR is an ongoing and urgent need in ecology and ecosystem sciences because of the pressing need to predict and manage systems given climate and land-use disturbances.

Authors cultured a soil microbial community in chemostats with varying dilution rates (disturbances) and surveyed species abundances at the steady state. At a constant dilution rate, an U shaped DDR (diversity-disturbance relationship) is observed: diversity is the lowest at intermediate dilution rate. When dilution rate is allowed to fluctuate, increased diversity is observed. Moreover, the U-chape is erased when fluctuation in dilution rate is very large (one large dilution per day instead of many smaller dilutions per day). These phenomena can be explained by a resource competition mathematical model where growth rate is a saturable function of resource, but not by a Lotka-Volterra model or if growth rate is a linear function of resource. Finally, by simulating perturbations at different fluctuation frequencies, authors obtained diverse DDRs including U shape, peaked, increasing, decreasing, and flat.

A major strength of the work is a blending of modeling and empirical approaches. The figures are informative and framework is explained clearly.

Reviewer #2 (Public Review):

This work uses a throughput continuous culture system with simplified soil microbial communities to investigate how diversity-disturbance relationships (DDRs) change with different disturbance "intensities" (here, defined as mortality rate or dilution rate in a continuous system) and "frequencies" (here, defined as the number of dilution events that occur per day to achieve the desired mortality rate). Understanding the mechanisms that support different DDR is an ongoing and urgent need in ecology and ecosystem sciences because of the pressing need to predict and manage systems given climate and land-use disturbances.

A major strength of the work is a blending of modeling and empirical approaches. It includes an ambitiously-designed study that uses a controlled, high-throughput microbial community experimental system to observe disturbance outcomes and uses those observations to build their proposed quantitative framework. The figures are informative and framework is explained clearly. The authors propose and name a new mechanism, "niche-flip" that describes resource competition at varying disturbance "intensities" - this is an interesting proposal and I suggest that it is explored more fully as a potential mechanism (see weaknesses).

Weaknesses of the work are the use of definitions that are generally inconsistent with the disturbance ecology literature, and the inability to separate the disturbance event characteristic of "intensity" from the biological outcome of mortality. The authors conclude that DDRs are contextual, which is supported by their modeling and data, but I suggest that they consider that diversity as an outcome in itself may not be the most informative metric of what mechanism(s) drive context-specific outcomes. The authors have a lot of compositional data that could also be examined to understand whether their "niche-flip" mechanism is supported.

This work is likely to advance our understanding of the myriad of outcomes of DDR and what potential mechanisms may support those DDR in natural ecosystems.

Major comments:

Comment 1. Ecological definitions and interdependence of disturbance outcomes/attributes

The authors define disturbance "intensity" as the average mortality rate but claim that this is a disturbance characteristic. However, mortality rate is not a characteristic of a disturbance event, but rather an effect/outcome of a disturbance on the biological community. The key distinction is that disturbance characteristics (also called traits or aspects) are defined relative to the environment, while disturbance outcomes (also called effects, impacts, or responses) are defined relative to the biology of interest, in this case a microbial community. So, changes in diversity of the community, as a result of a disturbance, is a biological outcome of the disturbance. An average mortality rate, what the authors call "intensity" (L40) would be such an outcome.

The authors' definition of "intensity" is not in agreement with the disturbance ecology literature, including the references cited in this current work. For example, in reference #18 (Miller et al. 2011 PNAS) disturbance aspects include intensity, timing, duration, extent, and interval. Specifically, Miller et al. 2011 defined intensity as the magnitude of the disturbance (e.g., a flood's maximum stage). Notably, Miller's definition of intensity is more aligned with the author's definition of "fluctuation," which the authors define as the "magnitude of deviations from the average". In the current work, the disturbance "event" cannot be separated from the biological outcome because of the nature of the continuous culture system. The system is not being disturbed with, for example, a change in pH or salinity or another environmental variable that results in microbial mortality, but rather the loss of viable members from the community through control of the flow-through. So, the mortality is both the precisely controlled disturbance "event" and "outcome" in the continuous culture.

To summarize, the premise of the article is confusing, because one of the two disturbance "characteristics" considered is, rather a disturbance outcome. This may seem like mincing words and to each paper its own definitions, but because this work seeks to reconcile DDRs as reported across many studies, and because many of the previous ecology studies that have investigated or reported DDRs are not using analogous terms, the work could further confusion rather than serve as a reconciliation. When different definitions are applied that mix disturbance aspects with biological outcomes of disturbance, readers will have to work hard to understand this work in context with the existing literature. I suggest revising the introductory section to be consistent in terminology with the ecology literature and to be framed not only as disturbance characteristics, but also outcomes. I also suggest adding discussion of how an inability to distinguish disturbance event from outcome may influence interpretation of this work and its broader application. I suggest adding clarification/discussion of "how intensity and fluctuations interact" (e.g. L200): as the authors define intensity and fluctuation of the disturbance event, intensity is not independent of the biological disturbance outcome of mortality in the given model system. So, how the two "disturbance components interact" is not able to be examined independently from the biological outcome (mortality, resulting diversity).

Comment 2: Compositional evidence for the proposed "niche flip" mechanism and suggestion for deeper consideration of population-level response to disturbance outcomes that collectively contribute to emergent diversity values.

Regarding the "niche flip" - it is unclear whether there is compositional evidence for any swap in niche preference/space among particular community members. Figure S8 may offer evidence, but I could not deduce it from the busy bar charts. Could population/ASV level analysis be conducted on each member to assess their dynamics and ask whether the dynamics support the proposed niche-flip as a DDR mechanism?

Related, there seems to be possible evidence of a "fluctuation" rate threshold, after which there is a major compositional shift in the microbial community. Consider Figure 3: At all "intensities", there is a shift in microbial community composition between "fluctuation" rates of 4/day and 16/day (3d, Fig S8). This threshold/shift is not also apparent in the Shannon diversity in Fig 3f. This could be an example in which diversity as a metric in itself is not as informative/useful outcome for disturbance responses, as identical Shannon diversity values can result from different community compositions that are themselves the outcomes of different mechanisms. I see from the PCoAs (Fig S9) that the authors were exploring potential compositional clustering by day, frequency, and dilution - the most "obvious" clustering to the eye is indeed by "frequency" and between 4/day and 16/day (red/blue separation along both axes, which also supports a potential threshold/shift. Generally, it would have been good to report statistical tests (e.g., PERMANOVA or equivalent) for these PCoA categories (where it makes sense, nested and term interactions as well) - is there statistical support for compositional threshold shift between 4/16?

Reviewer #3 (Public Review):

This manuscript focuses on the relationship between diversity and disturbance. The authors study this relationship in experimental microbial communities. These communities as subject to different levels of disturbance, which is identified as the dilution rate. The authors find a non-monotonic relationship between diversity and dilution rate. In presence of temporal fluctuations, the non-monotonic relationship becomes less evident, disappearing for strong enough fluctuations. The experimental findings are well explained by a consumer-resource model with Monod response.

The results of the paper are a very interesting combination of experimental and theoretical work. The manuscript is well written and easy to follow.

Experiments. The data support the main result of the paper. The U-shaped disturbance-diversity relationship (DDR) is robust (e.g., independent of the measure of diversity). The experimental setup is innovative.

Theory. A main strength of the manuscript is the clarity in which the model reproduces the experimental data. It is also interesting that alternative models (Lotka-Volterra and consumer-resource with linear response) do not reproduce the data, therefore indicating the relevance of the data themselves. The main weakness of the paper is that, in the end, the mechanism behind the non-monotonicity of the DDR is not completely clear. The authors discuss how it emerges with two species and two resources in presence of a trade-off between maximal growth rate and resource-limited growth rate: at low dilution rate, the species with high maximal growth rate wins, while at high dilution rate the one with resource-limited growth rate dominates. This mechanism is clear with two species (in which diversity can transition between 2 and 1). It is unclear what happens for more species and resources. In particular, the role of the tradeoff --- which is central in the pairwise competition case --- is unclear: the U-shapes relationship is observed also in absence of the tradeoff for multispecies communities.

Reviewer #1 (Public Review):

The rapid evolution of target site resistance to pesticides in plant and insect populations poses a challenge to evolutionary models, as it requires a sufficient supply of adaptive genetic variation that is sometimes difficult to reconcile with classical population genetic theory. Kreiner et al address this conundrum by conducting an in-depth study of the evolutionary patterns associated with the evolution of target site resistance to acetolactate synthase (ALS) inhibiting and protoporphyrinogen oxidase (PPO) inhibiting herbicides in Amaranthus tuberculatus, an agricultural weed that is widespread in North America. Several of the mutations responsible for resistance to these herbicides have been previously characterized molecularly, so the adaptive alleles are known. Kreiner et al focus specifically on the question of how often these individual resistance mutations have arisen within different populations, whether this occurred from de novo mutations or standing genetic variation, and how resistance mutations have subsequently spread across the landscape. They find remarkable parallelism in the evolution of resistance mutations, detecting instances of repeated mutational origin of individual mutations even within localized subpopulations, as well as some examples that appear more consistent with evolution from standing variation. Their interpretation is that such rapid adaptation was facilitated by a massive recent population size expansion. They also present results suggesting that the geographic distribution of resistance mutations has been shaped by both intra- and inter-locus allelic interactions due to haplotype competition.

This is a well-written study that presents intriguing results on the evolutionary dynamics of resistance evolution. The conducted analyses stand out through the application of cutting-edge population genetic methodology. In particular, the use of ancestral recombination graph (ARG)-based methods to infer tree-sequences along the genome is an innovative approach that allows the authors to disentangle independent mutational origins of resistance mutations, date individual resistance alleles, and infer selection coefficients over time at these loci. The implementation of these novel analyses appears sound. To the extent that we can trust them to provide correct results, the conclusions are well supported by the data.

While the use of sophisticated new methodology clearly represents a major appeal of the study, it also raises some concerns about the robustness of the results. At present, we simply do not yet have a very good understanding of how accurate the results from ARG-based inference methods are in the light that some of the assumptions underlying these methods are certainly violated in real-world populations. Spatial population structure, complex selection scenarios, variable mutation and recombination rates, or phasing errors are just some of the factors that could potentially mislead the resulting estimates. The error bars provided by these approaches, for instance for the allele age estimates shown in Figure 3, paint just part of the picture, given that they reflect only stochasticity in the MCMC analyses, but not any systematic errors due to violation of the underlying assumptions.

However, it is also clear that a thorough analysis of all potential factors that could limit the robustness of the conducted analyses and lead to biases in the results would be a serious undertaking, which I would consider as beyond the scope of the present study. To do this rigorously would presumably require comprehensive simulation analyses and, ideally, evaluation against positive controls where the true tree-sequences are known. Nevertheless, as I outline in my specific recommendations below, I believe that there are a number of simpler tests the authors could easily perform to at least test the robustness of their results to phasing errors, misspecification of the recombination rate, and the use of different demographic inference methods.

Reviewer #2 (Public Review):

In this manuscript, the authors investigate the origins of herbicide resistance alleles in an agricultural weed A. tuberculatus.

The authors collected genomic data from 18 populations across the Mid West of the US and Ontario in Canada. They focus on known mutations in two genes that are targeted by herbicides (PPO and ALS). One deletion in PPO and two non-synonymous mutations in ALS are common (8-34%) and lend themselves to more in depth analysis. A sweep analysis around the target genes that compares chromosomes with and without a resistance mutation reveal an increase in long range haplotypes (as measured by XPEHH) and a decrease in diversity on chromosomes with the PPO deletion, but not for the two common ALS alleles. This shows that the common resistance alleles are not due to a hard sweep.

Next the authors use a phylogenetic approach (taking into account recombination) to determine how often each of the three common alleles has originated. They find 6 and 2 origins for the two ALS alleles and 3 origins for the PPO deletion. They then map these origins onto the 18 sampled populations and see that some of the origins are found only in certain regions, but others are spread across Ontarios and the US. Next, the authors try to determine *when* the alleles arose and whether they had already been present as standing genetic variation before being picked up by selection. Finally, the authors try to determine interactions between the alleles.

In my opinion, the map with the origins of the alleles (fig 2B) is the key result of the paper. It shows multiple origins, co-occurrence of origins (local soft sweeps), and it shows that there is migration that allowed alleles to spread to different locations, but not enough to spread the alleles evenly across all locations. In a way, this result is a worst-case scenario for those who try to prevent weed resistance, because the results show that resistance evolved at least 9 times independently (which means it is hard to prevent) and also that the resistance alleles are able to spread across space - so even if a farmer or community can prevent evolution, resistant plants may arrive from elsewhere. The parts of the paper that deal with the ages of the alleles and genomic interactions of the alleles are not as convincing to me.

Reviewer #1 (Public Review): 

The paper is very well written and the results are well presented. I only have minor comments. 

The introduction insists on the idea that the ability to regenerate might be ancestral (line 45) but convergent evolution is an extremely common phenomenon. The hypothesis of convergent evolution cannot be excluded here. In any case, whether convergence or ancestrality, one can ask whether the mechanisms underlying organ regeneration are the same in various taxa.

Reviewer #2 (Public Review): 

Abrams et al. show that under certain conditions (high nutrient level, insulin intake, leucine intake and hypoxia) the frequency of arm regeneration in the cnidarian Aurelia aurita increases compared to control ('low food') conditions. The authors argue that similar effects on appendage regeneration can also be found in widely divergent organisms (fruit flies and mice). They suggest that all animals may have an intrinsic capacity to regenerate, which could be revealed by simple (e.g. nutritional) interventions. The idea that many animals possess latent regenerative abilities which could be activated by simple genetic interventions (e.g. by activating specific genes or signalling pathways) has attracted significant interest in the field of regenerative biology, as it underpins hopes that a wide range of regenerative therapies might even be possible in humans. Abrams et al. provide an example of how simple, non-genetic interventions could also influence the extent of regeneration. 

Strengths:

The authors nicely describe the regenerative capacity of the Aurelia aurita juvenile. They show that after large amputation (removal of 3 of 8 arms), a few individuals in their natural habitat are able to regrow partially one arm. Taking advantage of this inherent ability, they screen numerous factors in order to identify ones that enhance this response. Their screen includes factors that have been identified as modulators of signalling pathways, metabolism, the immune system and stress responses in other species. Out of >40 factors or conditions tested, Abrams et al. identify four - high nutrient level, insulin intake, leucine intake and hypoxia - that increase the frequency of arm regeneration in Aurelia. The authors show that the regenerated arms can be functional, as they regrow nerves, muscles and contract simultaneously with the remaining arms. 

Having identified factors that enhance arm regeneration in Aurelia, the authors test whether similar conditions could influence the extent of regeneration in two widely divergent systems: the legs of the adult fruit fly Drosophila, which are thought to completely lack the ability to regenerate, and the digit tips of the mouse, which have a limited regenerative ability. These bold tests yield some intriguing preliminary results. 

Weaknesses:

The work presented on Drosophila is intriguing because the adult legs of flies were not thought to be capable of any regeneration. One of the major constraints is that growth in arthropods is limited by the hard exoskeleton (cuticle) surrounding the body. Periodic molting allows these animals to grow in a stepwise fashion (shedding the old cuticle and forming a new one), but adult flies do not molt, so it is unclear how an adult regenerating leg would break that constraint. Abrams et al. report that a small proportion (~1%) of amputated legs regrow part of the limb when the flies are kept on a medium supplemented with leucine, glutamine and human insulin. The number of legs in which this has been observed is small and the extent of regeneration is variable and not well documented in relation to the site of amputation (which is unmarked). A more detailed documentation of the regrowth would be needed to validate the authors conclusions. 

The work on mice focuses on the regeneration of digit tips, a relatively well-studied example of limited regeneration in these rodents. Mice are known to be able to regenerate the tips of their digits when these are amputated near the distal end, but cuts proximal to the base of the nail fail to regenerate. The authors focus on regeneration of digits amputated near this boundary. They report that animals whose drinking water is supplemented with leucine, glutamine and sucrose are more likely to regenerate part of their digit tips when amputated at the base of the nail. These data are intriguing, but the number of observations is limited (few digits with patterned regeneration) and variation in the site amputation does not make it easy to draw firm conclusions on the extent of regeneration compared with controls. 

Overall, the authors propose that similar nutritional interventions have similar effects in 'inducing' regeneration in widely divergent animals, revealing a widespread intrinsic capacity of animals to regenerate. The claim that these treatments 'induce' regeneration seems exaggerated, given that appendage regeneration in Aurelia and in mouse digits can occur to a variable extent in untreated animals. These treatments appear to shift the probability and the extent of regeneration. The data on Drosophila legs are more surprising and deserve further analysis. 

The idea that the same nutritional interventions may have similar effects on regeneration in diverse animals is intriguing. A minor caveat: the nutritional interventions tested in each species were not identical; in Aurelia high-nutrient, insulin and leucine treatments were tested separately, in Drosophila leucine and insulin were combined, in the mouse leucine and sucrose were combined. Future work could determine which components in these treatments (nutritional, metabolic or hormonal) are responsible for the observed responses in each species.

Reviewer #3 (Public Review): 

In this article, Abrams et al. aim at identifying a potential conserved strategy for inducing appendage regeneration in Metazoa. Appendage regeneration is a process shared by many animal lineages, raising the possibility of a common strategy at play. Using a large-scale screening of molecular and physical putative modulators of regeneration, they identify the amino acid L-leucine and the growth hormone insulin/sucrose as molecules potentially able to trigger appendages regeneration in three species, the cnidarian Aurelia, the ecdyzosoan Drosophila and the mammal Mus musculus. The question addressed in this paper is really interesting and some results, especially in Drosophila and Mus musculus, are intriguing but need to be confirmed and extended. 

Strengths: 

One main strength of this study is the use of three different models to approach a biological question in a comparative manner. Such a strategy is still rare and the authors are commended for it. 

An impressive number of various molecular and physical putative modulators was screened in Aurelia. 

While not being a specialist, statistics appeared to be performed in a correct manner and the number of individuals used in the different experiments is appropriate. 

Results showing tibia regeneration in Drosophila and digit regeneration in adult mice are impressive. 

Weaknesses: 

The evolutionary statement underlying the entire study is not fully accurate. While it is true that many animal phyla include species that can regenerate and also that some studies start to identify common molecular components in regeneration, the question of regeneration being ancestral or not is still debated. It is indeed highly tempting to consider regeneration as ancestral but this is not proven yet (see Lai and Aboobaker 2017) and the possibility of convergence have to be considered too. In addition, appendage regeneration versus whole body regeneration versus structure or organ regeneration may not rely on similar mechanisms. 

The strategy put in place for identifying putative triggers is questionable and more information about, (i) the reasons to select and test such parameters, (ii) what the different drugs are doing in theory (components of signaling pathways targeted, for ex), (iii) how exactly the various tests have been done, frequency of scoring, different concentration tested, number of individuals per condition, frequency of drug administration etc etc are missing. It appears really surprising that no modulators of signaling pathways (notably the wnt b catenin, known to be involved in many developmental and regeneration contexts, especially in cnidarians) are involved in any sort in such process. 

Concerning the results with cnidarians, two aspects puzzle me: the high variability in the regenerative response between batches and the way the amputations were done in ephyra. In the context of appendage regeneration, what justify to perform an amputation that remove almost the half body of the ephyra and not just one arm. Basic information about regeneration occurrence in the context of amputation of only one arm are missing. 

More importantly, the results shown, sometimes in extremely low proportions compared to the controls (ie in the case of drosophila), are not supported by other approaches. It would be really important to have some clues about the molecular mechanisms underlying such process and its induction.

Reviewer #1 (Public Review):

The experiments are exceptionally well done and the analysis is detailed and comprehensive. The figures are mostly good although a few are a bit complex, and I found the force-directed graphs to be not very helpful in the static figures, although extremely useful in the interactive browser (maybe the interactive version could be called out more clearly in the figure legends).

Here are some suggestions for revisions that might further improve the manuscript:

• In both the abstract and beginning of results, it would be helpful to describe the libraries a bit more clearly: all combinations of mutations separating the germline and mature antibody in the VH domain among sites contacting the epitope (at least that's my understanding).

• It is also never clearly stated in results whether it is a single chain (scFv) antibody, and if the HA is trimeric. If so, is there a potential for avidity so that the measurements are Kd, apparent for the multivalent interaction rather than monomeric Kd?

• The calculating of the probabilities of different paths is cool! Is anything known about the actual maturation of these antibodies? I know there are longitudinal data on FI6v3 from the patient from whom the antibody was isolated, but wasn't sure for these two antibodies.

• I personally was very curious how the specific epistasis models the authors used compare to global epistasis models. This is well explained in the appendix, but might be helpful to mention in another sentence or two in the main text as well.

• The figures are really informative but are also numerous and dense. I really appreciated panels like Figure 5A,B, which are easy to interpret and pretty simple!

• In the non-global-epistasis model, is anything done to handle the censoring of the data at the high and low end of the affinity scale? I was confused by this because line 115 says values outside the range are pinned to the boundaries, but then in Appendix (line 1282) it seems to suggest censoring isn't an issue.

Reviewer #2 (Public Review):

The binding landscapes of two broadly neutralizing antibodies (bnAbs), CR9114 and CR62621, which bind to the conserved stem region of influenza hemagglutinin through their VH regions and display varying levels of breadth, were studied. CR9114 and CR6261 contain 14 and 18 mutations in the VH chain between somatic sequences and constructed germline sequences. To decipher the contribution of individual mutations as well as pairwise and higher-order interactions between mutations, the authors made combinatorially complete mutational libraries for both the antibodies. Libraries excluded mutations distant from antigen contacts in the crystal structure (CR9114: 2, CR6261:3). Single-chain variable fragment libraries were constructed and displayed on the yeast surface. Their equilibrium binding affinities were determined using the Tite-seq method against selected antigens (CR9114: H1, H3, and Influenza B HA; CR6261: H1 and H9 HA). Both CR9114 and CR6261 libraries showed expectedly different patterns of breadth. Breadth conferring mutations in CR9114 exhibit nested structure, giving rise to the hierarchical structure of the binding landscape. Examination of the extensive pairwise and higher-order epistasis between mutations revealed key sites with strong synergistic interactions that are highly similar across antigens for CR-6261 and different for CR-9114. It was suggested that features of the binding affinity landscapes strongly favor the sequential acquisition of affinity to diverse antigens for CR-9114 and are highly constrained, while the acquisition of breadth to more similar antigens for CR-6261 is less constrained. The study indicates that the evolutionary pathways to bnAbs are highly dependent on epistatic and pleiotropic effects of mutations. The experimental and computational procedures used to generate and analyze the data are clearly described and there is wealth of binding affinity data that will be of interest to those studying antibody:antigen and more generally protein:protein interactions.

Major points:

1) Unlike other studies where antibodies were isolated from single cell sorting of memory B-cells, the present bNAbs were isolated from phage display libraries. These libraries (Throsby et al, 2008; Dreyfus et al, 2012) were constructed from pooled IgM+ from 10 (not 3 as incorrectly stated in manuscript) (CR6261) or 3 (CR9114) healthy donors and scFv fragments were cloned and screened using phage display against H5HA for CR6261 (Throsby et al, 2008) and against sequential panning against Has from H1, H3 and both B lineages for CR9114 (Dreyfus et al, 2012) respectively. Use of phage display methodology which involves multiple rounds of PCR as well as panning, means that mutations can be introduced during library construction. Hence the resulting sequences isolated need not accurately reflect antibody gene sequences present in the donors. Further the multi-round panning process with diverse HAs (especially for CR9114) biases the resulting sequences selected, altering the order of the panning steps might result in a different selected sequence. Additionally it is unclear what the natural selection pressures are. It is likely that they would be for increased neutralization breadth and potency which is not straightforwardly related to improved breadth of binding to soluble HA where the stem accessibility is much higher than it is in the viral context. The known polyreactivity of many bNAbs including stem directed bNAbs (PMID: 33049994) is another confounding factor.

2) CR6261 was selected by panning against H5 HA whereas the vaccinated individuals presumably primarily had experienced H1. Since the antibody binds to the conserved stem of HA what is relevant to interpret the mutational landscapes is not the overall antigenic diversity of HA (Figure 1B) but the diversity of the stem epitopic region. If these regions are similar in H1 and H9 HA that would explain the observation that multiple mutations in the antibody are tolerated in both cases. In the case of CR9114, the mature antibody binds best to H1, less well to H3 and weakest to B HA. Unsurprisingly the same pattern is reflected in the mutational data. Given the greater diversity in HA stem epitope sequence between H1, H3 and B relative to that between H1 and H9 it is also unsurprising that there should be sequential acquisition of breadth in the latter CR9114 case (see also 1 above).

Minor points:

1) It would be useful to have list the amount of surface area each residue in the paratope for both antibodies contributes to binding where such information is available.

2) Is it possible to convert the effect scores into a free energy of binding contribution? Are there error estimates for the effect scores that would allow one to assess whether apparent differences in effect scores are statistically significant? It is surprising to see significant second order effects at relatively large distances (Figure 2F). What is the suggested explanation?

3) Larger epistatic effect scores occur between residues with the largest contributions to binding. Is this expected?

Reviewer #3 (Public Review):

Phillips et al. aimed to characterize the binding affinity landscapes of two influenza bnAbs, CR9114 and CR6261. Mutant libraries of CR9114 and CR6261 that contained all possible evolutionary intermediates back to their corresponding unmutated germline sequences were constructed. Tite-seq was then used to measure the binding affinity of all variants in the mutant library of CR9114 against HAs from H1 (group 1), H3 (group 2) and influenza B, and the mutant library of CR6261 against HAs from H1 and H9 (both are group 1). The binding landscape of CR9114 showed a hierarchical structure, where most variants bind H1, but only a subset of high affinity variants to H1 bound H3 and a subset of high affinity variants to H3 bound influenza B. In contrast, the binding affinities of CR6261 variants to H1 and H9 correlated well. Mutations with large first order effect are at the HA-binding interface, whereas mutations with strong pairwise interactions tend to be close in space. Additionally, a subset of mutations that dominated the higher-order interactions largely determined the overall structure of the binding affinity landscape. At the end, the authors demonstrated that sequential antigen selection is important for the affinity maturation of CR9114 to cross-react with H3 and influenza B HAs, but not so much for that of CR6261 to the two group 1 HAs (H1 and H9).

Overall, this data in this study is of exceptionally high quality. The authors' claims are mostly well supported. But some additional explanations need to be made.

Reviewer #1 (Public Review): 

Tracking the evolutionary optimization a catalytic RNA, via the emergence of a new structural fold is an exciting development. This work captures the emergence of a new pseudoknot in the catalytic core of a two domain RNA polymerase ribozyme. The work is convincing, timely and will be of significant interest to evolutionary biologists. Biologically speaking, the emergence of new folds is frozen in time. So we can admire the fact that RNaseP has evolutionary flexibility in its substrate recognition domain and not in its catalytic core. We can appreciate that the ribosome through its detailed set of A-minor interactions encodes its evolutionary progression from a simple and ancient gene duplication to the very complex enzyme we see today. Despite having these powerful arcs of evolutionary history we rarely, if ever, get to see the emergence of new RNA structures, that demonstrably improve RNA functionality. Tracking in 'real-time' such evolutionary progressions therefore provides powerful insights into how RNA folds are optimized after their initial emergence. Thus in one sense the initial emergence of the RNA polymerase ribozyme described here happened all at once from random sequence. A new domain called the accessory domain conferred to the ligase core the ability of polymerization (Johnston et al, 2001). How such catalytic systems evolve after this initial 'birth' is key to understanding the evolution of catalytic RNAs early in the evolution life. This work provides a glimpse as to how such optimizations take place and was a pleasure to read.

Reviewer #2 (Public Review): 

Portillo et al describe continuing progress in improving a polymerase ribozyme and furthermore, analyse the structural changes that underlie these improvements over the course of in vitro evolution. They describe the most active polymerase ribozyme yet, which is now able to synthesize the highly structured RNA ligase catalytic core with good efficiency. 

Mapping of the structural changes using in line probing they find a distinct structural remodelling of part of the catalytic core of the polymerase ribozyme that correlates with enhanced catalytic activity. Such progress is encouraging as it suggests that - contrary to previous suggestions - the polymerase ribozyme catalytic core does not occupy an isolated fitness peak in the adaptive landscape and is impervious to further evolutionary optimization. Furthermore, these results suggest that there is, in principle, no obstacle to reaching ever more active polymerase ribozymes ultimately capable of self-replication. Once again the ceiling is raised for RNA replicase capabilities boding well for the potential of an RNA-based genetic system. 

The key claims of the manuscript are well supported by the data, but several important questions are raised that require further discussion and some experimental investigation: 

1) The authors show that the rearrangement correlates with improved activity, but there is little data on how the structural rearrangement alters and improves polymerase activity. Previous generations of polymerase ribozymes were particularly limited in substrate / template binding and processivity and generally had modest fidelity, and the rearrangement could be influencing any of these parameters (see points 2 and 5 below). This requires some investigation, perhaps through product sequencing or assay of activity in the absence of template tether. What are the reasons for the improved activity and remaining limitations that prevent ribozyme self-synthesis? 

2) The parental class I ligase ribozyme synthesized by 52-2 is reported to have a catalytic rate of 0.3 h-1 or as stated in the manuscript" ...accelerating the rate ... of ...ligation by 1,500-fold...". I think this statement is potentially misleading for non-experts in the field, giving the impression of an impressively active product. The original class I ligase is one of the most powerful RNA catalysts ever described, reaching catalytic rates of up to 375 min-1 (Biochemistry 39 : 3115) under optimal conditions and therefore four to five orders of magnitude faster. It may well be that the precise construct and conditions used here conspire to give a somewhat slower catalytic rate, but it seems rather unlikely that it would be compromised to such an extent. Activity should be compared to an equivalent construct transcribed using T7 RNA polymerase and assayed under equivalent conditions. To better understand any discrepancy in activity between protein-prepared, 52-2-prepared and 24-3 prepared RNA, the authors should sequence the 52-2 synthesis products. This would also provide information on ribozyme fidelity (see 1). 

3) The authors state "Tertiary structural remodeling of RNA is known to occur in nature, ..., but .... structural innovation had not previously been observed in an experimental setting." This statement should be tempered in the light of previous studies such as morphing one ribozyme activity / structure into another (Science 289: 44), evolution of a new GTP binding fold from the canonical ATP aptamer (Rna 9: 1456) or indeed experiments from the senior author's own laboratory on the evolution of ribozymes with a reduced nucleotide content (e.g. Nature, 402: 323). 

4) The authors state "... an RNA polymerase ribozyme was seen to undergo a tertiary structural change, similar to changes inferred to have occurred in nature based on phylogenetic analysis (Bokov & Steinberg 2009, Fox 2010)." This statement should be rephrased and different citations used. The changes seen in the polymerase ribozyme do not obviously resemble the structural changes inferred to have happened during ribosome evolution such as hierarchical domain addition and insertion of expansion segments. Considering the focus of the paper, the authors should more directly compare the rearrangement seen here to inferred examples of transformation from biological RNAs. Does in vitro evolution offer different routes of structural changes to biological evolution? Such a significant structural rearrangement during evolution whilst maintaining activity is somewhat surprising. Certainly, the notion that structure is conserved to support activity during sequence divergence is deeply embedded in our understanding of protein evolution. More extensive discussion of these possibilities is needed to strengthen the paper, together with analysis of the implications for e.g., library design during RNA in vitro evolution. 

5) The new P8 helix element is well supported by the data, although I have some doubts as to whether A16 should be assigned as part of this duplex. As the authors identify, A16 has functions beyond base-pairing, and in the parental ligase makes critical contacts to the primer-template using its Watson-Crick face. How is this compensated for? If the J1/3 a-minor interactions are preserved, might the role of the new stem be to position these residues and thus the primer/template? 

6) It would be of great interest, although potentially technically challenging, to investigate to what extent structural changes affect the apo form / ground state of the polymerase ribozyme compared to the holoenzyme (with primer-template / NTPs bound). This would be of particular relevance in the context of a recently described variant of the same polymerase ribozyme (Science 371: 1225), which has been proposed to undergo a significant structural rearrangement at the level of the holoenzyme towards a highly processive conformation.

Reviewer #3 (Public Review): 

The authors have monitored an additional phase in the in vitro evolution of polymerase ribozymes derived from the 1994 Bartel & Szostak ligase ribozyme. Prior phases had appended large domains and engineered or selected abilities to synthesize small amounts of large RNAs with simple repeats and/or to synthesize large functional RNAs (aptamer, ribozyme, tRNA). A large amount of work has gone into this system, both because of its robust catalysis and because of the potential for polymerase ribozymes to drive in vitro evolution of molecular systems composed entirely of RNA (for origins-of-life or SynBio applications). 

The present work heavily mutagenized a single clone from the Rnd 38 population (10% per position), then carried out 14 more cycles of selection for synthesis of a functional hammerhead ribozyme with mutagenic PCR in the amplification steps. and slightly lower magnesium concentrations than prior rounds (down from 200 mM to 50 mM). The resulting species was 3x to 23x more active in 50 mM Mg2+ than was the wt ribozyme or the Rnd 38 clone from which it was derived. 

Major Strengths: 

The primary 'new' finding of the paper is the acquisition of a new structure feature in which a portion of a stem (p7) and loop (L7) gains pairing interactions with a previously unpaired region (J2/3?) to form a new pseudoknot that was not present in the 'wt' ribozyme. This feature is supported by in-line probing data, by kinetics of ribozymes carrying disruptive and compensatory mutation, and by RNASeq data from 19 of the 52 rounds of the selection that document the appearance and development of this feature. Thus, the major claims are well supported by the data. 

An intriguing observation that is not significantly developed is that the 52-2 ribozyme exhibits a burst phase for nucleotide incorporation that may be as much as 100x faster than the later processive elongation rates. Many questions immediately come to mind: Why does it slow down after the initial burst? How many nucleotides can be made at 'burst speed'? Does a structural accommodation accompany the change from burst phase to processive elongation phase? If so, when and why does it occur, and what are the structural differences between ribozymes in these two phases? No doubt these questions will be explored in future studies. 

Figure 5 is one of my favorite parts of the paper. Eighteen clusters with >1% representation in any given round (among the 19 rounds analyzed) were identified, and their fractional representations were plotted as a heat map across the 52 Rnds of the selection (5B). The potential of each sequence to form stem P8 is also shown (5A). While I would like to see a network analysis defining evolutionary precursor-product relationships, I cannot trace the evolutionary ancestor of any of the sequences shown (with a few exceptions in the early Rnds). That is not surprising for the last few sequences, since they arose from heavy (10%) mutagenesis of clone 38-6 and are likely independent of each other. However, as noted by the authors and in the figure, stem P8 was established long before Rnd 38 - including several clones from as early as Rnd 11! Very cool. The lack of evident (by eye) evolutionary connections among these species indirectly supports the claim in the abstract that "multiple paths to the new structure were explored by the evolving population." 

Minor Weakness: 

The only significant weakness is that while the paper does demonstrate that the pseudoknot is necessary, it is not clear whether it is sufficient. Specifically, there is no evaluation of whether rate improvements can be obtained simply by introducing any of the derived P8 pseudoknots into earlier forms of the ribozyme, or if instead the pseudoknot must be accompanied by secondary mutations elsewhere in the molecule.

Reviewer #1 (Public Review):

The paper by -Blackwell et al. develops the ideas developed in the influential paper by Dash et al. (2017) which defined a similarity matrix for CDR3s TCRdist which is based on a weighted combination of local and global similarity measurements. In this paper they use the metric to develop the idea of a meta-clonotype, a set of similar TCRs which enrich for TCRs directed at the same antigen. They demonstrate that these meta-clonotypes show greater publicity than individual clonotypes, and show evidence of HLA-restriction. The authors speculate that the metaclonotype may be a useful biomarker. They provide open-access software tools for defining meta-clonotypes in antigen enriched repertoires. 

The major findings are: (1) Meta-clonotypes are more public than clonotypes, a result which seems not unexpected, given that meta-clonotypes include many different sequences; (2) Meta-clonotypes show evidence of HLA restriction, again predicted given the well-established fact that specific antigens can be recognised by sets of similar TCRs. 

The concept of a metaclonotype is an interesting one which could have widespread use in analysis of TCR repertoire. However, the impact could be much greater, by sharpening the focus of the paper, and adding detail and clairty to the idea of teh clonotype. In particular, while the introduction correctly points out that prediction of SARS-Cov_2 clinical outcome, or better understanding of the role of coronavirus prior exposure in determining outcome are important unanswered questions, this paper does not address these questions. 

A substantial portion of the paper is devoted to analysing data obtained using the MIRA assay (Klinger et al PLoS One 10 :e0141561) to define SARS-COV-2 responses, and it is not always clear whether the objective is to evaluate the accuracy of this data set, or to test the power of the meta-clonotype approach.

Reviewer #2 (Public Review): 

Summary of main aims: The main aim of this paper is to build a framework for TCR meta-clonotypes for finding similar TCRs across individuals (or different repertoires). The majority of the investigations performed in this work have the objective of showing the data properties of meta-clonotypes as well as the metaclonotypes' usefulness for the analysis of antigen-specific TCR data and disease-labeled immune repertoire data. 

Major strengths: Building meta clonotypes is a possible path towards a better coverage of immune repertoire biology as well as inter-individual repertoire comparison. TCRdist3 is an efficient method for building meta-clonotypes that enables the study of the specific characteristics of meta-clonotypes. So, far clusters of similar sequences have not been investigated in depth. The author team is making a significant step forward in this direction by characterizing meta clonotypes in differentially antigen-specific-clone-enriched repertoires and by relating the results to generation probability, HLA, sex and immune status. 

Major weaknesses: Although the authors show a significant amount of data, I am not sure if these data convey sufficient intuition about the characteristics and behavior of meta-clonotypes. The authors seem too focused on relating meta-clonotypes to immune status instead of focusing on the specific biological characteristics of metaclonotypes. Furthermore, the authors fail to convincingly show that the background repertoires chosen for meta clonotypes are robust and to what extent meta-clonotypes are sensitive to changes in the background repertoire. The authors also do not convincingly differentiate themselves from previous approaches that have used network analysis and generation probability in order to find clusters of similar sequences (very much conceptually similar to the approach taken here). Finally, the authors do not provide detailed descriptions of how the comparison of meta-clonotypes across repertoires is handled as well as potential sequence redundancies across meta-clonotypes (in potentially different individuals). I believe that all of the perceived shortcomings are readily addressable in a revision. 

All in all, this manuscript is an important steps towards a better understanding of immune receptor biology. tcrdist3 is an evolution of a previously published method (tcrdist) that is here used to build meta-clonotypes. After reading the paper, it remains slightly unclear (addressable in a revision) as to how useful they are for understanding repertoire biology as well as how to use them in practice in terms of robustness and sensitivity.

Reviewer #3 (Public Review):

Mayer-Blackwell et al introduce a new framework for leveraging antigen-annotated T cell receptor (TCR) sequencing data to search for similar TCRs in bulk repertoire data, which potentially recognise the same antigen peptide. They introduce the notion of meta-clonotype, a T cell receptor (TCR) feature consisting of a main TCR sequence ("centroid") and a distance radius around it (+/- a CDR3 motif), with distance measured according to their previously published TCRdist method (Dash et al, 2017). The meta-clonotypes benefit from increased publicity over exact clonotype matching, and enhance the ability to find potentially relevant TCRs in repertoires from unrelated individuals, which are usually highly diverse, predominantly private, and subject to sampling constraints. The idea of meta-clonotypes is very interesting, and will provide a very useful tool in future repertoire analyses. For example, public databases of annotated TCRs (e.g. VDJdb) can be used to derive the set of meta-clonotypes for a variety of antigens, which can in turn be searched for in bulk repertoire data to identify e.g. memory to previous antigen exposure, immune status etc. 

The tool for performing the analysis, tcrdist3, is open-source, well-documented with instructions and examples, and the statistical analysis has been well-thought out. It is also useful to have the comparison to the current alternative of k-mer based TCR distance (i.e. GLIPH2), and the added flexibility for the user to define the precise distance metric to be used in the tcrdist3 tool. 

The authors then apply their method to analyse TCR beta sequences from COVID-19 datasets that have been publicly released by Adaptive Biotechnologies through the immuneRACE project. They use the MIRA set, the peptide-enriched set, to identify the meta-clonotypes, and then search for these in an independent cohort of COVID-19 bulk repertoires from 694 individuals. The authors find that a large proportion of the meta-clonotypes were more abundant in patients expressing the relevant restricting HLA allele, and suggest this could potentially lead to the development of disease biomarkers. The set of sars-cov-2 related meta-clonotypes is a useful resource in itself, as researchers generating other COVID-19 TCR datasets will be able to utilize this set of meta-clonotypes to search and potentially stratify patients in their own generated data. 

There are a few areas were further detail / examples would strengthen the paper's claims, in particular in the application of the tcrdist3 method to the COVID-19 data. 

1) Bulk TCR data from repertoires with past antigen exposure are likely to contain varying sizes of clones due to the proliferation of responding T cells and a remaining memory population. Due to the sharp drop in size between a TCR sequencing sample and the entire repertoire, clones above a particular size relative to the sample size are highly unlikely to have been sampled by chance, and identifying significantly/meaningfully expanded clonotypes in a sample is often used to identify a potentially antigen-recognising set of TCRs. The authors demonstrate the detection of meta-clonotypes in the repertoire sets, but it is somewhat unclear how the abundance of a clonotype conforming to a particular meta-clonotype is addressed. For example, there may be rationale for treating the following cases differently: meta-clonotype A is instantiated by (i) a unique clonotype with abundance 1; (ii) a single clonotype with abundance 1000; (iii) 100 different clonotypes (i.e. a "dense neighbourhood" around this meta-clonotype). If used to develop biomarkers, perhaps some degree of granularity in how the frequency/occurrence of meta-clonotypes is calculated would be helpful here. 

2) The authors focus their analysis on detecting meta-clonotypes from MIRA sets with strong evidence of HLA-restriction. They report 59.7% of these meta-clonotypes were more abundant in patients expressing the corresponding HLA allele. This means that over 40% of meta-clonotypes with strong HLA restriction were more abundant in repertoires with other HLA types. This point could be further elucidated by comparing results with the control repertoires from the COVID-19 set, from MIRA sets with low evidence of HLA restriction, or combining the sets of low and high evidence of HLA restriction (i.e. HLA agnostic results). 

3) The MIRA55 set is used as an illustrative example throughout the manuscript, which familiarises the reader with this dataset as they are reading the paper. However, the claims made by the paper about MIRA sets / strong HLA evidence MIRA sets could be strengthened by providing an indication of how measured characteristics of the MIRA55 set compare to the other sets being assessed. 

4) There is some discussion throughout the manuscript about using the sars-cov-2 meta-clonotypes to identify differing clinical outcomes such as disease severity. Perhaps the dataset does not have sufficient power to allow for such sub-analysis, but a method of using meta-clonotypes to differentiate between patients based on the occurrence of meta-clonotypes in their repertoire is not provided [e.g. the number of observed clonotypes, the density distribution around clonotypes etc.)

Reviewer #1 (Public Review): 

The authors present a cryo-EM structure of an assembly intermediate of the human mitochondrial ribosome (mitoribosome) purified in the presence of GMPPCP to inhibit GTPBP7, one of the GTPases involved in the assembly of the large ribosomal subunit, thereby stabilizing the structure. The trapped intermediate shows clear density for assembly factors NSUN4 and MTERF4, an RNA methyltransferase and transcription termination factor, respectively, both of which are important for the correct folding of the 16S ribosomal RNA. The spatial resolution achieved by the authors is sufficient to pinpoint some of the molecular interactions, at the amino acid side chain level, that are functionally relevant. These details, and correlating point mutations at different interaction sites with developmental defects in C. elegans, provide strong support for the authors' proposed role of NSUN4 and MTERF4. 

Nevertheless, the use of the non-hydrolysable GTP analog, GMPPCP, as well as purification of intermediates known to be transient introduce some uncertainty in the relevance of the observed structures. While showing that point mutations at interaction sites have a phenotype in C. elegans, structural details of the interactions visualized in the trapped intermediate may still be different under native conditions. 

Overall, this work adds important structural detail to our understanding of how the peptidyl transferase center folds correctly, an essential step in the assembly pathway of mitoribosomes. Insights gained by the authors may also be relevant for the biogenesis pathway of cytoplasmic ribosomes, and they will help understand disease mechanism associated with defective mitoribosome biogenesis.

Reviewer #2 (Public Review): 

This paper is one of several recently released studies describing presumed late intermediates of mitochondrial LSU biogenesis. The authors of this study are experts in ribosome structure determination and RNA processing in C. elegans. In the course of another study to interrogate mitochondrial translation, the authors purified mitochondria from PDE12-/- HEK293T cells that show defects in translation and isolated mitochondrial ribosomal subunits by sucrose gradient preparation in the presence of the non-hydrolysable GTP analog, GMPPCP. They identified a sub-population of mitoribosome large subunits with H68-71 of the 16S rRNA in a partially unfolded state and the proteins NSUN4, MTERF4 and GTPBP7 bound that is incompatible with translation. This structure is potentially interesting because it presents a plausible new intermediate of mitoribosome biogenesis and a possible mechanism for ribosome quality control during recycling. 

The cryo-EM map presented is consistent with an open unfolded conformation of H68-71 and the binding sites of NSUN4, MTERF4 and GTPBP7 as the authors propose. The binding sites of NSUN4 and MTERF4 are also consistent those presented in 2 other concurrent studies, providing further support for this interaction interface. Unlike the concurrent studies by Hillen et al., and Cipullo et al., the LSU is close to fully mature in this structure, making interpretation of the precise role(s) of NSUN4 and MTERF4 in mitoribosome maturation complex. The binding location of GTPBP7 in this model is also different from that observed by Hillen et al., and Cipullo et al., but is consistent with the binding site of the bacterial ortholog RbgA, which can also function in mitoribosome biogenesis, providing evolutionary support for the observed binding site. 

The authors found that mutations in the C. elegans orthologs of MTERF4 and GTPBP7 predicted from their model to interrupt the interaction with 16S rRNA cause sterility, mitochondrial proteotoxic stress and, for MTERF4, developmental delays. However, the authors did not confirm that these mutants interrupt the interaction with the LSU and that the mutant proteins were expressed at or above the level of their wt counterparts. This makes it difficult to determine whether the observed physiological effects were due to loss of this interaction or down-regulation of the proteins. 

The authors' suggestion that this complex might form during mitoribosome recycling is intriguing, especially since this complex was isolated from PDE12-/- cells which show translational defects. As the authors acknowledge, this role for NSUN4, MTERF4 and GTPBP7 is at this point speculative. 

In summary, this study presents a plausible intermediate of mitoribosome biogenesis, highlighting potential roles for NSUN4, MTERF4 and GTPBP7 in late mitoribosome biogenesis and speculatively mitoribosome quality control. Further work is required to determine whether the complex presented represents an on-pathway intermediate of mitoribosome assembly in vivo.

Reviewer #3 (Public Review): 

The overall goal of this study was to capture structures of the assembly intermediates of the human large mitoribosomal subunit that involve GTPases. Authors used a non-hydrolysable GTP analog to arrest the bound form of assembly factor(s) that would otherwise be released in the absence of a GTP analog. While authors succeeded in capturing high-resolution structural intermediates, they failed to some extent in establishing a direct correlation with the point mutations and the biochemical studies carried out in a model organism C. elegans. At times the two studies seem to be disconnected.

Joint Public Review:

The authors set out to assess the role of the ion channel TRPM7 in the regulated release of neurotransmitters and hormones. They used a TRPM7 KO mouse line and studied KO and WT control chromaffin cells and neurons using electrophysiological and imaging techniques. The basic finding is that TRPM7 seems to be activated during the endocytotic process that follows secretory vesicle fusion, and that this TRPM7 activation boosts Ca2+ influx into the cytosol upon endocytosis, resulting in augmentation of endocytosis. This is a quite unexpected discovery of a phenomenon that seems to have been missed in the many previous studies on chromaffin cell secretion - possibly due to the specific rectifying characteristics of TRPM7. The authors show further that TRPM7 KO synapses are characterized by less rapid synaptic depression, and link this phenomenon to altered endocytosis. Overall, key aspects of the data presented are convincing, novel, interesting, and important. On the other hand, there are multiple items that require clarification. 

Strengths:

1) By examining the effects of TRPM7 loss in both chromaffin cells and dissociated hippocampal neurons, the authors are able to deploy a broad spectrum of techniques (each appropriate for the respective preparation) to dissect different aspects of the problem at hand. 

2) Using cell attached membrane capacitance measurements, the authors show convincingly that when individual vesicles fuse, the time between exocytosis and endocytosis (i.e. membrane fission) is clearly increased upon TRPM7 deletion. 

3) Amperometric analyses of secreted catecholamines show clearly that this delay in the fission time course does not impact the ability of vesicles to release cargo to the extracellular space. 

4) The discovery that that the presence of TRPM7 allows for a divalent cation current to pass through the newly incorporated patch of membrane added to the plasma membrane during exocytosis is novel and interesting. 

5) Deletion of TRPM7 slows down, by ~50%, synaptic vesicle endocytosis as assayed by either Synaptophysin-pHluorin or VGluT-pHluorin measurements in dissociated primary hippocampal neurons. This is a quite strong and hence striking effect. 

6) The authors show convincingly that the impact of TRPM7 loss can be rescued by re-expressing a normal variant of the protein but not one that harbors a point-mutation that renders is non-conductive, indicating that ion flux through the channel may be required for the phenomenon analysed. 

7) The authors show (using pHluorin-based measurements) that rapidly removing Ca2+ after a burst of exocytosis also slows down synaptic vesicle recycling kinetics - to the same extent as TRPM7 KO does. This indicates that the impact of removing external Ca is occluded in the KO background. 

Weaknesses:

1) A basic analysis of the morphological properties and protein expression (e.g. of components of the endocytosis machinery) of TRPM7 KO chromaffin cells and neurons is missing. 

2) The presentation of the results is often very brief, which hampers readability. 

3) The authors did not thoroughly describe the rationale for several experimental strategy choices. 

4) The experimental approach to test whether the slowed decay of the pHluorin signal is due to a slowing of the fission process or to a defect in vesicle reacidification was addressed, but the kinetics of the particular experiments are not very likely to resolve any differences even if they were there. 

5) The authors report measurements of changes in nerve terminal intracellular Ca2+ based on readouts of Synptophysin-GCaMP6f in the KO versus the WT. These measurements reflect largely what is happening during a stimulus train, where there is minimal impact of the TRPM7 KO, and not what is happening during recovery (e.g. at the 60 s time point) - where according to the authors the presence of external Ca2+ and Ca2+ flux through TRPM7 is important. 

6) There are some deficits with regard to linking the present experiments to what is already known about TRPM7. 

7) Several experimental details need to be clarified and the discussion requires redaction, taking into account prior knowledge about TRPM7 and considering alternative explanations for some of the key findings. 

8) The fact that TRPM7 loss slows down synaptic vesicle recycling and in turn alters synaptic depression would be of broad interest. However, there is no direct evidence that TRPM7 channels are present on synaptic vesicles, despite very extensive proteomics knowledge about the protein content of purified synaptic vesicles. Thus, it remains unclear whether and how the mechanism of a TRPM7-mediated Ca2+ influx is built into the secretory process, whether after exocytosis the channel appears on the plasma membrane, or if instead the channel is permamently present on the plasma membrane and is only activated - by as yet unknown mechanism - upon exocytosis.

Reviewer #1 (Public Review): 

Nava Gonzales et al. have reconstructed in unprecedented detail the morphology of olfactory sensory neurons (OSNs) within their sensilla in D. melanogaster, characterising the majority of sensory hairs, and OSNs types. To that end they used 8 datasets - 7 of which had been previously published - of serial block-face electron microscopy (SBEM) images where different individual OSN classes were genetically labelled in each dataset. The morphometric dataset collected will be a reference point for the field of olfaction research in Drosophila, and furthermore might inspire similar analyses of other sensory systems, building our understanding of how peripheral morphological features contribute to sensory neuron processing. In addition, they made several observations that warrant follow up studies in the future. These include: 1) Finding what seems to be new sensillum types, and identification of variation in the number of neurons within a single sensillum class, including empty sensilla. 2) mitochondrial enrichment in the dendritic base of certain OSN classes, 3) the presence of extracellular vacuoles within the sensillum lymph, likely derived from the tormogen accessory cell. The paper is purely descriptive but is a valuable addition to the literature and the claims made in the paper are well justified by the results. I have a few comments that I detail in the below. 

- The authors should include more detail as to how the different sensillum classes were identified. The only information given is: "Within a morphological class, sensillum identity was determined by the number of enclosed neurons, the relative position of the sensillum on the antenna, as well as by genetic labelling when this information was available", and "we distinguished ab2 from ab3 by its characteristic antennal location". However, it is worth noting that while sensilla distribution across the antennae is heterogeneous and indeed specific sensillum types are restricted to particular domains, the distribution of many sensillum types follows a "salt and pepper" pattern, intermingling with each other. This is specifically the case for ab2 and ab3 sensilla, both found in partially overlapping regions of the antennae. Therefore, a more detailed description in the methods as to how each sensillum type was assigned will aid the reader understand how the authors reached their conclusions. Furthermore, the authors should avoid circular arguments, such as the one presented for ab2 sensilla, where the identification was made based on position (with the caveat highlighted above) and on the difference in size, but this difference is then used as part of the results, making the argument circular. 

- Following on this point, one of the novel basiconic sensilla identified abx(3) is undistinguishable in terms of morphological features from ab3 sensilla. How was it then distinguished from ab3? Was it due to the lack of genetic marking? This is not explicitly stated in the manuscript and needs to be specified. Furthermore, the authors propose that this sensillum type could be an ab1 sensilla that is missing the ab1D neuron. How did they arrive to this conclusion? If it was based on location, this needs to be explained more explicitly. A suggestion is to show in Figure 1 a diagram of an antennae and indicate from where in the antennae each of the datasets was taken. Furthermore, in subsequent figures it would be good to show on a schematic antennae the approximate location of the described sensilla, and specify from which dataset they were reconstructed. 

- I have some concerns regarding some of the claims made for ab2 sensilla, as these are based on a single sensillum reconstruction (Table 2, n=1 for ab2 sensilla). 

- The discovery of a large number of mitochondria in the inner dendritic segment of some OSN classes but not others is intriguing. Although there seem to be no correlation between this and the size of the soma and therefore spike amplitude generated by each OSN (see ab5A vs ab5B sensilla). It would be interesting if the authors could generate some graphs correlating the number of mitochondria with some physiological parameters previously published, such as spike amplitude, and resting spike frequency of each OSN type. 

- Their findings on at4 sensilla imply that this sensillum type should be reclassified as at4_T2 and at4_T3, because at4_T2 contains only two neurons expressing Or82a and Or47b, while at4_T3 sensilla contains three neurons, expressing Or82a, Or47a and Or65a. This is extremely interesting and predicts that there would be more Or82a and Or47a neurons in the antennae than Or65a neurons, something unexpected given the previous assumption of a single at4 sensillum type with 3 neurons. Based on this finding the authors claim: "We show that not all ORNs expressing the same receptor are house in a singular sensillum type". This statement should be rephrased as it was known before that the same receptor can be housed in two sensillum types, as it is the case for Or35a being hosted in both ac3i and ac3ii sensilla, being paired with either Ir75b or Ir75c. 

Besides these comments, the manuscript provides plenty of novel and intriguing findings that will set the bases for many future investigations.

Reviewer #2 (Public Review): 

Gonzales et al., took advantage of high-end automated, volume-based EM technology, and genetic labelling thus providing an extensive 3D morphometric dataset of 122 olfactory receptor neurons (ORN, that is about 10 per cent of the reported number of ORNs on the antenna of Drosophila melanogaster) grouped in 33 ORN types and housed in 13 of the 19 known antennal sensilla types. For the ORNs morphometric measures, such as ORN soma size and dendritic branching pattern are analyzed. In addition, over 500 sensilla, derived from eight data sets, are identified, including new morphological types. Cellular features, such as empty sensilla, mitochondria number, extracellular vacuoles and extensive dendritic branching in distinct ORNs are described. In selected cases the structure and relationship to the supporting cell in sensilla (thecogen, tormogen and trichogen) are depicted. The studies goes beyond previous structural work done in this field by covering a large number of sensilla and its olfactory receptors. 

The sheer number and completeness of the data strongly complements our knowledge of the sensilla assembly and ORN types in Drosophila. Of particular interest is the ORN cell variability but also their generic structural features (such as soma size for the A and B neuron) reported in a large number of identified ORNs. All olfactory sensilla types (basiconica, trichodea, coelonica) are covered in this study. Therefore, the data presented here are valuable for the experimental neurobiologist for comparing functional properties in ORNs (from own single cell ORN recordings), and is also of potential use for comparative studies in other insects outside the Drosophila neuroscience community. 

In general, the manuscript is well organized. The figures, including figure legends, are nicely designed to give a comprehensive overview that is mostly well to read with the accompanying text. See, my suggesting for improvements below. 

The morphometric analysis is restricted to ORN macroscopic features, such as cell size and dendrite branching pattern of ORNs, cellular features, such as mitochondria distribution, or the relationship to the sensilla supporting cells are only analyzed in exemplified cases. 

I do recommend for a publication in e-life providing the authors make an effort for a more detailed discussion of their findings, and a more comprehensive introduction, e.g. for essential sensilla components such as support cells. 

For a wider audience of the neuroscience community the manuscript would much benefit from: 

1) by expanding your discussion with respect functional significance of your findings: How does your classification of ORN types compares to previous anatomical and functional studies ? Is an 'empty sensillum' a novel finding ? How are physiological responses on the receptor level correlate with neurons' soma size and number of mitochondria ? Some ORNs express more than one receptor, as shown recently previous work by the Potter lab: Task (2020) Widespread Polymodal Chemosensory Receptor Expression in Drosophila Olfactory Neurons 2020.11.07.355651 . 

2) The Table 2, that gives a summary of your result, should be more informative and presented in broader context of what is known on the receptors you describe. . Please, give a reference to the DoOR database (http://neuro.uni-konstanz.de/DoOR/default.html) that provides an excellent overview of functional and anatomical properties of ORNs. Additional columns, e.g. ORN corresponding glomeruli for the their representation in the antennal lobe, -DoOR response, -OR co-receptors, or -best ligand by of would be very valuable. For Figure 1, a clearer description of the location and representation of the genetically /non-genetically ORN and sensilla types is necessary. A nice overview is given by Grabe (2016), see Figure 1, here. 

3) Do you plan to make your datasets publicly available in an open source platform ? In particular, the non-genetically labelled, but identified ORN types are candidates for other researchers to explore cellular features in more detail. Can you make statements of the preservation of the ultrastucture in these preparations ? <br> Such efforts were made for the Drosophila brain connectome with data repositories provided by HHMI Janelia Research Campus and further suggestions for appropriate software (https://www.janelia.org/project-team/flyem).

Reviewer #1 (Public Review): 

Because researchers who use "BrainAge" (the prediction error between one's chronological age and an estimated brain health age) as their metric of choice must rely on the assumption that this estimated BrainAge index is a reflection of accumulated within-person effects of time on that person, this assumption needs to be verified or falsified. The current study aimed to test that assumption by utilizing several large-scale datasets that contained both cross-sectional and longitudinal data collected from structural MRI scanning. BrainAge studies by definition (because they are based on comparing an individual to a sample "norm") utilize cross-sectional data. However, cross-sectional estimates of age-related brain differences has been shown in other studies to not be a reliable predictor of true within-person change over time. 

Applying several instantiations of machine learning to a training set (n = 38,582) and a test set (n = 1372) of data from the UK Biobank database the authors compared cross-sectional BrainAge to true longitudinal change in brain structural features and computed a longitudinal BrainAge to compare to true longitudinal change in brain structure. This was then applied to an independent replication dataset (Lifebrain n = 3292). 

Importantly, the study results found that BrainAge score was not predictive to true brain aging, neither cross-sectionally nor longitudinally. The results did show that BrainAge was significantly predicted by measures available by the time of an individual's birth (birth weight n =770 and a polygenic score taken from a GWAS n = 38,163). These two findings taken together suggests that BrainAge index is not a reliable indicator of brain aging (over time) and certainly does not reflect "accelerated aging", but rather BrainAge index appears to be influenced by factors already present at birth. 

Given the mis- and over-interpretation of BrainAge scores, and the mis-representation of what cross-sectional designs can say about true longitudinal change that is rampant in the field, this study is timely and important. As such, it has many strengths including a very large sample size across several independent datasets, a good and proper use of computation of BrainAge scores (including correcting for age-bias and the inclusion spread), use of linear and GAM fits, and generalizing results across other machine learning algorithms and across independent datasets. 

On the whole, this is a successful study and the authors achieved their aims. While the interpretation and discussion of the findings does not overstep, some terminology could be clearer or more accurately defined. For example, using the term "early life influences" seemed a bit over-encompassing being only indexed by weight at birth and a genetics score. The reason this terminology precision is important is because early life influences connotes a rich set of biological and environmental variables during childhood and adolescence that impact one's ultimate brain health in middle and older age. I recommend changing to a term that better reflects "indices already available at birth". Otherwise this oversteps, conceptually. Another weakness is that the sheer amount of detail and variables involved in the study are not provided until very late in the manuscript (since the format of Intro-Results-Methods-Discussion-Supplemental is used). This manuscript was incredibly difficult to parse, and this is from a reviewer directly in this area of study -- a reader not in this direct field has little chance of successfully parsing this manuscript as currently written. I found it to be a necessity to simultaneously read the Methods and Supplemental Info to be able to read and understand even the abstract, intro, and discussion. There were minor confusions and errors in the figures and captions, etc that I detail in the review to authors. 

Having said that, other than readability, the strengths far outweigh the weaknesses of this submission and it stands to have a high influence on the research in this field, aimed particularly at urging caution in users of (and readers of) the BrainAge metric, and as a higher, more generalized point, the limitations of assumptions of true aging and brain change from cross-sectional data.

Reviewer #2 (Public Review): 

This work provides a new set of evidence on the necessity of longitudinal data for experimental designs to understand individual changes of brain aging. Such evidence were derived with a large-scale dataset UKB and replicated in another, and thus offered strong statistical power to detect small or moderate effects. To achieve their aims, the authors employed the BrainAge method and demonstrated the lack of its association between cross-sectional and longitudinal derivations. Adults' BrainAge metrics were related to the measurements of their birth weight and polygenic scores. This calls cautions in interpreting cross-sectional indices of the aging brain as well as concluding their validity as markers of individual-level brain aging process.

Joint Public Review: 

Lewis and Grandl propose that Piezo1 channels density does not have an effect on pressure sensitivity and that these channels do not cooperate in the nominal absence of membrane tension. To arrive at this conclusion the authors combined single channel measurements along with stochastic simulations of Piezo1 spatial distributions. An important element of this study is the use of two types of cells, one with intrinsically low level of Piezo1, and another with overexpressed channel. An interesting technical aspect is the use of a ramp pressure protocol, which overcomes the drawbacks of the standard step pressure method and help better estimate mechanosensitivity. 

The topic of this manuscript is timely and relevant as the study of this family of ion channels is still very new and the details of their gating mechanism are unknown, and the central conclusion is important for understanding the physiological role of Piezo1 is various types of mechanically sensitive cells. 

Although the manuscript is a tour de force, there are some concerns that should be addressed to validate the conclusions. 

1) Patch imaging to validate conclusions. We note that the authors departed from their previous strategy of patch visualization and direct tension determination (Lewis and Grandl, elife 2015). We do understand how tedious these experiments are, but the use of this approach in some of the trials would make this particularly study cleaner and more conclusive. The authors never mention tension as the parameter driving gating, and they work exclusively in the units of pressure. They postulated that their pipettes ranging between 3 and 6 MOhm in resistance provide 'standard' patches of given size and curvature. In fact, this range of resistances is large and roughly translates into a 1.4-fold difference in pipette diameters. If we look at Supplemental Fig 2D, we see that the largest pipettes (2-2.5 MOhm) produce 5 times the current recorded by 4-5 MOhm pipettes. These patches are unquestionably larger and no one can expect that the midpoint of channel activation by pressure is the same. The authors stated that only pipettes > 3MOhms were included into analysis, but this does not exclude actual size variation among patches. Fig. 4F,G clearly shows that patches containing 0-20 channels have higher P50. It is very likely that these patches are smaller and therefore will require higher pressure for activation. We expect that imaging can help here. The authors do not need to cover the entire range of densities; only two sets of measurements with low (native) and high (overexpression) densities would be sufficient. We suggest that you choose reasonably large pipettes (2.5-3 MOhm) so you can reliably image cell-attached patches. Two sets of tension-Po curves, their tension midpoints and slope factors, recorded from HEK cells transfected at low and high expression levels will provide a clinching result. 

2) Inactivated and silent channels. Because Piezo1 channels inactivate and require the removal of the stimulus to be reactivated, the presence of basal tension in the patch will render some Piezo1 inactivated. Given that the basal pressure differs between electrodes, different patches will always contain some number of 'silent' channels, which are physically present in the membrane, but do not manifest themselves functionally. This makes the estimation of a true number of channels in the patch virtually impossible using electrophysiology alone. The reviewers discussed this point at length. It was agreed that a few additional experiments (outlined below) would be ideal for mitigating all concerns on this issue. However, it was also agreed that the patch-imaging experiments requested above should be sufficient for supporting the paper's main conclusions, and therefore the experiments suggested below are suggested but not required. If the suggested experiments are not performed, we request that the authors mention / discuss the 'silent channel' point and indicate that their experiment provide a good estimation of the total number of channels, rather than the exact number. <br> Suggested experiments: 

a) The authors claim that their novel stimulation protocol for single channel analysis allows them to measure the number of channels in each patch with high accuracy. The pressure ramps, used in this manuscript,¬ last about 3,000 ms at + 60mV. However, according to the same group (Wu et al, 2017, Cell Reports) Piezo1 inactivates within 90-100 ms at +60 mV. Hence, it is quite possible that in those patches the authors are underestimating the number of channels. It is not clear why the authors did not measure macroscopic currents using similar conditions as the ones used for single channel measurements. Particularly, macroscopic currents should be measured at positive potentials (to slow down inactivation), instead of at -80 mV, where Piezo1 tends to inactivate more rapidly than at positive potentials. 

b) The authors should consider perfusing Yoda1 (Piezo1 specific agonist) at the end of the pressure ramp experiments to reveal that there are indeed no more channels in the patch membrane than those visible ones with pressure. 

c) The representative traces on Figure 1B and 1D suggest that -60 mmHg does not saturate the response of Piezo1. The authors should add a control square pulse at the end of the experiment (equal to or larger than -60 mmHg) to ensure the currents are saturated.

Reviewer #1 (Public Review): 

This is a well-written, timely manuscript describing a robust and user-friendly machine learning method tailored for high-throughput studies of proteins (PARROT). The authors provide an accessible description of the method and several case studies demonstrating its utility. The software source code is readable and I was able to run their example analyses. The documentation is excellent and was easy to navigate. I think the tool will be useful for the many groups doing high-throughput experiments that are not machine learning experts.

Reviewer #2 (Public Review): 

The analysis of large data sets obtained from omics or other approaches is often the most time consuming and difficult step of a study. Deep learning and related computational approaches offer the possibility to train a software on a certain data set and then analyze large new experimental data sets. The authors describe the software architecture and demonstrate the application of the system on three different topics: prediction of phosphorylation, prediction of transactivation potential of peptides and prediction of aggregation propensity. They compare the results of their new software PARROT with other existing software tools. 

Overall, the performance of the new software tool seems excellent. In particular its flexibility will make PARROT a very useful tool for the analysis of large data sets.

Reviewer #1 (Public Review):

This paper aims to address the question of whether the rotational dynamics in motor cortex may be due to sensory feedback signals rather than to recurrent connections and autonomous dynamics as is typically assumed. This is indeed a question of importance in neural control of movement.

The authors employ both analyses of motor cortical data and simulation analyses where a neural network is trained to perform a motor task. For the simulations, the authors use a neural network model of a brain performing arm control tasks. Importantly, in addition to the task goals, the brain also receives delayed sensory feedback from the muscle activity and kinematics of the simulated arm. The brain is modeled either using a stack of two recurrent neural networks (RNN) or using two non-recurrent neural network layers to investigate the importance of autonomous recurrent dynamics. The authors use this framework to simulate the brain performing two tasks: 1) posture perturbation task, where the arm is perturbed by external loads and has to return to original posture, and 2) delayed center-out reach task. In both tasks, the authors apply jPCA to units of the trained network, simulated muscle activity, and simulated kinematics and investigate their rotational dynamics. They find that when using an RNN in the brain model, both the RNN layers and kinematics show rotational dynamics but the muscle activity does not. Interestingly, these conclusions for both tasks also hold when networks without recurrent connections are used instead of the RNNs. Also importantly, the rotational dynamics also exist in the sensory feedback signals about the limb state (e.g. joint position, velocity). These results suggest that recurrent dynamics are not necessary for the emergence of rotational dynamics in population activity, rather sensory feedback can also achieve the same.

The authors perform similar jPCA analyses on monkey motor cortical (MC) or somatosensory cortical activity during the same two tasks and find largely consistent results. As with simulations, neural population activity and kinematics show rotational dynamics but muscle activity, which is explored only in the posture task, does not. Importantly, population activity in both motor and somatosensory cortices shows rotational dynamics. This observation is more consistent with the view that rotational dynamics emerge due to inter-region communications and processing of sensory feedback and planning, rather than autonomous dynamics within the motor cortex.

The approach of the paper is interesting and valuable and the questions being addressed are very important to the field. To further improve the paper and the analyses, there are several major comments that should be addressed to fully support the conclusions and clarify the results:

Major:

1) In the Methods, the authors explain how they model a non-recurrent network as follows: "We also examined networks where we removed the recurrent connections from each layer by effectively setting Whh, Woo to zero for the entire simulation and optimization (NO-REC networks)". However, if this is the only modification, it still leaves recurrent elements in the network. For example, if we set W_{hh} to zero, equation 2 will be:

h_{t+1} = (1-a) * h_t + a * tanh(W_{sh} * s_t + b_h)

where a is a constant scalar (seems to be equal to 0.5). This is indeed still a recurrent neural network since h_{t+1} depends on h_t. If their explanation in the Methods is accurate, then the current approach restricts the recurrent dynamics to be a specific linear dynamic (i.e. "h_{t+1} = (1-a) * h_t + ...") but does not fully remove them. The second layer is also similar (equation 3) and will still have recurrent linear dynamics even if W_{oo} is set to 0. To be able to describe networks as non-recurrent, the first terms in equations 2 and 3 (that is (1-a)*h_t and (1-a)*o_t) should also be set to 0. This is critical as an important argument in the paper is that non-recurrent networks can also produce rotational dynamics, so the networks supporting that argument must be fully non-recurrent. Perhaps the authors have already done this but just didn't explain it in the Methods, in which case they should clarify the Methods. However, if the current Method description is accurate, they should rerun their NO-REC simulations by also setting the fixed linear recurrent components (that is (1-a)*h_t and (1-a)*o_t) to zero as explained above to have a truly non-recurrent model.

2) Assuming my comment in 1 is addressed and the results stay similar, the authors show in simulations that even without recurrent dynamics (referred to as the NO-REC case), rotational dynamics are observed in the simulated brain during both tasks (Figure 8). This result is used to suggest that the sensory feedback is what causes the rotational dynamics in the brain model in this case. However, I think to fully demonstrate the role of feedback, additional simulations are also needed where the sensory feedback is removed from the brain model. In other words, what would happen if recurrent and non-recurrent brain models are trained to perform the tasks but are not provided with the sensory feedback (only receive task goals)? One would expect the recurrent model to still be able to perform the task and autonomously produce similar rotational dynamics (as has been shown in prior work), but the non-recurrent model to fail in doing the task well and in showing rotational dynamics. I think adding such simulations without the feedback signals would really strengthen the paper and help its message.

3) A measure of how well each trained network is able to perform the task should be provided. For example, is the non-recurrent network able to perform the tasks as accurately as the recurrent models? The authors could use an appropriate measure, for example average displacement in the posture task and time-to-target in the center-out task, to objectively quantify task performance for each network. Another performance measure could be the first term of the loss in equation 5. Also, plots of example trials that show the task performance should be provided for the non-recurrent networks (for example by adding to Figure 8), similar to how they are shown for the recurrent models in Figures 2 and 6.

4) An important observation is that rotational dynamics also exist in the sensory signals about the limb state. This may imply that the task structure that dictates the limb state and thus the associated sensory feedback may play an important role in the rotations without the recurrent connections. While the present study will be a valuable addition regardless of what the answer is, this is an important point to address: What is the role of the task structure in producing rotational dynamics? In both the posture task and the center-out task, the task instruction instructs subjects to return to the initial movement 'state' by the end of the trial: in the posture task the simulated arm needs to return to the original posture upon disturbance, and in the center out task the arm needs to start from zero velocity and settle at the target with zero velocity. Is this structure what's causing the rotational dynamics? This is an important question both for this paper and for the field and the authors have a great simulation setup to explore it. For example, what happens if the task instructions u* instruct the arm to follow a random trajectory continuously, instead of stopping at some targets? With a simulated tracking task like this, one could eliminate obvious cases of return-to-original-state from the task. Would the network still produce rotational dynamics? Of course, I don't expect the authors to collect experimental monkey data for such new tasks, rather to just change the task instructions in their numerical simulations to explore the dependence of observed rotational dynamics on the task structure. I think this will help the message of the paper and can be very useful for the field.

5) It would be beneficial if the authors could elaborate in the discussion on intuitive explanations of why sensory feedback can produce rotational dynamics even with no internal recurrent dynamics in the brain model. To me, it seems like sensory feedback is providing a path for recurrence to exist in the overall brain-arm system, so the non-recurrent neural networks can learn to exploit that path to effectively implement some recurrent dynamics. Some intuitive explanations like this will be helpful for readers.

6) One main result in data from non-human primates is that there exist rotations also in the somatosensory cortex not just in motor cortex. A more thorough discussion of prior work on rotational dynamics or lack thereof across brain regions and behavioral tasks is important to add here. For example, besides the works cited by the authors, there are other works such as (Kao et al., 2015; Gao et al., 2016; Remington et al., 2018; Stavisky et al., 2019; Aoi et al., 2020; Sani et al., 2021) that discuss or show rotational dynamics in various brain regions and behavioral tasks and should be cited and discussed.

7) The authors state that "In contrast, rotational dynamics appear to be absent in... MC activity during grasping driven by sensory inputs (Suresh et al., 2020)." There are other papers that study dynamics during reach-grasps and still finds rotational dynamics and modes (Abbaspourazad et al., 2021; Vaidya et al., 2015) and should be cited and discussed. The recent paper on naturalistic reach-grasps (Abbaspourazad et al., 2021) also argues for the involvement of a large-scale network in these movements, which further supports the authors' interpretation that "This interpretation of motor control emphasizes that the objective of the motor system is to attain the behavioural goal and this requires feedback processed by a distributed network." A discussion of this point made in this recent paper in the intro/discussion is important. Finally, there is a recent paper that argues for the input-driven nature of motor cortex (Sauerbrei et al., 2020) and is cited/discussed by the authors but briefly and mainly in the discussion. I think given the relevance of this recent paper to the core message here, it should also be briefly discussed in the introduction to better set up the work.

Minor:

1) The Methods are clear and comprehensive, but just to make understanding of the simulation setup easier, it would help to have a diagram of the computation graph for the recurrent and non-recurrent networks that shows their number of units, activations/nonlinearities, RNN cell type, etc., added as supplementary figure.

2) Again, to help more clearly convey the simulations, it would help to show the task goals (x*) that are inputs to the simulated brain for example trials in each task (for example added to Figures 2 and 6).

3) Similar to how VAF is shown on top of all plots of jPC planes, it would be helpful to have the rotation frequency for each jPC plane noted next to it. Currently it is difficult to find the jPC frequency associated with each plot from the text.

4) I am a bit surprised by how different the null distributions are for modeling muscle activity (Figure 3F) and kinematics (Figure 3H). The null distribution is simply the R2 for a constrained or unconstrained dynamic model fit to a subsampled version of the neural activity. The only difference between the null distributions in Figure 3F and Figure 3H seems to be the downsampled dimension, which for muscle activity is 6 and for kinematics is 4 (per equation 1). Any insight will be welcome as to why down sampling the population activity to 4 (Figure 3H) results in so much worse R2 compared with down sampling it to 6 (Figure 3F)?

References:

Abbaspourazad, H., Choudhury, M., Wong, Y.T., Pesaran, B., Shanechi, M.M., 2021. Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior. Nature Communications 12, 607. https://doi.org/10.1038/s41467-020-20197-x

Aoi, M.C., Mante, V., Pillow, J.W., 2020. Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nature Neuroscience 1-11. https://doi.org/10.1038/s41593-020-0696-5

Gao, Y., Archer, E.W., Paninski, L., Cunningham, J.P., 2016. Linear dynamical neural population models through nonlinear embeddings, in: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 29. Curran Associates, Inc., pp. 163-171.

Kao, J.C., Nuyujukian, P., Ryu, S.I., Churchland, M.M., Cunningham, J.P., Shenoy, K.V., 2015. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications 6, 7759. https://doi.org/10.1038/ncomms8759

Remington, E.D., Narain, D., Hosseini, E.A., Jazayeri, M., 2018. Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics. Neuron 98, 1005-1019.e5. https://doi.org/10.1016/j.neuron.2018.05.020

Sani, O.G., Abbaspourazad, H., Wong, Y.T., Pesaran, B., Shanechi, M.M., 2021. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nature Neuroscience 24, 140-149. https://doi.org/10.1038/s41593-020-00733-0

Stavisky, S.D., Willett, F.R., Wilson, G.H., Murphy, B.A., Rezaii, P., Avansino, D.T., Memberg, W.D., Miller, J.P., Kirsch, R.F., Hochberg, L.R., Ajiboye, A.B., Druckmann, S., Shenoy, K.V., Henderson, J.M., 2019. Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife 8, e46015. https://doi.org/10.7554/eLife.46015

Vaidya, M., Kording, K., Saleh, M., Takahashi, K., Hatsopoulos, N.G., 2015. Neural coordination during reach-to-grasp. Journal of Neurophysiology 114, 1827-1836. https://doi.org/10.1152/jn.00349.2015

Reviewer #2 (Public Review):

In this interesting study, Kalidindi et al. investigate the dynamics of responses in cortical areas during motor control.

In a landmark paper, Churchland, Cunningham ... Shenoy suggested surprisingly that the dynamics of peri-movement related responses in neurons could be well described by a dynamical system with imaginary components to their eigen values and resulting in rotational structure. One possibility is that MC is an autonomous and rotational dynamical system with such imaginary eigen values. Since Churchland et al. 2012, and a companion paper Sussillo et al. 2015 originally appeared, there have been many discussions on how to interpret this result. For instances sequences of neural activity could lead to rotations, or smoothness in time, or nonnormal dynamical systems etc.

Here, in this new study the authors argue through a convincing combination of modeling and analysis that even without local recurrent connections in motor cortex, one could generate rotational structure in the dynamics. This paper is another attempt to understand these rotations and tries to argue that perhaps it is misleading to view the MC as a purely autonomical dynamical system and that feedback is an integral part of the process of generating these dynamics. I particularly liked the use of data from 5 monkeys in the paper and also the various perturbations to the model and the fact that the model has realistic feedback.

These arguments are based on training a 2-stage RNN with feedback and use it to solve a task where they perturb the arm. They then analyze these networks and neural data and show 2 key results: Rotations can occur in non-motor cortices and recurrence is not necessary for rotational structure. Both of these results are important and deepen our understanding of rotational structure in areas involved in motor control.

Weakness emerges from the fact that feedback in itself is a form of recurrence and not something that is mystically leading to rotations. This was something that Sussillo et al. 2015 made plain: "it should be stressed that, although our model reveals a robust dynamical solution to the problem of producing multiphasic EMG, the scope of the recurrent circuitry, cortical, central and/or feedback, supporting those dynamics remains an open question". In my opinion, the lack of a common framework in the paper for understanding local recurrence and longer time scale feedback reduces the impact of the study, because without such a framework, it feels like a slew of observations.

These results build on Churchland et al. 2012, Sussillo et al. 2015, Sauerbrei et al. 2020, Suresh et al. 2020 and provide insight into how and when does rotational structure emerge in motor cortex. In my opinion, the study is interesting and provides food for thought and robust discussion for the many researchers working on motor control.

Reviewer #3 (Public Review):

The authors trained monkeys to perform a posture perturbation task and showed that monkey motor cortex (MC) activity exhibits significant rotational dynamics during movement. These findings suggests that near-autonomous dynamics may not be the only explanation for rotational dynamics in the MC, as sensory inputs to the MC are likely important for solving this task. To validate this idea, the authors trained recurrent neural networks (RNNs) to actuate a two-link arm model and perform a similar posture perturbation task. Importantly, these RNNs receive delayed sensory feedback about about the state of the arm and the muscle activity produced. They found that the trained RNNs and the sensory inputs provided to these RNNs both exhibited rotational dynamics, while the corresponding muscle activity did not. This suggests that sensory inputs could be the source of rotational dynamics in the RNNs. Indeed, the authors found that monkey somatosensory cortex also exhibited rotational dynamics during the posture perturbation task. Moreover, feedforward networks trained to perform the task also exhibited rotational dynamics, suggesting that recurrent connections are necessary for the emergence of rotational dynamics.

This work provides an alternative explanation for the emergence of rotational dynamics in the motor cortex (MC). In doing so, the authors (1) dispel the idea that the only explanation for rotational dynamics is that the MC is a near-autonomous dynamical system and (2) show that rotational dynamics in the MC is consistent with the well-supported view that the MC uses sensory feedback for online motor control. The central thesis of this study is well-supported by both analyses of neural recordings and simulation experiments. In particular, the authors showed that RNNs driven by sensory inputs could also exhibit rotational dynamics. While this fact alone does not prove that sensory inputs induce rotational dynamics in the MC, it certainly shows that sensory feedback could be a potential mechanism. Overall, this is an important contribution that connects recent electrophysiological studies of population dynamics in the MC with the long-standing view of the MC as a feedback controller.

Reviewer #1 (Public Review):

There is continued speculation on the extent of within-host adaptive evolution of acutely infecting pathogens, including SARS-CoV-2 and influenza. Previous studies have found little evidence of positive selection during influenza infections of healthy adults. Here the authors examine within-host influenza dynamics in two interesting populations: children experiencing likely their first infections with H3N2, and children and adults infected with the newly emerging H1N1pdm09. The authors extend previous observations of adults infected with H3N2 to children, showing that despite potentially higher viral population sizes and/or longer infections, H3N2 largely experiences purifying selection within hosts. H1N1pdm09 infections, in some contrast, show some evidence of positive selection. The authors analyze specific substitutions in different genes, finding some evidence of CTL escape/reversion and epistasis through stabilizing mutations. Using a simple model, the investigators contend that H3N2 reaches mutation-selection equilibrium late in infections.

This is a generally accurate and interesting analysis that enriches our understanding of within-host influenza dynamics. It is valuable to see the dynamics of (mostly) primary infections, where little antibody pressure is expected, and also some impact of the cellular immune response.

My primary reservations concern the analysis of H1N1pdm09:

First, the authors describe a higher rate of nonsynonymous substitutions early in infection, but the statistics backing this claim are unclear. Figure 2B shows box plots suggesting this trend, but the caption describes typically only two samples per day. In that case, it's better to plot the data points directly. Is there really statistical power to claim a significant trend over time and meaningful difference from H3N2?

Second, the authors interpret individuals infected with H1N1pdm09 infections as being as naive to the virus as ~2 year olds experiencing their first H3N2 infection (ll. 352-354). Setting cellular immunity aside--- which maybe we shouldn't---at least two studies found substantial targeting of an epitope on H1N1pdm09 HA that was homologous to H1N1 HA epitopes from the late 1970s and early 1980s (Linderman et al., 2014, PNAS, and Huang et al., 2015, JCI). In other words, there likely is some adaptive immune pressure with these H1N1pdm09 infections.

Finally, it is curious that mutation-selection balance is posited for H3N2 but not H1N1pdm09. Obviously there's not much real "balance" in infections that are so short, and the H1N1pdm09 infections appear shorter than H3N2. As there is likely some preexisting immunity shortening infections with H1, does this imply the mutation-selection balance story is unlikely to hold for H3N2 in older children and adults? What evolutionary dynamics can convincingly be ruled out after more careful consideration of the H1N1pdm09 temporal trends?

Reviewer #2 (Public Review):

At the global level, influenza evolution is characterized by positive selection and antigenic drift. While similar dynamics have been seen in chronically infected individuals, multiple studies of acute infections have been characterized by limited diversity and a lack of antigenic selection. Here the authors leverage a unique dataset of deeply sampled, longitudinal isolates from individuals whose infection lasted up to two weeks. The intermediate length of these infections helps bridge observations from studies of acute and chronically infected hosts. Additionally, the data set is comprised of endemic H3N2 isolates as well as H1N1pdm09 isolates from infections early during the 2009 pandemic. The dataset provides insight into host-level differences between emerging and endemic viruses. Although there is little evidence of within-host antigenic selection the authors do uncover a few mutations found in multiple samples at later time points. Their detailed analysis shows these may be the result of positive selection and epistatic interactions. Additionally, the study reveals increasing rates of nonsynonymous substitution over time and simulations show these trends would be expected under mutation-selection balance with most NS mutations being mildly deleterious. Nonsynonymous rates are also higher in H1N1pdm09 isolates as could be expected of a virus that is less adapted to its host.

Disentangling biological phenomena from methodological artifacts is a challenge in any deep-sequencing, within-host study. The increase in nonsynonymous and nonsense mutations seen in later samples with high Ct is consistent with the author's conclusions, but it is also consistent with PCR errors which are common in low titer samples. Although the authors have applied quality and depth thresholds to help mitigate against these artifacts, figure 1 figure-supplement 2 appears to show that some variants used in the analysis were only found in 1 of the multiple overlapping amplicons. These variants are potentially PCR artifacts and may indicate other variants at similar frequencies are also artifacts. The same phenomena might also just be a consequence of imperfect variant detection at low frequencies. It would be interesting to see if the same general trends in the estimated rates are observed if the variant-calling stringency is increased to exclude these such variants. Longitudinal sampling is a key strength of this study. Observing the same mutation at different time points suggests they are unlikely to be random PCR artifacts. And the abundance of nonsynonymous mutations seen in H1N1pdm09 isolates is maintained across minor allele frequencies. In general, the major conclusions appear robust to random PCR error.

This is a thorough study of a unique dataset, that combines a cross-sectional and longitudinal analysis to uncover general trends (NS/S rates over time) and specific events (parallel evolution at later time points) that shape within-host influenza evolution. The authors support their conclusions with a diverse array of quantitative analyses (e.g. transmission-bottlenecks, with-host evolutionary rates, haplotype reconstruction). This study helps unite previous observations from acute and chronic infections and is an important step in a fuller understanding of how evolutionary forces act across biological scales.

Reviewer #3 (Public Review):

The authors analyze deep sequencing data from H3N2 and pandemic H1N1 infections, primarily from children and young adults. The pandemic H1N1 samples came from the first year of the pandemic, just after the virus's emergence into human hosts, and the authors often had access to longitudinal samples from the same infection. The authors used within-host variants detected to estimate evolutionary rates at different times throughout the infection. They identify several instances of seemingly recurrent mutations, and they perform simulations to determine how synonymous and nonsynonymous mutations would accumulate over time given different assumptions about the distribution of fitness effects. The manuscript's findings largely reinforce prior findings about influenza's evolutionary dynamics within hosts and at transmission, though the authors analyze longitudinal samples from longer infections than in previous studies.

Reviewer #1 (Public Review): 

This manuscript is a follow-up of an earlier manuscript using the LRET technology, but extends the study by identifying a new "open" state and using experimental distance constraints to provide molecular models of the different states. All in all, the manuscript is well written, the experiments are described in sufficient details and experiments are done to high quality with the appropriate controls. The data corroborate the partially open state as published early, but extend the study to a second, open state. It is very good to see that the observed states are not only present in the catalytic head but the authors also use the full-length protein and find similar states. However, in the present manuscript, I find the conceptual advance with respect to the mechanism of MR somewhat limited. The authors curiously do not include any DNA in their structural studies, so the observed states are only relevant for the free MR complex, but not the complex "in action" bound to DNA where quite different conformations might occur. As one consequence, the structurally proposed states do not directly correlate with the functional nuclease states that are necessarily bound to DNA. Perhaps as a consequence, in the author's model, Rad50 is merely a gate-keeper for Mre11, but this is not the case as recent structural work shows that Rad50 forms a joint DNA binding surface with Mre11. Likewise, biochemical studies are done with physiologically unclear/less relevant 3' exonuclease activity only, but not with the physiological important 5' endonuclease activity. In my opinion, it is important for a publication in a journal with the scope of eLife and addressed to a broad audience to provide structural analysis in the presence of DNA and validate the structures using the endonuclease activity. 

Specific recommendations: 

1) Instead of using the physiological unclear exo activity, I suggest to use the more relevant endonuclease activity to validate the mutants. 

2) Since the authors mutated one side of newly identified/proposed salt-bridges, I also suggest to test whether a charge reversal on both sides of the salt bridge rescues the phenoptype. I find this important because MR has quite many conformations, and mutating a single residue might not unambiguously validate the proposed conformation, a rescue by a charge reversed salt bridge is much stronger. 

3) Since all LRET experiments are done without DNA, the authors do not capture relevant DNA processing states and comparison of structural (w/o DNA) and biochemical data (w/ DNA) is not really justified, in my opinion. Also, they might miss critical conformations. Is there a technical reason for not including DNA in the LRET studies? 

4) If the authors want to claim processive movement coupled to partially open/open state interchanges, they should provide experimental evidence. Where would the energy come from for such a movement, this is not clear from the model? 

5) The SAXS data for the "open" state do not validate the model, in my opinion. Experimental data and model are not inconsistent, but the curve looks to me as if the open state is perhaps much more flexible (i.e. an ensemble) or extended? Please comment. 

6) Distance errors for the full complex are much smaller than those for the catalytic module only (Fig. 1d). Does that mean that the full complex is more rigid, please comment?

Reviewer #2 (Public Review): 

This study investigates the ATP-dependent global conformational changes of the P. furiosus Mre11-Rad50 (MR) complex using Luminescent Resonance Energy Transfer (LRET), the molecular docking program HADDOCK, and Small-angle X-ray scattering (SAXS). LRET use a luminescent lanthanide donor and a fluorophore acceptor pair, and provides a measurement of the distance between donor and acceptor molecules calculated from the donor and fluorophore lifetimes. Authors engineered a set of LRET probes throughout the NBD of Pf Rad50 via single-cysteine mutations. The network of distances provided three conformations of the ATP-bound Pf MR(NBD) complex in solution: open, partially open, and closed, which were modelled by HADDOCK. These models were tested using site-directed mutagenesis by disrupting interacting domains and evaluated using SAXS. The work also investigates how these conformational changes induced by ATP-binding affect the exonuclease activity of Mre11 dimer. Overall, this is a solid and very well presented study. However, this work also presents some concerns. It is unclear how SAXS modelled data obtained from HADDOCK models corroborate the three conformations proposed by the authors. Additionally, simulation of SAXS data from the open conformation - obtained under ATP-binding saturating conditions - matches well with SAXS experimental curves of the apo-MR complex. This is unclear and should also be addressed. Experiments including the non-hydrolysable ATP analogue AMP-PNP and in the absence of nucleotide should bring light on the question on the coupling between ATP binding and hydrolysis with MR complex conformational changes.

Reviewer #3 (Public Review): 

In this study, first, multiple LRET pairs were designed, purified and analyzed, which revealed three distinct groups. The measured distances between the respective pairs were then given as constraints for molecular modeling. The authors then modelled three distinct conformations: closed (nuclease-inactive), partially open and open (latter two being active). Based on the models, several mutants that disrupt the equilibrium between the conformations were designed and biochemically analyzed. This an interesting study that is focused on an important problem. Overall, the study appears to be well done, described and presented. The functional difference between the partially open and open conformations remains however unclear.

Reviewer #1 (Public Review): 

This paper describes, in extensive detail, the transcriptome and synaptogenic function of astrocytes in the developing visual cortex (VC), a widely used model for neural development. Using bulk RNAseq and detailed histology, they determine the changing astrocyte transcriptome during VC development and show the expression of key synaptogenic genes are timepoint and layer specific during development, providing an essential resource for understanding how astrocytes change and impact the development of VC circuits. 

In the second part of the paper, the authors set out to understand what drives this layer specific expression of key synaptogenic genes by attenuating glutamatergic transmission and blocking astrocyte calcium signaling. These results demonstrate that neuronal activity drives the changes in astrocyte function and layer-specific expression of synaptogenic secreted proteins. Collectively, the authors convincingly demonstrate that neuronal cell-type and astrocyte interactions drive VC development. These findings have broader implications for development of the brain and neural circuits in health and disease.

Reviewer #2 (Public Review): 

In this paper by Allen and colleagues the development of cortical astrocytes is examined using molecular profiling approaches. The central questions examined are two-fold: 1) deciphering layer specific organization of astrocytes and 2) how neuronal activity can influence layer specific astrocyte profiles that relate to synaptogenic gene families. These are important and timely questions that need to be addressed and also touch on astrocyte diversity, an emerging topic in glial biology. Furthermore, this paper rigorously analyzes the expression characteristics of key synaptogenic genes (GPC- and Thbs- family) that are expressed in developing astrocytes. Figures 1-3 assess developing astrocytes in the cortex and very elegantly show that these synaptogenic genes are expressed in a spatial and temporal specific manner across different layers-this is the very first rigorous analysis of the expression profiles of these genes across space and time. Figures 4/5 are neuronal and astrocyte manipulations, showing that broad expression of these genes is regulated by neuronal activity and ca2+ activity in astrocytes; again a very nice demonstration of how astrocyte gene expression profiles are influenced by the neuronal microenvironment. Finally, figures 6/7 are single cell RNA-Seq analysis of developing astrocytes and importantly reinforces the concept that changes in neuronal activity alters astrocyte profiles and subpopulations. Together, this a very well conducted study that provides important and new information on the following topics: astrocyte diversity, neuron-astrocyte interactions during development, and the ground truth underlying the expression of key astrocyte synaptogenic genes. I don't have any real concerns about the data or interpretation. This is an excellent paper that will provide a new framework for understanding how neurons shape developing astrocytes in the cortex.

Reviewer #3 (Public Review): 

The authors investigated astrocytic expression of known synapse-regulating genes and how they changed during different developmental stages at temporal and spatial levels using experimental strategies of astrocyte RNA-seq and FISH. Moreover, the authors revealed that expression of synapse-regulating genes is also regulated by input from thalamic neuronal activity (vGlut2 KO mice) and astrocyte calcium activity in vivo (IP3R2 KO mice). Finally, the authors investigated astrocyte molecular changes in an unbiased way using snRNA-seq for VGlut2 cKO and Ip3r2 cKO mice. 

Overall, the findings regarding astrocytic gene expression alterations of synapse-regulating genes in the visual cortex regulated by neuronal input from the thalamus and astrocyte calcium activity are novel and of interest to the field. The experiments are well designed and well performed by experts in the area with a track record of high quality work.

Reviewer #1 (Public Review): 

This manuscript describes a freely available software tool, PGfinder, for the analysis of LC-MS datasets for bacterial peptidoglycan structure. This tool has the potential to greatly increase the speed and broad applicability of these analyses. 

The authors are correct that a great limiting feature of PG structural analysis efforts has been interpretation of LC-MS datasets. Specific searches of the data using known or expected muropeptide masses produced the expected molecules, but would in some cases miss unpredicted but potentially important PG fragments. A strength of the PGfinder platform is the relatively automated generation of a database of predicted muropeptide masses with which to search the MS dataset. While the user must specify an initial set of expected monomer muropeptides, based on some knowledge of the crude PG structure of the species/strain, a complex database is then constructed including all possible cross-linked muropeptides and molecules with a variety of known PG chemical modifications. 

While this requirement for the initial muropeptide dataset might be considered a weakness if one wants to begin characterizing PG for an unstudied organism, an initial database containing monomers from a wide variety of PG chemotypes should be easily produced. This would allow an initial crude analysis to identify the basic structural PG parameters, allowing the production of a highly detailed database for that species. 

As the authors point out, the vast majority of PG structure studies are targeting minor changes across environmental or growth conditions, or between mutant strains within a species, and the database construction in this version of the tool is ideal for these analyses. 

A great strength of LC-MS-based PG analysis is the detection and identification of minor muropeptide components. An example is the peptide-denuded glycans thought to be present in cell poles that were shown in this study. An automated system to identify these minor components greatly increases the likelihood of their identification. The PGfinder platform was shown to produce highly reproducible results across technical replicates. 

The PGfinder tool was applied to PG of three different species and was found to produce comparable results to traditional analyses, but in each case with significantly greater sensitivity in the detection and identification of minor muropeptides, often with unexpected structural modifications. This sensitivity and somewhat unbiased structure determination is a great strength. 

A minor weakness inherent in MS-based analyses is the accuracy of the quantitative analysis. While the quantitative MS-data across samples is highly reproducible, some PG structural parameter values differed from those determined using other methods, such as UV detection. It is not yet clear if these differences were due to biological differences between the samples used or if the ion-count MS data is different from the UV-based data. Ion-count analyses in proteomic studies have been shown to be significantly affected by ionization efficiency of different molecules. Again, the strong PGfinder reproducibility across samples is excellent for observing differences between strains or growth conditions. The absolute accuracy of the quantitative values will remain to be seen.

Reviewer #2 (Public Review): 

The authors provide a novel and generalizable framework for the quantitative analysis of peptidoglycan (PG) composition across different bacteria. The software they develop uses MS data, which allows for high-confidence muropeptide identification across different bacteria. Authors demonstrate the reliability and utility of their methodology through analyses of cell walls first for the relatively well-characterized model species E. coli, followed by a comparative analyses between two C. difficile isolates. 

In their approach, authors first applied their pipeline to analyze E. coli PG, which has been amongst the most well-studied cell walls. They achieved an unprecedented level of detail and uncovered previously unidentified low-abundance muropeptides. The authors next compared PG composition two C. difficile isolates, which has not been as well characterized. They noticed significant differences in the abundance of several muropeptides between the two strains, pointing to both new biological insights as well as an approach that allows detailed quantitative comparisons. Both results in E. coli and C. difficile could pave the way for uncovering novel biology. Finally, they analyzed an existing MS dataset of P. aeruginosa PG. The results produced by their software compared favorably to the published PG composition, reinforcing the validity of their approach. 

Strengths: Overall, the paper presents solid data and the provided conclusions are within reason. One of their greatest strengths is their open access effort, especially as it relates to transparency around their methodologies (open access, Jupyter notebook) and comparisons with alternative commercial tools. This would be a considerable contribution to the field - finding a way to unify approaches or at least allow for more direct comparisons between studies by different groups using different methodologies or analysis pipelines. This alone could really enable more future discoveries in the field of bacterial cell wall biology. 

Weaknesses: While their observations point to potentially interesting PG moieties and differences between conditions/strains, the experiments in the paper focus on methodologies and stop short of demonstrating any biological importance for the PG fragments and modifications identified. However, this is outside the scope of their effort. Further, it is highly likely that diversity of PG composition is, in fact, important for a wide range of cellular processes and phenotypes, based on previous studies. Thus, the biological impact of their work will depend on how widely their pipeline is adopted to explore this.

Reviewer #3 (Public Review): 

Despite knowing the general composition and structure of peptidoglycan for over 50 years as a heteropolymer of two amino sugars (N-acetylglucosamine and N-acetylmuramic acid) and attached short peptides, only recently have we discovered its true complexity and the significant differences that exist between bacterial species, and even strains. This understanding has been made possible largely by the application of high-pressure liquid chromatography to separate enzyme-generated fragments of peptidoglycan coupled with the large advances in mass spectrometric analyses. Recent reports indicate that over 100 different muropeptides (combinations of the amino sugar disaccharides and variations of the stem peptides) comprise the peptidoglycan (sacculus) of the bacteria studied to date, and this new tool promises to facilitate a much greater understanding, and more importantly, the significance of this diversity. However, for this tool to be applicable to study of many important pathogenic bacteria, the muropeptide MS library would have to be expanded to include an important modification, namely O-acetylation. The manuscript has been prepared with care and attention to detail, while being clear and concise. This reviewer has only a few minor edits that the authors should consider, and one that, unfortunately, permeates the entire manuscript, including both main and supplemental figures and tables (namely, the use of J as the abbreviation for diaminopimelic acid (Dpm/Dap).

Reviewer #1 (Public Review): 

In this study, Sharma and colleagues report that intracellular transport of glutamine and asparagine in osteoblast progenitors and their descendants is critical for amino acids synthesis, overall protein synthesis, osteoblast terminal differentiation, and bone development. The authors use a variety of in vivo and in vitro assays for the testing of their working model. The paper expands and deepens our knowledge of the role of amino acid metabolism in osteoblast function and bone development. 

The paper provides novel and interesting information. The authors' conclusions are largely supported by the data as shown.

Reviewer #2 (Public Review): 

Osteoblasts are highly anabolic cells that display a high proliferation rate and secrete ample amounts of extracellular matrix, indicating that these cells have a specific metabolic profile. Here, using a set of in vivo and in vitro experiments, Sharma et al describe that SLC1A5-mediated glutamine and asparagine uptake is critical to sustain osteoblast anabolism. While the experimental setup is robust, this concept has already been put forth, questioning therefore the novelty of the results. In addition, some of the author's claims are insufficiently supported by the presented data. Especially the metabolic role of asparagine in regulating osteoblast differentiation remains enigmatic. The main concerns are detailed below. 

1) Based on their data, the authors propose that the main mechanism whereby SLC1A5 regulates osteoblast proliferation and differentiation is via glutamine uptake, while asparagine only contributes to a lesser extent. Importantly, the concept that glutamine metabolism regulates proliferation and differentiation of osteogenic cells by sustaining anabolic processes has already been described recently, even by the same research group (Yu Y. Cell Metab. 2019; Stegen S. JBMR 2021), questioning the novelty of the present study. Moreover, no metabolic rescue experiments were performed to unequivocally demonstrate that the defect in amino acid/protein synthesis in SLC1A5-deficient cells was causing the decrease in osteoblast proliferation and differentiation. In addition, Gln and Asn tracing (carbon and nitrogen) in SLC1A5-deficient cells would confirm that Gln and Asn uptake via SLC1A5 is important for osteoblast functioning. 

2) Using isotopic labeling experiments, the authors demonstrate that asparagine-derived carbon and nitrogen label several amino acids that are critical for protein synthesis, albeit at a lower level compared to glutamine. Based on these observations, they claim that the decrease in osteoblast differentiation upon asparagine depletion also occurs via a defect in protein synthesis. However, proliferation, EIF2a phosphorylation and COL1A1 levels were not affected in asparagine-deprived conditions, questioning that the decrease in differentiation is resulting from impaired protein synthesis. Further experiments to decipher the metabolic role of extracellular asparagine are therefore warranted to avoid overinterpretation of the data, including protein/matrix synthesis, analysis of amino acid levels in Asn-deprived conditions and rescue with Asn-derived metabolites. 

3) To inactivate SLC1A5 in vivo, the authors use the Tet-off Osx-GFP::Cre mouse line. Importantly, newborn Osx-Cre mice display severe craniofacial abnormalities, which may complicate correct interpretation of the in vivo data, especially when analyzing at embryonic stages. Do the authors observe a similar defect in osteoblast function when SLC1A5 was deleted postnatally? This might be especially relevant because the phenotype seems to wane off over time, as knockout mice at P0 only display a craniofacial phenotype, whereas long bones appear to be normal.

Reviewer #3 (Public Review): 

This work by Sharma et al studied the role of aa transporter, ASCT2, encoded by Slc1a5 gene, that transports mostly Glmn and Asn, in osteoblasts (OB). They use gene targeting in vitro and in vivo using Sp7-Cre driven cKO. They found that ASCT2 deletion impairs OB differentiation in vitro as well as mostly intramembranous ossification in vivo by interfering with proliferation and protein synthesis. Mechanistically, they show that Glmn uptake via ASCT2 is important for aa synthesis in OBs. 

This group has shown before that Glmn is essential for OB metabolism. The current work further investigates this phenomenon and identifies ASCT2 as the key mechanism of Glmn uptake into OBs. 

The work is logically structured and carefully done with appropriate in vivo and in vitro controls. A variety of methods is used to confirm their findings, such as in vivo immunodetection and in situ hybridization and in vitro metabolic tracing. The conclusions are well justified by the data. 

Minor comments are: <br> -MicroCT methodology is not adequately described and needs to be expanded.

Reviewer #1 (Public Review):

The authors aim at characterizing the cellular organization in epithelial sheets by reconstructing the shape of lung epithelial cells from light sheet microscopy images. The find that in each imaging plane, the organization follows the laws of Lewis and Aboave-Weaire, which describe the organization of the apical surface of tightly packed cell monolayers, but that the organization can differ substantially between different planes. Equivalently, the authors observe frequent cell neighbor exchanges as the imaging plane moves from the basal to the apical side. The authors achieve a very good reconstruction of static and dynamic monolayers. The finding of frequent neighbor exchanges as one moves along the apicobasal axis can potentially change our image of epithelial monolayers, which so far mostly considers these cells to have the shape of prisms and frusta to which so-called scutoids have recently been added.

The quantification of the packing uses the same methods as for cell packing in two dimensions and underlying mechanism proposed by the authors neglects contributions from the dimension along the apicobasal axis. The authors reasoning behind the observed Aboave-Weaire's and Lewis' laws utilizes the same arguments as for the cell packing in apical layers. The differences between the cellular organization in different layers is ascribed to the position of the nuclei along the apicobasal axis. Here, the authors take correlations for causes and this discussion is missing any three-dimensional elements (except for the nucleus position). Explicitly, the authors state that the origin of the observed laws is a minimization of the lateral cell-cell surface energy in each plane. However, the cells are oblivious to the planes and the analysis should include the cell-cell interaction energy of the whole cell surface. Furthermore, the nucleus with its stiffness against deformations would need to be included in this analysis. Finally, according to the authors, the changing nucleus position along the apicobasal axis is at the origin of the neighbor exchanges. Apart from a correlation, there is no data supporting this claim.

Reviewer #2 (Public Review):

Through detailed analysis of growing mouse embryonic lung explants, these authors investigate the statistical and physical relationships underlying three-dimensional cell organization in pseudostratified epithelial tissues. The authors find that tissue curvature plays a minor role in their tissue of interest, but that cell cross-sectional area and neighbour statistics conform to previously proposed geometrical 'laws' and can be explained with a minimisation of lateral cell-cell contact surface energy, which in turn follows from nuclear packing and dynamics. Overall, this work constitutes a significant investigation into the drivers of complex three-dimensional cell shapes and tissue structures, the primary aims appear to be largely supported by the data provided, and the work should be of interest to many in developmental biology.

Reviewer #3 (Public Review):

Gómez et al. study cellular packing in epithelial tissues. The authors dissect how 2D cell packing statistics change along the apico-basal axis by examining different cross-sections parallel to the epithelial surface. They obtain 3D ex-vivo data, both fixed and live, from the developing mouse pseudostratified lung epithelium, which they analyze using 3D cell segmentation. They compare these experimental data to known topological invariants (Euler characteristic), existing phenomenological relations (Aboav-Weaire law, Lewis' law), and phenomenological relations by the authors (quadratic cell area scaling, dependence of hexagon fraction on cell area variability), which they had proposed in an earlier paper (Kokic et al., 2019).

The authors moreover discuss changes in the 2D cell neighbor relations that occur in the lung epithelium along the apico-basal axis, which they call T1L transitions ("lateral" T1 transitions). Recent work had already discussed such T1L transitions and proposed that they can be induced by epithelial curvature. The authors of the current manuscript first tested this existing hypothesis on their experimental data on both tubular parts and tips of the developing mouse bronchioles, and they conclude that curvature cannot explain the T1L transitions they observe. However, they demonstrate that the T1L transitions in their data are strongly correlated with variations in the cross-sectional cell area and the nucleus positions in the pseudostratified epithelium.

1. This paper will be of interest for anybody working on cell packing in tissues and the mechanics of epithelia. In the past, 2D cellular packing arrangements in epithelia have most often been studied at the apical side only, because of technical limitations. Using state-of-the-art imaging and image analysis techniques, this manuscript goes a step further and studies how the 2D cellular packing changes along the apico-basal axis. The only other papers that I am aware of that have started to address this question are Gómez-Gálvez et al., Nat. Comm., 2018, which the authors cite, and Rupprecht et al., MBoC, 2017, which first discussed apico-basal changes in cell neighbor relations as far as I know. Hence, being among the first papers to address this question, the current manuscript would be of interest to the community.

2. A major conclusion that T1L transitions are correlated with changes in the cross-sectional cell area and the nucleus positions are well supported by the data. While the authors seem to claim causation here, this is not backed by the experimental data presented.

3. The topological and phenomenological relations discussed seem to be reflected by the data. However, estimations of uncertainties would be required to better judge this point (e.g. in Fig. 2 e,f).

4. In lines 165-197, the authors discuss in how far the observed numbers of T1L transitions per cell can be consistent with curvature-induced transitions as discussed earlier (e.g. Gómez-Gálvez et al.). To this end, they also use theoretical predictions derived in the Supplemental Material (SM). Unfortunately, there appear to be several problems with the derivation in the SM.

a) Most importantly, in Eq. S5, epsilon is derived for the situation displayed in Fig. S1b. However, after a T1L transition on the blue line, the formula will qualitatively change (e.g. the "1+" should go from the numerator to the denominator, and the meaning on n changes as the cells abutting the blue line are now the other two cells). Hence, computing the derivative in Eq. S6 to see how epsilon changes during a T1 transition seems highly problematic.

b) The angle integral, first formula on the second page of the SM, appears to evaluate to exactly zero, which is different from what the authors obtain.

Unfortunately, these two points cast strong doubt on the predicted formula in Eq. S8, Fig. S2, blue curve in Fig. 3g. As a consequence, the conclusions drawn in lines 165-197 of the main text are not sufficiently convincing.

5. The authors compare to predictions from their earlier preprint (Kokic et al., 2019), where they say that Lewis' law is replaced by a quadratic dependency of cell area on cell neighbor number when the cell area fluctuations become large. However, it is not clear whether this transition between linear and quadratic prediction is smooth or discontinuous. Moreover, the magnitude of cell area fluctuations where the transition is expected to occur seems unclear. In these aspects, the theoretical prediction seems elusive, which makes it harder to critically compare it to the experimental data.

Minor comments / potential sources of confusion for readers:

6. There seems to be a typo in Eq. (2). What is likely meant is m_n = 5 + 8/n.

7. It is unclear how the "T1L transitions per cell" are counted (e.g. when talking about "up to 14 cell neighbour changes per cell" on line 154, in lines 165-197, or in Figs. 3d, 8b). Do the authors refer to the number of T1L transitions divided by the number of cells, or the average number of times a cell is involved in a T1L transition? The latter number should be at least four times the former, because at least four cells are typically involved in a single T1L transition. The caption to Fig. 3d suggests that the former is meant, while lines 304-307 in the discussion suggest the latter is meant.

Reviewer #1 (Public Review): 

This is a very interesting study that examines the neural processes underlying age-related changes in the ability to prioritize memory for value information. The behavioral results show that older subjects are better able to learn which information is valuable (i.e., more frequently presented) and are better at using value to prioritize memory. Importantly, prioritizing memory for high-value items is accompanied by stronger neural responses in the lateral PFC, and these responses mediate the effects of age on memory. 

Strengths of this paper are the large sample size and the clever learning tasks. The results provide interesting insights into potential neurodevelopmental changes underlying the prioritization of memory. <br> There are also a few weaknesses: 

First, the effects of age on repetition suppression in the parahippocampal cortex are relatively modest. It is not clear why repetition suppression effects should only be estimated using the first and last but not all presentations. The consideration of linear and quadratic effects of repetition number could provide a more reliable estimate and provide insights into age-related differences in the dynamics of frequency learning across multiple repetitions. 

Second, the behavioral data show effects of age on both initial frequency learning and the effects of item frequency on memory. It is not clear whether the behavioral findings reflect the effects of age on the ability to use value information to prioritize memory or simply better initial learning of value-related information on older subjects.

Reviewer #2 (Public Review): 

Nussenbaum and Hartley provide novel neurobehavioral evidence of how individuals differentially use incrementally acquired information to guide goal-relevant memory encoding, highlighting roles for the medial temporal lobe during frequency learning, and the lateral prefrontal cortex for value-guided encoding/retrieval. This provides a novel behavioral phenomenology that gives great insight into the processes guiding adaptive memory formation based on prior experience. However, there were a few weaknesses throughout the paper that undermined an overall mechanistic understanding of the processes. 

First, there was a lack of anatomical specificity in the discussion and interpretation of both prefrontal and striatal targets, as there is great heterogeneity across these regions that would infer very different behavioral processes. 

Second, age-related differences in neural activation emerged both during the initial frequency learning as well as during memory-guided adaptive encoding. While data from this initial phase was used to unpack the behavioral relationships on adaptive memory, a major weakness of the paper was not connecting these measures to neural activity during memory encoding/retrieval. This would be especially relevant given that both implicit and explicit measures of frequency predicted subsequent performance, but it is unclear which of these measures was guiding lateral PFC and caudate responses. 

Third, more discussion is warranted on the nature of age-related changes given that some findings followed quadratic functions and others showed linear. Further interpretation of the quadratic versus linear fits would provide greater insight into the relative rates of maturation across discrete neurobehavioral processes. 

Although hippocamapal and PHC results did not show a main effect of value, it seems by the introduction that this region would be critical for the processes under study. I would suggest including these regions as ROIs of interest guiding age-related differences during the memory encoding and retrieval phases. Even reporting negative findings for these regions would be helpful to readers, especially given the speculation of the negative findings in the discussion.

Reviewer #3 (Public Review): 

This paper investigated age differences in the neurocognitive mechanisms of value-based memory encoding and retrieval across children, adolescents and young adults. It used a novel experimental paradigm in combination with fMRI to disentangle age differences in determining the value of information based on its frequency from the usage of these learned value signals to guide memory encoding. During value learning, younger participants demonstrated a stronger effect of item repetition on response accuracy, whereas repetition suppression effects in a parahippocampal ROI were strongest in adults. Item frequency modulated memory accuracy such that associative memory was better for previously high-frequency value items. Notably, this effect increased with age. Differences in memory accuracy between low- and high-frequency items were associated with left lateral PFC activation which also increased with age. Accordingly, a mediation analyses revealed that PFC activation mediated the relation between age and memory benefit for high- vs. low-frequency items. Finally, both participants' representations of item frequency (which were more likely to deviate in younger children) and repetition suppression in the parahippocampal ROI were associated with higher memory accuracy. Together, these results data add to the still scarce literature examining how information value influences memory processes across development. 

Overall, the conclusions of the paper are well supported by the data, but some aspects of the data analysis need to be clarified and extended. 

Empirical findings directly comparing cross-sectional and longitudinal effects have demonstrated that cross-sectional analyses of age differences do not readily generalize to longitudinal research (e.g., Raz et al., 2005; Raz & Lindenberger, 2012). Formal analyses have demonstrated that proportion of explained age-related variance in cross-sectional mediation models may stem from various factors, including similar mean age trends, within-time correlations between a mediator and an outcome, or both (Lindenberger et al., 2011; see also Hofer, Flaherty, & Hoffman, 2006; Maxwell & Cole, 2007). Thus, the results of the mediation analysis showing that PFC activation explains age-related variance in memory difference scores, cannot be taken to imply that changes in PFC activation are correlated with changes in value-guided memory. While the general limitations of a cross-sectional study are noted in the Discussion of the manuscript, it would be important to discuss the critical limitations of the mediation analysis. While the main conclusions of the paper do not critically depend on this analysis, it would be important to alert the reader to the limited information value in performing cross-sectional mediation analyses of age variance. 

It would be helpful to provide more information on how chance memory performance was handled during data analysis, especially as it is more likely to occur in younger participants. Related to this, please connect the points that belong to the same individual in Figure 3 to facilitate evaluation of individual differences in the memory difference scores. 

I would like to see some consideration of how the different signatures of value learning, repetition suppression and reported item frequency, are related to the observed PFC and caudate effects during memory encoding. Such a discussion would help the reader connect the findings on learning and using information value across development. 

A point worthy of discussion are the implications of the finding that younger participants demonstrated greater deviations in their frequency reports for the development of value learning, given that frequency reports were found to predict associative memory accuracy.

Reviewer #1 (Public Review): 

Tan et al. resequence the whale shark genome using long-read technology (PacBio), improving a previously available assembly, which was obtained from the same source DNA used in this study. The analyses of this improved genome led to a gapless assembly, which, together with the annotation, led the authors to analyze several features of the whale shark genome, including gene family gains/losses, the evolution of immune genes important for patter recognition receptors, rates of substitution in whale shark compared to other vertebrates, the evolution of genes potentially related with the emergence of gigantism and cancer rate. 

Whale sharks constitute a charismatic group of vertebrates for several reasons. First, they belong to a poorly studied group, and their genomic properties inform us about gene families dating back to the split between osteichthyes and chondrichthyes, i.e. to the origin of gnatostomes. Additionally, they have a unique biology, associated with their large size, which can inform us on the evolution of gigantism in vertebrates. 

Tan et al. indeed assemble a gapless genome for whale shark, with large contiguity (contig N50 of 10^5), i.e. providing a novel resource for shark genomes and for the study of early vertebrate evolution. However, repetitive regions that are not included in the assembly, account for over 700 Mb. The authors could provide more information about the genome assembly, the coverage, and a chromosome-level map of the genome. This improved assembly, together with the annotation, lead to exploring several aspects of the whale shark genome evolution. They reveal gene family gains and losses specific to the lineage leading to whale sharks. It is however not entirely clear to what extent the novel assembly enabled these findings compared to the previous assembly. 

This work provides an important resource for the study of the evolution of Pattern Recognition Receptors in early vertebrates, potentially identifying a novel class of TLR (i.e. TLR29) in whale sharks. This work further suggests that a diverse set of PRR is fully compatible with the evolution of lymphocyte-based adaptive immunity. In other words, the authors hypothesize that the evolution of adaptive immunity did not lead to the functional loss of innate immune effectors. 

Using comparative genomics, the authors explore the genomic basis of gigantism and cancer evolution. First, they adopt the two-cluster test to study substitution rates in single-copy orthologs, confirming that cartilaginous fish have slower rate of substitution to other vertebrates - confirming a previous finding. However, whale shark substitution rate does not differ from other sharks (i.e. non-giant sharks), suggesting that gigantism may not be directly or uniquely correlated with a slow substitution rate. I would recommend the authors to expand on the use of the two cluster test in the result section. Overall, the manuscript would greatly benefit from leveraging on its main asset, which a genome assembly with relatively high contiguity. Even if the authors study the genome of one individual, they could provide more information regarding changes in heterozygosity along the genome.

Reviewer #2 (Public Review): 

While the new assembly is certainly not the major selling point, I find the computational analysis very deep and comprehensive. The authors show that different groups of pattern recognition receptors undergo different modes of evolution, from very stable (RLRs) to showing expansions in diverse lineages (TLRs and especially NLRs), and thus reveal general evolutionary patterns of innate immune genes. Furthermore, they confirm that large animals in general have lower rates of protein evolution but show that the whale shark does not evolve at lower rates compared to smaller sharks. Interestingly, gene families that evolved at different rates in lineages including large animals are enriched in cancer genes.

Reviewer #3 (Public Review): 

The manuscript by Tan et al., leverages the group's improved whale shark genome assembly to elucidate patterns of genomic evolution in vertebrates. The authors use long read PacBio sequencing to assemble an improved reference genome for the whale shark, a member of cartilaginous fish group, which as a group lacks genomic data relative to other vertebrate groups. After summarizing the results of the new genome, the authors use the new assembly to infer gene family gain and loss during vertebrate evolution. They found that vertebrate genome duplications that occurred early in vertebrate evolution coincided with, but were not necessarily the cause of, a burst of new gene family generation in early vertebrates. Building on this analysis, the group was able to identify gene families gained during in vertebrate, jawed vertebrate, and bony vertebrate evolution with immune-related gene innovation coinciding with jawed vertebrate and bony vertebrate evolution. 

Next, the group used the new whale shark reference genome to assess how the emergence of the adaptive immune system (as cartilaginous fishes are the most evolutionary distant group from humans to possess both innate and adaptive immune systems) influenced immune evolution by analyzing pattern recognition receptor (PRR) evolution in jawed vertebrates. They found that, while there is PRR turnover and diversification among jawed vertebrates, a core set of PRRs found in the last common ancestor of jawed vertebrates is well conserved. This signifies that innate immunity is still required in vertebrates but PRR gain and loss is relatively slowed versus organisms lacking an adaptive immune system. The authors then attempt to find conserved evolutionary mechanisms that may be associated with vertebrate gigantism. They could not find any robust evolutionary pathways associated with vertebrate gigantism but did find that gene families involved in cancer were more likely to have a shifted gain/loss rate for vertebrate giants than would be expected by chance. 

Genomic sampling from a wider array of evolutionarily distant species is critical to understanding the evolution of physical traits and molecular pathways. With this new/refined assembly of the whale shark genome, the authors have greatly improved the quality of an understudied cartilaginous fish genome, making its use for comparative genomic and evolutionary studies more robust and powerful. Indeed, the authors themselves make use of this new reference, notably to interrogate the impact of the adaptive immune system on innate immune evolution through the lens of PRR genes in that species, or the potential evolutionary drivers of gigantism. 

Although the new genome assembly has promise, this reviewer has several major concerns that need to be addressed before conclusions are supported and before the manuscript is compatible with reproducible science practices. 

A major comment relates to the PRR evolution analysis, which is not sufficiently explained. For instance, the authors say that they used subsampling of a previously used set for the TLR analysis. The nature of the original set is not described satisfactorily (i.e. species, database, etc; see point 4 below), the method and depth of subsampling is not explained (i.e. software used, % of coverage, random or guided), and the scientific rationale for such a choice instead of using a full set is not given. In addition, why does the TLR analysis include more species than for NLR and RIG-like? When trying to infer NLR repertoire shifts in vertebrates, the authors compare humans (mammal, bony vertebrate), zebrafish (bony fish), and whale shark (cartilaginous fish). The selection of other included species in that analysis needs to: (i) be transparent, including information about how the other species/sequences are selected and obtained and (ii) be unified, i.e. include the same taxonomic depth for all considered groups.

Reviewer #1 (Public Review): 

In their paper, titled 'Group II truncated haemoglobin YjbI prevents reactive oxygene species-induced protein aggregation in Bacillus subtilis', Imai et al., suggest that the protein YjbI acts as a hydroperoxide peroxidase and therefore it may protect cell-surface cells from oxidation. Using AFM and contact angle measurements they show that yjbI mutants lead to changes in cell surface properties as well as to the formation of more hydrophilic biofilms, relative to the wild-type (WT) strain. Since both tasA and yjbI mutants experienced a similar phenotypic behaviour, the authors linked between the two proteins, TasA and YjbI, and in a series of biophysical and biochemical tests they tried to establish this link. This study touches upon an important question, how do biofilms protect themselves from reactive oxygene species (ROIs), that is nicely described in the introduction; The link between the above proteins in very interesting and relevant to the main question proposed in the study. However, the experiments presented does not always directly support the conclusions made. 

The points that I find necessary to clarify/extend: 

1) A major claim in the paper is that biofilms that do not harbour the tasA gene (tasA-) are flat, and therefore their contact angle is low, indicating that they are less hydrophobic than WT strains. However, the phenotype of biofilms of tasA mutants are normally not that flat (see for example Romero et al., PNAS 2010; Vlamakis et al., Genes and Development, 2008; Erskine et al., Molecular Microbiology 2018). As a matter of fact, even the WT biofilms that are used as a control in this study are much more flat than the biofilms that serve as standards in the papers referenced above. 

2) Figure 1. The authors use AFM phase imaging to probe differences in cellular stiffness. This AFM mode is not quantitative and the differences presented could also result from differences in adhesion between the tip and the sample. A more quantitative means to evaluate stiffness is a direct measurement of moduli in Force mode, a standard AFM module. 

3) Line 147. The authors link between the lack of monomeric TasA in YjbI mutants and the formation of covalent cross linking in TasA aggregates. This is a strong statement that unfortunately is not supported by any of the experiments described in the manuscript. 

4) The authors seek to make a connection between YjbI and TasA. However, this link is either not well established or only hinted indirectly in this manuscript, through precipitation assays, contact angle measurements and growth curves. To establish such a link, a more molecular approach is advised. Experiments that would provide a direct link between the two proteins and mark specific molecular changes of the proteins include for example titration NMR studies of labelled proteins (at least one of the proteins). In cases where the authors need to show protein localization to the cell surface, it would be of help to use TEM or high-end fluorescence microscopy. 

5) This paper suggests that the protein YjbI acts as an electron donor. Given that there are other proteins with a similar role (in other organisms), it would be nice to show whether there is any homology (by sequence and/or structure) to these proteins.

6) (Minor point). The use of Pymol to demonstrate that the YjbI's pocket could serve as a binding site for haem molecule is nice, but using Molecular Dynamics (or any other calculation) would be more quantitative and convincing of the specificity of the interaction.

Reviewer #2 (Public Review): 

In this study, Imai et al. uncover a role for the truncated haemoglobin protein YjbI in biofilm formation by the model bacterium B. subtilis. They show that yjbI gene disruption results in altered biofilms, with increased wettability and different matrix stiffness relative to cells. The absence of YjbI activity results in aggregation of the amyloid-like TasA matrix protein, and the biofilm wettability defect of the yjbI mutant can be recapitulated by anti-YjbI immune serum, suggesting that YjbI is located on the cell surface. Absence of YjbI also modestly increases the sensitivity of cells growing on agar plates to the oxidant AAPH. Using the model protein substrate BSA, purified YjbI can at least partially reverse oxidant-induced BSA aggregation in vitro, convincingly showing the YjbI has protein hydroperoxide peroxidase activity, which is evidently an unusual enzymatic activity. Finally, the authors examine lipid peroxidation and conclude that YjbI is not involved. The results are interesting in that they connect YjbI to a biofilm phenotype and convincingly show protein hydroperoxide peroxidase activity by a truncated haemoglobin protein, an activity not previously attributed to this class of proteins. 

The experiments are largely well done, but some of the corresponding conclusions are overinterpreted, connecting ideas without experimental support. Moreover, the yjbI mutant has a narrow and relatively mild phenotype. 

1) The paper identifies two separate properties of YjbI: its mutant phenotype with respect to biofilm formation, and its peroxidase activity against oxidant-induced aggregation of TasA and BSA. The authors conclude that these properties are connected, but this is not formally tested. While purified YjbI can reverse hydrogen peroxide-induced aggregation of purified TasA in vitro, and the yjbI mutant shows more TasA in the insoluble fraction of B. subtilis pellicle lysates, these experiments do not show that the TasA aggregates in pellicle lysates are caused by peroxidation, nor do they show that TasA aggregation is normally kept at bay by YjbI peroxidase activity (it is possible that YjbI has a separate role in biofilm integrity). Some experiments that might lend support to this connection include examining the biofilm phenotype of a catalytically dead point mutant of YjbI (perhaps Y25 or Y63, l. 298, or other residues informed by the crystal structure of Giangiacomo et al.) to establish whether peroxidase activity is important for biofilm formation. Such a mutant would be particularly valuable, as it could also be used to test whether inactivation of enzyme activity affects other phenotypes (cell stiffness, for example). Another approach would be to use a soluble antioxidant molecule, purified YjbI, or another peroxidase to see if the yjbI biofilm can be rescued. 

2) The authors conclude on the basis of the AFM data in Fig. 1 that yjbI mutant cells are less stiff than WT cells, but the data only show relative stiffness. It is also unclear why a change in cell envelope stiffness would relate to biofilm wettability (ll. 130-131). If there truly is a change in cell envelope stiffness, a high-resolution, head-to-head AFM comparison of planktonically grown cells would be informative. 

3) The data in Fig. 2F showing hypersensitivity of yjbI mutant cells to AAPH were generated in an unusual way: stationary-phase liquid culture was spotted on an LB plate, and the colonies were "fractionated" at the noted intervals and resuspended in saline for OD measurement. Measuring sensitivity to AAPH just in shaking liquid planktonic culture would make this phenotype more convincing. Under non-biofilm forming conditions, is a surface-associated peroxidase important for cell growth or survival under oxidant challenge?

Reviewer #1 (Public Review): 

This study provides evidence for previously unknown relationship between oncogenic protein kinase A (PKA) signaling and MYC family members. Specifically, the authors have employed a combination of systems biology and biochemical assays to capture mediators of oncogenic PKA signaling in a fibrolamellar carcinoma and melanoma cell line. This lead to identification of Aurora A and PIM kinases as potential effectors of constitutively active PKA. Aurora A and PIM kinases have been previously shown to stabilize MYC proteins. Accordingly, evidence is provided that the effects of PKA/Aurora A and PKA/PIM axis are mediated via MYC. Collectively, these findings suggest a model whereby the effects of aberrant PKA signaling are mediated via Aurora A and PIM kinases and related feedback mechanisms that ultimately result in stabilization of MYC proteins. Importantly, PKA-driven cancer cell lines exhibited high sensitivity to Aurora A kinase inhibitors in cell culture-based assays. These findings not only provide pioneering insights into oncogenic PKA signaling, but may also have implications for developing therapeutic approaches for neoplasia that harbor constitutively active PKA. 

Strengths: 

This study addresses the role of aberrant PKA signaling in cancer, which represents a major gap in knowledge in cancer biology. Systems biology approaches and dissection of signaling networks downstream of constitutively active PKA are found to be exciting in the context of this study and likely to provide a wealth of information for future studies. Results from samples obtained from fibrolamellar carcinoma patients partially confirmed correlations observed in cell lines, which was seen as an advantage. Notwithstanding that, it was thought that orthogonal genetic validation may in some cases be warranted, pharmacological approaches using e.g. Aurora A inhibitors hold a promise for accelerated translation of observed findings into the clinic. 

Weaknesses: 

The major drawback of the study is the lack of in vivo models to validate observations garnered from the cell lines. This is particularly important considering that experiments carried out in samples from fibrolamellar carcinoma patients suggested additional Aurora A and PIM kinase-independent mechanisms of PKA-driven increase in MYC levels and likely in neoplastic growth may be implicated in vivo. In addition, it was thought that more mechanistic evidence is required for linking PKA to PIM kinase, especially because different PIM kinases were implicated in stabilization of MYC in fibrolamellar carcinoma vs. melanoma cell lines. Finally, although pharmacological approaches were appreciated, due to potential issues with the specificity of the inhibitors, it was thought that orthogonal genetic approaches are warranted to further corroborate the proposed model.

Reviewer #2 (Public Review): 

Protein kinase A (PKA) is often stimulated and contributes to cancer growth, yet the downstream kinase signaling cascades remain unclear. Here the authors use a global phsophoproteomics and kinome activity profile to show that not only is the RAS/MAPK pathway activated, as expected, but the authors also suggest Aurora kinase A (AURKA) and PIM kinases are activated to stabilize the expression of MYC expression; a potent oncoprotein associated with poor prognosis and aggressive disease. The authors use a number of different cell lines in this study, but focus on fibrolamellar carcinoma as PKA is known to contribute to this disease. 

Strengths: It has been notoriously difficult to map kinases and their substrates as these protein-protein interactions are not always amenable to traditional biochemical techniques due to their labile nature, and kinase substrate consensus sites are often overlapping and not highly specific. Thus, the authors' pipeline to delineate such kinase cascades is quite novel and useful. They apply it here to determine PKA signaling in cancer using sophisticated computational strategies and then validate with classic molecular techniques. 

Weaknesses: The lack of mechanistic evidence linking aberrant PKA activation with regulation of MYC family members was considered to be a major weakness of the study. As it stands, it is hard to delineate whether observed changes in the levels of MYC family members are indeed a consequence of aberrant PKA signaling. It also remains unclear which MYC phosphorylation sites are implicated in the context of neoplastic PKA function and whether MYC family members are regulated at the level of protein stability or mRNA translation. Moreover, some methodological issues (e.g. using single siRNAs) were also observed. Collectively it was thought that these weaknesses should be addressed to corroborate author's conclusions.

Reviewer #3 (Public Review): 

The study by Chan, Gordan et al, utilizes state of the art global kinome profiling to map the shared signaling networks driven by diverse genetic changes resulting in PKA activation in human cancer. Among the many kinases whose activity is modulated by active PKA, they authors centered their study on Aurora Kinase A (AURKA), and its ability to regulate c-MYC and n-MYC protein levels. They propose an AURKA-MYC regulatory network, and a possible positive feedback loop mediated by the kinase PIM2, which can be disrupted by AURKA inhibition. The study has many elements of novelty, which could be of translational importance. The strength of the study is the use of global kinome profiling and the identification of multiple candidate signaling nodes downstream from PKA. The weakness is the limited mechanistic information provided on possible direct regulatory processes intervening in kinase activation, and the need to enhance the rigor of the studies and to provide quantitative analysis of the data to increase the confidence regarding the proposed novel mechanisms at this stage.

Reviewer #1 (Public Review): 

In this work Warneford-Thomson et al. developed an approach for surveillance screening for SARS-CoV-2, which involves the isothermic amplification of a region of the SARS-CoV-2 nucleocapsid gene using RT-LAMP, followed by detection with deep sequencing. High-throughput and cost effectiveness is achieved by two sets of barcodes that allow up to about 37,000 samples to be combined into one deep sequencing run. Moreover, the authors demonstrate they can do the detection from saliva collected on paper, which should make sample collection easier. 

The main strength of the work lies in the technical aspects, including setting up multiple controls such as a detection of a human gene, and multiplexing with detection of the influenza virus. 

The main weakness is that there are multiple other papers either published or archived that use RT-LAMP for SARS-CoV-2 detection, deep sequencing for SARS-CoV-2 detection, or both. These are cited in the current work, which is very well written and presented. Whether this method is better than the others which have the same aim of developing cost-effective and high-throughput detection is not conclusively demonstrated as only 8 clinical saliva samples are examined. 

Furthermore, the requirement for deep sequencing and batching many samples for cost-effectiveness will, in most situations, greatly increase turn-around time. This will make surveillance much less effective, since by the time results are fed back, the asymptomatically infected individual would have had more opportunity to transmit the infection to others. However, the deep sequencing step may be very useful for surveillance of circulating SARS-CoV-2 spike sequences to detect emerging variants within a population, provided this method can be modified to do it.

Reviewer #2 (Public Review): 

In 'COV-ID: A LAMP sequencing approach for high-throughput co-detection of SARS-CoV-2 and influenza virus in human saliva', Warneford-Thomson et al. present a novel methodology to perform large numbers of COVID-19 tests in parallel. Their approach takes unprocessed saliva and requires only a small number of experimental steps before the results are sequenced overnight to generate many thousands of results. This straightforward experimental design should allow the protocol to be expanded to a number of settings where population-level monitoring is required in order to contain outbreaks and reduce transmission. In this paper, the authors demonstrate the efficacy of their approach and perform a large number of benchmarking experiments to quantify its sensitivity, specificity and limitations of detection. They are able to detect artificially created infections (spike-ins) with as low as 5 virions per µL and all clinically available samples agreed with the standard RT-qPCR test. This method can detect both SARS-CoV-2 and Influenza infection and can also be applied to saliva samples which have been collected on filter paper, a strategy which will further simplify the testing regime. 

The authors have spent much time testing this approach but these have largely been limited to analysing artificially created infections. The only results which were obtained were from eight clinically derived samples which are presented in Figure 2E. Although all results from this approach agreed with the standard clinical test this is a small number of tests compared to the total number of tests which are reported in this paper. It is also only a small proof-of-principle experiment to justify a quick rollout of this technology. 

The potential for this technology to perform rapid, high-throughput SARS-CoV-2 testing alongside the potential for very low sequencing costs (Figure 4G) is impressive. It is noted in the manuscript that this will require 96 unique barcodes but only 32 are tested here. All but three of these 32 work for the SARS-CoV-2 N2 primers and required STATH control but how will the remaining 67 primers be derived (i.e. is it realistic that this can be made to work to deliver the promise of this approach)? 

Overall, this is an interesting paper which has very clear real-world application to helping to defeat the ongoing COVID-19 pandemic, but some extra validations are needed to fully demonstrate its performance in clinical and/or public health settings.

Reviewer #1 (Public Review):

This paper aims to characterize mutations that act in trans to affect a single gene's expression in yeast. Trans-acting mutations potentially play an important role in variation and disease within species and in phenotypic evolution. The authors have previously described the mutational architecture and natural variation in the gene's cis-regulatory activity, creating a powerful experimental model for the causes of phenotypic variation. Trans-acting variation is much more challenging, because the mutational target space is the whole genome. The authors use a forward-genetic screen and bulked segregant analysis to identify 52 point mutations that affect their focal transgene's activity, and they identify an additional 17 by directly searching within a handful of candidate genes. The paper includes elegant validation using genome engineering to confirm the mapped variants are causal.

With this collection of trans-acting mutations in hand, the authors can compare their characteristics to a set of mutations that do not have detectable effects on the transgene. Overall, they conclude that trans-acting mutations are enriched for genes that are known to sit upstream of the focal gene in transcriptional cascades, but the majority of mutations are in other kinds of genes, outside the network of transcription factors.

This work is a valuable contribution to the authors' important experimental assault on the genetics of regulatory variation, a useful complement to their previous work on cis-regulatory mutations and polymorphisms. They provide evidence that experimentally defined regulatory networks have predictive value for the location of trans-acting mutations, and they reinforce the result (well established and widely accepted, but important to show in this kind of rigorous way) that trans-acting variation is distributed across a wide range of cellular and molecular functions. There are also some useful fine-grained results, such as the absence of mutations in a known regulator, RAP1, probably due to pleiotropic constraints, and an excess of mutations in iron homeostasis.

Because the dataset of trans-acting mutations is relatively modest in size (necessarily- it's a heroic effort to identify this many), many of the enrichments are also modest. In particular, the finding that mutations are enriched in eQTL regions holds for only two of three previous eQTL studies, and involves a slight elevation over the baseline that 66% of the genome is in eQTL regions. Because both the eQTL and the mutations were discovered by bulked-segregant analysis, biases in mappability will affect both similarly, and so I do not find the enrichment for overlapping hits to be completely persuasive.

This work is important in substantial measure because of its contribution to the larger yeast TDH3 model trait project, which is a landmark research program for understanding phenotypic variation and evolution. On its own, the results in this manuscript would be difficult to generalize to regulatory variation more broadly. There are narrow reasons for this (yeast has a distinctive compact CDS-dense genome; the focal transcript is YFP and so has no endogenous post-transcriptional regulation; only one class of mutations assayed), but the bigger reason is that the researchers are only able to discover mutations with effects above a particular size. Even among the 82 mutant strains they start with, some 36 strains have altered YFP levels but no successfully mapped causal variants. The authors do a great job of listing and evaluating possible explanations, one of which is simply that the strains carry multiple mutations of small effect. All but one of the successfully mapped variants consists of missense and nonsense mutations. I think it's important to note that this represents a particular range of the effect-size distribution of mutations affecting the YFP phenotype. We know from the authors' earlier work that there are lots of mutations that can affect gene expression in cis, and so the absence of trans-acting cis-regulatory variants here is parsimoniously interpreted as due to their small effects. In general, work in other systems (particularly human genetics) has shown that even molecular traits are often hugely polygenic, affected by thousands of variants of tiny but non-zero magnitude. With a forward screen of the sort performed here, it's difficult to know how much of the phenotypic variance is due to unmapped small-effect variants, but two lines of evidence suggest it may be a lot: first, the absence of mappable causal mutations in 36/82 mutants, and second, the differences between EMS mutant strains and their matched single-site mutants. The authors commendably report and discuss these issues but to my mind they neglect them in drawing inferences and generalizations from their findings.

Reviewer #2 (Public Review):

Fabien Duveau et al. tried to characterize mutations in trans-regulation effects on expression of the TDH3 by using EMS mutants with TDH3 reporter in Saccharomyces cerevisiae. This work is an extension of works of Gruber et al. (2012) and Metzger et al. (2016) with specific mutation effect on TDH3 expression. They found that these trans-regulatory mutations that have effects on expression of TDH3 reporter were enriched in coding sequences of transcription factors. They found that the trans regulatory mutations with effect are associated with natural variants of trans within S. cerevisiae. In summary, the data is well described and supports their claims. The method of study could be used for study the mechanism how regulatory network works.

Strengths:

This work provides a new general trans regulatory network on a specific focal gene. This work confirms that trans-regulatory mutations with effects on targets are often located in coding sequences, and they are correlated with natural variation within yeast. This would help insight on evolutionary of trans, and function analysis of TF as well.

This paper provides a model that predicts the effects of trans regulatory changes on the expression of one specific focal gene. The technique in principle works in the same way across many different types of gene regulation network.<br> BSA-Seq and permutation were applied for single site mutation effect on the focal gene out of mutated strains. The method is well designed for identifying trans regulatory mutation effects on expression of focal gene.

I appreciate that the authors provide the raw data and data processing in the supplements. It helps someone really interested with this work dig up the data with detail.

Weaknesses:

Although the paper does have strengths in principle, some weaknesses of the paper would cause the quality of data presented. In particular:

1) The authors did a lot of analyses on trans regulatory mutations on TDH3, however, no cis-regulatory mutation was discussed. In the previous work of Metzger et al. (2016), there are 235 cis mutations, and their effects on expression of TDH3 are even stronger than trans mutations. Secondly, the authors only consider the promoter of TDH3 as cis regulation. However, cis regulation should include enhancers and repressors as well.

2) Since some mutations would change the development or growth rate of yeast, thus change the expression of focal gene, this kind of change is hard to say trans regulatory effect.

3) For the statistics of this paper, there is some weakness. A) The method did not explain the details why choose G-tests for statistics. Why this is better than other tests, especially chi-squared test? B) If there are more than two categories tested the significance from their null expectation, Post-hoc test should be performed. However, I only see Figure 2-suppl5. A has such kind of test. C) Is the threshold of expression difference of 1% too sensitive? I read the cited paper of "altering 162 fluorescence by 1% or more (Duveau et al., 2014)" in page 4 line 162, and can not find what is the threshold based on.

4) The result of GO analysis seems robust. The significant genes for GO are a bit two less and it would cause the bias of GO analysis. Most presented enriched GO terms only have 2-3 significant genes and it means only few genes have big effects on enrichment. The suppl. Table 8 shows more than 150 enriched GO terms of BP enriched.

5) There are two dimensions of activity change in trans regulation because of mutations, one is the ability of binding (structure change) and another is the abundance of transcriptional factors.

6) As many transcriptional factors works as a complex, this study considers each trans regulator independent.

Reviewer #3 (Public Review):

Duveau et al. present a dissection of the genetic architecture and mutational spectrum of new unlinked (trans acting) variation influencing the expression of a single gene. By using a smart approach that combines a promoter of interest, a reporter gene, mutagenesis and identification of regulatory variation influencing target gene expression by fluorescence sorting, the authors trace the source of regulatory variation in trans factors. The results bring significant novel insight into the architecture of regulatory networks. Regulatory networks have been dissected by using a variety of approaches, including mapping of expression variation (eQTL), targeted functional test and the approach used here, mutagenesis. The mutagenesis approach is useful to discover novel variation not yet filtered by natural selection or drift, meaning that it is useful to trace regulatory interactions in a "neutral" setting. Previous research has been very successful to study regulatory networks but has one clear weak point; the architecture and source of trans acting mutations have been hard to study, either because of technical/statistical constraints (e.g. eQTL's), or because of low scalability (targeted approaches). The present work fills a significant gap in our understanding of the nature of trans effects by identifying and functionally testing a large number of trans acting mutations. The results broaden our understanding of the mutational sources of trans variation.

Overall, the manuscript is very extensive, the results are clearly explained and the conclusions are well supported by the data. I have identified a few general aspects in which the manuscript could be improved, explained below.

The authors posit that "we know comparatively little about the genomic sources, molecular mechanisms of action and evolutionary contributions of individual trans-regulatory mutations." I generally agree with this point. However, the genetic architecture of expression variation has been extensively studied using genetic mapping (in contrast to mutagenesis used here), with a few significant insights into the architecture of trans-acting variation. I think elaborating on the strengths/weaknesses and commonalities/differences between mutagenesis versus eQTL strategies would benefit the reader, as well as recognising what ground has already been covered by eQTL approaches, and what is the novelty of the authors work.

The architecture of the trans-acting mutations was in majority simple; most trans-effects associated with single SNPs with ~37% (i.e. 17/46, if I have interpreted the results correctly) of trans-effects being due to multiple mutations. This finding can appear surprising, as trans-effects are often perceived in the literature as "complex". It would be beneficial to note that eQTL studies commonly find single genes influenced by many (putatively) trans-acting loci, however, this is distinct from the findings here. In this experiments, 69 loci influence Ptdh3-YFP expression in trans (which is consistent with the "complex" architecture of trans effects in eQTL studies). What is shown here is that the majority of these trans effects are single loci. These findings highlight the novelty gained from this experiment; the resolution in eQTL studies rarely allows to identify the individual mutations and their number attributed to a trans (or cis) effect. Furthermore, the fact that more than a third of trans effects are associated with more than one SNP is intriguing. If I have interpreted the results correctly, the authors test the effect of individual mutations (which is a notable feat in itself) to conclude that in such cases there are single SNPs that account for the association, the rest being driven to high frequency by linkage disequilibrium. The testing of SNP effects is however done in an individual SNP-basis. This choice leaves the possibility of multiple SNPs acting in association to change expression in trans (i.e. epistatic effects). It would benefit the reader to clarify if it is possible to test two mutations at once to start with, and if it is, what was the reasoning behind not doing so. Further, because I consider this analysis giving novel insight into the genetic architecture of trans-effects not presented in earlier analyses, I'd encourage the authors to elaborate on the possibility of epistatic trans effects.

The mutagenesis approach in yeast the authors used is very powerful, but it naturally has drawbacks. The regulatory landscape in yeast is arguably simpler compared to e.g. metazoa or plants, in that the cis-regulatory regions are predominantly closely linked to target genes, the genes in majority do not have introns and post-transcriptional regulation of mRNA through e.g. splicing is rare. These features distinguish the systems, as in animals and plants introns are a very prominent source of regulatory elements (close to half of all enhancers are intronic in many animals), and alternative splicing of e.g. transcription factors are known to play major roles in transcriptional regulation. Further, chromatin is a very important layer in metazoan and plant gene regulation. To benefit the general readership, it would be informative to further elaborate on the significance of the findings for researchers studying other organisms. In addition, it would help to clarify what aspects of the differences in the regulatory landscape the authors think are important to distinguish.

Reviewer #1 (Public Review): 

This study explored the distance-decay relationship for soil prokaryotic communities in alpine and temperate grasslands. With large amounts of natural samples (258 soils) across 4000 km distances, this survey on soils and microbial communities is pretty massive and extensive. Their results showed that in contrast to traditional linear relationship within the same biome, a significant U-shape pattern was found across both two grassland types. More interestingly, further analysis showed homogeneous selection process instead of geographic distance effects may contribute to the soil prokaryotic community patterns, mainly attributed to dissolved nutrients. It subverted the popular standpoint that microbial distribution pattern as same as macroorganisms had clear distance-decay relationship. The habitat environments, especially the soil dissolved nutrients, could be more important on shaping microbial distributions on large scale.

Reviewer #2 (Public Review): 

In this manuscript, Zhang et al. collected 258 topsoil and subsoil samples from northern Chinese grasslands on a scale up to 4,000 km. They found that prokaryotic communities in those samples did not follow the common distance-decay relationship, which (they believed) was caused by homogeneous environmental selection. In general, it is a well-versed manuscript. It presents an excellent sampling campaign that generates a large number of precious soil samples. Lack of distance-decay relationship is an interesting observation, but it has been observed in other large-scale biogeographic studies (e.g., References 90 and 91). This study took samples throughout a transect from northeastern China to southwestern China. Both ends of the transect are cold climates, which explains the U shape of distance-decay relationship. This is rare since such transects are difficult to find in other places of the world. Therefore, the U shape pattern is an exception to large-scale biogeographic patterns. I like most of the results here and applaud the tremendous efforts to collect samples since China is a vast country, but I disagree with the authors that their results have challenged any tenet of the microbial biogeographic field.

Reviewer #1 (Public Review): 

Xiangyi Li and colleagues developed a conditional gene-based method, MGCA, for the identification of risk genes from GWAS summary statistics. The authors performed extensive simulations to demonstrate the statistical power of MGCA, and the superiority of isoform QTLs in identifying risk genes. The authors used publicly available GWAS data from schizophrenia to demonstrate the ability of MGCA to identify risk genes, biological pathways, and drug repurposing candidates. The results suggest MGCA is likely to benefit the field with improved gene prioritisation for fine-mapping and functional studies. 

The conclusions are supported by the data, but additional work is needed to compare the MGCA with existing gene-based results. This will help the community evaluate the added benefit of MGCA for the functional interpretation of GWAS results. 

1) Please directly compare MGCA with MAGMA at all levels of the analysis (i.e. risk gene identification, biological pathway analysis, and drug repurposing analysis). It is important to demonstrate the added benefit of MGCA to existing approaches. 

2) Similarly, a systematic comparison of MGCA_eQTL should be performed with S-PrediXcan (or a similar TWAS approach), which currently dominate expression-based GWAS secondary analyses. 

3) Given MGCA_eQTL and MGCA_isoQTL are compatible with GTEx, the method would benefit from the dissection of tissue-specific effects, which play an important role in complex disease aetiology 

4) Given the preservation of gene co-expression patterns across the brain structure (in GTEx), it might be worthwhile building consensus networks using WGCNA in order to simplify the results. 

5) The drug repositioning analysis could be extended to integrate drug annotations and biological pathway information using publicly available resources. This will further prioritise drug candidates for follow-up functional studies.

Reviewer #2 (Public Review): 

In this study, the authors develop a novel method, called MCGA, extending from their previous gene-based methods, to detect gene-trait association removing redundant signal. They further leverage expression QTL into their model to improve the resolution of gene-trait association. The overall structure is clear, and data is presented well. I am concerned about the simulation methods, and would like the authors to present some clarifications. 

1) When comparing MCGA-eQTL and MCGA-sQTL, the authors simulate a single isoform-trait association, and the simulated gene expression is averaged among isoforms, which is kind of unfair for MCGA-eQTL model. Hormozdiari et al reveal that sQTL contributes few to traits after conditioning on eQTL (Hormozdiari et al., 2018, doi: 10.1038/s41588-018-0148-2). I would suggest to simulating a case that gene-trait association is mediated by overall expression, instead of a single isoform (transcript); 

2) When comparing MCGA-eQTL and MCGA-sQTL, only power is considered. The authors should include the analysis to demonstrate the performance in control for false positive; 

3) When choosing a favorable exponent value c (1.432 chosen in the study), the authors found that the c value is robust to trait type, sample size or variant size, but the authors didn't explain what factors affect the choosing of c. Considering the potential application of MCGA method in other studies, the authors should explain what factor affects c value, and provide the guidance how to choose an optimal c; 

4) The mediation analysis result in Yao et al. estimates that 11% of trait heritability is mediated by gene expression (Yao et al., 2020, doi: 10.1038/s41588-020-0625-2), while in simulation section of this study, 100% of trait heritability is mediated by gene expression. Simulations mimicking real scenarios should be used; 

5) It is important to choose a background gene set when conducting GO enrichment analysis. It is not clear what kind of genes are used as control when evaluating significance; 

6) GTEx v8 contains samples from diverse populations, and it is crucial to handle the issue of population structure. Based on the description on https://pmg-lab-docs.readthedocs.io/en/latest/KGGSEE\_doc/KGGSEE.html#id18, it seems that eQTL/isoQTL were detected ignoring population structure. The authors should explain why they applied a pipeline like that, and show that their conclusion wouldn't be affected by the choice.

Reviewer #3 (Public Review): 

The manuscript, "MCGA: a multi-strategy conditional gene-based association framework integrating with isoform-level expression profiles reveals new susceptible and druggable candidate genes of schizophrenia", describes an approach to conduct gene-level association testing in GWAS data with integration of gene expression data. The authors have conducted comprehensive simulation studies for main modules involved in this framework, demonstrating the advantages of the MCGA strategy compared to established similar work. The method has also been applied to the analysis of schizophrenia GWAS, with several interesting discoveries. All methods proposed are implemented in the KGGSEE package, a command tool written in Java with good documentation, data resource and examples for the type of analysis proposed in this work. 

Overall, the framework is solid and the analyses performed are thorough. In particular, the simulation study and real data demonstration of advantages of isoQTL over conventional eQTL is novel and interesting. With the user friendly software available, I can envisage that MCGA will receive interest from the community and be adopted to many projects. 

My major reservation on the methods is the component using conditional analysis to identify gene specific signals. Even though the MCGA framework is as solid as the methods it is based on, alternative methods are available for gene-level association analysis that takes into consideration of contribution from multiple SNPs and the LD without having to rely on conditional analysis. For example, fine-mapping approach such as SuSiE (https://github.com/stephenslab/susieR) uses summary statistics and LD, and can produces gene-level evidence of association in terms of Bayes Factor, when a gene region is analyzed. Such an approach does not have a potential type I error issue, is efficient enough to analyze multiple genes in LD with each other. Most importantly it provides inferences directly for multiple genes accounting for LD, without having to rely on conditional analysis. Conditional analysis, as a greedy algorithm, suffers an obvious limitation: suppose genes A and B are two causal genes in weak LD with each other. A non-causal gene C physically in between A and B are correlated with both A and B. Then C may have a stronger marginal signal than either A or B. A conditional analysis may identify C, and conditional on C, association signals of the true causal genes A and B will become weaker. I therefore am not convinced that a conditional analysis such as ECS is the best approach on which MCGA should be based.

Reviewer #1 (Public Review): 

Vafidis et al. propose a model of a form of synaptic plasticity in head direction cells of the Drosophila central complex, in which visual input arriving at axon-proximal locations supervises other input arriving at axon-distal locations. The authors show that this proposed plasticity rule can tune the system to perform accurate path integration and adjust to changes in gain. 

The proposal is an interesting idea that maps the extended morphology of the E-PG neurons that represent head direction in flies to a very specific functional prediction. This prediction is inspired by work in mammalian pyramidal neurons, where it appears that inputs arriving at different parts of the neuron can either be modified through learning or serve as the learning signal itself. The model functions well, and successfully produces a network that is capable of path integration. 

A concern about the model is that it is unclear whether this activity-dependent plasticity rule is actually needed to reproduce results consistent with what is shown in flies. The trained networks actually perform better than flies do in experiments, and noise must be added to make the performance comparable. Also, adult flies do not appear to be capable of adjusting to changes in gain, as the model does. 

The general ideas of the model may be applicable to a variety of systems that require some form of path integration, and thus although this study develops a model that is specific to the fly head direction system the architecture could be easily extended to other scenarios.

Reviewer #2 (Public Review): 

The ring attractor model serves as a fundamental framework to study how the brain encodes and computes based on continuous variables. One of this computation is angular path integration- the ability of animals to orient themselves using idiothetic cues to update the internal representation of their location. However, these models are usually pre-engineered and require precisely tuned connectivity, but it is unclear how such tuning emerge in the system. 

This computational work provides a model that learn to path integrate by adjusting synaptic efficacies, guided by allothetic cues. Tailored to the head direction system of the fly, this work replicates some of the experiments and findings in the literature and provides a mathematical explanation for how symmetric connectivity arise in such recurrent networks when supervised learning scheme is assumed. 

Strengths of the paper:

The paper addresses an interesting question, of the ability of the head direction system to path integrate. Putting in context, it's been almost 30 years that a ring attractor network was suggested to model the head direction system. The idea behind this model is that due to a symmetry in the recurrent connectivity in the network, a continuous attractor emerges in the system. In classical models, networks with 2 or 3 rings are used to both generate a persistent and continuous representation, but also to allow to perform angular integration. In recent years, it was suggested that a 3-ring-like structure plays a key role in the head direction system of the Drosophila. 

The strength of the paper is in building of a mechanistic network model that learns to path integrate based on a supervised learning scheme. The learning rule is local, suggesting its being biologically plausible, and following training the system develops symmetric recurrent connectivity that are reminiscent of the Fly's head direction system. Moreover, the authors provide a mathematical derivation, which shows how symmetric connectivity emerges in the network. 

Because the ability to path integrate in the system is based on learning, and not on pre-engineered connectivity, the network can also adapt to changes in the gain between the allothetic and idiothetic signals. This is consistent with the literature, where it was recently shown that the head direction system of the mouse can adapt to such gain modulations. 

The learning rule is based on a two-compartment model, which serves as a coincidence detector between external and internal inputs arriving at different compartments. The authors analyze the Fly's connectome data (visual and recurrent inputs to the E-PG neurons) to support this hypothesis. 

Weaknesses of the paper:

The paper has three main weaknesses. 

The first is that it is unclear if symmetry is a necessary outcome of the learning scheme, or if it depends on the way the network is initialized. In their mathematical derivation the authors assumed that the solution is symmetric, and while checking that this assumption is self-consistent, it is unclear if other non-symmetric solutions exist. Specifically, it is unclear if different initializations of the recurrent and feedforward connectivity will give a different- possibly asymmetric- solution. Moreover, the authors assumed a specific network architecture, which was tailored to the Fly's head direction system. It is unclear if the symmetric solution the authors found is specific to this architecture. 

The second issue is with the support the authors provide to their two-compartment model assumption. The authors analyzed the spatial locations of inputs to an E-PG neuron and claimed that the visual and recurrent inputs are spatially segregated. While this is a nice use of the new connectome data, there is no statistical analysis to support this claim, and only one example is shown in the manuscript. 

Third, the mechanism in the model seems to strongly rely on saturation of the E-PG neurons. However, it is not clear if this is a biologically relevant regime for these neurons and the authors do not address this problem. 

Finally, while the authors did a great job in building a mechanistic model that resembles the fly's head direction system, they fail to provide testable predictions. Such predictions are in general not necessary to have, but with the detailed characterization of this system in the fly, together with a mechanistic model that is tailored specifically to this system, I feel that the manuscript (and the community) will benefit from devoting a paragraph or two in the discussion to suggest new experiments, in line with their mechanistic model.

Reviewer #3 (Public Review): 

In this manuscript by Vafidis et al., the authors propose a learning mechanism that adjusts the connectivity of a head direction cell network to obtain robust angular integration. Ring attractors have been proposed long ago to account for head direction cell tunings. However, in general, continuous attractors require precise (or fine-tuned) weights in the neuronal connectivity of the circuit. It seems, a priori, unreasonable to think they are developmentally obtained, and a process requiring experience would be more likely. Hence the mechanism proposed by the authors is attractive and elegant. 

In general, the manuscript is well written and structured. The work is of timely interest, especially since the insect head direction cell circuit is currently receiving much attention.

test

Reviewer #1 (Public Review):

This study examines the use of terahertz wave modulation (THM), a technique for transmitting terahertz wave electromagnetic energy to the cochlea with the aim of improving the sensitivity of the cochlear outer hair cells. ABR obtained with and without THM suggests that sensitivity thresholds were improved by 10 dB when using THM. Whole-call patch clam recordings from outer hair cells suggest that THM significantly increases both K+ and MET currents of the cochlear outer hair cells. These results are convincing and potentially important for understanding normal cochlear physiology.

On the other hand, the numerous claims about translational applicability of this work seem overstated.

61-65 This is incorrect. For example, optogenetics or stem cell use are not currently seen as "treatment for hearing impairment" and, in fact, the manuscript says as much later in the paragraph. Also, pharmacological treatment is rarely effective, and only in limited circumstances.

283-294 The discussion of near-infrared vs THM is misguided. Near-infrared has been proposed as a possible alternative technology to stimulate spiral ganglion neurons, thus replacing cochlear implants. This is plausible, even though feasibility has not yet been demonstrated. In contrast, THM does not seem like a plausible alternative to cochlear implants. Patients who are candidates for cochlear implantation may not have enough (or any) outer hair cells, which are the target for THM.

295-299 "In comparison with wearing hearing aids, stem cell differentiation and transplantation (Oshima et al., 2010; Li et al., 2003; Chen et al., 2012), optogenetics (Huet et al., 2021) and electronic cochlear implantation (Wilson et al., 1991; Kipping et al., 2020; Gang et al., 2008), THM requires no traumatic surgery, cumbersome equipment, or genetic manipulation, and is thus more suitable for use in human subjects." In the described experiment, optic fibers had to be placed close to outer hair cells. That seems to require "cumbersome equipment" and obviously would require surgery for use in humans.

The data show that sensitivity was improved by 8.75 dB. In practical terms this is a very small change. Sensitivity improvement of 10 dB (and much more than that) can be obtained non invasively and on a frequency dependent basis using traditional amplification.

Any neural stimulation technology would require not only spatial selectivity but also temporal responsiveness. It seems that THM could meet the former criteria but the latter is unknown. In other words, for any practical application it would be necessary to show that modulation of a THM signal can be perceived by listeners. However, this criticism is moot if the claims about clinical applicability of THM are removed.

Reviewer #2 (Public Review):

This manuscript uses mid-infrared light to enhance the currents from natural stimuli (mechanical and voltage) of hair cells. The authors show increased voltage-gated K+ current and MET currents while being illuminated with mid-infrared light. Based on molecular dynamics simulations, the authors hypothesize that the augmented voltage-gated K+ currents are due to stimulation of C=O groups in the selectivity filter which allows K+ ions to pass through the pore more quickly to increase conductance; there was no hypothesis as to why MET currents were augmented. The authors also demonstrate improved ABR thresholds when the cochlea was illuminated with the mid-infrared light, demonstrating a potential therapeutic application. The enthusiasm for the novelty of this work is reduced because other work has shown that neurons can be excited by near-infrared (~2 microns) wavelength due to thermal stimulation and changes in cell capacitance, so this work mainly differs in their proposed mechanism and the longer wavelength of light (8.6 microns). Additionally, the Hudspeth group (Azimzadeh et al, 2018, PMC5805653) has shown thermal gating of MET channels using ultraviolet light and infrared light (1.47 microns). If the THM mechanism is indeed different from thermal stimulation, this would be a novel therapeutic mode, however, the data are not yet convincing that thermal stimulation is not the mechanism of action.

The authors hypothesize that the increase in K+ current through voltage gated channels is due to increasing the speed of movement of the K+ ions through the selectivity filter, which they modeled with molecular dynamics simulations. However, the simulations are not validated with experimental manipulations.

It was unclear to this reviewer whether the temperature effect would be measurable with the technique used. It appears that the temperature measuring system is rather large as compared to the cell, therefore it would likely measure changes in bulk solution temperature and not necessarily a local or micro-scale change in temperature that the cell may be responding too. Additionally, Littlefield and Richter has suggested that temperature changes on the order of 0.1 degrees Celsius are sufficient to evoke action potentials (Littlefield & Richter, 2021, PMC8035937), which is well within the temperature changes observed by the authors. At the longer wavelengths used in this study, the absorption of water is generally even higher as well, suggesting even greater temperature changes with the same power. In vestibular hair cells a 10 deg Celsius increase in temperature led to a 50-60% increase in peak MET current (Songer & Eatock, 2013, PMC3857958).

In figure 1, when THM is on, there appears to be an increase in the inward current without any mechanical stimulation. There is no discussion of this, and this could be a baseline effect that is not aimed at simply enhancing existing conductances. The increase in K+ conductance seen in the voltage-gated K channel cannot account for this increased inward current, since K+ conductance is outward. THM itself could also activate a small amount of MET current, maybe via the thermal effect demonstrated by Azimzadeh et al. This increased conductance could also be from the Tmc1 leak conductance that the authors have published on previously.

Line 232-233: With regard to the ABR data, data is not shown about whether an OABR can be elicited. The data show that once the THM is turned on and then a click stimulus is presented, there is no response; however, this experiment does not really test whether the THM can evoke an OABR since many repetitions are required to get the ABR waveform out of the noise. If THM is on and the stimulus is below threshold, then there is unlikely going to be an evoked response since the THM stimulus is not synchronized with the ABR recording. The authors need to show that THM onset stimulation that is synchronized with the ABR recording does not result in an ABR waveform.

Reviewer #1 (Public Review): <br> Lamm and colleagues explore how the brain regulates our empathic response to others in genuine versus simulated pain. Using a clever experimental design, the authors sought to adjudicate between two conflicting perspectives in the literature - whether the empathic response reflects the alignment of neural activity typically engaged for the affective component of self-experienced pain or whether such activity is better accounted by an automatic response to salient perceptual cues (pain vividly expressed on the face). They used carefully designed videos of actors responding to painful stimuli or pretending to respond in pain. The experimental design provided an elegant way of teasing apart underlying affect, controlling for the perceptual saliency of the facial pain expression. 

Empathy-related activity in the anterior insula was found to reflect affective sharing; a fundamental component of the empathic response. Using dynamic causal modelling, the authors explored the interactions between affective responses and self-other differentiation, focusing on the right supramarginal gyrus as a major hub previously been implicated in the self-other distinction. They found a dynamic partnership between the anterior insula and the right supramarginal gyrus which enables humans to track the genuine emotional response of another person and to regulate one's own response accordingly. 

This study is very well-conducted with clear hypotheses that are grounded in theory and novel methodology that enables the authors to explore their hypotheses in a carefully controlled manner. The interpretation of findings is measured, limitations are appropriately discussed and directions for future study are presented. 

Overall, these findings provide a novel lens through which we can understand the processes by which humans respond to the genuine suffering of others and how we regulate our responses accordingly.

Reviewer #2 (Public Review): 

The authors provide an experiment in which videos with painful and non-painful stimulation of unknown persons are shown. In the genuine condition one person is shown who undergoes a painful experience, whereas another person in the pretentious condition is shown who undergoes a non-painful experience, but has a facial expression of pain (both persons are only pretending). The authors found higher rating of pain expression, painful feeling in others and higher unpleasantness related to the participant for videos with painful stimulation in the genuine condition. Mass-univariate fMRI activity in a regression analysis revealed a relation between higher unpleasantness related to the participant that was associated with activation in the anterior insular cortex (aIns). The authors further build a dynamic causal model (DCM) that included the aIns and the rSMG and showed that neural activity during videos with painful stimulation that was modulated in genuine vs. pretended condition. The authors interpret these findings by a model in which genuine (vs. pretented) pain in others modulates the influence of aIns activity on the rSMG to regulate self-other distinction. 

An account of the major strengths:

The experimental design is well controlled for pain vs. no pain condition. The genuine vs. the pretended condition allows to disentangle visual expression of pain from actual pain in others (at least in the pilot study). <br> The DCM is an elegant method to test the modulatory influence of genuine vs. pretended pain on two brain regions. 

Weaknesses of the Methods and Results:

1) In my view, the experiment does not allow to unambiguously disentangle self vs. other distinction (as mentioned in the abstract "..we investigated how affect sharing and self-other distinction interact.."). For example, genuine vs. pretended pain could be distinguished from the participants own experience in a comparable way. The higher rating of unpleasantness for genuine pain in others does not necessary mean that the participants cannot separate own from others experiences. 

2) The experimental design does not unambiguously allows to disentangle genuine vs pretended pain from other factors, such as the differences in pain expression, painful feeling in others and higher unpleasantness in these two conditions. I understand that the intensity pain expression, painful feeling in others and unpleasantness for others is inherently tied to genuine vs. pretended pain. But the author already saw that the instruction of "genuine vs. pretented" influenced the ratings of pain expression. Hence, this allows two interpretation of the results: either the influence from the anterior Insula on the rSMG is driven by higher perceived pain expression, painful feeling in others and unpleasantness or by the conditions of genuine vs. pretended pain. Or (more likely) by an interaction between these factors. <br> It would, for example help to explore the association between the aIns-rSMG interaction pain expression ratings (or painful feeling in others or higher unpleasantness) in videos with genuine pain und pretended pain separately. <br> The author should further discuss this point that different factors (pain expression, etc) contribute to the differences between genuine vs. pretended pain. 

3) The multiple regression analyses revealed an association between the unpleasantness for the participants and the aIns, when accounting for the painful expression and the pain experienced by the other. This, however, does not reveal the specificity of the aIns for encoding the unpleasantness for the participants. <br> It might well be that variance is shared in the association between the aIns and pain expression and pain by the other and unpleasantness for the participants, but simply strongest for unpleasantness. <br> Such ambiguity could be resolved by additional multiple regressions of 1) pain expression (controlling for pain by the other and unpleasantness for the participants) and 2) pain for the other (controlling for pain expression and unpleasantness for the participants). 

4) Is the regression biased by the differences between conditions in the aIns in both fMRI signals and the ratings? 

5) The inclusion of the rSMG into the DCM model is not straight forward for me. It could have been based on previous literature, but then the aMCC should have been added as well. Furthermore, while the implication of the rSMG in distinction of self vs. others is established, the actual process in this experiment cannot be revealed. The authors state that the rSMG is involved in action observation or imitating emotions (page 9, line 200). 

Whether Results support their conclusions:

The results support the distinction between the experimental conditions of genuine vs. pretended pain in the aIns and as a modulatory influence on the connectivity between the aIns and the rSMG. However, the authors aimed to test if genuine vs. pretended pain modulate regulatory influences from the aIns on the rSMG that are connected to self-other distinction (as proposed in the discussion page 8, line 170). Yet, any insights about self-other distinction are only inferred reversely, since there is no outcome that indicates how well participants distinguished between themselves and the other person. For example in the discussion the authors state that: " we thus propose that the higher rSMG engagement in genuine pain conditions reflects an increasing demand for self-other distinction imposed by the stronger shared negative affect experiences in this condition". This is not supported by the results. <br> Furthermore, the title mentioned automated responses to pretended pain, which I could not understand, given the current results. 

Likely impact of the work on the field:

These results are expected to advance the field, since they allow to disentangle visual expressions of pain from genuine pain in others. Thereby, this work could resolves the question about neural processes that are specific to pain in others beyond other salient cues.

Reviewer #1 (Public Review): 

Insulin-secreting beta-cells are electrically excitable, and action potential firing in these cells leads to an increase in the cytoplasmic calcium concentration that in turn stimulates insulin release. Beta-cells are electrically coupled to their neighbours and electrical activity and calcium waves are synchronised across the pancreatic islets. How these oscillations are initiated are not known. In this study, the authors identify a subset of 'first responders' beta-cells that are the first to respond to glucose and that initiate a propagating Ca2+ wave across the islet. These cells may be particularly responsive because of their intrinsic electrophysiological properties. Somewhat unexpectedly, the electrical coupling of first responder cells appears weaker than that in the other islet cells but this paradox is well explained by the authors. Finally, the authors provide evidence of a hierarchy of beta-cells within the islets and that if the first responder cells are destroyed, other islet cells are ready to take over. 

The strengths of the paper are the advanced calcium imaging, the photoablation experiments and the longitudinal measurements (up to 48h). 

Whilst I find the evidence for the existence of first responders and hierarchy convincing, the link between the first responders in isolated individual islets and first phase insulin secretion seen in vivo (which becomes impaired in type-2 diabetes) seems somewhat overstated. It is is difficult to see how first responders in an islet can synchronise secretion from 1000s (rodents) to millions of islets (man) and it might be wise to down-tone this particular aspect.

Reviewer #2 (Public Review): 

Kravets et al. further explored the functional heterogeneity in insulin-secreting beta cells in isolated mouse islets. They used slow cytosolic calcium [Ca2+] oscillations with a cycle period of 2 to several minutes in both phases of glucose-dependent beta cell activity that got triggered by a switch from unphysiologically low (2 mM) to unphysiologically high (11 mM) glucose concentration. Based on the presented evidence, they described a distinct population of beta cells responsible for driving the first phase [Ca2+] elevation and characterised it to be different from some other previously described functional subpopulations. 

Strengths: 

The study uses advanced experimental approaches to address a specific role a subpopulation of beta cells plays during the first phase of an islet response to 11 mM glucose or strong secretagogues like glibenclamide. It finds elements of a broadscale complex network on the events of the slow time scale [Ca2+] oscillations. For this, they appropriately discuss the presence of most connected cells (network hubs) also in slower [Ca2+] oscillations. 

Weakness: 

The critical weakness of the paper is the evaluation of linear regressions that should support the impact of relative proximity (Fig. 1E), of the response consistency (Fig. 2C), and of increased excitability of the first responder cells (Fig. 3B). None of the datasets provided in the submission satisfies the criterion of normality of the distribution of regression residuals. In addition, the interpretation that the majority of first responder cells retain their early response time could as well be interpreted that the majority does not. 

A major issue of the work is also that it is unnecessarily complicated. In the Results section, the authors introduce a number of beta cell subpopulations: <br> first responder cell, <br> last responder cell, <br> wave origin cell, <br> wave end cell, <br> hub-like phase 1, <br> hub-like phase 2, <br> and random cells, <br> which are all defined in exclusively relative terms, regarding the time within which the cells responded, phase lags of their oscillations, or mutual distances within the islet. These cell types also partially overlap. Their choice to use the diameter percentile as a metrics for distances between the cells is not well substantiated since they do not demonstrate in what way would the islet size variability influence the conclusion. All presented islets are of rather a comparable size within the diffusion limits. 

The functional hierarchy of cells defining the first response should be reflected in the consistency of their relative response time. The authors claim that the spatial organisation is consistent over a time of up to 24 hours. In the first place, it is not clear why would this prolonged consistency be of an advantage in comparison to the absence of such consistency. The linear regression analysis between the initial and repeated relative activation times does suggest a significant correlation, but the distribution of regression residuals of the provided data is again not normal and non-conclusive, despite the low p-value. 50% of the cells defined a first responder in the initial stimulation were part of that subpopulation also during the second stimulation, which is rather random. 

One of the most surprising features of this study is the total lack of fast [Ca2+] oscillations, which are in mouse islets, stimulated with 11 mM glucose typically several seconds long and should be easily detected with the measurement speed used. 

And lastly, we should also not perpetuate imprecise information about the disease if we know better. The first sentence of the Introduction section, stating that "Diabetes is a disease characterised by high blood glucose, ..." is not precise. Diabetes only describes polyuria. Regarding the role of high glucose, a quote from a textbook by K. Frayn, R Evans: Human metabolism - a regulatory perspective, 4rd. 2019 „The changes in glucose metabolism are usually regarded as the "hallmark" of diabetes mellitus, and treatment is always monitored by the level of glucose in the blood. However, it has been said that if it were as easy to measure fatty acids in the blood as it is to measure glucose, we would think of diabetes mellitus mainly as a disorder of fat metabolism. "

Reviewer #3 (Public Review): 

Pancreatic beta cells in each islet of Langerhans act cooperatively to produce a coordinated response to blood glucose, which takes the form of roughly synchronous electrical oscillations that result in coherent pulses of insulin secretion. However, it has become more appreciated recently that the cells are heterogeneous and that there may subgroups of cells within the islet that contribute in different ways or control different aspects of the collective behavior. 

This paper addresses a subset of cells, termed first responders, that are the earliest to transition into activity when glucose is stepped up from a sub-stimulatory level. It is shown that the first responders determine the first transient phase of electrical activity, and implicitly secretion, that precedes the start of steady-state oscillations of the second phase. The first phase is of interest for the pathogenesis of diabetes because it is (or claimed to be) one of the first indications of the disease. The clinical data on this are actually ambiguous, but nonetheless the first phase is an important aspect of how secretion from islets is organized. This is clear from the existence of a subset of readily releasable insulin vesicles, but the electrical activity correlates that synergize with vesicle availability are less well understood. As such, the paper is an important contribution to both islet biology and potentially diabetology. 

The approach is to use a genetically encoded calcium indicator, GCamps, and confocal imaging to identify cells that show the earliest rise in calcium and then to verify that this property remains consistent when the glucose stimulus is repeated about an hour later. Further testing over 48 h shows the first responder slowly fading. This thus appears to be a matter of continuous variation of cell properties within the islet and moreover one that is persistent but decaying over long-enough periods of time, rather than a discrete sub-type of beta cell. This is confirmed by simulations using an islet model in which first responders emerge from random variation of properties. The text is a bit ambiguous about whether we should think of the glass as half full or half empty and could be clarified in this regard. 

The properties that matter are the density of KATP channels and gap junctional coupling which are both lower. This is also confirmed by simulations. The intriguing suggestion is made that there may be a reciprocal negative feedback relationship between these quantities that regulates their variation over time. This would be rather different from a persistent genetic difference and would be a good subject for future investigation. 

Interestingly and perhaps surprisingly, increased sensitivity to glucose is not a feature of the first responders. In contrast, other putative "leader" or "hub" cells that have been identified for the second phase of electrical activity are proposed to have increased glucose sensitivity. This and other features lead to the conclusion that the two types of leader cells are probably distinct. The much-discussed topic of hub cells provides interesting context and relevance to the paper, but its results stand by themselves and can be judged independent of hubs.

Reviewer #1 (Public Review):

The authors carry out a series of cell biology and genetics experiments in Arabidopsis to implicate CLE40 in an additional negative feedback loop. CLE40 has an expression pattern complementary to that of CLV3, so that CLV3 and CLE40 antagonistically control shoot meristem size by controlling expression of the transcription factor WUSCHEL in a CLV1- and BAM1-dependent manner.

Reviewer #2 (Public Review):

Loss-of-function cle40 mutants have small meristems, and promoter fusions show that the CLE40 gene is active in the peripheral meristem zone around the CLV3 expression domain. Lines expressing WUSCHEL from the CLV3 promoter have enlarged meristems that lack CLE40 expression, and the CLE40 expression domain is larger than normal in loss-of-function wuschel mutants, so WUSCHEL suppresses CLE40 expression. Protein fusions of CLAVATA1 show localization at the center of the meristem and in leaf primordia in the apical dome, and of the BAM1 receptor-like kinase in many tissues of the shoot tip, but with strong expression in a cylinder around the center of the shoot apex. Overlapping expression of CLE40/BAM1 and CLV3/CLV1 is shown and mutant combinations show that the proteins act in pairs. Analysis of WUSCHEL expression in clv1, clv3, cle40 and bam1 mutant backgrounds shows that CLE40 and BAM1 promote expression, ad both genes affect the shape of the apical dome. A model for patterning of the apical dome by CLV3/CLV1, CLE40/BAM1 and WUSCHEL is proposed.

Reviewer #3 (Public Review):

Maintaining the balance between stem cell proliferation and cell differentiation is an essential challenge of all stem cell niches. In the shoot apical meristem of plants, these functions are spatially separated into the central zone and peripheral zone, respectively. How these zones communicate to give rise to proper stem cell behavior has been a research focus for many years.

In this manuscript, the authors suggest that the small secreted peptide CLE40 and the receptor kinase like protein BAM1 form a novel pathway that contributes to meristem homeostasis by stimulating the expression of the central stem cell inducer WUSCHEL primarily from the meristem periphery. Importantly, this pathway acts antagonistically to the well-studied CLV pathway, which is only active in the center of the meristem and is molecularly highly similar to the CLE40/BAM1 system. This model is experimentally supported mainly by analysis of spatial localization patterns in the meristem using transcriptional and translational reporters and by the analysis of genetic interactions.

The findings of the authors are novel, highly relevant and would certainly be of great interest for the plant community. However, the manuscript could be substantially improved to provide better support for the conclusions laid out.<br> Of major concern are the reporter genes and imaging data: Partial colocalization and exclusion from CZ and OC are one of the main arguments of the authors to claim that CLE40/BAM1 function together and antagonistically to CLV3/CLV1 in controlling WUS expression.

Working with reporters as proxies for endogenous gene expression needs to be backed up by proper controls. Given the central importance of the reporters for the conclusions it is essential to show that the regulatory sequences used for the CLE40 reporter are sufficient to rescue a cle40 mutant; the observed expression of the reporter is consistent across the majority of different T1 lines and, most importantly, that the pattern reported here is consistent with in situ data for endogenous CLE40 mRNA. The authors have previously published in situs for CLE40 that do not show the exclusion from the CZ and OC (Hobe et al., 2003, Figure 2a,c), which urgently needs clarification.

Figures 2, 4 and 5 show imaged meristems in great detail but each focus only on a single sample. I strongly recommend to also include quantitative data on multiple samples to substantiate the claims. This could be likely be done with standard software, such as MorphographX.

Whereas the inhibitory effect of WUS on CLE40 is convincingly shown using ectopic WUS expression and the hypomorphic wus7 allele (Figure 2) the quantification of WUS positive cells in Figure 7 is problematic. Although it was done over multiple samples it heavily relies on manual scoring, which is prone to bias. The same is true for the width/height measurements of different meristems. An unbiased computational image analysis would certainly give more reliable results.

One major point that the authors try to establish is that the CLE40 signal that eventually leads to reduction in meristem size is transduced via the BAM1 receptor. However, only genetic interactions, which are complicated by intricate feedbacks, are show to substantiate this claim. For a strong statement on CLE40/BAM1 ligand/receptor interactions, advanced imaging technologies available to the authors or biochemical experiments would be necessary. Similarly, the genetic studies need some clarification: The authors show that cle40 and bam1 single mutants as well as cle40/bam1 double mutants all show a comparable reduction in meristem size, suggesting epistasis. In contrast, a reduction in meristem size can not be observed if cle40 is combined with clv1, which according to the proposed model appears to be unexpected. The interpretation of the genetic experiments is complicated by the well-known fact that BAM1 expression is regulated by the CLV pathway and loss of CLV signaling leads to ectopic expression of BAM1 in the OC which can partially compensate for the loss of CLV1, due to the molecular similarity of the two receptors. The shift of BAM1 expression from the PZ towards the OC could explain why there is no significant reduction in meristem size since CLE40 induced signaling at the PZ would be inhibited by the lack of the BAM1 receptor. To clarify the specific interaction of CLE40 with BAM1 and/or CLV1 the authors could try to restore BAM1 levels in the PZ of cle40/clv1 mutants by expressing BAM1-GFP from an appropriate promoter (e.g. RPS5 or UBQ10). This experiment would allow to distinguish between the genetic interaction of CLE40 with CLV1 from the feedback between CLV1 and BAM1 expression.<br> Overall, the manuscript could be strengthened by inclusion of additional molecular data probing the directness of WUS inhibiting CLE40 and/or BAM1 expression.

Reviewer #1 (Public Review): 

The manuscript by Nakamura et al builds on previous publications reporting that research grants from Black Investigators scored worse and were less successful than those from White investigators. The authors of the current study attempt to examine the role of several variables (PI race, gender,degree, career stage, etc) on scoring of scientific reviews. The authors examined whether blinding reviewers to PI identity impacted review scores. They examined 1,200 applications, broken down to 400 from Black investigators, 400 from matched White investigators and 400 from un matched/random White applicants. The authors find that applications from White applicants scored better than those from Black applicants. The authors redacted information in the application and found that this worsened scores for the White applicants but had no impact on Black applicants. The study had several strengths including the fact that it used an experienced group of NIH grant reviewers and in fact the first author Dr. Nakamura was previously head of the CSR. Another strength is that the grant applications were actual grants submitted to NIH between 2014-2015. The study has several inherent weaknesses including that it is very hard to completely blind the reviewers to variables such as institution, career stage, PI identity, race etc. Redaction is also very time consuming and may affect the overall impact of the submitted application. Nevertheless the results provide new information to help better define the issues surrounding reviewer bias and potentially help in the search for possible solutions.

Reviewer #2 (Public Review): 

This manuscript details an investigation into whether blinding NIH grant reviewers to the name and institution may affect their review scores. They demonstrate that unblinded grants lead to slightly higher scores for white applicants than blacks, however, a deeper dive demonstrates that grantsmanship and history of prior funding can be even greater predictors of scores regardless of race. 

Overall the manuscript touches on a presently vogue topic and that is of equality in outcomes and systemic racism. The major limitations of the study however, are ironically demonstrating the very topic that the manuscript tries to address. There are no considerations in the manuscript or mention of applications from Asian, Hispanic or Native American applicants, as the authors distill the problem literally down to only Black and White.

Reviewer #3 (Public Review): 

This is a study of peer review merit evaluation for a set of R01-mechanism grant applications that were originally reviewed in the National Institutes of Health's Center for Scientific Review in 2014 and 2015. The goal was to try to determine the effects of redacting the identity of the scientists serving as Principal Investigator (PI) on these applications on the merit scoring by experienced peer reviewers. The explicit focus of this study was on whether differential effects of redacting identity were obtained for applications with Black versus white PIs. This study fits into a broader perspective in which the NIH has identified a consistent, very large, reduction in the rate of funding applications with Black PIs. This study represents an attempt to determine if explicit knowledge of the race of PIs affects peer review scoring of the applications. The major outcome of the study was that redaction of the PI identity reduced the Black/white gap by about half; this was due to a reduction in the scores of identity-redacted applications with white PIs, relative to their scores with identity provided. 

Major strengths of this work include pre-registration of the design and analytical plan for the study, the inclusion of two control data sets with white PIs (one matched to the primary experimental group, one randomly selected), selection of reviewers from a pool with experience on the study sections in which the target applications had been reviewed, and assessment of the success of reviewer blinding with post-review surveying. 

There are several methodological issues which limit confidence in translating these experimental results to actual NIH review processes. Of the nine scientific review officers who were used to recruit and assign reviewers, only four had any prior experience in real review panels. The integral role that reviewer recruitment and application assignment has on scoring was thus only poorly reproduced for this study. In a related vein, the reviewers provided written critiques but did not meet in study section panels, this eliminates a substantial part of the determination of an application's voted scoring. Individual reviewers also completed a wide range of reviews (1-29) which also has major implications for how a given application is reviewed in real panels with the reviewer "load" much more consistent. These concerns are supported by a perplexing finding of an advantage for the applications with white PIs in the experimental standard-identity review condition, given that this group was matched for original scores with the applications with Black PIs. 

The study at present omits critical analysis / data depiction which would be of tremendous value. First, the correlations of original review scores with those obtained in the standard-format experimental condition have not been presented. This is key not just for the above mentioned discrepancy for the score-matched group of applications, but as a more general issue of replication/repeatability for the NIH. Second, there is no direct presentation of the differences in the experimental scoring for the applications in redacted and standard guise. 

Overall, the study presents a highly credible attempt to investigate an issue of significance and impact for NIH and NIH-funded scientists. While there are significant caveats, the limitations are mostly transparent. The main conclusions are supported. This adds evidence to the obvious conclusion that the substantial bias against the funding of NIH proposals with Black PIs is a "death of a thousand cuts" scenario where no one single major cause can be found. Importantly, it shows that changing to blinded review is likely only to have very limited impact and it will do so mostly by reducing the advantage for applications with white PIs that results from their identity being known.

Reviewer #1 (Public Review): 

This study by Parker et al is an in-depth analysis of the chemical "rules" that underlie liquid-liquid phase separation (LLPS) by the Drosophila melanogaster (D.m.) Cdt1 replication initiation protein. Given that Orc1 and Cdc6 replication initiation proteins have similar internal disordered regions (IDR) that promote phase separation, the findings of the current report are likely to generalize to the initiator class of IDRs that induce LLPS. 

The author's provide an excellent intellectual background on IDR composition and determinants for LLDS in many different systems. This allows the authors to effectively communicate to readers the comparison of the initiator IDR with a wide variety of IDRs that induce LLPS in other types of proteins. The analysis suggests that the initiator class of IDR is quite distinct from some of the most commonly studied proteins that lead to IDR-induced LLPS. 

A strength of the study is that it uses several techniques to analyze inherent LLPS formation in the D.m. Cdt1 replication initiator protein. This includes uses of hydrophobic disruption agents, salt, use of various polyanions as scaffolds, and use of PEG as a crowding agent. The authors mutate the Cdt1 IDR sequence in many ways, following a well explained logistical reasoning for identification of the determinants within the IDR that are required for LLPS formation. Thus they explain their studies of the IDR for aromatic residues, for short repeats, for linear and branched hydrophobic residues. The authors then perform extensive mutagenesis to test their analysis of the IRD sequence. The mutagenesis includes deletions of large segments of the IDR, complete total scrambling of the IDR sequence, removal of hydrophobic residues, replacement of hydrophobic residues. In each case, the mutants are assayed for relative efficiency of LLPS formation using either PEG or DNA to induce LLPS. 

The authors conclude that the Cdt1 IDR amino acid composition, but not its sequence, is required for LLPS formation. This was a surprise. They found that aromatic residues were not required, unlike the case for some other types of proteins, but that the hydrophobic residues are required, regardless of sequence context. Furthermore, they determine that the hydrophobic residues mediate inter-IDR interactions and that these provide the main force behind LLPS formation. They find that interaction with a polyanion, like DNA (or numerous other types of polyanions), helps the phase separation process, but is not the main driving force of LLPS by Cdt1. 

Overall, this study is an important "next step" in the study of LLPS, and the authors convincingly identify the chemical determinants that are necessary for LLPS formation in Cdt1. The results are clear, and the data support the author's conclusions. Furthermore, the results with Cdt1 are likely to extend to the Orc1 and Cdc6 initiator proteins, which have IDRs that appear to have similar characteristics to Cdt1. The authors make the interesting proposal that this "like-recruits-like" IDR interaction in the LLPS may contribute to the basis by which these "initiator LLPS particles" specifically attract initiator proteins while excluding other types of proteins that undergo LLPS. 

The studies of this report may form a template for identification of the determinants within the IDRs in other types of proteins that undergo LLPS.

Reviewer #2 (Public Review): 

In this paper, the authors study the Drosophila replication initiation protein Cdt1 in vitro. This study builds on previous work by the Berger and Botchan labs in which the authors reported that Cdt1 as well as Cdc6 and ORC, other replication initiation factors, undergo liquid liquid phase separation (LLPS) when combined with a short stretch of DNA in vitro. This phase separation is dependent on the intrinsically disordered region (IDR) in the N-terminus of the Cdt1 protein. Importantly, the condensed phase recruits the Mcm helicases required complex that is required for replication initiation. In addition, the IDR seems to be regulated by cyclin dependent kinases, and dephosphorylation at the end of mitosis could allow the formation of initiator condensates on DNA to initiate recruitment of other replication factors (Parker et al., 2019). 

In the current manuscript, the authors address the questions of which molecular hallmarks of Cdt1 IDR control DNA-dependent phase separation. They find that positively charged amino acids play a crucial role while aromaticity - which was reported to drive phase separation of other proteins - is not important for Cdt1 phase separation. Indeed, other negatively charged polymers can phase separate with Cdt1 as well. Interestingly, the charge distribution in the IDR does not seem to be important for the phase separation process. However, because Cdt1 molecules with a more uniform charge distribution have a different elution profile in size exclusion chromatography, the charge distribution may serve another unknown function. In addition, the authors describe an essential role of hydrophobic amino acids (in particular Leucin, Isoleucin and Valine) in driving the self-interaction of Cdt1 molecules that is required for phase separation. 

In conclusion, the authors propose that phase separation of replication initiation factors on DNA requires both, the electrostatic interactions of the positively charged Cdt1 IDR with the DNA polymer, as well as the hydrophobic IDR-IDR interaction which mostly involve Leucin and Isoleucine. 

Overall, this is a good follow up work dissecting, in detail, the molecular interactions required for replication initiation factor phase separation. As in their previous work, the experiments are well performed and of good quality. The authors created a series of Cdt1 mutants that, in combination, elucidate which types of interactions contribute to phase separation. However, given that studying the effect of mutations on the phase separation behaviour of Cdt1 is the main focus of this work, the simple 'depletion assays' which the authors performed throughout the work do not give sufficient quantitative insight and phase diagrams should be prepared for wt and mutant proteins. In addition, the core assumption of this work is that all protein-dependent phase separation is driven by aromatic residues. While this was shown for several proteins, there are other examples which includes Michael Rosen's work on NCK and N-WASP (Li et al., Nature, 2012) or work on the nucleolar protein NPM1 (Mitrea et al. or Ferrolino et al., Nat Commun, 2018), to just name a few. Further, many examples have been described in which the interaction between a protein and a nucleic acid is driving phase separation (e.g. Shrinivas et al., Mol. Cell, 2019 or Guillén-Boixet et al./Sanders et al./Yang et al., Cell, 2020 or Boeynaems et al, PNAS, 2019), similarly to what the authors described for DNA and some of which has been shown to be driven by basic amino acids. 

Although it was not a prior authors goal of this study, we learn very little about the relevance of the presented work for replication initiation in vivo. Is the recruitment of other replication factors influenced by the balance of electrostatic and hydrophobic interactions, why are the mutants not tested for recruitment of other initiation factors? How does this work compare to older, established systems for replication initiation? While RNA is freely diffusing inside the cell, this is not equally true for the long DNA polymer. It is therefore unclear, how replication phase separation could work inside the cell. 

Other major points: 

Considering that this is an in vitro study of Cdt1 solely, and the main assays are a pelleting assay, as well as fixed time point fluorescent microscopy, differences among mutants are hard to judge. Phase diagrams of wildtype and mutant proteins should be prepared. As well saturation concentrations should be determined and microscopy images of the different phase separation assays should be provided. Biophysical parameters of condensates are not addressed at all and would give considerable insight into the role of the different amino acid interactions. 

The authors show that hexanediol does not dissolve Cdt1 droplets. In addition, they convincingly establish that phase separation of Cdt1 is driven by the interaction between basic amino acids and RNA as well as between interactions of hydrophobic residues but not aromatic amino acids. This gives interesting insight into the type of interactions that are disrupted by hexanediol. Given that this chemical is widely used in the literature to probe for phase separation, this is an important finding of their work and should be discussed further. 

The authors dissect in detail which molecular interactions are driving phase separation of Cdt1. A picture emerges in which Cdt1 is soluble in the aqueous solution due to charged (mostly negatively charged) amino acids. However, through binding to DNA those charges are neutralized, exposing almost exclusively hydrophobic amino acids to the aqueous environment of the cell. This is a likely explanation for how those hydrophobic interactions are driving phase separation in the presence of DNA. This model (or an alternative model provided by the authors) should be discussed in more detail, for which the authors are suggested to re do Fig. 6.

Reviewer #3 (Public Review): 

The central hypothesis of the manuscript is that initiator proteins have different characteristics that lead to their phase separation away from other proteins that undergo LLPS. Consistent with the authors' hypothesis, they find that ORC, Cdc6 and Cdt1 IDRs have distinct sequences and that LLPS by these proteins is resistant to 1.6 hexanediol, in contrast to many other LLPS-forming proteins. In the rest of the paper, the authors analyze the requirements for Cdt1 LLPS in detail. They find that the sequence composition of the IDRs associated with these initiation proteins are distinct from other proteins involved in LLPS. The studies provide strong evidence that the role of DNA in the induction of LLPS by Cdt1 is as a multivalent ion. Finally, the show that branched chain hydrophobic but not aromatic residues are important for Cdt1 LLPS formation. 

The strength of this manuscript are the detailed studies of the requirements for the Cdt1 IDR to undergo LLPS. The authors provide clear evidence as to the sequence determinants of Cdt1 LLPS. The authors use the identification of Cdt1-Cdt1 interactions in the absence of DNA to identify critical aspects of the sequence for self-interaction and then show that they are important for the DNA-dependent LLPS formation. 

The weaker part of the paper is evidence that these observations can be extended to the other 'initiator IDRs'. It would substantially improve the manuscript to test whether similar mutations in another initiator IDR (either ORC or Cdc6) have the same consequences. It would also be nice to determine whether the mutations that prevent Cdt1 from phase separating on its own would also be enough to prevent it from joining an ORC-DNA or Cdc6-DNA LLPS event. 

Overall, this manuscript describes a thorough analysis of the determinants of LLPS by DNA and Cdt1.

Reviewer #1 (Public Review):

In this report, the authors describe chromatin accessibility and RNA-seq data in B cells from three mouse models of neurodevelopmental disorders (Kabuki syndromes 1 and 2 and Rubinstein-Taybi syndrome type 1) caused by mutations in related epigenetic regulatory genes. They used ATAC-seq to profile chromatin accessibility and a novel bioinformatics approach to overlay the peaks across different mouse models. They report that the accessibility profiles can distinguish one KO mouse model from another. Matched RNA-seq data demonstrate that many of these ATAC-seq peaks overlap with transcriptional changes, and may be relevant to disorder biology. Overall, the paper is well written, and the conclusions are supported by the data presented.

Reviewer #2 (Public Review):

Luperchio et al. have explored examples of Mendelian Disorders of the Epigenetic Machinery (MDEM) to look for common systemic functional epigenetic variation in mouse models of Kabuki Syndrome type 1 (KS1) and 2 (KS2), and Rubinstein-Taybi syndrome 1 (RT1). These MDEMs are caused by mutations in the histone lysine methyltransferase KMT2D that targets H3K4 (KS1), histone lysine demethylase KDM6A that targets H3K27me3 (KS2), and the histone acetyltransferase CREBBP (RT1). Due to certain common immunological phenotypes across these disorders, the authors compared isolated B cell (CD19+) via ATAC-seq and RNA-seq from the mutant mice against age- and sex-matched wild-type littermates. They identified 463 loci and 249 genes that show shared disruption in the 3 models examined, with potential functional impact, including on IgA deficiency in KS1 and RT1.

Strengths

Mouse models have strong advantages for experimental control. Also, the authors have examined isolated cell-types to reduce the impact of cell-type heterogeneity issues. There is clear merit in examining MDEMs, which possess defined monogenic causes for their epigenomic abnormalities, and as such are excellent models to disentangle specific changes in regulatory mechanisms with potential for novel pathogenic understanding.

Weaknesses

Whilst the authors recognise the strong skew in their findings, with almost all of the shared disrupted peaks being unidirectional towards an open chromatin state - that all three specific MDEMs would not predict - they don't give an adequate explanation for these observations. Also, the authors find that many of the loci identified are not known to be direct KMT2D, KDM6A or CREBBP targets. Therefore, the results identified are found to not fit clearly within functional pathways. To account for their indistinct findings, the authors state this is "counterintuitive to the function of the individual causative genes and may either suggest a previously unexpected role for them or an undescribed systemic compensatory response."

The authors grouping approach in their ATAC-seq analysis, where prior to alignment they merged all the individual bam files into genotype-specific meta-samples, will lead to a loss of power and resolution in their findings. They do correctly exclude intervals overlapping the most recent ENCODE blacklisted regions and also overlay their findings with DHS in B cells for further support to reduce the likelihood they are false positives. However, although different from human population studies, some genetic variability does exist even within these mice substrains including C57BL/6J - as highlighted in recent analysis (Mortazavi et al. BioRxiv 10.1101/2020.03.16.993683).

Reviewer #1 (Public Review):

Sokolsky et al. propose a new statistical model class for descriptive modeling of stimulus encoding in the spiking activity of neural populations. The main goals are to provide a model family that (G1) captures key activity statistics, such as spike count (noise) correlations, and their stimulus dependence, in potentially large neural populations, (G2) is relatively easy to fit, and (G3) when used as a forward encoder model for Bayesian decoders leads to efficient and accurate decoding. There are also three additional goals or claims: (C1) that this descriptive model family can serve to quantitatively test computational theories of probabilistic population coding against data, (C2) that the model can offer interpretable representations of information-limiting noise correlations, (C3) that the model can be extended to the case of temporal coding with dynamic stimuli and history dependence.

The starting point of their model is a finite mixture of independent Poisson distributions, which is then generalized and extended in two ways. Due to the "mixture", the model can account for correlations between neurons (see G1). As any mixture model, the model can be viewed in the language of latent variables, which (in this case) are discrete categorical variables corresponding to different mixture components. The two extensions of the model are based on realizing that the joint distribution (of the observed spike counts and the latent variables) is in the exponential family (EF), which opens the door to powerful classical results to be applied (e.g. towards G2-G3), and allows for the two extensions by: (E1) generalizing Poisson distributions in mixture components to Conway-Maxwell-Poisson distributions, and (E2) introducing stimulus dependence by allowing the natural parameters of the EF to depend on stimulus conditions. They call the resulting model a Conditional Poisson Mixture or CPM (although the "Poisson" in CPM really means Conway-Maxwell-Poisson). E1 is key for capturing under-dispersion, i.e. Fano Factors below 1. For the case of discrete set of stimulus conditions, they propose minimal, maximal versions of E2; depending on which natural parameters are stimulus dependent. In the case of a continuum of stimuli (they only consider 1D continuum of stimulus orientations, e.g. in V1 encoding) they also consider a model-based parametric version of the minimal E2 which gives rise to Von Mises orientation tuning curves.

Strengths:

- Proposing a new descriptive encoding model of spike responses that can account for sub-poissonian and correlated noise structure, and yet can be tractably fit and accurately decoded.

- Their experiments with simulated and real (macaque V1) data presented in Figs. 2-5 and Tables 1-2 provide good evidence that the model supports G1-3.

- Working out a concrete Expectation Maximization algorithm that allows efficient fits of the model to data.

- Exploiting the EP framework to provide a closed form expression for the model's Fisher Information for the minimal model class, a measure that plays a key role in theoretical studies of probabilistic population coding.

As such, the papers makes a valuable contribution to the arsenal of descriptive models used to describe stimulus encoding in neural population, including the structure and stimulus dependence of their higher-order statistics.

Weaknesses:

1) I found the title and abstract too vague, and not informative enough as to the concrete contributions of this paper. These parts should more concretely and clearly describe the proposed/developed model family and the particular contributions listed above.

2) I was not convinced about claims C1 and C2 (which also contribute to the vagueness of abstract), but I think even without establishing these claims the more solid contributions of the paper are valuable. And while I can see how the model can be extended towards C3, there are no results pertaining to this in the current paper, nor even a concrete discussion of how the model may be extended in this direction.

2.1) Regarding C1, the claim is supposed to follow from the fact that the model's joint distribution is in the exponential family (EF), and that they have reasonably shown G1-G3 (in particular, that it captures noise correlations and its Bayesian inversion provides an accurate decoder). While I agree with the latter part, what puzzles me is that in the probabilistic population coding (PPC) theoretical models that claim can be quantitatively tested using their descriptive model are, as far as I remember/understand, the encoder itself is in EF. By contrast here the encoder is a mixture of EF's and as such is not itself in EF. Perhaps this distinction is not key to the claim - but if so, this has to be clearly explained, and more generally the exact connection between the descriptive encoder model here and the models used in the PPC literature should be better elaborated.

2.2) Regarding C2, I do not see how their results in Fig 5 (and corresponding section) provide any evidence for this claim. As a theoretical neuroscientist, I take "interpretable" to mean with a mechanistic or computational (theoretical) interpretation. But, if anything, I think the example studied in Fig 5 provides a great example of the general point: that even when successful descriptive models accurately capture the statistics of data, they may nevertheless not reveal (or even hide or mis-identify) the mechanisms underlying the data. In this example's ground-truth model, the stimulus (orientation) is first corrupted by input noise and then an independent population of neurons with homogeneous tuning curves (and orientation-independent average population rate) responds to this corrupted version of the stimulus. That is a very simple AND mechanistic interpretation (which of course is not manifest to someonw only observing the raw stimulus and spiking data). The fit CPM, on the other hand, does not reveal the continuous input noise mechanism (and homogeneous population response) directly, but instead captures the resulting noise correlation structure by inferring a large (~20) number of mixture components, in each of which population response prefers a certain orientation. For a given stimulus orientation, the fluctuations between (3-4 relevant) mixture components then approximate the effect of input noise. This captures the generated data well, but misses the true mechanism and its simpler interpretation. Let me be clear that I don't take this as a fault of their descriptive model. This is a general phenomenon, despite which their descriptive model, like any expressive and tractible descriptive model, still can be a powerful tool for neural data analysis. I'm just not convinced about the claim.

2.3) Regarding C3, I think the authors can at least add a discussion of how the model can be extended in this direction (and as I'm sure they are aware, this can be done by generalizing the Von Mises version of the model, whereby the model I believe can be more generally thought of as a finite mixture of GLMs).

Reviewer #2 (Public Review):

Sokoloski, Aschner, and Coen-Cagli present a modeling approach for the joint activity of groups of neurons using a family of exponential models. The Conway-Maxwell (CoM) Poisson models extend the "standard" Poisson models, by incorporating dependencies between neurons.

They show the CoM models and their ability to capture mixture of Poisson distributions. Applied to V1 data from awake and anesthetized monkeys, they show it captures the Fano Factor values better than simple Poisson models, compare spike count variability and co-variability. Log-likelihood ratios in Table 1 show on-par or better performance of different variant of the CoM models, and the optimal number of parameters to use for maximizing the likelihood [balancing accuracy and overfitting] and are useful for decoding. Finally, they show how the latent variables of the model can help interpret the structure of population codes using simple simulated Poisson models over 200 neurons.

In summary, this new family of models offer a more accurate approach to the modeling and study of large populations, and so reflects the limited value of simple Poisson based models. Under some conditions it gives has higher likelihood than Poisson models and uses fewer parameters than ANN model.

However, the approach, presentation, and conclusions fall short on several issues that prevents a clear evaluation of the accuracy or benefits of this family of models. Key of them is the missing comparison to other statistical models.

1) Critically, the model is not evaluated against other commonly used models of the joint spiking patterns of large populations of neurons. For example: GLMs (e.g. Pillow et al Nature 2008), latent Gaussian models (e.g. Macke et al Neural Comp 2009), Restricted Boltzmann Machines (e.g. Gardella et al PNAS 2018), Ising models for large groups of neurons (e.g. Tkacik etal PNAS 2015, Meshulam et al Neuron 2017), and extensions to higher order terms (Tkacik et al J Stat Mech 2013), coarse grained versions (Meshulam et al Phys Rev Lett 2019), or Random Projections models (Maoz et al biorxiv 2018).

Most of these models have been used to model comparable or even larger populations than the ones studied here, often with very high accuracy, measured by different statistics of the populations and detailed spiking patterns (see more below). Much of the benefit or usefulness of the new family of models hinges on its performance compared to these other models.

2) As some of these models are exponential models, their relations to the family of the models suggested by the authors is relevant also in terms of the learned latent variables. Moreover, the number of parameters that are needed for these different models should be compared to the CoM and its variants.

3) The analysis focuses on simple statistics of neural activity, like Fano Factors (Fig. 2) and visual comparisons rather than clear quantitative ones. More direct assessments of performance in terms of other spiking statistics for single neurons and small groups (e.g., correlations of different orders ) and direct comparison to individual spiking patterns (which would be practical for groups of up to 20 neurons) would be valuable

Reviewer #3 (Public Review):

The authors use multivariate mixtures of Poisson or Conway-Maxwell-Poisson distributions to model neural population activity. They derive an EM algorithm, a formula for Fisher information, and a Bayesian decoder for such models, and show it is competitive with other methods such as ANNs. The paper is clear and didactically written, and I learned a lot from reading it. Other than a few typos the math and analyses appear to be correct. Nevertheless there are some ways the study could be further improved.

Most important, code for performing these analyses needs to be publicly released. The EM algorithm is complicated, involving a gradient optimization on each iteration - it is very unlikely people will rewrite this themselves, so unless the authors release well-packaged and well-documented code, their impact will be limited.

Second, it would be nice to extend the model to continuous latent factors. It seems likely that one or two latent factors could do the work of many mixture components, as well as increasing the interpretability of the models.

Third, it would be interesting to see the models applied to more diverse types of population data (for example hippocampal place field recordings).

Fourth, how does a user choose how many mixture components to add?

Reviewer #1 (Public Review):

This work from Park et al represents a large, ambitious study utilizing a variety of mouse models (several novel) to establish mechanisms underlying cardiac pathologies observed upon loss of imprinting at the H19/IGF2 locus. The studies indicate 1) that mice recapitulate key cardiovascular features observed in humans with BWS, 2) that developmental cardiomegaly and progressive cardiomyopathy are distinct, non-correlated phenotypes driven by disparate mechanisms (upregulation of IGF2 and reduction of H19, respectively), and that H19 associated pathologies are driven by reduced interaction with let7 microRNAs. There is considerable novelty and potential impact of the work, as it presents substantial mechanistic insight into the consequences of LOI and the development of BWS. The authors use a variety of appropriate approaches, including mouse echocardiography, tissue IHC, pressure myography, and in vitro studies of cell size regulation to support these primary conclusions. In its current form, however, some primary conclusions are insufficiently supported, and additional quantification and controls would be needed.

Major

1) The conclusion of transient neonatal cardiomegaly that resolves by 2 months is insufficiently supported. Increased cell surface area is useful, but can be driven by cell spreading, not necessarily hypertrophy, and no data is shown at the 2-month time point to suggest reversion of cardiomegaly.

2) It seems an important validation is needed for the Let7 binding site deletion model - I do not see any data confirming that the gene editing was indeed successful nor that Let7 binding to H19 was effectively disrupted. Further, it is unclear at what age the Let7 binding site deletion mice were assayed for cardiomegaly/hypertrophy. The HW/TL values (WT = 7.5) are different from most others reported throughout the manuscript.

3) Many observations or conclusions are not sufficiently supported by quantification. For example, there is a lack of quantification of any western blots.

Reviewer #2 (Public Review):

BWS caused by loss of imprinting of H19 and IGF2 results in overexpression of the growth factor IGF2 and reduced expression of H19. Because overgrowth and various other phenotypes associated with this disorder can result from misexpression of either of these genes, the investigators have used a mouse model that recapitulates the human phenotypes. Specifically, they use a series of endogenous mouse mutations and transgenes to test the role of Igf2 overexpression and H19 loss in neonatal cardiomegaly and adult onset progressive heart pathology, including fibrosis and reduced ventricular function. The use of H19/Igf2 ICR mutations to increase Igf2 expression shows that the neonatal cardiomegaly phenotype is largest caused by increased expression of IGF2. This phenotype ultimately resolves when IGF2 decreases. In contrast the adult phenotype is consistent with loss of H19, which is rescued using an H19 BAC. Finally, because H19 encodes a 2.3 kb lncRNA and a microRNA, the researchers used two additional mutants that test the function of each, demonstrating that reduced H19 expression can cause progressive heart pathologies and that interaction of H19 lncRNA with let7 microRNA is essential for normal cardiac physiology.

This is an interesting and elegant manuscript that shows the importance of the H19/IGF2 axis for normal heart physiology. Moreover, this work brings together some of the disparate functions that have been ascribed to H19. The work is convincing and the only remaining issues concern the level of expression of H19 and Igf2 in the various mutant backgrounds and target cells of IGF2 expression. Also, it is unclear if BWS individuals with H19/IGF2 lesions have heart disease later in life, and it would have been helpful for the readers if the authors had commented on this.

Reviewer #3 (Public Review):

Park et al were seeking to understand the effects of loss of imprinting at the H19/Igf2 locus on the physiology of the developing mouse heart. With the extensive molecular characterisation of the mechanisms of gene regulation at this locus in the public domain, they used this knowledge to move to the next step of making a detailed phenotypic study using a suite of mouse mutants.

Park et al utilize a suite of different approaches including choosing a set of mouse mutants that together have the capacity to ratchet up and down the expression of the two imprinted genes Ig2f and H19, and because of their known coordinate regulation, they can also distinguish their individual effects. This results in a predicted gene expression ratio and the resultant phenotypes in these mutants are characterized thoroughly. Heart phenotypes were examined in WT, LOI, LOI+BAC Tg and H19 deficient neonates which had different levels of each Igf2 & H19 and represent genetic rescues. Dysmorphology of the heart was observed and characterised. In aged animals, cardiac hypertrophy was evident in mice with LOI. Echocardiography revealed mild CV phenotypes in LOI mice.

The morphology of the heart overall and in detail (eg aorta strength/size) are investigated. Cardiac hypertrophy is assessed using markers for cell proliferation and cell assays are employed and revealed that exogenous IGF2 peptide induces cellular hypertrophy in WT cardiomyocytes through mTOR pathways.

However the authors noticed that the phenotypes under study elicited some odd parent of origin behaviours, namely, LOI female adult mice showed weak phenotypes compared to male mice but they were equivalent in neonatal mice. The authors therefore thought that neonatal hypertrophy and adult disease phenotypes were complicated by something unknown. This called for a genetic rescue experiment to carefully check for H19 and Igf2 expression independently, something feasible given the molecular detail in which these two genes have been studied mechanistically and the strains of mice available. It was revealed that H19 does not contribute to neonatal hypergrowth, instead, hypertrophy results from biallelic Igf2. This they justify by published work on TOR signalling.

The study undertook transcriptomic analysis of WT vs H19 deficient hearts followed by gene enrichment studies to pin point EMT deficiencies which they went on to validate with assays in isolated cells in culture. They study determined that endothelial cell transition was increased in the absence of H19. Prior studies pointed to let7 miRNAs binding H19 and to find out whether without this, cardiomyopathy results, CRISPR/Cas9 genome editing to delete let-7 binding sites in the H19 gene indeed supported this.

This work overall shows show that neonatal cardiomegaly is dependent on increased Igf2 compared to WT. Deletion of H19 is sufficient to induce cardiomyopathy in adult mice. A role in the regulation of cardiac endothelial cell growth is attributed to H19 through let7. A huge body of work.

Overall, the authors achieved their aims and their results support their conclusions.

This work moves ahead from mechanism to functional characterisation of these imprinted genes and focuses on mammalian development and physiology in the heart. It does this through a thorough and exhaustive examination of morphology, cell biology and quantitative methods in what are an informed and well characterised set of mutant strains. There is a lot of work in this study and it will impact researcher's views of the importance of imprinted genes in normal development. The importance of tight gene expression levels in development and differentiation and how this can be achieved using co-ordinately regulated gene pairs. The H19/Igf2 locus and its control elements are of interest because in humans there is an association with BWS. It will therefore provide an excellent model in which to study cardiomyopathy as it related to the human disease BWS.

Reviewer #1 (Public Review): 

In this paper, the authors attempted to fill some major knowledge gaps with regards to mitochondrial function in circulating immune cells in humans. The majority of the immunometabolism literature focuses on the mouse as a model system, so this contribution is a welcome addition to the field. Major strengths of the results of this contribution include the unmasking of cell type specific mitochondrial parameters that are hidden when measuring PBMCs in aggregate. In addition, the contributions of platelet contamination serve as a warning to prospective investigators conducting similar studies. The study methods are quite extensive and well thought out. Some minor weaknesses to take into consideration are interpretations of some of the data. Oftentimes distinctions between clear statistically significant findings and trends do not seem to be made, leaving the reader to scour the text and figures to make sure something was not missed. There are a lot of data to take in and understand, certainly there are probably some figures which could be excluded. Overall, these are fixable things, and the authors have done a fine job in achieving their goal to fill the knowledge gap about mitochondrial parameters in immune cell subtypes in humans. This contribution is the first of its kind in this field and will serve as a reference for those conducting immunometabolism studies on human immune cell populations.

Reviewer #2 (Public Review): 

In this manuscript, Rausser et al., have assessed age-, sex- and time-driven differences in mitochondrial phenotypes with human PBMCs and their subsets. 

A major finding of this manuscript was that metabolic analysis of bulk PBMC masks a lot of differences that occur due to other factors, especially changes in leukocyte composition. From my perspective in immuno-metabolism, it is already very clear that i) different leukocytes have very different metabolic profiles and ii) the metabolic profile of a defined leukocyte subset can also change due to age-, infection-related changes in both composition and cell activation. Accordingly, analysis of bulk PBMCs will have limited value for defining biological mechanisms due to the complexity of cell subsets within that tissue sample. Perhaps researchers outside immunology are not aware of the complexity inherent in analysis of PBMCs, so there may be value in highlighting these differences, but that finding was of limited novelty. 

The broader observations in the manuscript are often consistent with what is known in the field of immuno-metabolism. However, the mitotype approach is a niche method of assessment of mitochondrial activity. It is unclear how the measures of mitochondrial activity used overlap with other more common measures of immune cell metabolism such as metabolic dyes, mitochondrial imaging, MetFlow, or Seahorse analyses. I remain unclear of how MHI relates to other more common measures of immune cell metabolism and mitochondrial capacity/health- for example, a correlative analysis of the MHI metric and Seahorse assays to validate whether changes in MHI track with a functional measure of mitochondrial flux across ages, sexes or individuals. 

One uncertainty I have is that the group numbers in each age/sex group seemed small (n=2-3) and there are a large number of comparisons. The authors did identify some robust changes in leukocyte composition that are commonly found with ageing, such as a decline in CD8 T cell % and naïve CD8 T cells, in particular, so this group size may be enough to identify robust differences. I do not have the expertise to assess the robustness of the statistical tests performed- other reviewers may be able to comment better there and I defer to those- but I wanted to note my uncertainty on this point. 

One major variable that has not been assessed is variability in processing of bloods. The authors highlight that there is marked variation in samples collected from the same individual week to week but also highlight that platelet contamination can have a major impact on the readouts and that the range of variation in the age/sex cohort is similar to an individual's variation. So does the variability in the mitochondrial assays reflect variation in processing? It would be instructive to sample from the same individual sequentially over 5 days or even the same individual on the same day, process separately, and then perform these assays. 

More broadly, I suggest that, rather than the value of this manuscript being in its biological findings, its value may be more validation of the metric that the authors have developed- the MHI measure. Re-framing to stress this validation with limitations and considerations, rather than the biological findings, could be helpful. 

While this publication has a distinct focus on metabolic phenotypes, the authors would need to include a discussion of their work with regard to a recent publication that has looked at sexual dimorphism and age-related changes in PBMC composition: https://www.nature.com/articles/s41467-020-14396-9 

Fig3 b-i: I'm unclear why CD8 memory phenotype cells are missing from g-i, although they are present in the graphs b-e.

Supp Fig 2: On the CD4 vs CD8 gate, the gates are mislabelled as CCD4 and CCD8 

I'm unclear on how PBMCs were processed for all of the workflows. In particular, it is unclear whether the cells were frozen and then defrosted at any point. Some PBMC subsets will withstand this process better and PBMCs may require resting time after thawing to return to normal metabolic activity.

Reviewer #3 (Public Review): 

The authors characterize the mitochondrial profile of immune cell subsets vs. bulk PBMCs in a small cohort. They successfully point out the heterogeneity between the subjects with regards to the composition of PBMCs. The composition of innate and adaptive immune cell subsets indeed affect the mitochondrial parameters obtained from the bulk PBMCs, therefore, the cell subsets should be handled separately for such analyses. The leads obtained from this study would be useful for further research.

Reviewer #1 (Public Review):

This study identifies some of the mechanisms of action of MOR-acting opioids on the respiratory network. It determines the impacts of MOR activation of various types of neurons of the respiratory network including the preBotzinger Complex (preBotC), the site of respiratory rhythmogenesis. It shows that MOR inhibition may be due to a reduction in spiking in the pre-inspiratory phase as well as a reduction in synaptic transmission. This study uses a combination of in-vitro electrophysiology and optogenetics as well as in-vivo optogenetic experiments which are the state-of-the-art techniques in neuroscience. The experiments performed are technically challenging especially in this region of the medulla. Therefore these data are unique as they elucidate network level mechanism of opioid inhibition in relatively "intact" preparations. This study has limitations inherent to the animal preparations used which include in-vitro conditions, immature brain development in some conditions, and adult, anesthetized animals in others. However, these limitations are expected in these challenging animal models.

Overall, this study proposes new mechanisms of opioid inhibition that may be translated to other model systems. A better understanding of these mechanisms is critical to identify new therapies and eventually new pain killers without the respiratory side-effects of opioids.

Reviewer #2 (Public Review):

This study leverages an emerging consensus regarding respiratory rhythmogenesis to better understand how opioids depress breathing. Since 1991, the pre-Bötzinger Complex (PBC) has been proposed as the central pattern generator for breathing, which has been extensively studied in the transverse slice, but using conventional electrophysiological techniques, definitive functional-anatomical identification of the rhythmogenic constituents of the respiratory CPG was impossible. Studies focusing on the developmental origin of these networks identified a specific subpopulation of dbx1+ neurons born between E10.5-E11.5 that were shown to generate respiratory rhythm in PBC (Bouvier et al., 2010). In earlier work by this same group, reporter genes targeting these populations, the authors of this study were able to robustly identify the PBC both in vivo, and in their novel horizontal slice preparation (Baertsch et al., 2019), and validate the general rhythmogenic mechanism they elaborate on here. Thus, this study builds on the slow but steady progress in this field that has emerged from a collaborative, multidisciplinary approach.

Despite an emerging consensus regarding the functional anatomy of the PBC, a mechanistic account of rhythmogenesis remains elusive. Persistence of rhythmic activity following fast Cl--mediated inhibition ruled out models in which crossed inhibition generated rhythmic output (Feldman and Smith, 1989). After neurons with endogenous bursting properties were found in PBC, pacemaker-driven network models were developed (Butera et al., 1999), but these fell out of favor when it was shown that respiratory rhythm persisted following blockade of conductances essential for endogenous bursting (Del Negro et al., 2002). Currently, there is general agreement that respiratory rhythm arises out of network interactions between neurons that begin spiking hundreds of milliseconds before the inspiratory burst onset, described as the "percolation period" in this manuscript. Although recently a model in which respiratory rhythm arose out of network interaction between pre-I neurons without the requirement of pacemakers (Guerrier et al., 2015), definitive experimental evidence supporting this account has not yet emerged, and somewhat more qualitative "burstlet"-based mechanisms have been proposed. (Kam et al., 2013). Because a rigorous functional taxonomy of respiratory network constituents has not yet been completed, delineating how opioids depress breathing remains challenging.

In this study, the authors elucidate how the network respond to opioids by selective targeting of neurons directly modulated by opioids. They do this by expressing light-activated inhibitory and excitatory conductances in neurons expressing the mu-opioid receptor (MOR), via the Oprm1 gene. In addition, optogenetic manipulations were also carried out on the same mouse lines in adults, in vivo. Because light could be used to identify MOR-expressing neurons, the authors were able to robustly differentiate between direct and network-mediated effects of opioids on neuronal activity. By comparing the effect of light-induced hyperpolarization of Oprm1+ neurons with the effect of opioids, the authors also robustly establish that OIRD cannot be ascribed to hyperpolarization of MOR-expressing neurons alone, and that impaired synaptic transmission also contributes.

A less obvious and equally important finding is that the specificity and accuracy of this optogenetic approach highlights why networks that control breathing have been so difficult to study. If a specific gene can be found that enables optogenetic manipulation of a discrete, functionally homogeneous population of neurons, the reverse-engineering potential of this approach is maximized. What was found here was that among PBC neurons, taxonomized into 4 classes (pre-inspiratory, inspiratory, expiratory, tonic), roughly half of the neurons probed in each group were found to be Oprm1+. This partial overlap with all groups weakens inferences drawn from optogenetic manipulations. While incorporation of optogenetic methods offers a robust way to differentiate between direct effects of opioids on members of each group that express MORs from indirect, network-mediated changes in activity, the impact of opioids on essential rhythmogenic networks is more difficult to assess, since the methods used here cannot selectively target pre-inspiratory neurons, which are the only population proposed to have dedicated rhythmogenic function. While it is possible that other, yet-to-be-identified genes are expressed in all pre-inspiratory neurons, and only in this population, enabling a highly targeted analysis of the functional role of pre-inspiratory neurons in respiratory rhythmogenesis using an optogenetic or other gene-targeting approach, the absence of success in past efforts to selectively pick out essential respiratory rhythmogenic constituents suggests that essential constituents are themselves heterogeneous, and overlap genotypically with non-rhythmogenic neurons.

Compounding the conceptual challenges posed by the effort to delineate the impact of a drug on heterogeneous respiratory networks are the complexities of opioid pharmacodynamics (Williams et al., 2013). Within 5 minutes of initial exposure, receptor phosphorylation and arrestin binding rapidly blunt responses to opioids; on an overlapping but slower timescale, receptor endocytosis and recycling further alters the effect of opioids; all these processes vary with opioid agonist. Insofar as these factors are clinically relevant, they would suggest that OIRD is most acute in naïve subjects whose response is not blunted by desensitization or tolerance, and may contribute to the significantly lower mortality risk associated with opioid substitution programs as compared to abstinence treatment programs (Sordo et al., 2017). In this study, opioids were administered sequentially to generate dose-response curves. If it were the case that in some experiments the order in which opioid doses were administered was randomized, the impact of desensitization on OIRD could be estimated.

By including a robust, high-level low-dimensional model of respiratory rhythm generating networks to test whether their conjecture that OIRD arises out of the interaction between presynaptic hyperpolarization and synaptic depression, the authors are providing a blueprint for how empirically motivated hypotheses can be validated and tested using modeling. This multidisciplinary approach will gain in power as more detailed mechanistic accounts of respiratory rhythmogenesis continue to evolve.

Baertsch, N.A., Severs, L.J., Anderson, T.M., and Ramirez, J.M. (2019). A spatially dynamic network underlies the generation of inspiratory behaviors. Proc Natl Acad Sci U S A 116, 7493-7502.

Bouvier, J., Thoby-Brisson, M., Renier, N., Dubreuil, V., Ericson, J., Champagnat, J., Pierani, A., Chedotal, A., and Fortin, G. (2010). Hindbrain interneurons and axon guidance signaling critical for breathing. Nat Neurosci 13, 1066-1074.

Butera, R.J., Jr., Rinzel, J., and Smith, J.C. (1999). Models of respiratory rhythm generation in the pre-Botzinger complex. I. Bursting pacemaker neurons. J Neurophysiol 82, 382-397.

Del Negro, C.A., Morgado-Valle, C., and Feldman, J.L. (2002). Respiratory rhythm: an emergent network property? Neuron 34, 821-830.

Feldman, J.L., and Smith, J.C. (1989). Cellular mechanisms underlying modulation of breathing pattern in mammals. Ann N Y Acad Sci 563, 114-130.

Guerrier, C., Hayes, J.A., Fortin, G., and Holcman, D. (2015). Robust network oscillations during mammalian respiratory rhythm generation driven by synaptic dynamics. Proc Natl Acad Sci U S A 112, 9728-9733.

Kam, K., Worrell, J.W., Ventalon, C., Emiliani, V., and Feldman, J.L. (2013). Emergence of Population Bursts from Simultaneous Activation of Small Subsets of preBotzinger Complex Inspiratory Neurons. J Neurosci 33, 3332-3338.

Sordo, L., Barrio, G., Bravo, M.J., Indave, B.I., Degenhardt, L., Wiessing, L., Ferri, M., and Pastor-Barriuso, R. (2017). Mortality risk during and after opioid substitution treatment: systematic review and meta-analysis of cohort studies. BMJ 357, j1550.

Williams, J.T., Ingram, S.L., Henderson, G., Chavkin, C., von Zastrow, M., Schulz, S., Koch, T., Evans, C.J., and Christie, M.J. (2013). Regulation of mu-opioid receptors: desensitization, phosphorylation, internalization, and tolerance. Pharmacological reviews 65, 223-254.

Reviewer #3 (Public Review):

Opioid induced respiratory depression (OIRD), characterized by a pronounced decrease in the frequency and regularity of breathing, involves suppression of neuronal activity within the brainstem inspiratory rhythm generator- the preBötzinger complex (preBötC), which contains rhythmic neurons expressing mu-opioid receptors (MOR). In this paper, Baertsch and colleagues sought to define in greater detail cellular- and network-level mechanisms of OIRD in the preBötC from in vitro and in vivo electrophysiological studies and computational modeling. The main important conclusion supported by the authors' results is that mechanisms involving hyperpolarization of preBötC excitatory neurons and depression of excitatory synaptic transmission in preBötC circuits explain perturbations of rhythmic neuron activity and the inspiratory rhythm caused by MOR activation.

Strengths of the study include: (1) the authors employ a combination of electrophysiological, optogenetic, and computational approaches to dissect mechanisms, (2) the authors employ optogenetic identification and manipulation of neurons with the MOR encoding gene Oprm1, (3) important new data on electrophysiological phenotypes of MOR-expressing neurons is presented, (4) the authors analyze effects of MOR activation on neuron spiking activity during different phases of the inspiratory rhythm, (5) the authors present novel electrophysiological evidence of MOR-induced depression of preBötC excitatory network synaptic transmission, and (6) simulations with a model of the preBötC excitatory network can reproduce experimentally observed perturbations of inspiratory rhythm when a combination of neuronal hyperpolarization and reduction in strength of excitatory synaptic transmission is implemented. Weaknesses include: (1) experimental data on neuronal hyperpolarization caused by MOR activation is required to justify some the authors conclusions, and (2) parameterization of the preBötC network model used requires further explanation.

Reviewer #1 (Public Review):

This study investigates how the recently discovered CTPase activity of bacterial ParB promotes the separation of ParS-containing sister chromosomes within the ParABS system. Using fluorescence visualization the authors were directly visualizing ParB-binding to ParS as well as the diffusive spreading of the protein along the DNA. They furthermore systematically and comprehensively probed under which conditions ParB-mediated DNA condensation was obtained that is considered to be essential for the chromosome segregation. The data of the authors convincingly shows that CTP binding by ParB stimulates its association with ParS and most importantly promotes its diffusive spreading from ParS. Diffusive spreading along the DNA contour was demonstrated using protein roadblocks. This provides sufficient evidence for protein diffusion along the DNA contour, although the authors did not achieve to visualize the diffusion of single ParB complexes along DNA directly. By testing a broad range of conditions (ParB concentration, presence of Mg2+, hydrolyzable and non-CTP, number of ParS sites), the authors demonstrated that CTP binding rather than hydolysis is sufficient for ParB to promote DNA condensation. The two different types of observation show together that the CTP-mediated diffusive spreading of ParB from ParS drives the downstream DNA condensation. Moreover, the nanomechanical DNA condensation experiments together with the combined direct fluorescent visualization represent a helpful methodological development for future studies of this system. Overall, the presented work is a clear and comprehensive study that provides direct and unambiguous evidence for the recently suggested models of ParB-mediated DNA condensation and its stimulation by ParS and CTP.

Reviewer #2 (Public Review):

This manuscript investigates parS DNA binding and condensation by B. subtilis ParB protein in single molecule experiments using optical traps and magnetic tweezers. The work follows up on the recent discovery of ParB's ability to bind and hydrolyze CTP. The authors show that CTP addition stimulates ParB binding to DNA molecules harboring clustered parS sites and promotes the spreading of ParB onto neighboring DNA. Moreover, ParB binding is shown to lead to the condensation of DNA with clustered parS sites, again an activity that is stimulated by CTP or the non-hydrolysable analog CTPgS.

While the work carefully observes and describes ParB activity in vitro and reports findings that are largely consistent with recent publications, a potential weakness of the study concerns the use of artificially clustered parS sites on the DNA test substrates and the absence of similar activity on more natural substrates with a single parS site, together raising doubts about the physiological relevance of the discoveries.

Reviewer #3 (Public Review):

ParBs bind to a centromere site called parS to form large condensed complexes at which ParBs are observed to spread many bp away from parS, but the mechanisms of spreading are under debate. The current study is based on recent discoveries that ParBs bind and hydrolyse CTP, and that CTP promotes spreading. Here the authors directly visualize fluorescent ParBs bound to parS on DNA that is stretched out because it is tethered at both ends. They examine the condensation using magnetic tweezers on DNA tethered at one end and pulled by a magnet at the other end. They find that CTP does promote parS-specific DNA binding and spreading, and this activity requires CTP but not hydrolysis. The results extend their previous published analyses in the absence of CTP, in which the authors observed spreading but it was not parS-specific and required higher ParB concentrations. Their results recapitulate many of the properties of spreading that have been observed in vivo, including specificity for parS and the influence of roadblocks. The results are consistent with a model proposed by Soh et al (2019) in which CTP locks ParB as a clamp around DNA by promoting N-terminal domain self association, and that once clamped, it slides along the DNA away from parS; that is, sliding is proposed to be the mechanism of spreading. The results however are also consistent with spreading by cooperative interactions of ParBs with those bound next to them, so these data do not directly support sliding by ruling out other alternatives. Since this distinction is not resolved by the current results, one could look at these results as confirmatory. However there is important value to these results. As the authors state, this is the first time ParB molecule binding on linear DNA at and around parS has been directly visualized in single molecule studies; that is, with resolution for parS vs closely surrounding regions. The ability to view the complexes directly at this resolution on the DNA directly tests ParB/parS localization and the influence of spreading roadblocks. Second, the specificity for parS (or lack thereof) has long been a problem for the in vitro study of ParB binding to parS in biophysical experiments. Also, the authors show that sliding still prefers to reside close to the parS region (it is not "free"), suggesting that lateral (and perhaps bridging) protein-protein interactions play roles in complex architecture.

Reviewer #4 (Public Review):

Francisco et al. investigate the role of CTP and hydrolysis in the binding of ParB to parS sequence and non-specific DNA at the single-molecule level. Using optical tweezers, they show the specific binding of ParB to parS sites, and demonstrate that this process is enhanced by the presence of CTP or CTPS. They find that lower density ParB proteins are also detected in distal non-specific DNA in the presence of parS, and that ParB spreading is restricted by protein roadblocks. Furthermore, using magnetic tweezers, they show that parS-containing DNA molecules are condensed by ParB at nanomolar protein concentration, which requires CTP binding but not hydrolysis. These finding show the significance of CTP-dependent ParB spreading and impact the understanding of the mechanism of DNA bridging and condensation by ParB networks.

Based on these results, the authors propose a model for ParB-mediated DNA condensation, which requires one-dimensional ParB sliding along DNA from parS sites. Overall, the experiments were carefully done and thoroughly controlled. The manuscript provides critical insights that can be strengthened by addressing the following minor concerns:

1) Did the authors observe the diffusion of isolated ParB foci along DNA? This will provide strong evidence for the proposed diffusion/sliding model.

2) Based on the sliding clamp model, ParB spreading and diffusion result in DNA condensation by forming large DNA loops. Is it possible to show the dynamic spreading of ParB while keep the same numbers of ParB on DNA? For example, can the authors incubate ParB-containing DNA in channel 4 (ParB channel) at a certain time for the loading of ParB on parS sites, and then move it to the buffer channel without free ParB as well as with CTP or CTPS, where the images are acquired at the long interval time to minimize the photobleaching. The fluorescent intensity of the ParB during the spreading process can be analyzed. If the intensity remains constant through spreading in the presence of CTPS but significantly decrease in the presence of CTP, this data will strongly demonstrate the proposed spreading and CTP hydrolysis-dependent dissociation mechanism.

3) In Figure 2, the authors show the spreading of ParB can be blocked by EcoRI. Can the authors show that EcoRI is bound at the specificity positions? The spreading blockage by protein roadblocks showed in optical tweezers experiments potentially hints that the roadblocks may affect the DNA condensation. Can the authors apply the magnetic tweezers to show the affection of protein roadblocks to DNA condensation in vitro?

Reviewer #1 (Public Review):

Yao Rang and collaborators find that heterologous expression of TMEM120A from mouse and human in cells that lack Piezo1 does not result in poke- or stretch-activated currents in whole cells or excised patches, and further detect no mechano-sensitive currents when the purified human protein is reconstituted in giant unilamellar vesicles. Together with high-quality positive controls with Piezo1, Piezo2 and TMEM63a, the results presented here call into question a previous proposal (Beaulieau-Laroche et al., Cell 2020) that TMEM120A functions as the long sought-after mechano-activated channel responsible for detecting painful touch.

Although the evidence supporting a channel function for TMEM120A is not strong, it remains to be ruled out that the discrepancies between the two studies arise from the different methods that were used to deliver the mechanical stimuli, as mentioned by the authors in the Discussion, or from the C-terminal mCherry tag attached to human TMEM120A in this study that was not present in the construct used by Beaulieau-Laroche et al.

Upon determination of the structure of full-length human TMEM120A in nanodiscs using cryo-EM, the authors find that the protein forms a dimer with six transmembrane helices per subunit and a cytosolic N-terminal coiled coil domain. Surprisingly, the authors find a density attributable to coenzyme-A (CoASH) located within a highly conserved cytosolic cavity at the transmembrane domain. The authors provide evidence from mass-spectrometry and isothermal titration calorimetry (ITC) to demonstrate that CoASH binds TMEM120A, and solidify their conclusions by showing that mutation of a residue close to the CoASH density in TMEM120A disrupts binding measured by ITC. The authors show that a potential ion-conduction pathway in their TMEM120A structure would be occluded by CoASH on the cytosolic side and on the extracellular side by a series of not well-conserved residues. Finally, the authors solve a structure in detergents where no density for CoASH is observed, as expected from spectroscopic data showing that detergent-reconstitution results in loss of CoASH binding. In this structure, a conformational change is suggested to occur on the cytosolic cavity entrance where a loop becomes reoriented to occlude the cavity that is otherwise occupied by CoASH. Together, the data presented paints an intriguing alternative for the function of TMEM120A proteins with a role in metabolism or CoA transport.

Although the main conclusions are well supported by the evidence, it is challenging to appraise many of the interesting structural observations pointed out by the authors because the experimental data (i.e. the density) is in most cases not depicted. Some of these observations for which only the model rather than the experimental data is shown include the hinge-like motif at the dimer interface (Fig. 3C), the CoASH binding site (Fig. 4E and Fig. 4 Supplement 1C), the difference in the conformation of the IL5 loop between the apo and CoASH-bound structures (Fig. 5), the extracellular constriction of the possible ion-conduction pathway (Fig. 4 Supplement 1D and Fig. 5 Supplement 2), as well as the comment that the observed density in the structures cannot accommodate other CoA-derivatives, for which data is not shown. The relatively low resolution at which the data were obtained raises concerns regarding many of these detailed observations.

Reviewer #2 (Public Review):

In this manuscript, Rong et al., address the possibility that TMEM120A, also known as TACAN, might not be a mechanosensitive ion channel. They use extensive electrophysiological characterization to convincingly show that cells heterologously transfected with either human or mouse TMEM120A do not exhibit mechanosensitive currents above background in cell lines or when purified and reconstituted in giant unilamellar vesicles. They also solve the cryo-EM structure of TMEM120A and find that it does not resemble an ion channel (i.e., there is no obvious pore). Interestingly, there is a density consistent with coenzymeA in the structure, thus suggesting an alternative function for TMEM120A. Further evidence for this interaction is shown through biochemical analysis, as disrupting a residue proposed to form a π−π stacking interaction between TMEM120A and coenzymeA reduces the binding affinity as assayed by ITC.

Overall, the impact of this manuscript is extremely high, as it refutes a recent report that TMEM120A/TACAN is a high-threshold (pain) mechanosensitive ion channel, and further suggests an alternative function for this protein. The experiments are extremely carefully done, and the authors attempted to replicate the electrophysiological function of TMEM120A with high numbers and with appropriate positive and negative controls. The inclusion of structures (Coenzyme-A bound and apo) and corresponding biochemical analyses provide strong support for the direct binding of Coenzyme-A by TMEM120A.

Reviewer #3 (Public Review):

TMEM120A protein was recently reported to mediate mechanosensitive currents in response to painful stimuli. In the present manuscript, the authors aimed to elucidate TMEM120A mechanism of action by solving the structures and complementing them with functional characterization. In contrast to the recent report, the authors could not observe TMEM120A-mediated currents in response to mechanical stimuli neither in transfected cells nor in liposomes. Furthermore, the structure of human homolog HsTMEM120A revealed a co-purified endogenous ligand, which was shown to be coenzyme A (CoASH). The authors went on to solve the structure in the absence of CoASH, revealing a different conformation of HsTMEM120A. Together, structural and functional data point towards a conclusion that TMEM120A might not be a mechanosensitive channel, but might rather be important for fatty acid metabolism. The conclusions of the manuscript are supported by the presented data.

Strengths:

1. The authors conclusively show that TMEM120A does not mediate poking- or stretch-induced currents when compared to well-characterized mechanosensitive channels Piezo1 and TMEM63a.

2. The authors employed several approaches to confirm the identity of the co-purified ligand. Firstly, the presence of CoASH in the purified protein sample was confirmed by mass spectrometry. Secondly, the binding of CoASH to TMEM12A was confirmed by ITC. Thirdly, using the obtained structure the authors identified CoASH-interacting residues and show that mutating one of the key residues (Trp193) reduced TMEM120A affinity for its ligand.

3. The observation that CoASH dissociates from TMEM120A during size exclusion in detergent, but not in lipid environment allowed to solve a ligand-free TMEM120A structure, which revealed a different conformation at the entrance to CoASH-binding site and is possibly relevant for the mechanism of action.

Noteworthy, 3 other studies (Niu et al., 2021, Xue et al., 2021, Ke et al., 2021) independently arrived at the conclusion that TMEM120A is probably not a mechanosensitive channel, further supporting the results of the present study.

Weaknesses:

Despite the advances presented in this manuscript, physiological function of TMEM120A and its mechanism of action remain obscure, other than that it is probably not a mechanosensitive channel. However, the goal of the authors was to understand how TMEM120A might mediate mechanosensitive currents, while establishing its role in lipid metabolism is outside of the scope of this manuscript.

Regardless, this work provides an important insight into TMEM120 family and will serve as a basis for future investigations.

Reviewer #1 (Public Review): 

In this study, the authors developed an elegant toolset called HiLITR for identifying the genes that are involved in protein localization to a specific subcellular organelle. The basic strategy is exquisitely designed: Two distinct types of organelle-specific membrane anchored proteins are respectively fused to the TEV protease domain and a transcription factor (TF). Colocalization of the two proteins induces release of the TF by proteolytic cleavage. The TF switches on the expression of a fluorescent protein enabling the amplification of localization signal. The expression levels of fluorescent proteins can be quantified and sorted by FACS. 

In combination with the CRISPRi screening employing a pooled sgRNA library, this strategy turns into a powerful high-throughput platform to discover genes that influence protein localization in various cellular compartments. Applying this method to protein localization in mitochondrial and ER membranes led to an unexpected discovery of the genes, SAE1 (SUMO activating enzyme) as a regulator of the tail-anchored (TA) protein insertion to mitochondrial membranes and EMC10 as an antagonist in the insertion of TA proteins to ER membranes. 

The basic workflow is thoroughly designed and optimized (e.g., the construct design, the choice of targeting sequences, the strategies to filter out false positive hits, FACS analysis, nontargeted and targeted identification of the genes affecting localization, validation of the identified genes, etc.). The triple filtering strategy (i.e., TA screen, SA screen and ER screen) is impressive since this not only enables filtering out false positives but also provides a way to investigate mislocalization or rerouting of TA proteins to ER membranes. 

Overall, this is an excellent study contributing to our understanding of protein localization and mislocalization. The manuscript reasonably well supports the conclusion. Nonetheless, there are several concerns that authors could further address: 

i) It would have been helpful to discuss how this method could be evolved to address more complex problems in protein localization and mislocalization. For example, the current version focuses on single membrane-spanning peptides as a localization signal, but the scientific community would be also interested in the localization problems of membrane proteins with multiple TM segments or larger water-soluble domains. In such case, how could the accessibility issue between TF and protease be overcome? 

ii) Although this manuscript majorly focuses on the tool development, more in-depth explanations on the role of the identified genes (SAE1 and EMC10) would have helped readers to appreciate the significance of this work. 

iii) Signal amplification can be a double-edged dagger since it can magnify small differences more than what is actual. A statement would be needed how authors translate the HiLITR results into the actual effect of an identified component (e.g., HILITR vs Western blotting).

Reviewer #2 (Public Review): 

In this work, Coukos et al. describe the development of a genetic reporter system that involves the use of chimeric, photoactivatable substrate proteins that can be used to monitor the targeting of a tail-anchored (TA) protease to various organelle membranes. The authors present a strategy to couple these sensors with fluorescence activated cell sorting (FACS), deep sequencing, and CRISPRi libraries in order to identify genes that mediate membrane targeting. This study documents extensive optimization efforts and numerous controls to ensure the output of these screens are valid. Furthermore, the results include numerous examples of previously characterized insertases (i.e. core subunits of the ER membrane complex, or EMC) as well as the discovery of two novel genes that play a central role in the targeting of TA proteins to the outer mitochondrial membrane (OMM) or to the endoplasmic reticulum membrane (ERM). In follow up investigations, the authors show that the loss of the SUMO E1 ligase component SAE1 is critical for the targeting of TA proteins to the OMM. Using an array of quantitative cellular assays, the authors then confirm the specificity of the knockdown, and show that the disruption of SUMOylation results in the mis-targeting of endogenous substrates. Using another variation of this assay, the authors also discover that, while the knockdown of core EMC subunits decreases the targeting of TA proteins to the ERM, knocking down EMC10 results in an increase in the targeting of these substrates to the ERM. The authors also verify that this subunit specifically appears to antagonize this insertase activity of the EMC. Overall, this study provides both new tools for pooled genetic screening and identifies novel components of the topogenic machinery in human cells. These results have a clear impact on our understanding of membrane insertion pathways and are likely to influence efforts to develop new screening platforms. 

Strengths: 

This study includes both an impressive number of controls and several counter screens that make this approach both comprehensive and robust. The described approaches are also likely to be somewhat adaptable given the modular architecture of the HiLITR sensor proteins. 

Validation processes both confirm the roles of these genes in each respective process and provide evidence for the accuracy of the results. These efforts along with the detailed methods sections set a high standard for future screens that employ similar approaches. 

The novel roles of the SAE1 and EMC10 subunits suggest new factors that may control the efficiency of OMM and ERM targeting pathways. These findings are sure to inspire a slew of follow up studies centered around the mechanistic roles of these proteins in the context of each pathway. 

Weaknesses: 

Chimeric TA proteases represent an artificial substrate. While these screens clearly pick up the central machinery involved in these pathways, the characterization of such substrates has limited impact on our understanding of how the spectrum of native substrates navigate the partially redundant topogenic pathways within the cell. 

The authors characterize two of the more robust and biochemically interesting hits in their follow up studies. Nevertheless, it is unclear how many of the other hits are likely to be relevant due to a lack of biological replicates and to the lack of metrics to describe the precision of the observed effect sizes. 

The authors' efforts to characterize the effects of SAE1 and EMC10 knockdowns confirm the screening results and show that the activities of these proteins are important for targeting. However, these studies do not establish the mechanistic roles of these proteins within each insertion pathway. This will undoubtedly require additional investigations.

Reviewer #3 (Public Review): 

Pooled sgRNA library-based genetic screen has been a powerful and efficient approach to comprehensively identify essential regulators for key cellular processes. However, the employment of pooled sgRNA library was limited due to the reliance on cell proliferation or FACS. Therefore, it has been difficult to use pooled sgRNA library to screening for genes involved in protein targeting which is not usually related to cell survival or could be easily detected by flow cytometry. To circumvent this problem, the authors developed a smart assay based on location-activated transcription. They engineered the cells with an mCherry gene which could be turned on by a transcription factor. The transcription factor was anchored to the interacellular compartment of interest via a TEV site and LOV sequence, allowing the exposure of the TEV site by blue light activation. In another construct the TEV protease is linked with a targeting sequece to the same compartment. Therefore, of the protease is correctly targeted, it will cleave the TEV site located on the linker of the transcription factor in the presence of blue light, leading to the relocation of the membrane bound transcription factor to the nucleus to trigger mcherry expresss. The assay allows for the translation of the protein location signal to fluorescence production and could be employed in FACS analysis. Using this assay and pooled sgRNA library screening, the authors identified key regulators in tail-anchored protein targeting to the mitochondria and the ER. The study will deepen our insight into how tail-anchored protein targeting is control in the cell. Importantly, the principle of design could be widely applied and broaden the employment of pooled sgRNA library screening in cell biology. The overall concept is novel and the data is properly controlled and support the conclusion reached.

Reviewer #1 (Public Review): 

The authors performed high-throughput sequencing and performed transctiptome analysis of neurons in the adult superior colliculus (SC). This revealed that there are potentially 19 molecularly distinct neural cell types in the SC. Genes enriched is specific types have lamina specific expression patterns as do SC neurons that project to the LPTN and ZI. From this analysis two Cre-lines were chosen that express in the LPTN (Cbln2) or ZI (Pitx2) SC neurons. Using these lines and a number of tracing/recording techniques and behavioral assays they show that these two populations of neurons are involved in different circuits with the Cbln2 neurons responding to visual stimuli and necessary for the looming response, and the Pitx2 neurons responding to touch stimuli and involved in predator detection. This work will be of wide interest to researchers who aim to understand the relationship between molecular identity and function. 

The strengths of the paper are the identification of genes in the SC that are likely to be cell type specific, the characterization of two such cell types, and the finding that different cell type are used for specific behaviorally relevant tasks. There is also a large number of independent techniques used to validate the hypothesis.

Reviewer #2 (Public Review): 

This work presents an integrated characterization of neuron types in the mouse superior colliculus, using the combination of molecular, anatomical and functional methods. This midbrain area is involved in sensorimotor processing in a wide range of behavioral contexts including predator escape and prey capture. Like the cerebral cortex, the superior colliculus is organized in layers with specialized input-output connectivity. Neural circuits engaging these layers have been studied extensively in the past. However, neuron types in the superior colliculus had never been characterized systematically with molecular data. 

This paper is an initial step towards the complete multimodal characterization of neural types in this important brain area. The study is based on an impressive array of challenging experiments, including single-cell RNAseq, patch-seq, high-resolution viral tracing, calcium imaging and behavioral experiments. Using single-cell RNA seq as an entry point, the authors identify two types of glutamatergic neurons expressing distinct markers, including Cbln2 and Pitx2. These types localize to distinct layers of the superior colliculus, and have distinct input-output connectivity. Behavioral and manipulation experiments indicate that these two types are part of two distinct circuit modules, involved in predator avoidance and prey capture, respectively. 

From these data, the general conclusion proposed is that transcriptomically-defined neuron types in the superior colliculus belong to neural circuits engaged in distinct behaviors. 

The paper is well written and the data support the main conclusions. 

Strengths:

The wide range of methods used in this paper is impressive. This is a complete story that truly embraces the multidisciplinary spirit of modern neuroscience. With the multimodal characterization of neuron types, the authors achieve several goals. First, they go beyond a simple description of cell clusters in the single-cell transcriptomic space, and corroborate the conclusion that these clusters are distinct cell types using orthogonal lines of evidence. Second, the multimodal data are key to bridge molecular data to physiology and behavior. Using the single-cell RNA sequencing data as an entry point, the authors were able to identify cell-type specific marker genes and transgenic lines which were instrumental for precise circuit mapping and manipulation experiments. 

This paper will be an important reference for researchers interested in the superior colliculus because it introduces new resources. Single-cell RNA sequencing data from the mouse superior colliculus are not new, but this dataset is larger and analyzed thoroughly. Furthermore, the paper reports the generation and characterization of a new transgenic line, the Cbln2-IRES-Cre line, created by CRISPR knock in. 

Weaknesses:

The choice of experimental breadth comes with the cost of depth, perhaps unavoidably. The size of the single-cell RNA sequencing dataset is still relatively modest, and the true diversity of neuron types in the superior colliculus might have been underestimated. 

The combination of tracing and Patch-Seq experiments indicates that Cbln2 and Pitx2 are expressed in neurons projecting to the LPTN and to the ZI, respectively. However, the present data do not exclude the possibility that other neuron types in the superior colliculus, that do not express those markers, are projecting to LPTN or ZI. 

The circuit model proposed in Figure 7L appears oversimplified. The model proposes that predator escape and prey capture behaviors are associated to the projections of Cbln2 and Pitx2 neurons to LPTN and ZI, respectively. However, as Figure S6 shows, Cbln2 and Pitx2 neurons project to many other brain areas. This is consistent with previous literature. Presumably several of these downstream brain areas, besides LPTN and ZI, are engaged in those behaviors.

Reviewer #1 (Public Review): 

In this manuscript, Choppakatla et al. reconstitute chromatin assembly on sperm DNA in Xenopus meiosis II extracts to test the role of linker Histone H1. They find that depletion of embryonic isoform histone H1.8 increases the chromosomal levels of Topoisomerase 2A, as well as condensin I and II. Using in vitro-assembled nucleosome arrays, together with purified condensin and linker histone H1, they provide evidence that linker histone H1.8 competes with condensin and Top2A binding. They show that histone H1 depletion extends chromosome length, in a manner dependent on condensin I. Hi-C analysis suggested shorter chromatin loops in histone H1.8-depleted extracts, dependent on condensin I. This led the authors to conclude that histone H1.8 limits the association of condensin I with chromosomes to reduce the number, and thereby, increase the length of loops, shortening the chromosomes. The last part of the paper (Figures 5 and 6) explore the interplay between histone H1,8, condensin and Top2A in chromosome individualisation, arguing for a role of H1.8 in preventing chromosome dispersion, hypothesised to facilitate chromosome capture at metaphase. 

Strengths: 

• Draws together two important questions (role of linker histone H1, how chromosome size is controlled) into a potentially important mechanism.

• Experiments are carefully conducted and controlled.

• The paper is well written and presented.

Weaknesses: 

• The in vivo significance is unclear. Although the in vitro studies are extremely informative, a more thorough discussion of the biological importance of the mechanisms proposed would be useful, particularly with relevance to the cell cycle.

• The latter part of the paper exploring chromosome individualization is partly contradictory and difficult to contextualise.

Reviewer #2 (Public Review): 

This is an interesting study performed using frog cycling extracts. The authors show that depletion of an embryonic histone, the H1.8 linker histone, leads to an increase in the binding of two important effectors of chromosome shape, topo II and Condensins. The increased loading of these two effectors leads to longer chromosomes that are less individualized in extracts. The major strength of this study is the elegant use of the frog extract/biochemistry to carefully dissect the contribution of chromatin-binding proteins to the overall shape and dimensions of chromosomes. The major caveat of this study is that it is based exclusively on in vitro observations, and validation experiments with purified Condensins were performed with nonstoichiometric complexes. The biological rationale justifying a role for an early embryonic histone in the reduction of chromosome length is also unclear. Cells in early embryos are typically very large and should be less dependent on size-reduction mechanisms provided by histone H1.8. One would assume that chromosome size should be maximally reduced in small somatic cells and promoted by somatic linker histone H1 subtypes. The authors did not provide an explanation for this apparent contradiction.

Reviewer #3 (Public Review): 

In the current manuscript, Choppakatla et al. address the contribution of the linker histone H1 to mitotic chromosome assembly in the Xenopus egg cell-free system. They show that the presence of this histone limits the binding of condensins I and II as well as topoisomerase II. Depletion of H1 from the egg extracts results in assembly of longer and thinner chromosomes, and increases dispersion of individualized chromosomes. Hi-C analyses indicate that average loop size is shortened and the DNA amount in each layer of mitotic loops is reduced in the absence of H1, a phenotype attributed to the increased presence of condensin I. 

Strengths: 

- Experiments are carefully designed and performed, the figures are clear and properly labeled and the manuscript is written with clarity. <br> - Combination of classical assays (immunodepletion+chromosome assembly followed by image analysis) with Hi-C analyses (to my knowledge, this is the first time that Hi-C is used for chromosomes assembled in Xenopus extracts) as well as in vitro reconstitution of topoII and condensin binding to nucleosomal arrays to test the effect of H1. <br> - The results support most of the conclusions of the manuscript, explain the previously reported effect of H1 depletion on chromosome assembly and are consistent with previous reports regarding the contribution of condensin I and condensin II to chromosome organization. 

Weakness: 

- The last part of the manuscript regarding "chromosome individualization" is a bit confusing, probably because it is unclear what this process entails in molecular terms. On the one hand, the authors mention the existence of entanglements between metaphase chromosomes that must be removed to allow complete individualization of chromosomes before segregation. On the other hand, chromosomes assembled in the egg extract cluster together (even in the absence of a spindle). The correlation between these two phenotypes and the actual contribution of the different factors (H1, condensins, topoII) is unclear.

Reviewer #1 (Public Review): 

Martín-Fernández et al. present an important advance in the understanding of how Nrp1 controls formation of the corpus callosum. It had previously been shown that the neocortical Nrp1 gradient (high medial, low lateral) is important for controlling the separation of axon bundles from two different cortical areas (S1 and M1) within the corpus callosum midline, and therefore also controlling the homotopic pattern of contralateral targeting (Zhou et al., 2013). The major advance of the work presented here is showing that the Nrp1 gradient within S1 acts in a similar fashion as between S1 and M1, as well as elucidating some further details about the mode of action. The conclusions regarding these further details are generally supported by the results, although select further controls to ensure comparability between conditions are recommended. This is an important addition to the field of axon guidance and callosal development, and presents intriguing insights into the complex mechanics of exuberance, refinement and competition occurring in the corpus callosum that, while not completely elucidated here, do provide candidate mechanistic processes for future work to follow.

Reviewer #2 (Public Review): 

While midline crossing of callosal axons has been extensively studied, how axons make precise connections with contralateral target areas is less understood. Neuropilin 1 (Nrp1), a well-known guidance receptor, mediates the crossing of callosal axons at the midline through its interaction with the Semaphorin (Sema) 3C and the segregation of motor from somatosensory (SS) callosal axons within the corpus callosum (CC) through its interaction with Sema3A. Here, the authors address the role of this guidance cue in the invasion of SS callosal axons to homotopic or heterotopic SS cortical areas. Using in utero electroporation of an shRNA against Nrp1 (shNrp1) or a CAG-Nrp1 construct to respectively down- or up- regulate Nrp1 expression in SS layer 2/3 neurons combined with stereotaxic injection of CTB to label callosally projecting neurons, the authors study the importance of Nrp1 expression levels in establishing primary (S1) and secondary (S2) somatosensory interhemispheric homotopic and heterotopic connections. They conclude that neurons overexpressing Nrp1 project contralaterally in the S1/S2 but not the S2 column. Neurons with lower levels of Nrp1 can project contralaterally both in the S1/S2 and the S2 columns. This study suggests that precise controls of guidance cue expression levels are necessary for the proper establishment of cortical connections.

Reviewer #3 (Public Review): 

In this paper Martin-Fernandes et al. investigated establishment of topographically organized connections between cortical hemispheres. Specifically, they studied homotopic connectivity, when cortical neurons project to the same topographic region in the opposite hemisphere versus heterotopic projections, when neocortical neurons project to other regions. They focused on primary and secondary somatosensory areas of the neocortex (S1 and S2). They hypothesized that a well known axon guidance molecule, Neuropilin1, that has a gradient of expression in the neocortex, is a potential controller of homotopic connectivity. They show that manipulation of Neuropilin1 levels in the neocortical neurons would change their target areas in the opposite hemisphere. Using in utero electroporation (IUE) of both Neuropilin1 "gain-of-function" and "loss-of-function" genetic constructs into developing brain, the authors find that neurons that downregulate Neuropilin1 become less selective in projecting to homotopic area S1 and target S2 area as well. On the other hand, neurons in the areas S1 and S2 with artificially elevated levels of Neuropilin1 can only project to S1 area of the opposite hemisphere. Interestingly, the data also indicate that this selectivity is achieved mostly at postnatal stages due to selective elimination of axons that project to heterotopic areas. These data support a model of cortical area map establishment that is based on gradients of expression of certain signaling molecules.

Strengths: 

The authors used technically challenging combination of in utero electroporation of precise cortical regions with axonal labelling. This approach allowed genetic manipulation of local neurons in restricted neocortical regions. The conclusions of this paper are mostly well supported by data. 

Weaknesses: 

In utero electroporation as well as other in vivo gene manipulation techniques do not allow fine manipulations of expression gradients. Therefore some conclusions of the paper are not fully supported. Although the data presented in the paper clearly show that Nuropilin1 expression level is important for establishment of homotopic connections, it does not show directly that the gradient of expression indeed is in play, as suggested by the authors. Another week point is that there is no direct evidence that the Neuropilin1 protein level follows the mRNA expression gradient. 

Therefore it remains an open question, whether it is a gradient of expression or a sharp border of cellular response to higher-lower levels of Neuropilin1 that controls area specific connections within somatosensory cortex. 

Another week point is that the paper relies on in utero electroporation solely. This technique with all its advantages, has some disadvantages too. One of them is high variability of individual experiments. On the other hand, it targets only subsets of cells, and therefore is not the best to address cell extrinsic mechanisms, especially those that involve expression gradients.

Reviewer #1 (Public Review):

Tong and colleagues recorded intracranial EEG activity in 21 epilepsy patients to explore the relationship between high frequency LFP activity (high frequency activity - HFA), ripples and neuronal spiking activity during a verbal episodic memory task. Results show an association between ripple amplitude/rates, HFA activity, and successful memory (correct versus incorrect memory recall trials). The dataset is rare and unique in that it is from the human brain and recording neural activity from multiple scales (single neuron, LFP, iEEG) within the same subjects. The study tackles important questions with regards to how ripples relate to broadband LFP activity as well as single neurons in the human brain. The authors also include elegant analyses to characterize the timing of spiking activity with respect to high and low frequency activity and relevant control analyses to take into account possible artifacts and epileptic-related activity. My main concern with the study is whether the authors are truly isolating ripple activity in the human brain as claimed. Their threshold for ripple activity is quite low and it thus seems very possible that many of their "ripple" events are rather high frequency activity events that reflect spiking activity. That being said I think this is an important study to share results from in that it provides unique characterization of the relationship between high frequency activity and spiking in the human brain, as well as how it relates to human memory.

Reviewer #2 (Public Review): 

Recordings from human patients with implanted electrodes provide high temporal resolution, localized measurements of brain activity that can reveal neural correlates underlying a wide variety of cognitive functions. These intracranial electroencephalography (iEEG) recordings are typically made with large electrical contacts, however, and thus represent a complex and poorly understood averaging of voltages from the underlying tissue. As such, it is difficult to know exactly what patterns of neural activity these signals correspond to and how to compare them to spiking and local field potential (LFP) recordings more commonly acquired in non-human animals. Tong et al. carried out simultaneous iEEG recordings from surface contacts as well as spiking and LFP recordings from implanted electrode arrays to directly address the relationship between these signals. 

They present quantifications (e.g. Figure 2B) of the relationships between the amount of spiking activity and the amplitude of events detected in the LFP and iEEG. Their results showing the relationships among these signals are very important for the field, and it is very helpful to see that there are clear correlations across scales. 

The context in which they present these results is problematic, however. They focus on "ripple" events, detected as periods where the power in a 80-120 Hz band exceeds an arbitrary threshold for an arbitrary length of time. To be fair, the application of similarly arbitrary thresholds is common in the human, primate, and rodent literatures, and several important results have arisen from the analysis of these events. These results can be understood as claiming that a set of high amplitude events have certain properties (e.g. they are related to memory retrieval), but should not be understood as establishing that there is some specific threshold that separates real events from others. 

Here they go beyond these analyses and make the claim that these ripple events correspond to real, discrete events that, as their title indicates, "reflect a spectrum of synchronous spiking activity." The problem here is that they do not present any criteria for defining a real, discrete event. Indeed, they conclude that "the continuum of activity that [they] observe in [the] data ... suggests that strictly adhering to predefined criteria for what constitutes a ripple may run the risk of overlooking functionally meaningful events". Without a clear definition of what should and should not be considered to be a discrete event, we are left with the current situation where each study uses their own set of criteria, picks out a set of high amplitude events, and uses those for subsequent analyses. 

A second major challenge to understanding the current manuscript is the ambiguity of the physical relationships between the LFP and iEEG recording sites. While it might be obvious to human physiologists, details such as the distance between the LFP and iEEG contacts and the site areas of each type of electrode are critical for interpreting how closely the data from each could be expected to be related. 

We would also like to highlight an instance of a common statistical error in Fig 1 I: the authors conclude that the difference between correct and incorrect is significant in true data and insignificant in the ripple-removed data, and therefore the 70-200Hz power band modulation on correct trials is significantly informed by 80-120Hz ripple events. The statistical problem is further described in Nieuwenhuis et al., Nature Neuroscience 2011. 

Finally, we would also like to note the difficulty of characterizing a single deflection in the LFP or iEEG signal as a low frequency oscillation, given the large potential for measurement variability of the frequency of that oscillation as described in Fig. 4. This large deflection is to be expected when a concentrated amount of synaptic input drives a burst of spiking, as we would expect in the case of the increased spiking during ripples. In the hippocampus, this deflection is the sharp wave component of the sharp-wave ripple; it appears to take a similar form in cortical ripples. While unsurprising, it is well worth observing that the iEEG reflects this coincident deflection, but it should not be characterized as a 2-10Hz oscillation.

Reviewer #3 (Public Review): 

In this study, authors systematically investigated the iEEG ripples, LFP ripples, and their relation with each other and single units from micro channels obtaining LFP. They found that the amplitude of LFP ripples reflects the sum and alignment of underlying spiking activities. Meanwhile, the amplitude of iEEG ripples reflects the number and alignment of LFP ripples. More interestingly, the amplitude of ripple events is functionally relevant. In general, I find that the data analyses and methods are sophisticated and the results are interesting. It extends our understanding of ripple events and is of interest to a wide audience.

Reviewer #1 (Public Review):

Energy in the form of ATP is key for any function of the cell. Most organisms have a series of protein complexes in their membrane that transport proton against a gradient using the redox reactions and this is then used by the enzymes called ATP synthases that generate ATP, the energy needed to sustain life. The first enzyme in this electron transport chain is called complex I and uses the oxidation of NADH to the reduction of ubiquinone, which is coupled to proton translocation. The unique shape of this complex had been realized from early electron microscopy studies and they can be divided into a soluble domain that comprises all the co-factors needed for electron transfer and the membrane domain that has modules for proton translocation. Depending on the organism, this complex can be compact with only the core 14 subunits as found in many bacteria or decorated with a number of other subunits as found in eukaryotes. These additional subunits are implicated in the stability and regulation of the complex. With the advances in cryoEM, in the last few years there has been a flurry of structures from different organisms, the composition and arrangement of subunits, potential functional states and description of the mechanism. An important factor to consider is that these complexes are labile and the detergents which are used for purification can have a profound effect on the structure as well as any conclusion drawn from it. In the field of Bioenergetics, one of the key questions that have fascinated scientists for a long time is how the electron transfer is coupled to proton translocation and few different hypotheses has been put forward.

Adding to the growing number of complex I structures, the manuscript by Kolata and Efremov discusses the structure of a complex I from Escherichia coli, a well-studied enzyme and amenable to easy genetic engineering, essential to verify proposed mechanisms. The key observations of the current work and future prospective are discussed below.

The E. coli complex I is known to be labile and previous attempts to crystallize the whole complex had yielded a low-resolution structure of the membrane domain. The authors have overcome this by introducing a tag in the bacterial genome using CRISPR technology minimizing the number of steps needed for enrichment. The manuscript also addresses one of the concerns of the complex I structures i.e., effect of detergent environment by determining the structure of a mesophilic bacterial complex I in nanodisc. Despite the presence of the lipidic environment, substantial number of particles have only the soluble peripheral arm and only half the number of particles exist as full complex indicating the extreme instability of this complex. By extensive computational classification to address heterogeneity, the maps have been improved both for the full complex and the peripheral arm at a very high resolution (2.1 Å), allowing to build almost a complete protein model as well as a number of water molecules, key in understanding the mechanism. Differences in E. coli complex with structures of other enzymes are discussed, in particular the junction of the peripheral arm and the membrane domain and TMH8 in the NuoM subunit of the membrane domain. With all the structural and biochemical details, the authors propose a coupling mechanism that is different from those proposed previously and involves the idea that protons enter the ubiquinone cavity via the periplasmic or intra-membrane side (in organelles) and this is coupled to 3 protons being transported by the membrane domain in the opposite direction. Thus, for each reduction of ubiquinone 4 protons are translocated in two pumping cycles.

While the mechanism is simple and elegant, the absence of a structure with bound ubiquinone (the cavity for ubiquinone is inferred from other structures) and with NADH, different states observed by classification, dissociation of the peripheral arm - all of which asks for some caution and is a caveat. Nevertheless, these structures are important and will be the platform for further studies (addition of NADH, ubiquinone, pH etc.,) and the proposal can be verified by further biochemical and computational studies.

Reviewer #2 (Public Review):

Summary of what the authors were trying to achieve:

Kolata and Efremov set out to achieve a cryoEM structure of functional E. coli respiratory complex I (proton-pumping NADH-ubiquinone oxidoreductase) reconstituted in lipid nano-discs by single particle cryo-electron microscopy (cryoEM).

Strengths and weaknesses of the methods and results:

A strength of the paper is that E. coli respiratory complex I is one of the most studied homologues of the enzyme with many important functional and mutagenesis studies published. For this reason, the complex I field has been anticipating the structure for some time.

A strength of the paper is the production of a E. coli cell line harboring a Twin-Strep tag on the complex I subunit NuoF using the Crispr-Cas9 system. This allows the authors to develop a single step purification of the complex and will be very useful for any future work on E. coli complex I.

A strength of the paper is the multiple methods used to test the integrity of the nanodisc reconstituted complex (i.e., size exclusion chromatography and mass photometry) and the functional assays demonstrating inhibitor sensitive NADH:Q1 oxidoreductase activity.

A strength of the paper is the high-resolution structure of the peripheral (cytoplasmic) arm of the complex. This structure reveals several features unique to complex I and suggests different strategies for stabilization of the peripheral arm subunits have evolved in different lineages.

A weakness of the paper is the disruption of the complex during cryoEM grid preparation resulting in about half of the observed particles missing the membrane arm and likely also contributing to the disorder and biased orientation seen in the intact complexes. This leads to poor density in the membrane arm for all of the intact complex I structures presented and large variations in the local resolution of the membrane arm focused refinement.

A weakness of the paper is the disorder of important functional regions of the complex, namely the NuoH TMH1, whose disorder is unique to these nanodisc E. coli structures, and the NuoA TMH1-TMH2 loop. As the NuoH TMH1 forms part of the entry to the quinone tunnel of the complex, its absence in the structure leads to concerns regarding the function of the nanodisc preparation. Its absence it curious as this suggests flexibility of the helix, as pointed out by the authors, but the authors also state that there is not enough room in the nanodisc to accommodate this helix (given the visible density for the lipid and membrane scaffold protein). These observations suggest denaturation or unfolding in this region of the complex as opposed to simple flexibility. Unfortunately, the NADH:Q1 functional data do not fully address these concerns at Q1 is far more soluble that the native Q8 substrate of the complex. Although the Q1 activity is sensitive to the inhibitor Piericidin A, which clearly demonstrates that the Q1 reduction is occurring in the native quinone binding site as Piericidin A binds specifically at that site, this does not preclude the possibility of Q1 accessing this binding site via a different path. In fact, the structures indicate that given the flexibility in the connection between peripheral and membrane arms of the complex, the quinone binding site is likely open to the cytoplasm. This leads the authors themselves to conclude that the structures presented are likely disrupted/uncoupled states in which the energy converting mechanism of the complex is not likely possible.

A weakness of the paper is the building of atomic models into regions of the map which do not contain sufficient detail to warrant atomic models. This is particularly the case for the intact models of complex I as well as the membrane arm focused maps and results in low map-model correlations (0.58-0.71). The models were clearly highly restrained during refinement, resulting in good geometry, as is necessary for low resolution regions. But being able to restrain the geometry is not sufficient for placing atoms into regions where the density is weak or absent. If additional information was used in building/constraining the model, such as the X-ray structure, the regions of the model that are biased towards the X-ray structure model needs to be made clearer. Also, in several places in the membrane arm map residues bulge out of the density (side chain and main chain) leading to possible frame shifts with respect to the match between subsequent residues in the model and the map (see NuoM Ile168 for example).

A weakness of the paper is that several specific claims are made about the positions of side chains but, when investigated, the density for those side chains is poorly resolved. An example of this is NuoH Lys274, which is in a low-resolution region of the map and although is fit as well as possible must be considered low confidence given the local resolution (nearby residues Phe277 and Phe282 have almost no side chain density for example).

A weakness of the paper is that the conformational changes seen between the membrane and peripheral arm of the complex in the different 3D classes are difficult to interpret. It is unclear if they are mechanistically significant or, perhaps more likely given the amount of broken complex observed, due to partial disruption of the complex before it completely breaks apart.

A strength of the paper is the interesting and original mechanistic proposal put forward by the authors. But a weakness is that it is unclear how this proposal stems from the structural data presented. Also, the arguments presented are difficult to follow in their current form and warrant a more detailed discussion with the requisite thermodynamic treatment. This may warrant a more complete discussion in an appendix or unless the authors can more convincingly show how the data presented in the paper suggests their proposed mechanism perhaps a separate review article. Furthermore, the proposed mechanism, as presented would make a simple prediction that in the absence of NuoM and NuoL (or equivalent subunits in other species) complex I would not pump any net protons. Experiments that are relevant to this prediction have been done in E. coli (NuoL deletion) and Y. lipolytica (nb8m deletion that results in loss of both NuoM and NuoL subunits). See https://pubmed.ncbi.nlm.nih.gov/21417432/ and https://pubmed.ncbi.nlm.nih.gov/21886480/. In both cases the complex is still able to pump protons. The behavior of the NuoL deletion in E. coli is reconcilable with their proposed mechanism as NuoM is still present, however, the case of the nb8m deletion in Y. lipolytica is more difficult to reconcile with their proposed mechanism. The authors would need to address these experiments in order to include their proposed mechanism.

Appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

Overall, despite the many strengths of this paper detailed above it is unclear whether the authors achieved their goal of a structure of functional E. coli respiratory complex I reconstituted in lipid nano-discs. It appears that under the current grid preparation conditions that the complex is under excessive stress resulting in partial denaturation and partial-to-complete dissociation. Given the clear biophysical data presented on the intactness of the complex in solution, this disruption likely occurs during grid preparation and further optimization of grid conditions may resolve this issue. With the current maps more work needs to be done to improve the map-to-model correlation and to clearly indicate the regions in the models where this correlation is low.

Likely impact of the work on the field, and the utility of the methods and data to the community:

As the first structure of respiratory complex I from the important model organism E. coli this work will have a big impact on the field. This is due to the history of studies on complex I that have been performed using this organism. The structure of the peripheral arm presented here is itself a major advance for this field and presents several important new insights into the evolution of complex I in different lineages. Due to the problems outlined above the structure of the membrane arm and proposed mechanism are more difficult to evaluate and if the limitation of the models are not made more clear this could lead to misinterpretation by non-experts in the structural biology.

Reviewer #1 (Public Review):

The authors investigate the effect of manipulating participant's beliefs about how much pain another individual is really experiencing, on participants' explicit rating of the other's pain, and on participants' willingness to donate money to the suffering person. Towards their aim, the authors designed and collected the results of 6 experiments, that collected behavioral and imaging data (EEG and fMRI). Authors manipulated the beliefs of how much pain the other should be in, by altering participants beliefs about the identity of the person shown in the presented pictures: in the majority of the experiments participants believed to watch facial expressions either collected from a suffering patient or an actor mimicking the patient's facial expression. Participants were then asked to rate how much pain the person in the picture felt, how much unpleasantness the participants themself felt while watching the pictures, and how much money participants are willing to donate to one of the suffering people in the pictures.

A major strength of the paper is the introduction of slightly different modification to the paradigm across the six studies, which allowed them to reliably replicate the effect of their manipulation on participant's behavior; to identify whether the believes-manipulation mediated participant's behavior and brain activity; to touch upon the time at which the manipulation acts on brain activity; and finally to identify the network that is modulated by the manipulation. Through the six experiments the evidence accumulates toward the authors' conclusion that manipulating the belief of participants about how much pain another people is really feeling, influences the amount of pain participants report to perceive, and the amount of money they are willing to donate. Furthermore, the results show that belief-manipulation partially mediates the effects on both behavior and neuronal activity.

The major limitation of the manuscript lies in the framing and interpretation of the results, and therefore the evaluation of novelty. Authors claim for an important and unique role of beliefs-of-other-pain in altruistic behavior and empathy for pain. The problem is that these experiments mainly show that behaviors sometimes associated with empathy-for-pain can be cognitively modulated by changing prior beliefs. To support the notion that effects are indeed relating to pain processing generally or empathy for pain specifically, a similar manipulation, done for instance on beliefs about the happiness of others, before recording behavioural estimation of other people's happiness, should have been performed. If such a belief-about-something-else-than-pain would have led to similar results, in terms of behavioural outcome and in terms of TPJ and MFG recapitulating the pattern of behavioral responses, we would know that the results reflect changes of beliefs more generally. Only if the results are specific to a pain-empathy task, would there be evidence to associate the results to pain specifically. But even then, it would remain unclear whether the effects truly relate to empathy for pain, or whether they may reflect other routes of processing pain.

Reviewer #2 (Public Review):

The authors performed six experiments examining the influence of beliefs regarding pain experience on behavioral and neural indices of empathy for pain and altruistic behavior. They demonstrate that manipulations that to reduce beliefs that individuals making painful expressions are actually in pain (e.g., revealing them to be actors, indicating that their treatment has been successful, etc.) attenuates subjective judgments of pain intensity, real monetary donations to these targets, and P2 amplitudes, and further, that regions involved in perspective-taking and emotion regulation are sensitive to representations of pain beliefs. While I think that the authors have done an admirable job in laying out the evidence for their argument across six well-devised experiments, I do think that the manuscript has some room for improvement. In particular, I hope that the authors can offer stronger grounding in the background literature and clarify some task and stimulus details.

1. In laying out their hypotheses, the authors write, "The current work tested the hypothesis that BOP provides a fundamental cognitive basis of empathy and altruistic behavior by modulating brain activity in response to others' pain. Specifically, we tested predictions that weakening BOP inhibits altruistic behavior by decreasing empathy and its underlying brain activity whereas enhancing BOP may produce opposite effects on empathy and altruistic behavior." While I'm a little dubious regarding the enhancement effects (see below), a supporting assumption here seems to be that at baseline, we expect that painful expressions reflect real pain experience. To that end, it might be helpful to ground some of the introduction in what we know about the perception of painful expressions (e.g., how rapidly/automatically is pain detected, do we preferentially attend to pain vs. other emotions, etc.).

2. For me, the key takeaway from this manuscript was that our assessment of and response to painful expressions is contextually-sensitive - specifically, to information reflecting whether or not targets are actually in pain. As the authors state it, "Our behavioral and neuroimaging results revealed critical functional roles of BOP in modulations of the perception-emotion-behavior reactivity by showing how BOP predicted and affected empathy/empathic brain activity and monetary donations. Our findings provide evidence that BOP constitutes a fundamental cognitive basis for empathy and altruistic behavior in humans." In other words, pain might be an incredibly socially salient signal, but it's still easily overridden from the top down provided relevant contextual information - you won't empathize with something that isn't there. While I think this hypothesis is well-supported by the data, it's also backed by a pretty healthy literature on contextual influences on pain judgments (including in clinical contexts) that I think the authors might want to consider referencing (here are just a few that come to mind: Craig et al., 2010; Twigg et al., 2015; Nicolardi et al., 2020; Martel et al., 2008; Riva et al., 2015; Hampton et al., 2018; Prkachin & Rocha, 2010; Cui et al., 2016).

3. I had a few questions regarding the stimuli the authors used across these experiments. First, just to confirm, these targets were posing (e.g., not experiencing) pain, correct? Second, the authors refer to counterbalancing assignment of these stimuli to condition within the various experiments. Was target gender balanced across groups in this counterbalancing scheme? (e.g., in Experiment 1, if 8 targets were revealed to be actors/actresses in Round 2, were 4 female and 4 male?) Third, were these stimuli selected at random from a larger set, or based on specific criteria (e.g., normed ratings of intensity, believability, specificity of expression, etc.?) If so, it would be helpful to provide these details for each experiment.

4. The nature of the charitable donation (particularly in Experiment 1) could be clarified. I couldn't tell if the same charity was being referenced in Rounds 1 and 2, and if there were multiple charities in Round 2 (one for the patients and one for the actors).

5. I'm also having a hard time understanding the authors' prediction that targets revealed to truly be patients in the 2nd round will be associated with enhanced BOP/altruism/etc. (as they state it: "By contrast, reconfirming patient identities enhanced the coupling between perceived pain expressions of faces and the painful emotional states of face owners and thus increased BOP.") They aren't in any additional pain than they were before, and at the outset of the task, there was no reason to believe that they weren't suffering from this painful condition - therefore I don't see why a second mention of their pain status should *increase* empathy/giving/etc. It seems likely that this is a contrast effect driven by the actor/actress targets. See the Recommendations for the Authors for specific suggestions regarding potential control experiments. (I'll note that the enhancement effect in Experiment 2 seems more sensible - here, the participant learns that treatment was ineffective, which may be painful in and of itself.)

6. I noted that in the Methods for Experiment 3, the authors stated "We recruited only male participants to exclude potential effects of gender difference in empathic neural responses." This approach continues through the rest of the studies. This raises a few questions. Are there gender differences in the first two studies (which recruited both male and female participants)? Moreover, are the authors not concerned about *target* gender effects? (Since, as far as I can tell, all studies use both male and female targets, which would mean that in Experiments 3 and on, half the targets are same-gender as the participants and the other half are other-gender.) Other work suggests that there are indeed effects of target gender on the recognition of painful expressions (Riva et al., 2011).

7. I was a little unclear on the motivation for Experiment 4. The authors state "If BOP rather than other processes was necessary for the modulation of empathic neural responses in Experiment 3, the same manipulation procedure to assign different face identities that do not change BOP should change the P2 amplitudes in response to pain expressions." What "other processes" are they referring to? As far as I could tell, the upshot of this study was just to demonstrate that differences in empathy for pain were not a mere consequence of assignment to social groups (e.g., the groups must have some relevance for pain experience). While the data are clear and as predicted, I'm not sure this was an alternate hypothesis that I would have suggested or that needs disconfirming.

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Reviewer #1 (Public Review):

The authors reanalyse previously published data to identify evolutionary patterns in HIV evolution during three drug treatment. Specifically they assert that the rate of resistant evolution is constant over time, that mutations occur sequentially (rather than multiple occurring at once) and that the order in which mutations occurs is largely consistent. They then use an understanding of the pharmacokinetics of the drugs in the body to explain some of the patterns. In my opinion, these are important observations that may have an important impact on HIV treatment (though this is not my area of expertise, which is in antibiotic combination therapy against bacteria).

My main concern with this work is the absence of formal statistical analyses to support the authors' interpretation. These assertions seem to be based on a visual analysis of the data. In my opinion, formal statistical analyses should be performed. Also, I am not certain the evidence for the predictable ordering of mutation is sufficient.

In particular, the statements regarding the rate of drug resistance evolution, the proportion of patients with 0-3 drug mutations, and the ordering of mutations do not receive formal statistical analysis, but are important to the interpretation. Indeed, formal statistical analysis does not appear in this manuscript.

Without these analysis, it does not seem possible at present to assess whether the authors have achieved their aims.

Reviewer #2 (Public Review):

Overview:

This perspective piece considers two models of within-host HIV dynamics in the presence of triple drug therapy. The authors explain how the two models of spatial heterogeneity of drug concentration within the body and temporal fluctuations of drug concentration could give rise to three patterns that they characterise in drug resistance data. Namely, that DRMs evolve consistently through time (after a transient phase), that mutants evolve one mutation at a time, and that these multi-drug resistant mutants occur in a particular order.

General comments:

I found the paper engagingly written and mostly very clear. I particularly liked the clear figures.

I must admit that the paper was rather unusual in its presentation. The paper discusses the points in great detail and - while very clear - I wondered whether there is ample opportunity to shorten and focus the piece.

I found myself a little disappointed that the authors had stopped short of doing any modelling, especially given their remarks in the introduction about the need to match models to data, and the lack of a framework for understanding how best to recapitulate clinical data.

Finally, I found it hard to pick out the new points being made by the authors from the previous literature, as well as the implications of these new ideas. For example, spatial and temporal differences in drug concentration have been used to explain viral rebounds etc. (which the authors discuss), however, is the central point in this paper that these two models of viral dynamics could also explain the three-fold pattern (as described in the Overview)? Perhaps the motivation could be clarified. I'm sure this will be a case of shortening and clarifying the introduction. (This confusion was compounded somewhat by the lack of quantitative analysis as the point above.)

Reviewer #1 (Public Review):

This manuscript aims at reporting a novel role of a small fluoroquinolone resistance qnrD plasmid and elucidating relevant mechanisms that allow extreme low sub-inhibitory levels of aminoglycoside antibiotics to induce SOS response in E. coli.

The authors identified two qnrD plasmid-encoded products, FAD-dependent oxidoreductase and FNR-type transcriptional repressor, which contribute, respectively, to elevated nitric oxide production and reduced nitric oxide detoxification, subsequently resulting in the SOS response. The contribution from the oxidative stress was ruled out. These novel findings were achieved in vitro by using a range of clearly-defined genetic backgrounds of isogenic strains (include gene deletion mutants and in-trans complementation) and by measuring the expression of SOS response indicator genes, which is the strength of the study. The experiments were overall conducted both logically and carefully for understanding the mechanisms responsible for observed qnrD plasmid-dependent induction of SOS response by aminoglycosides, followed by proposing a justifiable model that summarizes the relevant nitrosative stress pathways leading to the SOS response.

However, the target(s) (molecules) with which aminoglycoside interact as inducers remain unknown, although the aminolgycoside effect is dependent on the GO repair system and indirectly results in DNA damage. Moreover, the magnitude of gene expression changes affected by aminoglycosides in the presence of a qnrD plasmid was relatively small, mostly within about 2- to 3-fold alterations at the transcriptional level, raising an open question on the interpretation of the significance of aminoglycoside-induced SOS response in the contribution to DNA mutations and resistance emergence. The latter warrant future additional in vitro and in vivo studies to shed light on the importance of aminoglycoside-triggered SOS response.

Reviewer #2 (Public Review):

Strengths and Weaknesses:

The manuscript from Babosan and coworkers is nicely written and presented and describes a well-conducted experimental work using appropriate methodology. Overall, this is an interesting manuscript describing an important phenomenon, the induction of the SOS response by aminoglycosides (AGs) in cells carrying a plasmid conferring resistance to another drug class. The main points are clearly addressed and demonstrated: SOS induction by AGs in cells carrying the qnrD-containing plasmid; the involvement of nitrosative stress; identification of ORFs 3 and 4 as the responsible for the nitrosative stress. The major weakness of the work is that it does not assess whether the SOS induction by AGs leads to increased mutagenesis as speculated. Also, the qnrD plasmid containing ORFs 3 and 4 is not commonly found in E. coli (Table S3 and Figure S1). It is adequate that most of the work has been performed in this genetically tractable organism. Nevertheless, it would be important to assess whether the same AG-mediated SOS induction occurs in bacteria that usually carry this type of plasmid (P. mirabilis or other Morganellaceae). This is a simple qRT PCR experiment which could add a lot to the significance of the paper. Perhaps the nitrosative stress (which is obviously a deleterious effect) is particular to E. coli and that could be one of the reasons why this particular plasmid architecture occur rarely (or never?) in this species.

Other important points for consideration by the authors:

- How the experiments in figure S2 addressed plasmid stability during growth by measuring OD? Plasmid stability in the experimental conditions is an important issue.<br> - Comparing Figures 3B and 1E, it can be observed that deletion of both recF and recB have significant effects on SOS induction by AG in cells carrying the qnrD plasmid. These results are very important for their model and a qRT measurement of sulA in recF and recB mutants would be an important confirmation for the results obtained with plasmid based assays, which could be influenced by the reporter plasmid copy number and stability in the different genotypes analyzed.<br> - The complementation of ORF 3 and 4 mutants (Figures 4A and 4C) is incomplete, the same SOS induction level as observed in the wt is never reached. Could this be due to the deletion of one ORF influencing on the expression of the other? Additionally, a more proper comparison with cells carrying the empty expression vector may help to solve this issue.

Reviewer #3 (Public Review):

The authors use the term "aminoglycosides" in multiple sections of the manuscript, including the title, the abstract and the discussion. However, only one aminoglycoside (tobramycin) has been used throughout the study. Gentamicin was used at the beginning of the study but disappears at some point. Please clarify.

In contrast with the detailed analysis described in the initial sections, the information provided on the products of ORF3 and ORF4 is scanty. This is unfortunate as these products and their activities are the main findings of the story. Hence, more detailed characterization of these putative proteins seems advisable to support the authors's model. Bioinformatic analysis should be shown in more detail, the existence of the predicted proteins should be proven and the putative DNA binding activity of the ORF4 product should be shown. Otherwise, the possibility of indirect effect will remain open.

If 8-oxoG is the problem indeed, assays in a strain lacking formamidopyrimidine-DNA glycosylase (fpg) might provide support to the authors' hypothesis about NO production.

The magnitude of SOS induction reported in the manuscript is small. Hence, the authors' hypothesis that induction of the SOS response will increase the mutation rate can be questioned as error-prone repair is unlikely to operate under such circumstances. To sustain the authors' hypothesis, mutation rates should be tested.

Absolute numbers of beta-gal activities should be provided when lac fusions are used. This would help the reader to assess the magnitude of the phenomena described in the manuscript.

Reviewer #1 (Public Review):

The authors succeed in conveying a clear and concise description of how intrinsic heterogeneity affects continuous attractor models. The main claim, namely that resonant neurons could stabilize grid-cell patterns in medial entorhinal cortex, is striking.

I am intrigued by the use of a nonlinear filter composed of the product of s with its temporal derivative raised to an exponent. Why this particular choice? Or, to be more specific, would a linear bandpass filter not have served the same purpose?

The magnitude spectra are subtracted and then normalized by a sum. I have slight misgivings about the normalization, but I am more worried that , as no specific formula is given, some MATLAB function has been used. What bothers me a bit is that, depending on how the spectrogram/periodogram is computed (in particular, averaged over windows), one would naturally expect lower frequency components to be more variable. But this excess variability at low frequencies is a major point in the paper.

Which brings me to the main thesis of the manuscript: given the observation of how heterogeneities increase the variability in the low temporal frequency components, the way resonant neurons stabilize grid patterns is by suppressing these same low frequency components.

I am not entirely convinced that the observed correlation implies causality. The low temporal frequeny spectra are an indirect reflection of the regularity or irregularity of the pattern formation on the network, induced by the fact that there is velocity coupling to the input and hence dynamics on the network. Heterogeneities will distort the pattern on the network, that is true, but it isn't clear how introducing a bandpass property in temporal frequency space affects spatial stability causally.

Put it this way: imagine all neurons were true oscillators, only capable of oscillating at 8 Hz. If they were to synchronize within a bump, one will have the field blinking on and off. Nothing wrong with that, and it might be that such oscillatory pattern formation on the network might be more stable than non-oscillatory pattern formation (perhaps one could even demonstrate this mathematically, for equivalent parameter settings), but this kind of causality is not what is shown in the manuscript.

Reviewer #2 (Public Review):

The manuscript takes a remarkably systematic and unbiased approach to the question of whether the models, that explain in terms of recurrent connectivity the grid firing patterns observed in the entorhinal cortex of rodents and other species, would really work given heterogeneity in the values of several parameters, that in the standard models are assumed to take a fixed value. Since the real degree of heterogeneity is difficult to measure, but even more because it is difficult to relate measured quantities to the parameters in simple models which perforce include many artificial components, the authors proceed by setting 5 levels of heterogeneity in 3 parameters, and by simulating the network with each parameter made heterogeneous alone, or all 3 in combination. In short, grids are destabilized (Figure 2), but with interesting differential effects on different aspects of the activity patterns (Fig. 3). This pars destruens is clear, strong and entirely convincing. It is concluded by showing that heterogeneities act on the slow components of the dynamics (Fig. 4).

The pars construens demonstrates that similar networks, but comprised of units with different dynamical behavior, essentially amputated of their slowest components, do not suffer from the heterogeneities - they still produce grids. This part proceeds through 3 main steps: a) defining "resonator" units as model neurons with amputated low frequencies (Fig. 5); b) showing that inserted into the same homogeneous CAN network, "resonator" units produce the same grids as "integrator" units (Figs. 6,7); c) demonstrating that however the network with "resonator" units is resistant to heterogeneities (Fig. 8). Figs. 9 and 10 help understand what has produced the desired grid stabilization effect. This second part is on the whole also well structured, and its step c) is particularly convincing.

Step b) intends to show that nothing important changes, in grid pattern terms, if one replaces the standard firing rate units with the ad hoc defined units without low frequency behavior. The exact outcome of the manipulation is somewhat complex, as shown in Figs. 6 and 7, but it could be conceivably summed up by stating that grids remain stable, when low frequencies are removed. What is missing, however, is an exploration of whether the newly defined units, the "resonators", could produce grid patterns on their own, without the CAN arising from the interactions between units, just as a single-unit effect. I bet they could, because that is what happens in the adaptation model for the emergence of the grid pattern, which we have studied extensively over the years. Maybe with some changes here and there, but I believe the CAN can be disposed of entirely, except to produce a common alignment between units, as we have shown.

Step a), finally, is the part of the study that I find certainly not wrong, but somewhat misleading. Not wrong, because what units to use in a model, and what to call them, is a legitimate arbitrary choice of the modelers. Somewhat misleading, because the term "resonator" evokes a more specific dynamical behavior that than obtained by inserting Eqs. (8)-(9) into Eq. (6), which amounts to a brute force amputation of the low frequencies, without any real resonance to speak of. Unsurprisingly, Fig. 5, which is very clear and useful, does not show any resonance, but just a smooth, broad band-pass behavior, which is, I stress legitimately, put there by hand. A very similar broad band-pass would result from incorporating into individual units a model of firing rate adaptation, which is why I believe the "resonator" units in this study would generate grid patterns, in principle, without any CAN.

Reviewer #1 (Public Review):

I think that it is important for the authors to consider that for most (if not all) SARS-CoV-2 variants, increased transmissibility of the virus has not been directly demonstrated. While it is clear that numerous variants have emerged and will continue to emerge, the rapid upsurge of cases with a variant may be related to many factors (e.g. host susceptibility due to immunity or genetic factors, virus seeding events, predominant replication in particular age cohorts, ...) that cannot simply be captured as "transmissibility of the virus". Even for B.1.1.7 and D614G mutants, the direct evidence of increased transmissibility in humans is extremely limited if available at all. Most studies erroneously simply take the increasing occurrence of particular lineages or mutations in sequence databases as a measure of increased "transmissibility", which should be avoided, also in the present manuscript. Increased transmissibility can only be derived from field studies where transmission is measured directly.

On several occasions in the manuscript (e.g. page 3, page 4 L58-59, page 9 in submitted version), the authors seem to suggest that changes that lead to increased "transmission" or binding affinity and changes that lead to immune escape are mutually exclusive. But the opposite might be true. Viruses may escape from antibody-mediated immunity by amino acid substitutions in linear or structural antibody-binding epitopes. However, viruses may also escape from antibody-mediated immunity through altered protein density on virion surfaces (e.g. less Spike) and/or altered affinity, making it harder for antibody to inhibit virus attachment. As an example, increased affinity may facilitate virus replication with less dense Spike protein, allowing more effective antibody escape. Lower affinity but more dense coverage of Spike may reduce accessibility of critical virus parts by antibodies. Several viruses are known to escape from antibody-mediated neutralization through changes in affinity/avidity.

In relation to the previous point, it is important that authors mention some limitations of the present work in the discussion. SARS-CoV-2 virion attachment to cells is not just a matter of spike protein binding and certainly not of a monomeric RBD. Escape from antibodies and effects on affinity are heavily influenced by the entire (trimeric) spike protein, including its N-terminal domains. Such components are not taken into account in the present experimental designs, and this should be discussed, as e.g. the NTD can be important in attachment and antibody-mediated neutralization.

The authors suggest that the pandemic virus as it spread across the globe initially did not have "optimized" affinity. However, in the first months of the pandemic, there was relatively limited variation in spike protein sequences. The major variants emerged only later and mostly in areas where population immunity was building up. Again, this begs the question whether natural selection is occurring as a consequence of receptor affinity or immune escape?

Reviewer #2 (Public Review):

Barton and colleagues investigated the effect of common SARS-CoV-2 RBD mutations and two ACE2 mutations on the RBD/ACE2 interaction. They concluded that the N501Y, E484K and S477N increased receptor binding while the K417N/T had the opposite effect. Double and triple mutants were also included. The ACE2 mutations (that are rare in the human population) also increased binding to most RBD mutants. The study is well-performed and written clearly.

The primary conclusions of the manuscript were supported by the results. However, the interpretation was too speculative. In the abstract (lines 14-17), the authors suggest that the 501 and 477 mutations enhance transmission solely based on data on the RBD-ACE2 interaction. It is unknown whether increased affinity to ACE2 is beneficial for transmission. In addition, increased RBD affinity to ACE2 does not mean that the whole spike or virus particle also binds stronger to ACE2. Lastly, increasing ACE2 affinity does not necessarily increase binding to cells (for example S1A binding to sugars or spike abundance can also influence this).

The overall impact on the field will be limited as there is substantial overlap with already published studies. The observation that the N501Y and E484K increase receptor binding while the K417N/T mutations decrease binding was already made prior by Laffeber et al (2021; J Mol Biol). Laffeber et al also investigated double and triple mutants and came to similar conclusions. Liu et al (2021) confirmed that the N501Y increases binding whereas the K417N/T have opposing effects (Liu et al., 2021 mAbs). The observation that the Y501N increases ACE2 affinity has been made by several groups (e.g. Liu et al 2021 Cell research; Starr et al 2020 Cell)

The S477N and ACE2 mutations on the ACE2-RBD interaction were not investigated prior. In addition, the authors indicate correctly that they performed the SPR experiments at 37 degrees Celsius, whereas others did SPR experiments at room temperature.

Reviewer #3 (Public Review):

Barton et al. performed detailed measurements and analysis of the binding of the SARS-CoV-2 receptor-binding domain (RBD) and mutations in currently circulating variants of concern (VOC) against the human receptor ACE2 using SPR. K417N/T that occurs in VOCs B.1.351 and P.1 was shown to decrease binding to ACE2, where N501Y (found in B.1.351, P.1, and B.1.1.7) increased binding. S477N and E484K exhibited more modest effects on binding to ACE2. The gain of binding by N501Y and E484K was found to make up for loss of binding caused by K417N/T in B.1.351 and P.1, respectively. Some of these effects have been noted in other publications. The authors also showed that two natural variants of human ACE2 (S19P and K26R), which are found in samples in the gnomAD database (0.4% and 0.03% respectively), also affected bind to SARS-CoV-2 RBDs. The authors then discussed possible causes of the variations of the relatively wide range of affinity values cited in the literature, including measurement at non-physiological temperature, mass-transport limitations and rebinding, and protein aggregation. These are all good points that should be taking into account in assessing absolute versus relative binding. The strengths of this paper are the comprehensive and careful analyses of wildtype and mutant SARS-CoV-2 RBD and ACE2 in one study. Points to be further considered include:

1) The ACE2 receptor exists naturally as a dimeric form and the RBD is a component of the SARS-CoV-2 spike trimer. The assay format here was monomeric RBD binding against monomeric ACE2 throughout this study. While the measurements are indeed carefully executed and under more physiological conditions than many other reported studies, the authors should discuss potential avidity effects, the consequences of mutations on the accessibility of the RBD in VOC versus wildtype, and impact of other domains such as the NTD, in the context of their monomeric ACE2 measurements with isolated RBD here.

2) As shown in Figure S2, RBD WT, K417N, K417T, KN/EK, KT/EK, and S477N, the ~30kDa monomeric proteins were flanked by additional ~60kDa bands (which correspond to the smaller peaks to the left of the main peaks) some of which bleed through to the main fraction to different extents, whereas RBDs SA, UK1, UK2, BR, and E484K, do not seem to have as much or any of these extra species. Can the authors comment on whether these contaminants are RBD-dimers as observed before (Dai et al. 2020)? If yes, would such dimers affect the affinity and kinetics?

Reference:

Dai, Lianpan, et al. (2020). A universal design of betacoronavirus vaccines against COVID-19, MERS, and SARS. Cell 182: 722-733.e11.

Reviewer #1 (Public Review):

Yao Rang and collaborators find that heterologous expression of TMEM120A from mouse and human in cells that lack Piezo1 does not result in poke- or stretch-activated currents in whole cells or excised patches, and further detect no mechano-sensitive currents when the purified human protein is reconstituted in giant unilamellar vesicles. Together with high-quality positive controls with Piezo1, Piezo2 and TMEM63a, the results presented here call into question a previous proposal (Beaulieau-Laroche et al., Cell 2020) that TMEM120A functions as the long sought-after mechano-activated channel responsible for detecting painful touch.

Although the evidence supporting a channel function for TMEM120A is not strong, it remains to be ruled out that the discrepancies between the two studies arise from the different methods that were used to deliver the mechanical stimuli, as mentioned by the authors in the Discussion, or from the C-terminal mCherry tag attached to human TMEM120A in this study that was not present in the construct used by Beaulieau-Laroche et al.

Upon determination of the structure of full-length human TMEM120A in nanodiscs using cryo-EM, the authors find that the protein forms a dimer with six transmembrane helices per subunit and a cytosolic N-terminal coiled coil domain. Surprisingly, the authors find a density attributable to coenzyme-A (CoASH) located within a highly conserved cytosolic cavity at the transmembrane domain. The authors provide evidence from mass-spectrometry and isothermal titration calorimetry (ITC) to demonstrate that CoASH binds TMEM120A, and solidify their conclusions by showing that mutation of a residue close to the CoASH density in TMEM120A disrupts binding measured by ITC. The authors show that a potential ion-conduction pathway in their TMEM120A structure would be occluded by CoASH on the cytosolic side and on the extracellular side by a series of not well-conserved residues. Finally, the authors solve a structure in detergents where no density for CoASH is observed, as expected from spectroscopic data showing that detergent-reconstitution results in loss of CoASH binding. In this structure, a conformational change is suggested to occur on the cytosolic cavity entrance where a loop becomes reoriented to occlude the cavity that is otherwise occupied by CoASH. Together, the data presented paints an intriguing alternative for the function of TMEM120A proteins with a role in metabolism or CoA transport.

Although the main conclusions are well supported by the evidence, it is challenging to appraise many of the interesting structural observations pointed out by the authors because the experimental data (i.e. the density) is in most cases not depicted. Some of these observations for which only the model rather than the experimental data is shown include the hinge-like motif at the dimer interface (Fig. 3C), the CoASH binding site (Fig. 4E and Fig. 4 Supplement 1C), the difference in the conformation of the IL5 loop between the apo and CoASH-bound structures (Fig. 5), the extracellular constriction of the possible ion-conduction pathway (Fig. 4 Supplement 1D and Fig. 5 Supplement 2), as well as the comment that the observed density in the structures cannot accommodate other CoA-derivatives, for which data is not shown. The relatively low resolution at which the data were obtained raises concerns regarding many of these detailed observations.

Reviewer #2 (Public Review):

In this manuscript, Rong et al., address the possibility that TMEM120A, also known as TACAN, might not be a mechanosensitive ion channel. They use extensive electrophysiological characterization to convincingly show that cells heterologously transfected with either human or mouse TMEM120A do not exhibit mechanosensitive currents above background in cell lines or when purified and reconstituted in giant unilamellar vesicles. They also solve the cryo-EM structure of TMEM120A and find that it does not resemble an ion channel (i.e., there is no obvious pore). Interestingly, there is a density consistent with coenzymeA in the structure, thus suggesting an alternative function for TMEM120A. Further evidence for this interaction is shown through biochemical analysis, as disrupting a residue proposed to form a π−π stacking interaction between TMEM120A and coenzymeA reduces the binding affinity as assayed by ITC.

Overall, the impact of this manuscript is extremely high, as it refutes a recent report that TMEM120A/TACAN is a high-threshold (pain) mechanosensitive ion channel, and further suggests an alternative function for this protein. The experiments are extremely carefully done, and the authors attempted to replicate the electrophysiological function of TMEM120A with high numbers and with appropriate positive and negative controls. The inclusion of structures (Coenzyme-A bound and apo) and corresponding biochemical analyses provide strong support for the direct binding of Coenzyme-A by TMEM120A.

Reviewer #3 (Public Review):

TMEM120A protein was recently reported to mediate mechanosensitive currents in response to painful stimuli. In the present manuscript, the authors aimed to elucidate TMEM120A mechanism of action by solving the structures and complementing them with functional characterization. In contrast to the recent report, the authors could not observe TMEM120A-mediated currents in response to mechanical stimuli neither in transfected cells nor in liposomes. Furthermore, the structure of human homolog HsTMEM120A revealed a co-purified endogenous ligand, which was shown to be coenzyme A (CoASH). The authors went on to solve the structure in the absence of CoASH, revealing a different conformation of HsTMEM120A. Together, structural and functional data point towards a conclusion that TMEM120A might not be a mechanosensitive channel, but might rather be important for fatty acid metabolism. The conclusions of the manuscript are supported by the presented data.

Strengths:

1. The authors conclusively show that TMEM120A does not mediate poking- or stretch-induced currents when compared to well-characterized mechanosensitive channels Piezo1 and TMEM63a.

2. The authors employed several approaches to confirm the identity of the co-purified ligand. Firstly, the presence of CoASH in the purified protein sample was confirmed by mass spectrometry. Secondly, the binding of CoASH to TMEM12A was confirmed by ITC. Thirdly, using the obtained structure the authors identified CoASH-interacting residues and show that mutating one of the key residues (Trp193) reduced TMEM120A affinity for its ligand.

3. The observation that CoASH dissociates from TMEM120A during size exclusion in detergent, but not in lipid environment allowed to solve a ligand-free TMEM120A structure, which revealed a different conformation at the entrance to CoASH-binding site and is possibly relevant for the mechanism of action.

Noteworthy, 3 other studies (Niu et al., 2021, Xue et al., 2021, Ke et al., 2021) independently arrived at the conclusion that TMEM120A is probably not a mechanosensitive channel, further supporting the results of the present study.

Weaknesses:

Despite the advances presented in this manuscript, physiological function of TMEM120A and its mechanism of action remain obscure, other than that it is probably not a mechanosensitive channel. However, the goal of the authors was to understand how TMEM120A might mediate mechanosensitive currents, while establishing its role in lipid metabolism is outside of the scope of this manuscript.

Regardless, this work provides an important insight into TMEM120 family and will serve as a basis for future investigations.

Reviewer #1 (Public Review):

The well studied tendons in the fly embryo are formed by rather simple specialized epithelial cells that stay in the epithelium and attract the end of an attaching myotube. This study investigates the more complex 'long tendons' of the adult legs that form in the leg discs during pupal stages. The authors find that the long tendon precursors undergo an interesting collective migration process to re-shape into a higher order long tub-like structure. This elongation process had not been studied before.

By performing a transcriptomics analysis of sorted leg disc tendon precursors (0h APF), followed by systematic RNAi knock-down studies of all transcription factors enriched in the tendon precursors, the authors identify the Kr-like transcriptional regulator dar1 as key regulator of long-tendon development. RNAi specificity was confirmed by analyzing 2 different lines both resulting in locomotion defects and by confirming the tendon defect by the analysis of dar1 null alleles that rarely survive until pupal stages.

Dar1 protein is enriched in stripe positive tendon precursors of the leg discs (but not in the tendons of the wing disc). Dar1 knockdown animals show largely missing or too short internal tendons in the legs, hence strongly affecting the attachment of the adult leg muscles. This explains the observed locomotion defects of these flies.

Excellent imaging during early stages of pupal development shows that dar1 is required for the extension of the long leg tendon precursors into a long tube. Mechanistically, dar1 organizes actin-rich long filopodial extensions during tube elongation and hence equips these cells specifically with their collective migration ability to form a long tube.

Epistasis experiments show that dar1 acts downstream of the tendon master regulator stripe, making long tendons different from other tendons. Interestingly, the amount of stripe positive tendons cells is reduced upon dar1 knock-down, resulting in the hypothesis that dar1 is needed to recruit and specify the correct number of stripe positive tendons cells to form the larger long tendons in a non cell autonomous manner. Such a model makes sense, as likely the invagination and migration of tendon precursors into a long tube will recruit additional epithelial cells to turn on stripe and to follow the invagination as nicely illustrated in the model in figure 8.

I am not sure if the term 'master regulator' for dar1 for the long tendons is justified. Do the authors have access to a UAS-dar1 line to see if 'normal' tendons in the wing disc or the embryo can be changed to 'long tendons' when expressing dar1? If not, the term tendon identity gene might be more appropriate.

It would have been nice to include Dar1 antibody staining of dar1 know-down leg to confirm antibody specificity.

Together, this is an excellent study that genetically and molecularly characterizes a novel gene controlling an interesting morphogenesis process that was not well understood. As these complex long tendons are somewhat similar to mammalian tendons, these findings could also be relevant for mammalian tendon development and diversification.

Reviewer #2 (Public Review):

In their manuscript titled "Transcriptomic and genetic analyses identify the Krüppel-like factor dar1 as a master regulator of tube-shaped long tendon development," Quentin et al. identify Dar1 as a novel transcriptional regulator of tendon development. They present the first transcriptomic profiling of Stripe-expressing tendon progenitors in the leg disc, highlighting similarities to well-characterized tubulogenesis systems. After performing a candidate screen of 31 genes identified in this data, they focus on the transcription factor Dar1. Dar1 is selectively expressed in tendon cells of the leg and its knockdown results in both tendon and muscle defects. The long tendons are too short due to insufficient tube extension and decreased filipodial arborization. The number of tendon precursors is also greatly reduced upon knockdown of dar1. As dar1 expression is downstream of Stripe and there is little alteration of cell proliferation and death rates in the leg disc, the authors suggest Stripe positive cells are recruited during long tendon tubulogenesis and this recruitment is non-autonomously regulated by Dar1.

There are several new observations, approaches and datasets presented in this work that are of value to the muscle field and the broader Drosophila community. First, the FACS-based RNA-Seq approach and associated protocol for tendon cells in leg can be adapted for other imaginal discs, dissectible tissues and cell types. Second, this RNA-seq dataset expands on earlier observations suggesting long-tendon development proceeds via tube-formation and thus is a valid model to study the important process of tubulogenesis. Third, the candidate climbing and survival screen combined with the RNA-Seq data provide multiple candidate genes, and notably transcription factors, that may play a role in tendon specification or development and will be useful for future studies. Lastly, this work identifies and nicely characterizes the tendon phenotype after knockdown of Dar1, ultimately placing Dar1 downstream of Stripe in the regulatory hierarchy. Given the apparent conservation of Krüpel-like factors in tendon development, this finding is of general interest and may point to a conserved mechanism fueling long tendon elongation.

On a critical note, only select genes of interest and select GO terms are included from the RNA-Seq data, and a broader or more systematic analysis is not presented. While the title refers to Dar1 as a master regulator of tendon development, this is not addressed experimentally. The manuscript is largely descriptive, and while the characterization of Dar1 indeed raises interesting inferences notably about Stripe-positive cell recruitment, there are no experiments designed to test potential mechanisms of non-cell autonomous effects or even regulatory targets of Dar1 in tendon. The manuscript also does not address non-autonomous effects on muscle number, size and morphology in the ventral tibia, for example, and does not effectively place this result in the context of what is known about bi-directional muscle-tendon signaling. Lastly, the authors have previously characterized tendon-cell recruitment in the leg to be Notch dependent, and indeed they find enrichment of the Notch signaling pathway in their RNA-seq analysis, but the findings for Dar1 are not integrated in this context.

In conclusion, this manuscript identifies a new transcriptional regulator of tendon development and offers an insightful and informative description of the phenotype. It arrives at mechanistic inferences that provide a framework for future studies to identify the non-cell autonomous mechanism of tendon-cell recruitment, to elucidate the regulatory mechanisms of long-tendon extension and tube formation and to investigate the transcriptional hierarchy that defines long-tendon cell identity in Drosophila.

Reviewer #3 (Public Review):

In the introduction the authors frame some key background questions for the study concerning the specificity of muscle-tendon interactions, and the processes governing tendon development. To investigate they have set out to identify genes regulating the development of the long tendons in the Drosophila leg. These share similarities with autopod tendons in mouse and also their morphogenesis is similar to that of some tubule development.

To investigate, the authors have profiled the RNAs (total RNA transcriptomic analysis) that are enriched in the tendon progenitors in comparison to the limb disc as a whole. From the large number of enriched genes, the consequences of knocking 31 transcription factors whose expression was >1.5-fold higher in the tendon precursors, using a viability assay. Several gave phenotypes and their homologues had been reported to have expression in mouse limb tendons. Ultimately 1 gene was chosen for further analysis, Dar 1, and conventional analysis shows that it is involved in normal tendon development and its expression is dependent on the key tendon regulator stripe. The tendons from Dar1 knock-down animals don't extend as well, have reduced amounts of actin protrusions and have slightly fewer cells. The data demonstrating this provide good evidence that Dar1 plays a role in the development of the tendons and, interestingly is specific for the leg tendons. It's not required in the wing tendons.

The work is nicely done but it falls short of addressing the questions set. The initial RNA seq data are a good starting point and the analysis of dar1 expression and knock-down are sound. More could have been made of the primary data and the authors would need to include properly the "data not shown" about some of the other genes tested. The basic phenotypic analysis supports the conclusions that Dar1 as is an additional player in long tendon development, but its identification does not significantly add to the understanding of how tendons grow, navigate correctly and interact with the right muscle. What step is Dar1 regulating? There is a suggestion that it may regulate the cytoskeletal genes but there are no concrete data to support that. Could dar1 be involved in specifying leg versus wing tendons (the former being more tube like) given its expression in leg tendons only.

The results identifying a new gene involved in tendon morphogenesis will be of interest to those directly in the field.

Reviewer #1 (Public Review): 

The authors explore a gap in knowledge pertaining to the non-immediate effects of intragroup interactions on individual behaviour. The main strength of this research is analysing both natural and simulated situations to demonstrate behavioural patterns to suggest that interactions between individuals may have long-lasting effects on an entire social group. The manuscript could improve by better highlighting the significance of their specific findings in a broader context. For instance, more discussion into the implications of delayed responses to intragroup conflict are desired, and a more in-depth introduction of the relationship between conflict bystanders and conflict resolution would be ideal. The authors achieve their goal of understanding the immediate and non-immediate effects of intragroup conflict on dwarf mongoose, however the conclusions can be expanded to enhance the significance of this research in relation to broader literature. This work will encourage others to consider multiple antecedents when observing behaviour, including those occurring hours beforehand. I believe that this research is significant and unique, however the emphasis of the novelty of these findings could be enhanced throughout the manuscript.

Reviewer #2 (Public Review): 

Morris-Drake et al. investigate the effect of within-group conflict on behaviour of bystanders in wild dwarf mongooses (Helogale parvula). They test whether hearing a foraging displacement between a dominant and subordinate in the social group changes the behaviour of bystanders in both the immediate and longer term, using both natural occurrences of foraging displacements, and playbacks of displacement calls to experimentally simulate conflict. They show that bystanders are more vigilant immediately following natural and simulated conflict, but that there is no change in bystanders' affiliative behaviour (measured by grooming) in the minutes after conflict. By contrast, they find that, overall, occurrences of grooming decrease in the evening following conflict but that there are contrasting patterns of grooming between certain individuals within the social group: aggressors are groomed by bystanders less, but bystanders groom each other more. Together, these data are presented as evidence that information about within-group conflict is obtained through displacement calls, and that this information is used by bystanders to shape social interactions even some time after the occurrence of conflict. 

Strengths: 

1)The study's aim to test whether there are delayed effects of within-group conflict is novel and the finding that aggressive interactions between social partners can affect the behaviour of fellow group members for some time after provides evidence that within-group conflict can have effects on social behaviour over varying temporal scales. 

2) The use of acoustic stimuli to simulate within-group conflict is novel and provides a new angle to previous work on this topic. The indication from the study that individual identity can be obtained from these acoustic calls, and that the information can be used at a later time to inform social interactions between bystanders and protagonists or victims is interesting and adds to a growing body of evidence that acoustic information can play a strong role in shaping social behaviour. 

3) The combination of natural observations and experimental simulation of within-group conflict, in the form of foraging displacements, provides a systematic examination of the effects of within-group conflict on affiliative behaviour. Experiments and natural observations comprise pair-matched treatment and controls which provide a robust test. The effort of performing such rigorous experiments on a wild population is highly commended and the neat experimental design goes some way to countering the lack of statistical power that the study is exposed to from small sample sizes. 

Weaknesses: 

1) The modelling framework used in some statistical analyses is not the most appropriate given the nature of the data. 

2) The difference in the context of post-conflict behaviour in the immediate (during foraging) and the longer term (during resting) is not really acknowledged in the interpretation of the results. While this does not affect the interpretation of evidence of delayed post-conflict increases in affiliation (grooming), there could be an argument that it would not be possible to detect changes in affiliation immediately following conflict because grooming behaviour is not observed during foraging and therefore does not represent the most suitable metric of affiliation in this context.

3) The study finds increased vigilance in the minutes following both natural and simulated occurrences of foraging displacement but this result is not given much attention in the discussion of the results. Vigilance was not measured in the hours following simulated conflict, as grooming behaviour is, and so there is no test of whether there is a delayed change in avoidance behaviour in the same way as there is a delayed change in affiliative behaviour. 

4) Some claims for evidence for delayed post-conflict management are a little overstated given the small sample sizes in the study.

Reviewer #1 (Public Review): 

Adhesion GPCRs are a relatively understudied GPCR family. One of the mechanisms they are activated is by a tethered agonist peptide that interacts with the transmembrane domain to activate the receptor. The authors first show that a knock-in mouse expressing a non-cleavable GPR116 mutant to prevent the release of the tethered agonist peptide phenocopies the pulmonary phenotype of GPR116 knock-out mice, demonstrating that tethered agonist-mediated receptor activation is indispensable for GPR116 function in vivo. They then use mutagenesis and activity assays to find residues in the tethered agonist which are most important for receptor activation, as well as mutating loops in the extracellular face of the receptor to find residues important in mediating the response to the tethered agonist. The authors also highlight residues important for differential mouse vs. human GPR116 activation and propose that ECL2 is very important for GPR116 activation. <br> The work is overall solid. However, there are some concerns with the lack of expression data shown, with the lack of citing and mentioning previous work about the peptide-receptor interactions, with the model and some structural interpretations that needs to be carefully made.

Reviewer #2 (Public Review): 

This manuscript describes studies on the structural determinants of activation for the adhesion GPCR (aGPCR) GPR116 both in vitro and in vivo. The authors define key residues for activation on the receptors' N-terminus (the "tethered agonist") and the extracellular loops. Thus, the studies provide novel insights into the structural determinants of GPR116 activation. However, some interpretational issues (detailed below) complicate some of the authors' conclusions. Specific comments are as follows: 

1) Results section, first paragraph, last sentence: The authors write, "These results taken together indicate that the H991A mutant is capable of proper trafficking to the membrane, is able to response to exogenous peptide, but is unable to be cleaved and activated by endogenous ligands in vivo." The last part of this sentence represents an over-interpretation, as the data shown in Figure 1 do NOT show that the non-cleavable receptor is unable to be activated by endogenous ligands in vivo. It is entirely conceivable that a non-cleavable aGPCR could still be activated by endogenous adhesive ligands if those ligands were to change the position of the tethered agonist in manner that alters receptor signaling activity. 

2) The data shown in Fig. 1B (surface expression of non-cleavable H991A mutant) need to be quantified in some way in order to be interpretable. 

3) Results section, second paragraph, penultimate sentence: The authors write, "These data demonstrate that while the non-cleavable receptor is fully activated in vitro by exogenous peptides corresponding to the tethered agonist sequence, cleavage of the receptor and unmasking of the tethered agonist sequence is critical for GPR116 activation in vivo." However, the non-cleavable GPR116 mutant actually has two key differences from WT: i) lack of full liberation of the tethered agonist sequence, and ii) lack of liberation of a free NTF, which might dissociate from the CTF and have important in vivo physiological actions on its own. Isn't it conceivable that the lack of a freely mobile NTF contributes to the similarity in lung phenotype between the non-cleavable knock-in mutant and the GPR116 knockout? Based on the data shown in Figure 2, how can the authors claim these data demonstrate that unmasking of the tethered agonist is critical for GPR116 activation? The data could equally be interpreted as showing that liberation of a free NTF is critical for the physiological effects of GPR116 in vivo. 

4) Figure 3: If the authors' hypothesis is that the tethered agonist must be liberated in order to allow activation of GPR116, then why do ANY of the Flag-tagged mutant constructs exhibit constitutive signaling activity? Doesn't the N-terminal Flag tag prevent the tethered agonist from being exposed? How can these data be reconciled with the authors' model? 

5) The data shown in Fig. 3D are lacking statistical comparisons, so it is not possible to tell whether any of the differences between the mutants are statistically significant. 

6) The data shown in Fig. 4D (surface expression of the ECL mutants) need to be quantified in some way. 

7) In interpreting the results of the ECL mutations on GPR116 signaling activity, it is unclear why the authors so explicitly propose that these data demonstrate that the tethered agonist must be interacting with ECL2. Isn't it possible that ECL2 mutants with impaired receptor signaling activity simply lock the receptor in an inactive state? In this way, the effects of the ECL2 mutations could be explained without invoking a physical interaction between the putative tethered agonist and ECL2.

Reviewer #3 (Public Review): 

The data reported in this study highlight the physiological relevance of an autoproteolysis event which exposes a receptor's tethered ligand leading to its activation through a network of residues stabilizing the receptor's active conformation. With an emphasis on the orphan adhesion G Protein Coupled Receptor (aGPCR) member GPR116, the authors describe that a proteolytically-deficient mutant of the receptor recapitulates many of the phenotypic elements that are characteristic of lung defects observed in a GPR116-deficient mouse line previously developed and analyzed by the same group, thus alluding to the critical role played by an aGPCR canonical intramolecular cleavage in maintaining physiological receptor function. While the authors bring a quintessential support for this hypothesis by analyzing lung cells phenotypes in this CRISPR-generated genetically modified mouse mutant, these observations come amidst a divided field. Indeed, attempts to verify if aGPCR functions rely on a strict requirement for proteolytic cleavage have led to conflicting data and have highlighted the need for deeper range analytical strategies. Adding to this controversial hypothesis, some aGPCR members lack the ability for autoproteolytic cleavage, thus further interrogating the functional relevance of this event as a pan-aGPCR activation mechanism. In order to further support their hypothesis and due to limited knowledge on the nature of a potential endogenous ligand for GPR116, the authors have undertaken an elaborate structure-function analysis of the relationship taking place between the cryptic peptide sequence and GPR116 receptor domains. Their experimental strategy focused on the peptide's ability to activate receptor functions by exploiting a cross-species characteristic underlining the human receptor's differential ability to transduce ligand-mediated intracellular signaling cascades compared to its mouse homologue. This mutational scanning strategy conducted on the tethered ligand peptide as well as on critical receptor domains identified in cross-species comparison led the authors to delineate the activation determinants that accompany the ligand-receptor complex in achieving a conformational state capable of engaging intracellular effectors leading to the modulation of intracellular calcium levels. While the lack of constitutive activity detected in the full-length receptor underlines an absolute requirement for extrinsic factors to induce physiological activating conditions (such as a yet unknown endogenous ligand, mechanical force or other) which consequently may point to a different pattern of ligand/receptor activation determinants, the description of residues constituting the intramolecular network of activation is not expected to deviate significantly from the data reported in this study. Although the study denotes the need for assays directly addressing the delineation of the binding pocket (such as binding assays among others), the data reported by the authors represent an initial assessment of structural requirements linked to the stabilization of an active conformational state. In the realm of GPCRs, this report is among very few recent studies highlighting the interplay between the agonist peptide and the second extracellular loop in aGPCRs, a description that unifies aGPCRs' structural features with the current knowledge on the binding and activation mode of GPCRs from other subfamilies. 

Although this study provides significant advancements for the field of aGPCRs it also contains noticeable scientific shortcomings, that would be good to attend to. 

Function of aGPCR cleavage in mammalian tissue physiology- Hint of the importance of aGPCRs' autoproteolysis is provided by the generation and analysis of the cleavage-deficient GPR116 H991A/H991A mouse line. However, a more complete in vivo functional strategy is needed in order to sort out a few important details. Of a contentious nature is the phenotype resulting from the replacement of Histidine991 by an Alanine. Although it is clear that this substitution leads to the loss of autoproteolytic activity, it is not clear if the actual substitution with alanine does not constitute the primary origin of the phenotypes observed in mice. In view of the high level of conservation of this amino acid within the GAIN domain of various GPR116 species as noted by the authors, one would have to try to discard the possibility of the loss of Histidine being the root cause of the changes and not the resulting loss of autoproteolytic activity. Also, a more detailed and systematic characterization of GPR116 H991A/H991A mouse line is warranted since the authors point to phenotypic similarities with GPR116 knock-out mice. 

Assessing agonist-receptor activation efficacy- As receptor constructs are expressed at varying levels throughout the study and because receptor activity is tightly correlated with cell membrane localization, it would have been helpful if the authors quantified receptor expression levels and incorporated this data as a normalizing criterion for their experiments throughout. This would greatly help to better understand the functional effects observed in the study. 

Correlating activation determinants with binding determinants- Peptide ligands are likely to possess multiple points of contact with their cognate GPCR, so the fact that single receptor point mutations are able to completely abolish receptor activation likely suggests that a critical activation network may have been perturbed rather than a complete loss of binding determinants from the receptor's ligand-binding pocket. Evidently, the reporter assay measuring calcium transients relates to the functional aspect of GPR116. This aspect represents the compound effect of various conformational states emanating from ligand interacting with the receptor's binding pocket and subsequent ligand-induced conformational transition allowing the receptor to couple to intracellular effectors. Thus, the mutational analysis presented here by the authors does not allow for a proper dissection of both events. Indeed, a given mutation could be impeding the receptor's transition to an active state while maintaining a proper ligand binding. As noted the C1088 mutation results in an unresponsive receptor probably because the active conformation cannot be properly reached and not necessarily because this residue is part of the binding pocket. Binding assays would be more appropriate to describe binding pocket determinants.

Reviewer #1 (Public Review): 

The authors generate a unique dataset profiling the transcriptomes of late-stage secretory endometrium from women with past pregnancies confirmed to be (i) with sPE or (ii) without sPE, divided into pre-term and full-term pregnancies. They divide the dataset into training and testing samples in order to identify a signature of sPE. Intriguingly, they observe long lasting anomalies in the transcriptomes of endometria from women with prior sPE, which indicates that elements of PE pathogenesis survive the end of the pregnancy. This observation is consistent with the fact that a previous preeclamptic pregnancy is one of the best predictors for a subsequent preeclamptic pregnancies. The authors then perform functional enrichment analysis on the signature, which appears to be related to hormonal signaling (i.e. estrogen and progesterone signaling). <br> Although this study presents a seemingly well-controlled dataset (with stratified controls at pre-term and full-term) and on a rarely assessed tissue (post-pregnancy endometrium), there are a number of concerns on the processing of the data and the statistical methods employed (for instance the use of fold-change thresholding, which is known to lead to extremely poor FDR control in standard processing pipelines). There is also not enough information about how some analyses were performed for full transparency and reproducibility of studies in the long-term.

Reviewer #2 (Public Review): 

Garrido-Gomez et al. perform RNAseq on human endometrial biopsies from women with a history of sPE and women who never had sPE, to better define the signature of decidualization that may lead to sPE. The authors report on 166 differentially expressed genes in sPE compared to control, and many of which are connected to hormonal signaling through progesterone receptor-B and estrogen receptor 1 pathways. Therefore, changes in these genes could connect some of the dots between decidualization impairment and the development of sPE during pregnancy. The strengths of this manuscript include the use of human endometrial biopsies, a training and validation set of samples and the rigorous analyses to define the decidualization disorder genotype/phenotype of sPE. While this is a mostly descriptive study, the conclusions are mostly well supported by the data; however, some aspects of the patient population need to be further explained. 

The clinical definition of sPE and whether it was early or late onset is not included anywhere in the manuscript. Please clearly define these variables and discuss how these pathways may impact the timing of disease onset. 

The conclusion (and title) that these gene signatures could be useful for preconception or early prenatal screening is overreaching since all these biopsies were collected from women who already experienced sPE and not from women who had yet to be diagnosed with PE. As risks of many diseases (i.e. cardiovascular and metabolic disease) increase in women with a history of PE, sPE itself might have a long-lasting impact on the endometrial environment, independent from decidualization.

Reviewer #1 (Public Review):

Boczek and colleagues have investigated the phase-transition properties of the RNA-binding protein FUS and its ability to form condensates in the presence of HspB8. The phenomenon of phase transition of proteins that contain structurally disordered regions or so-called low-complexity domains (LCDs) leads to condensates of increased local protein concentration that are also observed in a variety of (stress) granules, or other intracellular assemblages/"bodies" in a cell. The ability of a certain class of proteins to form such condensates provides a novel concept in molecular biology that might explain the local action of proteins in essential cellular pathways. Recently, several studies have addressed this phenomenon and found mechanisms, e.g. through protein-protein interactions and protein modifications that lead to, or disrupt, condensation of proteins.<br> In the work presented here, Boczek et al. introduce an interesting approach to monitor the properties of proteins in condensates and during protein ageing. The methodological novelty of this approach is the use of protein-protein crosslinking to study the conformation of the RNA-binding domain of FUS in condensates in the absence and presence of HspB8. In the past, such studies of proteins that undergo phase transition/condensation were performed mainly by using NMR.<br> I consider the approach of Boczek and colleagues to be an interesting one, which might make possible an alternative view of how protein properties change in phase transition.

Strengths of the work:

1. The presence of Hsp8B in stress granules containing FUS or hnRNPA1 protein has been observed in cells in previous studies, and the authors here provide an idea of the molecular basis of how the FUS and HspB8 protein conformations and their interactions change in condensates. Crosslinking might indeed be used as an alternative assay method to monitor changes in protein conformations and interactions in a time-resolved manner, and the results can moreover be quantified.

2. The authors provide a model of how HspB8 interacts with FUS, i.e. through interaction of the aCD domain with the RRM of FUS, and they show that the binding of FUS to RNA probably competes with its binding to Hsp8B.

3. The authors performed studies with the mutated HspB8 present in Charcot-Marie-Tooth (CMT) disease and found, by crosslinking and FRAP, that the interaction between FUS and mtHspB8 is strengthened by the mutation, indicating that this interaction is not reversed in the disease.

Weaknesses of the work:

This is almost entirely an in vitro study, lacking in vivo data to support the model.

Reviewer #2 (Public Review):

In this work, the authors aim to understand the nature of protein-protein and protein-RNA interactions within stress granules by means of time-resolved chemical cross linking coupled to mass spectrometry.

Through careful control of conditions through reconstituting stress granules in vitro using the archetypal phase-separating model protein, FUS, the authors develop an excellent system to allow detailed structural and dynamical interrogation of the interactions within droplets.

A particular focus is examining the role that the small heat-shock protein HSPB8 - known to be associated with stress granules - might have in preventing maturation of the FUS-rich droplets.

The authors demonstrate convincingly that HSPB8 associates with FUS, with arginines in the HSPB8 N-terminal region targeting it to the droplets, and the alpha-crystallin domain associating with the FUS RNA-recognition motif (in competition with RNA).

Excitingly, the authors observe that a pathogenic variant of HSPB8 associated with Charcot-Marie-Tooth Disease does not display the same effect as the WT molecular chaperone in stabilising FUS.

In sum, this study provides a clear mechanism by why HSPB8 interacts with FUS, leading to a general hypothesis for how small heat-shock proteins associated with phase-separation play a protective role in vivo.

Reviewer #3 (Public Review):

This manuscript describes a detailed investigation of the effect of aging in Fus-based droplets. These droplets can change from a liquid-like phase to a solid-like phase which is correlated with the transition from a normal cellular state as seen for example in stress granules to a disease state with fibrils being the end point. The authors use cross-linking techniques to characterize this aging process in structural detail and investigate the effect of adding small heat shock proteins. These chaperones can partition into the droplets based on their own unfolded domain and interact with the RNA binding domain of FUS via their a-crystalline domain. Through a series of clever mutation experiments the authors demonstrate the relevance of these interactions for preventing the transition into the disease, fibrillar state.

Reviewer #1 (Public Review):

In this manuscript, Ransdell et al study the gating mechanisms contributing to the generation of the resurgent sodium currents (INaR) in cerebellar Purkinje neurons. Using both experimental data and simulations, the authors propose that INaR is a result of fast inactivating sodium channels transitioning into an open state on membrane hyperpolarization and propose that the current decay is due to slow accumulation of recovered channels into another slower inactivated state. The data are compelling and uphold the idea that additional intrinsic gating mechanisms in NaV channels other than blocking particles can also generate INaR. The manuscript is well written supported by good quality data and analysis.

Reviewer #2 (Public Review):

This manuscript presents experiments on the kinetics and voltage-dependence of resurgent sodium current in cerebellar Purkinje neurons and a kinetic model for resurgent current. Much of the basic experimental characterization of resurgent current behavior repeats earlier data from Indira Raman and her colleagues, with no major differences (except for the magnitude of resurgent current relative to transient current, which is much larger here than in previous reports). There are also some new experimental protocols, notably those in Figures 5 and 8. The main point of the manuscript is a reinterpretation of the mechanistic basis of resurgent current. Raman and her colleagues suggested that the origin of resurgent current could be voltage-dependent open channel block by a blocking particle of some kind, with resurgent current representing flow of current through the channels when the blocking particle exits the channel on repolarization of the membrane. Here, the authors argue that that a particular kinetic model based on this idea presented by Raman and Bean (2001) does not explain some of the experimental behavior (notably decline of resurgent current during maintained depolarizations to strongly positive voltages) and present a different kinetic model that does account for this behavior, which is based on transitions of the channel between three distinct inactivated states. A key distinction is that in the Raman-Bean model, there are no direct transitions between the inactivated state corresponding to occupation by the hypothetical blocking particle and the "normal" inactivated state, so that channels have to pass through the open state in transitioning from the blocked state and the normal inactivated state, while in the new model, channels can transition between "fast" and "slow" inactivated states without opening. This correctly predicts that resurgent current declines during maintained depolarizations to +20 mV, while in the Raman-Bean model this does not occur, because channels stay in the blocked state at such positive voltages.

The work in the manuscript, and especially the suggestion that the phenomenon of resurgent current may not be tied to a blocking particle of some kind, seems to me to be interesting and significant, and at the least should stimulate further work on the molecular mechanisms involved. If the authors are correct, then searching for proteins with "blocking particle" motifs may be pointless. Personally, I would still favor a blocking particle mechanism, even after the results and arguments in this manuscript. It seems to me possible that simply allowing blocked channels to slowly inactivate with the blocking particle in the channel (which seems very plausible given that normal inactivation is now known to be an allosteric effect rather than occupancy the pore by the domain III-IV linker) could largely fix the limitations of the Raman-Bean model. Another argument in favor of the blocking mechanism is that behavior similar or identical to normal resurgent current is produced by free charged peptide pieces from the beta4 subunit, even if experiments have now shown that the beta4 subunit is not necessary for resurgent current in Purkinje neurons. However, the authors' arguments should at the least provoke new experiments - for example, it would be interesting to know whether "normal" non-resurgent channels blocked with beta4 peptide can inactivate while occupied by the blocking particle.

Reviewer #3 (Public Review):

INaR is related to an alternative inactivation mode of voltage activated sodium channels. It was suggested that an intracellular charged particle blocks the sodium channel alpha subunit from the intracellular space in addition to the canonical fast inactivation pathway. Putative particles revealed were sodium channel beta4 subunit and Fibroblast growth factor 14. However, abolishing the expression of neither protein does eliminate INaR. Therefore as recently suggested by several authors it is conceivable that INaR is not mediated by a particle driven mechanism at all. Instead, these and other proteins might bind to the pore forming alpha subunit and endow it with an alternative inactivation pathway as envisioned in this paper by the authors.

The main experimental findings were (1) The amplitude of INaR is independent of the voltage of the preceding step. (2) The peak amplitudes of INaR are dependent on the time of the depolarizing step but independent of the sodium driving force. (3) INaT and INaR are differential sensitive to recovery from inactivation. According to their experimental data the authors put forward a kinetic scheme that was fitted to their voltage-clamp patch-clamp recordings of freshly isolated Purkinje cells. The kinetic model proposed here has one open state and three inactivated states, two states related to fast inactivation (IF1, IF2) and one state related to a slower process (IS). Notably IS and IF are not linked directly in the kinetic scheme.

In my humble opinion, the proposed kinetic model fails to explain important experimental aspects and falls short to be related to the molecular machinery of sodium channels as outlined below. Still it is due time to advance the concepts of INaR. The new experimental findings of the authors are important in this respect and some ideas of the new model might be integrated in future kinetics schemes. In addition, the framework of INaR is not easy to get hold on with lots of experimental findings in the literature. Likely, my review falls also short in some aspects. Discussion is much needed and appreciated.

INaT & INaR decay:

The authors stated that decay speed of INaT and INaR is different and hence different mechanisms are involved. However at a given voltage (-45 mV) they have nicely illustrated (Fig. 2D and in the simulation Fig. 3H) that this is not the case. This statement is also not compatible with the used Markov model. That is because (at a given voltage) the decay of both current identities proceed from the same open state. Apparent inactivation time constants might be different, though, due to the transition to the on state.

Accumulation in the IS state after INaT inactivation in IF1 and IF2 has to proceed through closed states. How is this compatible with current NaV models? The authors have addressed this issue in the discussion. The arguments they have brought forward are not convincing for me since toxins and mutations are grossly impairing channel function.

Fast inactivation - parallel inactivation pathways:

Related to the comment above the motivation to introduce a second fast-inactivated state IF2 is not clear. Using three states for inactivation would imply three inactivation time constants (O->IF1, IF1->IF2, O->IS) which are indeed partially visible in the simulation (Fig. 3). However, experimental data of INaT inactivation seldom require more than one time constant for fast inactivation. Importantly the authors do not provide data on INaT inactivation of the model in Fig. 3. Fast Inactivation is mapped to the binding of the IFM particle. In this model at slightly negative potential IF1 and IF2 reverse from absorbing states to dissipating states. How is this compatible with the IFM mechanism? Additionally, the statements in the discussion are not helpful, either a second time constants is required for IF (two distinct states, with two time constants) or not.

Differential recovery of INaT & INaR:

Different kinetics for INaR and INaR are a very interesting finding. In my opinion, this data is not compatible with the proposed Markov model (and the authors do not provide data on the simulation). If INaT1 and INaT2 (Fig. 5 A) have the same amplitude the occupancy of the open state must be the same. I think there is no way to proceed differentially to the open state of INaR in subsequent steps unless e.g. slow inactivated states are introduced.

Kinetic scheme:

Comparison with the Raman-Bean model is a bit unfair unless the parameters are fitted to the same dataset used in this study. However, the authors have an important point in stating that this model could not reproduce all aspects of INaR. A more detailed discussion (and maybe analysis) of the states required for the models would be ideal including recent literature (e.g., J Physiol. 2020 Jan;598(2):381-40). Could the Raman-Bean model perform better if an additional inactivated state is introduced? Are alternative connections possible in the proposed model? How ambiguous is the model? Is given my statements above a second open state required? Finally, a better link of the introduced states to NaV structure-function relationship would be beneficial.

Reviewer #1 (Public Review):

This paper demonstrates that the collagen crosslinking proteins PLOD2 and LOXL2 are significantly increased in IPF and investigates the upstream pathway involved in their regulation; this paper is based on previous work from this group showing that collagen crosslinking is key in regulating collagen structure and function, and tissue stiffness, in human lung fibrosis progression. The authors interrogate several known fibrotic pathways and find that DMOG, a hypoxia mimetic, is the most potent up-regulator of PLOD2/LOXL2 and that activation of the hypoxia-associated HIF pathway is required for expression of PLOD2/LOXL2, possibly acting synergistically with TGF-beta in the up-regulation of PLOD2. Although the authors consider the fact that LOX and LOXL1 are also increased, and justify their focus on LOXL2 by its correlation with PLOD2 expression, it may not be justified to completely ignore LOX and LOXL1. Critically, collagen deposition appears to be independently regulated from its crosslinking. They confirm that HIF pathway activation leads to the structural and mechanical changes they previously described. Interestingly, they show using published gene expression datasets that this pathway is up-regulated in 2 fibroblast subpopulations found in idiopathic pulmonary fibrosis and that pathway activation is independent of oxygen status. This adds to a recent publication reporting that HIF activation can occur in normoxic conditions and indeed, the authors demonstrate that oxidative stress can activate this pathway and thereby collagen crosslinking in IPF fibroblasts (with consistent findings in human IPF tissue). These data collectively confirm that collagen crosslinking is a key component of the changes in IPF and elucidates the novel pseudohypoxic pathway that regulates it. The data are overall solid and convincing.

Reviewer #2 (Public Review):

Brereton et al. investigated the pathways potentially involved in the stiffening and accumulation of the ECM of lung fibrosis Following a study of the same group (Jones, 2018), they showed that Hypoxia-inducible factor (HIF) pathway was pathologically activated in sites of active fibrogenesis and responsible for " bone-type" pyridoline collagen cross-linking and altered collagen architecture. This alteration of the collagen architecture is responsible for the characteristic ECM stiffness of lung fibrosis responsible for ECM stiffness of lung fibrosis. In particular, they showed that LOXL2 and PLOD2 were responsible for collagen modification in the lung fibrosis foci.

Weaknesses:<br> 1. the evidence that LOXL2 and PLOD2 are involved in collagen cross-linking is indirect. The authors need to show direct involvement of LOXL2 and PLOD2, e.g. using siRNA to revert the stiffness phenotype. They also should show co-localization of LOXL2 and PLOD2 in area of more collagen deposition (COL1A1 and COL 3A) since there is a synergistic effect between TGF beta and HIF pathways in IPF foci.

2. The authors use primary lung fibroblasts for their experiments. They did not show any characterization of the donors for both healthy and IPF patients. The experiments of all the figures do not have always the right control. Since in IPF there is substantial genetic background, authors need to present data obtained from primary fibroblasts from at least 3 healthy as well as 3 IPF donors to support their hypothesis and conclusion. The selection criteria and characterization of the donors should be presented.

3. mRNA is not always a prediction of protein expression levels. The mRNA data have to be confirmed with immunostaining/WB data.

Reviewer #3 (Public Review):

This manuscript by Brereton et al. describes a specific role for Hypoxia-Induced Factor (HIF) in lung fibrosis. They discover that HIF is a critical factor that regulate post-translational modification of collagen, inducing abnormal 'bone-type' crosslinking, that cause the increased stiffness typical of fibrotic tissue. This mechanism is distinct from the known role of TGFb in increasing the collagen production in fibrosis.

HIF pathway was found increased in human fibroblasts from patients with lung fibrosis, and this was associated with reduced expression of Factor Inhibiting HIF (FIH). Furthermore, oxidative stress, known to be increased in lung fibrosis, promoted HIF activation in normal fibroblasts, and was also found co-upregulated with HIF in mesenchymal cells derived from IPF lungs. These results show a link between oxidative stress and HIF pathway as a modulator of aberrant collagen crosslinking in lung fibrosis.

The results of this manuscript are supported by well-designed experiments and clear results. The interpretation that HIF is acting in a pseudo hypoxic modality is not completely demonstrated by the data presented, but this do not reduce the importance of the finding that HIF is a critical modulator of crosslinking in lung fibrosis.

Reviewer #1 (Public Review):

ASIC channels are important physiological mediators of pain and have other functions as sensory transducers. Although several high-resolution structures of these channels exist, the terminal domains have not been resolved. Given that they likely mediate important molecular interactions, it is important to obtain at least a coarse-grained approximation to their positions and interactions within the channel.<br> Fluorescence methods such as FRET allow these kind of experiments and are used by Couch et at. to provide such an approximation in the ASIC1 channel.<br> The authors have made use of the ability of the DPA (dipicryl-amine) molecule to quench the fluorescence of fluorescent proteins (FPs) via FRET, providing a convenient molecular ruler.

The authors insert FPs in several positions in the N and C terminal regions of the channel and place DPA in the membrane. Estimation of the degree quenching allows to asses the relative position and eventually motions of the FPS with respect to the membrane.

Using this approach the authors have succeeded in estimating that the N terminus is very close to the membrane and the C-terminus hangs like a tail into the cytoplasm.<br> The attempt to measure relative motions of the FPs normal to the membrane, have, in my opinion, failed. The observed changes are to small and seem to be buried in statistical noise.

The paper however, is a nice collection of well executed experiments that provide interesting information and should be the basis of future, finer resolution, experiments aimed at understanding the dynamics of these intracellular regions.

Reviewer #2 (Public Review):

The authors set out to measure intracellular movements of ASIC channels using fluorescence techniques, and they have provided a detailed and thorough account. The manuscript is clearly and concisely written.

The authors measure proximity between the N and C terminals of individual subunits and the axial displacement of the same parts. They use previous work and exemplary control measurements to bolster their work.

This work outlines the scope of the movements of the intracellular termini, and provides a benchmark for future studies of its type. The manuscript represents the very best of careful, intelligent biophysics and cell biology.

Reviewer #3 (Public Review):

The structure of the acid-sensing ion channel ASIC1a, a proton-gated cation channel, has been determined in resting, desensitized, and toxin-bound open states. However, the intracellular N- and C-termini are not resolved in any of these structures. Couch et al. sought to outline their structures and any conformational changes associated with ASIC gating using FRET coupled with patch-clamp electrophysiology. The authors inserted fluorescent protein tags at the N-terminus and various positions on the C-terminus of ASIC1 and measured the distance between these tags and the plasma membrane using the membrane-embedded FRET acceptor dipicrylamine (DPA). Using fluorescence lifetime measurements, the authors demonstrated that the N- and C-termini of a given subunit are in close proximity to one another. By observing FRET between fluorescent proteins on N- and C-termini of the same ASIC subunit, the authors demonstrate that there is no substantial rearrangement between the intracellular termini during pH gating. However, they did observe that the N- and proximal C-termini do move relative to plasma membrane during the transition from the resting to the pH-desensitized state.

This paper should be of interest to those working on acid-sensing ion channels and of broader interest to those working on ion channels, receptors, and membrane protein structural biology. The study was well designed and the data are of high quality. The authors took great care to provide a conservative interpretation of their data. I have only minor concerns regarding sources of error, particularly with respect to interpretation of the small effects the authors observe in many of their FRET experiments.

• Figure 2D shows rather small changes in ΔF/F-15 mV between fluorescent protein labels inserted at different positions in the ASIC sequence, particularly for the YFP constructs. As this metric is determined from the top and bottom asymptotes for the Boltzmann fits shown in Figure 2C, it would be useful to have some estimate as to the error associated with the fits at extreme values. Perhaps the authors could provide fits to their data (as in Figure 2C), including confidence intervals, or some similar estimate as to the size of the expected error compared to the effect size in Figure 2D.

• Along those same lines, the authors use an interesting (and potentially generalizable) approach to reducing background from intracellular proteins in their experiments: co-transfecting their channels with empty plasmid DNA. What percentage of the remaining fluorescence signal is the result of intracellular background? How would that affect the data in Figure 2 and 3? Is the ΔF/Fnorm curve for YFP labeled positions in Figure 2-figure supplement 4 so flat because of contaminating background fluorescence?

• In Figure 3D, the FRET efficiency between CFP-cA1-cA1 and N YFP at a 1:15 ratio of the two plasmids is higher than the FRET efficiency between CFP and YFP in the same subunit, even though the authors conclude that fluorescent proteins on the same subunit show considerably more FRET than fluorescent proteins on neighboring subunits. Could this indicate that the N-termini of adjacent subunits are closer together than the N- and C-termini of a single subunit? If, on the other hand, this effect were entirely the result of crowding in the membrane why is FRET efficiency substantially lower when CFP-cA1-cA1 is co-expressed with C4 YFP? Wouldn't this construct produce a similar crowding effect?

• On page 23, the authors state that they detected no pH-dependent changes in FRET between their GFP tag on the N-terminus of ASIC1 and an RFP tag on the channel's C-terminus. However, Figure 4 shows a small, but significant change in fluorescence between pH 8 and pH 7.

• The interpretation of distances between various tagged position on ASIC and the plasma membrane in Figure 2 is based on using two different colored tags with two different distance dependences. However, the interpretation of the data from Figure 5 provided on page 25 is less clear. For example, the reduction in fluorescence from the N-terminal tag is interpreted as the tag moving closer to the plasma membrane. Without similar data from a YFP tag to verify, it seems equally likely that the reduction in fluorescence (at steady state) could result from a movement away from the plasma membrane.

Reviewer #1 (Public Review):

This study aimed to determine the rules governing the use of torpor and whether these rules differed between the breeding, fattening and migration periods of the annual cycle of the ruby-throated hummingbird. The authors evaluated changes in the relationships between evening body fat content and the torpor occurrence, torpor duration, time of torpor entry, and amount of energy expended before torpor entry during the breeding, fattening and migration periods. Authors showed that birds shift the rules on when to enter torpor depending on whether they are in a life-history stage where a lean body composition is advantageous such as during breeding or during times when accumulating energy stores is advantageous such as during migration: during breeding, ruby-throated hummingbirds go into torpor when body fat stores drop to 5% of body mass and are unlikely to enter torpor above this threshold within 75% of the night, whereas during migration they are more likely to enter torpor at high body fat content. Authors conclude that their findings demonstrate the versatility of torpor as an energy management mechanism throughout the annual cycle. They also suggest that torpor plays a role in driving pre-migratory increases in body mass during the fattening period.

This is a detailed and interesting study, however, it is not clear that this study has repeatedly and accurately defined a consistent rule governing torpor use in hummingbirds. Its findings, nonetheless, has implications for our understanding of the physiology of torpor.

Reviewer #2 (Public Review):

This study quantifies body composition, energy expenditure and torpor use in breeding and migrating Ruby-throated hummingbirds. The authors quantify body composition using QMR providing direct high-resolution measurements of fat storage. They additionally use flow through respirometry to measure energy expenditure allowing for accurate estimates of torpor use and night-time energy savings. Serial measurements across the breeding, fattening and migration seasons show how birds seasonally adjust their body fat threshold for torpor use (the adipostat) to support potential energy emergencies (e. g. cold snaps, storms that prevent foraging) during breeding season to a new higher fat setpoint during migration to support rapid fat accumulation to fuel migration.

A key strength of the paper is that the authors directly quantify body composition using QMR which provides rapid, repeatable, noninvasive measurements of body composition. Combined with the night-time respirometry measurements the study provides a really exciting and convincing picture of the changing context for torpor use with differing life history stages in hummingbirds.

This work also importantly provides insight into how climate change induced reductions in floral nectar with increased drought and higher temperatures during migration may be buffered by the increased use of torpor.

Reviewer #3 (Public Review):

This is an excellent study overall. It pushes frontiers with respect to our understanding of torpor. The design and methods are rigorous and well suited to some key questions in bird torpor biology: what determines the use, timing, and duration of torpor? The use of respirometry and QMR was a major strength of the study. The study convincingly uncovers an interaction between seasonal cycles and body fat in determining torpor use and bout length.

The study uses 16 wild Ruby-throated Hummingbirds that were each held in captivity during the course of a summer. The controlled conditions were necessary, but present both a strength and a weakness. The weakness is that environmental cues that may effect torpor - variation in temperature, precipitation, food availability, etc. - were not in effect (note: daylight cycles were varied in a semi-natural way, but that needed to be described in more detail). Body mass and composition was convincingly important for torpor initiation and length, but it was difficult to tell what factors caused variation in body mass and composition because all of the birds had ad libitum food; was it just individual variation? Were some birds more stressed than others by captivity? For this and other reasons, looking forward, I hope these investigators thought to collect genetic samples from each individual.

Reviewer #1 (Public Review):

This manuscript presents some new fossil remains from the lower back of one of the specimens of Australopithecus sediba, the Malapa Hominin 2 (MH2). The authors identified portions of four lumbar vertebrae (L1-L4), which refit with some previously found vertebrae. All in this, produce a nearly complete lower back of a female individual, which an invaluable finding to understand the functional morphology and evolution of purported adaptations to bipedalism in fossil hominins. They find that MH2 had both a lumbar lordosis and an intervertebral articular facets width from the upper to lower lumbar column ("pyramidal configuration") similar to that of modern humans. Also, they find that the overall vertebral shape is more similar to that of modern humans compared with that of great apes. These fossils allow other researchers to test existing hypotheses about the evolutionary process implied in the acquisition of obligate bipedalism.

Some of the conclusions of this paper are supported by the data, but other interpretations of the results need to be modified.

Strengths:

The paper present very important fossils and also includes a large comparative sample of lumbar vertebrae from modern humans and great apes. Also, includes other fossil remains from other species of hominins such as Neandertals, Australopithecus afarensis, and Australopithecus africanus. It also includes analyses from two methodologies, geometric morphometrics, and unidimensional linear and angular variables. This complete approach produces interesting and complementary results that give support to their conclusions.

Weaknesses:

The weaknesses of the paper are the lack of hypotheses and clear objectives of the work. Also, the methods are not explained in detail, which makes the paper hard to follow in some parts and difficult to replicate. The lack of hypotheses makes difficult to understand the use of some analysis. Finally, the interpretation of some results is not fully justified by the data, the authors need to focus on what all the results indicate, and not only on some of them.

Reviewer #2 (Public Review):

Williams et al. present newly discovered lumbar vertebrae of MH2 so that now almost the complete lumbar spine of this important australopithecine specimen is known. This allows better inferences for posture and locomotion in these early hominins than what was previously possible, particularly for lumbar lordosis. This study could, however, benefit from using the correct anatomical and taxonomical terminology (e.g., "costal process" instead of lumbar transverse process, and "australopithecines" instead of australopiths), and a more inclusive consideration of the literature. For example, it might help to discuss that Oreopithecus has been said to show a similar pattern of lumbar vertebrae wedging angles and pyramidal configuration of the articular processes, and that the same inferences for a human-like degree of lordosis have already been made previously based on the pelvic incidence of an alternative reconstruction of MH2. Moreover, lumbar lordosis (particular the wedging angles) should also be compared to the Homo erectus specimen KNM-WT 15000, and to the 95% range of variation of modern humans rather than to the 95% confidence interval for the mean of modern human lumbar lordosis. Finally, the authors could be more precise in saying to which vertebra of their great ape comparative sample they have compared the middle lumbar vertebra of MH2 and how they justify this.

Reviewer #1 (Public Review):

In this paper, Balsdon and colleagues test the hypothesis that perceptual decisions and confidence judgments are regulated by distinct (though overlapping) neural processes. Participants were required to make a categorical judgment regarding the orientation of a series of gabor stimuli while EEG data were recorded. In one condition participants made speeded judgments and in the other they were required to withhold their responses until the end of the sequence. The sequence length was varied to be shorter, the same or longer than the average response time of the speeded condition. In the delayed response condition, behavioural modelling indicated that participants tended to commit to their choice before the end of the longer trial sequence consistent with the imposition of a decision bound. In contrast, confidence judgments were better explained by an unbounded model in which evidence accumulation continued after the first-order choice. Neural signal analyses were also interrogated. Decoding analyses highlight earlier motor preparation in the longer condition consistent with premature commitment while regression analyses highlighted a corresponding attenuation of the neural representation of accumulated evidence. Meanwhile a distinct neural representation was associated with variations in the accuracy/optimality of confidence judgments and was found to persist throughout longer trials and was associated with distinct cortical generators.

This paper covers a very topical issue that would appeal to a wide range of readers and applies a number of very rigorous and sophisticated analyses.

At the outset the authors suggest that there are two major viewpoints on confidence representations - the first being that it is 'a mere consequence of perceptual processes' and the second being that it recruits specialised metacognitive resources. It is unclear whether this statement is made with reference to mathematical models of the decision process and/or our current understanding of the neural circuitry. In the case of the latter, the authors do not mention the extensive fMRI and lesion work pointing to a dissociation of the two. In the case of the former, the authors highlight the possibility that confidence judgments may be informed by additional sources of information that do not influence the initial perceptual choice. Here they include post-decisional accumulation as an example of 'an additional source of noise that does not influence the perceptual decision' but even 'first-order' models that are devised to jointly account for choice and confidence judgments through a single accumulation process assign a key role to post-decisional accumulation. Some reframing of the introduction would be beneficial to better clarify what the novel contributions of this paper really are with respect to this previous.

Reviewer #2 (Public Review):

This paper uses a clever manipulation of perceptual evidence streams to encourage people to make covert early decisions in a categorisation task (whether a stream of Gabors was tilted clockwise or counterclockwise). Modeling and behavioural data indicate that stimuli arriving after a covert decision continue to affect confidence, offering an opportunity to dissociate neural signatures of evidence accumulation and confidence formation. A "Free response" task allowed inference onto the status of (neural) evidence accumulation in the corresponding fixed-response conditions, via cross-classification analyses of EEG data. The results are consistent with previous findings that confidence is affected by post-decisional evidence accumulation. Sophisticated computational modelling here allowed the disentangling of neural representations of distinct phases of the perceptual decision process, from stimulus encoding to response preparation.

The key analyses focus on a distinction between decision and confidence encoding in the EEG data. The main approach here was to identify trials where computational model and behavioural data diverged - cases where behaviour (either choice or confidence, or both) either matches or mismatches the model on individual trials. By applying decoding techniques the authors were able to identify neural correlates of these suboptimalities. One concern here is that if the behavioural data deviates from a noise-free ideal-observer model, it's not clear what neural correlates of these deviations mean. One interpretation could be that they indicate subjects are using a different model - in which case identifying neural correlates of deviations are less informative. Another interpretation is that they are deviating from the assumed model on a fraction of trials, but if this is the case, analysing these deviations will not be able to identify neural correlates of the latent variables of the (otherwise well-functioning) model. In other words, it is not clear whether these analyses are identifying latent states tracking noise in a confidence representation (confidence in confidence?), latent states underpinning (psychological) confidence, or something else.

Understanding this is important to set the current findings in the context of previous work. For instance, classical lesion approaches have also revealed a distinction between confidence and performance in both humans and animals (eg Lak et al., 2014 Neuron). Others have used neural decoding approaches to dissociate confidence and performance-related activity (eg Cortese et al., 2016 Nat Comms). And other work has examined the relationship between post-decisional evidence samples and neural encoding of confidence (eg Murphy et al., 2015 eLife; Fleming et al. 2018 Nat Neuro). A key advantage of the current work in relation to previous studies is the use of sophisticated computational modelling to identify neural correlates of latent model variables. But it's ambiguous as to whether that approach is actually telling us about confidence, given the focus on suboptimalities. The discussion of the paper states clearly that the focus here is on confidence precision not magnitude, but the findings are then interpreted as revealing neural correlates of confidence (eg in the title and abstract, "computation of confidence"). To support this claim (and tackle issues highlighted above) it may be useful to decode confidence magnitude from the same data, and ask whether confidence precision modulates these or different signals.