Reviewer #2 (Public Review):

In the report by Brandao A. et al the authors used a zebrafish adult tail fin regeneration model to elucidate the role of metabolic adaptation in cell fate transition and cell proliferation during regeneration with a focus on bone regeneration. Firstly, the authors used transgenic reporter bglap:GFP to label mature osteoblasts and co-immunostaining with a pre-osteoblast marker runx2 to show that within 6 hours post amputation, osteoblasts show signs of dedifferentiation giving rise to pre-osteoblasts that re-enter the cell cycle between 12 - 24 hpa. The authors then use evidence from gene expression changes, metabolomic analysis, pharmacological perturbation experiments, cell proliferation analysis and histological detection of lineage markers to demonstrate that an immediate metabolic switch from OXPHOS to glycolysis precedes blastema formation in amputated tail fin stump. Importantly, blocking glycolysis with 2-DG suppressed mature osteoblast dedifferentiation and proliferation as well as blastema formation which resulted in failure of tail fin regeneration. In summary, this study has shown that a rapid metabolic switch from OXPHOS to glycolysis immediately after tissue damage is important for subsequent bone regeneration, and more specifically the authors provide evidence to show that glycolysis is required for both dedifferentiation and for cell proliferation, both processes are crucial for appropriate blastema formation.

This study established that metabolic switch is an early response to tissue damage and metabolic adaption is key for cellular responses during bone regeneration. The conclusions of this study are well supported by data provided. There are some details of data mentioned in the text that should be clarified.

The authors used Microarray transcriptome analysis to demonstrate dynamic gene expression response in 6hpa OBs compared to 0hpa OBs and stated on page 6 line 154 - 159 that at 6hpa, OBs undergo dramatic gene expression changes with 2200 differentially expressed genes, and a set of genes related to energy metabolism was also dramatically altered. However, in supplement figure 1 there was no mention of which genes related to energy metabolism are altered, there is no real data as to what kind of gene expression changes are happening in 6hpa OBs, because DE gene list is not provided. Do the gene expression changes reflect partial dedifferentiation of OBs? The authors should provide more details of their microarray analysis, or at least provide the DE gene list.

Reviewer #1 (Public Review):

This manuscript describes a new electrophysiological method to manipulate and control the capacitance of the cell membrane. Capacitance is an important property of the membrane of all cells, in particular neurons as applied in this study. Cell membrane capacitance determines how a cell responds to inputs from connected neighbors, and how it generates the activity that the networks it operates in relies on. In this study, the authors have described the theoretical/mathematical basis for the tool they developed, have developed the software necessary to run it (in two separate software platforms), and have tested the accuracy and the limitations of the method. Their results are thorough and clear. The applications that they discuss are interesting and potentially useful for a wide range of experimentalists especially in neuroscience.

Reviewer #2 (Public Review):

In the present article, Pfeiffer and colleagues describe a new development of the dynamic-clamp, capacitance clamp, which allows to mimic modifications of capacitance in biological neurons. The authors first demonstrate the feasibility of the technique in a computational neuron model and then apply it to dentate gyrus granule cells, showing that capacitance clamp can be applied to real neurons displaying a complex morphology with multiple compartments and associated capacitance values. While the "classical" dynamic-clamp systems have been used to simulate changes in synaptic inputs, passive and active intrinsic conductances, Pfeiffer and colleagues demonstrate that changes in membrane capacitance, another biophysical parameter influencing neuronal activity, can also be simulated to understand the specific impact of this parameter on neuronal output. As such, this tool is undeniably novel, especially since specific pharmacological manipulations of capacitance are very difficult to achieve. However, as mentioned by the authors, membrane capacitance per unit area does not appear to vary much across neuronal types, species, development, or specific physiological and pathological conditions. The main changes in membrane capacitance reported in the literature are those associated with neuronal growth, in which case it cannot be dissociated from changes in the overall input conductance (passive and active) of the neuron. Considering that membrane capacitance per unit area is essentially constant in all investigated contexts, the main concern about the present study is not on the novelty of the tool but on its potential use to address relevant biological questions.

Reviewer #1 (Public Review):

Phillips and Rubin investigated the biophysical mechanisms that underly the generation of respiratory related burstlets and bursts in the rhythmogenic pre-Botzinger circuit. They show, suing a computational modelling approach, that synaptically mediated intracellular calcium concentrations and Ca2+ release and uptake via the endoplasmic reticulum are a unifying mechanism of the generation of the respiratory rhythm and its recruitment at the motoneuronal level. This explanatory model is able to unify contradictory experimental findings and is further evaluated by modelling the effect of opioids on the generation of burstlets and bursts in pre-Botzinger complex.

The conclusions of this paper are mostly well supported by a sound computational modelling approach, however the current computational modelling data are largely based on experimental data of very few workgroups, while previous modelling approaches and experimental data that support anatomical network connectivity as a key feature for respiratory rhythm generation and transmission of burstlet/bursts to motorneuron pool were neglected.

The current model is exclusively focused on biophysical properties and in particular calcium dynamics to generate a unifying computational model for respiratory rhythm generation. However, a previous model and experimental data suggest the emergence of rhythmogenic activity in pre-Botzinger complex may be largely determined by the local network connectivity as well as by the connectivity of the pre-Botzinger complex with the extended medullary and caudal pontine respiratory circuit. It would be interesting if this crucial component of a truly unifying model could be added or at least needs to be discussed appropriately.

Reviewer #2 (Public Review):

Since its isolation in the transverse slice in 1991 (1), researchers have studied the preBotC with a focus on 2 related questions: how respiratory rhythm is generated, and how this rhythm is transformed into the pattern of inspiratory bursts, which are recorded at hypoglossal rootlets (XIIn), and in more intact preparations, from cervical ventral roots. The discovery of burstlets has provided support for the conjecture that the preBotC can be functionally parcellated into rhythm-generating and pattern-forming networks (2).

Others have proposed that burstlets are rhythmogenic (2-4), via a stochastic percolation mechanism that synchronizes tonic spiking into burstlets, which in turn give rise to inspiratory drive. Phillips and Rubin challenge this account for its lack of detail and argue that it is weakly supported by features of burstlets that can be otherwise accounted for (burstlet onset and slope; percolation of activation following photostimulation of network subsets). Their points are well-taken, but two points need to be made. First, the percolation conjecture has gained traction because other more conventional mechanisms for rhythmogenesis have been shown not to apply, as nicely summarized in (4, 5). In particular, rhythmogenesis arising out of the activity of endogenous bursters (as is the case in their model) has failed to find empirical support: optical recordings of respiratory networks under conditions of synaptic blockade have not revealed appreciable numbers of endogenous bursters (this despite the fact that groups are recording from slices expressing genetically-encoded Ca2+ indicators in glutamatergic neurons in preBotC, using two-photon microscopy), older studies using conventional patch clamp methods identified negligible numbers of endogenous bursters (6, 7), and pharmacological disruption of specific endogenous bursting mechanisms has not silenced respiratory rhythm in the slice (8). Second, and more importantly, the processes that they model mediate the amplification of burstlets to bursts, and thus don't have obvious bearing on whether or not burstlets are generated by stochastic percolation or endogenous burster activity. As they state (lines 605-607) "the findings about burstlets and bursts presented in this work would have been obtained if the burstlet rhythm was imposed (Fig. 1) or if burstlets were generated by some other means". Thus a discussion of the inadequacies of current percolation models of respiratory rhythmogenesis seems to be irrelevant to their main points, and fails to acknowledge that other more plausible mechanisms have been set aside because they were undermined by experimental findings. They should either explain why a percolation-based mechanism for the emergence of burstlets is incompatible with their model for CIRC-mediated amplification of burstlets to network bursts, or they should remove these arguments, which distract from their main findings.

It would be useful if the authors could elaborate on the applicability of their findings to less reduced preparations. The emergence of burstlets as well as the transition from predominantly burstlets to predominantly bursts is strongly dependent on network excitability, which is controlled by varying [K+]bath. Importantly, in their simulations burstlet activity falls silent at [K+]bath < 4 mM, and robust motor output only emerges for [K+]bath > 8 mM. While these modeling results jibe well with experimental results in the slice preparation, in more intact preparations, robust and stable respiratory rhythm is maintained at physiological levels ([K+]bath = 3 mM); in intact animals, 9 mM [K+]o is lethal. This raises the question of whether their model has explanatory power for respiratory rhythmogenesis in more intact preparations, or whether it is limited to describing fictive respiration in the slice.

Another aspect to this problem is that the rhythmogenic mechanism they have incorporated in their model has strong dependence on [K+]bath; this straight-forwardly accounts for the transition from quiescence to burstlets, since within their models, endogenous bursters are quiescent at lower excitability levels. Of greater interest is the extent to which the [K+]bath dependence of the transition from burstlets to inspiratory bursting is again due to their choice of endogenous burster implementation. This is important, because it might enable CIRCA-mediated transitions from burstlets to network bursting that show less steep voltage dependence, and which are robust at more physiological [K+]o, thus enhancing the generalizability of their model. This was certainly a feature of an early study, in which I(CAN) was proposed as a mechanism for endogenous bursting, with weaker voltage dependence than the I(NaP)-based endogenous bursters implemented here (7).

1. Smith JC, Ellenberger HH, Ballanyi K, Richter DW, Feldman JL. Pre-Botzinger complex: a brainstem region that may generate respiratory rhythm in mammals. Science. 1991;254(5032):726-9.<br /> 2. Kam K, Worrell JW, Janczewski WA, Cui Y, Feldman JL. Distinct inspiratory rhythm and pattern generating mechanisms in the preBotzinger complex. J Neurosci. 2013;33(22):9235-45.<br /> 3. Ashhad S, Feldman JL. Emergent Elements of Inspiratory Rhythmogenesis: Network Synchronization and Synchrony Propagation. Neuron. 2020;106(3):482-97 e4.<br /> 4. Kallurkar PS, Grover C, Picardo MCD, Del Negro CA. Evaluating the Burstlet Theory of Inspiratory Rhythm and Pattern Generation. eNeuro. 2020;7(1).<br /> 5. Del Negro CA, Funk GD, Feldman JL. Breathing matters. Nat Rev Neurosci. 2018;19(6):351-67.<br /> 6. Del Negro CA, Koshiya N, Butera RJ, Jr., Smith JC. Persistent sodium current, membrane properties and bursting behavior of pre-botzinger complex inspiratory neurons in vitro. J Neurophysiol. 2002;88(5):2242-50.<br /> 7. Thoby-Brisson M, Ramirez JM. Identification of two types of inspiratory pacemaker neurons in the isolated respiratory neural network of mice. J Neurophysiol. 2001;86(1):104-12.<br /> 8. Del Negro CA, Morgado-Valle C, Feldman JL. Respiratory rhythm: an emergent network property? Neuron. 2002;34(5):821-30.<br /> 9. Del Negro CA, Johnson SM, Butera RJ, Smith JC. Models of respiratory rhythm generation in the pre-Botzinger complex. III. Experimental tests of model predictions. J Neurophysiol. 2001;86(1):59-74.

Reviewer #3 (Public Review):

A key contribution of this study includes a demonstration that two sets of neurons coupled via excitation can drive network activity similar to that observed in mXII nerve during breathing. In particular, the authors formulate a link between synaptic excitation and intracellular calcium induced Ca2+ release mechanisms via positive feedback from ICaN. This forms the basis for recruitment of non-rhythmogenic neurons by rhythm generating neurons. Such a formulation seems to help explain the burstlet theory and support a percolation theory of network bursts put forward in the field.

The manuscript is well written. Figures and figure legends are clear, and justify the results stated. Methodology is well laid out; however, missed references in many places where it is not clear where the equations came from (e.g., equations 19 through 24 and elsewhere). The authors state that the model code "will" be available on ModelDB. This should instead be submitted with the manuscript for review. Later, the code must be made available on a GitHub repository for wide dissemination and future updates by others. ModelDB has models which can only be downloaded but not extended for wider use. Oftentimes this leads to lack of technical help for future users and limits model use and enhancement.

The authors put forward a plausible mechanistic explanation for Ca2+ dependent recruitment of non-rhythmogenic neurons by linking synaptic excitation from the rhythm generating neurons to CICR in the former. Although attractive, there are numerous factors which control intracellular Ca2+ signaling and buffering. It would be important to clarify whether the assumption that dendritic depolarization due to synaptic inputs directly contributes to CICR as postulated in this model (the term PsynCa*Isyn in equation 19), has any (direct/indirect) empirical support either in preBotz neurons or elsewhere to ensure that this is not purely conjectural.

Reviewer #1 (Public Review):

Berra and colleagues revisit several mechanistic questions mainly centered on the accessory helix of mouse complexin (mCpx) and its contribution to the 'fusion clamp' property of mCpx whereby mCpx-SNARE interactions prevent full assembly and subsequent membrane fusion. This clamping function is believed to help generate a metastable pool of release-ready vesicles at the synapse, and it has been studied in a wide variety of systems including mouse, fly, worm, squid, fish, and diverse in vitro biochemical preps over the past ~ 20 years. The authors derive several conclusions from their efforts, but most relevant is a reiteration of a previous proposal that the accessory helix region of mCpx stabilizes a pre-fusion clamped state via interactions with SNAREs.

Strengths:

There are several attractive features of this study. The single-vesicle fusion assay (which this lab has now published several papers on) provides an impressive minimal model fusion system with nearly complete control over all the relevant components including their absolute and relative concentrations, as well as a quantitative assessment of both docking and vesicle persistence time prior to fusion using mouse, worm and fly Cpx orthologs. The authors substantiate a claim made in a previous publication that efficient clamping by Cpx requires some sort of kinetic delay in vesicle fusion by employing DNA assemblies to slow fusion via a means entirely independent of SNARE assembly. By changing the Cpx concentration, the authors reveal a range of dose-dependent clamping that may be important for interpreting results from previous Cpx studies and further support the notion that the C-terminal domain of Cpx plays a role in its local enrichment.

Weaknesses:

A major selling point of this study is its reassessment of a long-standing conundrum in the complexin field: what is the mechanism of Cpx acting as a fusion clamp and why does its function appear to be so different between species? The results described here more or less echo previous claims using this new in vitro assay without providing much resolution. One the one hand, this in vitro assay suggests that mCpx1 is a potent fusion clamp while fly and worm orthologs are less efficient. Whereas using the currently best available synaptic data, one would conclude that the reverse is true: fly/worm Cpx is a potent fusion clamp while mCpx doesn't strongly clamp. Given that the apparent discrepancy between vertebrate and invertebrate Cpx mechanism remains an outstanding question in the field, it was a bit of a missed opportunity to compare them. Did the worm and fly Cpx proteins perform poorly because they were not paired up with their conspecific SNAREs and Syt1s? Would mCpx1 fail to clamp efficiently using invertebrate SNAREs/Syt1? We know that mCpx doesn't suppress spontaneous neurotransmitter release efficiently in the fly (Xue 2009, Cho 2014) or worm (Wragg 2017). And fly Cpx potently suppresses spontaneous fusion in mouse cultured neurons much more than mCpx (Xue 2009), suggesting that, in the context of synapses, fly Cpx can efficiently clamp. None of these past examples specifically and precisely controlled for expression levels, so a rigorous comparison has yet to be done. However, this current study does not clarify these long-standing questions that have confounded the field for years. Moreover, while this in vitro assay has many advantages, it is not entirely clear whether it is a sufficiently good model of clamping at the synapse to resolve some major questions. Perhaps using pre-assembled tSNAREs rather than relying on accessory fusion proteins such as Munc18 and Munc13 and proteins that remodel/diassemble the SNARE complex such as NSF/alpha-SNAP lead to significantly different energy barriers for SNARE assembly and fusion. In this scenario, the relative impact of Cpx may differ from its impact at the synapse. Regardless of these details, the study is technically nice and provides some interesting assessments of various Cpx accessory helix mutations and invertebrate Cpx for researchers of synaptic molecular mechanisms to explore further.

Reviewer #2 (Public Review):

Fast SNARE-mediated membrane fusion triggered by calcium is tightly regulated by accessory proteins including synaptotagmin and complexin (Cpx). In this paper, the authors used vesicles containing ssDNAs to demonstrate the clamping effect of Cpx. Moreover, besides previous known clamping effect of Cpx variants, the author also showed that Cpx doesn't promote triggered fusion with 1 mM Ca++. Several major concerns have to be addressed; particularly, the high Ca++ level and low effective Cpx concentration are two critical technical issues.

Reviewer #3 (Public Review):

This work builds, albeit incrementally, on three recent papers - Ramakrishnan 2018, 2019, and 2020 - the most recent of which was published in eLife. The eLife paper led to an interesting model in which complexin was found to operate synergistically with synaptotagmin, with synaptotagmin delaying fusion long enough for complexin to clamp most of the SNARE complexes and synaptotagmin clamping the rest in a Ca2+-releasable form. The goal of the current manuscript was to gain a better understanding of this interesting system.

What then are the main contributions of this manuscript? First, for the purpose of delaying fusion long enough to allow the complexin clamp to form, the authors are able to replace synaptotagmin with duplex DNA. Unlike the complexin/synaptotagmin machine, however, this system once clamped cannot proceed to fusion, so there's no way to know whether, or in what way(s), the clamped state resembles the physiologically clamped state.

Second, the authors compare two Ca2+-triggered fusion reactions, with and without complexin. (In the absence of complexin, synaptotagmin will clamp provided it is superstoichiometric to the SNAREs.) They conclude that complexin does not contribute to the probability or speed of Ca2+-triggered fusion. However, they only test a single, high concentration of Ca2+ that leads to almost complete fusion in both cases, leaving open the possibility that at physiological Ca2+ they would find a difference in release probability after all. As for the speed of fusion being the same, the authors acknowledge that they don't have the temporal resolution to measure that speed - it's basically over in a single time step, so again it's not possible to know whether the speeds with and without complexin might differ.

The other main goal of the paper is to gain more insight into the molecular nature of the complexin clamp by studying complexin mutants. The authors favor the "zigzag array" model they proposed in 2011, but I the new data really move the needle very far in resolving that debate. All but one of the mutations studied compromise complexin's clamping ability. As the authors know, it is relatively easy to break a machine, and that would seem especially true when all of the mutants they studied involve quadruple substitutions (or a quadruple insertion) within a single ~25-amino acid stretch. Thus, although the results seem to be consistent with a zigzag array model, they cannot in my view be used to rule out alternative models (such as the model proposed by Malsam, 2020).

I am puzzled by the authors' claim (in the Abstract) that their in vitro work "establishes" the "physiological relevance" of the complexin clamp. Since no physiological experiments are performed, it would seem that such a claim must be based on new and improved agreement between experiments performed and literature data. Yet I was unable to find evidence of this.

Finally, how is the reader to evaluate the claim (lines 288ff) that "the only plausible way for both CPX(cen) and CPX(acc) to interact with the SNAREpins is if CPX interacts with different pre-fusion SNAREpins as observed in the pre-fusion CPX-SNARE X-ray crystal structure (Kummel et al. 2011)"? Doesn't Malsam et al. (2020), Fig. 5 (which seems to be undercited) present an alternative model?

Reviewer #1 (Public Review): 

This manuscript describes a large field experiment in which diverse maize lines were grown under high and low nitrogen conditions. The authors surveyed the root microbiome and performed several different analyses in search of evidence of host control over the microbiome. These analyses include clustering the 16S amplicon sequencing data into 'microbial traits', searching for evidence of heritability and selection, GWAS and transcriptomics. The final section of the paper looks for patterns across the different analyses and highlights a single microbial trait, a corresponding GWAS hit and evidence that one allele correlates with both microbial abundance and plant health. 

The strengths of this manuscript include an impressive dataset, nicely compiled figures and an impressive compilation of distinct analyses to reduce complexity and reveal interactions. This manuscript is interesting on its own and will also serve as a template for future analyses on similar or distinct datasets. In several places, this manuscript could be improved by more clearly articulating the logic behind choices made for the analyses (see specific comments below). 

1. How the 'microbe traits' are defined is critical for interpreting the rest of the paper and so needs to be explained more clearly in this manuscript. From the text, I interpret that ASVs were first clustered into genera and then further clustered based on differential abundance between high and low N in both years. If so, this would seem to exclude a bunch of microbes that were specific to the 202 genotypes planted only in the second year. However, Supp Fig 2 shows a handful of 'microbe traits' that are not differentially abundant (grey). Please clarify. Also, the authors should clarify how the 'names' for each microbe trait defined. 

2. Throughout the manuscript, I expected more consideration of which microbe traits overlap between the different analyses. This comes in only at the final figure and so we never get a sense of how the different analyses overlap. For example, 150 microbe traits are defined and assessed for heritability and evidence of selection. I was not clear on whether the traits that were found to be heritable overlapped with those under selection (as one might expect). Lines 387-395 and Supp Fig 5 attempt to synthesize the different analyses but should be expanded to help the reader understand overlap between the analyses. 

3. I gather that the authors performed the GWAS separately for each 'microbe trait' and nitrogen condition, but then searched for 'hot spots' where the data from different microbe traits was considered in a pooled manner. The logic behind this decision is not clear to me. Why would we expect different microbe traits to be co-localized in the genome? Line 346-349 indicates that no plant loci were found to associate with microbe 'traits' under both nitrogen treatments and speculate as to why (dynamic interactions or not enough statistical power). The traits were defined based on robust differential abundance between nitrogen treatments and if I understand correctly, the GWAS was run separately for each trait and nitrogen treatment, so it seems logical that this method would only yield microbe trait associations that are differential between nitrogen treatments. If I did understand this correctly, I recommend emphasizing this point as it seems to indicate that the methods are working as expected. 

4. Having access to expression data for 298 genotypes is amazing. It would seem logical to try to more directly connect the MAPLs and microbe traits with this expression data. Do the lines that show association with the microbes also show higher expression of the corresponding gene? The authors generated additional RNAseq data from 2 week old plants from 4 genotypes but the logic for the selection of these lines is missing and I am not sure about the relevance of this since the samples were collected from young plants. Is a nitrogen treatment effect observable at 2 weeks? The authors conclude that the gene expression data is consistent with host control over root microbiome (line 371-373) but, as is, I'm not convinced that this analysis supports that statement. Fig 3C is striking on its own, but based on panel B, I suspect that a similar pattern would be observed for 'third leaf' and 'germinating shoot' so it is harder to make a direct connection with the microbe traits. 

5. The authors report that 62 microbe traits associated with canopy coverage, a very exciting result! However, again, this confuses me based on how the microbe traits were defined. To be considered a microbe trait, the microbes had to show differential abundance across the treatments. The logic for how this could manifest in phenotypic changes in both treatments needs to be elaborated. 

6. The final figures summarizes correlations for one microbe trait across the different analyses and looks very promising, especially for noisy field data. The authors are careful to not overstate this finding, perhaps a bit too conservative. They see a significant correlation between microbe abundance and canopy coverage that also correlates with allele frequency. The difference in canopy coverage by allele frequency is not significant, but shows a similar trend and this is not necessarily surprising given all the other factors that will influence this one trait. I expected a comment on gene expression of the genes in the locus and perhaps a peek at the other plant traits to see if any of them also show a similar trend.

Reviewer #2 (Public Review): 

This study reports on a field experiment done with 230 maize genotypes under 2 nitrogen fertilization regimens. The abundance of each taxon within the root microbiota of over 3000 samples were used to perform two separate analyses: (i) a genome-wide association study (GWAS), using the abundance of each taxon as a quantitative trait and (ii) a phenotypic study, identifying microbial taxons correlated with different plant phenotypes (mainly canopy coverage). 

This is an impressive manuscript that will help pave the way for a better understanding of heritability in the plant microbiota, which is a major open question in our field. In massive screening studies such as this one, it is often challenging to deliver a concise take-home message or a mechanistic insight. Here, the actual functions of "MAPL" genes are not looked at in detail, besides looking at their expression profiles across the plants (which is an interesting analysis in and of itself). I therefore urge the authors to make their data, in the form of supplementary tables, as accessible as possible for follow-up studies on the loci that they detected. 

This manuscript will provide a valuable and nearly unprecedentedly large dataset on the genetic control maize has on microbiome assembly. 

One methodological aspect that is unclear here, is how were the 150 "rhizobiome traits" defined? This seems like a taxonomic classification, but in this version it not made clear how it was performed. 

A second and related comment, is why are the only "rhizobiome traits" that the authors consider taxon abundances? Other quantities can be extracted from this dataset: alpha diversity, beta diversity (e.g the 1st and 2nd axes of the ordination). Perhaps a measure of absolute abundance can also be extracted from the ratio of bacterial to plastid/mitochondrial reads. These would provide a more holistic picture of how plant genotype influences the microbiome composition. 

There is some disconnect between the GWAS and the phenotypic comparisons. Indeed, the authors show that heritability is correlated with the correlation with canopy coverage, but I would assume that much of the phenotypic differences observed in the first place are a result of the genotypic variability. However, no unified model that accounts for the relationship between plant genotype, microbiome composition and plant phenotype is attempted. <br /> The relative abundance and the prevalence of ASVs is essentially ignored, beyond the initial screening described in the appendix. It would be important to know for example if heritable taxa are also abundant ones?

Reviewer #3 (Public Review): 

Strengths: 

The choice of 230 genotypes from a well-known maize diversity panel, with accompanying SNP genotype data, was a good one for this purpose, given the focus on selection during intensive breeding during the 20th century in heavily fertilized conditions. 

The very large dataset (N>3000 with replication of 230 genotypes) is a useful source of information on maize rhizosphere bacterial microbiomes, and the availability of the host genotype SNP data is an especially useful and unusual feature. The authors used a relatively newly developed (2018) Bayesian computational approach to characterize genetic architecture of rhizosphere composition, which is an interesting advance in the microbiome field. The same tool makes inferences about whether each SNP underlying rhizosphere features shows signatures of past selection (inferred as the variation in effect size relative to the minor allele frequency). 

Weaknesses: 

The BayesS results classifying rhizosphere-related SNPs as under positive, negative, or no selection appear to be over-interpreted. First, it is not clear that this method is meant for comparing current patterns of selection in contrasting environments (as in this N+/N- experiment), but rather for detecting signatures of selection in the distant past. Second, it IS clear that this method only reveals signatures of selection on a locus or SNP, and cannot confirm that selection is acting on a particular trait. The Zeng et al. 2018 paper states this quite clearly. The authors of this manuscript did not attempt to rule out that the loci classified as under selection do not have pleiotropic effects on (or are linked to) traits other than rhizosphere microbes. Occam's razor in this case suggests that these loci control root traits that are important for plant survival and also happen to affect microbiome composition. No functional benefit of these microbes was demonstrated beyond correlations with plant phenotypes. 

It is unclear how substantially different the N+/N- treatments were from each other. The entire experiment followed commercial corn, so presumably all plots had been fertilized within the past year. The soil chemical profiles were not subsequently tested, and basic analyses (such as comparison of plant growth in the two treatments) are missing. Furthermore, the assumption that microbiome differences between fertilization treatments are driven by some activity of the plant host (lines 94-99) is not justified - direct responses of the microbes to N addition would almost certainly be reflected in the rhizosphere, since rhizosphere microbiomes are almost entirely derived from the surrounding soil. No bulk soil samples were collected as controls to rule this out. 

The authors report how various patterns differ between the N+ and N- treatments- for example, more rhizobiome features had nonzero heritability in N- than in N+. However there is no statistical support for this apparent difference, i.e. no direct test of heritabilities in the two treatments. Nor does they test for possible differences (or lack thereof) in the magnitude of heritability between treatments. This incomplete style of analysis was repeated several times in the paper, e.g. for comparing patterns of selection between treatments, and for comparing correlations between rhizobiome features and plant traits between features. 

The methods sections contain inconsistencies and omissions that made it difficult to evaluate some of the claims. For instance, lines 147-148 describe collection of rhizosphere samples from entire root crowns, but the appendix (lines 774-778) describe collection of rhizosphere from roots that fit in 50 mL tubes. So it is unclear which part of the root crown was actually used, and whether the focal root type was consistent for all samples. Similarly, the appendix states that the B73xMo17 check genotype was used to correct for small-scale geographic differences (747-748), but no additional detail is provided nor are the results of this process reported. In general, the descriptions of statistical analyses lack important details. For example, by definition a constrained ordination (CAP) analysis requires a formula to be specified, but this was not reported in the paper, making it impossible to interpret the meaning of the constrained axes shown in the figures. Ordinations also require the use of a distance or dissimilarity metric, the choice of which affects interpretation - the metric used in this paper was not provided. 

Finally, many of the analyses throughout the paper take the form of testing 150 different rhizobiome traits, one by one, and then reporting the number of significant results (e.g., differential abundance between N+/N-, significant heritability, selection, correlations with plant traits). This suggests a potentially severe risk of false positives due to repeated multiple testing. After the p-values are corrected for the very large number of statistical tests (using Bonferroni, FDR, or similar) many of the conclusions might change.

Reviewer #1 (Public Review): 

The prefrontal cortex and the hippocampus are key brain regions for a number of major cognitive processes. While the direct neural routes connecting these brain regions have been well documented in the hippocampus-to-prefrontal direction, top-down messages from the prefrontal cortex to the hippocampus were thought to be essentially indirectly conveyed. The discovery of a direct prefrontal cortex-to-hippocampus pathways was thus a major and surprising discovery (Rajasethupath et al., Nature 2015). This has not really been confirmed so far by any subsequent work. The present study attempted to examine this issue by relying on multiple viral tracing strategies, both retrograde and anterograde, and also by analyzing data currently available in the Allen brain atlas. No evidence in support of the existence of this projection was found which raises important methodological issues. The work conducted here is rigorous and the authors clearly invested a fair amount of efforts to find out about this projection. Off course, a formal demonstration that something does not exist is logically impossible but the present attempt has a clear value. 

As strengths, they authors relied on multiple and complementary viral approaches to examine the extent to which the anterior cingulate cortex may directly contact the hippocampus, using both a rabies-based approach (as in the original paper) and retroAAVs for the retrograde strategy and more standard AAVs for the anterograde work. The fact that all these approaches were effective and consistent in reporting known direct hippocampal projections from multiple brain regions (other than the ACC) demonstrate that the present work has value. The confirmation from data mining in the Allen brain atlas adds further to this. 

Regarding potential weaknesses, I was a bit concerned by the low number of starter cells apparent in figure C (rabies-based approach). If very few are actually encountered, it somehow undermines the approach. A more complete description of the number of starter cells considered could clarify this issue. In addition, only pyramidal cells were considered here but long-range projecting GABA neurons have been recently reported in the prelimbic cortex (preprint from Malik et al., 2021) which suggests that this could be a possibility in the anterior cingulate cortex as well. So even if the starter cells identified in the present study were sufficient to detect other inputs to the hippocampus, I am not sure it is sufficient to completely rule out the existence of a scarce and potentially inhibitory projection to the hippocampus. I was also wondering whether the use of more classic but well established retrograde tracers (e.g. Fluorogold, Ctb) may have been useful here as viral vectors often come with their own limitations and specific tropisms and the particular retrograde viral vector used here was found to only bring quite disappointing findings (basically "off-target" labeling). Taken together, these two issues thus leave the entire retrograde approach with some possible flaws. I think it would be important that the authors clarify this by further discussion and/or new data/analyses.

Reviewer #2 (Public Review): 

The study by Andrianova et al. on the existence or non-existence of projections from the anterior cingulate area to the hippocampus provides a confirmation of the non-existence of this direct projection. Earlier data had not shown the existence of this connection; this was confirmed by the current experiments. The provided data are supporting the claim of the manuscript. The important difference between their data and the earlier "suggested" connection is a more careful analysis of possible labeling in neighboring brain areas which can easily lead to incorrect conclusions. Even so, it should be pointed out that proving something does not exist is harder then showing something could exist. Furthermore, they do point out that anterograde/retrograde tracers do not work exactly the same. Multiple tracers should always be used to confirm data. Finally, the putative direct connection was shown not to exist; this is important for modeling the flow of data between the hippocampus and anterior cingulate area.

Reviewer #1 (Public Review):

In this interesting and clearly written paper, which continues a line of work that originated with two 2019 papers from the same authors, Anderson et al. investigate the transcriptional diversity of microglia in developing mouse retina and how this diversity is impacted by phagocytosis of apoptotic neurons. Using single-cell RNA-seq they find substantial diversity among retinal microglia at P6/7 that echoes the diversity of microglia in early postnatal brain. Unlike in the brain, few P6-P7 retinal microglia are in the well-characterized homeostatic resting state. Instead, based on their gene profiles, the authors surmise that most retinal microglia are in a phagocytic (or "remodeling") state. Striking evidence for this hypothesis was provided by scRNA-seq experiments in which neuronal apoptosis was eliminated (using Bax mutants). When there are no apoptotic neurons to engulf, the authors find that most microglia become homeostatic, with matching reductions in remodeling cell clusters. Another interesting set of experiments investigates microglial survival factors and the role of TAM receptors Mer and Axl in phagocytosis/survival. Surprisingly, unlike previous reports in brain, Mer and Axl have distinct roles in retina. Mer is needed for clearance of apoptotic neurons, as expected, but Axl is important for microglial survival, especially in the context of Csf1R blockade.

Reviewer #2 (Public Review):

In the article titled "Neuronal apoptosis drives remodeling states of microglia and shifts in survival pathway dependence" the authors describe the heterogeneity of the microglial population in the early postnatal retina. In WT mice, the authors find both "homeostatic" microglia (expressing Tmem119 and P2RY12) and several clusters of "remodeling" microglia (expressing lysosomal and phagocytic components). In a Bax KO mouse lacking the significant apoptosis that usually occurs in the early postnatal retina, they find a decrease in the remodeling clusters of microglia, concluding that the lack of apoptosis in this mouse prevents the shift in microglial phenotype from homeostatic to remodeling. Several of these "remodeling" clusters in WT mice are relatively enriched after short term PLX3397 treatment, a CSF1R inhibitor commonly used to deplete microglia in mouse.

This paper provides interesting new phenotyping of retinal microglia and how they change with Bax KO and PLX treatment and has potential to be an impactful contribution. In particular, the idea that microglia engaged in tissue remodeling might be less CSF1R dependent could guide the development of new treatments and improve our fundamental understanding of eye development. At present, the central claim and primary new finding compared to a prior study from this group (Anderson et al 2019, Cell Reports), is that the identified remodeling clusters are not only CSF1R independent, but more specifically that expression of remodeling genes confers this survival advantage. On initial submission, this very interesting claim is partially supported by the data presented, but this support is greatly limited by technical issues (a lack of biological replicates for single cell data), and a lack of assessment of whether clusters inferred to be performing remodeling functions are actually doing so.

Reviewer #3 (Public Review):

The strength of the manuscript lies in its thorough and comprehensive characterization of multiple microglia states in the developing postnatal mouse retina. By the mean of scRNAseq analysis, the authors addressed the question of which environmental cues drive such heterogeneity.

This work allowed the identification of 11 microglia states that coexist in wild type postnatal retina, ranging from a so-called homeostatic state to some remodeling states, with for instance a disease-associated signature. The proportion of the different microglia states is context-dependent and the authors demonstrated that the spectrum of homeostatic to more remodeling clusters is driven predominately by neuronal apoptosis (resulting from waves of neuronal cell death during the postnatal period in the retina). On the other hand, microglia-expressed recognition receptors, Mer or Axl, required for clearance of apoptotic cells and survival in the absence of CSF1R signaling, respectively, do not mediate changes in microglia remodeling gene expression.

The manuscript is extremely clear and the data are thoroughly described. They perfectly support the main conclusions. Although the finding that developmental apoptosis impact retinal microglia gene signature is not novel (a manuscript from the authors and based on bulk RNAseq analysis was published in Cell Reports in 2019), the comprehensive scRNA-seq clustering analysis is an important addition, providing a valuable resource for the field.

As true for most of scRNAseq analysis, the authors deal with lists of many markers and names of ligands/receptors, so a good knowledge in the field is advised for an easy reading of the manuscript.

Reviewer #2 (Public Review):

In their manuscript, Gill et al describe a novel reprogramming approach by transient expression of Yamanaka factors in fibroblasts up to that maturation phase of reprogramming to test for rejuvenation of fibroblast. Guided initially by epigenetic clocks, it was determined that transient expression of Yamanaka factors in the range of 10-17 days resulted in a likely optimal level of rejuvenation while still maintaining their fibroblastic nature. The data supports this conclusion very well. There are some shortcomings with respect to characterization of the extent of the functional rejuvenation of fibroblasts and identification of limits of the protocol.

Reviewer #1 (Public Review):

Gill et al. investigated organismal rejuvenation, an emerging topic in longevity and aging research. They explored the timing of Yamanaka factor expression and developed a transient reprogramming-withdrawal protocol that supports restoration of cellular identity. They also used omics methods to prove that the transcription profile and epigenetic memory of fibroblast identity are preserved, while the age profile is reversed. They marked an outline of the genes specifically related to the rejuvenation of the epigenome and transcriptome.

This study fills an important gap in our knowledge and presents a fascinating finding that human cells may achieve age reversal without going to the full pluripotency state, which further offers an intriguing insight that the reversal of biological age can be somehow separated from the reversal of cell differentiation status. This manuscript should be of great interest to scientists working on aging, epigenetics and development.

1. Figure 1A discusses Horvath et al. multi-tissue and skin and blood clocks but more clocks could be applied. It is recommended to add Hannum et al., Levine et al. PhenoAge, Lu et al. GrimAge, and Teschendorff epitoc2 clocks.<br /> 2. The study reports substantial differences in DNA methylation and chronological ages, which might be due to passage number. The passage number of these cells should be listed, if possible. Additionally, there seems to be a deviation when applying EPIC chip data to the Horvath et al. clock compared to its original platform. The authors may address in the Methods section whether this inconsistency has been addressed.<br /> 3. It is discussed that fibroblast morphology is reversed. It would be good to quantify this morphological dynamics. For instance, whether cell size undergoes transition from mesenchymal to epithelial lineages and if any reversal is observed.<br /> 4. The findings relate to cells >39 years old. Would the same significant effect be observed in younger cells?<br /> 5. Citation 32 should be updated. Also, are there any common genes responsible for both rejuvenation and transient reprogramming?<br /> 6. Figure 1F (the PCA figure) has a disproportional percentage of PC1 and PC2. As PC1 represents the reprogramming trajectory, does PC2 have any obvious biological meaning?<br /> 7. Of the cells that failed to reprogram, do they undergo cellular senescence? What are their predicted ages?<br /> 8. It would be interesting to investigate multiple rounds of transient reprogramming, and the maximum number rounds that could be achieved.<br /> 9. Ohnishi et al. 2014 could be cited as a key article, which should not be missed when discussing the oncogenicity of these genes.<br /> 10. There are only 6 rejuvenation genes overlapping in figure 4f. Would DNAm age be reversed if all 6 genes are overexpressed? Would it erase cell identity?

Reviewer #3 (Public Review):

In this manuscript, the authors investigate the potential of cellular reprogramming for the rejuvenation of age-associated phenotypes in human fibroblasts. Importantly, compared to previous studies in this field, the authors go one step further in the degree of induction of cellular reprogramming by expressing the reprogramming factors for a longer time until the maturation phase (MPTRs). Using this approach, they demonstrate not only the rejuvenation of fibroblasts at the transcriptional and epigenetic level but also the restoration of cellular identity following partial dedifferentiation. First, the authors showed that DNA methylation age is progressively decreased following the expression of the Yamanaka factors using a doxycycline inducible lentiviral system. In addition, they demonstrate that although fibroblasts transiently lose their identity during reprogramming to the maturation phase (transient reprogramming intermediate), this identity is recovered after the expression of the factors has been terminated following doxycycline withdrawal. Subsequently, analysis of DNA methylation at promoters and enhancers associated with fibroblast identity, reveals what they authors refer to as epigenetic memory, which might be responsible for the restoration of fibroblast identity following termination of transient reprogramming. Lastly, the authors elegantly demonstrate the rejuvenation of human fibroblasts following reprogramming at the transcriptional and epigenetic level using clocks based on these analyses, as well as the restoration of epigenetic marks and expression of dysregulated genes associated with fibroblast function.

This is very interesting manuscript that follows up on previous studies demonstrating the amelioration of age-associated phenotypes by cellular reprogramming. Although the concept presented in the manuscript is not completely novel, the authors try to go one step further by investigating the rejuvenation of aging phenotypes and recovery of cellular identity following a more extensive and longer reprogramming protocol. This manuscript elegantly reinforces the potential of cellular reprogramming for the rejuvenation of aging and proposes a hypothesis for the restoration of cellular identity after cellular reprogramming based on the existence of a certain degree of epigenetic memory in the cells. This manuscript will be of interest to scientist working in cellular reprogramming, aging and epigenetics. Nevertheless, before publication the authors would need to address the points stated below:

Major points:

- The timeline of induction and termination of cellular reprogramming by addition and withdrawal of doxycycline is not very clear across the manuscript. This is a critical aspect of the manuscript that should be better explained at the beginning of the results as well as in the methods section. Specifically, the authors only refer to the time of analysis following withdrawal of doxycycline in the legend of Figure 1B. Even there, the authors mentioned that "Sorted cells were also further cultured and grown in the absence of doxycycline for at least four weeks". The precise timeline of induction and termination of reprogramming by addition and withdrawal of doxycycline is critical for the whole message of the manuscript and therefore should be explained in much more detail. How long after doxycycline withdrawal were the transient reprogrammed fibroblasts analyzed?

- In the same line, the authors described in the methods section that transient reprogrammed sorted cells were replated on irradiated mouse embryonic fibroblasts (iMEFs) in fibroblast medium without doxycycline. Were the negative control cells processed in the same way and plated on top of iMEFs? Was there any effect observed due to the growth of fibroblasts on top of iMEFs? How long were the cells cultured on top of iMEFs before culturing under normal conditions? Could the authors explain the rationale for the use of iMEFs during this protocol?

- There is a certain degree of contradiction between the results shown in some sections of the manuscript as well as the discussion of the results. In one hand, the authors claim that reprogramming can rejuvenate fibroblasts not only at the transcriptional but also at the epigenetic level, more specifically at the level of DNA methylation, and that this rejuvenation is maintained even weeks after the induction of reprogramming has stopped. On the other hand, the authors propose that epigenetic memory based on the absence of changes in DNA methylation in promoters and enhancer of fibroblasts-related genes allow the restoration of cellular identity following reprogramming. Why epigenetic age does not display this memory? Why some regions will maintain DNA methylation memory but the site used for the analysis of epigenetic age by DNA methylation show changes in methylation status and therefore rejuvenation? The authors should discuss in more detail this important aspect of their data.

- In Figure 3A the changes induced by reprogramming in the PC2 direction look orthogonal to the aging changes observed in PC1. Could the authors maybe use a more stringent criteria for the selection of age-related genes. How do they explain the direction of these changes? In the same line, have the authors tested the effect of MPTRs in young fibroblasts? Could young fibroblasts be included in Figure 3C?

- Based on the standard protocols used for the culture of fibroblasts using culture medium containing fetal bovine serum (FBS), I hypothesis that the recovery of cellular identity following reprogramming is mainly due to differentiation signals coming from factors present in the medium. For these reasons, knockout serum (KSR) is used at later stages (day 8) of reprogramming to allow generation of iPSCs. The authors should rule out the possibility that recovery of fibroblast identity is due to the culture of reprogramed fibroblasts in FBS containing medium. For this purpose, the authors should test whether the fibroblast identity can be recovered following doxycycline withdrawal by culturing the cell in KSR or 1% FBS containing medium instead of 10% FBS. This is a very important concept for the message that the manuscript tries to communicate regarding an epigenetic memory responsible for the recovery of fibroblast identity.

Minor points:<br /> - The authors claim that their maturation phase transient reprogramming protocol (MPTRs) induces further rejuvenation compared to previous studies because of the use of a longer induction timeline. Nevertheless, the authors mentioned that cells took around 50 days to reach a fully pluripotent state. This is a very long timeline to reach pluripotency compared to previous studies inducing reprogramming in mouse or human fibroblasts where typically iPSCs can be generated after 2 or 3 weeks following expression of the reprogramming factors. In the same line, secondary systems based on the use of cells isolated from transgenic mice carrying a cassette for the expression of Yamanaka factors have been shown to be very rapid and efficient in the generation of iPSCs. For these reasons, the authors should not use during their discussion a direct comparison with previous studies based just on time of induction since it is possible than systems with a more efficient or higher expression of the reprogramming factors and therefore a much more rapid alteration of cell fate have been used in previous studies.

- In Figure 1D, principal component analysis shows "Not reprogramming" fibroblasts represented by a cross that cluster between young, old, and rejuvenated fibroblasts. What cells are the authors referring to? Are these cells represented in the diagram included in Figure 1B? Why do they cluster differently compared to the other fibroblasts?

- The authors propose in the discussion that the use of transient reprogramming protocols like the one presented in the manuscript could be used in vivo to safely induce the rejuvenation of tissues and organs. Although previous studies have shown that this in fact the case, the authors should be aware that the recovery of cellular identity might be easier to achieve in vitro where differentiation signals like the ones in FBS are present during in vitro culture but not present anymore in an adult fully differentiated animal. For these reasons, the authors should be cautious when discussing the potential of in vivo application of these approaches and their safety.

- The figure legend of Supplementary Figure 2B shows iPSC but this population is not present in the figure.

Reviewer #1 (Public Review):

Oxygen consumption in mitochondria by the respiratory chain leads to a major source of reactive oxygen species. Mitochondrial glutathione is an important line of defence against free radical production. The ABC transporter Atm3 exports oxidized glutathione from the mitochondria to help maintain a suitable reducing environment. Here, the authors have determined structure of Atm3 in multiple conformations by single-particle cryo EM and have revealed new insights into local changes coupled to substrate export. They conclude that a lack of cysteine residues in Atm3 is a feature to avoid mixed-disulfides that could impair export, and that substrate-bound complexes are only transiently occupied in the presence of ATP.

Strengths: The structures of Atm3 are well resolved and the paper is very well written. The use of principle component analysis (PC) to assembly the different type IV exporters together with Atm3 is insightful and highlights that i) nucleotides are required for the stabilization of the closed, occluded, and outward-facing conformations and ii) transport substrates and related inhibitors have only been observed associated with inward-facing conformational states.

Weaknesses: The cysteine-exclusion hypothesis of Atm3 is interesting, but lacks experimental validation. The described local changes by TM6 in Atm3 are appreciated, but the coupling with substrate binding needs to be better clarified.

Reviewer #2 (Public Review):

The structural features are highly reminiscent of previous studies on the Atm subfamily of type I ABC exporters and type I ABC exporters in general, and therefore offer confirmation and reinforcement of current knowledge and not completely novel discoveries. Nevertheless, the in-depth analysis of the obtained data in light of ABC exporters (in general in particular those of the Atm subfamily) make this paper a very interesting read. The manuscript text as well as the figures are logical and easy to grasp. The paper contains two interesting learnings, namely that cysteines are not found close to the GSSG binding site within the Atm1-subfamily and that the closed conformation is not a unique discovery for NaAtm1. At the technical level, the work had been carefully executed (apart some open questions regarding the cryo-EM maps of the closed and outward-facing conformation).

Reviewer #3 (Public Review):

The authors aim to understand the functional roles of Atm transporters, a family of ABC transporters with cellular localization of mitochondria and play important roles in transition metal homeostasis. To understand their functions, the author captured the structures of Atm3 from plant Arabidopsis in several functional states by cryo-EM--inward-facing, inward-facing with substrate GSSH bound, nucleotide bound closed state and nucleotide bound outward-facing states. Although many of those functional states have been reported for Atm orthologues in other species, the authors did elegant analyses on how the rareness of cysteine residues in the transport pathways could be very important for efficient glutathione transport. Moreover, the authors systematically show the unlikelihood of capturing an ABC transporter with both substrate and nucleotides bound. Although conceptually, a functional state with substrate and nucleotides bound should be very transient to avoid backflow of the substrate, it is wonderful that the authors put in the effort to capture such state and present a systematic principal component analysis (PCA) to show it may not be possible to attain such state structurally. The usage of PCA on analyzing a plethora of ABC transporters with different functional states could be applicable to other systems.<br /> The conclusions of this paper are mostly well supported by data, with some aspects of analysis and discrepancy that need to be addressed.

1) In Figure 1, the authors report the ATPase activities of AtAtm3 are quite different in detergent and membrane environments, with a more than 10-fold decrease in basal activity from detergent to POPC containing nanodiscs. Is this composition a good representation of Arabidopsis mitochondria membrane? The nanodisc belts in Figure S4 looks quite tight. Could that contribute to the decrease in ATPase activity? In one of the papers you cited, Schaedler et. al, 2014, ATM3's activity is not stimulated by GSH versus in your data, 10mM GSH strongly stimulated AtAtm3. How would you explain the discrepancy? In terms of data analysis, the fit for 10mM GSH and 2.5mM GSSG ATPase activity in nanodiscs is poor visually, the addition of goodness of the fit in the figure could help the reader to assess the real quality of the data.

2) There are a couple of structural studies on Atm transporters available to date, and the authors did a couple of overall structural comparisons in Figure S3 and S5. However, it is still not clear what exactly those systems are similar or dissimilar, and what are new structural insights gained with these structures. It could be helpful to compare the substrate-binding sites side by side or have a cartoon representation of different functional states in different systems. The authors brought this reader's attention to "a ~20 amino acid loop between TM1 and Tm2 of AtAtm3 that would be positioned in the intermembrane space and is absent from the structures of ScAtm1 and NaAtm1" without further explanation. Does this loop have any proposed functional roles? Is it present in other Atm or ABC transporters?

Reviewer #1 (Public Review): 

Han et al. present a straight-forward discovery and description of an amino acid transporter that is locally required for neurotransmitter synthesis in Drosophila photoreceptor axon terminals. The question is somewhat specific: where does the precursor histidine come from to synthesize histamine? But as the authors argue, a specific amino acid transporter that locally generates the required precursor for local monoamine transmitter synthesis has not been shown for any other system. 

Strengths/novelty include: (1) this is a local phenomenon, providing precedence for other systems that uptake of a precursor and synthesis of a monoamine transmitter is a local business and may locally be exploited at synapses to regulate transmission. (2) a previous paper from 2008 in J Neurosci (Ni et al.) published a mutation in the TADR gene that leads to neurodegeneration. The authors now made a clean CRISPR null and it has no degenerative phenotype. They convincingly show a specific defect in histamine-dependent transmission. They further provide a strong biochemical characterization of the histidine uptake role for TADR and in vivo rescue with other histidine transporters. 

Weakness: there is no claim to generality, and little discussion of how similar the system may be to other, better known monoamine transmitter systems. As such the scope of the work may be limited. On the other hand, the entire synthesis and recycling pathway for histamine as a monoamine transmitter in an in vivo system may prove valuable to other systems in principle.

Reviewer #2 (Public Review): 

The manuscript by Han et al. describes the identification of TADR as an axonally localized transporter for Histidine, the direct precursor of the Histamine neurotransmitter used by photoreceptor cells in Drosophila. A role of TADR in vision was first identified in an RNAi screen and confirmed by a CRISPR mutant and rescue experiments. These findings, as well as the experiments supporting a role of TADR as histidine transporter and its localization to photoreceptor axons, convincingly support the main conclusions of the authors. 

The critical role of this axonal histidine transporter in vision are conceptually new and will be of interest to many neuroscientists. Somewhat unfortunately the authors are not able to resolve the conflicts with previously published results about a different TADR allele with structural phenotypes in photoreceptors (PMID: 19074021). However, given the convincingly documented and quantified experiments in the current manuscript, I believe it is warranted to just let this difference stand at this time. 

Major comments:

1. Despite the strong ERG phenotype, some 50% of the TADR mutant flies still show behavioral responses in the phototaxis axis, strongly arguing for a pathway acting in parallel to TADR. Comparison to a known blind mutant, such as HDC could clarify this issue. 

2. Such a second pathway is also consistent with the level of histamine still present in TADR flies. Although curiously this issue is not specifically addressed by the authors, the level appears to be significantly higher than in HDC mutants (Figure 5D). This should be addressed. 

3. Beyond referring to a "complete disruption of the tadr locus", the molecular details of the mutant should be better explained: Does the mutant result in an in-frame or an out-of-frame fusion of exon3 and 5? What parts of the protein are deleted?

Reviewer #3 (Public Review): 

The authors present a well-executed series of experiments and a convincing set of data supporting the idea that tadr is a histidine transporter essential for the function of photoreceptor cells. The manuscript nicely complements other papers from this group and others on transporters required for histaminergic signaling at photoreceptor cells. Indeed, this is an important paper for understanding synaptic signaling in the fly visual system. This is an important paper for understanding neuronal activity in the fly visual system anwill be of great interest to people in this field.

Reviewer #1 (Public Review): 

NPM1-mutated acute myeloid leukemia (AML) is a frequent AML subtype for which new therapeutic approaches are needed. Immunotherapy may represent a promising strategy, to be combined with or alternative to chemotherapy. Particularly, vaccination strategies may be successful in NPM1-mutated AML, as mutant NPM1 is known to be immunogenic and specific T-cells may control disease relapse. In this study, Tripodo et al. performed a handful of experiments to demonstrate the feasibility and the antileukemic efficacy of a dendritic cell (DC) vaccine armed with neutrophil extracellular traps (NETs) derived from NPM1-mutated myeloid cells. 

While this work has the major strength of being novel and performed in vivo, the models used in the study, the number of replicates and the quality of some of the data presented seem insufficient. 

One of the major issues is the lack of a formal demonstration of clear antileukemic activity of this approach in leukemic mice. The authors first used a non-leukemic model, then, in their second set of experiments, a subcutaneously injected AML model. In this regard, I am worried that no effect may be seen in AML models where leukemia is engrafted in the bone marrow or in leukemic genetically modified mouse models. Furthermore, it is unclear what would be the place of NET-DC among other DC vaccines, as there is no direct comparison in this study. 

Other issues include replicate numbers that need to be increased in some experiments, data representation which is not always appropriate and one panel which has been duplicated from a previously published work.

Reviewer #2 (Public Review): 

Here, the authors successfully demonstrated the immunogenic effects of a new dendritic cell-based vaccine on AML with NPM1 mutation. Using a transgenic mouse model that carries human NPM1 mutation and an elegant bone marrow chimera approach, they showed that the tumor growth is reduced by the vaccine. Furthermore, they provided evidence for increased CD8+ T cell presence and activity at the tumor site and increased anti-NPMc antibody levels in the serum. These findings are timely and novel and the new methods presented here would be of interest to a broad audience from immunology, inflammation and cancer fields.

Reviewer #1 (Public Review):

The manuscript by Shortill and colleagues describes a new sorting nexin heterodimer in budding yeast comprised of a Vps9 (Rab5-GEF) domain-containing, VARP-like protein (Vrl1) and a previously uncharacterized SNX-BAR protein the authors have named Vin1. The authors provide strong evidence that Vin1 and Vrl1 form a heterodimer, driven primarily by the ankyrin-repeat domain in Vrl1 binding to the N-terminal region in Vin1. This is a surprising result because both proteins have BAR domains and one would have assumed that these domains mediated heterodimerization as shown for other PX-BAR proteins. The protein-protein interaction studies were thorough and yielded novel insights into VINE structure and function.

The authors also find evidence that Vin1 interaction with phosphatidylinositol 3-phosphate (PI3P)-containing membranes facilitates the Vrl1/RabGEF-dependent activation of PI 3-kinase, suggesting a potential mechanism for amplifying PI3P production on endosomes or vacuoles. These conclusions are based on indirect readouts (such as GFP-FYVE or Vps26 endosomal localization are used as a proxy of PI3P levels) and a few assumptions (there is good genetic support for the Vrl1 Rab-GEF activity but no direct biochemical evidence), but the approaches are standard for the field and support the conclusions well.

Finally, the authors also present evidence that the Vrl1-Vin1 complex sorts a mannose-6-phosphate receptor-like (Mrl1) protein in the endosomal system although the data supporting this conclusion is not as strong. The differences in Mrl1 localization between cells with or without Vrl1 are subtle and not quantified in a manner that would clearly indicate a mislocalization phenotype. The title, abstract and final model of the manuscript emphasize this role of the Vrl1-Vin1 complex in protein sorting. Therefore, additional supportive data on this point would significantly strengthen the manuscript.

Reviewer #2 (Public Review):

This is an interesting study by Shortill and colleagues exploring the function of the yeast Vps9-domain family member Vrl1, building on previous work from the same lab identifying Vrl1 as a distant homologue of the mammalian retromer-interacting Rab GEF VARP. It presents an interesting combination of yeast genetics, biochemistry and imaging with molecular modelling using the AlphaFold2 machine learning algorithm to predict protein domain structures and protein-protein interactions and validation of these prediction in cells. Major findings include that (i) Vrl1 contains not only RabGEF and ankyrin-repeat domains but also PX and BAR domains with some homology to the retromer-interacting SNX-BAR proteins Vps5/Vps17, (ii) Vrl1 forms an obligate complex with another poorly studied SNX-BAR protein Vin1/Ykr078w required for recruitment and activity at yeast endosomes, (iii) surprisingly, the interactions between Vrl1 and Vin1 are primarily mediated through Vin1 N-terminal sequences interacting with the AnkRD domain of Vrl1, and (iv) this complex is important for mediating tubulovesicular transport of the transmembrane cargo Mrl1. The authors conclude these proteins cooperate to regulate Rab activation and cargo transport at endosomes, and propose to name this the "VPS9 GEF-Interacting Sorting Nexin" or VINE complex. For context I have expertise in the structural biology and biochemistry of endosomal trafficking proteins and have some experience using AlphaFold2, but I do not have specific technical expertise in the yeast assays reported here.

The paper is well written, and I found the data to be convincing and supportive of the final conclusions. I did not find any major weaknesses, and I think with minor changes this will be of great interest to those in the field of membrane trafficking and the role of sorting nexin proteins in endosomal sorting. The use of AlphaFold2 also provides an interesting example of how this novel tool can be used to guide molecular and cellular studies of protein function.

Reviewer #1 (Public Review):

The study simultaneously explored the associations of self-reported egg consumption with plasma metabolic markers, and of these markers with the risk of CVD in a nested case-control study in the China Kadoorie Biobank (CKB). In general the authors achieved their aims, and the results support their conclusions. The authors found that egg consumption was associated with several metabolic markers, and these associations were directionally opposite to associations between these metabolites and risk of CVD. The major strengths of the study include relatively large sample size, accurately identified CVD and its subtype events, collection of as many covariates as possible, and the quantification of a wide range of metabolites based on NMR platform. These results not only potentially reveal at the small molecule level that lipid metabolism metabolites may play a role in the beneficial effects of egg consumption on CVD, but also provide Chinese population-based evidence for the formulation of strategies and policies to encourage egg consumption. The causal roles of lipid metabolites in the association between egg consumption and CVD risk can be not verified because of the cross-sectional nature of the study.

Reviewer #2 (Public Review):

The submitted manuscript by Pan et al discusses the associations of self-reported egg consumption with plasma metabolic markers and these plasma metabolic markers with the risk of cardiovascular diseases.

1) The current study did not consider the difference between hypercholesterolemia patients and participants without hypercholesterolemia. Diagnosed hypercholesterolemia patients preferred to eat less egg, which is the richest source of dietary cholesterol as the authors mentioned. Hypercholesterolemia patients also had a higher risk of cardiovascular diseases. The potential bias should be considered.

2) Whether all the dietary assessments of participants in this study had reliable quality? The authors do not address the information about the exclusion of any unreliable dietary questionnaires.

3) Based on the results of this study, eggs could be a component in a healthy diet. However, the current evidence of egg consumption based on observational studies was not consistent. It is not appropriate to appeal for more health education and health promotion strategies and policies to encourage egg consumption.

Reviewer #1 (Public Review):

The paper is novel and informative; the authors' conclusions are supported by the data as shown. The paper is significant as it unequivocally proves that the key function of proline during bone formation is being incorporated into proline-enriched proteins rather than contributing to other metabolic processes.

The quality of the data is outstanding. However, it would be helpful to provide better documentation of whether and how proline affects replacement of cartilage by bone during the process of endochondral bone development.

Reviewer #2 (Public Review):

The goal of this work was to investigate the role of proline and its transporter SLC38A2 in osteoblasts (OB). OB marker proteins and regulators are enriched in proline. The authors first study the source of proline in OBs in vitro and find that it is mostly transported from outside. Then they determine that SLC38A2 amino acid transporter is a major transporter of proline in OBs. The role of proline after proline depletion and of SLC38A2 after its knockout is demonstrated by OB impairment. Genetic deletion of SLC38A2 in OB progenitors vivo, disrupts bone formation.

This is a well-designed mechanistic study done with appropriate controls and methodology. Conclusions are justified by the results. The impact for bone field is significant since OB metabolism and amino acid metabolism is not completely understood. Amino acid metabolism is especially important for highly synthetic cells, such as OBs.

Reviewer #3 (Public Review):

Osteoblast differentiation imposes a significant metabolic demand as these cells synthesize and secrete large amounts of extracellular matrix. Recent studies have highlighted an important regulatory role for amino acid metabolism in sustaining osteoblast biosynthesis. Here, using a combinatory transcriptomic and metabolomic approach, Shen and colleagues describe that SLC38A2-dependent proline uptake is essential for osteoblast differentiation. Although the role of proline in regulating cellular properties has already been put forth in other (malignant) cells, the concept that proline contributes to specific osteoblast-related proteins is novel and interesting. However, some of the authors' claims are not sufficiently supported by the provided data and additional experiments are therefore warranted. The main concerns are detailed below.

1. Based on their data, the authors state that there is a considerable enrichment of proline residues in osteoblast-related proteins (7.1%) compared to the average of all proteins (6.1%). However, it is not very clear how robust and relevant this change is, especially since other amino acids (Ala, Cys) show comparable changes. Unbiased proteomics approaches using biological replicates might therefore be warranted to avoid overinterpretation of the data.

2. Using 13C-proline tracing experiments, the authors show that after 72 hours more than 60% of the intracellular proline pool is 13C-labeled. They thereby claim that proline is not metabolized (line 160), although supporting data (carbon labeling of TCA cycle intermediates, glutathione, 1-Pyrroline-5-carboxylic acid) is lacking. This is especially relevant given the many metabolic fates of intracellular proline. Along the same lines, proline dehydrogenase (PRODH)-mediated proline catabolism is known to regulate electron transport chain (ETC) activity and ROS production. Are bioenergetics and/or redox homeostasis altered upon proline withdrawal or (genetic/pharmacological) SLC38A2 inactivation?

3. To study the role of SLC38A2-mediated proline uptake in bone cells in vivo, the authors use Sp7-tTA,tetO-EGFP/Cre mice. It is known that neonatal Cre-positive mice show severe craniofacial defects, which may hinder correct interpretation of the data, especially when analyzing at embryonic stages. Do the authors observe a similar phenotype in mice where SLC38A2 was deleted postnatally? The same mouse line can be used to answer this important question experimentally.

Reviewer #1 (Public Review):

Dillard et al. sought to identify signatures of urbanization using paired analyses of the human, coyote, and crested anole gut microbiota using 16S rRNA gene sequencing. They report increased similarity to the human gut microbiota in coyotes and anoles living in an urban environment, leading to the identification of multiple amplicon sequence variants shared between species.

The major strengths include the innovative use of two very distinct model systems to study urbanization coupled to robust methods for 16S amplicon analysis. The study is also conceptually innovative, proposing a testable hypothesis about microbial transfer from humans into the surrounding wildlife.

A major weakness is the reliance on 16S profiling to assess interspecies transfer events, which (as the authors point out) lacks sufficient resolution. Metagenomics would be a more reliable way to assess if the same strains are shared across species.

There's also a conceptual flaw in that the authors interpret any overlap in bacteria between host species to be a sign of human to wildlife transfer. The reverse direction is also possible, as is colonization of both species from a shared environmental reservoir. Furthermore, the reported trends are modest in effect size and the consequences of these events for wildlife health and disease are not established. This raises the question as to whether rare bacterial transfer events would matter given the numerous other factors that shape the microbial ecology and health status of wildlife.

Given these caveats, the hypothesis for human to wildlife transfer is not definitively established and warrants additional data.

Reviewer #2 (Public Review):

Dillard et al. compared urban and rural populations of coyotes, anoles, and humans to ask whether urbanization impacts on the gut microbiota are similar across species and in particular whether urban, rather than rural, animal populations have a more similar gut microbiota to humans. They find that overall composition of the gut microbiota in urban and rural populations do differ for both animal species and that the urban individuals have greater similarity to humans, and specifically urban humans. In addition they show that some microbial taxa enriched in urban animals were also at greater abundance in urban humans. The authors cannot distinguish whether the observed convergence between animals and humans in urban settings is due to transmission from humans to animals or due to similar ecology (most likely diets) in urban settings. These data expand previous single species assessments of urbanization impacts on the gut microbiota by showing effects in multiple host species and explicitly assessing whether human-associated taxa are overrepresented in the gut microbiota of urban wildlife.

The analyses conducted in this paper are generally robust, however there are some aspects of study design that weaken their interpretations.

1. The human gut microbiota data is drawn from a study not intended to test urbanization impacts directly. The three populations represented (US, Venezuela, and Malawi) differ in numerous ways beyond whether the population is urbanized (US) or not (Venezuela and Malawi). Urban animals are more similar to all human populations, not just the urban US population, indicating that the urban context alone may not explain the dynamics.<br /> 2. The human populations represented, as noted above, are not from the localities where wildlife samples were collected. As humans within a lifestyle group (e.g. urban or rural) can still vary considerably in their gut microbiota composition (at both the ASV and strain level), this greatly diminishes the likelihood of sharing ASVs between humans and wildlife in different localities. The authors rightly note that strain level data would be necessary to demonstrate horizontal transmission, but even with such data it is unlikely such a signal would be present in this dataset.<br /> 3. The urbanization impacts in the two wildlife species shown do not parallel one another. They move in distinct, arguably perpendicular ways in ordination space (Fig. 1), and have non-overlapping microbial taxa that recapitulate trends in humans. Urbanization impacts need not lead to a singular urban microbiome, but what it means to have a significant but totally distinct influence between animal hosts is not clear.

Reviewer #3 (Public Review):

I found this paper to be well written and well presented. The background and hypotheses are clearly outline in the introduction, leading towards simple predictions and presentation of results showing that urban animals have more similarity and overlap with the gut microbiomes of humans. While reviewing this paper, there were several instances where I wrote down thoughts or questions that the authors then answered or addressed in following paragraphs, and so this paper was a pleasure to read.

However, there are several places where the data could be discussed in a bit more detail to get a better sense of the magnitude of these effects. The microbiome field is now pressed to understand the relative importance of various ecological and evolutionary factors (e.g. diet versus evolutionary history), and so some discussion as to whether these changes appear to be considerably drastic or relatively minor in relation to other effects would benefit the paper.

Additionally, the authors could discuss predictions regarding how these changes in community structure might lead to changes to host performance and fitness (since these ideas are invoked in the introduction). Note, I believe that even small changes to the community structure could still yield large biological changes - but I would like to see that discussed. Within here, I request the authors to be sure to use careful language to explicitly signal where they are speculating versus concluding from their data. But, these requests are just more interpretation and context for these results, and do not detract from my interest in these data.

Reviewer #1 (Public Review):

The authors are using the honeybee and its gut microbial symbionts to understand the factors that enable the stable association between the host and its symbiont. They are also interested in understanding the factors involved in niche specificity, namely the mechanisms that are required for bacteria to colonize specific physical niches in the gut.

Here they focus on the bee gut symbiont Frischella perrara. A gammaproteobacterial that colonizes a restricted region of the gut (midgut and pylorus/ileum). One unique characteristic of this bacterial symbiont is that upon colonization of the gut it induces the scab phenotype, characterized by the accumulation of a brown material deposited on the host epithelium, which might be related to the host immune response to this symbiont.

The authors isolated spontaneous mutations in a regulator called IHF, and studied the phenotype of these mutations in vivo, using gnotobiotic model with bees mono-colonized with F. perrara. They determined the genes differentially expressed in a ihf mutant in comparison with WT, both in vitro and in vivo. With these transcriptomics experiments they observed that mutants in IHF affects expression of a large number of genes. Among the genes with low expression in the ihf mutant, were genes involved in production of colibactin, pili formation, production of the yellow pigment aryl polyene, and components of type VI secretion system (T6SS). They constructed deletion mutants in genes involved in these processes and show that the deletion mutants had distinct defects in gut colonization. Mutants affecting pili formation (presumably involved in adhesion to the host epithelium) or the production of the aryl polyenes were defective in colonization. Interestingly, mutants in the T6SS had only a mild colonization defect but were in impaired in inducing the scab phenotype. T6SS are usually thought to be important in interbacterial warfare, the results with the T6SS shown here with gnotobiotic bees mono-colonized with F. perrara provides evidence for the role of this system in a direct interaction with the host.

In conclusion, they showed that IHF is major regulator in F. perrara, involved in the regulation of the expression of colonization factors with distinct roles in colonization. It remains to be shown if the regulation of IHF is direct or indirect, but it is clear that this regulator is a major hub controlling many different factors required for colonization in F. perrara. There are homologues of this regulator in other bacteria, so it is possible it plays a role in controlling colonization factors in other symbionts.<br /> One interesting point that was not addressed here was if this regulator, or some of the factors that it controls will play a role in spatial occupancy during gut colonization. Given that this bacterium colonizes a particular section of the gut it will be interesting to see if any of the factors regulated by IHF play a role in distinct niche colonization.

Overall, the experiment included here are technically sound and the conclusions are well supported by the data. The paper is well-written and clear.

Reviewer #2 (Public Review):

This is a very interesting, well conducted and conclusive research paper. On the positive side, beyond the scientific results of the work, this reviewer emphasizes the development of a protocol for RNA extraction and sequencing in vivo as well as a directed mutagenesis protocol for this peculiar symbiont which constitute great technical assets for future research in the bee microbiota field. On the less positive side, this reviewer feels there is a lack of characterization of the molecular phenotype induced by ihfA* on host biology, maybe as a start at the transcriptional level. Also, it would be interesting to study the impact of the deletion mutant on the bacterial physiology (cell shape, growth...) to help rationalize how the mutant is compromised in its ability to colonize the bee gut. In addition, given the ecological context of the bee gut, competition assays between the mutants and wt strains of F.perrara in bi-associated animals or within the a more complex gut microbiota would help to further understand the impact of the mutations on the fitness of the bacterial strain in the bee gut.

Reviewer #3 (Public Review):

The manuscript by Schmidt and coauthors explores bacterial molecular factors influencing the colonization of the gut bacterial symbiont Frischella perrara in the honeybee gut, as well as the formation of the so called "scab phenotype". The authors elucidate a major role of the DNA-binding protein integration host factor (IHF) in regulating the expression of several colonization factors, which have been shown to be involved in other symbiotic systems. The use of a combination of techniques leads to the conclusion that specific pili genes, two Type VI secretion systems and the biosynthesis gene clusters for two secondary metabolites have particular effects on colonization success and/or the emergence of the scab phenotype.

The thorough and extensive work presented provides well-supported conclusions on the role of specific genes for colonization in one strain of F. perrara. In particular, the valuable integration of infection bioassays, gene expression analyses, analytical chemistry and genetic manipulation result in a solid and wide-ranging set of results. Also, the text is well written, and the experimental procedures and presentation of the results are properly designed and detailed.

It is generally interesting that they identify genes already discovered in other bacterial symbioses, as this reinforces and deepens on their relevance for establishment of bacteria in an animal host. Despite these links, a clearer case could be made on the relevance of the findings in connection to natural or non-experimental conditions in honeybees. It is not immediately clear to the reader why the scab phenotype might be interesting, how frequently it is found (at least not from this paper) and if it is associated to honeybee performance or health. Additionally, there is no information or discussion on whether the F. perrara strain used is representative for the system, whether other F. perrara strains are often found and have the same "wt" genotype for the IHF gene. It also seems relevant to think about the possible impact of other microbial associates colonizing the gut, since most conclusions are drawn from assays in gnotobiotic bees.

In summary, I find that the experiments and conclusions of this work are well supported and properly presented. Placing the motivation for the work and the interpretations in a more general context would be useful to better convey their relevance.

Reviewer #1 (Public Review):

The authors show that the general transcription factor complex TFIIH is required for chromosome condensation in Xenopus egg extracts. Inhibition of the ATPase activity of the TFIIH subunit XPB or depletion of XPB both strongly impaired chromosome condensation. Inhibition showed a discernible effect after 5 min, even when chromosomes were already condensed. This loss of condensation was associated with a loss of condensin, but not topo II from chromosomes, Interestingly, both the condensation defect and the loss of condensin localization could be counteracted by slightly reducing the histone concentration in the egg extract. Based on this and the known DNA unwinding activity of TFIIH, the authors propose that TFIIH may act by promoting nucleosome-free regions for condensin to bind DNA and create DNA loops. Although an interaction between TFIIH and condensin was detected, whether this interaction is functionally important remains unclear.

The experiments are well-conducted and clearly and logically presented, but could be strengthened by adding a quantification of the phenotypes observed.

The strength of the paper lies in the identification of a novel contributor to chromosome condensation, which is a fundamental process in cell division that has been much studied and is still little understood.

Weaknesses are that it remains unexplored how exactly TFIIH promotes condensation, and that it remains unclear whether TFIIH plays a role in condensation in somatic cells or other species. The authors show that the role of TFIIH in promoting condensation can be observed both by using Xenopus sperm and using mouse sperm, which suggests - but is far from proving - that it might have a conserved role.

Reviewer #2 (Public Review):

The authors of this paper provide exciting and convincing evidence that the transcription machinery is involved in mitotic condensation in the frog egg extract system. This opens up a lot of interesting new questions and lines of research that promise to add significantly to the field of chromosome biology. Weaknesses in the work are minor and primarily have to do with lack of image data quantification. A few of the conclusions are a bit too speculative based on the experiments performed, but the overall quality of this work is very high.

Reviewer #3 (Public Review):

How DNA is compacted into chromosomes during mitotic and meiotic cell division is a question of great interest. Over the years, the prowess of Xenopus egg extracts to condense sperm DNA into mitotic chromatid structures resulted in the identification and characterisation of protein complexes that contribute to this function. However, the precise contributions of Condensin I, Condensin II, and DNA topoisomerase remain unclear, especially in the context of DNA substrates with different histone compositions.

In this study, the Kelly lab used Xenopus egg extracts to investigate a possible link between transcription and the structure of mitotic chromatids. They discover that mitotic chromatid formation is impossible in the presence of triptolide (TPL), a small compound that covalently inhibits XPB, a subunit of the transcription initiation TFIIH complex. Mitotic chromatids form normally following exposure to other transcription inhibitors, RNAase treatment, or ERCC1 depletion, demonstrating convincingly that TPL effects are transcription- and nucleotide excision repair independent. This is a surprising and interesting finding.

In figure 2, the authors demonstrate that TPL addition results in a rapid (5-10 minutes) decondensation of chromosomes. This effect is reversible since a ten-fold dilution of TPL restores chromosome condensation, underscoring that maintaining condensed chromosomes is an active process (see point 2 below).

Although TPL-treated decondensed chromosomes lack XPB and Condensins (Fig 3), TPL exposure results in chromosome decondensation before XPB and Condensin levels are reduced (Fig 4).

Following the effects of TPL addition over time is insightful. It is namely intriguing to see that chromatids already decondense in the first 5-10 minutes following TPL exposure, XPB levels go down later, and total Condensin levels do even increase in this period. A direct interaction between XPB and Condensin, suggested based on data shown in Fig3C, requires more evidence.

Interesting insight comes from an experiment in which the authors reduce the levels of H3/H4 on chromatin mouse sperm DNA through immunodepletion. Following a mild (24%) reduction of H3/H4 levels, chromosome condensation was no longer inhibited in the presence of TPL. It would be good if this effect was quantified beyond the representative images shown in panel A. XPB localisation to chromatin with reduced H3/H4 levels was still abolished by TPL, but Condensin levels were not. The ability to uncouple XPB and Condensin localisation is exciting.

Reviewer #1 (Public Review):

Summary:

In this paper, authors used S. cerevisiae to explore the mechanism of tolerance to paraquat, a drug that produces the superoxide anion with electrons from the respiratory chain or other NADH dehydrogenases. They find that yeast adapt to PQ after a few generation, recovering growth by 50 % from the 2.5-3 fold growth inhibition caused by PQ. Adaptation is paralleled by mitochondrial fragmentation, but is not the result of a mitophagic response, the loss of part of mtDNA consistently in similar regions, and the inability to respire. Growth adaptation, the loss of mtDNA and the ability to respire are recovered after a few generations after stress release, but long, chronic PQ exposure causes irreversible mtDNA total loss. Adaptation appears to depend on the presence of SOD2 and is absent in the Rtg1 and Rtg3 mutants of the retrograde response. Sequence data analysis also indicate the duplication of chromosomes II, III and V, the duplication of Chr II and V having an additive effect on the adaptation to paraquat.

General comments:

The reversible loss of mtDNA as an adaptive response to PQ stress is novel and interesting, and the data provided support this conclusion. The evaluation of PQ stress tolerance by real time DNA sequencing represents a huge work task and is a reflection of the rigor used throughout the experiments. The paper however suffers from its style, very elliptic, the use of complicated, long sentences, the use of terms such as growth cycles, generations etc. that have not been clearly defined at start, which makes it difficult to read and to understand the protocols used, for instance when PQ is added and removed. For instance, if cells adapt to PQ stress by decreasing their doubling time, the severity of growth rate reduction should be initially quantified. The authors are encouraged to take this criticism seriously. Below are a few specific points that do not detract from the general very positive impression of this paper.

Specific comments:

1. page 4, and S1 legends says that " we see PQ causes doubling time to increase", but where do we have to see that? It is not clear how the growth rate is calculated? How are experiments performed on solid, liquid medium? The figure only shows gene expression data? What is a growth cycle and what is its length? What do you mean by 240 generations? What is the length of one generation in hours? What is a population, and what is the difference with "clonally reproducing cell populations? At best a picture of the plates used to monitor growth should be shown for one to understand how it is done. How do you calculate that 106 min doubling time reduction equals 49.3 % of the maximum possible reduction? And what is this maximum? Fig. 1B is confusing: it is understandable that cells are exposed to PQ enough to adapt, then grown without PQ, and then again with PQ, but over the generations shown in the picture, do one not expect to see adaptation after a few "cycles"?

2. Page 7. The experiment described in S6A cannot be used to rule out signaling by H2O2: adding 3 mM H2O2 to cells that have already adapted to PQ, whether or not by use of an H2O2 signal, amounts to a severe H2O2 stress, exacerbated by the lack of a functional respiratory chain (petite cells are more sensitive to H2O2, relative to WT). One way of tackling this question would be to see whether adaptive doses of H2O2 (100-300 microM) prior to exposure to paraquat would speed up growth adaptation or not (cross adaptations have been described in the past). Similarly, the WT PQ adaptative response of cells lacking Yap1 or other antioxidants does not prove anything: signaling by H2O2 is mostly localized in confined areas, and this should persist even in a Yap1 mutant.

3. Page 8. The need of SOD2 for PQ adaptation to occur is not really convincing because of the sickness of SOD mutants in general. Further, it shows that there is no adaptation at 12 microG/mL PQ, but then adaptation occurs at a higher dose, but slower, relative to WT. What is the point authors want to make? That SOD by dismutation of the superoxide anion produces H2O2 needed for signaling? But, authors already ruled out the need for H2O2 to signal adaptation? Please don't be too peremptory in your conclusion on this experiment. In addition, it is hard to follow the writer: "in four populations, the copy number..." then "two of these fail to adapt" then "the remaining four populations", but which ones? Lastly the text of Fig 4c indicates 12.5 mG/mL, but the figure 50?

Reviewer #2 (Public Review):

The authors used extended yeast cell culture to determine how cells adapt to paraquat-induced oxidative stress by following the deletion pattern of mtDNA and the role of nuclear genes such as SOD2, MIP1, RTG2 and RTG3 in the process. The idea that specific deletions of mtDNA genes may reduce superoxide production endogenously which may help cells to grow is novel. However, there are several major concerns that severely diminish the validity of the conclusion. First, the manuscript suffers from overinterpretation of the experimental data. The key observation in the manuscript is that growth adaptation to paraquat correlates with mtDNA deletions in the COX1-VAR1 region. There is no data showing that loss of these genes actually reduces superoxide production and is causative for adaptive growth. In fact, yeast mitochondria produce superoxide mainly in the bc1 complex. Loss of Cox1 would be expected to increase electron leak at the bc1 complex. Secondly, the authors overlooked the dynamic nature of mtDNA mutations in yeast and the data on mtDNA deletions are incorrectly interpreted in many places. Thirdly, the budding yeast is anaerobic and the physiological implication of mtDNA mutations would be different from mammalian cells. The authors made many unfunded claims for the implication of the work to mitophagy, cancer therapy and aging-related diseases. Finally, describing paraquat-induced mtDNA mutation as a regulatory "gene editing" program is inappropriate.

Reviewer #1 (Public Review):

In this study, Guggenmos proposes a process model for predicting confidence reports following perceptual choices, via the evidence available from stimuli of various intensities. The mechanisms proposed are principled, but a number of choices are made that should be better motivated - I develop below a number of concerns by order of importance.

1) Lack of separability of the two metacognitive modules.

Can the author show that the proposed model can actually discriminate between the noisy readout module and the noisy report module? The two proposed modules have a different psychological meaning, but seem to similarly impact the confidence output. Are these two mutually exclusive (as Fig 1 suggests), or could both sources of noise co-exist? It will be important to show model recovery for introducing readout vs. report at the metacognitive level, e.g., show that a participant best-fitted by a nested model or a subpart of the full model, with a restricted number of modules (some of the parameters set to zero or one), is appropriately recovered? (focusing on these two modules)

This raises the question of how the two types of sigma_m are recoverable/separable from each other (and should they both be called sigma_m, even if they both represent a standard deviation)? If they capture independent aspects of noise, one could imagine a model with both modules. More evidence is needed to show that these two capture separate aspects of noise.

2) The trade-off between the flexibility of the model (modularity of the metacognitive part, choice of the link functions) and the generalisability of the process proposed seems in favor of the former. Does the current framework really allow to disambiguate between the different models? Or at least, the process modeled is so flexible that I am not sure it allows us to draw general conclusions?

Fig 7 and section 3 of the results explain that all models are similar, regardless of module of functions specified; Fig 7 supp shows that half of participants are best fitted by noisy readout, while the other half is best fitted by noisy report; plus, idiosyncrasies across participants are all captured. Does this compromise the generalisability of the modeling of the group as a whole?

3) More extensive parameter recovery needs to be done/shown. We would like to see a proper correlation matrix between parameters, and recovery across the parameter space, not only for certain regimes (i.e. more than fig 6 supp 3), that is, the full grid exploration irrespective of how other parameters were set.

The recovery of the three metacognitive bias parameters is displayed in Fig 4, but what about the other parameters? We need to see that they each have a specific role. The point in the Discussion "the calibration curves and the
relationships between type 1 performance and confidence biases are quite distinct between the three proposed metacognitive bias parameters may indicate that these are to some degree dissociable" is only very indirect evidence that this may be the case.

1.8: It would be important to report under what regimes of other parameters these simulations were conducted. This is because, even if dependence of Mratio onto type 1 performance is reproduced, and that is not the case for sigma_m, it would be important to know whether that holds true across different combinations of the other parameter values.

Is lambda_m meaningfully part of the model, and if so, could it be introduced into the Fig 1 model, and be properly part of the parameter recovery?

4) An important nuance in comparing the present sigma_m to Mratio is that the present model requires that multiple difficulty levels are tested, whereas instead, the Mratio model based on signal detection theory assumes a constant signal strength. How does this impact the (unfair?) comparison of these two metrics on empirical data that varied in difficulty level across trials?

Relatedly, the Discussion paragraph that explained how the present model departs from type 2 AUROC analysis similarly omits to account for the fact that studies relying on the latter typically intend to not vary stimulus intensity at the level of the experimenter.

5) 'Parameter fitting minimizes the negative log-likelihood of type 1 choices (sensory level) or type 2
confidence ratings (metacognitive level)'.
Why not fitting both choices and confidence at the same time instead of one after the other? If I understood correctly, it is an assumption that these are independent, why not allow confidence reports to stem from different sources of choice and metacognitive noise? Is it because sensory level is completely determined by a logistic (but still, it produces the decision values that are taken up to the metacognitive level)?

6) Fig 4 left panels: could you clarify the reasoning that due to sensory noise, overconfidence is expected, instead of having objective and subjective probability correct aligning on the diagonal? Shouldn't the effects of sensory noise average out? In other words, why would the presence of sensory noise systematically push towards overconfidence rather than canceling out on average?

7) The same analysis as Fig 6 but for noisy readout instead of noisy reports do not show the same results: both sigma_m and m-ratio vary as a function of type 1 performance. Does this mean that the present model with readout module does not solve the issue of dependency upon type 1 performance?

8) In Eq8, could you explain why only the decision values consistent with the empirical choice are filtered. Is this an explicit modeling of the 'decision-congruence' phenomenon reported elsewhere (eg. Peters et al 2017)? What are the implications of not keeping only the congruent decision values?

Reviewer #2 (Public Review):

This paper presents a novel computational model of confidence that parameterises links between sensory evidence, metacognitive sensitivity and metacognitive bias. While there have been a number of models of confidence proposed in the literature, many of these are tailored to bespoke task designs and/or not easily fit to data. The dominant model that sees practical use in deriving metacognitive parameters is the meta-d' framework, which is tailored for inference on metacognitive sensitivity rather than metacognitive biases (over- and underconfidence). This leaves a substantial gap in the literature, especially as in recent years many interesting links between metacognitive bias and mental health have started to be uncovered. In this regard, the ReMeta model and toolbox is likely to have significant impact on the field, and is an excellent example of a linked publication of both paper and code. It's possible that this paper could do for metacognitive bias what the meta-d' model did for metacognitive sensitivity, which is to say have a considerable beneficial impact on the level of sophistication and robustness of empirical work in the field.

The rationale for many of the modelling choices is clearly laid out and justified (such as the careful handling of "flips" in decision evidence). My main concern is that the limits to what can be concluded from the model fits need much clearer delineation to be of use in future empirical work on metacognition. Answering this question may require additional parameter/model recovery analysis to be convincing.

Specific comments:

- The parameter recovery demonstrated in Figure 4 across range of d's is impressive. But I was left wondering what happens when more than one parameter needs to be inferred, as in real data. These plots don't show what the other parameters are doing when one is being recovered (nor do the plots in the supplement to Figure 6). The key question is whether each parameter is independently identifiable, or whether there are correlations in parameter estimates that might limit the assignment of eg metacognitive bias effects to one parameter rather than another. I can think of several examples where this might be the case, for instance the slope and metacognitive noise may trade off against each other, as might the slope and delta_m. This seems important to establish as a limit of what can be inferred from a ReMeta model fit.

- Along similar lines, can the noisy readout and noisy report models really be distinguished? I appreciate they might return differential AICs. But qualitatively, it seems like the only thing distinguishing them is that the noise is either applied before or after the link function, and it wasn't clear whether this was sufficient to distinguish one from the other. In other words, if you created a 2x2 model confusion matrix from simulated data (see Wilson & Collins, 2019 eLife) would the correct model pathway from Figure 1 be recovered?

- Again on a similar theme: isn't the slope parameter rho_m better considered a parameter governing metacognitive sensitivity, given that it maps the decision values onto confidence? If this parameter approaches zero, the function flattens out which seems equivalent to introducing additional metacognitive noise. Are these parameters distinguishable?

- The final paragraph of the discussion was interesting but potentially concerning for a model of metacognition. It explains that data on empirical trial-by-trial accuracy is not used in the model fits. I hadn't appreciated this until this point in the paper. I can see how in a process model that simulates decision and confidence data from stimulus features, accuracy should not be an input into such a model. But in terms of a model fit, it seems odd not to use trial by trial accuracy to constrain the fits at the metacognitive level, given that the hallmark of metacognitive sensitivity is a confidence-accuracy correlation. Is it not possible to create accuracy-conditional likelihood functions when fitting the confidence rating data (similar to how the meta-d' model fit is handled)? Psychologically, this also makes sense given that the observer typically knows their own response when giving a confidence rating.

- I found it concerning that all the variability in scale usage were being assumed to load onto evidence-related parameters (eg delta_m) rather than being something about how subjects report or use an arbitrary confidence scale (eg the "implicit biases" assumed to govern the upper and lower bounds of the link function). It strikes me that you could have a similar notion of offset at the level of report - eg an equivalent parameter to delta_m but now applied to c and not z. Would these be distinguishable? They seem to have quite different interpretations psychologically: one is at the level of a bias in confidence formation, and the other at the level of a public report.

Reviewer #1 (Public Review): 

The authors have developed a method (scQuint) for analyzing alternative splicing using scRNA-Seq. The method performs both visualization and clustering and also differential analysis, although the differential analysis modeling is not novel and has been adopted from a bulk RNAseq method (Leafcutter) and applied to pseudobulked data by grouping reads from cells within a cell type. Therefore, the method is not able to capture the true splicing variation at the single-cell level. Also, authors have only applied the method to Smart-Seq2 data and therefore it is not clear if their method is applicable to 10x data which has much higher throughout compared to Smart-Seq2 and is able to capture rare cell types but is more challenging for splicing analysis due to its 3' bias and lower coverage. 

Authors have applied their method to two mouse scRNA-Seq datasets: Tabula muris (from multiple tissues) and BICCN (from brain) and provided a comprehensive analysis of alternative splicing in mouse cell types. They have found that cell-type-specific splicing is ubiquitous in mouse cell types and splicing variation augments the total gene expression variation as there is little overlap between top differentially spliced and differentially expressed genes. They also found that a considerable fraction of cell-type-splicing events involve novel transcripts. They applied predictive machine learning models to show that cell types can be well distinguished by the splicing information and identifies relationships between the splicing changes in known splicing factors and the splicing changes in their target genes. 

The authors provide several biological findings regarding alternative splicing at cell-type-level and have shown how scRNA-Seq (despite being underutilized for splicing analysis so far) can expand our understanding of splicing mechanisms in single cells. Additionally, authors have made their data publicly available through interactive data browsers that can serve as a resource tool for future studies.

Reviewer #2 (Public Review): 

This paper proposes novel methods to study alternative splicing at the single-cell level. Their approach addresses the problem of non-uniform read coverage of transcript sequences by Smart-seq2 data and proposes a metric that evaluates differential intron expression by looking at intron groups or groups of AS events that share the same 3´splice site. In this way, coverage biases are canceled out for the evaluated intron group. Using this metric, they propose a VAE method for dimension reduction and show that it effectively clusters cell types better than PCA. They also propose a method for differential splicing analysis, which is an adaptation of a previous approach used for bulk RNA-seq. The methods are applied to a variety of datasets. Finally, an analysis of splice factor regulation is proposed. 

Strengths:

• The concept of using intron groups for control of coverage biases is interesting and results indicate that this is an effective way to capture the AS patterns that discriminate between cell types <br /> • The authors use a diversity of datasets (neural cell types and tabula Muris) to illustrate the applicability of their methods and show multiple cases of cell-type-specific alternative exon/intro usage and TSS. 

Weaknesses:

• Generally, I find that the statistical methods are insufficiently benchmarked as no controlled datasets are used, only indirect demonstration on experimental data. This is particularly true for the differential splicing methods as well as the splice factor regulatory models. A more formal demonstration of the performance of their approach is needed. <br /> • I am not sure of the biological novelty and significance of the biological insights presented in this paper. Some general messages are already known: cases of splicing dissociated from expression, the fact that splicing contributes to cell identity, or that many unannotated splicing events still can be found in neural tissues, is already known thanks to other approaches both based on short or long reads. 

Some specific remarks are: 

1. The authors state that most methods do not consider the different reads coverage along with the transcript sequence. This is not true, some popular RNA-seq methods such as RSEM do model the RNA degradation pattern, and hence non-uniform coverage. 

2. The author state that their method allows for novel isoform discovery since it does not rely on transcriptome annotations. However, this is not accurate: they might be able to detect new splice junctions or introns, but not isoforms, which refers to the concatenation of exons. This sentence should be amended. 

3. Some aspects of the methods are poorly described. For example, the procedure to obtain and use a pseudo count vector of PSI values to apply PCA for cell clustering is not clear. 

4. The authors implement a multinomial GLM for differential splicing proposed by LeafCutter. It is unclear how they deal with sparsity. While they mention this problem for PCA analysis but not for differential splicing. At some point, authors indicate that they only test introns detected in at least 50 cells in each condition, but this is an arbitrary value and it is unclear how this cutoff impacts accuracy in their analysis. A more formal explanation of the treatment of sparsity should be provided. Finally, in Figure 3, they show that their p-values are better calibrated than in LeafCutter, but it is not described how calibration was done. Moreover, this analysis does not reveal how well their method performs in terms of sensitivity, specificity, or FDR. In general, it is unclear how methods are benchmarked. I would suggest using synthetic data, where different intro PSI across cell types are modeled for single cells, and then use established performance metrics to evaluate their method. 

• It is not clear to me that the novelty of this paper is in terms of the discovered biology. The authors found the cell types can be separated by splicing patterns and highlight several genes with cell-type-specific alternative introns, which is consistent with previous knowledge. Also, the fact that neural cell types have extensive AS and contain many un-annotated splicing events has already been described. It would be interesting to know what novel biology is discovered by this approach that is not possible to identify by other approaches. 

5. The dendrogram comparison for the Tabula Muris dataset is interesting, but it is unclear what the biological significance of the finding is. 

6. I find the splicing factor analysis is rather speculative, as an association between splicing patterns of splicing factors and genes does not imply necessarily imply a regulatory role, especially taking into account that information about splicing factor binding sites were not used. The fact that authors recover one known association is not sufficient to validate the approach. For this analysis to be reliable, additional evidence of regulation should be provided. For example, is there an enrichment of SFBS for those associations where the logistic model was significant? 

In summary, the paper presents a promising concept for the alternative splicing analysis in single cells, but the method requires a more elaborated benchmarking and a better explanation of the novel biology discovered by this approach in comparison to existing approaches. Without this assessment, it is unclear whether the work can have a real impact on the community.

Reviewer #1 (Public Review):

The manuscript by Saint-Criq and colleagues reports an extensive study on the role of basolateral Na+-bicarbonate cotransport in bicarbonate secretion and pH regulation of human and mouse airway epithelium. The apical components of this process (CFTR, TMEM16A, SLC26A) have been extensively studied in the past, but the basolateral components are not understood. The present study shows that both human and mouse airway epithelial cells express the Na+-bicarbonate cotransporter SLC4A4, and that the protein localizes to the basolateral membrane. They further demonstrate that SLC4A4 function is essential for intracellular pH homeostasis, transepithelial bicarbonate secretion, and maintanance of pH of the airway surface liquid. Finally, knocking out SLC4A4 in the mouse results in a lung phenotype that resembles cystic fibrosis.

The experimens are carefully performed, the data seem to support the conclusions, and the findings represent a significant advance in understanding pulmonary anion secretion in health and disease. I have a number of suggestions for clarifications in figures and figure legends, to facilitate understanding of the presented complex sets of experiments for the reader.

Reviewer #2 (Public Review):

In this study a human in vitro airway cell model. i.e. primary hAECs expanded by conditional reprogramming, seeded on semi-permeable supports and differentiated under air-liquid interface conditions, resulting in enrichment of ciliated cells, and trachea from a systemic null-mouse for the electrogenic Na+-dependent bicarbonate (HCO3-) transporter/importer NBCe1 (Slc4A4) were used to evaluate the importance of NBCe1 in transepithelial HCO3- secretion and in alkalinization of airways surface liquid (ASL).

The results demonstrate unequivocally that the B-splice variant of NBCe1 is expressed at the basolateral membrane of ciliated cells in humans and in CCSP+ (Club) cells in mice, and that its absence in Slc4A4-KO mice results in ASL acidification and the induction of a muco-obstructive, CF-like phenotype in the airways. The identification of Slc4A4 as the main HCO3- importer in airway epithelial cells and the demonstration of its pivotal role in the maintenance of ASL pH and mucociliary clearance (MCC) are novel findings (albeit not entirely unexpected) that contribute to the notion that alkalinizing the ASL pH (e.g. with nebulized bicarbonate), aside restoring ASL volume, is an important strategy as a supportive treatment of cystic fibrosis (CF) and other muco-obstructive airway diseases.

The methodology used is sound and the conclusions of this paper are mostly well supported by data, but some aspects need further clarification and discussion.

Reviewer #3 (Public Review):

Saint-Criq et al have made some very nice and extensive studies of basolateral membrane bicarbonate transport in airway epithelial cells using both human primary cell cultures and mouse tissue derived from a wild type and/or an SL4A4 knockout line. The author's work points to an important role for the electrogenic SLC4A4 transporter in bicarbonate movement for both human and mouse airway epithelium. Although there is this similarity between mouse and human epithelia, the cell types in which this transporter operates differ in the two species and thus, the role of the transporter also seems likely to vary. A significant strength of this paper is the number of different approaches the authors take to investigating the function of this transporter, including immunohistochemistry, mucocilliary transport measurements, Ussing chamber recordings and airway surface liquid pH measurements. One of the key findings, that of a CF-like mucus phenotype in the mouse, bears directly on a current, much debated, topic about whether the CF mucus rheology arises from an effect of pH or from an effect on airway surface liquid hydration. In relation to this topic, future, more direct measurements of ASL volume and pH would further strengthen the author's case.

Reviewer #1 (Public Review):

Scheres and colleagues report 76 cryo-EM structures of recombinant tau filaments assembled in vitro. This is a scientific tour-de-force, and will provide an immense database that can be used by everyone working in the amyloid field. When this knowledge is combined with the structure of tau filaments in vivo, it will help shape the design of laboratory experiments in the future to generate the conditions to replicate the in vivo forms in vitro.

For one filament solved at 1.8 Å resolution, they determined the hand of these polymers from the carbonyl oxygens. But I am confused and puzzled about how the hand was determined or chosen for the rest. The reason that this is important is because it is stated: "Whereas all previously described tau filaments had a left-handed twist, phosphoserine-induced filaments were right-handed." Is it actually true that all previously described tau filaments had a left-handed twist, or was that assumed in many papers? I actually asked the authors of two recent papers in the amyloid field about what experimental evidence they had for stating that their filaments were left-handed. In both cases, they replied that this was an assumption based upon the literature, and it was a mistake to never state this in the paper (or, in one case, to actually imply that the filaments were observed to be left-handed). While I had thought that one might require almost near-atomic resolution to see the hand directly, a recent PNAS paper on a largely beta structure (10.1073/pnas.2120346119) showed that the hand could be determined at 2.5 Å resolution. I therefore think that it would help the amyloid field greatly to explicitly state in all cases when the hand has been determined, when it has been assumed, and test what resolution might be required to have confidence in describing the hand.

Reviewer #2 (Public Review):

Lovestam et al. report the results of cryo-EM studies of tau protein fibrils grown in vitro under a variety of conditions. One goal of this work is to identify conditions that yield the same tau fibril polymorphs that the MRC group has identified in earlier studies of fibrils that were extracted from AD, CTE, Pick's disease, and CBD. They find that certain conditions, influenced by agitation rate during fibril growth, salt concentration, the nature of cations, and crowding agents, and truncation of the tau construct, do yield fibrils that are identical to (or at least very similar in their core structures) to fibrils from AD and CTE tissue samples.

This work may represent the first example of the use of cryo-EM-based structures to evaluate the results of a broad screening of growth conditions. As such, this work demonstrates the value of "high-throughput" cryo-EM.

This work also shows that tau polymorphs found in brain tissue do not necessarily depend on the presence of brain-specific cofactors or brain-specific conditions, as the same polymorphs can in some cases be created in the complete absence of brain material and without seeding with brain-derived seeds.

This work also provides further examples of the amazing diversity of amyloid fibril structures that can arise from a single amino acid sequence and the sensitivity of polymorphism to subtle variations in growth conditions (e.g., agitation rate), as was first shown by other for amyloid-beta fibrils. Additionally, the authors find polymorphs with three-fold symmetry about the fibril growth axis (e.g., their "10a" and "15a" structures) as well as two-fold symmetric polymorphs (e.g., "4a","8a", "8b", etc.). This is interesting, because 40-residue amyloid-beta fibrils have also been shown to exhibit two-fold and three-fold symmetric polymorphs.

Reviewer #3 (Public Review):

In this article, Lövestam et al. reported 76 cryo-EM structures of in vitro assembled recombinant tau filaments, including 27 previously unobserved ones. Together with the recent Science paper from Scheres and Goedert research groups, the structure-based knowledge of amyloid assembly will be boosted several fold. Most importantly, a few in vitro conditions were found to replicate the amyloid structures from both Alzheimer's disease and chronic traumatic encephalopathy. Those findings will open up new avenues to quickly screen compounds that inhibit filament formation under in vitro conditions, as well as the (self-)assembly process of amyloid fibrils. Congratulations on this beautiful body of work!

Here is a minor point:

It seems all helical twists per ~4.8Å rise are assumed to be left-handed, meaning the twist of β-sheets was assumed left-handed. I wonder whether AFM or Tomography was ever used to determine the hand of the filaments experimentally. Some filaments reached to high resolution, such as 1.8 Å. The author stated: "At this resolution, the absolute handedness of the filament was obvious from the position of the main-chain carbonyl oxygens. Whereas all previously described tau filaments had a left-handed twist, phosphoserine-induced filaments were right-handed". This is nice, and it will further benefit the field if the authors can use their best filaments, let's say ten in terms of resolution, mirror the maps, and build atomic models into those mirror maps. Then within the original and mirror models, they can show whether β-sheet hydrogen bonds get worse in the mirrored map. We recently found this can be pretty indicative of the correct hand for cross-β filaments that go to ~3Å or better. It would be much better for the field to understand the hand of cross-β filaments rather than assuming it.

Reviewer #1 (Public Review): 

Tropical ecosystems comprise species-rich assemblages of myriad invertebrate groups that are difficult to work with. These invertebrates form complex, multitrophic food webs that recycle energy locked in dead wood. For a long time, progress in soil ecology has been hampered by the taxonomic impediment, but also by shortcomings in our understanding of basic natural history. Zhou et al. leapfrogged these obstacles by using stable isotopes (techniques that elucidate the position of a species in the food web and [old vs. -human- young] energy pathways) and metabolic rates (that together with abundance, provide a measure of how much energy a species recycles in a community). 

The strengths of this ms thus lie in the usage of comprehensive datasets (stable isotopes of δ13C and δ15N, and metabolic rates of 23 soil taxa) on a series of equally-comprehensive and well-defined metrics, used together for the first time in tropical ecosystems. This approach allows the authors to explore simultaneously changes in community, trophic and energy-pathway metrics, something rarely seen in the published literature.

Reviewer #2 (Public Review): 

This study examined the effect of land-use change on soil animal food webs in Sumatra, Indonesia using datasets of stable isotopes and metabolisms of 23 animal groups. They found that the calibrated 13C values of soil animals are generally higher in the rainforests compared to those in the plantations, and multidimensional metrics of the soil animal community (e.g., isotopic dispersion) differed across the land-use types. They also showed that the community food web metrics are influenced by environmental variables (e.g., soil pH and tree density and species richness. These results demonstrate that the conversion of rainforest to plantations could affect not only the above- but also the below-ground components of the ecosystems and likely the ecosystem functions. 

Strength: 

This is the first attempt to investigate the effect of land-use changes in soil food web structure with stable isotope and metabolism datasets. This study is based on a great amount of data of high quality (biomass and metabolism rates, high taxonomic resolution for some taxa) and collected from four land-uses, which were achieved through a well-organized project. It is also noteworthy that the measurement of stable isotopes of tiny soil invertebrates required the improvement of the continuous flow isotope ratio mass spectrometer. The collaborations among international groups and across different disciplines shows a direction to be followed in studies on global environmental issues. 

Weakness: 

The only weakness of this study is that the isotopic measurements were conducted on the high-rank taxonomic group level (order or family) based on an assumption that "the high-rank animal taxa in soil are generally consistent in their isotopic niches and reflect the trophic niches of species in most taxa" (L164-165). Although I am not sure if I understand this assumption correctly, the authors consider that different species in some taxa should have similar isotopic (trophic) niches. If so, it is difficult to accept this assumption because previous studies have already reported large variations in C and N isotope ratios (~ 6 permil) within the order or family (e.g., oribatids, collembola, ants, and earthworms). The isotopic variation exceeds those observed across the land-use types. Because of the considerable variation within the high-rank taxa, which species were included in the mixed samples (only 3 to 15 individuals for each taxonomic group, Line 176) should have affected the present results and thus the key claims in this paper. Therefore, it would be necessary to scrutinize whether the samples used for the isotopic analyses could represent the high-rank taxa and whether the isotopic values presented in this study are understandable in light of the previous knowledge of their biology. I suppose that this could be a main focus of this study. Based on the compiled isotopic datasets, previous work (Potapov et al. 2019, Funct. Ecol.) successfully shows that the high-rank taxa can be treated as trophic nodes in food web studies. However, the work does not demonstrate that the isotopic values of relatively few individuals of a high-rank taxon could be used as the representative values of the taxon.

Reviewer #1 (Public Review): 

The experiments are well designed, generally well controlled, and carefully conducted, and are thoughtfully and appropriately discussed. The authors make conclusions that are well supported by their results. 

When describing the aptamer knockdown of the PPS, the authors explain that the western blot was too noisy for monitoring the knockdown, which is frustrating for the reader and must have been frustrating for the authors. The authors instead counter-intuitively use qRT-PCR to monitor the transcript abundance of the PPS transcript in the aptamer system - this aptamer system is thought to be a modifier of protein, not transcription or transcript abundance. The authors describe that this has been seen once before (using aptamer knockdown of PfFis1), and the authors of that study speculate that the TetR-DOZI aptamer might be degrading the target mRNA. This is a plausible explanation, but it isn't quite clear from the description how this experiment was performed. The authors explain that the knockdown parasites grew normally for three days, but the parasites may be becoming sicker over this period. It's therefore possible that the decrease in PPS mRNA abundance is a product, rather than a cause of the growth defect. Sick or dying parasites could plausibly impact the PPS differently to the two chosen controls, particularly since both control genes chosen have substantially longer half-lives than the PPS mRNA (according to the Shock and DeRisi datasets). I therefore I suggest that this experiment be performed in an IPP rescue scenario (where the parasites aren't dying) with biological replicates. There is no explanation of the replicates here, but the error bars in 6C are implausibly small for real biological replicates. 

Line 342 "These results directly suggest that apicoplast biogenesis specifically requires synthesis of linear polyprenols containing three or more prenyl groups." - I think that this might be overinterpreting those results - there could be a number of different reasons why polyprenols of different sizes do or don't rescue, including different solubility, diffusion, availability of transporters, predisposition to break down to useable subunits. Perhaps this needs a caveat. 

Line 361 " the cytosolic enzyme, PF3D7_1128400" - I don't think we know the localisation of this protein based on the published data. The Gabriel et al study makes it clear the protein isn't apicoplast or mitochondrial, but it is punctate at stages in a pattern that doesn't look to me to be a straightforward cytosolic localisation (and the original authors don't describe it as cytosolic). 

Line 423 "with strong prediction of an apicoplast-targeting transit peptide but uncertainty in the presence of a signal peptide". I don't think this describes well the bioinformatic analysis of the N-terminus. Although the experimental data are convincing that this is an apicoplast-targeted protein, bioinformatically this would not be predicted as an apicoplast protein. There is no obvious signal peptide, and "uncertainty" is too vague a descriptor. None of the versions of signalP, nor psort, predict this as possessing a signal peptide (which by definition means that PlasmoAP absolutely rejects it), and there is no obvious hydrophobic segment at the N-terminus that we would normally expect of a signal peptide. The toxoplasma hyperlopit doesn't suggest that the Toxoplasma orthologue is apicoplast, and the protein isn't found in the Boucher et al apicoplast proteome. This is somewhat of a mystery. It doesn't diminish the solid localisation data, with the excellent complementary data from IFA as well as the doxycycline+IPP experiment, but it should be pointed out clearly that this localisation isn't to be expected from the sequence analysis. 

The section after line 344 "Iterative condensation of DMAPP with IP...", up until line 377 doesn't sit well within the section that has the heading "Apicoplast biogenesis requires polyprenyl isoprenoid synthesis". I suggest either creating a separate subheading for this material, or moving it into the start of the subsequent section "Localization of an annotated polyprenyl synthase to the apicoplast.".

Reviewer #2 (Public Review): 

Okada et al., investigated the role of isoprenoids for the Plasmodium falciparum apicoplast (morphology, elongation, inheritance of the organelle). Most known functions of isoprenoids (ubiquinone-, heme A- and dolichol-synthesis as well as protein prenylation) are found outside of the apicoplast. To investigate a putative role of isoprenoids for the apicoplast itself, the authors elegantly use drug treatments at specific time points throughout the intraerythrocytic life cycle combined with rescue through metabolite supplementation. A role of isoprenoids for the apicoplast has previously been suggested but had not been investigated in this detail. By generating a MiaA-KO line, which presents no defect in intraerythrocyctic Plasmodium, the authors demonstrate convincingly that isoprenoids must have other critical functions within the apicoplast, besides tRNA prenylation, until this study the only known function for the apicoplast. In their search to identify the essential role of isoprenoids for the apicoplast, the authors investigate the role of a putative second polyprenyl synthase (PPS), besides a known enzyme inside the cytosol, which is expected to synthesize the longer chain isoprenoids, that contribute to the aforementioned roles of isoprenoids outside the apicoplast. The authors demonstrate that this enzyme is found inside the apicoplast (based on co-localization and 'fragmented localization' during apicoplast disruption). An inducible knock-down using the aptamer/TetR-DOZI system, uncovers that the PPS is required for apicoplast elongation and inheritance and apicoplast structure can only be rescued through supplementation with the very long chain isoprenoid, decaprenol (C50). Finally, the authors perform mass spectrometry combined with stable isotope labelling, to demonstrate that the phytoene, β-carotene is not synthesised by the PPS, but may originate from the serum. Thus, the newly identified apicoplast PPS likely acts inside the apicoplast where it synthesises very long chain isoprenoids which are critical for the apicoplast itself. What function they play remains unclear, but the authors speculate that the isoprenoids contribute to the membrane structure of the organelle. 

The conclusions of this paper are well supported by the data presented. The data is outlined and structured in a very clear manner. The paper reports numerous findings which greatly enhance our understanding of apicoplast biology. The study could have been further improved through a biochemical characterization of the apicoplast PPS or through metabolomic analyses of the ko-strain, but these are beyond the scope of this study. 

Minor comments: 

The authors emphasize that this study reveals a previously unnoted interconnection between apicoplast maintenance and pathways that produce an output from the apicoplast to serve the cell. But is the prevailing view really that these two are separate? Isn't the interconnection already clear from many other studies and observations? E.g., the fatty acids produced inside the apicoplast provide membrane- and lipid- precursors for the rest of the cell as well as for the apicoplast itself (Botte et al., PNAS, 2013) (although not essential in Plasmodium blood stages). Other pathways that function inside the apicoplast such as the Fe-S cluster synthesis are critical to support enzymes that provide exported metabolites (e.g., IPP synthesis, IspG/H) and function in maintenance (e.g., MiaB) (Gisselberg et al., PLoSPath, 2013). Perhaps the authors could tone this conclusion down and acknowledge that maintenance and output are interconnected in other cases, which have been acknowledged in the literature. 

Could the authors elaborate more on the leader sequence predicting apicoplast localization for the PPS characterized here and discuss why it might have been missed in previous detailed study of apicoplast localised proteins (Boucher et al., PlosBiol, 2018)? 

Could the authors discuss conservation of the PPS gene(s) in other Apicomplexa with (e.g., T. gondii) and without (e.g., Cryptosporidium spp.) an apicoplast? This could be relevant for other people in the field and could give further insights into the enzyme's role in apicoplast maintenance.

Reviewer #3 (Public Review): 

The paper is very nicely written and was a true pleasure to read. The introduction is concise yet dense with all relevant background of our current understanding of functioning of the apicoplast in relation to IPP production and utilization. The rational of the experiments and the interpretation of the results are presented clearly and everything is discussed well in the context of the current understanding of the field. The main conclusion of the paper that isoprenoid is not solely essential for critical functions elsewhere in the cell, such as prenylation-dependent vesicular trafficking but also for apicoplast biogenesis via its processing by an essential polyprenyl synthase conserved with plants and bacteria is well substantiated and very exciting. The authors demonstrate an equally beautiful and clever use of available and newly generated genetic mutants in combination with complementary pharmacological interventions and metabolic supplementation. There are no true major weaknesses that could jeopardize the conclusions or change the interpretation of the results. However, the authors do consistently perform statistical analyses on data obtained from individual cells obtained in no more than two independent experiments, which in my humble opinion does not qualify for statistical analysis. That said, the results are so clear-cut that no statistics are required to convince me, or to quote Ernest Rutherford: '"If your experiment needs statistics, you ought to have done a better experiment."

Reviewer #1 (Public Review): 

Significance: A central puzzle in evolutionary biology (and philosophy of biology) is the evolution of new (collective) entities that can evolve on their own right (e.g. the evolution of multicellular organisms from single cells). These evolutionary transitions are often conceptualized in terms of fitness decoupling (a fitness increase of the collective even as the fitness of the component particles decreases). Using a life-history model, the authors show that fitness decoupling is not possible when the conditions for fitness are the same. Thus, this paper has the potential to change how we think about the evolution of new collective entities. 

Strengths: This paper is conceptually rich and the overall argument is clear. Re-analyzing previous data/models using their new framework highlights new patterns of fitness change in these transitions of individuality, and as such, it provides novel and exciting avenues of research. 

Weaknesses: While the overall argument is clear, some of the details can be hard to follow (even as someone familiar with the literature). The initial description of their model is fairly clear, but given its conceptual novelty, the paper does not spend enough time developing the different concepts of fitness at the particle level. 

Moreover, it is not entirely clear what is at stake: what is the role of fitness decoupling in our understanding of fitness transitions? And how does the proposed mechanistic ("trade-off breaking") model serve as a replacement? It seems to me like trade-off breaking is a characteristic of many evolutionary innovations, not only of major transitions. It seems even possible to envision groups that allow for an escape in a trade-off without leading to the evolution of a new "Darwinian" individual. 

For example, one could conceive of a trade-off in zebras between time spent foraging and protection against predators. Coming together temporarily as a group is likely to allow for values outside this trade-off space (similar to those in Fig. 6). One could even imagine a new mutation that makes zebras switch activities (foraging/watching) depending on their position within the group. This mutation is only available to zebras that form groups (the phenotype does not exist in the absence of a group). But I would still want to argue that there is more to the evolution of new levels of individuality. Trade-off breaking seems (potentially) a necessary, but not sufficient step in these transitions. 

And while the language of the authors is careful to not suggest sufficiency, it is not entirely clear how this approach helps us understand the particularity of these transitions.

Reviewer #2 (Public Review): 

This work reviews the influential "fitness decoupling" heuristic for understanding evolutionary transitions in individuality (ETIs), describes some of its limitations, and clarifies its interpretation. The review of the fitness decoupling account capably describes an interpretation of this framework that has frequently occurred in the literature, for example in Okasha 2006, Godfrey-Smith 2011, Hammerschmidt et al. 2014, Black et al. 2019, and Rose et al. 2020. However, it does not address the interpretation advanced by its authors, Richard Michod and colleagues, which they have clarified in several papers cited in the present work. Michod and colleagues have argued that the fitness decoupling account describes a changing relationship between the fitness of groups and the "counterfactual" fitness of their component cells, that is, the fitness the cells would have if they were removed from the group. This point is made explicitly in Shelton & Michod 2104 and Shelton & Michod 2020 and was present (though perhaps not as obvious) in Michod 2005 and later works, in contrast to the claim in the Glossary that this is a "relatively recent development of the fitness decoupling literature." The interpretation that Michod embraces is similar to what is here described as f2, the fitness of a "theoretical mono-particle collective", but that interpretation is not mentioned in the present work until Section 2.3. It is possible that an argument could be made that Michod and colleagues have not consistently interpreted fitness decoupling this way, or have made statements inconsistent with this interpretation, but no such argument is present in this work. Thus the impression conveyed is that Michod and colleagues consider decoupling of "commensurably computed fitnesses" possible, which is counter to their explicit statements on the topic. 

The description of the limitations of the fitness decoupling heuristic (Section 2) is useful and goes a considerable distance toward clarifying the ways in which fitness decoupling can rigorously be interpreted. However, the final assessment (Section 2.3) does not make a compelling case for its central argument, the lack of utility of the fitness decoupling concept. Elsewhere in the work, the ratcheting model of Libby and colleagues is referenced in comparison to the tradeoff-breaking approach, but Section 2.3 does not acknowledge the relationship between Libby and colleagues' model and the counterfactual interpretation of the fitness decoupling heuristic. For example, the argument in Libby and Ratcliff 2014 that "If any of the yeast that evolved high rates of apoptosis within clusters were to leave the group and revert to a unicellular lifestyle, they would find themselves at a competitive disadvantage relative to other, low-apoptosis unicellular strains." and in Libby et al. 2016 that "...if G cells were to revert to unicellular I cells, they would be quickly outcompeted" are counterfactual fitness arguments essentially similar to that of Shelton and Michod 2020 that "the fitness a cell would have on its own declines as the transition progresses." Section 2 makes a convincing case that commensurable fitnesses cannot be decoupled, but by fixating on commensurability, which is not relevant to the counterfactual interpretation of fitness decoupling, Section 2.4 fails to make a convincing case that "fitness-decoupling observations do little to clarify the process of an ETI." That is, "because they are not commensurable" does little to explain why the counterfactual interpretation of fitness decoupling "does little on its own to clarify the process of an ETI," since commensurability is not a claim that the the counterfactual interpretation of fitness decoupling makes. 

The model based on trade-offs and trade-off breaking is useful and likely to be of interest to theorists interested in ETIs. The observation that this model can reproduce the (counterfactual) fitness-decoupling observation is a useful in showing the how the two models relate. The result that counterfactual fitness decoupling is a consequence rather than a cause of the evolutionary dynamics is an important point (though perhaps obvious in retrospect, since counterfactuals, things to do not happen, can't be the causes of anything). 

The caution in Section 3.3 that "the same [counterfactual fitness decoupling] observation will be made in any situation in which short-term costs are compensated by long-term benefits, not solely during ETIs" is a good point, and it sets up the argument that trade-off breaking is a "genuine marker for an ETI". However, no convincing case is made that the same criticism, that the observed phenomenon is not unique to ETIs, is not equally true of trade-off breaking. Some nice examples of trade-off breaking in the context of ETIs are given, but these do not amount to an argument that trade-off breaking is only observed during ETIs. The life history literature includes examples of trade-off breaking that are not related to ETIs, so it is not clear that trade-off breaking is either a reliable indicator of ETIs or superior in this respect to counterfactual fitness decoupling. 

In the Discussion, the "inconveniences" associated with the fitness decoupling are cogent limitations of this heuristic. The "impossibility of decoupling between commensurable measures of fitness" is an important result, but it is not new and should thus probably not be presented as "[o]ur first main finding". Shelton and Michod 2014 includes a mathematical proof in the appendix that, given the model assumptions, "consideration of the births and deaths of colonies gives us exactly the same bottom line (fitness) as consideration of the births and deaths of lone cells." The second main finding, that "fitness decoupling observations cannot be reliably used as a marker for ETIs," is valid, but as described above, a convincing case is not made that trade-off breaking can be reliably used in this manner, either. Trade-off breaking may, however, be a useful way to think about ETIs in the other ways that are suggested, for example as key events and as stepping stones to new hypotheses.

Reviewer #3 (Public Review): 

Bourrat et al take on the idea of fitness decoupling in evolutionary transitions in individuality; using theoretical approaches and some available empirical data, they question the meaningfulness of fitness decoupling and suggest instead an alternative framework that looks at rare mutations that break tradeoffs between the lower and the higher level, e.g., a tradeoff between growth at the lower level vs survival or dispersal at the higher level. This is an important question and worthwhile endeavor, useful in guiding not only theoretical investigations but also empirical measurements.

Reviewer #1 (Public Review):

The authors present an interesting approach of COVID-19 pathogenesis with emphasis on the role of innate lymphoid cells as the main driver of lymphopenia in severe COVID-19.

The main strength of the manuscript is the novelty of approach being the first on that field. The main weaknesses are the difficulty in showing that the depletion of innate lymphoid cells is culprit for the lymphopenia of severe COVID-19 and of the subsequent hypoglobulinemia and the lack of further information on cytokine production capacity from these cells.

Reviewer #2 (Public Review):

In this manuscript the authors describe very rationally the hypothesis of association of ILC with age- and sex-dependent COVID-19 severity and moreover, through a series of well-designed experiments in both adults and children justify such a link. The manuscript is of great interest and the authors are to be congratulated for their meticulous approach. I only have some minor comments as follows. The authors should clarify when exactly blood sampling took place (at admission? during hospitalization? Before any treatment start?) as in Table 1 median duration of symptoms is about 20 days, quiet a long period; normally patients are admitted after the first week. If not at admission administered therapy especially corticosteroids may influence flow cytometry results. If therapy was administered this has to be part of the regression results in Table 3. A second minor comment is the absence in presentation of comorbidities as they can perhaps also influence results. If these data are unavailable, this is for sure a limitation and should be discussed.

Reviewer #3 (Public Review):

This study investigates the association between age, sex, the frequency of specific blood lymphocyte subsets, and COVID disease severity. In uninfected controls, they show a very clear link between age and changing lymphocyte frequencies in blood. This reveals a strong association between age and the frequency of total ILCs in blood and of so-called ILC-precursors (ILCp), which are highest at birth and decrease approximately twofold every 20 years. In addition, CD4 and CD8 T-cells were significantly lower in the oldest age category, while CD16+ NK cells appeared to increase with age. The authors also reveal a lower frequency of circulating ILCs in males than females, although the ranges are highly overlapping and the effect is weakly significant. By contrast, the lower CD4 count in males is striking and highly significant.

In SARS-CoV2 infected adults, as reported widely, infections were highest in young adults, and mortality was highest in the elderly and increased approximately 1 log for each 20 year age group. Linear regression revealed that age, sex, and ILC and NK cell frequency were inversely correlated with hospitalization, and the association between ILC and NK frequency was retained when age and sex were adjusted for. Subsequent multiple logistic regression analysis showed that adjusting for age, sex, and symptom duration, only ILC frequency was significantly associated with an increased risk of hospitalization, the duration of hospital stay, and an increase in CRP. The same association between ILC frequency, but not other lymphocytes, and COVID was observed in an additional paediatric cohort. However, in rare Paediatric cases who developed MIS-C, both ILCs and T cells were significantly reduced. To link blood ILCs to what is happening on the lung, bioinformatic analysis of sequence data generated from sorted blood ILCS in compared to that of published gut and lung ILC datasets. Although, the authors argue this shows blood ILCs are transcriptionally more closely aligned to lung ILCs, the validity of this analysis is hard to judge without more detail and, as the data does not come from COVID subjects, its value in supporting the manuscript is unclear. Finally, ILCs from uninfected males produced less amphiregulin, important in lung homeostasis and repair.

The loss of ILCs from circulation has been shown in several diseases, including HIV, TB, and COVID, as has the association between circulating ILCs and age. The strength of this manuscript is in using multiple regression to show that the association between ILC loss and disease severity is retained when age is controlled for, as age is such an important factor in COVID. However, the overall conclusion that elevated circulating ILCs support disease tolerance, while interesting, is not directly supported by any data. For example, a reduction in blood ILCs may indicate the recruitment of these cells to the lung, as has been shown for TB (Ardain et al Nature 2019). In which case, an increased frequency of blood ILCs may not be protective per se, but just reflective of less lung involvement. Therefore, I suggest the title and abstract be altered to reflect the data presented more directly.

Reviewer #1 (Public Review):

This is an innovative and interesting study using crowd sourced data to estimate overdispersion of COVID-19 clusters in US & Canadian classrooms. The writing is exceedingly clear and the linkage of the observed overdispersion of case clusters with specific classroom prevention strategies in Figure 7 is potentially extremely useful. I particularly appreciate that the authors very clearly state the limitations of the cluster size data in terms of ascertainment. The writing in these sections is excellent. Overall, the study thoughtfully addresses an important public healthy question with a novel and perhaps less expensive method. The study achieved its aims with caveats listed below.

There are several key weaknesses. As the authors describe honestly and thoroughly, the high potential for misclassification of clusters is a real limitation. This is likely to be of higher relevance for the US data. Perhaps this is too subjective on my part, but the Canadian data seems likely to be more complete and less biased, particularly in terms of including singlet events with n=1 case. It also strains belief that the true cluster distribution would differ markedly between Canada and the US based on overlapping demographics, culture, class size, etc....For this reason, I would favor labeling the Canadian data as more representative of reality and interpreting the analysis accordingly. It seems fair to use the US data as a likely surrogate of what occurs when the model is applied to incomplete datasets with overrepresentation of large clusters. I would therefore consider excluding all data from the US in the main figures and writing the US data into a separate section at the end of the results associated with a supplementary figure. Overall, I think it is fair to assume that the Canadian dataset is more complete and more representative, not just of Canada but also of the US.

To harp on this a bit more, It is particularly worrisome that public health officials might interpret US time to cluster analyses in Figure 6 literally when the Canadian estimates are more likely to approximate the truth. Similarly, differing estimates for Rc between Canada and the US are alarming. As written, a public health official could interpret the Rc in schools in Florida as 7! This is false. Accordingly, all estimates of k in the US are likely to be deeply misclassified.

In the discussion, the authors fail to state the most obvious contributor to overdispersion which is aerosolization. Notably, influenza virus is associated with equivalently heterogeneous contact networks, similarly high variation in viral load and an overlapping major route of transmission. Yet, its degree of overdispersion is substantially less than SARS-CoV-2, SARS, or MERS, likely due to less aerosolization. Accordingly, influenza is much less commonly associated with large super-spreader events. Please see Goyal et al (Elife, 2021).

Next, I am puzzled by one result which is the Canadian data in Figure 6. This panel suggests that clusters involving more than 12 cases never will happen. This is probably not correct. I think that the issue is that this analysis fails to account for the rarity, but high importance of larger super-spreader events. I am assuming that this figure is showing average values as it is directly extrapolated from parameter values. It would be more useful to show the range of expected times needed to see a cluster of different sizes. This would require stochastic simulation which could be performed by drawing randomly from a distribution with given values for Rc & k. The result would likely be a wide range in time to cluster for given set of Rc & k values. Without accounting for stochasticity, this figure is misleading and should probably be removed.

Another final limitation is regarding data presentation in the figures. Multiple suggestions are provided for the authors to strengthen scientific messaging.

Reviewer #2 (Public Review):

This manuscript has a number of strengths. First, the paper concerns a topic of considerable importance and interest as a safe return to in person education will be critical for safe reopening of societies. Second, there have been limited analyses of school transmission clusters, and they present the opportunity to better understand school transmission risks. Finally, the authors integrate an analysis that estimates transmission risk with a previous infection risk framework, which provides actionable guidance to school administrators concerning the most effective mitigation measures.

However, there are a number of weaknesses in the analysis. First, the manuscript relies on reported cluster data rather than systematically collected datasets. This causes issues related to reporting biases such as differences in reporting standards across jurisdictions and a propensity to miss smaller case clusters. Second, the modeling methodology relies on assumed ascertainment rates of infection, which appear sensitive to assumptions related to the proportion of cases that are detected (particularly in low ascertainment ranges). Finally, it is not fully clear what constitutes a cluster within a school in the dataset and the methodology. This makes it difficult to interpret the fitted model results, particularly for analyses comparing the cluster size across regions.

Overall, given my concerns related to the underlying data, I find it difficult to interpret the results without significant alterations to the methodology and manuscript overall.

Reviewer #3 (Public Review):

The strength of this paper lies in its simplicity. The authors have, as above, fitted simple negative binomial models to available school outbreak case distributions. The sensitivity analysis in which plausible variation in ascertainment fraction does relatively little to cluster size estimates is also important.

More exploration of mechanisms underlying Canada-US differences would be helpful.

Reviewer #1 (Public Review):

The goal of health scientists is to find better ways to treat patients for the various ailments. One of the most significant challenges we face is developing safer and more efficacious therapeutics. In creating safer therapeutics our goal as scientists is to understand and minimize adverse effects, usually caused by off-target effects. In some cases, the adverse effects are caused by on-target but related to a complex signal transduction pathway. To dissect this complexity, Avet et al developed a novel set of biosensors to assess the coupling specificity of 100 therapeutically relevant G protein-coupled receptors (GPCRs) to various G protein isoforms and arrestins. In their manuscript, the authors tested reference ligands defining each receptor and assessed their capacity to interact with a 12 different G proteins and 2 arrestins, each contributing to distinct signal transduction pathways. The novel screen and results obtained with reference ligands will have a direct impact on understand adverse effects of currently marketed therapeutics but also on the discovery of novel, safer therapeutics.

Reviewer #2 (Public Review):

The authors describe unique resources for studying GPCR coupling to different G proteins. The manuscript describes a full set of "effector membrane translocation assay" (EMTA) tools that can be used to assess activation of Gi/o, Gq and G12/13 families, and pair this with a Gs membrane translocation assay to assess activation of 12 G proteins and 2 arrestins by 100 GPCRs. The data presented represents a valuable resource for investigators interested in the precise signaling pathways that mediate physiological events, and the tools described are likely to be useful for many specific studies of individual receptors, including efforts to discover ligands that display functional selectivity (bias) between G protein pathways or between G proteins and arrestins. The authors make the case that their system has a unique advantage in that most of the G proteins studied need not be modified. While this advantage is not empirically demonstrated in the manuscript (assay systems are not directly compared), the availability of multiple assay systems based on different principles is obviously advantageous. The manuscript also includes some very nice observations, such as the ability to detect signals produced by endogenous receptors and G proteins, and a near-universal assay system that combines Gz and G15 assays. The overall utility of the assay system, which should have significant impact on the field, is well-supported by the data.

Reviewer #3 (Public Review):

Building on previous work (Namkung, Nat communications 2016 & Science Signaling 2018) the authors now develop a set of new enhance bystander BRET (ebBRET) biosensors to measure the GPCR coupling with all G proteins classes and barrestin. Using this set, they then map the effector coupling possibilities of 100 GPCRs in HEK293 cells. The biosensors optimally function upon heterologous expression of GPCRs and the relevant G proteins, although the authors show that they can be used when endogenous expression levels and/or effector coupling are sufficient. The authors claim that since GPCR and g proteins are untagged that these biosensors represent potential advantages over alternative developed technologies, although it is not clear if a systematic comparison has been done and differences revealed. From their work, the authors show that combining two biosensors to measure Galpha15 and GalphaZ coupling provides a quasi-universal biosensor to measure GPCR activation that could be useful to identify off-target activation or polypharmacology.

Strengths:

This is an impressive quantity of work describing the possible GPCR coupling for 100 GPCRs (in one cell type), which are an important class of drug targets. The authors have now assembled a collection of ebBRET sensors (some previously reported ones and some new ones) that monitor activation of all heterotrimeric G proteins and b-arrestin that could be used to profile ligands and uncover functional selectivity at G proteins or b-arrestin pathways. These new sensors called EMTA, are based on the plasma membrane translocation of effectors. EMTA was found to be suitable for the detection of constitutive activity, inverse agonism, biased signaling and polypharmacology. The uncovering that combining Galpha15 and GalphaZ biosensors could enable monitoring activation of virtually all GPCRs could also lead to a new screening platform and/or allow to identify off-target activation or polypharmacology properties of various ligands.

Weaknesses:

Although one can imagine potential use for the information reported in the manuscript and from the new tools to study GPCR signaling, the present study is mostly descriptive and stops short of exploring some new biology made possible by these new technological advances. Given that in most contexts biosensors are used with overexpression of GPCRs and G proteins, some concerns exist about the relevance of the information presented, especially in the absence of validation.

Reviewer #1 (Public Review):

In this paper, Qin et al. investigated the molecular mechanism of phospholamban (PLN) linked dilated cardiomyopathy (DCM), using structural approaches combined with biophysical measurements. Structures of the catalytic domain of protein kinase A (PKAc) in complex with PLN peptides (both wild-type and the R9C and A11E DCM mutants) provide insights into the mechanism of substrate recruitment and how it is perturbed in the disease state. Qin et al. show convincingly that the mutant peptides all have lower affinity for PKA than the wild-type peptide, suggesting models in which heterozygous DCM mutations act via sequestering PKA and thereby preventing phosphorylation of the wild-type peptide may be incorrect.

The authors highlight significant differences between their structure of the WT-PLN:PKAc complex, which has a 1:1 stoichiometry, and a previous structure of the complex (PDB 3O7L), which has 1 PLN bound between two PKAc monomers (a 1:2 complex). The authors posit that the stoichiometry observed in 3O7L is an artifact of the crystal lattice, and does not occur in solution, supporting this with analysis of the elution volumes of the peptide complexes on size exclusion chromatography compared to PKAc alone. They further suggest that the AMP-PNP ligand included in the 3O7L structure is not bound, based on analysis of Fo-Fc maps calculated from the deposited coordinates. Inspecting 3O7L I am not convinced of this last point - it seems more likely that a technical error was made in assigning or refining the B-factor of the ligand in 3O7L, because there is clearly density present in SA-omit maps for the nucleotide.

Taking these results together, the authors suggest a mechanism for DCM, whereby mutations in PLN result in lower affinity for PKA, and consequently reduced phosphorylation. This seems plausible and well supported by the data, although in the ADP-Glo assay used here, the reductions in phosphorylation observed for some of the mutant peptides are rather modest. However, as the authors state, it is plausible that even relatively subtle changes in PLN phosphorylation could have substantial effects on Ca2+ homeostasis via increasing SERCA inhibition.

Reviewer #2 (Public Review):

Strengths:

The authors presented new high-resolution 3D crystal structures of the PKA catalytic domain (PKAc) in complex with PLN WT or mutant peptides (residues 8-22) containing the DCM-associated PLN mutations (R9C or A11E). These are novel and important data given that the present structures are dramatically different from those reported previously. The authors made convincing argument that the 3D model reported previously may result from a crystallization artifact.

By characterizing the interactions between the PKAc domain and PLN WT or DCM-associated mutant peptides using surface plasmon resonance (SPR) analysis, the authors convincingly showed that the DCM-associated PLN mutations at positions 9, 14, and 18 alter the conformation of the PLN peptide and reduce the binding affinity of the PLN peptide with PKAc. These data provide an explanation how some DCM-associated PLN mutations at these positions reduce the level of PKA-dependent phosphorylation of PLN.

The authors also performed nuclear magnetic resonance (NMR) to determine the structural dynamics of PLN WT, R9C, P-Ser16, and P-Thr-17 peptides. These NMR structures combined with the SPR analysis also support their conclusion that PLN phosphorylation and DCM-associated PLN mutations have an impact on its conformation.

Weakness:

The present study used PLN-derived peptides (aa 8-22). Although technically challenging, it is important to consider if the full-length WT or mutant PLN will behave the same as those observed with the peptides. This is especially crucial in light of the prior work showing substantially different structures using a different segment of PLN.

Although it is convincing that DCM-associated PLN mutations likely reduce the interaction between PKAc and PLN (assuming that the peptides behave the same as the full-length PLN with respect to interaction with PKA) and, as a result, the PKA dependent phosphorylation of the mutant PLN, it is unclear how this impaired interaction between PKA and PLN mutant could explain the effects of the DCM-associated PLN mutations on SERCA function (either reduced or enhanced PLN-dependent inhibition of SERCA, as proposed previously). In this regard, can the authors predict if the DCM-associated PLN R9C mutation reduces or increases SERCA inhibition based on the results of their present study?

It is also unclear how reduced PKA phosphorylation of mutant PLN could lead to DCM. PLN is unlikely to be significantly phosphorylated by PKA at rest (in other words, PLN is likely to be phosphorylated by PKA during stress, i.e. during the adrenergic fight-or-flight response). Therefore, it is puzzling how such reduced PKA-dependent phosphorylation of PLN would significantly affect the PLN function during the absence of flight-or-flight response.

Given that the DCM-associated PLN mutations have significant effects on the conformation of PLN itself, at least in the form of short-peptides, it is possible that these mutations could affect the folding, oligomerization, trafficking, degradation, etc., in addition to PKA-dependent phosphorylation. The relevance and contribution of reduced PKA-dependent PLN phosphorylation to DCM remain unresolved.

Reviewer #3 (Public Review):

This manuscript describes an elegant study utilizing the crystal structures for the elucidation of the disease mechanism of familial dilated cardiomyopathy. It has been known for decades that the mutations in PLN are associated with DCM, but the underlying mechanism remains controversial. In my opinion, Prof Yuchi and co-authors did excellent job on revealing the high-resolution crystal structures of PKA-phospholamban complexes, representing both the native and diseased states. Combined with various of biophysical and biochemical methods, including SPR, ADP-glo, thermal melts, NMR, etc, the authors systematically investigated the correlations between the PLN conformation, the binding affinity, and the phosphorylation level. The mechanism of PKA phosphorylation on another related substrate, ALN, was also convincingly revealed. The results are very helpful for understanding the pathological mechanism of PLN-related DCM. More importantly, the atomic structures of PKA-phospholamban complexes lay a solid foundation for the structure-based rational design of therapeutic molecules that can reverse the effects of the DCM-causing mutations in the future, e.g. by stabilizing the interactions between PLN and PKA.

Reviewer #1 (Public Review):

The current manuscript describes a model of Ewing sarcoma wherein the cre-inducible expression of human EWSR1-FLI1 in wild type zebrafish causes the rapid onset of small round blue cell tumors (SRBCTs). The tumors appear to resemble human tumors, expressing CD99 as well as elevated ERK1/2 signaling. Proteomics indicated that progression was associated with dysregulated extracellular matrix metabolism in general and heparan sulfate catabolism in particular. Accordingly, targeting heparan sulfate proteoglycans with Surfen reduced ERK1/2 signaling and tumour cell growth in vitro and in the zebrafish model. Overall, this study reveals a model that may be used to better understand the evolution of sarcoma. However, greater comparisons may be needed in order for this model to be used as a model of human disease.

Reviewer #2 (Public Review):

The authors of this paper have generated an inducible transgenic zebrafish that expresses human EWSR1-FLI1, the most common oncofusion in Ewing sarcoma, a common bone and soft tissue cancer in children and adolescents. These zebrafish represent the first in vivo genetic model of Ewing sarcoma that develops spontaneous tumors solely from expression of the oncofusion, with tumor formation in defined anatomic sites. Specifically, these tumors tested positive for CD99 expression and PAS staining, two diagnostic markers of Ewing sarcoma. Additionally, increased ERK1/2 signaling was observed in both the initial cellular outgrowths and mature adult tumors and transgenic embryos displayed an upregulation of proteins associated with extracellular matrix reorganization, proteoglycan metabolism, and protein synthesis. This led the authors to examine the efficacy of surfen, a sulfated heparan sulfate antagonist that interferes with proteoglycan signaling, which resulted in the impairment of ERK1/2 signalling and decreased proliferation and survival in two Ewing sarcoma cell lines. In vivo, surfen treatment inhibited the formation of EWSR1-FLI1-associated cellular outgrowths in zebrafish embryos and rescued abnormal fin shape in transgenic larvae. Thus, this Ewing sarcoma transgenic zebrafish line may shed light on additional mechanisms contributing to tumorigenesis and can serve as a robust drug screening tool to further identify therapies to target Ewing sarcoma.

Strengths

Overall, this is a well-written manuscript. The authors did a thorough job showing their novel zebrafish transgenic line recapitulates Ewing sarcoma phenotypes and that these phenotypes were specific to the expression of the oncofusion. Their data nicely shows the comparable histology between the zebrafish and human tumors, especially as they are the first group to demonstrate the successful immunohistochemical staining with CD99, diagnostic (but not entirely specific) for Ewing sarcoma. Additionally, they have described a quantifiable phenotypic readout (Ccurv analysis) for drug discovery that can be used to determine the efficacy of potential therapeutic compounds for Ewing sarcoma. This study demonstrates the utility of using a representative zebrafish model to uncover key mechanisms of disease progression and opportunities for molecularly targeting these mechanisms to rescue disease phenotypes.

Weaknesses

The ability of surfen treatment to cause morphological changes and inhibit cell proliferation and survival in two Ewing sarcoma cell lines would need to be further compared to other cancer cell lines, especially those that do not have upregulated ERK1/2 signaling to determine specificity of proteoglycan metabolism in Ewing sarcoma progression. Surfen treatment decreased expression of active ERK1/2 in the Ewing sarcoma cell lines, and a similar approach should be used to investigate if decreased pERK1/2 expression is observed in vivo in correlation with decreased cellular outgrowths. Additionally, images showing the ability of surfen to rescue normal fin curvature would make these results more compelling.

Reviewer #3 (Public Review):

In this manuscript, Vasileva et al developed an Ewing sarcoma in vivo model by introducing EWSR1-FLI1 fusion oncogene into zebrafish. They used the Cre/loxp system to delay the expression of the oncoprotein, thus avoided the developmental toxicity of EWSR1-FLI1 which they observed in previous studies. Injected zebrafish developed malignant small round blue cell tumors similar to human Ewing sarcoma at multiple levels, including histology, immunohistology of Ewing markers and elevated ERK1/2 signaling. Embryonic proteomics analysis showed that proteins involved in ECM and proteoglycan metabolism were alternated by EWSR1-FLI1 in zebrafish embryos, especially the enzymes involved in heparan sulfate proteoglycan catabolism. Similar signatures were also identified in a human Ewing sarcoma microarray dataset. Based on these findings, the authors used a sulfated heparan sulfate antagonist, surfen, to treat human Ewing sarcoma cell lines and observed reduction of pERK1/2, cell proliferation and colony formation. Subsequently, they added surfen to the fish water containing the injected zebrafish to evaluate the in vivo tumor suppression effect of this compound. This is an interesting and important study defining the first robust animal model of Ewing sarcoma caused by EWSR1-FLI in vivo. And the authors use the model to document changes in protein expression that suggest treatment with a novel agent that is in use in humans for another condition.

Reviewer #1 (Public Review):

Hauser et al, analyze two large datasets of GPCR-G protein interactions/couplings ("Inoue" and "Bouvier"), comparing and combining them with the widely-used literature-based Guide to Pharmacology (GtP) database. As the Inoue and Bouvier datasets were based on different experimental setups, this enables the identification of which couplings are supported by more than one method. The authors also establish a normalization protocol that enables to move from qualitative to quantitative comparisons and identify couplings that might be either below are above a rigid threshold. Overall, the paper describes a new resource and the methodologies used to build this resource. The resulting coupling map is available through the GPCRdb website, a widely used resource in the field.

The authors have thus improved the ability of researchers to assess prior results and compare them to their own new data. This resource clearly and significantly upgrades options currently available and will likely be of interest and prove quite useful to scientists both in academia and in industry.

Weaknesses include:

- The data is described mostly by broad numbers, such as the number of receptors or coupling in a subset, or percentages. While this is helpful to understand the data, this reviewer found it hard to follow the mountain of numbers. A suggestion would be to add a section where the authors pick selected examples of particular experimental data and show how their combine database can resolve previously unanswered (or wrongly answered) questions of GPCR/G protein coupling.

- The paper does not reveal new biological findings. For example, while some emphasis is placed on new data on G15, it would be helpful to take the extra step and use this to suggest new biological insights.

- The authors cautiously label couplings supported by only one dataset as "unsupported". It would seem more helpful to grade couplings by a reliability scale, providing users with a wider set of data. Perhaps only couplings that are directly conflicted by negative data should be labeled as unsupported?

- Given that this manuscript includes authors from both the Inoue and Bouvier studies, I can understand why they are not directly assessing which of the two datasets (in relation to the GtP) might be more accurate. Nevertheless, I believe this assessment should be done and that the advantages and disadvantages of the two experimental systems discussed clearly.

In contrast to the larger volume of the "weaknesses" section, the strengths of this manuscript are clear and robust - this is a very useful resource that is described well and with many details.

Reviewer #2 (Public Review):

This study is a meta-analysis of previously reported studies on G protein-coupled receptor (GPCR) coupling to G proteins. The data sets are from three distinct sources: a compendium compiled by the International Union of Basic & Clinical Pharmacology (IUPHAR), and two data sets compiled by two separate laboratories. Each of these data sets describes the coupling of members of the superfamily of non-sensory GPCRs (~200 genes) to the large family of G protein alpha subunits (~20 genes). The authors try to arrive at a consensus for receptor-G protein coupling from the three data sets, as well as identify and highlight differences or incongruencies. Compiling these vast data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies. A better understanding of receptor-G protein selectivity and/or promiscuity will ultimately help in identifying safer therapeutics.

Reviewer #1 (Public Review):

Wang et al. adapt a new statistical framework on a multi-site multi-year database to investigate the effects of environmental variables on the temporal stability of plant communities and biomass productivity in Chinese grassland. The authors show with several lines of evidence that 1. the temporal stability of the region is due to spatial asynchrony of community dynamics, 2. this stability relies on dominant species, but less so on other community metrics, and 3. reductions, but also increasing variability in water availability reduces the stability of the system, with rather important future consequences to humans living in the region.

A significant strength of the ms lies in solid statistics. Wang et al. apply to a real dataset a new framework (and two pathways, i.e., community-level vs. population-level metrics) with formulas the authors develop (in special for dominant species). Additionally, they provide a summary/test of the effect of environmental variables in shaping regional stability with SEM analyses. This new framework may be one that the larger ecological and ecosystem academic communities, interested in temporal changes of ecological processes across large spatial scales, are looking for.

Reviewer #2 (Public Review):

The authors analyse an impressive dataset of field data collected across Inner Mongolian Grasslands to test theory concerning the mechanisms promoting temporal stability of plant biomass.

Overall, the analyses seem solid, and the paper is based on strong theory, but the overall message is diluted by a large number of different analyses, making the analysis, results, and interpretation confusing in several places.

The unfocused nature of the analysis and presentation of the results makes it difficult to evaluate whether the authors achieve their aims, and whether their results support the conclusions. My general impression is that they do, but the number of different analyses, supplementary results, etc., really complicates the narrative and interpretation.

The paper is an interesting test of theory, and a practical test of the theory outlined in a previous paper (Wang et al.) could be a real asset to anyone aiming to explore the mechanisms promoting temporal stability across scales. The dataset too is a large and potentially useful one.

That said, without a clearer narrative and streamlined set of analyses, it is difficult to interpret the potential impact of this work - which is a shame, because clearly the work put in was considerable. By focusing on only a few key analyses and results, interpretability and potential impact could be much improved.

Reviewer #1 (Public Review):

The authors demonstrate a molecular mechanism responsible for the rewiring of stroma cells that makes them supportive of acute myeloid leukaemia cells. That metabolic interactions takes place between leukaemia cells and the bone marrow microenvironment is an emerging topic, which hopes to provide new targets for the development of less toxic and more effective leukaemia treatments. It has already been shown that inhibiting gap junctions reduces AML growth in vivo (reference 10 in the manuscript), and here the authors provide evidence for a potential mechanism explaining that finding. Here, the authors demonstrate using co-culture models that AML cells use gap junctions to offload ROS to stroma cells, which in turn provide acetate that fuels AML cells' metabolism. This is linked to a transcriptional rewiring of stroma cells leading to overexpression of gap junctions themselves and of genes encoding enzymes involved in acetate production. The work is very interesting, and it should be feasible for the authors to fill a few gaps that would allow this to become a complete demonstration of the phenomenon uncovered.

Reviewer #2 (Public Review):

The manuscript by Vilaplana-Lopera et al. highlights a novel interesting metabolic cross-talk between the stroma and AML cells through production of acetate. The main conclusion of the paper, ie that acetate production is increased in co-culture most likely because of stroma production and is taken up by AML cells, is supported by the data. Some of the other conclusions re the utilisation of acetate in AML cells and the exact mechanism leading to acetate production might require further experimental work to be fully supported. The authors make an effort to validate their findings in primary AML cells model and in vivo although the mechanistic insight is mostly done in AML cell lines grown on a MS-5 stromal layer. As a result, whether the described interaction is also present in other stroma/AML combination remains to be demonstrated.

Technically the paper appears rigorous although I would not be able to comment on the technical aspects of the NMR analysis and I would hope some of the other reviewers can comment on that.

Reviewer #3 (Public Review):

Vilaplana-Lopera et al., present a study which predominantly focusses on the in vitro interaction of three different human AML cell lines with the murine bone marrow stromal cell line MS-5. The authors describe a novel observation where the co-culture of AML and MS-5 cell lines results in elevated production and secretion of acetate via a mechanism which requires direct contact between these two cell types. Importantly, increased extracellular acetate could also be detected in both an in vitro setting where primary patient AML samples were co-cultured with MS-5 cells; and in an in vivo mouse model of MLL-AF9-driven AML. In the context of in vitro co-culture, the authors demonstrate that AML cell lines are capable of taking up extracellular labelled acetate and incorporating this into various TCA-related metabolites. Underlying this observation, the authors found evidence of metabolic changes in stromal cells under co-culture conditions, namely relating to elevated glucose metabolism and production of acetate via upregulated glycolysis. Following on from this observation, evidence is presented to suggest that AML cells transfer reactive oxygen species (ROS) to stromal cells via gap junctions upon co-culture, where the ROS can then mediate a non-enzymatic decarboxylation of pyruvate into acetate. The authors speculate that this metabolic reprogramming of stromal cells by AML cells may help support the increased energy demands of AML cells by producing acetate which can then be metabolised in the TCA cycle.

Strengths of the study

The authors have succeeded in documenting a novel phenomenon where leukemia cells are able to metabolically reprogram stromal cells, resulting in a very interesting model where acetate is shuttled from niche cells to AML cells, apparently to the benefit of the AML cells.

The authors have validated some key aspects of their cell line data using both patient samples and a mouse model of AML.

NMR-based tracing of metabolites unequivocally demonstrates that AML cells are capable of taking up and subsequently metabolising extracellular acetate in the context of co-culture with stromal cells.

The concept that the intercellular shuttling of ROS is responsible for the catabolic conversion of pyruvate to acetate in stromal cells is highly novel.

Weaknesses of the study

The biological relevance of this mechanism is not clear, since coculture does not alter the growth of AML cell lines. Therefore, the resulting transfer of acetate appears to have no obvious impact on leukemia cell biology.

The authors have not directly demonstrated that acetate generated by stromal cells is metabolised by AML cell lines.

The use of NAC as a method to reduce ROS confounds interpretation of the data in this particular experimental setting, since NAC is precursor for several substrates in the metabolic pathways that are examined in this study. That is, NAC is the pro-drug for the generation of intracellular glutathione and can also be deacetylated to generate intracellular acetate.

The experiments which explore the mechanistic role of gap junctions in this phenomenon do not show any direct impact on acetate production nor secretion by stromal cells.

The culture conditions that have been used for growth of primary AML and control samples appeared to have been altered between different experimental repeats, which makes interpretation of data difficult. In addition, it is not entirely clear what cell type has been used as a control cell population for these experiments.

Reviewer #1 (Public Review):

Starting with iPSCs the authors build a 2D cell culture system and observe the gradual process of muscle differentiation in order to view myofiber formation in real time. Authors perform a detailed study looking at the time course of muscle development both in terms of expression of markers in real time as well as transcriptomic analysis. During their descriptive studies the authors note general rules of myofiber formation, myotube differentiation follows the emergence of stable attachment of foci as well as a seemingly coordinated strong induction of sarcomeric genes. These rules are consistent with a uniform cue for myofiber formation which they hypothesize to be tension. They test this hypothesis using laser microsurgery and explore the hypothesis that force-resistant integrin-based fiber-fiber attachments stabilise the myofibers and serve as a mechanism to explain their other observations.

Overall this is an important pioneering study. It stands on it's own but opens up an exciting avenue of research. The ability to view the entire process of myofiber and sarcomere formation in real time allows tissue engineering approaches to be used including varying the matrix (they currently use minimal culture conditions), varying the mechanical environment (using stretchable tissue media), and performing knockout screens to understand the process better. Moreover, since the authors start with iPSC this technique could be used to generate muscle cultures from patients to provide clinical insight.

Reviewer #2 (Public Review):

The article from Q Mao et al describes that muscle cells derived from iPSC differentiate into myofibers bundles that attach to each other at their ends in an integrin-dependent manner. To do so, the cells progressively arrange themselves together to form domains of alignment. The authors also observe that formation of sarcomeres creates tension forces which are in turn needed for building additional sarcomeres, ending in the stabilization of the myofibrils. Those results bring light into the collective behavior effect of muscle cells on their maturation.

The study will interest both researchers in the muscle field but also biophysicists interested in collective cell behavior.

Reviewer #3 (Public Review):

The manuscript by Mao et al., applies previously described protocol of in vitro muscle differentiation from iPS-derived human myogenic progenitor cells to test whether tension is required for the formation of sarcomeric pattern in multi fibrillar context of human muscle.

Authors elegantly describe and document, step-by-step, in vitro muscle differentiation from myotubes to mature myofibers organized in bundles. They apply quantitative and bioinformatics-assisted methods that clearly demonstrate that this process is biphasic with the formation of self-organising bundles of myofibers whose number and area increase rapidly until day 7 and then remain stable until day 15. Interestingly, this correlates with the transcriptional burst of several genes encoding sarcomeric components and the appearance of more and more regular striated patterns of titin, actin and MyHC along the myofibers. Strikingly, bundles of myofibers tend to cluster their ends in a way to form foci in which myofibers from adjacent bundles attach to each other. Authors suggest that this organisation and attachment between myofibers allows generation of tension, which increases during differentiation and is necessary for the formation of sarcomeres. This view is supported by an increased level of expression of actin, integrin, myosin and N-term titin in mature myofibers but also by micro-lesion experiments that allow measuring tension. Overall this is well executed and documented with innovative methods to study uncovering capacities of myofibers to self-organise in vitro and providing new standards for analysing iPS-derived muscle differentiation.

Reviewer #1 (Public Review):

Quigley et al. provide new insights into persister cell formation in Mycobacterium tuberculosis, a notorious pathogen provoking infectious diseases worldwide. The study is most relevant as M. tuberculosis is particularly tolerant to antibiotic treatment because of a dormant state. The authors show, at the single-cell level, that low ATP Mt cells are killed more slowly by antibiotics compared to high ATP cells, indicating the critical importance of ATP levels in drug tolerance. Further, the authors claim that increased noise in ackA expression, involved in carbon metabolism, results in higher survival. However, this link is currently less clear and could be further strengthened to make solid proof of the underlying molecular mechanisms. To conclude, this manuscript is very interesting and may lead to improved understanding of persistence in relation to ATP levels and noise in an important pathogen.

Reviewer #2 (Public Review):

This manuscript by Quigley & Lewis investigates the impact of bacterial intracellular ATP concentration on the ability of Mycobacterium tuberculosis to survive-but not grow-upon exposure to antibiotics. This manuscript follows others (PMID 33872303, 34982597) from the same laboratory that convey similar findings but focuses on a different organism, Mycobacterium tuberculosis. First, the authors show that cells treated with bedaquiline (BDQ) (the concentration used is not indicated), a drug that leads to a decrease in the intracellular ATP pool, are better able to survive subsequent exposure to antibiotics of other classes. Although this is compelling, it should be noted that BDQ leads to other changes in treated cells, such as rapid collateral vulnerability of another metabolic enzyme, glutamine synthase (PMID 31501323); thus, the increase in the number of bacteria surviving antibiotic treatment following exposure to BDQ might not be solely due to BDQ's influence on intracellular ATP. Next, rather than measuring ATP of the bulk culture, the authors used a radiometric FRET-based ATP biosensor to measure ATP levels in single cells so that they could sort low-ATP from high-ATP cells and showed that the former survive exposure to a combination of rifampicin and streptomycin better than the latter; of note, the difference in survival between the two populations is much lower than the survival difference between cultures with and without BDQ pre-treatment, suggesting that additional effects of BDQ contribute to the generation of persisters. The authors further used an acetate-based minimal medium to identify enzymes contributing to energy production under the hypothesis that variations in levels of expression of these enzymes might allow isogenic bacterial cells to have heterogeneous ATP levels. The authors used this medium supplemented with different concentrations of acetate to show that growth on lower acetate concentrations led to a higher number of cells with low ATP levels, better survival upon exposure to a combination of rifampicin and streptomycin, and a wider distribution of ATP levels. This led the authors to hypothesize that a wider distribution of ATP levels, encompassing ATP levels low enough to allow for persister formation, stems from greater variation, or noise, in gene expression. To prove their hypothesis, the authors measured transcript levels of genes involved in the conversion of acetate into acetyl-CoA in single cells with either low or high ATP levels and showed that cells with low ATP levels have lower transcript levels than cells with high ATP levels and that ackA transcript levels vary among cells with high ATP levels. This is the only conclusion that can be extracted from this experiment; the statement in the discussion (lines 210-211) that says "This shows considerable noise in the expression of the acetate kinase AckA in low ATP M. tuberculosis cells when acetate is the sole carbon source (Figure 4A)." is erroneous. To prevent variability in AckA expression, the authors overexpressed AckA, which led to a decrease in the number of persisters upon exposure to a combination of streptomycin and rifampicin; the authors concluded from this experiment that noise in AckA expression was responsible for the generation of cells with low ATP levels that survive antibiotic treatment. As is, the data provided do not support these conclusions; in Figure 4B, the authors should show the distribution of ATP levels in single cells as they did in Figure 2 and verify that the proportion of cells with low ATP levels is decreased upon overexpression of AckA. AckA overexpression should be demonstrated by Western blot or qRTPCR data. In the same vein, the authors should quantify the extent to which the CV decreases when AckA is overexpressed as in Figure 3. Finally, showing that knocking down ackA leads to an increase in cells with low ATP levels and better survival upon to exposure to antibiotics would strengthen the authors' conclusions.

Reviewer #3 (Public Review):

The manuscript by Quigley et al. is a technically-ambitious and potentially important study linking noisy metabolic gene expression with ATP levels and persister formation in M. tuberculosis. This work addresses a critical question - the mechanism of persister formation in this important pathogen. Using a reporter for intracellular ATP and single cell expression analysis, the authors conclude that stochastic expression of the acetate kinase gene correlates with ATP levels and persister formation during growth on acetate. They then test the importance of this variation in expression level, showing that constitutive ackA transcription reduces the abundance of persister cells. Overall, this is a compelling hypothesis that is consistent with work in other bacterial systems, and could lay the foundation for new therapeutic strategies. Acknowledging the technical challenge of studying small cellular subpopulations and the effort that has already gone into this work, there are a number of issues that should be addressed:

1) Based on the effect of preexposure to BDQ (Figure 1A and B) the authors conclude that "Tolerance of these mechanistically unrelated antibiotics shows that a decrease in ATP causes multidrug tolerance." This conclusion is consistent with the data, but the authors did not show that other bacteriostatic antibiotics don't have the same effect.

2) The relationship between growth and persister formation in Figure 3 seems overinterpreted. Since the growth curve ends at the same time that the ATP measurements are made (1 week) it is difficult to know what growth phase these cultures are in. As a result, statements like the following are hard to support: "The level of ATP dropped in the order 20 - 10 - 5 mM acetate (Figure 3C, D), showing that growth is not affected by relatively small changes in ATP", and "Notably, apart from linking noise to persister formation, this experiment shows that growth rate per se does not determine tolerance." As the growth curves end at the time of analysis, how do the authors know if the low acetate cultures are entering stationary phase?

3) The correlation between ATP CV and persister fraction in figure 3i is very weak and driven by a single point that does not seem to be representative. I don't see a biologically-driven correlation here, and the statistics agree.

4) The populations sorted in figure 2A differ in a number of respects. They differ in ATP concentration dependent FRET signal, as intended, but they also differ by more than 10-fold in constitutive YFP fluorescence. This suggests a difference in plasmid copy number, and the inverse correlation between YFP and ATP signals suggests a dependency. One could hypothesize that the high ATP cells have low plasmid copy number because they are replicating more rapidly. This is mainly an observation that the authors may consider commenting on, and is relevant for the next point. The conclusion that low ATP cells are enriched for persisters is sound.

5) The single cell qPCR study is a great approach, but there are technical issues that limit interpretation. The mRNA levels were normalized to the origin of replication of the ATeam plasmid. Based on YFP fluorescence in Figure 2A, the copy number of this plasmid appears to vary between single cells and is correlated with ATP levels (see point 5). It seems quite plausible that low ATP cells have a low ackA/plasmid ratio because plasmid copy number is increased.

6) The effect of ackA expression on antibiotic tolerance (figure 4D) are quite compelling. However, it is not clear if this effect is related to ATP levels. The product of ackA (acetyl phosphate) could have unanticipated ATP-independent effects on the cell if the enzyme is overexpressed.

Reviewer #1 (Public Review):

This study by Andreatta et al. defines the transcriptional state of virus-specific CD4 T cells in both acute and chronic LCMV infection over time. They identify 6 distinct cell states in both infections: Th1, Tfh, T central memory precursors (Tcmp), Th1 memory, Tfh memory and T central memory (Tcm) and characterize how the proportions and gene expression of each of these states are altered across time. In acute infection, Th1 effector function is downregulated as the cells transition to early memory cells, but the loss of Tfh effector function appears to be delayed until later memory timepoints. Tcmp and Tcm are transcriptionally distinct from the other states and have memory markers at early timepoints. During chronic viral infection, Th1 effector cells are lost with time, but Tfh function appears to be maintained. Further, using single cell TCR sequencing analysis, the authors determine that CD4 T cell responses are private amongst different individuals and that most TCR clonotypes can differentiate into all subtypes and are independent of the TCR. Lastly, the authors use their data to create a reference atlas as a new computational resource to interpret and subset CD4 T cells from other single-cell datasets.

In this study the computational methods and analysis are strong and the conclusions are well supported. As attributed in the text, some of the changes observed confirm those that have been documented in other studies, but this reference atlas now assembles the data from these timepoints and serves as a useful tool for the field to analyze other CD4 datasets. A few points listed below, however, would clarify and enhance the study.

Reviewer #2 (Public Review):

The main strength of the paper is the parallel profiling of virus-specific CD4 T cells in different stages of acute and persistent infection, and the ease of publicly accessing the data and source code. These data extend previous studies, such as Khatun et al. JEM 2020 and Cicucci et al. Immunity 2019, by revealing single-cell transcriptome information on virus-specific CD4 T cells at different stages of infection.

The main drawback is the paper's advertised use as a 'comprehensive atlas of virus-specific CD4 T cells'. This study includes virus-specific T cells from a single organ (spleen) during infections with two clones of a single virus (LCMV). Therefore, its use as a reference atlas does not extend to other viruses or T cells from organs other than spleen during LCMV infection. If such samples were integrated with the splenic LCMV atlas, either new unique populations would be found and therefore not meaningfully annotated or they would be force-integrated with one of the splenic subsets, producing a potentially misleading and crude annotation. In this sense, the authors did not construct an atlas but rather a dataset on LCMV-specific splenic CD4 T cells which, like other datasets, can be compared with other single-cell sequencing datasets.

The methodology description does not include convincing evidence that the integration was successful in minimizing batch effects and retaining biological heterogeneity, virtually no data is presented in support of this point. Therefore, the scope of the work should be refined and the methodology significantly improved.

Reviewer #1 (Public Review):

Heo et al., 2021 determined the consequences of deleting DNA/RNA-binding protein TDP-43 on oligodendrocyte maturation and function using transgenic mouse models. While TDP-43 deletion in early oligodendrocyte maturation resulted in abnormal morphological changes and premature cell death, no changes were observed following TDP-43 deletion in mature oligodendrocytes. Transcriptomic analysis of oligodendrocytes following TDP-43 deletion revealed missplicing of cytoskeletal-associated genes, which suggests that TDP-43 plays a crucial role in oligodendrocyte cytoskeletal organization. These findings revealed TDP-43 as a key regulator of oligodendrocyte maturation, which may go awry in neurodegenerative disorders.

Reviewer #2 (Public Review):

The investigators examined the in vivo effect of the genetic inactivation of the mouse gene that encodes TDP-43 on distinct stages of oligodendrocyte lineage cells. The authors combined a mouse strain containing a conditional allele of the Tardbp gene, which encodes the TDP-43 protein, with several Cre driver lines that induce recombination at discrete stages of oligodendrocyte development. Oligodendrocyte progenitor cells (OPCs), pre-myelinating oligodendrocytes, myelinating oligodendrocytes, as well as mature oligodendrocytes maintaining a myelin sheath were examined in these studies. Interestingly, the loss of TDP-43 results in distinct outcomes depending on the developmental stage of the oligodendrocytes at the time of genetic inactivation. The work is exceedingly well done, the results presented are clear and convincing and the discussion of the work is reasonable and interesting. The study will have considerable impact on the assessment of various neurodegenerative disorders with TDP-43 alterations. The work provides strong support for the concept that oligodendrocyte alterations contribute to neurological disorders that were previous thought to be primarily cell autonomous to neurons.

Reviewer #3 (Public Review):

In both neurons and glia (astrocytes, microglia, and oligodendrocytes) of patients with amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD), the DNA/RNA-binding protein TDP-43 is mislocalized from the nucleus to the cytoplasm where it forms pathological inclusions. Because this subcellular redistribution leads to TDP-43 depletion from the nucleus, the pathogenic mechanism may involve (1) the loss of nuclear function, (2) a gain of cytoplasmic function, or (3) a contribution from both. Heo, Dongeun et al., teases apart this first possibility by investigating the depletion of TDP-43 within specific stages of the oligodendrocyte lineage in vivo and raising the possibility of glial damage in disease progression. The authors found that the consequences of TDP-43 deletion in oligodendrocytes was dependent on the stage of oligodendrocyte maturation. First, they find that deletion of TDP-43 from oligodendrocyte precursor cells (OPCs) resulted in their rapid death, however OPCs that retained TDP-43 expression repopulated to their normal density. Secondly, they find that constitutive deletion of TDP-43 from early premyelinating oligodendrocytes exhibited seizures and early lethality. Similar conditional deletion of TDP-43 from early premyelinating oligodendrocytes in the adult CNS induces abnormal morphological changes, motor discoordination (but no seizures), and premature lethality. Meanwhile, constitutive deletion of TDP-43 from myelinating oligodendrocytes did not lead to any gross phenotypes or shortened lifespan. However, early deletion led to oligodendrocyte degeneration and astrogliosis followed by oligodendrocyte regeneration. Interestingly, in both early and mature oligodendrocytes loss of TDP-43 lead to morphological changes, thinner myelin, less myelinated axons, and aberrant myelination. These results are very interesting and opens the door for further questions, such as why are the oligodendrocytes mislocalizing their myelination targets? Why does early deletion of TDP-43 cause such drastic phenotypes? Lastly, to understand the molecular consequences of TDP-43 deletion in both early and late myelinating oligodendrocytes the authors perform RNAseq on FACS isolated KO cells and controls. The authors uncover that loss of TDP-43 from oligodendrocytes in the adult CNS leads to altered splicing of key regulators of oligodendrocyte growth and morphogenesis.

These findings complement those of Wang, J et al 2018 that shows depletion of TDP-43 in mature oligodendrocytes in the spinal cord is indispensable for the proper functioning of mature oligodendrocytes, including myelination and cell survival. Although Wang, J et al 2018 saw no apparent harm to motor neurons of mice with TDP-43 deleted in mature oligodendrocytes, the mice did have progressive motor deficits and early lethality - similar to Heo, Dongeun et al.

Strengths:

To investigate the role of TDP-43 within distinct stages of oligodendrocyte maturation, the authors used four different Cre and CreER mouse lines: 1) Pdgfra-CreERT2, 2) Mobp-iCre, 3) Mog-iCre, and 4) Mobp-iCreERT2, thus allowing them to inactive Tardbp in both the developing and mature CNS. By performing thorough analysis at each discrete stage within the oligodendrocyte lineage, the authors uncovered differential requirement for TDP-43 in cell survival and structural maintenance as OPCs transform into early and late myelinating oligodendrocytes. These results are important because they elucidate the contribution of a DNA/RNA-binding protein to oligodendrocyte development. Additionally, the results of the conditional deletion of TDP-43 from early premyelinating oligodendrocytes in the adult CNS is critical for understanding how nuclear depletion of TDP-43 from oligodendrocyte might contribute to disease pathogenesis.

The authors also performed bulk RNAseq on early and late myelinating oligodendrocyte controls and TDP-43 KO cells. In doing so, not only did they uncover hundreds of differentially expressed (DE) genes between each control and KO, but thousands of DE genes between the two controls. This experiment also confirmed that Mobp-iCre and Mog-iCre mouse lines were able to target different stages of oligodendrocyte development. This dataset is very exciting to both the developmental glial biology community and to those trying to understand the molecular mechanisms within glia that contribute to neurodegenerative disorders.

Minor weaknesses:

In Figure 1, the authors observe that in the cKO mice, the OPCs are dying because they observe a lack of NG2 staining. Is it possible the OPCs have changed to another cell identity that is NG2- in the absence of TDP-43? Tunnel staining would clarify that indeed the cKO OPCs are dying. Furthermore, the authors note that despite the extensive death of OPCs, they do not see signs of GFAP+ astrogliosis. Is there instead an increase in microglia activation? Throughout the paper, the authors use only GFAP+ astrogliosis to measure widespread inflammation. It would be more compelling to also look at the contribution of microglia or other inflammatory markers to measure inflammation.

As shown in Figure 3, loss of TDP-43 in oligodendrocytes at early and mature stages leads to similar profound phenotypes within both the Mobp-TDP43KO and Mogp-TDP43KO mouse lines. However, only early when TDP-43 is deleted using the Mobp-TDP43KO, are there severe physical phenotypes in the mice and early lethality. However, the authors show that there is no change in the density of ASPA+ mature oligodendrocytes in Mobp-TDP43KO and Mogp-TDP43KO at any stage. If there is an increase in the turnover of oligodendrocytes and oligodendrocyte number stays the same, can the authors speculate in their discussion what they believe is causing the severe seizure and lethality phenotypes in the Mobp-TDP43 KO mice? The authors mention that there is an increase in astrogliosis. Are they suggesting this change in astrocyte activity could promote the severe phenotypes and early lethality? Because motor neuron number is not affected by TDP-43 deletion, but no direct measurements of motor neuron activity were taken, it is hard to make sense of the phenotypes observed.

Reviewer #2 (Public Review):

The authors study tubulinopathy causing alpha tubulin mutations which impact microtubule cytoskeleton building block – the alpha/beta tubulin – and lead to human to neuronal and developmental defects. The authors study the mechanism of Valine 409 to alanine or isoleucine in alpha tubulin. Their studies reveal a major impact of these mutant alpha tubulins on neuronal development and migration. Studies of this conserved alpha tubulin valine in budding yeast (410 in the yeast) reveal an enhancement in polymerization and depolymerization. Most critically the authors find that the mutations interfere with ability of Tumor Overexpressed Gene (TOG) domain regulators from binding these mutant tubulin. The work reveals for the first time a mechanistic link between ability of TOG domain Regulators of microtubule dynamics and developmental defects caused by tublinopathy.

Microtubule polymerization dynamics are essential for cell development and cell division. The alpha/beta tubulin dimer is the conserved microtubule building block. A range of mutations, termed tubulinopathies, are found in human alpha and beta tubulin genes and result in range of defects in microtubule polymerization, their regulation or their capacity to be used as tracks for microtubule-based motor proteins. Understanding the mechanism for tublinopathies has been hampered by the lack of molecular understanding in the mechanism of these mutations within tubulins and how they impact alpha/beta tubulin capacity to polymerize into microtubules, or be regulated by conserved proteins.

The authors focus on two related tubulinopathy mutations in alpha tubulin, where valine 409 is mutated to Isoleucine (V409I) or to alanine (V409A), with the alanine mutations being the more severe. The authors study the mutants and wild type alpha tubulins in neuronal cells showing how they impact neuronal migration and neurite initiation, revealing the V409A severely impacts neuronal migration compared to V409I and wild type tubulin. This correlates with side neurite initiation, which is not well studied property even in neuronal cultures.

The authors then turn to budding yeast and introduce these mutations in the budding yeast tub1 and tub3 and show that V409I and V409A increase both the polymerization rate and depolymerization rates with alanine mutant being more severe. The authors correlate the location of a mutant with impact on Tumor Over-expressed gene (TOG) domain binding. These domains interact with alpha-beta tubulins in the curved conformation and their binding sites, are suggested to weaken upon tubulin straightening when incorporating into microtubules. The authors show that budding yeast Stu2p TOG domain arrays recruit wild type tubulin from yeast extracts in vitro, in contrast to the V409A and V409I which do not bind and become enriched with TOG domains. The authors postulate that the defects in polymerization are likely related inhibiting yeast TOG protein Stu2p binding . Stu2 plus-end tracking is significantly decreased in the V409A and less so in the V409I compared to wild type. When stu2p expression is decreased in living yeast cells, the authors observe no impact on microtubule dynamics in the mutants. In vitro studies of purified tubulins reconstitute these alpha/beta tubulin mutants in vitro showing that they somewhat impact tubulin polymerization. Although pure tubulin mutant studies were not successful in the case of the V409A , 1:1 ratio mixing with wild type tubulin lead strong enhancement of polymerization and depolymerization rates.

The authors conclude: the two mutants impact microtubule dynamics in two ways :<br /> 1) Impacting the ability of TOG array proteins from binding microtubules through defects in the alpha tubulin TOG interaction interface. the mutants disable the TOG domain alpha/beta tubulin interaction interface in different degrees of severity with the alanine mutant being more severe.<br /> 2) Impact on the curved conformation of alpha/beta tubulin and its ability to form a straight conformation. The authors suggest the alpha-tubulin mutations enhance the straight conformation enhancing the microtubule stabilizing state of these proteins.

Assessment:<br /> Overall, the manuscript presents very important data to exploring the mechanism of tubulinopathy causing mutations and their impact on microtubule dynamics. The approach presented fairly thorough and the effort undertaken to understand these mutations spans multiple systems with multiple levels of analysis. However, the work lacks a structural presentation of the mutants and structural models for potential functional effects. These models are very valuable to making careful interpretation for the mechanism of the mutation. The one of the authors conclusions regarding the enhanced straightening of the tubulin mutants is likely not well supported by the data.

Major concern:

The main concern is the interpretation for the V409A and V409I mutant mechanism relating to straightening the tubulin dimer. Although the authors present the location of the mutation near the alpha-beta tubulin interface, they do not present structural view of this mutant. Valine 409 in alpha tubulin resides at H11' region of C-term domain of alpha tubulin. Although this region is near the alpha/beta tubulin interface, V409 extending towards the solvent exposed surface. I compared the position of Valine 409 in both the straight and curved tubulin conformations. Thus, generally the impact of this mutant would be predicted to affect alpha/beta-tubulin binding to regulators such as TOG array proteins. This mutant is not likely to impact alpha-beta tubulin conformation. The authors make a claim that the enhancement in tubulin polymerization/depolymerization observed with V409A and V409I is related to its conformation. However there is another interpretation. The inability of the soluble pool of the V409A and V409I mutants to bind TOG proteins, increases the effective polymerizing concentration for microtubule dynamics. So the observed enhancement in microtubule polymerization maybe related to the dissociation of V409A or V409I from TOG proteins in the cytoplasm, which may substantially increase of soluble tubulin concentration available for polymerization within cells. These mutants may lead to a higher effective tubulin concentration resulting in higher polymerization rates. The in vitro polymerization experiments do not provide convincing supporting the enhanced dynamics of the mutants, in particular for V409A, due to the need to mix with wild type tubulin.

Reviewer #1 (Public Review):

Two different mutations in a single alpha-tubulin residue, V409, are found in patients who have cortical malformations caused by abnormal neuronal migration. In this manuscript Hoff and colleagues set out to determine the effect of these disease-associated mutations on microtubules. The authors first determine that V409A and V409I disrupt the migration of cortical neurons in mouse brains and alter the neurite growth of cultured cortical neurons, consistent with the V409 mutations being associated with neuronal migration disorders in humans. The authors then delve into the effect of the mutations on tubulin and microtubules, and here the authors to turn to a yeast model (human V409 is equivalent to yeast V410). In yeast, V410 mutant tubulin results in microtubules that polymerize faster and have fewer catastrophes. Based on decreased affinity between the V410 mutants and the microtubule polymerase Stu2 (the yeast XMAP215 ortholog), the authors ultimately suggest a model in which the disease-associated V409 mutations enhance microtubule polymerization by altering tubulin dimer conformation.

Overall, the authors do a nice job of probing the effects of the disease-associated V409/V410 mutations on neuronal morphogenesis and microtubule behavior. However, a major concern is that is unclear how the defects in microtubule behavior uncovered in yeast relate to microtubule function in neurons and how these perturbations might then disrupt neuronal morphogenesis. The strength of the manuscript is the characterization of the tubulin mutations in yeast; the mouse cortical neuron experiments are nice to include but are predominantly descriptive and have some weaknesses (not all the conclusions drawn from the cortical neuron experiments are supported by the authors' data). Also, while consistent with their data, the authors' model that the V409/V410 mutations disrupt tubulin conformation seems somewhat speculative.

Reviewer #3 (Public Review):

In this article Hoff et al. address the molecular, cellular and developmental consequences of two mutations in 𝛼-tubulin that in human cause malformations of cortical development (lissencephaly and pachygyria). In a first part, the authors show that overexpression of mutated 𝛼-tubulin alters neuronal positioning in the developing neocortex and affects neuronal morphology and microtubule acetylation in culture. In a second part, they endogenously insert these mutations in yeast and demonstrate altered microtubule dynamics, including faster polymerization and reduced catastrophe. They show that this effect also occurs in vitro, independently of any microtubule regulator and is therefore due to intrinsic effects on tubulin polymerization. Finally, they show that these mutations are able to rescue microtubule defects in Stu2/XMAP215 loss of function, confirming that these tubulin mutants stimulate microtubule polymerization.

The conclusions of the yeast experiments are novel and well supported by the data. Some aspects of the neuronal experiments are less convincing and would require additional experiments and clarifications:

1. The link between the neuronal positioning defects observed in vivo in figure 1 and the mild branching phenotype observed in figure 2 is not clear. The authors discuss branching defects as a cause of altered multipolar to bipolar transition but this is far from established. Moreover, the authors do not show here if the defect is indeed due to a block at the multipolar stage or to a bone fide neuronal migration phenotype.

2. The neuronal positioning defects are documented in figure 1 and reveal reduced fraction of cells is the fourth quartile. The representative image is however not convincing for V9409I, and no statistical test is provided. The mild defects may suggest a delay in migration, and the same analysis one day earlier (3 days post-electroporation) would be informative.

3. Figure 2C shows that in mutant tubulin-expressing neurons, neurites resist better to cold-induced retraction. The authors first present this as an assay to probe for neurite retraction defects, which may be a cause of the neurite branching phenotype. It is however not clear whether this cold treatment is a proper assay to test this. Live imaging of neurite growth and shrinkage at 37oC would be a much more convincing assay. The alternative interpretation of this experiment, which the authors make later on in the manuscript, is more convincing: that this is a readout for increased microtubule stability. The experiment is not quantified.

Reviewer #1 (Public Review):

Qi J, et l. investigated how trabeculation is regulated during early cardiogenesis of zebrafish. They claim an essential role for protrusion from the endocardial cells (EdCs) in cardiac trabeculation. The protrusions originate from EdCs express Apelin receptor B (Aplnrb) and respond to Apelin released by monolayered myocardium. Conversely, monolayered cardiomyocytes expressing ErbB proliferate to become multilayered cardiomyocytes (trabeculation) in response to Neuregulin 2a (Nrg2a). Nrg2/Erb2 signaling activates Erk in the cardiomyocytes, thereby leading to proliferation as evidenced by the Erk signaling monitoring transgenic zebrafish line. The authors further confirmed that not only Nrg2a-ErbB signaling but also protrusion is necessary for trabeculation by showing that the lack of protrusion in the endocardium (overexpression of dominant negative form of IRSp53) resulted in failure of trabeculation in the endocardium-specific Nrg2-overexpressingtransgenic fish.<br /> The explanation and interpretation steps overall support their conclusions, because the experiments were well designed and were done by high-quality imaging techniques and by using several important transgenic zebrafish lines. The present manuscript would be improved by some revision to convince readers interested in the role of protrusions in trabeculation.

Reviewer #2 (Public Review):

This manuscript by Qi and Helker et al. describes the presence of endocardial protrusions in the zebrafish heart that may be functionally analogous to endocardial sprouts recently described in the mouse heart, which appear necessary for determining trabecular architecture. The paper focuses on the necessity for Apelin/Apelin receptor b signaling for formation of protrusions and subsequent activation of the neuregulin signaling pathway via Erbb2/Erk activation, and for cardiomyocyte extrusion to form the trabecular layer. The authors combine elegant zebrafish reporters and imaging, as well as mutant lines and pathway inhibitors to make the case. Endocardial sprouting/protrusion formation appears to be analogous to endothelial sprouting in developmental vascular beds occurring in response to hypoxia and signalling gradients, and which involves metastable Notch-driven cell fate changes. Whereas Apelin signaling has been shown to be involved in endothelial bed sprouting, there are few pathways that have been identified to be involved in endocardial sprouting in mouse hearts. This is the first comprehensive description of endocardial protrusion formation and "touchdown" in zebrafish hearts and the data reveal that elaboration of protrusions is necessary for neuregulin signaling. These are valuable contributions to the field of heart development and chamber formation, and deepen our conviction that trabeculation is a deeply conserved process worthy of detailed study in the zebrafish model. For the most part the authors justify their claims. The high quality imaging and movies allows for quantitative measurement of morphological progressions, and without doubt the range of lineage, signaling and intracellular compartmental reporters and mutations make this a high quality study. Weaknesses in the manuscript in its current form relate to the lack of clear definitions of the individual steps of trabeculation and how this then relates to the interpretation of genetic or over-expression phenotypes, particularly those surrounding nrg2 over-expression. This leads to some uncertainty about conclusions. Insufficient evidence is provided for how protrustions relate spatially to cardiomyocytes "chosen" to extrude from the outer layer to become trabeculae. Notch signaling, one of the key pathways involved in trabeculation, is dealt with at a fairly superficial level.

Reviewer #1 (Public Review):

The premise of this paper is that a significant amount of microbial diversity might be maintained not purely through resource partitioning, as has been the thrust of multiple recent papers over the last few years, but perhaps also through "physical" differences between organisms---here manifested by the detachment rate of heterotrophic bacteria from resources in the form of particulate matter. I completely agree with that premise, and agree that this is an underexplored niche axis that is important to account for when seeking to understand coexistence and diversity.

As with any mathematical model, the assumptions made are critical to get right, and different assumptions about the details of resource uptake, dispersal, and competition may lead to different conclusions. So my comments primarily relate to some of these mathematical choices, as well as to their explanation in the text.

-- In framing the paper, I think the authors are right to focus on dispersal and detachment as under-explored mechanisms. But readers will benefit from reference to other work (even on particle-associated microbes) related to resource diversity, succession, and crossfeeding. That can only help put the current study in context with other mechanisms for the maintenance of microbial diversity.

-- There is a population growth process when a cell settles on a new particle. This is assumed to be logistic growth, though in the end, it seems likely that the precise dynamics of the growth process don't matter so much as the final abundance (carrying capacity). However, this seemed subtle to me for three reasons:

(i) Will detachment rate directly affect carrying capacity?

(ii) Is carrying capacity occurring when microbes fill out the surface of a particle, or when they have eaten the entire volume of a particle?

(iii) If the former, will particles continue to be shed from the particle as growth continues approximately linearly?

It's possible that none of this matters too much if all that's important is a final population size. However, it might help to clarify the process for readers if we have a conceptual picture of what this final population size represents (surface of particle being filled? or volume of particle entirely eaten up) and if there is a truer picture of the dynamics than logistic growth.

-- The relationship between the trade-off (between different detachment rates) derived in Eq 2 versus the optimal detachment rate (derived in the methods) is framed a little confusingly. If I understand correctly, the "trade-off" actually comes from the condition that a population will have net non-negative growth rate in the absence of other populations with different strategies. So it may be reasonable to frame this as a threshold---a necessary condition rather than a sufficient condition for a given population to persist. The reason I say this is that it is a bit confusing to have a trade-off that suggests a range of detachment rates can coexist so long as they differ in their carrying capacities, since it is then stated that the optimal detachment rate outcompetes all the others. Maybe I misunderstood something important being assumed about the carrying capacity for the optimal case, but a trade-off that also has an optimum is an odd outcome.

-- In the end, it seems critical that for multiple strategies to be maintained in the population that there is not only whole-particle mortality (which in effect is highly correlated catastrophic dynamics for an individual microbial population), but that the inflow of resources itself fluctuates. Did I interpret that correctly? Readers may appreciate a slightly clearer description of how this environmental stochasticity differs from the previous possibility of whole-cell mortality, and this also left me wondering how to quantity the kind of environmental stochasticity that will generally lead to multiple strategies coexisting.

-- In summary, I think this is a terrific idea and promising analysis that will bear fruit. But I also wanted to understand how robust is the outcome of coexistence to the various assumptions in the model.

Reviewer #2 (Public Review):

Using a mathematical model of particle-associated bacteria growth, detachment and colonization, the authors found that bacteria with different detachment rates can coexist at steady state only in the presence of particle-wide mortality, via a trade-off between net growth and mortality on particles. This phenomenon is remindful of the competition-colonization tradeoff, an ecological mechanism used to explain diversity via a tradeoff between colonizing and survival abilities, but is distinct from it as the two species considered here only differ for the rate at which they detach from particles. Species with low detachment rates can reap the growth benefits of residing for longer on particles consuming particulate organic matter, but face an increased mortality due to increased risk of predation and viral infection. Conversely, species with high detachment rates elude such risks, at the expense of a reduced growth benefit. This manuscript adds to the recently renewed interest on applications of optimal foraging theory to the study of microbial growth on marine snow.

The strengths of this work are that the results have a clear intuitive explanation and that the parameters used for the analysis are realistic, except perhaps for those related to bacteria-particle encounter rates that are not well explained in the manuscript and ignore bacterial motility, which can greatly increase their effective diffusion coefficient with respect to the Stokes-Einstein estimate used in this work. The authors have investigated a broad range of parameters to provide generality to their numerical results. The relationship between differences of particle detachment rates and biodiversity is, to the best of my knowledge, original, and interesting.

The simulation data presented in the paper support the claims put forth by the authors, but some imprecisions and gaps in the methods section limit the reproducibility of their results in the current version of the manuscript. For example, the details of bacteria-particle encounter rates are insufficiently explained in the methods section, given that the expression for the encounter rate alpha is never reported explicitly. Publication of the computer code (not available at this stage) would clarify these doubts and allow assessing the validity of the results with more confidence.

Together with other papers published recently from this and other groups, this manuscript expands our understanding of the factors affecting the foraging strategies and coexistence of microbial species on particles. This manuscript focused exclusively on detachment rates, but many other strategies are thought to affect the growth and diversity of particle-associated microbial communities, such as attachment strategies (Yawata et al, PNAS 15:11, 2015), nutrient concentration sensing (Yawata et al, PNAS 117:41, 2020), microbial interactions (Datta et al, Nature Communications 7:11965, 2016), and others (Fernandez et al, The ISME Journal 13, 2019). Although it makes sense to focus on a single process for the purposes of this investigation, a broader discussion of the relationship between the role of detachment rates and other relevant quantities is warranted in the discussion section to clarify the generality and limitations of this work. For example, differences in relevant traits such as search strategy, motility and metabolic interactions may also affect coexistence on particles.

Reviewer #1 (Public Review):

How biological pattern scales with the size of the cell or organism is a long-standing question in developmental and cell biology. While there are many mechanisms to form a gradient, only a few convey scaling of the gradient with size. In this study, Datta et al present a theoretical study that analyzes the formation of an intracellular cytoplasmatic gradient as a result of membrane transport of the biomolecules and their release in a pole of the cell.

The authors provide two main requirements for the cytoplasmatic gradient to scale with cell size:

1. The cell geometry should be close to spherical or more generally grow while maintaining its spheroid proportions, and not grow via elongation.<br /> 2. The binding of the cytoplasmatic fraction of the biomolecule to the membrane (e.g. to motor proteins) should be close to irreversible.

A strength of this manuscript is that it invokes a cellular mechanism that is imaginable and could be achieved through several biochemical implementations and can inspire experimental studies. The analytical arguments and the figures can be understood by biologists that value theory while the analogies to electrostatics and analytical solutions are informative for readers with a physics background.

A weakness of this paper is that the biological examples the authors provide are not convincing. A deeper search in the literature or change in some of the simulation parameters can improve the study, and give the "impetus for experiments" the authors wish to provide.

For example, the size range of yeast is an order of magnitude smaller than simulated. In Giardia, a system that the authors are well familiar with, the gradient does not scale with size. This is in accordance with the theory, but to show the theory's applicability, data on how the gradient scales with the radius of the flagella is required and not a general agreement. Achieving a gradient that doesn't scale is much easier and does not require the theory. The Bicoid system, which has been studied extensively, also does not appear to be a good example for the implementation of this mechanism. The c. elegans embryo system cited also does not appear to fit the model. There is very good data on the gradient formation in most of the systems described, which can be used to test the predictions of this model on scaling.

Altogether, the theoretical grounds of the model presented by Datta et al are sound, and the results are interesting and valuable to the developmental/cell bio community. To make this study better, the authors would need to show greater relevance to existing data.

Reviewer #2 (Public Review):

The authors propose a novel concentration scaling mechanism through transport and diffusion. While this is somewhat surprising, given extensive past work on transport-diffusion systems, I have not encountered the arguments presented by the authors in prior work myself.

For the polar transport case considered in Figure 2, the problem that is solved is essentially that of the concentration gradient resulting from a point source near an absorbing boundary (the boundary is perfectly absorbing and all captured proteins are transported to the anterior pole). Indeed, the resulting concentration profile would only depend on the geometry of random walks starting from the anterior pole and reaching the absorbing boundary. Thus, in this limit, it's a straightforward result that the concentration gradient does not depend on diffusion coefficient or transport speed, as stated in the abstract.

However, I have two main concerns regarding the proposed mechanism. The first is regarding the steepness of the concentration gradient. In the results of Fig. 2, the proteins are emitted at distance epsilon from the cortical boundary. From there, they locally perform 1D diffusion to the boundary, so most of them would readsorb once they diffuse a distance epslilon. Only a small fraction would extend past epsilon, which I assume is why the concentration drops by orders of magnitude beyond epsilon. Is such a concentration drop realistic given typical numbers of proteins in cells?

Secondly, it should be clarified if the proposed size scaling is independent of the specific choice of the distance epsilon of the point of protein release from the anterior pole. I don't see any reason why this distance should increase with cell size as epsilon = 0.05 R (on page with equation 5). It's unclear if the size scaling of the concentration gradient might be dependent on the assumption epsilon ~ R (though after plotting the expressions presented in equations 5 and 6, it seems to me that the scaling is independent of epsilon at distances larger than epsilon).

Finally, what seems to me to be the most important is to provide better quantitative biological examples of systems likely to exploit such mechanisms. A lot is known already quantitatively about several model organisms/cell systems. The absence of quantitative comparisons is suggesting that perhaps the proposed mechanism might not withstand further scrutiny. For example, how might this mechanism control the scaling of the budding yeast septin ring mentioned in the introduction? What would be the corresponding kon rate given the known density of actin cables? And can one use the knowledge from this work to check if the systems mentioned in the introduction (such as MEX-5, Pom1) might realize or not the proposed mechanism?

Reviewer #1 Public Review:

Using auxin-inducible degron alleles the manuscript shows that the TTT chaperone component TELO2 is required to stabilize TRRAP and facilitate its interactions with SAGA and TIP60 complex components. These data are convincing. Through genomic analysis they found that TRRAP plays a role in the repression of a number of interferon inducible genes through direct interaction with the IRF9 master regulator gene. They then try to demonstrate that both SAGA and TIP60 are involved in repression of IRF9, however, this aspect is incomplete and not convincing.

Reviewer #2 Public Review:

The manuscript by Detilleux and coworkers continues studies by the Helmlinger group on the conserved TRAPP subunit of the SAGA and TIP60 (NuA4 in yeast) complexes. In this manuscript the authors create auxin-inducible degron allelles of TRAPP and of its TTT chaperone TELO2 in the HCT116 colorectal cancer cell line. In previous work the group showed that the TTT complex stabilizes TRAPP, and indeed degradation of TELO2 reduces nuclear accumulation of TRAPP. As expected direct auxin-induced degradation of TRAPP is more rapid, and inhibits HTC116 cell growth already after one day instead of two days for TELO2. Loss of TRAPP from the SAGA and TIP60 is significant, but not complete. RNAseq analysis showed that reduced TELO2 and TRAPP mostly leads to a reduced expression of genes, including MYC and E2F target genes, which is expected given the documented role of TRAPP as a co-activator for MYC ad E2F.

Unexpected is the increase expression of the interferon type 1 group of genes (ISGs). The authors investigated regulation of the ISG pathway to find that TRAPP depletion mostly affects IRF9 expression at the mRNA and protein levels and to a lesser extent IRF7 expression. IRF9 and IRF7 are critical transcription factors for the ISG pathway and these observations offer an explanation for the induction of interferon type 1 genes after TRAPP depletion. The authors continue to show by 4SU labeling that IRF9 and IRF7 are transcriptionally induced and by CUT&RUN-PCR that TRAPP binds to the promoter regions of these genes. Re-expression of TRAPP reverses these effects. In order to dissect which of the TRAPP containing complexes several SAGA and TIP60 complexes are targeted by siRNA knock-down, but this does not provide clear distinction between SAGA and TIP60. In general, the exact mechanistic details of TRAPP-mediated repression of gene transcription have not been worked out, but the current work provides a strong basis for future studies addressing this.<br /> In conclusion, this study clearly demonstrates that TRAPP directly inhibits expression of the IRF9 and IRF7 transcription factors and thereby inhibits the ISG pathway in colorectal tumor cells. This is an interesting observation by itself as until now TRAPP was only known for its positive effects on gene transcription.

Major open issues:<br /> 1) it is unclear why the authors did not choose to sequence the DNA from the TRAP CUT&RUN experiment, but rather performed (a more cumbersome) PCR analysis. A genome-wide CUT&RUN dataset for TRAPP would have allowed a direct comparison with their TELO2 and TRAPP depletion RNAseq datasets.<br /> 2) it would be interesting to know the mechanistic basis for TRAPP as an inhibitor of IRF9 and IRF7 gene expression. I.e. how is TRAPP recruited to the promoters of these genes, is there a correlation with altered chromatin stats and/or histone modifications or variants at these promoters?<br /> 3) while the data clearly show that TRAPP acts a repressor of the IRF9 and IRF7 genes, it is not entirely clear whether this relies on the SAGA and/or TIP60 complexes. To delineate the contribution of (other subunits of) these complexes one would need to create auxin-inducible degradation alleles of core subunits like, SUPT20H, SUPT7L, TAF5L of SAGA and EP400, TIP60, MRG15 etc of TIP60.<br /> In particular, the issues 2 and 3 should be guiding future work.

Minor issues:<br /> 4) The siRNA knockdown data are inconclusive and can be removed without a loss of impact of this work.<br /> 5) The number of subunits shared between yeast and human SAGA depends on the organism. While in yeast five subunits (Taf5, Taf6, Taf9, Taf10 and Taf12) are shared, human SAGA shares only four (TAF9, TAF9b, TAF10 and TAF12) with human TFIID. This should be corrected on page 3, line 14.

Reviewer #1 (Public Review):

Canonical miRNA-targeting involves pairing between the miRNA seed region (nucleotides 2-7, counting from the miRNA 5' end) and a target mRNA. Pairing downstream of the seed can also influence target recognition, and in some cases 3' pairing can compensate for imperfect seed complementarity. In this study, McGeary et al. investigated the features of such miRNA 3' compensatory sites in a high-throughput manner by adapting the RNA bind-n-seq (RBNS) method used previously to characterize binding of purified Argonaute2-miRNA complexes to a random pool of target RNAs.

Strengths<br /> To focus on 3'-compensatory sites, which are rare in random libraries, the authors designed libraries of RNAs containing imperfect seed complementarity followed by 25 nucleotides of random sequence. This approach allowed investigation of a range of 3' pairing possibilities far more extensive than any previous work. Results provide several unexpected findings. Contrary to the prevailing model that miRNA nucleotides 13-16 are most efficacious for 3' pairing, the authors found the optimal position varies between miRNA sequences and is often shifted to include G nucleotides in the miRNA. The number of unpaired nucleotides bridging seed and 3'-paired regions is also a factor-certain let-7 sites preferring an offset of +4 target nucleotides, indicating a high affinity target-binding mode previously unknown. Additionally, the contribution of miRNA 3' pairing correlates poorly with predictions from nearest-neighbor parameters. Overall findings greatly expand insights into miRNA 3' pairing and provide metrics for improving target prediction.

Weaknesses<br /> Conclusions are drawn entirely from RBNS data sets, leading to a few limitations. Affinity measurements are limited to relative KD values, making comparison to other work in the field indirect and potentially problematic. For example, let-7 target sites in lin-41 have 11-19 3' compensatory pairing, +1nt offset, which (based on Fig. 2B and 2C) has a greater relative KD than the let-7 8mer canonical site. However, a recent result showed an in vivo lin-41 reporter with two 8mer sites is less repressed than same reporter bearing the wild type 3'-compensatory sites (1). In the absence of KD values and/or cellular repression data for these specific sites the noted differences are difficult to reconcile. Additionally, analyses assume miRNA-target complementarity directly correlates with physical pairing between miRNA and target. However, because physical pairing occurs within the Argonaute2-miRNA complex, this may not always be the case

1. Duan, Ye, Isana Veksler-Lublinsky, and Victor Ambros. "Critical contribution of 3'non-seed base pairing to the in vivo function of the evolutionarily conserved let-7a microRNA." bioRxiv (2021).

Reviewer #2 (Public Review):

In this manuscript, McGeary, Bisaria, and Bartel provide key additional insights into how 'compensatory' 3' binding contributes to the affinity of Ago2-miRNA complexes for target sites with imperfect seed matches.

At the core of this work is an impressive and elegant series of experiments using the Ago2-RNA bind'n'seq (AGO-RBNS) technique they recently developed. Here they focused on three 'programmed' libraries designed to have an imperfect seed match (edit distance 1) for either let-7, miR-155, or miR-1 preceding a 25 nt randomized sequence.

A detailed analysis of the sequences enriched upon incubation with Ago2 loaded with the corresponding miRNA challenges previous assumptions regarding the role of critical residues within compensatory sites. This study uncovers marked variability between different miRNAs with respect to the ability of 3' matches to compensate for imperfect seed matches. In particular they show that although for some miRNAs extensive 3' pairing can lead to a binding affinity comparable to the binding affinity of a perfect 8-mer seed match, for others the effect is much more modest.

Another important finding is the discovery of two 3'-pairing modes for some miRNAs (let-7 in particular), one requiring the presence of a more extensive 'bulge' in the target but resulting in higher affinity.

These observation will have important implication in the design of improved prediction algorithms and for the interpretation of CLIP experiments. Some of the findings reported here, for example the interesting observation that the nature of the seed mismatch profoundly affects the impact of the compensatory 3'-pairing, will prompt follow-up structural studies to be fully understood at the mechanistic levels.

The manuscript is impeccably written and the experiments are well controlled and beautifully illustrated. Their results are consistent with the authors's interpretation. The vast scientific literature existing on the topic is appropriately cited and the statistical analyses used are, as far as I can judge, appropriate.

Overall, this is a strong and important manuscript that will be widely appreciated by the broad scientific community and by the non-coding RNA field.

Reviewer #3 (Public Review):

The Bartel Lab tackles the elusive role of the 3' part of miRNAs to contribute to the binding of target RNAs. In short, the presented data lead to the following conclusions:

1- The positions most important for 3′ pairing differed between different miRNAs;<br /> 2- Compared to Grimson et al. 2007, the authors show that preferred pairing often does not correspond precisely to positions 13-16, but it does always at least partially overlap such stretch of nucleotides;<br /> 3- Two distinct 3′-binding modes seem to exist. Yet, arriving to that conclusion (that is at the core of the title) is not easy for the reader (see below);<br /> 4- Increasing miRNA length can sometimes improve 3′ binding affinity, but it cannot substitute for other features required for high affinity to the miRNA 3′ region.<br /> 5- Central to the paper and underlying several analyses, the authors show that parameters derived from interactions of purified RNAs in solution are not directly relevant to miRNAs associated with AGO2;<br /> 6- GG/GC/CG dinucleotides in positions 13-16 most likely participate in productive 3' pairing, and extra Gs beyond this stretch also favor.<br /> 7- Importantly, there is a functional difference between 3′-supplementary and 3′- compensatory pairing in regard to the presence of mismatches in the seed.<br /> 8- By using chimeric miRNAs, the authors separate effects of seed-mismatches, to those effects derived from the length, position, offset, and nucleotide-identity preferences of the 3′ region;<br /> 9- Finally, the two different 3' binding modes presented in this manuscript help rationalizing some aspects of target-dependent miRNA degradation (TDMD).

The title:<br /> Should the term "seed mismatch" be included to highlight one of the most important aspects of the paper?

The Introduction:<br /> Well-written and informative, but perhaps too long.<br /> The authors should explain why they have chosen Ago2 for all their experiments, when they continuously refer to "AGOs" in the Introduction.

The results:<br /> Specific comments:<br /> The authors jump from Fig. 1A to Fig. 1C. Fig. 1B is mentioned at the Introduction. Should Fig. 1B be moved to the supplement?

The authors mainly focus on let-7a and two well-known miRNAs: miR-1 and miR-155. The RNA bind-n-seq analysis reveals different binding behaviors. Are those miRNAs representatives? In how much the analysis provided by the authors get close to a (nearly) full picture of 3' miRNA binding modes?

The (many) figures displaying color-gradient squares to calculate Kds are elegant but I would argue that replacing some of them by tables and numbers would be more informative and less demanding for the eye of the reader.

I would also suggest to bring back TargetScan at the Discussion (as in the previous paper by Mc Geary et al. 2019), to highlight the benefits of the biochemical approach on top of the powerful and universally used TargetScan.

A general comment goes towards the presentation of the data. In contrast to other manuscripts, the authors rely on a unique type of data, that emerges from binding assays on nitrocellulose membranes, and their quantification. For a better visualization, I would encourage the authors to include examples of such bindings and quantifications.

Reviewer #1 (Public Review):

This study uses Drosophila as a model to study a specific mutant in a gene encoding a nuclear pore protein, whose counterpart in human leads to a rare disease called XX-ovarian-dysgenesis. Intriguingly, the fly mutants mimic the syndromes identified in human patients, such as failures in ovary development and function. The authors use fly as a model to study the molecular and cellular mechanisms underlying the phenotypes, which should provide insight to our understanding of this known human disease.

Reviewer #2 (Public Review):

In this manuscript Shore et al determine that Nucleoporin 107 (Nup107) is required in developing female somatic cells [intermingled cells (ICs)] for proper ovarian development. The authors propose that Nup107 is required for proper orientation of ICs during development to ensure proper function of escort cells during adulthood. They show that loss of Nup107 results in ectopic germline stem cells (GSCs) away from the GSC niche (primarily cap cells and terminal filament cells) and that these ectopic GSCs display hypermorphic Bone Morphogenic Protein (BMP) signaling. The authors also find that Nup107 regulates expression of the transcription factor doublesex (dsx) and share a common transcriptional target of AdamTS-A. Through knockdown/rescue experiments, the authors show that expression of Dsx-F can rescue phenotypes observed with ovarian somatic knockdown of Nup107 and that ovarian somatic knockdown of AdamTS-A mimics loss of Nup107 and dsx (including loss of escort cell membrane protrusions and enhanced BMP signaling). These data provide an interesting non-cytoplasmic to nuclear transport mechanism for Nup107 in regulating oogenesis.

This is a well written manuscript with proper experimental analysis (sufficient n's, proper statistics). However, it is unclear how the authors came to some of their conclusions and how their results significantly enhance findings from prior studies exploring how disruption of organization of somatic cells of the developing female gonad influences adult escort cell protrusions and preventing expansion of BMP signaling to ensure proper germline stem cell cyst differentiation.

Points to consider:<br /> 1. It was unclear reading the first part of the paper how the adult phenotypes were connecting to defects during larval development. It would further strengthen the rationale for describing the adult phenotypes first if the authors perhaps show the larval gonad defects (abnormal IC stacking/arrangement) prior to showing adult phenotypes. This would also help with citing previous studies that have also found the correlation with incorrect IC placement and ectopic GSC-like cells in adulthood (e.g., Tseng et al., 2018, Stem Cell Reports).<br /> 2. It would also help the reader to know that nuclear pore complex proteins have roles outside of nuclear-cytoplasmic transport early in the manuscript as opposed to only in the discussion.<br /> 3. It is unclear the model the authors are proposing for the Nup107-Dsx-AdamTs relationship. Are the authors proposing that Nup107 can regulate the import of Dsx or is directly regulating dsx transcription (based on the RNA-sequencing results)? A little more explanation would be helpful.

Reviewer #3 (Public Review):

This is an interesting story, providing molecular explanation for XX-OD, caused by mutations in Nup107 gene. Overall, the experiments are thoroughly conducted, and the results are important in understanding XX-OD. However, there are some issues that need to be addressed, as the data presented in this study still leaves some gaps that need attention.

1. Dsx is the major target of Nup107. Although it is clear that Dsx is downregulated in Nup107 mutant, how exactly Nup107 regulates Dsx expression remains entirely unclear. Does Nup107 functions as transcriptional regulator of Nup107?

2. Nup107  Dsx axis is required for escort cells to encapsulate germ cells to allow the downregulation of BMP signaling in germ cells, which in turn allows differentiation of germ cells. Whereas this axis appears to operate, the relationship between germ cell encapsulation and BMP signaling is quite unclear. Is encapsulation upstream or downstream of BMP modulation? Authors provide the evidence that adamTS-A, which modulates the amount of extracellular BMP ligands, is the downstream target of Dsx, and adamTS-A appears to regulate encapsulation. This makes it unclear whether encapsulation is required to down regulate BMP, or BMP regulates encapsulation (and if the latter, what is achieved by encapsulation that leads to germ cell differentiation?)

3. Dsx regulates germ cell differentiation in a female specific manner. I am somewhat puzzled by distinct phenotypes of Dsx depletion described in this paper (germ cell differentiation defect in female only) compared to other previous reports on Dsx function in male and female germline. Is there any sex-transformation phenotype? The part of this manuscript that describes Dsx appears to be detached from the context (published literature on Dsx), and it's somewhat difficult for me to interpret the results.

Reviewer #1 (Public Review):

This paper assesses the role of the molting hormone ecdysone in the coordination of growth and patterning in the Drosophila wing disc in response to food deprivation. Experimentally they use third instar-specific death of the prothoracic glands (PG), the source of ecdysone, to eliminate the hormone and the developmental time course of the appearance of two bristle markers, Achaete which normally begins progressing at about 5 hr after ecdysis to the third instar when the ecdysone titer just begins to increase and Senseless which only appears after the attainment of critical weight (a size that signals the ability to metamorphose without further feeding) after which small peaks of ecdysone occur. Then they assess the response of the experimentally manipulated larvae to 20-hydroxyecdysone (20E) in their food. The experimental data are then modeled. Interestingly, the growth rates and Achaete patterning through developmental times are best fitted by a Gompertz function whereas the Senseless patterning over time shows a typical linear regression. Also, they assess the effects of manipulating the insulin-signaling pathway. They show that nutrition regulates disc growth by both ecdysone-dependent and ecdysone-independent means, but regulates patterning (i. e., differentiation) only by ecdysone levels. Thus, the growth response to the hormone is quite plastic whereas patterning requires a threshold level of ecdysone in order to begin. These two different responses to ecdysone and nutrition also seem to be typical of other holometabolous insects as evidenced by the published examples in Lepidoptera that they cite.

This group carefully stages animals and has accumulated a wealth of background information necessary for this type of study. They have wisely chosen two differentiation markers that begin at different times relative to the larva's physiological state with respect to nutrition and hormone levels, i.e. one that begins before critical weight when ecdysone titers are very low and one that begins after critical weight when the ecdysone titer rises in pulses. The data are well presented and generally support their conclusions.

The genetic destruction of the prothoracic gland by the cell death gene Grim is an accepted method. They need to indicate in this case, how long the prothoracic gland is detectable in the third instar after exposure to the elevated temperature to inactivate the GAL80. For instance, are any gland cells still functional at the time of critical weight? How does this affect the onset of Achaete progression which normally occurs beginning at 5 hr after ecdysis which they show is dependent on low levels of ecdysone? Do you get the same result if you place them at the elevated temperature 12 hr after ecdysis to the 2nd instar, a time when the ecdysteroid titer has already risen to cause the molt to the third instar?

One concern with the paper is why the authors begin contrasting the effects of temperature on embryonic development and wing disc patterning in the Discussion on p25. This discussion seems irrelevant to the experimental manipulations done in this paper concerned with nutrition and hormone levels. Nothing was done relative to environmental temperature effects except to genetically kill the prothoracic gland, the source of ecdysone. I recommend omission of this section of the Discussion.

Reviewer #2 (Public Review):

Alves et al investigate hormone-dependent control of developmental plasticity. While some traits exhibit robustness (ie. Low variability) in the face of changing environmental conditions, others exhibit plasticity (ie. High variability). The authors focus on how the ecdysone steroid hormone links nutritional status in the larval stages of D. melanogaster development to robust and plastic traits in the wing. Prior work from this group and others demonstrated that nutritional status controls production of ecdysone, a key hormone signal that coordinates developmental timing in insects. In addition, ecdysone has been shown to promote wing growth during the larval stages of development, and it determines the timing of wing patterning. However, whereas wing growth (and hence wing size) is a plastic trait (e.g. restricted nutrition leads to smaller wings), wing patterning is a robust trait (e.g. small and large wings have the same constituent parts). It is unclear how ecdysone might coordinate both growth plasticity and patterning robustness. The authors combine several existing methodologies to support their conclusions. These include two types of precise measurement at successive stages of larval development (wing disc size, and spatiotemporal expression of the sensory organ patterning genes Achaete and Senseless); while conceptually straightforward, these dissections and measurements are challenging to execute. They manipulate systemic ecdysone levels by genetically ablating the prothoracic gland in a developmentally regulated manner. They control nutritional status by culturing larvae in normal food or starvation conditions. Lastly, they add defined amounts of ecdysone directly to the food to control systemic hormone levels. The combination of these approaches allows them to test three stated hypotheses: (1) ecdysone's effects on growth and patterning occur at different times. (2) ecdysone's effects on growth and patterning are interdependent. (3) ecdysone regulates growth and patterning independently.

The authors' highly quantitative approaches confirm the role for ecdysone in controlling wing disc growth and patterning, and they support a role of ecdysone in independently regulating wing growth and patterning (Hypothesis 3). Importantly, these quantitative approaches extend understanding by convincingly demonstrating that nutrition regulates disc growth in both an ecdysone-dependent and an ecdysone-independent manner - supplementing starved larvae with ecdysone only partially rescues disc growth. They also demonstrate that nutrition regulates disc patterning only through ecdysone - supplementing starved larvae with ecdysone fully rescues disc patterning. Lastly, they propose that robustness of wing patterning is due to a threshold response to ecdysone, whereas plasticity of wing size is due to a graded response to ecdysone. While the data supporting these last conclusions are provocative, they are not decisive. In particular, additional data are needed to strengthen the conclusion that disc size is controlled by a graded response to ecdysone. A final weakness of the paper is that the authors have not provided a causal link between the proposed graded response to ecdysone and plasticity of wing growth.

Reviewer #3 (Public Review):

Strengths:

The authors investigated the role of ecdysone in the plasticity of wing disc size and the robustness of wing disc patterning.

The authors use the genetic tools available in the model species Drosophila melanogaster to ablate the prothoracic glands that produce ecdysone. They also manipulate ecdysone level by adding it to the food or manipulating the activity of the insulin pathway in the prothoracic glands where it regulates ecdysone synthesis.

The authors measure ecdysone level in control or starved larvae with normal or ablated prothoracic glands fed with different concentration of ecdysone. They show that starvation increase ecdysone level in both genotypes and that supplementing the food with ecdysone is efficient in increasing ecdysone level in larvae.

They use normal or poor medium to analyse the role of nutrition on wing growth and patterning.

They follow size of wing imaginal discs and the progression of the patterning of these discs using neurogenic proteins (Achaete, Senseless) expressed in the developing peripheral nervous system (sensory bristles).

They show that nutrition regulate growth by ecdysone and another mechanism and that nutrition regulates pattering only via ecdysone.

Interestingly, growth shows a linear response to ecdysone level, whereas patterning shows a threshold response.

The linear response explains how nutrition induced fluctuation of ecdysone concentration leads to size plasticity and induce a robust patterning by initiating it only when a particular ecdysone level is reached.

The manuscript is very well written. The authors make three conflicting hypothesis in the introduction that they test experimentally. The experiments are rigorously designed and performed with appropriate controls. Their interpretation is justified. The methods used such as the rigorous staging of the patterning or the manipulation of ecdysone level by different means will be useful to other researchers. This article will have a strong impact as it illustrates how a single hormone coordinates the plasticity of size and the robustness of patterning of an organ.

I do not see weaknesses in this article.

Reviewer #1 (Public Review):

Velez-Aguilera use molecular genetics and live imaging after fertilization in C. elegans zygotes to document a novel role for spindle elongation in promoting dis-assembly of the nuclear lamina prior to the merging of the sperm and egg genomes into a shared space during mitosis. The authors take advantage of their previous work showing that a lamin transgene that converts 8 PLK-1 kinase target sites to alanine stabilizes the nuclear lamina and results in failure of genome union or mixing. Here they show that decreased cortical pulling forces on spindle microtubules that limit elongation enhance the 8A lamin transgene defects, and that the 8A lamin transgene expression that stabilizes the lamina opposes spindle elongation. In addition, the authors show that decreased cortical pulling forces can result in failed lamina breakdown, and increased cortical pulling forces can promote early dis-assembly. However, while decreased cortical pulling forces can enhance the 8A lamin defects, increased cortical pulling forces (generated by klp-7 knockdown) do not rescue 8A lamin breakdown or the genome union defects, indicating that lamina breakdown is a "pre-requisite" for envelope scission. The authors' data are compelling and strongly support their conclusions, although some points require clarification.

Reviewer #2 (Public Review):

Velez-Aguilera et al. investigated the role of cortical pulling forces on nuclear scission during first division in C. elegans embryos. Mitosis in C. elegans is semi-open, and the nuclear envelope partially breaks down during spindle elongation and reforms after division is finished. Previous studies found that nuclear rupture requires depolymerization of lamin, a supporting polymer network beneath the nuclear membrane controlled by PLK-1 activity. A particular mutation in lamin, LMN-1 8A, stabilizes it, prevents nuclear membrane scission, and causes nuclear abnormality in later divisions. The author investigated the role of cortical pulling forces in nuclear envelope breakdown by perturbing the GPR-1/2, a well-studied gene in C. elegans, to manipulate cortical pulling forces. They found that in LMN-1 8A, removing GPR-1/2 generated a significantly severe paired nuclei phenotype. They found that this is primarily due to the absence of nuclear membrane scission in those embryos. To test whether cortical pulling force contributes to nuclear membrane scission, they measured the timing of this event in efa-6 and klp-7(RNAi) embryos, where pulling forces are enhanced and found that under these conditions, embryos go under premature nuclear scission. It has been known that spindle length, measured as the distance between the two centrosomes, is regulated by cortical pulling forces. The authors found that spindles are shorter in LMN-1 8A, which indicates force-balance is perturbed in these embryos. Further genetic perturbation experiments concluded that cortical microtubule pulling forces and PLK-1 activity together contribute to nuclear membrane scission and proper chromosome segregation.

While the experiments are carefully done, I see a few confusing points and concerns about the conclusions of the paper:

1- It is unclear whether nuclear membrane scission is a phenomenon controlled by mechanical forces, cell cycle, or both. Some of the experiments in the manuscript suggest mechanical forces control it, but some are not very supportive of this. For example, gpr-1/2 (RNAi) in lmn-1 8A supports the hypothesis that mechanical forces are important for nuclear scission. However, the lack of scission in hcp-3 (RNAi) in lmn-1 WT, where chromosome alignment is prevented, suggests a cell-cycle dependence mechanism. It would be good if the authors could elaborate more on this issue.

2- The relation between membrane scission and paired nuclei phenotype is unclear. It seems that paired nuclei phenotype in daughter cells is a direct consequence of lack of membrane scission earlier during first mitosis. The authors showed that in gpr-1/2 (RNAi) in lmn-1 WT, 83% of embryos have no nuclear membrane scission. However, only 5% of gpr-1/2 (RNAi) in lmn-1 WT embryos showed paired nuclei phenotype (Fig 2A). Is this an inconsistency? If not, it would be good if the authors could explain this in more detail.

3- Similarly, why in klp-7 (RNAi) lmn-1 8A there is paired nuclei phenotype while the membrane scission happens (figure 5D), and in hcp-3 (RNAi) in lmn-1 WT, there is no paired nuclei phenotype, while membrane scission is completely prevented? It seems that one needs more knowledge/explanation about nuclear membrane scission and paired nuclei phenotype to understand these results. The short speculation at the top of page 11 does not seem enough.

4- The authors suggested an interesting mechanism for spindle length regulation in a one-cell stage embryo as a force balance between cortical pulling forces and membrane tension. To my knowledge, the role of the nuclear membrane had not been discussed for spindle length regulation. This could be interesting to highlight in conclusions.

5- The connection between plk-1ts experiments and the cortical pulling forces is not clear. The authors start with the proposal of testing the effect of pulling forces on membrane scission, but by this point in the manuscript, it seems that pulling forces have a secondary effect on scission. By the end, the authors argue that lamina depolymerization, chromosome alignment, and cortical pulling forces are all important for nuclear membrane scission. If so, it would be good if the authors could indicate which one is the primary factor and how the others are comparable to the primary factor.

Reviewer #1 (Public Review):

Reinitz et al use APP transgenic mice and fetal neural progenitor cells expressing mutant APP to document an neurogenesis defect in which APP mutant cells proliferate early but have reduced regenerative capacity. This reduced regenerative capacity is measured by the ability of neural progenitor cells to form neurospheres. This correlated with the increased expression of Cdkn2a in with age and aberrantly increased expression in the APP transgenic mice. Crossing the APP mice with Cdkn2a knock-out mice reversed these deficits as did crossing the APP mice with mice haploinsufficient for USP16, a gene that promotes Cdkn2a expression. RNAseq analysis showed that USP16 haploinsufficiency also rescued aberrant increases in the BMP signaling pathway occurring in the APP transgenic mice. Inhibition of BMP signaling also rescued neurogenesis phenotypes in human fetal neural progenitors that expressed an APP mutation. Thus, the authors claim that decreasing expression of USP16 is beneficial and a potential therapeutic target because it acts on these two independent pathways, Cdkn2a (senescence) gene expression and BMP signaling. For the most part the data presented in this work supports the conclusions and is informative for the field. A conceptual weakness is how this may relate to late-onset SAD which is not caused by APP mutations. Finally, the claim that these two USP16-impacted pathways are completely independent could be strengthened by experiments testing whether Cdkn2a expression is affected when APP transgenic neural progenitor cells are treated with BMP inhibitors.

Reviewer #2 (Public Review):

Reinitz et al., present evidence that cell intrinsic NPC defects predate neuropathological hallmarks of a mouse model of Alzheimer's disease. They found that their is a limited capacity for neurosphere formation from NPCs extracted from mice harboring APP mutations (Tg-SwDI model). Importantly, these NPC defects occurred prior to observable neuropathological hallmarks or cognitive defects associated with this model suggesting that these NPC defects are cell intrinsic. The authors extended these findings by demonstrating that impaired NPC self-renewal occurs in human fetal NPCs expressing APP mutations. Tg-SwDI mice showed Cdkn2a, a gene associated with inhibition of NPC self renewal, was increased. Tg-SwDI mice lacking Cdkn2a showed rescue of the NPC self renewal defect, suggesting this pathway could be exploited to rescue associated disease phenotypes. Due to potential negative effects of Cdkn2a loss, the authors demonstrated another protein associated with the BMI1/Cdkn2a pathway could be targeted. The authors found that genetic reduction of USP16 mitigated the NPC self renewal defects and rescued cognitive deficits in the Tg-SwDI. The findings presented by the authors provide compelling evidence that NPC self renewal defects are an early phenotype in AD and potential therapeutic target.

The data presented by the authors support their conclusions and provide an exciting potential therapeutic avenue, however incorporation of additional data would further strengthen their conclusions and the manuscript.

1) Determine whether reduction of Cdkn2a leads to increased neurosphere formation capacity in WT in Fig 3A. Is this rescue of NIC frequency specific to the Tg-SwDI, or does it result in a more general increase in NPC self renewal?

2) The author should include WT APP as a control in the analysis of pSMAD1/5/8 positive cells and its subsequent reduction by LDN-193189 in Fig.5.

3) The authors should include representative images of GFAP and Thioflavin staining used for quantification in Fig. 6

4) The authors' conclusion that Tg-SwDI/Usp16+/- rescues cognitive defects rely on one test and would be strengthened by additional cognition tests.

Reviewer #3 (Public Review):

Clarke and colleagues describe early neural stem cell defects in a transgenic mouse model for rare familial variants of Alzheimer's disease. They described that early, long before the onset of typical Alzheimer symptoms, the mice show an initial over-proliferation of stem cells, which is followed by an exhaustion of stem cells and downstream impairments. They next show that manipulation of proteins that have been studied extensively in the context of proliferation can alleviate the phenotype. A particular strength of the manuscript is the dogma-free approach and the general frame of the study is highly innovative. However, the authors make several assumptions that are not sufficiently backed-up by the data.

Reviewer #1 (Public Review):

The authors aim to test how mutational robustness is maintained across a different variants of a single protein skeleton. The assumption has been that similarity in structure will equate a similarity in robustness to change.

The work uses four proteins at different levels of robustness and utilized the unique properties of fluorescent proteins to assay the function of a massive mutant collection. This gives exquisite sampling immediately around each of the four proteins starting sequence but does leave the more distant spaces between the proteins unsampled.

The partial connection of mutational robustness/fitness optima to environmental robustness is intriguing as it suggests that overly-optimized proteins will end up being less robust to environmental or mutational changes. The partial nature of this suggests that environmental and mutational robustness may be independently selectable parameters in protein design.

Reviewer #2 (Public Review):

This manuscript experimentally measured the effects of mutations for a large number of variants of four green fluorescent proteins (GFPs) and compared the topology of the protein fitness landscape between four GFPs. The authors have performed various biophysical experiments to obtain diverse thermo and kinetic stability data for GFPs to explain the difference in the mutational responses (robustness) between GFPs. Further, the authors fit the experimental data using various models to identify mutational epistasis in each GFP. Then the authors tested their models by synthesizing and characterizing genes that contain up to 48 mutations. Interestingly, the prediction was mostly successful using a highly fragile GFP template, as the experimental data exposed epistasis while such information was not extracted in highly robust templates. The authors suggest that their platform is useful for designing and predicting new functional proteins.

This is an interesting study that has generated a massive amount of experimental data using deep mutational scanning using four GFP templates (with one data set from the authors' previous publication). The scale of the data is unprecedented. The neural network approach is novel. However, some of the overall findings, e.g., absence of correlation between sequence distance and mutational robustness, are not particularly new and surprising, and the authors overstate some of their findings. Some of the data description in the manuscript is unclear and can be improved.

Reviewer #3 (Public Review):

Somermeyer and coauthors performed a large-scale mutational analysis of four homologues of the green fluorescent protein (GFP). They show that two homologues were more resistant to the accumulation of mutations than the other two, and that this mutational robustness was related to a decreased number of negative epistatic interactions between mutations, rather than to reduced fitness effects of individual mutations. The authors then related mutational effects to the structures and biophysical properties of the proteins, finding the expected relationship between the effects of mutations on predicted protein stability and their effect on fluorescence. Finally, they used the data to train neural network models and design new GFP variants which retained near-wild-type function, despite differing from the original sequence by as many as 48 mutations.

This is an excellent study. The manuscript is clearly written, the experimental and analytical methods are state-of-the-art, and the conclusions are convincing and have important implications in the areas of biotechnology and molecular evolution. The neural network approach for the prediction of mutational effects and for the design of new variants works surprisingly well, as judged by its ability to produce distant, but fully functional variants of GFP.

Reviewer #1 (Public Review):

The paper by Liu et al investigates the question of whether the mitochondrial protein import component Tom70 might be involved in the coordination of biogenesis and localization of mitochondrial proteins. It follows a smart hypothesis that positions Tom70 in a coordinating role of nuclear-encoded mitochondrial gene expression and subsequent protein incorporation. The paper shows that Tom70 overexpression uniquely promotes the expression of numerous mitochondrial proteins and that Tom70's mitochondrial localization is required for this. The data then suggest that both mtDNA and a combination of transcription factors are involved in Tom70 controlled nuclear gene expression. The authors then find that Tom70 is also required to dampen nuclear mitochondrial gene expression during import stress. Importantly, Tom70 and numerous other import machinery components become depleted with age in yeast, and the same is true for mitochondrial membrane potential. This is a very strong part of the paper. Tom70 OE rescues this effect, and Tom70 OE extends survival. Finally, suggestive data show that the age-dependent Tom70 depletion is due to reduced expression and enhanced degradation. 

This is an interesting study that uses cutting-edge methods. However, a clear focus of the paper is somewhat missing. The paper touches on many topics that remain unresolved. These include the role of CR and the role of LCD-containing TFs as an explanation for the age-dependent decline of Tom70. There is a role of mtDNA in the Tom70 OE but the link to transcription factors remains unaddressed. For example, degradation of Tom70 is investigated via MDCs, but is autophagy involved? There is a large amount of data in the manuscript that cover a lot of territory, but further mechanistic insights would significantly enhance the paper.

Reviewer #2 (Public Review): 

The authors test the hypothesis that components of the TOM complex regulate efficient mitochondrial biogenesis by coordinating the synthesis of mitochondrial proteins with the rate of mitochondrial protein import. In general, the experiments are well developed and the findings and topic are likely of broad interest. The weaknesses are mainly related to the underdeveloped approaches and the vagaries related to the mechanism(s) by which Tom70 influences transcription of mitochondrial components. 

The authors performed a survey of TOM components (proteins required for protein translocation from the cytosol into mitochondria) and found that overexpression of Tom70 was sufficient to increase accumulation of 4 GFP-tagged mitochondrial proteins that localize to each of the four mitochondrial sub-compartments suggesting that Tom70 has a unique role in mitochondrial biogenesis. 

Interestingly, the authors demonstrate that Tom70 is required to limit transcription of mitochondrial components when Tim23 is impaired. In doing so, Tom70 prevents the aggregation of mitochondrial proteins that fail to be imported into mitochondria. 

The authors demonstrate that mtDNA is required for the increase in mitochondrial component transcription upon Tom70 overexpression. This is an exciting observation. However, experiments to understand the phenomenon are not considered. 

Interestingly, overexpression of Tom70 prevents the decline in mitochondrial function typically observed in aging cells, while Tom70-deletion accelerated the loss of mitochondrial function.

Reviewer #3 (Public Review): 

Mitochondria are autonomous double-membrane-bound organelles in eukaryotic cells. They synthesize ATP to meet the energy needs of the organism through oxidative phosphorylation. This cardinal role in energy production makes mitochondria a key player in metabolic, degenerative, and age-related diseases. Dysregulation of mitochondria is ubiquitous in diabetes, obesity, cardiovascular disease, cancer, etc. Research in the past decades have made huge progress in our understanding of mitochondrial biology. 

The current manuscript explores the dual role of Tom70 in coupling the highly orchestrated process of the transcriptional activation of nuclear genome-encoded mitochondrial proteins and the import of nascent mitochondrial proteins that are synthesized in the cytosol. With the help of cutting-edge techniques, the authors have demonstrated satisfactorily that Tom70 regulates the transcription of specific mitochondrial proteins and do so through transcription factors. Using quantitative imaging, the authors show that Tom70 regulate the mtDNA content. Further, the Tom70 protein was shown to play a crucial role in the feedback loop that ensures coupling of mitochondrial protein synthesis and import of these proteins into the mitochondria. Tom70 was also shown to have a crucial role in age-related defects in the mitochondria leading to mitochondrial dysfunction which seems to be conserved across various organisms. The conclusions of this paper are mostly well supported by data, but some clarifications are needed.

Reviewer #2 (Public Review):

By investigating the difference of humoral memory responses generated by infection and vaccination, authors are trying to find the epitope of pre-existing antibodies cross-reactive to endemic coronaviruses. Authors believe that the broadly-reactive epitope is potentially valuable for vaccine design. A strong point of this manuscript is the detailed description of those distinct humoral responses using human serum and a series of recombinant Spike domain proteins. Pre-existing antibodies reactive to the S2 domain conserved among beta coronavirus species are unique in the convalescent group. Although the observation is suggestive of the importance of protection, as the authors also pointed out, the link between biological importance in protection and their findings from serum studies is missing. The experimental methods are fairly designed, and the results are very informative. The authors provide adequate data to reach their goal of finding epitope of cross-reactivity among species. Further authors demonstrated that infection brings those cross-reactive antibodies than trimerized recombinant Spike protein-based vaccination. This pandemic is a unique occasion to study the immune responses and their memory generation against pathogen humans haven't met before. By using precious human materials, the findings are beneficial for future vaccine research.

Reviewer #1 (Public Review):

The authors investigated the plasma antibody responses of CoV-2 infected or vaccinated against CoV-2 spike and cross-reactive endemic CoV spike. They showed the correlation of CoV2 S2 IgG responses and OC43 S2 IgG responses in convalescent plasma. To investigate cross-reactivity of plasma antibody more directly, the authors affinity-purified plasma antibody with CoV-2 spike and OC43 spike, and showed the presence of non-neutralizing S2 cross-reactive antibody in CoV2-infected individual. Finally, the authors showed stabilized CoV-2 spike-vaccinated cohort had strong IgG responses against CoV-2 but not to OC43 spike. However, their data also showed other stabilized CoV-2 spike-vaccinated cohort had milder IgG responses against OC43 spike, which need to be clarified further.

Reviewer #1 (Public Review):

In this study the authors attempted to benchmark different methods for gene signature enrichment methods in single-cell RNA-seq data. They compared 2 single-cell based methods (AUCell, SCSE), their own developed method (JASMINE) and a popular method used in bulk RNA-seq studies (ssGSEA). For the benchmarking they collected 10 studies of scRNA-seq data from tumors, and performed several statistical analysis. In all the analyses ssGSEA performed worse by having lower specificity.

In this reviewer opinion, this is an important and understudied subject. In many scRNA-seq studies there is use of gene signatures to make a point, but to my knowledge there haven't been any deep-dive assessment of the performance of the methodologies used. Specifically, many studies use ssGSEA, but there haven't been any assessment of its reliability in single cell studies. As the authors note, gene dropouts in scRNA-seq may have major effect on reliability of this method.

My problem with this study is that the benchmarking is focused only on comparison of statistical measurements, but in my opinion, this is problematic here. The aim of an enrichment method is to identify biological relevant pathways. There is no analysis here that looks at the relevance of the significant signatures.

Here is an example of the main weakness - the main analysis performed by the authors was identifying gene sets that significantly distinguish between cancer cells and the rest of the cells in the tumor. This a problematic comparison, since the cancer cells are epithelial cells (in most of the studies used) and the "normal" cells are stromal cells, mostly immune. Those are not comparable "normal" cells, and therefore it is expected that all immune-related pathways will be significant. The authors find much more down-regulated gene sets in ssGSEA compared to the other methods, but why are they wrong? If they are all immune related, I would actually conclude that ssGSEA is better than the other methods.

Another weakness in this study is that the authors relate to the methods as if they are black boxes. If the main results of this study is that bulk gene expression methods (only one method is assessed, so I don't understand the title) cannot be used in single-cell data, I would expect to learn from the study what in the methodology makes it problematic.

The bottom line is that the only conclusion I can deduce from this study is that ssGSEA provides different results from newer single-cell specific methods.

Reviewer #2 (Public Review):

Noureen et al. benchmark four methods for quantifying the activity of gene expression signatures in single cell data, including one they developed, called JASMINE. They point to an imbalance in the number of expressed genes between tumor and normal cells, which they claim leads to a bias in performance of such methods.

The authors emphasize an important message -- considering cellular context when analyzing differentially expressed genes and gene signatures. Another strength is that the datasets on which these methods were evaluated is relatively large. However, they evaluate only four signature-scoring methods - a major weakness of the study. The new method they propose includes formulations which lead to unintended mathematical behavior and hamper interpretation.

Reviewer #3 (Public Review):

In this manuscript, Noureen et al. benchmarked four methods for the gene expression signature analysis for single-cell RNA sequencing data. They showed that cancer cells consistently expressing more genes than normal cells is the major factor to cause the bias. They also developed a method, JASMINE, and benchmarked this method with other methods. They finally suggest that cellular contexts should be taken into consideration for single-cell data analysis.

The topic of the study is important, considering a large amount of single-cell data released recently. The manuscript is well-organized and well-written. The strength is that the manuscript provides clear guidance for future benchmarking of the single-cell data analysis.

Reviewer #1 (Public Review):

Zaffagni et al. investigated the host cell response in a transcriptome level upon expression of viral proteins of SARS-CoV-2. They found that expression of Nsp14, highly conserved non-structural protein induces a dramatic remodeling of transcriptome that mimics SARS-CoV-2 infection. They revealed functional impacts of Nsp14 in various transcriptomic aspects such as transcript abundance, alternative splicing, and transcriptomic remodeling in a time course manner. They found IMPDH2, the rate-limiting enzyme in GTP biosynthesis as a key mediator of Nsp14 effects on host transcriptome, posing IMPDH2 and Nsp14 as a therapeutic target against SARS-CoV-2.

The paper revealed various transcriptomic effects upon Nsp14 expression. But biological relevance of infected cells should be verified on these effects. It would be better to explain the mechanistic link among these observations and some data need to be further validated to support their conclusions.

1. Are the alternative splicing pattern and increased circRNAs upon Nsp14 expression also observed in SARS-CoV-2 infection?

2. The authors showed that IMPDH2 contributes to Nsp14-mediated transcriptome changes. I wonder whether catalytic activity of IMPDH2 also affects the alternative splicing events mediated by Nsp14 expression. Given that the GC content is associated with the sensitivity to Nsp14-mediated alternative splicing, I am curious whether increased GTP level upon Nsp14 expression could be related to the alternative splicing events. How are the alternative splicing events when IMPDH2 inhibitor (MPA) was treated to Nsp14-expressed cells (comparing Nsp14+MPA to Nsp14+DMSO as Figure 6E)?

3. It would be nice to provide information of a responsible domain of Nsp14 for its effect on the host transcriptome. Also, I wonder whether this domain is required for its interaction with IMPDH2, which would further validate the IMPDH2-mediated Nsp14 effect on host transcriptome.

4. To make their conclusion "IMPDH2 is a key mediator of the effects of Nsp14 on the transcriptome of the hosting cell." more compelling, a rescue experiment using wild-type or catalytic dead mutant of IMPDH2 is needed. Or at least, the authors should confirm whether MPA effect on Nsp14-mediated transcriptome change can be reproducible using another IMPDH2 inhibitor.

Reviewer #2 (Public Review):

The authors were interested in understanding the functional consequences, as read out by changes in the RNA levels of human cells, that each protein product of the SARS-CoV-2 genome produce. Overall the study achieves these goals through multiple deep sequencing assays and investigates the data generated deeply, considering multiple mechanistic possibilities to explain the genome-wide results. They additionally nicely provided more focused evidence for how their high throughput data can be narrowed to insights on the individual gene level, as was the case for the data exploring IMPDH2's role in Nsp14's cell remodeling abilities. Given the nature of the sequencing data, this study provides a useful resource for others to examine DEGs for Nsp14 as well as the other viral proteins. Further, and more broadly, the work provides an interesting example of how a singular protein, in this case Nsp14, can have rapid and dramatic effects one the entire cell state; additional mechanistic work surrounding this ability could be of interest in the future.

Strengths of paper as submitted:

Broad scope of investigation for transcriptome changes across all known SARS-CoV-2 ORFs

Robust data on the dramatic impact that Nsp14 expression has on the transfected human cells

Correlation between individual-Nsp14 expression and changes during bonafide SARS-CoV-2 infections is strong

Mechanistic dissection to demonstrate a lack of import for the exonuclease domain in the observed transcriptome changes

Establishment of a function for the interaction between Nsp14 and IMPDH2, and some relevance for viral infection

Weaknesses of paper as submitted:

Lack of understanding of where the tagged proteins are expressed

Lack of understanding of the relative expression levels of each target

Lack of systematic understanding of how the viral proteins operate in concert (or even pair-wise) as they might during a native infection

Reviewer #1 (Public Review):

This is a very solid and exciting study. I have several suggestions, comments and questions:

The authors focused on examining the role of C129 as a regulator of PTPN22 redox sensitivity based on a published crystal structure of the catalytic domain. It would be great if they could demonstrate the existence of the disulfide bond between C129 and C227 also experimentally (in T cells). To this end, there are other cysteine residues in the vicinity of C227 such as the C231 that might be involved in the redox regulation PTPN22. The authors should at least discuss the their possible involvement.

How is mutation of C227 affecting T cell function? Are the effects similar with those of C129S?

Although the in vitro evaluation of the PTPN22 activity is of highest quality, it would be good to demonstrate that C227 redox status is modified under physiological conditions. 25-100 µM H2O2 is a high concentration that might not be reached within a cell and might be lethal for T cells.

C129 seems not to be mutated in patients with autoimmunity but is an excellent tool to test the importance of C227 redox regulation and the findings of this study suggest that its over-oxidation will support autoimmune responses. When considering the clinical relevance of the study, a drug that will protect the oxidation of the catalytic cysteine and/or stabilize the disulfide bond would have beneficial effects. The authors could test such pharmacological modulators in isolated T cells.

The authors discuss that NOX2-derived ROS most likely originate from antigen presenting cells. I fully agree with this discussion. However, some studies have proposed that NOX2 plays an important role also in T cells, a finding which was not confirmed by other following studies. It would be great if the authors could address this controversial issue in regards to their findings.

Fig. 1: Is the addition of bicarbonate affecting the pH and thus the activity of PTPN22?

The H2O2 concentration dependence of PTPN22_C129S should also be shown as for WT (see Fig. 1B)

Quantification of the slope based on only 3 measuring points is not accurate (Fig. 1D).

The pinna thickness measurements shown in Fig. 3B and C suggest that in NCF1 mice C129S has no effect. However, the thickness in NCF1 mice is already much higher than in WT mice (compare B and C). Does this mean that NOX2-derived ROS are the only factor that affects C227 redox properties?

The results shown in Fig. 5D could be moved to a supplementary figure.

The calcium measurements are not convincing and the differences are rather small. The y axis labels show 50K, 100K etc. Are this ratio values? If yes the imaging settings need to be optimized. Why is the mutant labeled as Pep? How is the C129S affecting calcium signaling? These observations need be examined in more detail or maybe calcium is not playing an important role.

I would suggest a more extensive evaluation of the proteomic data presented in Fig. 6D. The results might be very exciting and can further increase the impact of this study. Is 24h BSO treatment not toxic for the T cells (ferroptosis)?

Reviewer #2 (Public Review):

In this paper, the authors examine the acute regulation of the PTPN22 tyrosine phosphatase, an important negative regulator of T cell antigen receptor signaling, but reversible oxidation. PTPN22 has a cysteine residue C129 in close proximity to its catalytic cysteine, C227, allowing the formation of a disulfide bond between them, rather than an irreversible oxidation of C227, under conditions of reactive oxygen species (ROS) production. By mutating C129 to serine, the authors report that PTPN22 function is significantly altered during T cell activation, resulting in increased downstream effects of TCR signaling and worse autoimmune arthritis. Using a loss-of-function mutant of the NOX2 machinery, they provide further evidence that ROS production is key to PTPN22 inactivation during TCR activation.

The methods used are appropriate, well controlled, and the results clearly presented and interpreted. Experiments range from protein chemistry in vitro to assessment of T cell function in ptpn22-C129S expressing animals in an autoimmune arthritis model.

The proposed model of PTPN22 regulation fits well into the current understanding of PTP regulation and the role of PTPN22 in TCR signaling. Indeed, the very rapid increase in tyrosine phosphorylation of numerous proteins after TCR ligation is readily explained by not only tyrosine kinase activation, but by a simultaneous and transient inhibition of PTPN22.

Reviewer #3 (Public Review):

The manuscript by James, Chen Hernandez et al. reveals a novel function for PTPN22 oxidation in T-Cell activation. The authors used a broad array of methods to demonstrate that PTPN22 is catalytically impaired in addition to being more sensitive to reversible oxidation in vitro. In the characterization process, the authors found that PTPN22 could be directly reduced by Thioredoxin Reductase and that oxidation of PTPN22 oxidation could be easily monitored by the appearance of a faster migrating band in non-reducing gels. Supporting the hypothesis that the catalytic Cysteine forms a disulfide with a backdoor Cysteine (Cys129), the authors found that this C129S mutant is prone to oxidation and cannot be reduced back to its active form by Thioredoxin Reductase. Using a new mouse model in which this key Cysteine of PTPN22 is mutated to a Serine residue (PTPN22C129S mutant) and can presumably not form a stabilizing redox intermediate between the catalytic Cys residue and this backdoor Cys (C227-C129), the authors study how the oxidation prone mutant affects T-Cell activation. The authors find that the C129S mutant mouse showed an increased T-Cell dependent inflammatory response that was dependent on activation of the reactive oxygen species-producing enzyme NOX2. This data adds an interesting redox twist to the function of PTPN22 in T-Cells that contributes to conversation on the protective effects of reactive oxygen species against inflammatory diseases in vivo.

Strengths:

The in vitro characterization of the WT and C129S mutant form of PTPN22 is very thorough. Determination of the Km and Kcat highlights the differences between the two enzymes that go beyond redox regulation of the phosphatase. The reduction studies are masterfully done and highlight a novel reduction mechanism that merits to be further studied in cells. Demonstrating that PTPN22C129S is prone to oxidation in vitro is a key and technically challenging result that may be applicable to other members of the PTP family that also form disulfides with a backdoor cysteine. Showing that PTPN22C129S mice (backcrossed to B6Q mice making them susceptible to autoimmune arthritis) displayed higher T cell activation in two models (DTH and GPI), in addition to studies in T cells stimulated with collagen, increased this reviewer's confidence that the PTPN22C129S mouse exhibited T-cell-dependent inflammatory response phenotype similar to the PTPN22 knockout phenotype. Validation of T-cell signaling events in PTPn22C129S T cells were in line with the in vitro characterization of the phosphatase.

Weaknesses:

Although the paper has many strengths, some important weaknesses need to be addressed by the authors. In particular, the authors need to characterize better their mouse model and determine if PTPN22 is reversibly oxidized following TCR activation. If PTPN22 is oxidized, does it form an intramolecular disulfide between C227 and C129? The proposed model, that PTPN22C129S is more prone to oxidation, also has to be validated in vivo. Although this could be technically challenging in theory, the authors have shown that the migration pattern of the oxidized enzyme is different that of the reduced enzyme. Another major issue is that PTPN22 does not appear to be expressed in CD4+ T cells unless these cells are activated in vitro with anti-CD3/CD28 for 24 hours. This makes acute CD3-stimulation of CD4+ T cells studies - such as the measurement of acute calcium influx in Fig. 5E - very difficult to interpret. Perhaps the authors should explain why acute signal transduction studies in Figure 6 were performed in lymph node cells. If the reason is that PTPN22 (WT and C129S mutant) expression is higher, the authors should provide immunoblots for PTPN22 in these cells.

Since the PTPN22C129S mouse model has not been sufficiently validated, the claims of the authors are unfortunately weakened and the underlying molecular mechanisms do not completely support their conclusions. However, given the clear in vitro work provided in figures 1 and 2, it is this Reviewer's opinion that the authors can address the issues related to the oxidation status of PTPN22 and of PTPN22C129S in vivo, support their claims, and make a significant contribution to the field.

Reviewer #1 (Public Review):

This manuscript reports a very timely and interesting single-molecule fluorescence resonance energy transfer (smFRET) study of SARS-CoV-2 spike (S) proteins derived from the ancestral, Wuhan-1 strain (carrying an aspartic acid at amino acid position 614; D614) and the B.1 variant strain (carrying an aspartic acid-to-glycine mutation at amino acid position 614; D614G). The aim of the study was to characterize the dynamics with which the angiotensin-converting enzyme 2 (ACE2) receptor-binding domain (RBD) of the D614 variant of S undergoes transitions between its "down" conformation, in which the receptor-binding motif (RBM) is occluded, and its "up" conformation, in which the RBM is accessible and to assess the effects that the D614G mutation has on these dynamics. Moreover, the authors sought to elucidate if and how the binding of ACE2 or each of a set of antibodies that target different S epitopes to the D614 and D614G variants of S alter these dynamics. Finally, the authors attempt to determine whether the dynamic effects imparted by each of the antibodies interfere with or enhance ACE2 binding to the D614 and D614G variants of S. Interestingly, the authors find that the D614 and D614G variants of S intrinsically fluctuate between the up and down conformations, with the D614G mutation shifting the conformational equilibrium towards the up state through a mechanism in which the mutation stabilizes S in the up state while having little to no effect on the down state. In a very elegant set of results, the authors show that binding of ACE2 also shifts the conformational equilibrium towards the up state through a very similar mechanism, which, in the case of the D614G variant of S, pushes the conformational equilibrium even further towards the up state. The authors then show that binding of each of the antibodies tested to the D614 variant of S again shifts the conformational equilibrium of S towards the up state through a very similar mechanism. One of the most interesting observations in this regard is that even the antibodies targeting epitopes distal to the RBD allosterically exert this conformational effect on the RBD. The authors further observe that the effects of antibody binding to the D614G variant of S are smaller than those of antibody binding to the D614 variant of S. Finally, the authors beautifully show that, together with consideration of whether binding of a particular antibody would be expected to sterically obstruct the RBM, the effects of antibody binding on the conformational equilibrium of the RBD can regulate ACE2 binding.

Major strengths of this study are the carefully designed and biophysically and biochemically validated S constructs. Careful controls demonstrate that fluorescent labeling of the D614 and D614G variants of S does not seriously impair the ability of ACE2 to bind to S. Moreover, the representative smFRET trace shown in Fig 1 indicates that the smFRET data are of very high quality. In addition, the supporting and complementary fluorescence correlation spectroscopy and ELISA data are also of extremely high quality. Additional strengths of this work are the wide-ranging and unique exploration of the effects that the D614G mutation, ACE2 binding, and antibody interactions have on the dynamics of S as well as the very relevant investigation of if, and to what extent, the dynamic effects imparted by antibody-binding to S interfere with or enhance ACE2 binding to S.

The study does have a few weaknesses. One concern regards how the authors determined that the smFRET traces were best analyzed using a two-state conformational model. The representative smFRET state shown in Fig 1 suggests the possible existence of more than two FRET states and, correspondingly, more than two conformational states. Nonetheless, there is no discussion in the manuscript regarding how the authors arrived at the conclusion that the data are best described by a two-state model. Another concern regards how the authors determined if, and to what extent, some of the ligand-binding interactions had reached equilibrium/saturation. The methods report that S was incubated with ligands for periods of 60-90 min at room temperature prior to performing the experiments and that the experiments were performed in the continued presence of the ligands, but some of the interactions are quite high affinity and the study does not report if and how the authors determined whether the 60-90 min, room temperature incubations were enough to achieve equilibrium/saturation. Moreover, the number of smFRET traces obtained and analyzed at each experimental condition is lower than what is reported in typical smFRET studies. The relatively low number of smFRET traces exacerbates a somewhat related concern regarding the reproducibility and error analysis of some of the experiments. Specifically, it is not entirely clear whether the smFRET experiments were independently repeated or whether the various types of error analyses applied to the histograms, occupancies, and rates extracted from the smFRET experiments are fully justified.

Reviewer #2 (Public Review):

Diaz-Salinas et al. use single molecule FRET to probe the conformational changes in a recombinant viral spike glycoprotein from the SARS-CoV-2 virus. The authors introduced enzyme-labeling sites spanning the receptor binding domain (RBD) that report on the conformational transitions in the presence of a soluble ACE2 receptor along with a series of neutralizing antibodies that target different regions of the spike glycoprotein. The work includes important controls to verify that labeling does not destroy native function and antigenicity. A key result is identifying allosteric antibodies that can modulate the conformation even when they bind outside the RBD. Based on kinetic analysis of FRET transitions, the authors conclude that ACE2 binds through a mechanism of conformational selection rather than induced fit and that neutralizing antibodies affect the dynamic timescales of these interconversions. This allosteric modulation of spike protein affects the interaction with the ACE2 receptor with some antibodies inhibiting ACE binding while others enhanced binding. Based on this data, the authors conclude that the point mutant, ACE2 and some antibodies act by lowering the free energy of the bound state while antibodies targeting S2 lower the transition barrier height to facilitate the conformational change. The model is well supported by the kinetic data. Previous smFRET and structural studies identified this conformational switch linked to receptor interactions. The use of recombinant proteins provides an accessible assay for examining this important protein. The interpretation of FRET efficiency values is qualitative buts provides an accessible metric for the critical conformational change. The work provides important information about a naturally occurring point mutant and the effect of interactions with an array of antibodies.

Reviewer #1 (Public Review): 

This paper presents analysis of an impressive dataset acquired from sibling pairs, where one child had a specific gene mutation (22q11.2DS), whereas other child served as a blood-related, healthy control. The authors gathered rich, multi-faced data, including genetic profile, behavioral testing, neuropsychiatric questionnaires, and sleep PSG. 

The analyses explore group differences (gene mutation vs. healthy controls) in terms of sleep architecture, sleep-specific brain oscillations and performance on a memory task. 

The authors utilized a solid mix-model statistical approach, which not only controlled for the multi-comparison problem, but also accounted for between-subject and within-family variance. This was supplemented by mediation analysis, exploring the exact interaction between the variables. <br /> Remarkably, the two subject groups were gender balanced, and were matched in terms of age and sex. 

There are some aspects requiring clarification. In the discussion section, some claims come across as too general, or too speculative, and lack proper evidence in the current analysis of in the references. Furthermore, the authors seem to treat their (child) participants with the gene mutation as forerunners of (adult) schizophrenic patients, to whom their repeatedly compare the findings. However, less than half of these children with 22q11.2DS are expected to develop psychotic disorders. In fact, they are at risk of many other neuropsychiatric conditions (incl. intellectual disability, ASD, ADHD, epilepsy) (cf. introduction section). Furthermore, the liberal criteria for detecting slow-waves, along with odd topography of the detections, limit the credibility of the slow-wave-related results. Lastly, we cannot be sure whether the presented memory effects reflect between-group difference in general cognitive capacities, or, as claimed, in overnight memory consolidation. 

Generally, the current study introduces dataset connecting various aspects of 22q11.2DS. It has a great potential for complementing the current state of knowledge not only in the clinical, but also in sleep-science field.

Reviewer #2 (Public Review): <br /> This study examines 22q11.2 microdeletion syndrome in 28 individuals and their unaffected siblings. Though the sample size is small, it is on par with many neuroimaging studies of the syndrome. Part of the interest in this disorder arises from the risk this syndrome confers for neuropsychiatric disorders in general and psychosis specifically. The authors examine sleep neurophysiology in 22q11.2DS and their siblings. Principal findings include increase slow wave and spindle amplitudes in deletion carriers as compared to controls. 

Strengths of this manuscript include: 

- The inclusion of siblings as a control group, which minimizes environmental and (other) genetic confounds <br /> - The data analyses of the sleep EEG are appropriate and in-depth <br /> - High-density sleep EEG allows for topographic mapping 

Weaknesses of this manuscript include: 

- The manuscript is framed as an investigation of the psychosis and schizophrenia; however, psychotic experiences did not differ between 22q11.2DS and healthy controls. Therefore, the emphasis on schizophrenia and psychosis does not pertain to this sample and the manuscript introduction and discussion should be carefully reframed. The final sentence of the abstract is also not supported by the data: "... out findings may therefore reflect delayed or compromised neurodevelopmental processes which precede, and may be biomarkers for, psychotic disorders". <br /> - What is the rationale for using a mediation model to test for the association between genotype and psychiatric symptoms? Given the modest sample size would a regression to test the association between genotype and psychiatric symptoms be more appropriate? <br /> - From Table 1, which presents means, standard deviations and statistics, it is hard to tell if there is a range of symptoms or if there are some participants with 22q11.2DS who met diagnostic criteria for a the listed disorder while others who have no or sub-threshold symptoms. This is important and informs the statistical analysis. Given the broad range of psychiatric symptoms, I also wonder if a composite score of psychopathology may be more appropriate. What about other psychiatric symptoms such as depression? <br /> - The age range is very broad spanning 6 to 20 years. As there are marked changes in the sleep EEG with age, it is important to understand the influence of age. The small sample size precludes investigating age by group interactions meaningfully, but the presentation of the ages of 22q11.2DS and controls, rather than means, standard deviations and ranges, would be helpful for the reader to understand the sample. Also, a figure showing individual data (e.g., spindle power) as a function of age and group would be informative. The authors should also discuss the possibility that the difference between the groups may vary as a function of age as has been shown for cortical grey matter volume (Bagaiutdinova et al., Molecular Psychiatry, 2021). <br /> - There is a large group difference with regards to full scale IQ. IQ is associated with sleep spindles (e.g., Gruber et al., Int J of Psychphsy, 2013; Geiger et al., SLEEP, 2011). For this reason, the authors should control for IQ in all analyses. <br /> - The authors find greater power in the delta and sigma bands in 22q11.2DS compared to their siblings. Looking at the Figure 2, it appears power is elevated across frequencies. If this were the case, this would likely change the interpretation of the findings, and suggest that the sleep EEG likely reflects changes in cortical thickness between controls and 22q11.2DS participants. <br /> - Along the same lines as the above comment, it would be interesting to examine REM sleep and test how specific to sleep spindles and slow waves these findings are.

Reviewer #3 (Public Review): 

In this study, Donnelly and colleagues quantified sleep oscillations and their coordination in in young people with 22q11.2 Deletion Syndrome and their siblings. They demonstrate that 22q11.2DS was associated with enhanced power the in slow wave and sleep spindle range, elevated slow-wave and spindle amplitudes and altered coupling between spindles and slow-waves. In addition, spindle and slow-wave amplitudes in 22q11.2DS correlated negatively with the outcomes of a memory test. Overall, the topic and the results of the present study are interesting and timely. The authors employed many thoughtful analyses, making sense out of complicated data. However, some features of the manuscript need further clarification. 

1) Several interesting results of the manuscript are related to altered sleep spindle characteristics in 22q11.2DS (increased power, increased amplitudes and altered coupling with slow waves). On top of that the authors report, that the spindle frequency was correlated with age. I was wondering whether the authors might want to take these individual (age-related) differences into account in their analyses. The authors could detect the peak spindle frequency per participant and inform their spindle detection procedure accordingly. This procedure might lead to an even more clear cut picture concerning altered spindle activity in 22q11.2DS. 

2) The authors state in the methods section that EEG data was re-referenced to a common average during pre-processing. Did the authors take into account that this reference scheme will lead to a polarity inversion of the signal, potentially over parietal/occipital areas? This inversion will not affect spindle related analyses, but might misguide the detection of slow waves and hence confound related analyses and results. 

3) I have some issues understanding the reported slow wave - spindle coupling results. Figure 5A indicates that ~100 degrees correspond to the down-state of the slow wave. Figure 5E shows that spindles preferentially clustered at fronto-central electrodes between 0 and 90 degrees, hence they seem to peak towards the slow wave downstate. This finding is rather puzzling given the prototypical grouping of sleep spindles by slow wave up-states (Staresina, 2015; Helfrich, 2018; Hahn, 2020). Could it be that the majority of detected spindles represent slow spindles (9-12 Hz; Mölle, 2011)? Slow spindles are known to peak rather at the up- to down-state transition (which would fit the reported results) and show a frontal distribution (which again would fit to the spindle amplitude topographies in Fig 3E). If that was the case, it would make sense to specifically look at fast spindles (12-16 Hz) as well, given their presumed role in memory consolidation (Klinzing, 2019). In addition, is it possible that the rather strong phase shift from fronto-central to occipital sites is driven by a polarity inversion due to using a common reference (see comment 2)? <br /> Apart from that I would suggest to statistically evaluate non-uniformity using e.g. the Rayleigh test (both within and across participants). 

4) Somewhat related to the point raised above. The authors state that in the methods that slow wave spindle events were defined as time-windows were the peaks of spindles overlapped with slow waves. How was the duration of slow waves defined in this scenario? If it was up- to up-state the authors might miss spindles which lock briefly after the post down-state upstate, thereby overrepresenting spindles that lock to early phases of slow waves. Why not just defining a clear slow wave related time-window, such as slow wave down-state {plus minus} 1.5 seconds? 

5) The authors correlated the NREM sleep features with the outcomes of a post-sleep memory test (both encoding and an initial memory test took place pre-sleep). If the authors want to show a clear association between sleep-related oscillations and the behavioural expressions of memory consolidation, taking just the post sleep memory task is probably not the best choice. The post-sleep test will, as the pre-sleep test, in isolation rather reflect general memory related abilities. To uncover the distinct behavioural effects of consolidation the authors should assess the relative difference between the pre- and post-sleep memory performance and correlate this metric with their EEG outcomes.

Reviewer #1 (Public Review): 

This paper evaluates the decoding performance of sensorimotor electrocorticography (ECoG) and subthalamic nucleus (STN) local field potentials (LFPs) during a grip-force task in patients with Parkinson's disease. ECoG signals showed better decoding performance compared to STN LFPs or the combination of the two. Gamma band power seemed to provide the most information across power bands. Individuals with less impairment showed the best decoding performances, which was also impacted by both structural and functional connectivity with the chosen electrode contact. Overall, the authors took a comprehensive approach for evaluating decoding performance using a variety of algorithms in a pseudo real-time fashion to establish potential superiority of ECoG over the sensorimotor cortex compared to STN LFPs for using power to predict grip force production. 

Strengths: 

Despite being a retrospective analysis, the authors used appropriate methodological considerations to evaluate pseudo real-time decoding performance with algorithms and methods that could be translated to a true real-time performance. The authors evaluated power bands relevant to the technological capabilities of current adaptive neurostimulators. The authors provided sufficient support for both the superiority of ECoG over STN LFPs, as well as signals from the contralateral side compared to ipsilateral signals. These findings held true for both different timings (i.e., the duration of time over which to calculate a given band's power relative to movement onset) and algorithm complexity. 

The authors also investigated the contribution of factors other than just the neural signals themselves on decoding performance. The finding that impairment level was related to decoding performance will be relevant for any potential clinical utility of these types of decoding algorithms. Similarly, it is well-known that electrode location will impact the relevance of a given electrode's signal. The author's attempt to address this in a multi-faceted fashion by first evaluating the impact of electrode distance to relevant anatomical markers (hand-knob region of motor cortex and dorsolateral STN), and then also evaluating the roles of both structural and functional connectivity profiles for a given contact location. These significant findings may help explain the wide spread observed in decoding performance. 

Although the focus of this paper is in the context of PD, the proposed methods are applicable to many domains of brain computer interfaces and neural signal decoding. 

Weaknesses: 

A wide-range of decoding performance is seen across participants with a group of good performers and a group of low-performers. The findings from the secondary analyses on impairment levels and electrode location/connectivity may explain some of these differences, but it is unclear to what extent or if other factors are at play. 

The cohort is notably small, especially considering the heterogeneity of electrode location. Although this does not limit the major finding of the superiority of sensorimotor ECoG over STN signals, it does limit the ability to understand the observed individual differences in decoding performance. 

The authors are unable to fully rule-out that the superiority of ECoG signals is not dependent on the task performed (i.e., grip force). The authors claim that the findings support the utility of additional ECoG in adaptive DBS research for PD patients. Although it is certainly expected that sensorimotor ECoG would provide a richer signal compared to STN LFPs due to both signal size, complexity, and relation to movement, the data in this paper is inherently limited to the grip task performed. 

Conclusion: 

The methods used to evaluate sensorimotor ECoG and STN LFP signals for grip force production are of interest for both the PD field (e.g., aDBS and neurophysiological underpinnings of behavior and impairment) as well as the generalized field of brain-computer interfaces. The authors address how a multitude of factors (timing of signal, location of recording electrodes, frequency bands used, impairment level etc.) may impact decoding performance, which can inform/guide future work. The current work shows strong evidence for the superiority of sensorimotor ECoG compared to STN LFPs for decoding grip force production in PD.

Reviewer #2 (Public Review): 

Merk et al., compare grip force decoding performance between cortical ECoG electrodes and subthalamic LFP and find that electrodes over cortical regions perform better. They first compare a simple linear regression model, and then use several more sophisticated techniques to decode grip performance for each electrode for both contra and ipsi movements. Overall the claim that ECoG electrodes decode grip force with higher accuracy than subthalamic LFP seems supported with their data, although there are some inherent limitations of the clinical data that need to be addressed if not with data than in the discussion section. In addition, they find that decoding performance is negatively correlated with PD impairment and they use connectivity models to identify if decoding performance is related to connectivity profiles. 

I appreciate that this paper uses several different decoding techniques and attempts to decode grip force for contra and ipsi movements for each electrode. The main result of this paper is that neural signals from ECoG electrodes are superior to subthalamic LFP for movement decoding. Based on the analyses that the authors provide, these results seem to be of potential interest for clinical researchers interested in adaptive deep brain stimulation (aDBS) and basic science researchers interested in motor control. Although the difference in grip force decoding appears quite large, there are a couple limitations that I think the authors could address to make the paper even stronger. 

The first limitation when comparing cortical and subthalamic electrodes is that the size and structure of the probe may be different. This means that instead of comparing apples to apples, it is more like comparing apples to oranges. This does not completely undermine the result because the difference in decoding between the areas, even given experimental differences, is likely to be of interest to clinical researchers studying DBS. If the surface area of the electrode is different between the two regions, then this could be a factor in decoding performance that does not have to do with brain region. Additionally, the electrodes in the subthalamus nucleus are circular, which are likely targeting very different neural populations across the probe within the small nucleus, which is different from the cortical electrodes which are on the surface targeting neural populations which are adjacent. Both of these factors (e.g. size and shape) could contribute to differences in decoding performance regardless of brain region. I did not see details of the electrodes in the method section, but this would be important to report as surface area is related to the number of neurons/dendrites summing to create the LFP, and this might lead to qualitatively different results for something like hand gripping irrespective of area. Similarly, with the shape of the electrode. These details will be an important addition to the paper and something that others can continue to investigate (e.g., researchers who have different size or shape of electrodes in the STN). I am sympathetic that this is not a variable that the researchers can change given the clinical nature of DBS, but the surface area of electrodes in each area should be mentioned in the method section, and if the surface area of the electrodes are different, then it should also be mentioned as a limitation in the discussion section. Nonetheless, the results are likely to be of interest for clinical researchers, but they would need these details in order to compare to their own DBS system (there are now directional leads which have more electrodes and thus smaller surface area). 

The second possible limitation is whether you have fully explored the neural feature space. Although the cutoffs for frequency bands remain somewhat arbitrary, your selection of frequency bands seems very reasonable and seems to cover all the possibilities. One suggestion I have is that you also include the time domain data as a feature along with your frequency bands. Some papers have shown pretty good decoding with this feature - sometimes called the local motor potential. Here are some papers which discuss this feature in more detail. This could be an interesting addition especially if it performs well as it requires little preprocessing for studies doing online preprocessing and decoding. 

Flint, R. D., Wang, P. T., Wright, Z. A., King, C. E., Krucoff, M. O., Schuele, S. U., ... & Slutzky, M. W. (2014). Extracting kinetic information from human motor cortical signals. Neuroimage, 101, 695-703. 

Mehring, C., Nawrot, M. P., de Oliveira, S. C., Vaadia, E., Schulze-Bonhage, A., Aertsen, A., & Ball, T. (2004). Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. Journal of Physiology-Paris, 98(4-6), 498-506. 

Schalk, G., Kubanek, J., Miller, K. J., Anderson, N. R., Leuthardt, E. C., Ojemann, J. G., ... & Wolpaw, J. R. (2007). Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. Journal of neural engineering, 4(3), 264. 

Given how similar the descriptive power plots are, I am surprised that low gamma has much larger weights compared to high gamma or HFA. It looks like you aren't using regularization for your linear regression model. If your features (band pass filters) are highly correlated, the interpretation of the weights might not be meaningful. Have you thought about using ridge regression or lasso to deal with your seemingly highly correlated features? If not then I don't believe it makes sense to try and interpret the weights. It looks like you do use regularized regression later, but looking at the method section for your linear regression model there is no regularization term - so based on that it seems like for this first section it is just standard linear regression. I would suggest also using regularized regression for these analyses as interpreting the weights of linear regression with highly correlated features may be problematic. 

The correlation with decoding performance and motor PD impairment is intriguing and I think this analysis and the result is of value to both clinical and basic researchers. 

Although not dependent on your main claim, I had a difficult time understanding the logic and the methods of your last section which relates decoding performance with connectivity maps. For example, after reading the methods section, I was still unclear how you determined if a fiber was significant or not. I believe that this section needs more detail and clarity before publication. For example, you have analyses for structural and functional connectivity, but for the functional connectivity I could not find anything in the method section about what the patient was doing when this was computed - were the patients at rest, were they doing the same gripping task? These details are important for understanding the analyses and interpretation.

Reviewer #3 (Public Review): 

This work evaluates the effectiveness of signals recorded in the subthalamic nucleus (STN) and along sensorimotor regions of the cortex for decoding simple movements in patients with Parkinson's disease. The authors present this motor decode as a potential control signal for adaptive deep brain stimulation. A structured machine learning approach is presented that investigates the value of different sensor recording locations, signal components (frequency bands), and decoding model architectures. Additionally, a relationship between symptom severity and decoding performance is identified. With the recent advent of implantable closed-loop stimulators for neurological conditions such as Parkinson's disease, this paper addresses current knowledge gaps that may inform both surgical and engineering considerations for optimizing these new types of therapies. 

Strengths: 

The authors present a clean and principled model testing procedure with appropriate training, validation, and testing. In principle, this produces unbiased performance estimates for each model with its best possible configuration of hyperparameters. This provides a nice format in which hypotheses about different modelling aspects may be compared. The general pipeline may be built upon in future studies investigating control signals for closed-loop neuromodulation in a variety of neurological conditions. Data-driven machine learning approaches like those presented here allow for highly individualized settings of such neuromodulatory therapies. <br /> Although all analyses were performed offline, the data were processed in a manner that would be appropriate for the proposed use of providing control signals for real-time adjustment of deep brain stimulation. 

The analysis of feature importance (frequency band weights) in the linear models provides helpful intuition and sanity checks from a basic neuroscience perspective. By testing multiple decoder models, both linear and non-linear with varying levels of complexity, some intuition is also gained about the nature of the information content in these signals and how that differs across the recorded brain regions. The main result - that cortical recordings outperform STN recordings for decoding grip-force - is demonstrated in the more complex XGBOOST models. <br /> Consideration is given to multiple important factors contributing to decoder performance. A thoughtful combination of topographic features are considered, including the location of sensors with regards to specific neuroanatomical landmarks as well as connectivity profiles of the surrounding tissue. Investigating the relationship between connectivity profiles of the sensor locations - both structural and functional connectivity - is particularly intriguing. In principle, this could be incorporated into planning sensor implant locations. 

Weaknesses: 

A primary weakness of the paper is that some of the results and ideas are underdeveloped. While identifying a relationship between motor symptom severity and decoder performance is interesting on its own, there is no further investigation of this result for providing better intuition about why the relationship exists and what can or should be done about it. The authors discuss the potential impact beta bursts might have on decoder performance but do not pursue any analysis of their own data to determine whether those proposed hypotheses are supported. 

Similarly, it is reported that models combining signals from multiple sensors do not benefit from the added features, but there is very little follow-up or elaboration on this. There are many regularization and ensemble learning methods that ought to be tested before coming to strong conclusions based on this observation. It seems likely that given an optimal integration of these features, decoders would experience a performance benefit. While the specific XGBOOST models that they tested may have failed to benefit from larger feature combinations, the usefulness and generalizability of that knowledge is limited without more exhaustive testing and discussion. 

A Go/No-Go task is also used in the paper. This is potentially problematic, given that there may be modulation in the neural recordings associated with response inhibition. While the decoders are trained only to predict actual force production, it is possible that inhibitory responses could influence the decoder readout and impact the performance metrics on which every analysis of the paper hinges. It is also plausible that such inhibitory responses would asymmetrically affect the subthalamic decoders and the cortical decoders. Comparison of those two signal locations is the main topic of the paper, so this is a critical aspect to consider.

Reviewer #1 (Public Review): 

The authors have performed scATACseq on multiple timepoints during mouse male gonadogenesis and germ cell maturation during the fetal to neonatal transition (E18.5 and postnatal days 1,2,5). Clustering of thousands of cells revealed striking cellular diversity and led to the identification of cell populations that were not known before. This work may have far reaching implications, but additional validation is needed. 

The identification of novel transitional spermatogonia population in Figure 4D is intriguing. Independent validation by flow cytometry or in testis cross section to better allow the colocalization of nr5a1 and Oct4 and other germ cell markers would be important. 

Additional validation is needed to ensure that populations 1 and 2 in figure 4d are not to doublets. Providing violin plots for both soma and germ cell markers will be helpful. Is SF1 the only gene expressed in this unique germ cell population or are many other somatic markers expressed in the population. do these cells express well recognized SPG markers like Oct4+ , PLZF, GFRA? 

The IF validation in 5F is not as convincing that these cells are potentially Sertoli stem cells. IF in cross-sections will be easier to interpret- especially when co-stained with several germ, somatic, or novel markers of that population. purification of these cells and further characterization is needed. A hallmark of fetal Sertoli cells is to mediate the migration of endothelial cells to the seminiferous tubules during testicular cord formation. Is it possible to purify these cells to determine whether they have functional Sertoli cells properties in vitro using human umbilical vein endothelial cells (HUVECs). Do these cells have immune privilege properties - can they suppress proliferation of Jurkat E6 cells.

Reviewer #2 (Public Review): 

Liao et at performed single cell ATAC sequencing to reveal chromatin status in various cell types in the perinatal mouse testes. The chromatin status was then used to define cell types and identify potential transcription factors that control the progress of differentiation. This work could provide new insights into how various cell types acquire their fate in early testis development and establish a genomic framework that can be used to correlate with human data for infertility. The strength lies on the novelty of single cell analyses. The weaknesses include a lack of statistical power, the uncertainty on the correlation between chromatin status, gene expression, and transcription factor activity, and insufficient information and confirmation on some of the experiments and results.

Reviewer #1 (Public Review):

A great deal of effort has gone into identifying and studying the role force-gated ion channels in somatosensation. By far the best understood is molecule is Piezo2 which is required for mechanotransduction in a wide range of mechanoreceptors including most touch neurons and proprioceptors. Notably, the majority of responses to high intensity mechanical stimuli (i.e. mechanical pain) remain in mice with conditional ablation of Piezo2 expression and in human subjects who have inherited loss of function mutations in this gene. Very recently, Tmem120a, renamed TACAN, was proposed to function as a slowly adapting stretch-gated ion channel and be required for normal mechanical pain responses. However, this view has been challenged by a series of preprints. In this current study, Del Rosario and colleagues investigate the function of TACAN and find, similar to the other preprints, that heterologous expression of TACAN does not endow cells with any mechanosensitive currents. However, the authors make the unexpected observation that TACAN expression significantly reduces Piezo2 function but not other mechanosensitive ion channels (Piezo1 and Trek-1). The authors go on to provide TIRF imaging evidence that this effect is not due to Piezo2 trafficking to the plasma membrane or Tacan overexpression results rearrangements of the cytoskeleton. Finally, they show Tacan and Piezo2 are co-expressed in DRG neurons and the siRNA-mediation reduction of Tacan expression in neurons alters the relative proportion of mechano-sensitive neurons characterized based on their in vitro adaption properties.

Reviewer #2 (Public Review):

In this manuscript, Del Rosario et al. use electrophysiology and imaging to show that TMEM120A, a putative high-threshold mechanosensitive ion channel (also named TACAN), does not confer mechanosensitive currents to heterologous cells. Instead, they find evidence that TACAN negatively regulates the mechanosensitive ion channel Piezo2, but importantly, not other mechanosensitive ion channels (Piezo1 or TREK-1). They also find, contrary to a previous report, that siRNA mediated knockdown of TACAN does not reduce the proportion of slowly adapting mechanosensitive currents in DRG neurons (but see below for related concerns).

Overall, the potential impact of this manuscript is extremely high. Their extensive electrophysiological analysis adds to a growing body of preprints suggesting that TACAN is not, in fact, an ion channel, and go further to suggest a novel role of this membrane protein in regulating Piezo2. I do have several comments that must be addressed, specifically regarding the use of TIRF to quantify membrane expression and the analysis of the electrophysiological data.

Reviewer #3 (Public Review):

Del Rosario et al. investigate the function of the protein TACAN (TMEM120A) in cell lines and in sensory neurons from the dorsal root ganglion (DRG). TACAN was recently proposed to encode the slowly adapting (excitatory) mechanosensitive channel in sensory neurons, which is a candidate for detecting noxious mechanical stimuli (Beaulieu-Laroche et al., 2020, cited). Del Rosario et al. show that co-expression of TMEM120A reduced Piezo2 currents but did not inhibit two other well-known mechanosensitive channels, Piezo1 and TREK1. They went on to investigate the effects of TMEM120A siRNAs on endogenous mechanosensitive currents in DRG neurons. They show that siRNA-mediated knockdown of TMEM120A has no effect on slowly adapting mechanically activated currents (as argued by Beaulieu-Laroche et al., 2020) but increased the proportion of rapidly adapting currents (as expected from the expression study), supporting the idea that TMEM120A may act as a negative modulator of Piezo2 channel activity. While the authors succeeded in demonstrating the potential modulatory effects of TMEM120A on Piezo2 channel activity, they failed to demonstrate the physiological relevance of this potential modulation in native sensory neurons.

Reviewer #1 (Public Review):

The manuscript by Rekler and Kalcheim examines the role of neural tube-derived retinoic acid (RA) in neural crest development. They observe that the onset of expression of the RA-synthesizing enzyme RALDH2 in the dorsal neural tube coincides with the end of neural crest production. The authors propose that this local source of RA is essential to activate the transcription of Bambi other BMP inhibitors, leading to the disruption of BMP signaling. Loss of BMP activity at the dorsal neural tube would halt neural crest production, leading to the establishment of the definite roof plate. Thus, precise temporal regulation of RALDH2 in the dorsal neural tube would dictate the timing of neural crest production and the segregation of PNS and CNS progenitors.

Previous studies have already identified a role for RA in the control of the timing of neural crest production. Martinez-Morales et al (JCB 2011) have shown that during early trunk development, mesoderm-derived RA works with FGF signaling to jumpstart the BMP/Wnt cascade that drives neural crest migration in the trunk. Rekler and Kalcheim choose to focused on a distinct function of RA at a later timepoint. The main contribution of the present study is the demonstration that - at later stages - RA produced by the neural tube has the opposite effect, acting to inhibit the BMP/Wnt cascade and halt neural crest production. Thus, RA would be a major regulator of the timing of neural crest production, acting to both trigger and repress neural crest migration.

The study's strengths lie in an experimental strategy that allows the authors to manipulate RA function in a stage-specific manner and therefore uncover a later role for the signaling system in neural crest production. The authors also show that RA inhibition results in an incomplete fate switch and results in the generation of cells that share regulatory features of neural crest and roof plate cells. A significant limitation of the study is that the molecular mechanisms that endow RA signaling with stage-specific functions remain unknown. This is of particularly important since the early vs. late RA seem to have opposing effects, acting to either promote or terminate neural crest production.

Comments:

Previous studies have demonstrated that early RA production (presumably from the mesoderm) is necessary for the expression of early dorsal neural tube / neural crest genes like Pax7, Msx, Wnt1, and even BMP ligands. This is in contrast to the local source of RA, which presumably would be silencing these genes. Thus, mesoderm-derived RA would have the opposite effect in these progenitors than the RA synthesized in the neural tube. The study does not provide a mechanism that explains these stage-specific effects of the morphogen.

The effects of RA manipulation are often examined with non-quantitative techniques, like in situ hybridization (Fig. 2, 3). The incorporation of quantitative approaches (e.g., qPCR) would allow for the precise characterization of phenotypes (and better estimation of penetrance, etc.). Furthermore, the study lacks molecular/biochemical strategies to define the regulatory linkages between genes and pathways. This is a considerable limitation of the study since it prevents the establishment of a regulatory axis that would directly connect RA signaling to the BMP pathway.

The function (and the regulation) of RALDH2 at the dorsal neural tube has been studied thoroughly, and RA is a known player in the dorsal-ventral patterning of the CNS. It is not clear to what extent the phenotypes observed by the authors are due to the disruption of a neural crest-intrinsic mechanism or if they are secondary to the overall changes in the cellular organization of the neural tube caused by loss of RA.

The authors rely solely upon overexpression constructs to manipulate the activity of the RA signaling pathway, which may be prone to artifacts. Furthermore, both overexpression constructs aim at inhibiting RA activity. This limits the impact of the work since there is no demonstration that RA is sufficient to activate BMP inhibitors and halt neural crest production.

Reviewer #2 (Public Review):

The manuscript presents a novel role for RA signaling during development as the mediator of the switch that occurs in the dorsal neural tube after the neural crest cells have migrated and the roof plate forms. The finding is interesting and novel as the events that take place at the end of neural crest stage are poorly understood. The strengths of the manuscript are that the study is well planned and executed to show the interesting phenotype of delayed/disturbed roof plate formation accompanied with prolonged neural crest stage caused by inhibition of RA signaling in the dorsal neural tube. The results also show that RA signaling marks the RP territory and inhibits the DI1 interneurons from invading the region. The results bring novel information to the field. The original finding of the involvement of RA in the process was revealed in a RNAseq screen comparison between the neural crest and the roof plate (which was recently published by the same lab). However, the current study doesn't use any new technology such as high throughput screens or high resolution or live imaging etc., but rather relies mainly on "old fashioned" techniques: electroporation to induce transient inhibition of RA signaling in the dorsal neural tube followed by analysis of the phenotype by using chromogenic in situ hybridization. The chosen techniques are sufficient to convincingly show the point the authors want to make and the study serves as a reminder that fancy new techniques are not necessarily a requirement for creating a solid story. The manuscript is also well written and easy to follow.

Finally, the manuscript links the activation of RA signaling to the decline of BMP signaling and specifically the upregulation of BMP inhibitors in the dorsal neural tube at the end of the NC stage, but in its current form the proof of this proposed link remains weak. Similarly, the manuscript does not address the consequences of exposure of RA to the dorsal neural tube during NC stage and it thus remains unknown whether RA signaling is sufficient to end the NC stage and activate roof plate formation prematurely. Additional experiments of this kind would help clarify the role of RA in the dorsal neural tube and the reciprocal roles of the two signaling pathways (RA and BMP).

Reviewer #1 (Public Review):

Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models by Hajkariim et al introduces a pipeline for pre-processing and analyzing data from multiplex flow cytometry and other technologies. Preprocessing steps include algorithms for correcting common sources of bias in such data. Another key feature is a robust approach to measuring cell similarity across samples. Among the strengths are that the manuscript is well-written, the analysis pipeline is well-motivated, and illustrated with apt examples. The similarity measure is very interesting as well.

There are a few weaknesses as well. It is not completely clear to me how this pipeline agrees and disagrees with common practice in the field. References 1-3, cited to document ongoing analytic challenges, are all at least 5 years old. Comparisons to other approaches, including the use Jensen-Shannon Divergence for similarity, make a convincing case that the proposed method is both effective and computationally efficient, but it is not clear if the comparators represent true standard of practice, or mere straw men. Methodologies are complex and can be difficult to follow, especially the similarity measure.

Reviewer #2 (Public Review):

In this manuscript, Hajkarim et al developed compaRe, a user friendly software suite (written in R) for analyzing high-throughput, multi-parameter screening data. There are several modules included in the compaRe toolkit, which can be individually invoked to perform specific tasks, such as quality control, bias correction, pairwise comparisons, clustering and data visualization.

Strengths:

1 All of these modules are available as command-line version and a GUI version for users to use in data analysis, visualization and results interpretation.

2. The authors showed the utility of their toolkit in analyzing multiparameter mass and flow cytometric data from AML and MDS patient samples. Through this analysis using compaRe, the authors showed that they can identify patient heterogeneity and drug response profiles.

3. Overall, this is a well organized and written manuscript describing the development of the new compaRe toolkit. The method is clearly described, and the user manual/tutorial is easy to follow.

4. It seems like compaRe will be a useful toolkit for the research community, which is eager for a one-stop pipeline for analyzing high-throughout multiparameter screening data.

Weakness:

1. However, the current manuscript lacks comparison with other existing tools/methods in analyzing mass and flow cytometric data.

Reviewer #3 (Public Review):

Hajkarim et al. implement an algorithm in their presented toolkit compaRe to compare samples based on the similarities of samples, distinct from the more commonly used meta-clustering approaches, such as PhenoGraph, or dimensional reduction with Jenssen-Shannon Divergence analysis. Similarities among samples are calculated based on the proportions of cells within a sample belonging to an n-dimensional "hypercubes" (or "hypergridding" that is actually mass-aware and not blind) that are stratified by expression levels for n number of markers. The authors demonstrate that this method is much more time-efficient, obviates subsampling, and is robust to batch effects. This method is particularly appropriate for large-scale datasets, facilitating the comparison of numerous samples which would be helpful in screening efforts. The manuscript is written and presented well.

Major strengths:

1. The study demonstrates sufficiently strong support for the toolkit's ability to determine similarity across samples and its computing efficiency with Figure 2, an important advantage of this tool.<br /> 2. Compared to other approaches, the method is advantageous for identifying groups of samples that may be similar in a very large-scale dataset. CompaRe does not require (or make use of) manual expert annotation of meta-clusters. The workflow is efficient and unbiased.

Major weakness:

While the toolkit may clearly be useful in evaluating similarities across many samples, it does not seem to have clearly demonstrated its utility in exploring specific phenotypes in-depth within a high-parameter dataset.

Reviewer #1 (Public Review):

This study addresses the important question of understanding the cellular physiology of cholinergic interneurons in the striatum. These interneurons play a key role in learning and performance of motivated behaviors, and are central to movement disorders, psychiatric disease, and addiction. Their unique physiology, which includes tonic pacemaking activity and active conductances that shape integration of dendritic inputs, is critical to their function but is still incompletely understood. The authors cleverly integrate a series of innovative electrophysiological and optical approaches to gain insight into dendritic physiology of these neurons. Their creative approach yields some interesting and novel findings. However, there are technical and conceptual concerns that need to be addressed before these results can be readily interpreted. Some refinement of analysis and presentation, and potentially some additional experiments, will therefore be required to strengthen the conclusions and facilitate interpretation of the results.

Major concerns:

1) This manuscript focuses on differential physiology of proximal and distal dendrites contribute to physiological activity and integration of inputs in cholinergic interneurons, suggesting that NaP and HCN currents act in concert to selectively boost inputs onto proximal dendrites (from thalamus), relative to inputs onto distal dendrites (from cortex). The results presented in Figures 1-4 are consistent with a distinct physiology of proximal-vs-distal dendrites based on purely electrical properties. Indeed, Figure 5 initially appears consistent with this model as well, since thalamic inputs (onto proximal dendrites) are boosted by an NaP conductance, while cortical inputs (onto distal dendrites) are not. This raises a *key conceptual question*: why are cortical inputs onto distal dendrites not boosted? Any depolarization of distal dendrites must pass through proximal dendrites before reaching the recording electrode at the soma. Shouldn't this signal be subject to the same active and passive conductances, and consequently the same boosting that shapes thalamic inputs onto proximal dendrites?

2) The quasi-linear approach to characterizing active and passive membrane properties is promising, and the choice of a cable-based model is well supported. However, the model itself is rather opaque, which limits confidence in the interpretation of the results. Additional analysis and description should be presented to alleviate concerns about whether the experimental data, which has a limited number of measurable values, may be over-fit by a model with too many free parameters. For example, why is the radius of the dendrite a free parameter that is allowed to vary in the full field vs proximal experiment (Lines 253-256) - and isn't it a serious red flag that the value returned for proximal dendrites is smaller than for the full field? Additional tables (e.g. fixed and free parameters and how they were determined), and figures (plots of how those parameters influence the fits, and how the parameters interact with one another) would considerably strengthen confidence in the conclusions drawn by the authors.

3) Technically, the use of ChR2 to modulate dendritic currents is creative. While the authors rightly acknowledge that activation/deactivation kinetics of the ChR2 channel will contribute to filtering, this important point should be expanded with additional analysis and potentially with new experiments. Of particular concern is the transition of ChR2 channels to an inactivated state over the comparatively long oscillating light pulse in Figure 3 Inactivation of ChR2 is prominent over this timescale and would precisely co-vary with the shift in oscillation frequency. To address this, the authors should present a direct measurement of this inactivation and account for it in their analysis of the chirp data. Alternatively, the chirp stimulus could be presented backwards (starting at high frequency), so that comparison of forwards-vs-backwards chirp recordings could disentangle this artefact. Either one or both of these additional experiments would be critical for interpreting the roll-off in photocurrent responses at high frequencies reported in Figure 3.

Reviewer #2 (Public Review):

The paper by Oz and colleagues uses optogenetics and whole-cell patch clamp recordings from striatal cholinergic interneurons (CINs) to investigate their dendritic nonlinearities, in particular the hyperpolarization-activated h-current (HCN) and the persistent sodium current (NaP). The experiments are motivated by an elegant model for phase-shift and dendritic nonlinearity analysis and also support the firing patterns of putative CINs in sleeping monkeys. Using perisomatic and wide-field photostimulation, 2-photon imaging, and optogenetic circuit interrogation, the authors show the role of persistent sodium current in supporting action-potential backpropagation in CIN dendrites and synaptic amplification. The functional implications of the resonant properties of CINs are demonstrated in extracellular recordings from sleeping monkeys, showing modulation of the firing patterns in CINs but not striatal projection neurons. The results are interesting and the data presented is of high quality, combining several different methods and species.

Reviewer #1 (Public Review):

Proton pumps are necessary to set up gradients necessary for myriad biological processes. The malaria-causing parasite Plasmodium falciparum, uses two main pathways to achieve this, the vacuolar ATPase (V-type ATPase) and a more ancient vacuolar pyrophosphatase (PfPV1). The proton motive force set up across the parasite plasma membrane holds particular significance since it is necessary for transport of nutrients and waste products into and out of the cell. Motivated by the observation that the V-type ATPase is no expressed until several hours after the parasite has entered host cells, the present study examines the function of PfPV1. The authors demonstrate PfPV1 depletion blocks the early development of Plasmodium-specifically the transition from the ring to the trophozoite stage-and this is associated with changes to cellular pH and pyrophosphate levels, consistent with predicted functions. Complementation of the conditional knockdown suggests that pyrophosphatase activity alone is not sufficient to overcome the loss of PfPV. Overall, data supporting a critical role for PfPV1 in parasite energetics is compelling. However, the lack of several key controls somewhat weakens the conclusions of the paper when it comes to complementation of the mutants and description of which activities are needed for parasite survival. Because the proximal activities of the enzyme ATP generation and the proton motive force are incompletely examined, some of the major conclusions from the study remain speculative.

Reviewer #2 (Public Review):

In this work, the authors characterize a proton pump from the parasite Plasmodium falciparum that uses pyrophosphate as an energy source (PfVP1).

They looked at the expression and localization of the pump in different stages of the parasite and determined that it localizes to the plasma membrane and it is highly expressed in the ring stage.

They studied the biochemical function by expressing the gene in Saccharomyces followed by isolation of vesicles and measurements of proton transport and PPi hydrolysis.

They also characterized the biological role of PfVP1 in the parasites by creating conditional mutants that express PfVP1 when cultured in the presence of anhydrotetracycline (ATC). Upon removal of ATC the expression of PfVP1 is downregulated, which impacted growth and transition to the trophozoite stage. Mutant parasites struggled to progress through the ring state and failed to become trophozoites in the second intraerythrocytic cycle. They complemented the mutants with the yeast inorganic pyrophosphatase gene and the arabidopsis vacuolar pyrophosphatase.

Reviewer #3 (Public Review):

Solebo and coworkers investigated the energy requirements of blood-stage malaria parasites (the stage of infection that causes symptoms). Traditionally, parasites were thought to be somewhat quiescent during the first half of their life cycle in red blood cells and become metabolically active as they prepare for replication. Consequently, antimalarial drugs are more active against parasites during the second half of their life cycle. In this report, the authors show that the metabolic by-product pyrophosphate is an essential energy source for the development of early-stage malaria parasites and that it is consumed by a vacuolar pyrophosphatase (PfVP1). Knock down studies showed that PfVP1 is required for the development of early-stage parasites and localization studies established that it is located in the parasite plasma membrane. Characterization of PfVP1 heterologously expressed in yeast confirmed that it is a pyrophosphate hydrolyzing proton pump. Consequently, loss of PfVP1 in early-stage parasites results in reduced pyrophosphate consumption and a reduction in pH (accumulation of protons). The authors further show that a similar vacuolar pyrophosphatase from Arabidopsis thaliana can complement the loss of the parasite ortholog, but a general pyrophosphatase enzyme cannot. Consistent with this result, mutations designed to inactivate either the pyrophosphatase activity or the proton-pumping activity demonstrated that both activities are essential for the development and survival of early-stage parasites.

The conclusions of this paper are firmly supported by data, often from more than one type of experimental approach. The conclusions provide fundamental information about the stage of parasite development that has been hard to target with antimalarial drugs. The most energy-consuming process in a cell is the maintenance of membrane potential and in malaria parasites, it is known that proton pumps (rather than sodium pumps) are responsible for this process. Although PfVP1 was previously reported to be located internally in an organelle of the parasite, the data presented in this report clearly define its location on the plasma membrane and its essential role in maintaining the membrane potential. PfVP1 inhibitors could preferentially target early stage malaria parasites and the current results support efforts to find these inhibitors. Perhaps the most exciting aspect of this work is the potential to act synergistically and enhance the effect of current antimalarial drugs on early stage parasites. In this vein, the authors tested four antimalarial compounds in conjunction with knockdown of PfVP1 to determine whether there was enhanced activity. These experiments were not conducted in a systematic way and this is perhaps the only weakness of the paper.

Reviewer #1 (Public Review):

In this paper, Nunn and Goyal use long-read sequencing technology to investigate the origins and evolution of petite mutations in the S. cerevisiae mitochondrial genome. In brief, their results confirm much of what had been shown or hypothesized in a large number of older studies, many dating back to the 19070s and 1980s. Specifically, the results show that petite cells result from recombination events within the mtDNA that result in the development of small fragments, often complex concatemers, that have a higher density of replication origins compared to the WT mitochondrial DNA. The recombination events often involve short repeated sequences within the mtDNA and seem randomly distributed within the mt genome (as opposed to being enriched near replication origins). In a second part of the study, the authors find indirect evidence for the presence of both homoplasmic and heteroplasmic cells (i.e. cells that contain different species of mtDNA).

Overall, the major strength of this paper is that it uses modern technology to confirm many of the conclusions and hypotheses of previous studies.

The main problem with the text in its current format is that the general relevance of the findings is not explained well. In many organisms, mtDNA is indispensable or at least important for fitness, and mutants that lose the mitochondrial function are rapidly selected against. Moreover, the authors focus on the laboratory strain W303, which, like its more commonly used sibling S288C/BY4741, may have a higher petite frequency than feral yeasts because of mutations in genes like SAL1, MIP1, CAT5 (see https://doi.org/10.1016/S0076-6879(02)50954-X and 10.1534/genetics.109.104497). This makes one wonder whether the findings have a more broad relevance apart from laboratory S. cerevisiae strains. I believe there are possible links with mitochondrial diseases, but the authors do not explore these. The broader relevance and novelty are not always clear, but if the authors can draw very strong parallels with other systems, showing how their results help understand more general phenomena, it would increase the impact of the work. Interesting questions to discuss include: Do we observe similar phenomena in other species (e.g., mitochondrial human diseases?) Which general conclusions can be drawn?

Reviewer #2 (Public Review):

This work seeks to answer long-time outstanding questions in the field of yeast mitochondrial genetics using sequence analysis and biophysical modeling. The article uses new sequencing techniques to answer questions about the Petite genomes and by highlighting the significance of these findings outside the specific question of petite genome dynamics, the work could also be made more appealing to a wider audience of evolutionary geneticists.

The authors investigated the structural changes in yeast mitochondrial genomes by twice passaging spontaneous Petite phenotype colonies and comparing the long-read sequencing results of these cultured families to Grande (wild-type) colonies. Petite genomes were found to have a higher percentage of breakpoints than Grande genomes and these breakpoints are often clustered around replication origins. The authors also found that alternate structures, those that occur in lower frequencies than the primary mtDNA sequence, can give rise to "excision cascades" that in turn can result in new evolutionary pathways by bringing into close proximity areas of homology. Furthermore, using the percentage of alternate structure breakpoints within a colony, the contributions of heteroplasmic vs homoplasmic cells to the frequency of the alternate structure is able to be somewhat elucidated. Lastly, using crosses of Petite colonies with Grande colonies, the authors developed a model of suppressivity based on the biophysical parameters of mtDNA fitness.

Some questions and areas of clarification still remain within the paper to be addressed:

1. The heterogenous nature of mtDNA content within family 1 is not addressed. Is there some reason why these colonies remain heterogenous even after being twice passaged?<br /> 2. How does the mtDNA coverage of the sequenced Grande genomes compare to the coverage of Petite genomes shown in figure 1b? Adding similar sequencing coverage of the Grande genomes in this figure would be helpful for a wild-type comparison.<br /> 3. In figure 3a, please clarify if there is a difference between the black and grey circles on this plot. If these are just more densely occupied points, please make this clear in your plot.<br /> 4. In figure 3c, please clarify the objective of the circled diagrams in the green and orange alignments. How do these reflect what is going on in the larger model?<br /> 5. Figure 4e seems to directly reflect the criteria applied for the different types of alternate structures. For example, since the criteria for type III alternate repeats is that they share an existing primary alignment edge, they will have a shorter distance from the closest primary/alternate alignment edge as diagramed in figure 4e than the type I alternate repeats that occur within primary alignments. This plot seems unnecessary due to this circular logic. If the purpose of this figure is just to show that overall, the alternate repeats tend to occur close to alignment edges, this could be diagramed as a plot of all the breakpoints rather than broken down by categorical types.<br /> 6. On page 12, you use the term "non-periodic" primary structures. Please clarify if this is the same as "non-tandemly duplicated".<br /> 7. Please define the terms "heteroplasmic limit" and "homoplasmic limit". Are these the same as intracellular heteroplasmy and intercellular heteroplasmy.<br /> 8. Please clarify how it is known that the fraction of alternate structures found in cells grown on non-fermentable YPG media directly correlates with the heteroplasmic contribution and the fermentable YPD media correlates with the additive effect of the homoplasmic contribution.<br /> 9. How was mtDNA replication speed of the Petite families in figure 6b measured for use in the suppressivity rate calculation? Please add this to the accompanying methods section.

Reviewer #3 (Public Review):

This work reports the innovative sequencing of individual molecules to access the diversity of mitochondrial genomes in yeast cells and mechanisms driving the emergence and evolution of such diversity. The huge amount of data generated (needs to be made fully accessible to the reader with sufficient annotation) will certainly benefit the field and conceptual analyses performed already are elegant and convincing. The interest of this study to the general reader is in potential analogies between yeast and mammalian mtDNA rearrangements, recombination, and their relation to the aging process, but this issue is barely discussed in the manuscript.

I fully support and praise the idea of using nanopore sequencing in single-molecule analysis of mtDNA. The data are great and the analysis looks sound and convincing. Making analogies with animals (essentially human mtDNA, because this is where most of the research has been conducted) would benefit the manuscript.

There are similar phenomena in humans, and I can recommend a few publications that are closely related to what is reported in the current manuscript:

- Mapping of recombinants in human mtDNA and evidence for mtDNA recombination as an abundant ongoing intracellular process in humans (DOI: 10.1126/science.1096342),<br /> - Distribution and nature of mtDNA rearrangement breakpoints (in a very similar format to that of Fig 3 of the manuscript) - https://doi.org/10.1016/j.tig.2010.05.006);<br /> - Perhaps the most convincing ever study on the involvement of clonal expansions of mtDNA rearrangements in human aging pertaining to substantia nigra neurons (https://doi.org/10.1038/ng1778).

Joint Public Review:

The study "Mutated neuronal voltage-gated CaV2.1 channels causing familial hemiplegic 4 migraine 1 increase the susceptibility for cortical spreading depolarization 5 and seizures and worsen outcome after experimental traumatic brain injury" describes a higher susceptibility and number of cortical spreading depressions in mice bearing the S218L and R192Q mutations following experimental TBI induced by CCI. Increased number of CSD´S is associated with a slight increase in lesion size and edema. Moreover, a higher mortality was experienced in S218L transgenic mice. The study has been well conducted, the experimental procedures are well described.

The study provides the first essential step in understanding the higher susceptibility of mice with respective mutations to acute brain injury. Further subsequent investigations will be required to unravel underlying cellular and subcellular mechanisms.

Reviewer #1 (Public Review):

This paper provides experimental and modeling analysis of the inter-brain coupling of socially interacting bats, and reports that coordinated brain activity evolves at a slower time scale than the activity describing the differences. Specifically, the paper finds that there is an attracting submanifold corresponding to the mean (or "common mode") of neural activity, and that the dynamics in the orthogonal eigenmode, corresponding to the difference in brain activity, decays rapidly. These rapid decays in the difference mode are referred to as "catch up" activity.

There are two main findings:

1) Neural activity (especially higher frequency LFP activity in the 30-150Hz range) is modulated by social context. Specifically, the ratio of the averaged, moment-to-moment MEAN:DIFF ratio is much higher when the bats are in a single chamber, clearly indicating that the animals are coordinating their neural activity. This change also seems to hold -- although not as striking -- in lower-frequency LFP and spiking activity.

2) The time scales of the mean vs. difference dynamics are segregated: the "difference dynamics" evolve at a faster time scale than "similarity dynamics", seems to be well supported.

The basic finding is presented in Figure 1. The rest of the paper is focused on a modeling study to garner further insight into the dynamics.

Weaknesses:

This is an entirely phenomenological paper, and while it claims to garner "mechanistic insight", it is unclear what that means.

The basic idea of the model is simple and somewhat interesting, but the details are extremely complex. There are many examples of this, but the method used to "regress out" the behavior was very hard to interpret.

On the face of it, the model is extremely simple: a two-state linear dynamical system. However, this simplistic description buries extreme complexity. The model is extremely complex as involves a large number of parameters (e.g., time switching 'b' values, the values of which are completely unclear), the switching over time of these parameters based on hand-scored animal behavioral state, and the complex mix of markovian and linear dynamical systems theoretic results. Indeed, a fundamental weakness of the model is that the Markov chain is taken as an "input" to the 2-state linear systems model, as if somehow the neural state does not affect the state transitions. Further, the Markov assumption is not rigorously tested. No model selecting or other model validation appears to be done.

In short, the model, while very interesting, is so complex that it is literally impossible to evaluate. The authors report literally no shortcomings of their model. They do not report parameter estimation methods. They do not report fitting errors or other model validation metrics. The only evaluation is whether it can produce certain outputs that are similar to biological data. While the latter is certainly important, all models are wrong, and it essential to have a model simple enough to understand, both in terms of how it works and how it fails.

In general, while the basic finding is fairly interesting, and the experiments and their findings are highly relevant to the field, the modeling and its explication fall short.

It is not that it is wrong or bad; however, it is not clear that such a complex model increases our understanding beyond the experimental findings in Figure 1, and if it does, there has to be a major caveat that the model itself is not carefully vetted.

Reviewer #2 (Public Review):

In this paper, Wujie Zhang and Michael Yartsev investigate some of the basic underpinnings of inter-brain synchrony in socially interacting animals. The phenomena of inter brain synchrony is fascinating and has been observed in a variety of situations across different mammalian species. It has also been proposed to play a critical role in certain social behaviors. Here, the authors report that brain activity across two interacting bats display not only similarities but also important differences. The also use advance computational modeling to capture s these two characteristics as well as to demonstrate how they are affected by the presence and absence of interaction between animal pairs.

Reviewer #3 (Public Review):

The activity in the frontal cortex of mammals has been previously shown to become more correlated in socially interacting animals than when they are alone. In the current study, the authors examine the differences in brain activity that emerge during social interactions. The correlations and differences in activity were shown to occur over different time scales, with mean correlations occurring over longer time scales whereas differences occur over shorter time scales. The authors made a model of these processes that show how feedback may give rise to these phenomena.

Reviewer #1 (Public Review):

This is a manuscript by Han et al. to describe their findings that atmospheric particulate matter, one of the leading environmental risk factors for the global burden of disease, aggravates CNS demyelination via TLR-4/NF-κB-mediated microglia pathogenic activities. Using multiple in vivo and in vitro strategies, in the present study we demonstrate that PM exposure aggravates neuroinflammation, myelin injury, and dysfunction of movement coordination ability via boosting microglial pro-inflammatory activities, in both the pathological demyelination and physiological myelinogenesis animal models. TLR-4/NF-κB signaling mediated a core network of genes that control PM-triggered microglia pathogenicity. This study is interesting and novel, and the manuscript is well written.

Reviewer #2 (Public Review):

The present work by Han et al. demonstrates that PM exposure aggravates neuroinflammation, myelin damage, and motor coordination ability dysfunction by promoting microglial proinflammatory activity. In addition, the authors showed that PM exposure enhances microglial pathogenic activity by activating the TLR-4/NF-κB signaling axis. These data supply the direct testimony of PM-triggered demyelinating disorders, and establishes a systematic approach for studying the effects of environmental exposure on neurological diseases. This work is an interesting and complete research, and the paper is well designed and written. However, the manuscript raises the following concerns that need to be addressed:

Major comments:

1. In Fig 1, the authors showed that for both central and peripheral immunity, the percentages of Th17 (CD4+ IL17+) and Th1 (CD4+ IFN-γ+) cells were significantly increased under PM exposure. PM has already been reported to have some effects on peripheral immunity. The authors remain to examine the effects of PM on different T cells subsets in vitro?

2. In both Fig 2 and Fig 3, the authors show the activation of microglia and astrocytes by PM. Although in Figure 1, the authors mentioned that activated IBA1+ microglia and A2B5+ OPCs accumulated significantly in demyelinated injured areas in PM-treated mice, while PM inhalation had no significant effect on GFAP+ astrocytes. Microglia showed activation results by PM in three complementary animal models, which led the authors to select the final follow-up study subjects of microglia. Given the above results, the authors should examine the direct effects of PM on primary astrocytes, at least in vitro.

3. In Fig 4. The authors proposed that treatment of purified primary OPC with microglia-conditioned medium (MCM) in vitro prevented OPC differentiation. OPC are essential for remyelination after central nervous system injury. Therefore, it is necessary for the authors to investigate the direct effects of PM on OPC and thus better illustrate the effects of PM exposure on CNS demyelinating diseases.

4. In Fig 5. The authors predicted targeted genes for PM to induce microglial activation via the TLR-4/NF-κB signaling axis by RNA-seq and ChIP-seq. However, the manuscript would benefit if the authors also discussed the role of predicted target genes in PM-induced microglial activation, and what might be done subsequently.

Reviewer #1 (Public Review):

In this study the authors make a compelling case for Kv3.3 in the control of the presynaptic AP waveform at the calyx of Held within the mouse auditory brainstem. The authors previously showed a predominant expression of Kv3.1 and Kv3.3, but not Kv3.2 or Kv3.4, in the MNTB via mRNA in situ studies and postsynaptic recordings. The authors now show that deletion of Kv3.3, but not Kv3.1, in knockout mice cause a broadening in the immature presynaptic AP waveform. This broadening presumably leads to more calcium influx to the presynaptic terminal (although not shown), which increases the peak and charge of a post-synaptic AMPA-mediated EPSC. These broad presynaptic APs also correlated with faster rates of short-term depression, but faster recovery from depression, presumably through calcium-dependent mechanisms of synaptic release and vesicle recovery, respectively. In MNTB neurons, while Kv3.3KO mice were unable to maintain to steady state firing during 600Hz stimulation of the synaptic inputs, Kv3.1KO mice developed a depolarized plateau that took longer to recover to baseline membrane potentials compared to WT or Kv3.3KO mice. Importantly, Kv3.3 mice showed auditory response deficits, including increases in pre-synaptic AP halfwidth, synaptic delay, MNTB AP halfwidth, AP latency, AP jitter and spontaneous activity. These in vivo recordings are impressive. Overall, the study contains an extensive data set and makes a compelling argument for the uniquely important role for Kv3.3 in presynaptic transmission at the Calyx of Held synapse.

Reviewer #2 (Public Review):

In this manuscript, the authors tested specific roles of Kv3.1 and Kv3.3 channels at the calyx of Held presynaptic terminal, by combining global knockout (KO) mice, channel blocker, in vitro slice recording and in vivo extracellular recording. They found that Kv3.3 deletion but not Kv3.1 deletion increased presynaptic AP duration, postsynaptic EPSC amplitude, and short-term depression of EPSCs during high frequency transmission. Most interestingly, using in vivo extracellular recording in MNTB, the authors found that the neuronal response to sound in MNTB was delayed in the Kv3.3 KO mice, with higher spontaneous and lower evoked short-term firing, thereby reducing signal-to-noise ratio. Using computational modeling, the authors further showed that Kv3.3 deletion enhanced EPSC and short-term depression, indicating increased vesicle release probability and accelerated activity-dependent vesicle replenishment. Taken together, the authors concluded that Kv3.3 but not Kv3.1 enables short duration and temporal precision of APs to maintain transmission at high frequencies and during sustained synaptic activity.

Strengths:<br /> (1) Combine Kv3.1 and Kv3.3 global KOs with 1 mM TEA to dissect differential roles of the two channel subunits in regulating presynaptic APs and postsynaptic EPSCs.<br /> (2) In vivo recording of MNTB neuronal activities in response to sound in KO mice is highly interesting.<br /> (3) Overall, a substantial amount of electrophysiology results were included in this manuscript. It is technically solid.

Weaknesses:<br /> (1) In vitro slice recording was performed in mouse pups (P10-P25), whereas in vivo recording was performed in older mice (6 months). It is not clear why the experiments were done in mice with such huge age difference. The synapse might not be fully mature around P10-P25. Kv channels also tend to have increased expression during development. Were there any published results of immuno-electron microscopy to demonstrate the presence and expression levels of Kv3.3 and Kv3.1 channels in the calyx of Held terminals during these developmental stages?<br /> (2) There is potential compensatory effect from other Kv3 subunits in Kv3.1 or Kv3.3 global KO mice. Potential developmental changes caused by global KO should be more extensively discussed.<br /> (3) In Figure 2, mini EPSCs should also be analyzed to help dissecting the role of Kv3.3 at different stages of synaptic transmission.<br /> (4) Simultaneous pre- and post-synaptic recording will be challenging, but can more directly dissect the roles of Kv3.3 in synaptic transmission.<br /> (5) The potential effect of anaesthesia on in vivo recording in response to sound should be discussed.

Reviewer #3 (Public Review):

In this paper, the authors have used Kv3.1 and Kv3.3 KO mice and examined the roles of presynaptic firing. K channels are important in regulating presynaptic excitability and pharmacological dissection has been done by Takahashi and Forsyjte groups, but this study is novel because they have used KO mice. They used pre- and postsynaptic recording and in vivo physiology.

In Kv3.1 mice, the phenotype was not so strong. In Kv3.3 KO mice, they have found broader action potentials (AP) . In addition, they found increased EPSC amplitudes possibly due to broader APs. They also found deeper synaptic depression during repetitive activity, and increased recovery from synaptic depression, consistent with pharmacological study by Wang and Kaczmarek.

They also examined the effects on postsynaptic firing but have complex effects. In Kv3.3 KO mice, postsynaptic firing loses fidelity especially during the stimulus train to the presynaptic terminal. Although it is interesting, the effects can be due to presynaptic and postsynaptic. Kv3.3 channels are expressed in the postsynaptic cell. In addition, sound-evoked postsynaptic firing in vivo is more complex, because loss of Kv3.3 affects not only the calyx of Held synapse, but others, such as hair cells, auditory nerve fibers and cochlear nucleus. These points may be carefully discussed.

Reviewer #2 (Public Review):

This study uses cutting edge transcriptomics to decode the changes in transcript expression with neonatal development.

Strengths:

The study is sufficiently detailed so that the reader can evaluate the data and conclusions. Most importantly, the scientist who is able to analyze data from RNA-sequencing such as this will be able to seek answers their own questions about gene ontology and pathways involved in pituitary cell development.

The study is validated by the use of the organoid cultures, which recapitulate the transcriptome expression of the developing pituitary stem cells. An important strength is the fact that they were able to develop growth media that is optimal for neonatal pituitaries, as the organoid media used by many has been developed for adult cultures. This will be an important addition for many laboratories wishing to study organoid cultures from neonatal pituitary populations.

The study of the damaged neonatal pituitary (damaged by the ablation of somatotropes) is interesting and shows that the damage focuses on somatotropes and does not ablate stem cells. The study is worth further analysis by those who are interested in the impact of loss of somatotropes on pituitary cell populations.<br /> The populations subjected to scRNA-seq are available publicly and provide important tools for other researchers who want to decode stem cell activation.

Weaknesses:

1. The study is best analyzed by individuals who are well versed in bioinformatics approaches or by individuals who have access to this expertise. This is not a major weakness, only a precautionary remark.<br /> 2. This reviewer wonders about the use of the word "vividly" in the title and throughout the manuscript. Clearly these pituitary populations from the 7 day neonatal mice are maturing, however what about the study makes this maturation "vivid". The maturation is fairly ordinary and expected and not any more vivid than any other type of study of neonatal development. Vivid denotes a dynamic state and only one age was chosen for analysis of maturation.<br /> 3. Readers need to recognize that this transcriptome reflects gene activity in the PND 7 mouse and there may be additional changes during the second week of development, especially when prolactin cells begin to differentiate. This is not a major weakness because these types of studies are very expensive (in the US) and one must choose one's model carefully. The rationale for the use of 7 day old neonate could be spelled out (why not 4 or 5 day mice). One might guess however that this has to do with the size of the pituitary which is very tiny in the developing mouse.<br /> 4. It is impossible to remove the posterior pituitary and not also remove the intermediate lobe and the data show clearly that melanotropes were present in the PND 7 mouse as well as the adult.

Reviewer #1 (Public Review):

Improving the efficiency of genome engineering tools is a very important and competitive field. Moreover, the design of improved tools with a focus on models outside the mammalian cell editing dominated space is very welcome, as cross species activity should not be taken for granted. Thomas Thumberger et al focus on improving Cas9 activity in medaka and zebrafish embryos by rethinking about the "bells and whistles" attached to the enzyme in its commonly available variants.

They first develop an assay that links Cas9 activity to a phenotypic readout (retinal pigmentation controlled by oc2) in fish embryos. Using the first Cas9 variant reported to edit zebrafish embryos (Hwang et al 2013) as benchmark, they test an improved Cas9 variant (Zhang et al 2014) and their own heiCas9, and report up to 8-fold "enhanced activity" in medaka and 27-fold in zebrafish, when comparing to the Hwang et al variant. Improvements over the Zhang et al variant, tested only in medaka, were more modest.

Lacking a phenotypic assay in mammalian cells, the authors use genome editing outcomes deconvolution software (ICE and TIDE) on Sanger sequencing data to compare the editing efficiency of the Hwang variant, a commercially available Cas9, and heiCas9. HeiCas9 scores better in both analyses, and also shows a clear improvement at the ICE knockout score.

Finally, the authors move away from DSB inducing methods of genome editing and construct a heiBE4 (C to T base editor) variant. By adopting their phenotypic assay in medaka to introduce a stop codon (CAG>TAG) in oc2, they show stronger pigment loss phenotypes in injected embryos when using heiBE4, compared to the original BE4. They further quantify and confirm the high rate of C>T transitions by sequencing.

Overall, the manuscript is well written and the results are clearly presented. Boosting genome editing without modifying the primary sequence of the enzyme is a very interesting approach, and has been reported before (Liu et al 2021). Such methods could also be compatible with artificially evolved Cas9 variants (e.g. high fidelity, relaxed PAM recognition) or even other Cas enzymes (e.g. Cas12), providing an orthogonal approach to increase their activity.

The authors also provide evidence that the hei-tag is not restricted to the conventional DSB inducing approach, by trying a BaseEditor.

How their height-tag works to improve genome editing is not investigated in detail. Knowing the mechanistic underpinnings can help predict the usefulness or lack thereof across different organisms, developmental stages, or cell states. Moreover, the balance between ON- and OFF-target activity is not considered, an important parameter for cell culture experiments where outcrossing is not possible to segregate non-specific modifications of the genome. As a result, the mammalian cell culture data are interesting, but don't add much to the value of the hei-tag.

Reviewer #2 (Public Review):

The paper focuses on describing a novel tag (named hei-tag) consisting of two optimized NLS sequences and a Myc epitope separated by a linker peptide. This tag fused with Cas9 ORF increases the efficiency of genome editing in fish embryos following mRNA+sgRNA injection. The authors assess these results with a rigorous quantification of pigmentation reduction in fish embryos following the targeting of the pigmentation locus Oca2 in Medaka and zebrafish embryos. The same improved version of Cas9 is tested in mammalian cells in culture comparing the results with older version of Cas9. Finally the authors fuse the hei-tag to the base editor BE4-Gam showing also in this case an increased activity. The data would be more significant for the community if the injection of the Cas9 optimized construct would be tested as protein as this is the most efficient and commonly used approach in fish experiments. Similarly the comparison with the BE4-Gam should be extended to the more recent family of improved Cytidine Base Editors including the ancBE4Max that was optimized for nuclear shuttling among other properties. In the present manuscript the presence of the Myc epitope in the hei-tag is not tested in any of its possible applications and it remains unclear what is the utility of this part of the hei-tag system.

Reviewer #3 (Public Review):

In this manuscript, Thumberger et al. developed a novel high-efficiency tag to be used with existing CRISPR/Cas9 tools that boosted the editing efficiency in fish (medaka, zebrafish) and mammalian cell culture. Compared to the baseline gene editing methods chosen by the authors, hei-tag improved bi-allelic editing efficiency in medaka fish by about 30% and resulted in about 10% more indels in editing mammalian cells. The authors have also shown that hei-tag can be added to other Cas9-based techniques such as base editors to boost editing efficiency.

The authors have shown convincing evidence that hei-tag improves editing efficiency compared to the baseline methods they have chosen (JDS246-Cas9, myc-Cas9) in fish, demonstrating a boost in bi-allelic targeting efficiency of the oca2 gene in making eye pigment in medaka and zebrafish. Applications beyond gene knockout in fish have been carried out in mammalian cell culture, and also in base editing tools in medaka, implicating the potential broad application of the tool.

However, it is not clear under what scenarios hei-tag carries out significant and practical improvement compared to the state-of-art gene editing techniques. Especially concerning its original purpose of editing earlier and more efficiently in early embryos, the common strategy is to inject Cas9 protein rather than mRNA, which the authors did not account for. Even in the realm of RNA-based editing tools, it is questionable whether JDS246-Cas9, a construct originally made for mammalian gene editing, is the best baseline to compare to. Part of the ambiguity originates from a lack of systematic comparison of existing editing tools in the field, but the authors would need to ensure they are comparing to the state-of-art, and demonstrate the universality and limitation of hei-tag in practical use.

Overall, I think hei-tag would be a good addition to the exiting gene editing tools and has a potential to boost editing efficiency in many systems, although its practical improvement is yet to be solidly demonstrated. Further investigation of the impact of N-terminal tags and linker structure on Cas9 specificity and efficacy will be useful to guide future improvement of protein engineering.

Reviewer #1 (Public Review):

This study shows that the 5hmC DNA modification facilitates the formation of DNA-RNA hybrids during transcription. It is a hypothesis-driven study based on the known fact that 5hmC weakens the interaction between DNA and H2A.2B dimers and reduces the energy needed to separate the DNA strands. The study shows a correlation between 5hmC and R loops in mES cells and human HEK293 cells. This is shown first by dot blot, DRIP-qPCR at specific loci subjected to in vitro transcription in cells depleted of the TET enzymes Tet1 and Tet3 that convert 5mC into 5hmC, and later by bioinformatic analyses of genome-wide data of DNA-RNA hybrids and 5hmC of public databases, concluding that around 50% of active genes show an overlap between genes showing R loops and 5hmC. Thus there is a good correlation between R loop- and 5hmC-containing genes, but there are also regions with 5hmC with no R loops and the vice versa. Expression of TET fused to an inactive Cas9 increases R loops formation at the APOE locus. Although its physiological meaning is unclear, the study is nice and of interest but additional experiments are required to validate the model.

Reviewer #2 (Public Review):

These data describe a new potential role for 5hmC modified DNA in enforcing R-loop structures especially over the 3' ends of protein coding genes in both mESCs and human cell lines (HEK293). While these data are at present largely correlative, they certainly make an interesting connection between 5hmC DNA modification and a key feature of transcribed genes (R-loops) that are known to be associated with DNA damage in numerous pathological cellular conditions. Various additional controls are needed to fully justify the claims made. Especially the reliance on the S9.6 mab to detect R-loops and their level changes based on reducing or targeting TET enzymes (that metabolise mC to 5hmC) needs tight controls. The potential higher affinity of S9.6 for 5hmC vs unmodified DNA as a possible cause of increased signal needs to be investigated. Also, the redundancy of TET enzymes needs further investigation, rather than separately depleting either Tet1 and Tet3 (but not Tet2).

Reviewer #3 (Public Review):

Sabino et al. investigate the role of a rare DNA modification, 5-hydroxymethylcytosine (5hmC), in the formation of R-loops at transcribed loci. In an in vitro setting they demonstrate that 5hmC favors co-transcriptional R-loops formation. They further extend their findings in a cellular model, where they modulate the expression or the activity of TET enzymes, responsible for the conversion of 5mC into 5hmC. They observe that depletion of TET leads to a reduced amount of R-loops in cells, while targeting TET to a specific locus increases the formation of R-loops. Then, they take advantage of published datasets to show that 5hmC presence correlates with R-loops in active genes (and validate this observation performing PLA analysis), and with H2AX. Finally, they demonstrate that overexpression of RNaseH in mouse embryonic stem cells leads to differential expression of genes that contain 5hmC modifications and are involved in diapause establishment. Nevertheless, RNaseH overexpression does not affect cell cycle progression of mouse embryonic stem cells in the time points investigated.

This work provides new information on the interplay between epigenetic modifications, R-loops formation and transcription. In the discussion, the authors propose some interesting points to pursue in future research on this topic. Nevertheless, there are some important aspects of experimental setting, data presentation and statistical analysis that need improving.<br /> The main point of the manuscript, namely the fact that 5hmC favors co-transcriptional R-loops formation, is supported by data that in many cases lack statistical significance (Fig 1E, 2C). If this significance is not reached, the whole manuscript loses impact. The experiments where error bars and statistical significance are shown (Fig 1D, 2F) would be more convincing if the authors showed that 5hmC does not affect transcription levels in those particular settings.

The authors provide a quantification of dot-blots based on the normalization of the signal of interest (5hmC, 5mC, S9.6, Fig 1B, 2B, 2C) on dsDNA. From source data, it seems that these signals are quantified in two different membranes: this is not correct, should be carried out on the same membrane.

The proximity ligation assay performed in figure 4 to confirm the previous results is not convincing. Better resolution and magnification are needed to better gauge the signal. Single plane confocal images would be clearer. Moreover, the PLA signal seems to be mainly extranuclear. It is worth noticing that the use of S9.6 antibody for imaging techniques proved to be problematic for its non-specific binding to dsRNA (see Hartono et al., The EMBO Journal, 2021). Additional control conditions are necessary.

Reviewer #1 (Public Review):

In Figure 1A, the authors should show TEM images of control mock treated samples to show the difference between infected and healthy tissue. Based on the data shown in Figure 1B-E that the overexpression of GFP-P in N. benthamiana leads to formation of liquid-like granules. Does this occur during virus infection? Since authors have infectious clones, can it be used to show that the virally encoded P protein in infected cells does indeed exist as liquid-like granules? If the fusion of GFP to P protein affects its function, the authors could fuse just the spGFP11 and co-infiltrate with p35S-spGFP1-10. These experiments will show that the P protein when delivered from virus does indeed form liquid-like granules in plants cells. Authors should include controls in Figure 1H to show that the interaction between P protein and ER is specific.

Data shown in Figure 2 do demonstrate that the purified P protein could undergo phase separation. Furthermore, it can recruit viral N protein and part of viral genomic RNA to P protein induced granules in vitro.

Based on the data shown in Figure 4 using phospho-null and phospho-mimetic mutants of P protein, the authors conclude that phosphorylation inhibits P protein phase separation. It is unclear based on the experiments, why endogenous NbCK1 fails to phosphorylate GFP-P-WT and inhibit formation of liquid-like granules similar to that of GFP-P-S5D mutant? Is this due to overexpression of GFP-P-WT? To overcome this, the authors should perform these experiments as suggested above using infectious clones and these P protein mutants.

In Figure 5, the authors overexpress NbCK1 in N. benthamiana or use an in vitro co-purification scheme to show that NbCK1 inhibits phase separation properties of P protein. These results show that overexpression of both GFP-P and NbCK1 proteins is required to induce liquid-like granules. Does this occur during normal virus infection? During normal virus infection, P protein is produced in the plant cells and the endogenous NbCK1 will regulate the phosphorylation state of P protein. These are reasons for authors to perform some of the experiments using infectious clones. Furthermore, the authors have antibodies to P protein and this could be used to show the level of P protein that is produced during the normal infection process.

Based on the data shown in Figure 6, the authors conclude that phase separated P protein state promotes replication but inhibits transcription by overexpressing P-S5A and P-S5D mutants. To directly show that the NbCK1 controlled phosphorylation state of P regulates this process, authors should knockdown/knockout NbCK1 and see if it increases P protein condensates and promote recruitment of viral proteins and genomic RNA to increase viral replication.

Reviewer #2 (Public Review):

The manuscript by Fang et al. details the ability of the P protein from Barley yellow striate mosaic virus (BYSMV) to form phase-separated droplets both in vitro and in vivo. The authors demonstrate P droplet formation using recombinant proteins and confocal microscopy, FRAP to demonstrate fluidity, and observed droplet fusion. The authors also used an elaborate split-GFP system to demonstrate that P droplets associate with the tubulur ER network. Next, the authors demonstrate that the N protein and a short fragment of viral RNA can also partition into P droplets. Since Rhabdovirus P proteins have been shown to phase separate and form "virus factories" (see https://doi.org/10.1038/s41467-017-00102-9), the novelty from this work is the rigorous and conclusive demonstration that the P droplets only exist in the unphosphorylated form. The authors identify 5 critical serine residues in IDR2 of P protein that when hyper-phosphorylated cannot form droplets. Next, the authors conclusively demonstrate that the host kinase CK1 is responsible for P phosphorylation using both transient assays in N. benthamiana and a co-expression assay in E. coli. These findings will likely lead to future studies identifying cellular kinases that affect phase separation of viral and cellular proteins and increases our understanding of regulation of condensate formation. Next, the authors investigated whether P droplets regulated virus replication and transcription using a minireplicon system. The minireplicon system needs to be better described as the results were seemingly conflicting. The authors also used a full-length GFP-reporter virus to test whether phase separation was critical for virus fitness in both barley and the insect vector. The authors used 1,6-hexanediol which broadly suppresses liquid-liquid phase separation and concluded that phase separation is required for virus fitness (based on reduced virus accumulation with 1,6 HD). However, this conclusion is flawed since 1,6-hexanediol is known to cause cell toxicity and likely created a less favorable environment for virus replication, independent of P protein phase separation. These with other issues are detailed below:

1. In Figure 3B, the authors display three types of P-N droplets including uniform, N hollow, and P-N hollow droplets. The authors do not state the proportion of droplets observed or any potential significance of the three types. Finally, as "hollow" droplets are not typically observed, is there a possibility that a contaminating protein (not fluorescent) from E. coli is a resident client protein in these droplets? The protein purity was not >95% based on the SDS-PAGE gels presented in the supplementary figures. Do these abnormalities arise from the droplets being imaged in different focal planes? Unless some explanation is given for these observations, this reviewer does not see any significance in the findings pertaining to "hollow" droplets.

2. Pertaining to the sorting of "genomic" RNA into the P-N droplets, it is unlikely that RNA sorting is specific for BYSMV RNA. In other words, if you incubate a non-viral RNA with P-N droplets, is it sorted? The authors conclusion that genomic RNA is incorporated into droplets is misleading in a sense that a very small fragment of RNA was used. Cy5 can be incorporated into full-length genomic RNAs during in vitro transcription and would be a more suitable approach for the conclusions reached.

3. In Figure 4C, it is unclear how the "views" were selected for granule counting. The methods should be better described as this reviewer would find it difficult to select fields of view in an unbiased manner. This is especially true as expression via agroinfiltration can vary between cells in agroinfiltrated regions. The methods described for granule counting and granule sizes should be expanded (i.e. what ImageJ tools were used?).

4. In Figure 4F, the authors state that they expected P-S5A to only be present in the pellet fraction since it existed in the condensed state. However, WT P also forms condensates and was not found in the pellet, but rather exclusively in the supernatant. Therefore, the assumption of condensed droplets only being found in the pellet appears to be incorrect.

5. The authors conclude that P-S5A has enhanced phase separation based on confocal microscopy data (Fig S6A). The data presented is not convincing. Microscopy alone is difficult for comparing phase separation between two proteins. Quantitative data should be collected in the form of turbidity assays (a common assay for phase separation). If P-S5A has enhanced phase separation compared to WT, then S5A should have increased turbidity (OD600) under identical phase separation conditions. The microscopy data presented was not quantified in any way and the authors could have picked fields of view in a biased manner.

6. The authors constructed minireplicons to determine whether mutant P proteins influence RNA replication using trans N and L proteins. However, this reviewer finds the minireplicon design confusing. How is DsRFP translated from the replicon? If a frameshift mutation was introduced into RsGFP, wouldn't this block DsRFP translation as well? Or is start/stop transcription used? Second, the use of the 2x35S promoter makes it difficult to differentiate between 35S-driven transcription and replication by L. How do you know the increased DsRFP observed with P5A is not due to increased transcription from the 35S promoter? The RT-qPCR data is also very confusing. It is not clear that panel D is only examining the transcription of RFP (I assume via start/stop transcription) whereas panel C is targeting the minireplicon.

7. Pertaining to the replication assay in Fig. 6, transcription of RFP mRNA was reduced by S5A and increased by S5D. However, the RFP translation (via Panel A microscopy) is reversed. How do you explain increased RFP mRNA transcription by S5D but very low RFP fluorescence? The data between Panels A, C, and D do not support one another.

8. The authors relied on 1,6-hexanediol to suppress phase separation in both insect vectors and barley. However, the authors disregarded several publications demonstrating cellular toxicity by 1,6-hexanediol and a report that 1,6-HD impairs kinase and phosphatase activities (see below). doi: 10.1016/j.jbc.2021.100260, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445271/

9. The authors state that reduced accumulation of BYSMV-GFP in insects and barley under HEX treatment "indicate that phase separation is important for cross-kingdom infection of BYSMV in insect vectors and host plants."

The above statement is confounded by many factors, the most obvious being that HEX treatment is most likely toxic to cells and as a result cannot support efficient virus accumulation. Also, since HEX treatment interferes with phosphorylation (see REF above) its use here should be avoided since P phase separation is regulated by phosphorylation.

Reviewer #3 (Public Review):

Membrane-less organelles formed through liquid-liquid phase separation (LLPS) provide spatiotemporal control of host immunity responses and other cellular processes. Viruses are obligate pathogens proliferating in host cells which lead their RNAs and proteins are more likely to be targeted by immune-related membrane-less organelles. To successfully infect and proliferate in host cells, virus need to efficiently suppressing the immune function of those immune-related membrane-less organelles. Moreover, viruses also generate exogenous membrane-less organelles/RNA granules to facilitate their proliferation. Accordingly, host cells also need to target and suppress the functions of exogenous membrane-less organelles/RNA granules generated by viruses, the underlying mechanisms of which are still mysterious.

In this study, Fang et al. investigated how plant kinase confers resistance against viruses via modulating the phosphorylation and phase separation of BYSMV P protein. They firstly characterized the phase separation feature of BYSMV P protein. They also discovered that droplets formed by P protein recruit viral RNA and other viral protein in vivo. The phase separation activity of P protein is inhibited by the phosphorylation on its intrinsically disordered region. Combined with their previous study, this study demonstrated that host casein kinase (CK1) decreases the phase separation of P protein via increasing the phosphorylation of P protein. Finally, the author claimed that the phase separation of P protein facilitates BYSMV replication but decreases its transcription. Taking together, this study uncovered the molecular mechanism of plant regulating viral proliferation via decreasing the formation of exogenous RNA granules/membraneless organelles. Overall, this paper tells an interesting story about the host immunity targeting viruses via modulating the dynamics of exogenous membraneless organelles, and uncovers the modulation of viral protein phase separation by host protein, which is a hotspot in plant immunity, and the writing is logical.

Reviewer #2 (Public Review):

This paper aims to develop second-generation vaccines that protect against multiple SARS-CoV-2 variants of concern. For this purpose, the authors developed new vaccine candidates composed of SARS-CoV-2 spike protein derived from B.1.1.7 (alpha) and B.1.351 (beta) variants. The essential backbone of the vaccines they used contains alphavirus-derived sequences to be self-amplifying, and one containing spike protein of the Wuhan strain is already in clinical trials. They demonstrated no significant difference in virus removal and pathogenesis in the lower respiratory tract. However, the titer of in vitro neutralizing activity and virus removal ability in the upper respiratory tract were decreased against the strains different from the vaccine strain.

Overall, their data are convincing and valuable as a platform for a new vaccine against SARS-CoV-2 VoC in the future. Besides, I have some comments to strengthen their argument.

1) The challenge experiments in Figure 4, Figure 5, Figure 6, and Figure 7 lack data on infection protection against B.1.617.2 (delta strain). It is better to add B.1.617.2 to the challenge experiments and neutralizing assay in Figure 3. The addition of data against B.1.1.529 (Omicron) is ideal.

2) There are no data on T cell responses to vaccines, even in mice. If their vaccine can also induce T-cell responses, it would be more attractive. At least, it would be better to discuss the potential contribution of T-cell responses since alphavirus-based replicating RNA vaccines could be one of the nice vaccine platforms to elicit T-cell responses, according to previous works. (For example, McKay PF et al., Nat Commun. 2020 Jul 9;11(1):3523.)

Reviewer #1 (Public Review):

In this manuscript, the authors investigated that vaccine which is designated RNA replicons delivered by lipid inorganic nanoparticles (LION) exhibited the protective immune response against SARS-CoV2 variants by heterologous challenging. They also provide the evidence its significant efficacy to assess pathological analysis in the lung using hamster model. However, this study presented descriptive data with a few mechanistic studies in the immune response. Concerns with the manuscript are related to data describing the relevant of protective effects in vivo and the data supporting the interpretation of vaccination efficacy against multiple SARS-CoV2 strains.

Specific concerns:

In previous study (Erasmus et al, 2020a), the authors developed a novel vaccine and demonstrated that this novel vaccine harboring an alphavirus-derived repRNA induced antibody production responses in mice and macaques. In this manuscript, the authors demonstrated that this novel vaccine harboring SARS-CoV2 variant derived repRNA with pre-fusion type has significant cross-neutralization activity and protective immunity against SARS-CoV2 variant.

1. The antibodies which are produced after immunization by repRNA expressing pre-fusion stabilized spike protein/LION can bind to S1 or S2 or RBD? Please define the reactivity of antibodies and also compare to those from native form.

2. The authors demonstrated that this novel vaccine has significant efficacy with heterologous neutralizing activities. Please provide some evidence for reasons. F.i. this novel vaccine (with pre-fusion type) can induce the production of cross-reactive antibodies against SARS-CoV variant? And also it would be better to define the epitopes of these antibodies.

3. In the hamster model, this novel vaccination showed the significant protective effects on lung pathology. Please provide some data that a novel vaccination induce T cell responses in hamster by the frequency of antigen specific CD4 or CD8 T cell and cytokines.

Reviewer #3 (Public Review):

Summary:

The authors present a novel hypothesis of how cross-feeding could evolve in microbial communities. Their central idea is that the growth rate of isolated cells is reduced by fluctuations in essential metabolite concentrations which result from noise in enzyme expression. They hypothesize that the effect of these fluctuations could be reduced if cells share metabolites with each other, as this would average out the fluctuations. The authors term this process noise-averaging cooperation (NAC) and they present a mathematical model to formally analyze their hypotheses.

The authors first present a simple model for cells whose growth is limited by two essential metabolites. They show that fluctuations in enzyme levels decrease growth rates in isolated cells; the stronger the fluctuations (i.e., the larger the burst size of enzyme production) the more growth is reduced. Next, they show that it is beneficial for cells to share metabolites when enzyme levels fluctuate strongly. The optimal level of leakiness (i.e. cell permeability) depends on both cell density and the degree of enzyme fluctuations: high levels of leakiness are beneficial when cells grow in dense groups and face strong fluctuations, while low levels of leakiness are beneficial when cells grow in isolation and/or face weak fluctuations.

The authors subsequently generalize their model to an arbitrary number of metabolites and show that the growth reducing effect of metabolite fluctuations increases with the number of essential metabolites. They use previously published experimental data to suggest that E. coli suffers from noise induce growth inhibition and they suggest several experiments that could be done to test predictions from their NAC model. Finally, they show that their results are robust with regards to chosen growth function.

Strengths:

Cross-feeding plays an essential role in microbial communities and understanding its ecological consequences and evolutionary history is essential to understand natural communities (including ones affecting human health and disease) and improve the functionality of engineered ones. This question has been the focus of numerous theoretical and experimental studies, however the NAC framework presented by the authors provides a novel and alternative mechanism of why cross-feeding can be beneficial and as such it provides an important addition to these previous studies.

The NAC framework bridges two important subfields in microbiology: recent studies have focused on understanding how gene expression noise affects the physiology of cells and on how metabolic interactions affect the growth of microbial communities. The authors show that these two questions are related to each other, creating interesting opportunities for further investigations at the interplay between these two fields.

The model presented by the authors is both simple and realistic. It has relatively few parameters, which all have clear biological meaning, and which could potentially be estimated from data. Most assumptions can be justified (though see below) and the framework could easily be extended in future work to address additional questions about the interplay between metabolic noise and metabolic interactions. A particular strength of the framework is that it creates testable predictions, and the authors discuss some possible experiments to test these predictions in detail.

Weaknesses:

The conclusions are generally well supported by the model, however in my opinion the authors somewhat overstate the significance of their results, especially when it comes to comparing them to previous theories on cross-feeding.

Specifically, the authors suggest that the NAC framework is a generalization of the Black Queen Hypothesis (BQH), as it can provide an evolutionary explanation of why leakiness evolves. I think this misrepresents the relation between NAC and BQC for two reasons. First, although the authors give a plausible explanation of how leakiness could evolve, these conclusions are based on a purely ecological model and no actual evolutionary dynamics where investigated. NAC suggest that higher leakiness could be favored by selection because it buffers metabolic noise and increases cell growth rate, however it is not guaranteed to do so, as there is a potential for a conflict of interest between the individual and the group. E.g., consider a scenario where uptake rates of metabolites are fixed, but leakage rates can evolve. Higher leakage rates would increase the average growth in the population as it allows for cooperative noise buffering, however a mutant cell with a lower leakage rate would have a selective advantage as it does not pay the cost of losing metabolites, but still benefits from the noise buffering by taking up metabolites produced by the other cells. Whether leakiness can evolve under the NAC framework will thus critically depends on details of the evolutionary dynamics. In future work it would thus be essential to develop an evolutionary model to test if and when NAC allows for the evolution of higher levels leakiness. In the absence of such an evolutionary model it is not yet possible to determine whether NAC offers a more general theory than BQH.

Second, the authors argue that the main limitation of BQH is that it assumes leakiness without providing an evolutionary explanation for it. Although experiments have shown that many metabolites are leaky, the authors argue that cells potentially could have evolved low leakiness levels, and BQH does not explain this fact. The authors thus imply that BQH is an incomplete theory, and they suggest that NAC offers a (more) complete alternative as it could potentially explain why leakiness evolved. I do not agree with this last statement, as in my opinion a similar argument can be made against NAC. NAC is fully contingent on the assumption that metabolism is inherently noisy, and this is not necessarily the case. The degree of gene expression noise is an evolvable trait: under the right conditions, cells could potentially have evolved a largely noise free metabolism, and NAC does not offer an evolutionary explanation for the level of noise we observe now. It is not clear to me why the limitation of BQH (leaving leakiness unexplained) is any worse (or better) than the limitation of NAC (leaving metabolic noise unexplained). In my opinion neither limitation is an issue: the levels of leakiness and metabolic noise we observe in present days microbes are likely the result of complex evolutionary trade-offs that involve many processes in addition to cross-feeding. I thus do not see clear grounds to call NAC more general than BQH, instead I see these two theories as complimentary frameworks that both offer important insight into the question of how cross-feeding could have evolved.

Moreover, the authors neglect to discuss a third alternative explanation of how metabolite cross-feeding could have evolved. Recent experimental and theoretical work (see for example the work by the group of Christian Kost) has shown that some metabolic pathways display economies of scales: the marginal cost of producing additional metabolite goes down with the total amount of metabolites that is produced. In these cases, a division of labor based on cross-feeding becomes beneficial, as it reduces the overall production costs of metabolites and allows for faster growth of the community. This economy of scales model is complementary to NAC and BQH and a full explanation of cross-feeding will likely require apectes of all three models.

None of these issues decreases the overall value of the presented framework: NAC is an interesting and novel hypothesis that can help understand cross-feeding communities. However, in my opinion NAC should be seen as complimentary to existing theories and not as a replacement.

Finally, some of the model assumptions made by the authors have a debatable biological justification. For example, the authors assume that metabolites are degraded both in the cell and external environment, however I expect that most cross-fed metabolites are stable on the relevant time scales. Likewise, the authors assume that enzyme production is proportional to cell growth rate, while for many enzymes the transcription rate mostly depends on metabolite concentrations. Both these assumptions appear to strongly affect some of the authors conclusions: e.g., the decrease in the growth rate of isolated cells as function of permeability (Fig 1E) appears to depend strongly on the degradation rate, while the result that cells experience irreversible metabolic arrest depends critically on the fact that cells are unable to produce new enzymes when their growth halts. Care should thus be taken when comparing NAC predictions with experimental data and modifications of the relevant assumptions might be needed for such future work, however, the framework is flexible enough to allow for this, so this is not a major limitation.

Reviewer #1 (Public Review):

Lopez and Wingreen proposes the idea of noise-averaging cooperation (NAC), or within-population cross-feeding driven by noisy metabolism in microbes. The authors reasoned that since microbes are small, they are prone to noisy metabolism which limits growth rate. If related bacteria can share metabolites to average out noise (e.g in biofilm), then population growth rate can be improved and sometimes, the irreversible growth arrest of individuals can be prevented in theory. The authors predict substantial noise-driven growth inefficiencies from single-cell protein abundance data, review evidence for NAC, and propose how to detect NAC in microbial populations.

Although this paper would be greatly strengthened by experimental tests (some of which may not be too difficult to do), I did enjoy reading it, and the writing is clear and thoughtful. The problem of "cheaters" (cells that take metabolites but do not leak any) will naturally arise, although the problem is mitigated in biofilms. Discussions on that will be useful.

Reviewer #2 (Public Review):

The authors introduce the noise averaging cooperation (NAC) concept. When microbes' growth rates are limited due to the noise in vivo metabolite concentrations, sharing metabolites between microbes improves the growth rate of the whole community as the noise is averaged out. The NAC shows that metabolite leakage can be optimal for a group, suggesting a novel scenario to explain how microbial cross-feeding can evolve: first, the cells evolve to share their metabolites via leakage, and then gene deletion may occur, driving the evolution of microbial cross-feeding.

The model is simple that is easy to understand, and provides intuitive results. Also, biologically feasible conditions where the benefit of metabolite sharing can arise are clearly addressed. Cells should be in a crowded space such as a biofilm to share the metabolites. If a huge free space isolates cells, they cannot get the advantage. An experimental design to confirm the existence of NAC in nature is well discussed as well. Overall, the NAC is a novel approach and provides clear predictions on the evolution of metabolite leakage under the assumptions.

However, the model has the limitation that the obtained results strongly rely on the bursty behavior of enzyme production. Moreover, the model cannot explicitly show the evolution step on cross-feeding while it clearly shows that metabolite leakage is optimal. Thus, the current model can play a role as a stepping-stone, remaining a future investigation of the evolution of cross-feeding.

Reviewer #2 (Public Review):

This manuscript reported a new approach to conduct neural activity imaging and manipulation in two different cortical layers. Two periscopes, each constructed from a micro-prism, a GRIN lens and a multi-mode fiber, could be inserted to the brain at different depths, and each can either perform imaging or optogenetics. The authors demonstrated a few applications: stimulation of L5 soma and superficial layer dendrites to evoke backpropagating action potential; optogenetically stimulating cells in L2/3 and observing response in L5 to investigate interaction between cells in two different layers; and simultaneously recording axon terminals from posteromedial thalamic nucleus at two different depths in cortex. This works combines the ideas of fiber photometry to access deep layers and using microprism to turn the optical field of view by 90 deg.

Major strengths:

• Using microprism to perform layer specific imaging or optogenetics.<br /> • Low cost<br /> • Demonstrations of a few applications that require layer specific imaging and optogenetics.

Major weakness:

• As this is an inherently a variation of fiber photometry, there is a lack of cellular resolution and there is tissue damage.<br /> • Innovation is modest, as it is an incremental improvement of fiber photometry. Some of the applications may be performed through regular fiber photometry as well.<br /> • There is a lack of details on the optical setup and characterization of the periscope, i.e. how to choose the fiber, GRIN lens; optical throughput etc.

Overall, this research provides a new method to image/manipulate the neural activity of two different cortical layers. However, more details are needed on the optical setup and characterization of the periscope. The innovation of this work is modest.